Available Environmental Microbiology (“eMicro”) Apps and Tools

Below is a list and description of the apps available to anyone on OSC. Please keep in mind that this list is not 100% comprehensive and does not detail the methods underlying the tool. Where possible, citations have been included so users can read the original source’s documentation and theory.

Always check for the latest versions of Singularity containers and modules!

Some of this documentation is lifted from the iVirus project to avoid reinventing the wheel. Every effort is being made to ensure that both locations are up-to-date with the latest tools and literature.

One last thing to note: All of the eMicro singularity images are located at:

/users/PAS1117/osu9664/eMicro-Apps/

Additionally Microbial Informatics students can also find additional images at:

/fs/project/PAS1573/sif/

You must provide full paths to each image/container, or link them (see UNIX/LINUX Introduction and Guide).

Example:

$ module load singularity/current
$ singularity run /users/PAS1117/osu9664/eMicro-Apps/Prokka-1.12.0.img -h
# Alternatively
$ singularity run /fs/project/PAS1573/sif/fastqc_0.11.9--hdfd78af_1.sif

Alternatively, if you do not want to type out the full paths each time you run the container, you’ll want to add the

container location to your PATH.

$ export PATH=/users/PAS1117/osu9664/eMicro-Apps/:$PATH
$ Prokka-1.12.0.img -h
# OR, alternatively
$ export PATH=/fs/project/PAS1573/sif/:$PATH
$ fastqc_0.11.9--hdfd78af_1.sif

Keep in mind that NONE of these apps/tools should be run on the login nodes. Please create a job script and submit it or incur OSC’s wrath!

Also to note: There are several cases where these tools have been used in the CyVerse cyberinfrastructure. For these, there is a protocols.io link. We’re continually developing these protocols and trying to keep them up to date (though if it’s not broke and a current version, it’ll likely not be updated), so always make sure it’s the latest version.

For Sullivan lab members, also included are OSC module system, to use:

module use /fs/project/PAS1117/modulefiles  # Load Sullivan lab's modules
module load Prokka/1.13
# OR
module load Prokka/1.14.6
prokka -h

Quality Control [of Reads] and Read Mapping

Generally speaking, quality control (QC) is a technique applied to to [most commonly] raw read data. This ensures that the data going into the assembly (common next step) is of high quality. Poor read quality can result in mis- or incorrectly assembled sequences. Most frequently, read data QC involves trimming reads according to their quality scores. Although some assemblers do not require QC’d reads, we highly recommend it!

BBTools

Reference: http://sourceforge.net/projects/bbmap/

Reference (BBMerge): Bushnell, B., Rood, J., & Singer, E. (2017). BBMerge – Accurate paired shotgun read merging via overlap. PLOS ONE, 12(10), e0185056. https://doi.org/10.1371/journal.pone.0185056

Short description: BBTools is a suite of fast, multi-threaded bioinformatics tools designed for analysis of DNA and RNA sequence data. BBTools can handle common sequencing file formats such as fastq, fasta, sam, scarf, fasta+qual, compressed or raw, with autodetection of quality encoding and interleaving.

Note: This is SEVERAL tools, BBDuk (discussed below) is just one of them. We’ll be working on detailing this here, but in the meantime, any tool available on https://jgi.doe.gov/data-and-tools/bbtools/ is available through this image.

Singularity use:

module load singularity/current
singularity run /users/PAS1117/osu9664/eMicro-Apps/BBTools-38.97.sif

# For PAS1117
module use /fs/project/PAS1117/modulefiles
module load singularityImages
BBTools-38.97.sif

BBDuk (in the BBTools package)

Website: https://jgi.doe.gov/data-and-tools/bbtools/bb-tools-user-guide/bbduk-guide/

Short description: “Duk” stands for Decontamination Using Kmers. BBDuk was developed to combine most common data-quality-related trimming, filtering, and masking operations into a single high-performance tool. It is capable of quality-trimming and filtering, adapter-trimming, contaminant-filtering via kmer matching, sequence masking, GC-filtering, length filtering, entropy-filtering, format conversion, histogram generation, subsampling, quality-score recalibration, kmer cardinality estimation, and various other operations in a single pass.

Singularity use:

module load singularity/current
# Just adapter trimming
singularity run /users/PAS1117/osu9664/eMicro-Apps/BBTools-38.69.sif bbduk.sh in1=<input-pair1> in2=<input-pair2> out1=<trimmed-pair1> out2=<trimmed-pair2> ref=/bbmap/resources/adapters.fa ktrim=r k=23 mink=11 hdist=1 tpe tbo
# Just quality filtering
singularity run /users/PAS1117/osu9664/eMicro-Apps/BBTools-38.69.sif bbduk.sh in1=<trimmed-pair1> in2=<trimmed-pair2> qtrim=rl trimq=10 out1=<trimmed-and-quality-pair1> out2=<trimmed-and-quality-pair2>

Alternatively, run them both at the same time!

# Adapter and quality filtering *at the same time*
singularity run /users/PAS1117/osu9664/eMicro-Apps/BBTools-38.69.sif bbduk.sh in1=<input-pair1> in2=<input-pair2> out1=<qc-trimmed-pair1> out2=<qc-trimmed-pair2> ref=/bbmap/resources/adapters.fa ktrim=r k=23 mink=11 hdist=1 tpe tbo trimq=10 qtrim=rl minlength=35

BWA

Website: https://github.com/lh3/bwa

Reference: Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 25, 1754–60 (2009).

Short description: BWA is a software package for mapping DNA sequences against a large reference genome, such as the human genome. It consists of three algorithms: BWA-backtrack, BWA-SW and BWA-MEM. The first algorithm is designed for Illumina sequence reads up to 100bp, while the rest two for longer sequences ranged from 70bp to a few megabases. BWA-MEM and BWA-SW share similar features such as the support of long reads and chimeric alignment, but BWA-MEM, which is the latest, is generally recommended as it is faster and more accurate. BWA-MEM also has better performance than BWA-backtrack for 70-100bp Illumina reads.

Module use:

module use /fs/project/PAS1117/modulefiles
module load bwa/0.7.17-r1198

FastQC

Website: https://www.bioinformatics.babraham.ac.uk/projects/fastqc/

Short description: FastQC aims to provide a simple way to do some quality control checks on raw sequence data coming from high throughput sequencing pipelines. It provides a modular set of analyses which you can use to give a quick impression of whether your data has any problems of which you should be aware before doing any further analysis.

Singularity use:

module load singularity/current
singularity run /users/PAS1117/osu9664/eMicro-Apps/FastQC-0.11.8.sif <input-fastq-file>

Module use:

module use /fs/project/PAS1117/modulefiles
module load fastqc/0.11.5

Module use (directly from OSC):

module load fastqc/0.11.8

Kraken2

Website: https://github.com/DerrickWood/kraken2

Website: https://ccb.jhu.edu/software/kraken2/

Manual: https://github.com/DerrickWood/kraken2/blob/master/docs/MANUAL.markdown

Reference: Wood, D. E., Lu, J. & Langmead, B. Improved metagenomic analysis with Kraken 2. Genome Biol. 20, 257 (2019). https://doi.org/10.1186/s13059-019-1891-0

Short description: Kraken 2 is the newest version of Kraken, a taxonomic classification system using exact k-mer matches to achieve high accuracy and fast classification speeds. This classifier matches each k-mer within a query sequence to the lowest common ancestor (LCA) of all genomes containing the given k-mer. The k-mer assignments inform the classification algorithm

Singularity use:

module load singularity/current
singularity run /users/PAS1117/osu9664/eMicro-Apps/Kraken-2.1.2.sif

# To run against the standard database  # For PAS1573
singularity run /users/PAS1117/osu9664/eMicro-Apps/Kraken-2.1.2.sif --db /fs/project/PAS1573/modules/sequence_dbs/kraken2_dbs/standard --gzip-compressed --paired --classified-out Reads_R#.fastq.gz Reads_1.fastq.gz Reads_2.fastq.gz > kraken2_results

# To run against the standard database  # For PAS1117
singularity run /users/PAS1117/osu9664/eMicro-Apps/Kraken-2.1.2.sif --db /fs/project/PAS1117/modules/sequence_dbs/kraken2_dbs/standard --gzip-compressed --paired --classified-out Reads_R#.fastq.gz Reads_1.fastq.gz Reads_2.fastq.gz > kraken2_results

Note: Please check the kraken2_dbs folder for additional databases!

MultiQC

Website: https://multiqc.info/

Reference: Ewels, P., Magnusson, M., Lundin, S., & Käller, M. (2016). MultiQC: Summarize analysis results for multiple tools and samples in a single report. Bioinformatics, 32(19), 3047–3048. https://doi.org/10.1093/bioinformatics/btw354

Short description: MultiQC searches a given directory for analysis logs and compiles a HTML report. It’s a general use tool, perfect for summarising the output from numerous bioinformatics tools

Singularity use:

module load singularity/current
singularity run /users/PAS1117/osu9664/eMicro-Apps/MultiQC-1.7.sif

Module use:

module use /fs/project/PAS1117/modulefiles
module load MultiQC

NanoFilt

Website: https://github.com/wdecoster/nanofilt

Short Description: Filtering and trimming of long read sequencing data.

Reference: De Coster, W., D’Hert, S., Schultz, D. T., Cruts, M. & Van Broeckhoven, C. NanoPack: visualizing and processing long-read sequencing data. Bioinformatics 34, 2666–2669 (2018). https://doi.org/10.1093/bioinformatics/bty149

Singularity Use:

Forthcoming…

Module use:

module use /fs/project/PAS1117/modulefiles
module load Nanofilt/2.8.0

QUAST/MetaQUAST

Website: http://quast.sourceforge.net/

Manual: http://cab.cc.spbu.ru/quast/manual.html

Short Description: The project aim is to create easy-to-use tools for genome assemblies evaluation and comparison.

Reference: Gurevich, A., Saveliev, V., Vyahhi, N., & Tesler, G. (2013). QUAST: Quality assessment tool for genome assemblies. Bioinformatics, 29(8), 1072–1075. https://doi.org/10.1093/bioinformatics/btt086

Reference (using v4.x): Mikheenko, A., Valin, G., Prjibelski, A., Saveliev, V., & Gurevich, A. (2016). Icarus: Visualizer for de novo assembly evaluation. Bioinformatics, 32(21), 3321–3323. https://doi.org/10.1093/bioinformatics/btw379

Reference (using v5.x): Mikheenko, A., Prjibelski, A., Saveliev, V., Antipov, D., & Gurevich, A. (2018). Versatile genome assembly evaluation with QUAST-LG. Bioinformatics, 34(13), i142–i150. https://doi.org/10.1093/bioinformatics/bty266

Singularity use:

export SIF=/fs/project/PAS1573/sif

# QUAST
$SIF/quast.py contigs_1.fasta contigs_2.fasta --threads 48

# MetaQUAST
$SIF/metaquast.py contigs_1.fasta contigs_2.fasta ... --threads 48

# MetaQUAST can optionally be run with a list of reference genomes
$SIF/metaquast.py contigs_1.fasta contigs_2.fasta ... -r reference_1,reference_2,reference_3,... --threads 48

Module use:

module use /fs/project/PAS1117/modulefiles
module load quast/4.5

Samtools

Website: http://www.htslib.org/

Reference: Danecek, P. et al. Twelve years of SAMtools and BCFtools. Gigascience 10, 1–4 (2021).

Short description: Samtools is a suite of programs for interacting with high-throughput sequencing data

Module use:

module use /fs/project/PAS1117/modulefiles
module load samtools/1.10

SAMBAMBA

Website: https://github.com/lomereiter/sambamba

Reference:

Short description: Sambamba is a high performance highly parallel robust and fast tool (and library), written in the D programming language, for working with SAM and BAM files. Because of its efficiency Sambamba is an important work horse running in many sequencing centres around the world today.

Module use:

module use /fs/project/PAS1117/modulefiles
module load SAMBAMBA/0.7.1

Trimmomatic

Reference: Bolger, A. M., Lohse, M., & Usadel, B. (2014). Trimmomatic: A flexible trimmer for Illumina Sequence Data. Bioinformatics, btu170.

Short description: Identifies adapter sequences in raw sequencing reads and quality filters

Protocols.io: Trimmomatic on CyVerse

Singularity use:

module load singularity/current
singularity run /users/PAS1117/osu9664/eMicro-Apps/Trimmomatic-0.36.0.img PE -phred33 input_forward.fq.gz input_reverse.fq.gz output_forward_paired.fq.gz output_forward_unpaired.fq.gz output_reverse_paired.fq.gz output_reverse_unpaired.fq.gz ILLUMINACLIP:TruSeq3-PE.fa:2:30:10 LEADING:3 TRAILING:3 SLIDINGWINDOW:4:15 MINLEN:36

Module use:

module use /fs/project/PAS1117/modulefiles
module load trimmomatic/0.36-sulli
trimmomatic PE -phred33 input_forward.fq.gz input_reverse.fq.gz output_forward_paired.fq.gz output_forward_unpaired.fq.gz output_reverse_paired.fq.gz output_reverse_unpaired.fq.gz ILLUMINACLIP:TruSeq3-PE.fa:2:30:10 LEADING:3 TRAILING:3 SLIDINGWINDOW:4:15 MINLEN:36

Notes: Trimmomatic is a java jar file, and normally needs to be executed with “java -jar trimmomatic.jar [commands]”, but a tiny bash script has been written to automate this, which is why you can call “trimmomatic” without the java component.

Assembly

gsAssembler (aka Newbler)

Reference: Genivaldo, GZ; Silva, Bas E; Dutilh, David; Matthews, Keri; Elkins, Robert; Schmieder, Elizabeth A; Dinsdale, Robert A Edwards. “Combining de novo and reference-guided assembly with scaffold_builder”. Source Code Biomed Central. 8 (23). doi:10.1186/1751-0473-8-23.

Short description: De novo assembly based on overlap-layout-consensus

Notes on use: 454 Life Sciences was purchased by Roche in 2007 and shut down in 2013. There haven’t been any updates for the software since then, making it an increasingly aging tool.

Singularity use: We provide several versions of the tool on OSC, but please use the latest version unless you have a good reason otherwise (i.e. reproducing previous results). These are 2.3 and 2.5.

module load singularity/current
singularity run /users/PAS1117/osu9664/eMicro-Apps/Newbler-2.9.img -o output_dir /path/to/sff/file

The singularity container does contain the mapper, but for all intents and purposes, the tool uses runAssembly.

SPAdes

Reference: Bankevich A., Nurk S., Antipov D., Gurevich A., Dvorkin M., Kulikov A. S., Lesin V., Nikolenko S., Pham S., Prjibelski A., Pyshkin A., Sirotkin A., Vyahhi N., Tesler G., Alekseyev M. A., Pevzner P. A. SPAdes: A New Genome Assembly Algorithm and Its Applications to Single-Cell Sequencing. Journal of Computational Biology, 2012

Short description: SPAdes – St. Petersburg genome assembler – is an assembly toolkit containing various assembly pipelines

Protocols.io: Running SPAdes on CyVerse

Notes on use: SPAdes, as with many de Bruijn assemblers, can consume incredibly amounts of memory. In the context of viral metagenomics, it’s been known to use 2-3, and upwards of 6 TB of memory (and more if you give it more data!). There are multiple implementations on OSC using different runtimes and memory allocations.

Singularity use:

module load singularity/current
singularity run /users/PAS1117/osu9664/eMicro-Apps/SPAdes-3.15.5.sif

Module use:

module use /fs/project/PAS1117/modulefiles
module load spades/3.15.2

IDBA-UD

Reference: Peng, Y., et al. (2010) IDBA- A Practical Iterative de Bruijn Graph De Novo Assembler. RECOMB. Lisbon.

Peng, Y., et al. (2012) IDBA-UD: a de novo assembler for single-cell and metagenomic sequencing data with highly uneven depth, Bioinformatics, 28, 1420-1428.

Short description: IDBA-UD is a iterative De Bruijn Graph De Novo Assembler for Short Reads Sequencing data with Highly Uneven Sequencing Depth. It is an extension of IDBA algorithm.

Long description: IDBA-UD is a iterative De Bruijn Graph De Novo Assembler for Short Reads Sequencing data with Highly Uneven Sequencing Depth. It is an extension of IDBA algorithm. IDBA-UD also iterates from small k to a large k. In each iteration, short and low-depth contigs are removed iteratively with cutoff threshold from low to high to reduce the errors in low-depth and high-depth regions. Paired-end reads are aligned to contigs and assembled locally to generate some missing k-mers in low-depth regions. With these technologies, IDBA-UD can iterate k value of de Bruijn graph to a very large value with less gaps and less branches to form long contigs in both low-depth and high-depth regions. (taken from website)

Singularity use:

singularity run /users/PAS1117/osu9664/eMicro-Apps/IDBA-UD-1.1.3.sif --num_threads <threads> -r <reads-in-fasta-format> -o <output-dir>

Trinity

Reference: Grabherr MG, Haas BJ, Yassour M, Levin JZ, Thompson DA, Amit I, Adiconis X, Fan L, Raychowdhury R, Zeng Q, Chen Z, Mauceli E, Hacohen N, Gnirke A, Rhind N, di Palma F, Birren BW, Nusbaum C, Lindblad-Toh K, Friedman N, Regev A. Full-length transcriptome assembly from RNA-seq data without a reference genome. Nat Biotechnol. 2011 May 15;29(7):644-52. doi: 10.1038/nbt.1883. PubMed PMID: 21572440.

Short description: Trinity assembles transcript sequences from Illumina RNA-Seq data.

Singularity use:

module load singularity/current
singularity run /users/PAS1117/osu9664/eMicro-Apps/Trinity-2.9.0.sif

MEGAHIT

Reference: Li, D., Liu, C. M., Luo, R., Sadakane, K., & Lam, T. W. (2014). MEGAHIT: An ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph. Bioinformatics, 31(10), 1674–1676. https://doi.org/10.1093/bioinformatics/btv033

Short description: MEGAHIT is an ultra-fast and memory-efficient NGS assembler. It is optimized for metagenomes, but also works well on generic single genome assembly (small or mammalian size) and single-cell assembly.

Module use:

module use /fs/project/PAS1117/modulefiles
module load MEGAHIT/1.2.9

Singularity use:

module load singularity/current
singularity run /users/PAS1117/osu9664/eMicro-Apps/MEGAHIT-1.2.8.sif --k-list 21,41,61,81,99 -t <threads> -m 0.9 -1 <for-reads> -2 <rev-reads> -o <output-dir> --presets meta-sensitive (or meta-large for complex metagenomes like soils or oceans)

Binning

MetaBAT2

Reference: https://bitbucket.org/berkeleylab/metabat

Reference: Kang, D. D., Froula, J., Egan, R., & Wang, Z. (2015). MetaBAT, an efficient tool for accurately reconstructing single genomes from complex microbial communities. PeerJ, 3(8), e1165. https://doi.org/10.7717/peerj.1165

Short description: A robust statistical framework for reconstructing genomes from metagenomic data

Singularity use:

module load singularity/current
singularity run /users/PAS1117/osu9664/eMicro-Apps/MetaBAT2-2.14.sif

# Download test data (instructions from https://bitbucket.org/berkeleylab/metabat/wiki/Best%20Binning%20Practices)
wget https://portal.nersc.gov/dna/RD/Metagenome_RD/MetaBAT/Files/BestPractices/V2/CASE1/assembly.fa.gz
wget https://portal.nersc.gov/dna/RD/Metagenome_RD/MetaBAT/Files/BestPractices/V2/CASE1/depth.txt

# Run MetaBAT2
singularity run /users/PAS1117/osu9664/eMicro-Apps/MetaBAT2-2.14.sif -i assembly.fa.gz -a depth.txt -o resA1/bin -v

MaxBin2

Website: https://downloads.jbei.org/data/microbial_communities/MaxBin/MaxBin.html

Website (alt): https://sourceforge.net/projects/maxbin/

Reference (MaxBin1): Wu, Y.-W., Tang, Y.-H., Tringe, S. G., Simmons, B. A., & Singer, S. W. (2014). MaxBin: an automated binning method to recover individual genomes from metagenomes using an expectation-maximization algorithm. Microbiome, 2(1), 26. https://doi.org/10.1186/2049-2618-2-26

Reference (MaxBin2): Yu-Wei Wu, Blake A. Simmons, Steven W. Singer, MaxBin 2.0: an automated binning algorithm to recover genomes from multiple metagenomic datasets, Bioinformatics, Volume 32, Issue 4, 15 February 2016, Pages 605–607, https://doi.org/10.1093/bioinformatics/btv638

Short description: MaxBin2 is the next-generation of MaxBin () that supports multiple samples at the same time. MaxBin is a software for binning assembled metagenomic sequences based on an Expectation-Maximization algorithm. Users could understand the underlying bins (genomes) of the microbes in their metagenomes by simply providing assembled metagenomic sequences and the reads coverage information or sequencing reads. For users’ convenience MaxBin will report genome-related statistics, including estimated completeness, GC content and genome size in the binning summary page. Users could use MEGAN or similar software on MaxBin bins to find out the taxonomy of each bin after the binning process is finished.

Singularity use:

module load singularity/current
singularity run MaxBin2-2.2.6.sif

# Download test data
wget -O 20x.scaffold https://downloads.jbei.org/data/microbial_communities/MaxBin/getfile.php?20x.scaffold
wget -O 20x.abund https://downloads.jbei.org/data/microbial_communities/MaxBin/getfile.php?20x.abund

# Run MaxBin2
singularity run /users/PAS1117/osu9664/eMicro-Apps/MaxBin2-2.2.6.sif -contig 20x.scaffold -abund 20x.abund -out 20x.out -thread 4

Module use:

module use /fs/project/PAS1117/modulefiles
module load MaxBin/2.2.6

CONCOCT

Website: https://concoct.readthedocs.io/en/latest/

Reference: Alneberg, J., Bjarnason, B. S., de Bruijn, I., Schirmer, M., Quick, J., Ijaz, U. Z., … Quince, C. (2013).

CONCOCT: Clustering cONtigs on COverage and ComposiTion, 1–28. Retrieved from http://arxiv.org/abs/1312.4038

Short description: CONCOCT “bins” metagenomic contigs. Metagenomic binning is the process of clustering sequences into clusters corresponding to operational taxonomic units of some level.

Singularity use:

module load singularity/current
singularity run /users/PAS1117/osu9664/eMicro-Apps/CONCOCT-1.1.0.sif

See End-to-End Processing of a Microbial Metagenome for a more detailed explanation on usage.

MetaWRAP

Website: https://github.com/bxlab/metaWRAP

Reference: Uritskiy, G. V., DiRuggiero, J., & Taylor, J. (2018). MetaWRAP—a flexible pipeline for genome-resolved metagenomic data analysis. Microbiome, 6(1), 158. https://doi.org/10.1186/s40168-018-0541-1

Short description: MetaWRAP aims to be an easy-to-use metagenomic wrapper suite that accomplishes the core tasks of metagenomic analysis from start to finish: read quality control, assembly, visualization, taxonomic profiling, extracting draft genomes (binning), and functional annotation. Additionally, metaWRAP takes bin extraction and analysis to the next level (see module overview below). While there is no single best approach for processing metagenomic data, metaWRAP is meant to be a fast and simple approach before you delve deeper into parameterization of your analysis. MetaWRAP can be applied to a variety of environments, including gut, water, and soil microbiomes (see metaWRAP paper for benchmarks). Each individual module of metaWRAP is a standalone program, which means you can use only the modules you are interested in for your data.

Module use:

module use /fs/project/PAS1117/modulefiles
module load metaWRAP

DAS_Tool

Website: https://github.com/cmks/DAS_Tool

Reference: Sieber, C. M. K., Probst, A. J., Sharrar, A., Thomas, B. C., Hess, M., Tringe, S. G., & Banfield,

J. F. (2018). Recovery of genomes from metagenomes via a dereplication, aggregation and scoring strategy. Nature Microbiology, 3(7), 836–843. https://doi.org/10.1038/s41564-018-0171-1

Short description: DAS Tool is an automated method that integrates the results of a flexible number of binning algorithms to calculate an optimized, non-redundant set of bins from a single assembly.

Singularity use:

module load singularity/current
singularity run /users/PAS1117/osu9664/eMicro-Apps/DAS_Tool-1.1.1.sif

# You can test the installation (if you've git cloned the repository!)
git clone https://github.com/cmks/DAS_Tool.git
singularity run /users/PAS1117/osu9664/eMicro-Apps/DAS_Tool-1.1.1.sif -i DAS_Tool/sample_data/sample.human.gut_concoct_scaffolds2bin.tsv,DAS_Tool/sample_data/sample.human.gut_maxbin2_scaffolds2bin.tsv,DAS_Tool/sample_data/sample.human.gut_metabat_scaffolds2bin.tsv,DAS_Tool/sample_data/sample.human.gut_tetraESOM_scaffolds2bin.tsv -l concoct,maxbin,metabat,tetraESOM -c DAS_Tool/sample_data/sample.human.gut_contigs.fa --search_engine diamond -o DASToolTestRun --write_bins

Module use:

module use /fs/project/PAS1117/modulefiles
module load DAS_Tool

UniteM

Website: https://github.com/dparks1134/UniteM

Reference: https://github.com/dparks1134/UniteM (cite the repository)

Short description: UniteM is a software toolkit implementing different ensemble binning strategies for producing a non-redundant set of bins from the output of multiple binning methods.

Module use:

module use /fs/project/PAS1117/modulefiles
module load uniteM

Gene Callers

FragGeneScan

Reference: Mina Rho, Haixu Tang, and Yuzhen Ye. FragGeneScan: Predicting Genes in Short and Error-prone Reads. Nucl. Acids Res., 2010 doi: 10.1093/nar/gkq747

Short description: FragGeneScan is an application for finding (fragmented) genes in short reads

Singularity use:

module load singularity/current
singularity run /users/PAS1117/osu9664/eMicro-Apps/FragGeneScan-1.30.0.img

Prodigal

Reference: Hyatt, D. Prodigal (2.6.3) [Software]. Available at https://github.com/hyattpd/Prodigal

Short description: Fast, reliable protein-coding gene prediction for prokaryotic genomes.

Singularity use:

module load singularity/current
singularity run /users/PAS1117/osu9664/eMicro-Apps/Prodigal-2.6.3.img -i metagenome.fna -o coords.gbk -a proteins.faa -p anon

Module use:

module use /fs/project/PAS1117/modulefiles
module load prodigal/2.6.3
prodigal -i metagenome.fna -o coords.gbk -a proteins.faa -p anon

MetaGeneAnnotator (“MGA”)

Reference: Noguchi, H., Taniguchi, T., & Itoh, T. (2008). MetaGeneAnnotator: Detecting Species-Specific Patterns of Ribosomal Binding Site for Precise Gene Prediction in Anonymous Prokaryotic and Phage Genomes. DNA Research, 15(6), 387–396. https://doi.org/10.1093/dnares/dsn027

Short description: MetaGeneAnnotator is a gene-finding program for prokaryote and phage

Singularity use:

module load singularity/current
singularity run /users/PAS1117/osu9664/eMicro-Apps/MetaGeneAnnotator-1.1.0.img

MetaGeneMark

Website:

Reference: Zhu, W., Lomsadze, A. & Borodovsky, M. Ab initio gene identification in metagenomic sequences. Nucleic Acids Res. 38, 1–15 (2010).

Short description: ORF prediction…

Module use:

module use /fs/project/PAS1117/modulefiles
module load MetaGeneMark/3.38

gmhmmp <rest-of-command>

Annotation and Analyses

This is a catch-all category that doesn’t fit with the other sections.

CAT

Reference: https://github.com/dutilh/CAT

Short description: Contig Annotation Tool (CAT) is a pipeline for the taxonomic classification of long DNA sequences

and metagenome assembled genomes (MAGs/bins) of both known and (highly) unknown microorganisms, as generated by contemporary metagenomics studies.

Other notes: There are two versions of CAT. A pre-4.x version (“1.0.0”) and a post 4.x version (“4.3.3”). The new one is superior in all aspects, except database setup. The paths provided will not work unless you have the appropriate databases installed.

Singularity use:

module load singularity/current
singularity run /users/PAS1117/osu9664/eMicro-Apps/CAT-4.3.3.simg contigs -c {contigs fasta} -d 2019-03-31_CAT_database -t 2019-03-31_taxonomy

Module use:

module use /fs/project/PAS1117/modulefiles
module load CAT/4.3.3

Centrifuge

Website: http://www.ccb.jhu.edu/software/centrifuge

Reference: Kim, D., Song, L., Breitwieser, F. P., & Salzberg, S. L. (2016). Centrifuge: rapid and sensitive classification of metagenomic sequences. Genome Research, 26(12), 1721–1729. https://doi.org/10.1101/gr.210641.116

Short description: [Centrifuge] is a novel microbial classification engine that enables rapid, accurate and sensitive labeling of reads and quantification of species on desktop computers. The system uses a novel indexing scheme based on the Burrows-Wheeler transform (BWT) and the Ferragina-Manzini (FM) index, optimized specifically for the metagenomic classification problem. Centrifuge requires a relatively small index (4.7 GB for all complete bacterial and viral genomes plus the human genome) and classifies sequences at very high speed, allowing it to process the millions of reads from a typical high-throughput DNA sequencing run within a few minutes. Together these advances enable timely and accurate analysis of large metagenomics data sets on conventional desktop computers

Singularity use:

module load singularity/current
singularity run /users/PAS1117/osu9664/eMicro-Apps/Centrifuge-X.sif

Module use:

module use /fs/project/PAS1117/modulefiles
module load centrifuge/1.0.3

CheckM

Website: https://github.com/Ecogenomics/CheckM

Reference: Parks DH, Imelfort M, Skennerton CT, Hugenholtz P, Tyson GW. 2015. CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome Research, 25: 1043–1055.

Short description: CheckM provides a set of tools for assessing the quality of genomes recovered from isolates, single cells, or metagenomes. It provides robust estimates of genome completeness and contamination by using collocated sets of genes that are ubiquitous and single-copy within a phylogenetic lineage.

Singularity use:

module load singularity/current
singularity run /users/PAS1117/osu9664/eMicro-Apps/CheckM-1.0.18.sif

Diamond

Reference: B. Buchfink, Xie C., D. Huson, “Fast and sensitive protein alignment using DIAMOND”, Nature Methods 12, 59-60 (2015)

Short description: DIAMOND is a sequence aligner for protein and translated DNA searches, designed for high performance analysis of big sequence data.

Singularity use:

module load singularity/current
singularity run /users/PAS1117/osu9664/eMicro-Apps/Diamond-0.9.26.sif

Module use:

module use /fs/project/PAS1117/modulefiles
module load diamond/0.9.24

# OR
module load diamond/ 2.0.5

Prokka

Reference: Seemann T. Prokka: rapid prokaryotic genome annotation Bioinformatics 2014 Jul 15;30(14):2068-9. PMID:24642063

Short description: Prokka is a software tool to annotate bacterial, archaeal and viral genomes quickly and produce standards-compliant output files

Singularity use:

module load singularity/current
singularity run /users/PAS1117/osu9664/eMicro-Apps/Prokka-1.12.0.img

Module use:

module use /fs/project/PAS1117/modulefiles
module load Prokka/1.13

InterProScan

Website: https://github.com/ebi-pf-team/interproscan

Reference: Quevillon, E. et al. InterProScan: protein domains identifier. Nucleic Acids Res. 33, W116–W120 (2005).

Short description: InterPro is a database which integrates together predictive information about proteins’ function from a number of partner resources, giving an overview of the families that a protein belongs to and the domains and sites it contains.

Users who have novel nucleotide or protein sequences that they wish to functionally characterise can use the software package InterProScan to run the scanning algorithms from the InterPro database in an integrated way. Sequences are submitted in FASTA format. Matches are then calculated against all of the required member database’s signatures and the results are then output in a variety of formats.

Module use:

module use /fs/project/PAS1117/modulefiles
module load InterProScan/5.36-75.0

interproscan.sh <rest-of-command>

SortMeRNA

Website: https://github.com/biocore/sortmerna

Reference: Kopylova, E., Noé, L. & Touzet, H. SortMeRNA: fast and accurate filtering of ribosomal RNAs in metatranscriptomic data. Bioinformatics 28, 3211–3217 (2012).

Short description: SortMeRNA is a local sequence alignment tool for filtering, mapping and clustering.

The core algorithm is based on approximate seeds and allows for sensitive analysis of NGS reads. The main application of SortMeRNA is filtering rRNA from metatranscriptomic data. SortMeRNA takes as input files of reads (fasta, fastq, fasta.gz, fastq.gz) and one or multiple rRNA database file(s), and sorts apart aligned and rejected reads into two files. Additional applications include clustering and taxonomy assignation available through QIIME v1.9.1. SortMeRNA works with Illumina, Ion Torrent and PacBio data, and can produce SAM and BLAST-like alignments.

Module use:

module use /fs/project/PAS1117/modulefiles
module load SortMeRNA/4.2.0

sortmerna -h

PROSITE

Website:

Reference:

Short description:

Singularity use:

module load PROSITE/1.86
ps_scan.pl <rest-of-command>

HH-Suite

Website: https://github.com/soedinglab/hh-suite

Reference: Steinegger, M. et al. HH-suite3 for fast remote homology detection and deep protein annotation. BMC Bioinformatics 20, 473 (2019).

Short description: The HH-suite is an open-source software package for sensitive protein sequence searching based on the pairwise alignment of hidden Markov models (HMMs).

Module use:

module use /fs/project/PAS1117/modulefiles
module load hhsuite/3.2.0

MINCED

Website: https://github.com/ctSkennerton/minced

Reference: 1. Bland, C. et al. CRISPR Recognition Tool (CRT): a tool for automatic detection of clustered regularly interspaced palindromic repeats. BMC Bioinformatics 8, 209 (2007).

Short description: MinCED is a program to find Clustered Regularly Interspaced Short Palindromic Repeats (CRISPRs) in full genomes or environmental datasets such as assembled contigs from metagenomes. Iff you want to identify CRISPRs in raw short read data, in the size range of 100-200bp try using Crass (https://github.com/ctskennerton/Crass) MinCED runs from the command-line and was derived from CRT (http://www.room220.com/crt/)

Module use:

module use /fs/project/PAS1117/modulefiles
module load minced/1.0.0

Clust

Website: https://github.com/baselabujamous/clust

Reference: Abu-Jamous, B., & Kelly, S. (2018). Clust: automatic extraction of optimal co-expressed gene clusters from gene expression data. Genome Biology, 19(1), 172. https://doi.org/10.1186/s13059-018-1536-8

Short description: Clust is a fully automated method for identification of clusters (groups) of genes that are consistently co-expressed (well-correlated) in one or more heterogeneous datasets from one or multiple species.

Singularity use:

module load singularity/current
singularity run /users/PAS1117/osu9664/eMicro-Apps/clust-1.8.9.img data_path -o output_directory [...]

Please do read the extensive documentation on the Clust github page.

BamM

Website: http://ecogenomics.github.io/BamM/

Short description: Metagenomics-focused BAM file manipulation

Singularity use:

module load singularity/current
singularity run /users/PAS1117/osu9664/eMicro-Apps/BamM-1.7.0.sif

Note: This is no longer actively maintained. CoverM is a direct replacement.

CoverM

Website: https://github.com/wwood/CoverM

Short description: CoverM aims to be a configurable, easy to use and fast DNA read coverage and relative abundance calculator focused on metagenomics applications. CoverM calculates coverage of genomes/MAGs (coverm genome) or individual contigs (coverm contig). Calculating coverage by read mapping, its input can either be BAM files sorted by reference, or raw reads and reference FASTA sequences.

Singularity use:

For a directory of genome bins (each fasta file is a bin, all files having the “fna” extension) and the original fastq files used in the assembly…

module load singularity/current
singularity run /users/PAS1117/osu9664/eMicro-Apps/CoverM-0.6.1.sif genome --genome-fasta-directory <path-to-bins> -x fna --coupled <reads1.fastq> <reads2.fastq> --output-format sparse --min-read-percent-identity .95 --min-read-aligned-percent .75 --min-covered-fraction .75 > coverage_table.csv

GraftM

Website: https://github.com/geronimp/graftM

Reference: Boyd, J. A., Woodcroft, B. J., & Tyson, G. W. (2018). GraftM: a tool for scalable, phylogenetically informed classification of genes within metagenomes. Nucleic Acids Research, 46(10), e59–e59. https://doi.org/10.1093/nar/gky174

Short description: GraftM is a tool for finding genes of interest in metagenomes, metatranscriptomes, and whole

genomes. Using modular gene packages, GraftM will search the provided sequences using hmmsearch (HMMER) and place the identified sequences into a pre-constructed phylogenetic tree. The provides fast, phylogenetically informed community profiles and genome annotations.

Singularity use:

module load singularity/current
singularity run /users/PAS1117/osu9664/eMicro-Apps/GraftM-0.10.1.img

The latest version is 0.13.1. This will be updated.

Read2RefMapper

Website: https://bitbucket.org/bolduc/docker-read2refmapper

Protocols.io: Read2Ref on CyVerse

CyVerse App: https://de.cyverse.org/de/?type=apps&app-id=Read2RefMapper-1.1.0u3&system-id=agave

Short description: Read2RefMapper is a python-wrapper for a number of scripts and tools that allow for filtering coverage of BAM files against a reference dataset. It filters reads matching reference sequences for those references that are not covered over a specified threshold length, as well as alignment identity and alignment coverage. It is designed to be used in conjunction with Docker-BatchBowtie.

Singularity use:

module load singularity/current
singularity run /users/PAS1117/osu9664/eMicro-Apps/Read2RefMapper-1.1.1.simg --dir ${readsDir} --metagenome-sizes reads2refmapper_mysample.csv --num-threads 40 --coverages coverage_table.csv --cov_filter 70 --percent-id 0.95 --percent-aln 0.75 --coverage-mode tpmean --output-fmt png --dpi 300 --log read2refmapper.log

ClusterGenomes

Website: https://bitbucket.org/MAVERICLab/stampede-clustergenomes/

Short description: ClusterGenomes is a nucmer-based tool designed to cluster viral genomes. It can handle circular and short sequences with high accuracy.

Singularity use:

module load singularity/current

# Dereplicate
singularity run /users/PAS1117/osu9664/eMicro-Apps/ClusterGenomes-1.1.3.img -f <input-viral-genomes.fasta> -c <coverage> -i <identity> -o <output-directory>

Note: Both coverage and identity are 0 - 100, not 0.0 - 1.0.

DRAM

Website: https://github.com/shafferm/DRAM

Short description: DRAM (Distilled and Refined Annotation of MAGs [Metagenome Assembled Genomes]) is a tool for annotating metagenomic assembled genomes and VIRSorter identified viral contigs. DRAM annotates MAGs and viral contigs using KEGG (if provided by the user), UniRef90, [PFAM (https://pfam.xfam.org/), dbCAN, RefSeq viral, VOGDB and the MEROPS peptidase database as well as custom user databases. DRAM is ran in two stages. Additionally viral contigs are further analyzed to identify potential AMGs. This is done via assigning an auxilary score and flags representing the likelihood that a gene is metabolic and viral. The auxiliary score represents the confidence that a gene is viral in origin based on surrounding genes.

Module use: (This is always the most up-to-date version, barring the Wrighton lab’s constant updates!)

# For PAS1117
module use /fs/project/PAS1117/modulefiles
module load DRAM

DRAM.py annotate -i '<path-to-bins>/*.fa' -o annotation
DRAM.py distill -i annotation/annotations.tsv -o distill --trna_path annotation/trnas.tsv --rrna_path annotation/rrnas.tsv

# For PAS1573
export PATH=/fs/ess/PAS1573/modules/DRAM-1.4.0/bin:$PATH
DRAM.py annotate -i '<path-to-bins>/*.fa' -o annotation
DRAM.py distill -i annotation/annotations.tsv -o distill --trna_path annotation/trnas.tsv --rrna_path annotation/rrnas.tsv

Singularity use

Unfortunately, due to the size of the database, this is not currently possible. While we work on a solution, please use the module version!

dRep

Website: https://github.com/MrOlm/drep

Website: https://drep.readthedocs.io/en/master/

Short description: dRep is a python program for rapidly comparing large numbers of genomes. dRep can also “de-replicate” a genome set by identifying groups of highly similar genomes and choosing the best representative genome for each genome set.

Singularity use:

module load singularity/current
singularity run /users/PAS1117/osu9664/eMicro-Apps/dRep-2.3.2.sif

# You can test the installation
singularity run /users/PAS1117/osu9664/eMicro-Apps/dRep-2.3.2.sif bonus testDir --check_dependencies

# More rigorously check
git clone https://github.com/MrOlm/drep.git
cd drep/tests
singularity run /users/PAS1117/osu9664/eMicro-Apps/dRep-2.3.2.sif dereplicate output_dir -g genomes/*

# For genome de-replication
dRep.sif dereplicate outout_directory -g path/to/genomes/*.fasta

# To compare genomes
dRep.sif compare output_directory -g path/to/genomes/*.fasta

Module use:

module use /fs/project/PAS1117/modulefiles
module load dRep/2.4.2

NanoStat

Website: https://github.com/wdecoster/nanostat

Short Description: Calculate various statistics from a long read sequencing dataset in fastq, bam or albacore sequencing summary format.

Reference: De Coster, W., D’Hert, S., Schultz, D. T., Cruts, M. & Van Broeckhoven, C. NanoPack: visualizing and processing long-read sequencing data. Bioinformatics 34, 2666–2669 (2018). https://doi.org/10.1093/bioinformatics/bty149

Singularity Use:

Forthcoming…

Module use:

module use /fs/project/PAS1117/modulefiles
module load Nanostat/1.6.0

ViennaRNA

Website: https://www.tbi.univie.ac.at/RNA/index.html

Reference: Lorenz, R. et al. ViennaRNA Package 2.0. Algorithms Mol. Biol. 6, 26 (2011).

Short description:

Module use:

module use /fs/project/PAS1117/modulefiles
module load ViennaRNA/2.4.14

MetaPop

Website: https://github.com/metaGmetapop/metapop/

Reference: Coming soon!

Short description: MetaPop is a pipeline designed to facilitate the processing of sets of short read data mapped to reference genomes with the twin aims of calculating sample-level diversity metrics such as abundance, population diversity, and similarity across multiple samples, and assessing within-species diversity through the assessment of nucleotide polymorphisms and amino acid substitutions. To further facilitate understanding, the pipeline also produces graphical summaries of its results.

Singularity use:

# Load singularity
module load singularity

# Set variables
threads=40

# Inputs
input_contigs=data_dir/individual_fasta_dir/
input_coverage=data_dir/counts.txt
bam_dir=data_dir/BAMs

singularity run /users/PAS1117/osu9664/eMicro-Apps/MetaPop-0.35.sif -i $bam_dir -r $input_contigs --threads $threads -o $out_dir -n $input_coverage

MetaPop requires:

• input_contigs: a directory of fasta files representing the contigs/genomes - EACH genome must be its own FASTA file

• bam_dir: a directory containing BAM alignment files of reads against the contigs/genomes

• input_coverage: a tab-delimited file with the BAM filename (without the .bam extension) and the bp of that dataset

• out_dir: where to place the output files

Module use:

module use /fs/project/PAS1117/modulefiles
module load MetaPop/latest

python $(which metapop_main.py) -i $bam_dir -r $input_contigs --threads $threads -o $out_dir -n $input_coverage

MetaCHIP

Website: https://github.com/songweizhi/MetaCHIP

Reference: Song, W., Wemheuer, B., Zhang, S., Steensen, K. & Thomas, T. MetaCHIP: community-level horizontal gene transfer identification through the combination of best-match and phylogenetic approaches. Microbiome 7, 36 (2019).

Short description: MetaCHIP is a pipeline for reference-independent HGT identification at the community level.

Module use:

module use /fs/project/PAS1117/modulefiles
module load MetaCHIP

SingleM

Website: https://github.com/wwood/singlem

Short description: SingleM is a tool to find the abundances of discrete operational taxonomic units (OTUs) directly from shotgun metagenome data, without heavy reliance on reference sequence databases. It is able to differentiate closely related species even if those species are from lineages new to science.

Singularity use:

module load singularity/current
singularity run /users/PAS1117/osu9664/eMicro-Apps/SingleM-0.13.2.sif

# Generate OTU table from RAW metagenomic data
singularity run /users/PAS1117/osu9664/eMicro-Apps/SingleM-0.13.2.sif pipe --sequences my_sequences.fastq.gz --otu_table otu_table.csv --threads <threads>

# Summarize OTU table in Krona plot
singularity run /users/PAS1117/osu9664/eMicro-Apps/SingleM-0.13.2.sif summarise --input_otu_tables otu_table.csv --krona krona_plot.html

There are a lot more options are customization than is presented here. Check the documentation for more information. Remember, anything after “singlem” in a command can be copy-and-pasted after the “SingleM-0.13.2.sif” in the above examples.

VSEARCH

Website:https://github.com/torognes/vsearch

Reference: Rognes, T., Flouri, T., Nichols, B., Quince, C., & Mahé, F. (2016). VSEARCH: a versatile open source tool for metagenomics. PeerJ, 4(10), e2584. https://doi.org/10.7717/peerj.2584

Short description: VSEARCH is a fast, accurate and full-fledged alternative to USEARCH. It’s free, isn’t limited to 32-bit, but is only for nucleotide, not protein work. VSEARCH is “more accurate than USEARCH when performing searching, clustering, chimera detection and subsampling, while on a par with USEARCH for paired-ends read merging. VSEARCH is slower than USEARCH when performing clustering and chimera detection, but significantly faster when performing paired-end reads merging and dereplication.” (Rognes et al, 2016. PeerJ)

Long story short: it’s a free alternative to USEARCH’s 64-bit version. USEARCH does have a free 32-bit version, but that limits the available system memory to 4 GB, hardly sufficient to do large-scale metagenomic analyses.

Singularity use:

module load singularity/current
singularity run /users/PAS1117/osu9664/eMicro-Apps/VSEARCH-2.14.1.sif

Module use:

module use /fs/project/PAS1117/modulefiles
module load vsearch/2.6.0

Note: VSEARCH has a lot of options. So. Many.

Virus Analyses

“Consider something viral in your research” - Forest Rohwer

Cenote-Taker2

Website: https://github.com/mtisza1/Cenote-Taker2

Reference: Tisza, M. J., Belford, A. K., Domínguez-Huerta, G., Bolduc, B. & Buck, C. B. Cenote-Taker 2 democratizes virus discovery and sequence annotation. Virus Evol. 7, 1–12 (2021). doi:10.1093/ve/veaa100

Short description: Cenote-Taker 2 is a dual function bioinformatics tool. On the one hand, Cenote-Taker 2 discovers/predicts virus sequences from any kind of genome or metagenomic assembly. Second, virus sequences/genomes are annotated with a variety of sequences features, genes, and taxonomy. Either the discovery or the the annotation module can be used independently.

Singularity use:

module load singularity/current

# For PAS1117 users
module use /fs/project/PAS1117/modulefiles
module load singularityImages
Cenote-Taker2-2.1.3_osc.sif --contigs <contigs> --run_title <run title> --template_file <template file>

Notes: There is an extensive list of parameters for Cenote-Taker2 and their values. Call them with “–help”.

Notes: There is currently no eMicro equivalent, mainly due to the size of the required databases. We’re working to fix this issue.

For those who want to repeat the “defaults” of the CyVerse app:

module load singularity/current
singularity run /fs/project/PAS1117/modules/singularity/Cenote-Taker2-2.1.3_osc.sif --contigs testcontigs_DNA_ct2.fasta --run_title run_title --template_file 010226_6435_template.sbt --prune_prophage True --cpu 68 --mem 92 -am False --minimum_length_circular 1000 --minimum_length_linear 1000 --virus_domain_db standard --lin_minimum_hallmark_genes 1 --circ_minimum_hallmark_genes 1 --enforce_start_codon False --hhsuite_tool hhblits --isolation_source unknown --Environmental_sample False --molecule_type DNA --data_source original --filter_out_plasmids False --orf-within-orf False

CheckV

Website: https://bitbucket.org/berkeleylab/checkv

Reference: Nayfach, S. et al. CheckV assesses the quality and completeness of metagenome-assembled viral genomes. Nat. Biotechnol. (2020). doi:10.1038/s41587-020-00774-7

Short description: CheckV is a fully automated command-line pipeline for assessing the quality of single-contig viral genomes, including identification of host contamination for integrated proviruses, estimating completeness for genome fragments, and identification of closed genomes.

Singularity use:

module load singularity/current

# For PAS1117 users
module use /fs/project/PAS1117/modulefiles
module load singularityImages
CheckV-0.8.1.sif --help

# For eMicro
singularity run /users/PAS1117/osu9664/eMicro-Apps/CheckV-0.8.1.sif

CheckV Clustering

This uses two scripts already available in CheckV to deduplicate/dereplicate sequence data.

Website: https://bitbucket.org/berkeleylab/checkv

Reference: Nayfach, S. et al. CheckV assesses the quality and completeness of metagenome-assembled viral genomes. Nat. Biotechnol. (2020). doi:10.1038/s41587-020-00774-7

Short description: CheckV is a fully automated command-line pipeline for assessing the quality of single-contig viral genomes, including identification of host contamination for integrated proviruses, estimating completeness for genome fragments, and identification of closed genomes.

Singularity use:

module load singularity/current

# For PAS1117 users
module use /fs/project/PAS1117/modulefiles
module load singularityImages
CheckV-0.8.1-ClusterONLY.sif -i <input-fasta> -t <threads> -o <output-dir> --min-ani <min_ani> --min-qcov <min_query_coverage> --min-tcov <min_target_coverage>

# For eMicro and PAS1573
singularity run CheckV-0.8.1-ClusterONLY.sif -i <input-fasta> -t <threads> -o <output-dir>

# For additional help
CheckV-0.8.1-ClusterONLY.sif --help

Module use:

module use /fs/project/PAS1117/modulefiles
module load blast/2.11.0+
module load CheckV/2021.02.03
module load python/biopython3

CheckV-Deduplication.py -i <input-fasta> -t <threads> -o <output-dir> --min-ani <min_ani> --min-qcov <min_query_coverage> --min-tcov <min_target_coverage>

Notes: 206K viral contigs can be dereplicated to 52K in 1 hr 30 min

DeepVirFinder

Website: https://github.com/jessieren/DeepVirFinder

Reference: Ren, J. et al. Identifying viruses from metagenomic data by deep learning. (2018).

Short description: DeepVirFinder predicts viral sequences using deep learning method. The method has good prediction accuracy for short viral sequences, so it can be used to predict sequences from the metagenomic data.

Module use:

module use /fs/project/PAS1117/modulefiles
module load DeepVirFinder

Singularity use:

module load singularity/current

/users/PAS1117/osu9664/eMicro-Apps/DeepVirFinder.simg --help

DRAM-v

Website: https://github.com/shafferm/DRAM

Short description: DRAM (Distilled and Refined Annotation of MAGs [Metagenome Assembled Genomes]) is a tool for annotating metagenomic assembled genomes and VIRSorter identified viral contigs. DRAM annotates MAGs and viral contigs using KEGG (if provided by the user), UniRef90, [PFAM (https://pfam.xfam.org/), dbCAN, RefSeq viral, VOGDB and the MEROPS peptidase database as well as custom user databases. DRAM is ran in two stages. Additionally viral contigs are further analyzed to identify potential AMGs. This is done via assigning an auxilary score and flags representing the likelihood that a gene is metabolic and viral. The auxiliary score represents the confidence that a gene is viral in origin based on surrounding genes.

PAS1573 use: (This version is now out-of-date but these commands will still work)

export PATH=/fs/project/PAS1573/week10_pathways/DRAM/bin/:$PATH
# --skip_uniref if want faster, although, less sensitive results
DRAM-v.py annotate -i <path-to-VIRSorter_cat_contigs.fasta> --virsorter_affi_contigs <path-to-VIRSorter-VIRSorter_affi-contigs.tab> --output_dir DRAMv_annotate --threads 40
# optionally, --rrna_path dram_annotations/rrnas.tsv
DRAM-v.py distill -i DRAMv_annotate/annotations.tsv -o DRAMv_summarize

You’ll notice that the command to run the tool is different, this is because of the challenge in using Singularity to encapsulate the package + databases.

Module use: (This is always the most up-to-date version, barring the Wrighton lab’s constant updates!)

module use /fs/project/PAS1117/modulefiles
module load DRAM

DRAM-v.py annotate -i VIRSorter_cat1245.fasta -v VIRSorter_affi-contigs.tab -o viral_annotation
DRAM-v.py distill -i viral_annotation/annotations.tsv -o viral_annotation/distilled

IVA

Website: https://sanger-pathogens.github.io/iva/

Reference: Hunt, M., Gall, A., Ong, S. H., Brener, J., Ferns, B., Goulder, P., … Otto, T. D. (2015). IVA: Accurate de novo assembly of RNA virus genomes. Bioinformatics, 31(14), 2374–2376. https://doi.org/10.1093/bioinformatics/btv120

Short description: IVA is a de novo assembler designed to assemble virus genomes that have no repeat sequences, using Illumina read pairs sequenced from mixed populations at extremely high and variable depth.

Module use:

module use /fs/project/PAS1117/modulefiles
module load IVA

iva -f <forward-reads.fastq> -r <reverse-reads.fastq> <output-dir>

Singularity use:

module load singularity/current
singularity run /users/PAS1117/osu9664/eMicro-Apps/IVA-1.0.9.sif

# You can test the installation
singularity run /users/PAS1117/osu9664/eMicro-Apps/IVA-1.0.9.sif --test outdir

MARVEL

Website: https://github.com/LaboratorioBioinformatica/MARVEL

Reference: Amgarten, D., Braga, L. P. P., da Silva, A. M. & Setubal, J. C. MARVEL, a Tool for Prediction of Bacteriophage Sequences in Metagenomic Bins. Front. Genet. 9, 1–8 (2018).

Short description: MARVEL is a tool for recovery of draft phage genomes from whole community shotgun metagenomic sequencing data.

Module use:

module use /fs/project/PAS1117/modulefiles
module load MARVEL/0.2

Singularity use:

module load singularity/current
singularity run /users/PAS1117/osu9664/eMicro-Apps/MARVEL-0.1.simg

MetaPhinder

Website: https://github.com/vanessajurtz/MetaPhinder

Reference: Jurtz, V. I., Villarroel, J., Lund, O., Voldby Larsen, M., & Nielsen, M. (2016). MetaPhinder—Identifying Bacteriophage Sequences in Metagenomic Data Sets. PLOS ONE, 11(9), e0163111. https://doi.org/10.1371/journal.pone.0163111

Short description: Here we present MetaPhinder, a method to identify assembled genomic fragments (i.e.contigs) of phage origin in metagenomic data sets. The method is based on a comparison to a database of whole genome bacteriophage sequences, integrating hits to multiple genomes to accomodate for the mosaic genome structure of many bacteriophages. The method is demonstrated to out-perform both BLAST methods based on single hits and methods based on k-mer comparisons.

Singularity use:

coming soon…

Module use:

module use /fs/project/PAS1117/modulefiles
module load MetaPhinder

MetaPhinder.py -i <input-file> -o <directory> -d $BLAST_DB/ALL_140821_hr -b /fs/project/PAS1117/modules/MetaPhinder/bin/

Note: MetaPhinder’s help states that -o is a FILE, but specifying anything other than a directory (to be created) generates one of several errors (one is often: “Command line argument error”). Specifying a non-existent directory is the only way to avoid errors.

PhageTerm

Website: https://sourceforge.net/projects/phageterm/

Reference: Garneau, J. R., Depardieu, F., Fortier, L.-C., Bikard, D., & Monot, M. (2017). PhageTerm: a tool for fast and accurate determination of phage termini and packaging mechanism using next-generation sequencing data. Scientific Reports, 7(1), 8292. https://doi.org/10.1038/s41598-017-07910-5

Short description: Here, we developed a theoretical and statistical framework to analyze DNA termini and phage packaging mechanisms using next-generation sequencing data. PhageTerm was validated on a set of phages with well-established packaging mechanisms representative of the termini diversity: 5’cos (lambda), 3’cos (HK97), pac (P1), headful without a pac site (T4), DTR (T7) and host fragment (Mu). In addition, we determined the termini of 9 Clostridium difficile phages and 5 phages whose sequences where retrieved from the sequence read archive (SRA)

Singularity use:

module load singularity/current
singularity run /users/PAS1117/osu9664/eMicro-Apps/PhageTerm-1.0.12.sif
# OR
singularity run /users/PAS1117/osu9664/eMicro-Apps/PhageTerm-3.1.sif

Module use:

module use /fs/project/PAS1117/modulefiles
module load PhageTerm/1.0.11
# OR
module load PhageTerm/4.0.0

Note: PhageTerm is installed under numerous versions. Ensure you’re using the version you think you’re using.

PHANOTATE

Website: https://github.com/deprekate/PHANOTATE

Reference:

Short description: PHANOTATE is a tool to annotate phage genomes. It uses the assumption that non-coding bases in a phage genome is disadvantageous, and then populates a weighted graph to find the optimal path through the six frames of the DNA where open reading frames are beneficial paths, while gaps and overlaps are penalized paths.

Module use:

module use /fs/project/PAS1117/modulefiles
module load PHANOTATE/1.4.0

phanotate.py --help

PropagAtE

Website: https://github.com/AnantharamanLab/PropagAtE

Reference: Kieft, K. & Anantharaman, K. Deciphering active prophages from metagenomes. bioRxiv 2021.01.29.428894 (2021). doi:10.1101/2021.01.29.428894

Short description: PropagAtE (Prophage Activity Estimator) uses genomic coordinates of integrated prophage sequences

and short sequencing reads to estimate if a given prophage was in the lysogenic (dormant) or lytic (active) stage of infection. Prophages are designated according to a genomic/scaffold coordinate file, either manually generated by the user or taken directly from a VIBRANT (at least v1.2.1) output. The prophage:host read coverage ratio and corresponding effect size are used to estimate if the prophage was actively replicating its genome (significantly more prophage genome copies than host copies). PropagAtE is customizable to take in complete genomes or metagenomic scaffolds along with raw Illumina (short) reads, or instead take pre-aligned data files (sam or bam format). Threshold values are customizable but PropagAtE outputs clear “active” versus “dormant” estimations of given prophages with associated statistics.

Module use:

module use /fs/project/PAS1117/modulefiles
module load PropagAtE/1.0.0

PropagAtE_run.py --help

SAVAGE

Website: https://bitbucket.org/jbaaijens/savage

Reference: Baaijens, J. A., El Aabidine, A. Z., Rivals, E. & Schönhuth, A. De novo assembly of viral quasispecies using overlap graphs. Genome Res. 27, 835–848 (2017).

Short description: SAVAGE is a computational tool for reconstructing individual haplotypes of intra-host virus strains (a viral quasispecies) without the need for a high quality reference genome. SAVAGE makes use of either FM-index based data structures or ad-hoc consensus reference sequence for constructing overlap graphs from patient sample data. In this overlap graph, nodes represent reads and/or contigs, while edges reflect that two reads/contigs, based on sound statistical considerations, represent identical haplotypic sequence. Following an iterative scheme, a new overlap assembly algorithm that is based on the enumeration of statistically well-calibrated groups of reads/contigs then efficiently reconstructs the individual haplotypes from this overlap graph.

Module use:

module use /fs/project/PAS1117/modulefiles
module load SAVAGE

vConTACT2

vConTACT2 is a tool designed to classify viruses based on their shared gene content. It is intended for archaeal and bacterial viruses. It can work for some eukaryotic viruses, but may utterly fail or totally work - regardless - it hasn’t been vetted or tested for use with them.

Reference (V2): Bin Jang, H., Bolduc, B., Zablocki, O., Kuhn, J. H., Roux, S., Adriaenssens, E. M., … Sullivan, M. B. (2019). Taxonomic assignment of uncultivated prokaryotic virus genomes is enabled by gene-sharing networks. Nature Biotechnology. https://doi.org/10.1038/s41587-019-0100-8

Reference (Theory): Bolduc B, Jang H Bin, Doulcier G, You Z, Roux S, Sullivan MB. (2017). vConTACT: an iVirus tool to classify double-stranded DNA viruses that infect Archaea and Bacteria. PeerJ 5: e3243.

Protocols.io: Running vConTACT2 on VIRSorter output in CyVerse

Short description: Guilt-by-contig-association automatic classification of viral contigs

Singularity use:

module load singularity/current
singularity run /users/PAS1117/osu9664/eMicro-Apps/vConTACT2-0.11.1.sif

# For PAS1117 users
module load singularity/current
module use /fs/project/PAS1117/modulefiles
module load singularityImages
singularity run vConTACT2-0.11.1.sif

VIRSorter

Reference: Roux S, Enault F, Hurwitz BL, Sullivan MB. (2015) VirSorter: mining viral signal from microbial genomic data. PeerJ 3:e985 https://doi.org/10.7717/peerj.985

Short description: Identify viral contigs in a microbial metagenomes

Protocols.io: VIRSorter on CyVerse

Singularity use:

module load singularity/current
singularity run /users/PAS1117/osu9664/eMicro-Apps/VirSorter-1.0.5.img

VIBRANT

Website: https://github.com/AnantharamanLab/VIBRANT

Reference: Kieft, K., Zhou, Z., and Anantharaman, K. (2019). VIBRANT: Automated recovery, annotation and curation of microbial viruses, and evaluation of virome function from genomic sequences. BioRxiv 855387.

Short description: VIBRANT is a tool for automated recovery and annotation of bacterial and archaeal viruses, determination of genome completeness, and characterization of virome function from metagenomic assemblies. VIBRANT uses neural networks of protein annotation signatures and genomic features to maximize identification of highly diverse partial or complete viral genomes as well as excise integrated proviruses.

Singularity use:

module load singularity/current

# For eMicro and PAS1573
VIBRANT_DATA_PATH=/users/PAS1117/osu9664/eMicro-Apps/vibrant_dbs/20231102  # Optional
singularity run /users/PAS1117/osu9664/eMicro-Apps/VIBRANT-1.2.1.sif -i <input-fasta> -folder <output-dir>

# If VIBRANT_DATA_PATH is not specified, then you will need to specify -d /users/PAS1117/osu9664/eMicro-Apps/vibrant_dbs/20231102

# For PAS1117
module use /fs/project/PAS1117/modulefiles
module load singularityImages
VIBRANT-1.2.1.sif -i <input-fasta> -i <input-fasta> -folder <output-dir>

Note: There may be numerous DeprecationWarning. They can be safely ignored.

Module use:

module use /fs/project/PAS1117/modulefiles
module load VIBRANT/1.1.0
# OR
module load VIBRANT/1.2.1

ViralCC

Website: https://github.com/dyxstat/ViralCC.git

Reference: Du, Y., Fuhrman, J. A. & Sun, F. ViralCC retrieves complete viral genomes and virus-host pairs from metagenomic Hi-C data. Nat Commun 14, 502 (2023). https://doi.org/10.1038/s41467-023-35945-y

Short description: ViralCC is a new open-source metagenomic Hi-C-based binning pipeline to recover high-quality viral genomes. ViralCC not only considers the Hi-C interaction graph, but also puts forward a novel host proximity graph of viral contigs as a complementary source of information to the remarkably sparse Hi-C interaction map. The two graphs are then integrated together, followed by the Leiden graph clustering using the integrative graph to generate draft viral genomes.

Module use:

module use /fs/project/PAS1117/modulefiles
module load ViralCC/1.0.0
ViralCC pipeline -v <virus-contigs-FASTA> <sorted-BAM> <virus-contigs-CSV> <output-directory>

ViralRecall

Website: https://github.com/faylward/viralrecall

Reference: Aylward, F. O. & Moniruzzaman, M. ViralRecall-A Flexible Command-Line Tool for the Detection of Giant Virus Signatures in ’Omic Data. Viruses 13, 15–17 (2021).

Short description: ViralRecall is a flexible command-line tool for detecting signatures of giant viruses (NCLDV) in genomic data. Version 2 has been updated to focus more on NCLDV compared to version 1, but the original options are still available.

Module use:

module use /fs/project/PAS1117/modulefiles
module load Treemmer/Treemmer

python viralrecall.py -i examples/arm29B.fna -p test_outdir -t 2 -f

VIRIDIC

Website: http://rhea.icbm.uni-oldenburg.de/VIRIDIC/

Reference: Moraru, C., Varsani, A. & Kropinski, A. M. VIRIDIC — A Novel Tool to Calculate the Intergenomic Similarities of Viruses 12, 1268 (2020).

Short description:

Module use:

cp /fs/project/PAS1117/modules/viridic_v1.0_r3.6/* <current-directory>
./viridic.bash projdir=<output-dir> in=<fasta-file>

ViPTreeGen

Website: https://github.com/yosuken/ViPTreeGen

Reference: Nishimura, Y. et al. ViPTree: the viral proteomic tree server. Bioinformatics 1–2 (2017). doi:10.1093/bioinformatics/btx157

Short description: ViPTreeGen is a tool for automated generation of viral “proteomic tree” by computing genome-wide sequence similarities based on tBLASTx results. The original proteomic tree (i.e., “the Phage Proteomic Tree”) was developed by Rohwer and Edwards, 2002. A proteomic tree is a dendrogram that reveals global genomic similarity relationships between tens, hundreds, or thousands of viruses. It has been shown that viral groups identified in a proteomic tree well correspond to established viral taxonomies. The proteomic tree approach is effective to investigate genomes of newly sequenced viruses as well as those identified in metagenomes.

Module use:

module use /fs/project/PAS1117/modulefiles

ViPTreeGen --help

ViromeScan

Website: http://sourceforge.net/projects/viromescan/

Reference: Rampelli, S. et al. ViromeScan: a new tool for metagenomic viral community profiling. BMC Genomics 17, 165 (2016).

Short description: Tool for metagenomic viral community profiling

Module use:

module use /fs/project/PAS1117/modulefiles
module load viromescan
module load bowtie2/2.3.4.1
module load blast/2.4.0+

viromescan

VPF-Tools

Website: https://github.com/biocom-uib/vpf-tools

Reference: Pons, J. C. et al. VPF-Class: taxonomic assignment and host prediction of uncultivated viruses based on viral protein families. Bioinformatics 1–9 (2021). doi:10.1093/bioinformatics/btab026

Short description:

Module use:

module use /fs/project/PAS1117/modulefiles

Phigaro

Website: https://github.com/bobeobibo/phigaro

Reference: Starikova, E. V. et al. Phigaro: high-throughput prophage sequence annotation. Bioinformatics 36, 3882–3884 (2020).

Short description: Phigaro is a standalone command-line application that is able to detect prophage regions taking raw genome and metagenome assemblies as an input. It also produces dynamic annotated “prophage genome maps” and marks possible transposon insertion spots inside prophages. It is applicable for mining prophage regions from large metagenomic datasets.

Module use:

module use /fs/project/PAS1117/modulefiles
module load phigaro/2.2.3

phigaro -f <fasta-input> -o <output-file> -p --not-open -c $config

Note: $config is an environmental variable set to a specific file which information about the database locations

GRAViTy

Website: https://github.com/PAiewsakun/GRAViTy

Reference: Aiewsakun, P. & Simmonds, P. The genomic underpinnings of eukaryotic virus taxonomy: Creating a sequence-based framework for family-level virus classification. Microbiome 6, 1–24 (2018).

Short description:

Module use:

module use /fs/project/PAS1117/modulefiles
module load GRAViTy

Phylogenetics

BALi-Phy

Website: http://www.bali-phy.org/

Reference: 1. Redelings, B. D. Bali-Phy version 3: Model-based co-estimation of alignment and phylogeny. Bioinformatics 2–4 (2021). doi:10.1093/bioinformatics/btab129

Short description: BAli-Phy is software by Ben Redelings that estimates multiple sequence alignments and evolutionary trees from DNA, amino acid, or codon sequences. It uses likelihood-based evolutionary models of substitutions and insertions and deletions to place gaps. It has been used in published analyses on data sets up to 117 taxa.

Singularity use:

module load singularity/current
singularity run BALi-Phy-3.6.0.sif

BEAST2

Website: https://github.com/CompEvol/beast2, http://www.beast2.org/

Reference: Bouckaert, R. et al. BEAST 2.5: An advanced software platform for Bayesian evolutionary analysis. PLOS Comput. Biol. 15, e1006650 (2019).

Short description: BEAST is a cross-platform program for Bayesian inference using MCMC of molecular sequences. It is entirely orientated towards rooted, time-measured phylogenies inferred using strict or relaxed molecular clock models. It can be used as a method of reconstructing phylogenies but is also a framework for testing evolutionary hypotheses without conditioning on a single tree topology. BEAST uses MCMC to average over tree space, so that each tree is weighted proportional to its posterior probability. We include a simple to use user-interface program for setting up standard analyses and a suit of programs for analysing the results.

Module use:

module use /fs/project/PAS1117/modulefiles
module load BEAST2

ExaBayes

Website: https://sanger-pathogens.github.io/iva/

Reference: Aberer, A. J., Kobert, K. & Stamatakis, A. Exabayes: Massively parallel bayesian tree inference for the whole-genome era. Mol. Biol. Evol. 31, 2553–2556 (2014).

Short description: ExaBayes is a software package for Bayesian phylogenetic tree inference. It is particularly suitable for large-scale analyses on computer clusters.

Module use:

module use /fs/project/PAS1117/modulefiles
module load exa-bayes/1.4.1

GTDB-Tk

Website: https://github.com/Ecogenomics/GtdbTk

Reference: Chaumeil, P.-A., Mussig, A. J., Hugenholtz, P. & Parks, D. H. GTDB-Tk: a toolkit to classify genomes with the Genome Taxonomy Database. Bioinformatics 36, 1925–1927 (2019).

Short description: GTDB-Tk is a software toolkit for assigning objective taxonomic classifications to bacterial and archaeal genomes based on the Genome Database Taxonomy GTDB. It is designed to work with recent advances that allow hundreds or thousands of metagenome-assembled genomes (MAGs) to be obtained directly from environmental samples. It can also be applied to isolate and single-cell genomes.

Module use:

module use /fs/project/PAS1117/modulefiles
module load GTDB-Tk

IQ-Tree

Website: https://github.com/Cibiv/IQ-TREE

Reference: Nguyen, L.-T., Schmidt, H. A., von Haeseler, A. & Minh, B. Q. IQ-TREE: A Fast and Effective Stochastic Algorithm for Estimating Maximum-Likelihood Phylogenies. Mol. Biol. Evol. 32, 268–274 (2015).

Short description: The IQ-TREE software was created as the successor of IQPNNI and TREE-PUZZLE (thus the name IQ-TREE). IQ-TREE was motivated by the rapid accumulation of phylogenomic data, leading to a need for efficient phylogenomic software that can handle a large amount of data and provide more complex models of sequence evolution.

Module use:

module use /fs/project/PAS1117/modulefiles
module load IQ-TREE/2.0-rc1

MAFFT

Website: https://github.com/GSLBiotech/mafft

Reference: Katoh, K. & Standley, D. M. MAFFT Multiple Sequence Alignment Software Version 7: Improvements in Performance and Usability. Mol. Biol. Evol. 30, 772–780 (2013).

Reference (original): Katoh, K. MAFFT: a novel method for rapid multiple sequence alignment based on fast Fourier transform. Nucleic Acids Res. 30, 3059–3066 (2002).

Short description:

Module use:

module use /fs/project/PAS1117/modulefiles
module load mafft/7.429

Phylorank

Website: https://github.com/dparks1134/PhyloRank

Reference: https://github.com/dparks1134/PhyloRank (cite the github page)

Short description: PhyloRank provides functionality for calculating the relative evolutionary divergence (RED) of taxa in a tree and for finding the best placement of taxonomic labels in a tree.

Module use:

module use /fs/project/PAS1117/modulefiles
module load Phylorank

PhyML

Website: http://www.atgc-montpellier.fr/phyml/, https://github.com/stephaneguindon/phyml

Reference: Guindon, S. et al. New Algorithms and Methods to Estimate Maximum-Likelihood Phylogenies: Assessing the Performance of PhyML 3.0. Syst. Biol. 59, 307–321 (2010).

Short description: PhyML is a software package that uses modern statistical approaches to analyse alignments of nucleotide or amino acid sequences in a phylogenetic framework. The main tool in this package builds phylogenies under the maximum likelihood criterion. It implements a large number of substitution models coupled to efficient options to search the space of phylogenetic tree topologies. PhyTime is another tool in the PhyML package that focuses on divergence date estimation in a Bayesian setting. The main strengths of PhyTime lies in its ability to accommodate for uncertrainty in the placement of fossil calibration and the use of realistic models of rate variation along the tree. Finally, PhyREX fits the spatial-Lambda-Fleming-Viot model to geo-referenced genetic data. This model is similar to the structured coalescent but assumes that individuals are distributed along a spatial continuum rather than discrete demes. PhyREX can be used to estimate population densities and rates of dispersal. Its output can be processed by treeannotator (from the BEAST package) as well as SPREAD.

Module use:

module use /fs/project/PAS1117/modulefiles
module load PhyML/3.1

ProtTest

Website: https://github.com/ddarriba/prottest3

Reference: Darriba, D., Taboada, G. L., Doallo, R. & Posada, D. ProtTest 3: fast selection of best-fit models of protein evolution. Bioinformatics 27, 1164–1165 (2011).

Short description: ProtTest is a bioinformatic tool for the selection of best-fit models of aminoacid replacement for the data at hand. ProtTest makes this selection by finding the model in the candidate list with the smallest Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC) score or Decision Theory Criterion (DT). At the same time, ProtTest obtains model-averaged estimates of different parameters (including a model-averaged phylogenetic tree) and calculates their importance(Posada and Buckley 2004). ProtTest differs from its nucleotide analog jModeltest (Posada 2008) in that it does not include likelihood ratio tests, as not all models included in ProtTest are nested.

Module use:

module use /fs/project/PAS1117/modulefiles
module load prottest/3.4.2

PAML

Website: http://abacus.gene.ucl.ac.uk/software/paml.html

Reference: Yang, Z. PAML 4: Phylogenetic Analysis by Maximum Likelihood. Mol. Biol. Evol. 24, 1586–1591 (2007).

Short description: PAMLis a package of programs for phylogenetic analyses of DNA and protein sequences using maximum likelihood (ML). The programs may be used to compare and test phylogenetic trees, but their main strengths lie in the rich repertoire of evolutionary models implemented, which can be used to estimate parameters in models of sequence evolution and to test interesting biological hypotheses. Uses of the programs include estimation of synonymous and nonsynonymous rates (dN and dS) between two protein-coding DNA sequences, inference of positive Darwinian selection through phylogenetic comparison of protein-coding genes, reconstruction of ancestral genes and proteins for molecular restoration studies of extinct life forms, combined analysis of heterogeneous data sets from multiple gene loci, and estimation of species divergence times incorporating uncertainties in fossil calibrations

Module use:

module use /fs/project/PAS1117/modulefiles
module load PAML

Rascal

Website: ftp://ftp-igbmc.u-strasbg.fr/pub/RASCAL (no longer available?)

Reference: Thompson, J. D., Thierry, J. C. & Poch, O. RASCAL: Rapid scanning and correction of multiple sequence alignments. Bioinformatics 19, 1155–1161 (2003).

Short description:

Module use:

module use /fs/project/PAS1117/modulefiles
module load IQ-TREE/2.0-rc1

RevBayes

Website: https://revbayes.github.io/

Reference: Höhna, S. et al. RevBayes: Bayesian Phylogenetic Inference Using Graphical Models and an Interactive Model-Specification Language. Syst. Biol. 65, 726–736 (2016).

Short description: RevBayes provides an interactive environment for statistical computation in phylogenetics. It is primarily intended for modeling, simulation, and Bayesian inference in evolutionary biology, particularly phylogenetics. However, the environment is quite general and can be useful for many complex modeling tasks.

Module use:

module use /fs/project/PAS1117/modulefiles
module load RevBayes

TIM

Website: https://github.com/RomainBlancMathieu/TIM

Reference:

Short description: TIM detects and maps interactions between organisms onto a phylogenetic tree of a target group of organisms. Interactions are predicted from a species co-occurence-based network (such as one generated by FlashWeave).

TIM assumes that evolutionarily related organisms (refer to as query) interact with evolutionary related organisms (subject) (The reciprocal is not true).

Module use:

module use /fs/project/PAS1117/modulefiles
module load TIM/TIM
cp -r /fs/project/PAS1117/modules/TIM/TIM . && cd TIM
main.py Picornavirales.nwk connections.txt POS
downstream.py

Treemmer

Website: https://github.com/fmenardo/Treemmer

Reference: Menardo, F. et al. Treemmer: a tool to reduce large phylogenetic datasets with minimal loss of diversity. BMC Bioinformatics 19, 164 (2018).

Short description: Treemmer, a simple tool to evaluate the redundancy of phylogenetic trees and reduce their complexity by eliminating leaves that contribute the least to the tree diversity.

Singularity use:

coming soon…

Module use:

module use /fs/project/PAS1117/modulefiles
module load Treemmer/Treemmer

Treemmer_v0.3.py --help

Miscellaneous

SRA Toolkit

Website: https://www.ncbi.nlm.nih.gov/sra/docs/toolkitsoft/

Website 2: https://github.com/ncbi/sra-tools/wiki

Singularity use:

module load singularity/current
singularity run /users/PAS1117/osu9664/eMicro-Apps/SRA_Toolkit.sif

Module use:

module use /fs/project/PAS1117/modulefiles
module load sratoolkit/2.10.7

Entrez Direct

Website: https://www.ncbi.nlm.nih.gov/books/NBK179288/

Short Description: Entrez Direct (EDirect) provides access to the NCBI’s suite of interconnected databases (publication, sequence, structure, gene, variation, expression, etc.) from a Unix terminal window. Search terms are entered as command-line arguments. Individual operations are connected with Unix pipes to allow construction of multi-step queries. Selected records can then be retrieved in a variety of formats.

Module use:

module use /fs/project/PAS1117/modulefiles
module load Entrez-Direct

Clinker

Website: https://github.com/gamcil/clinker

Reference: Gilchrist, C. L. M. & Chooi, Y.-H. clinker & clustermap.js: automatic generation of gene cluster comparison figures. Bioinformatics (2021). doi:10.1093/bioinformatics/btab007

Short description: Gene cluster comparison figure generator - clinker is a pipeline for easily generating publication-quality gene cluster comparison figures.

Module use:

module use /fs/project/PAS1117/modulefiles
module load Clinker/Clinker
clinker --help

KronaTools

Website: https://github.com/marbl/Krona/tree/master/KronaTools

Manual: https://github.com/marbl/Krona/wiki/KronaTools

Reference: Ondov BD, Bergman NH, and Phillippy AM. Interactive metagenomic visualization in a Web browser. BMC Bioinformatics. 2011 Sep 30; 12(1):385.

Short description: Krona Tools is a set of scripts to create Krona charts from several Bioinformatics tools as well as from text and XML files.

Module use:

module use /fs/project/PAS1117/modulefiles
module load KronaTools/2.8
# There are a large number of kt* tools available, see the documentation for a full list
ktImportTaxonomy --help

SpClust

Website: https://github.com/johnymatar/SpCLUST

Reference:

Short description: SpCLUST is a package for divergent nucleotide sequences clustering. Contrarely to traditional clustering methods that focuses on the speed of clustering highly similar sequences, SpCLUST uses a Machine Learning Gaussian Mixture Model and targets the clustering accuracy of divergent sequences with the best possible speed. The current version of SpCLUST uses Edgar, R.C.’s MUSCLE module (www.drive5.com) for sequences alignment.

Singularity use:

module load singularity/current

singularity run SpCLUST.sif mpispclust -in test.fasta -out mpispclust_results.txt -alignMode fast -mdist BLOSUM62 -seqtype Amino
# OR
mpiexec -n 4 SpCLUST.sif spclust -in test.fasta -out spclust_results.txt -alignMode fast -mdist BLOSUM62 -seqtype Amino

SuperCRUNCH

Website: https://github.com/dportik/SuperCRUNCH

Reference: Portik, D. M. & Wiens, J. J. SuperCRUNCH: A bioinformatics toolkit for creating and manipulating supermatrices and other large phylogenetic datasets. Methods Ecol. Evol. 11, 763–772 (2020).

Short description: SuperCRUNCH is a python toolkit for creating and working with phylogenetic datasets. SuperCRUNCH can be run using any set of sequence data, as long as sequences are in fasta format with standard naming conventions

Module use:

module use /fs/project/PAS1117/modulefiles
module load SuperCRUNCH

Nonpareil

Website: http://enve-omics.ce.gatech.edu/nonpareil/, https://github.com/lmrodriguezr/nonpareil

Reference: Rodriguez-R, L. M., Gunturu, S., Tiedje, J. M., Cole, J. R. & Konstantinidis, K. T. Nonpareil 3: Fast Estimation of Metagenomic Coverage and Sequence Diversity. mSystems 3, 1–9 (2018).

Short description:

Module use:

module use /fs/project/PAS1117/modulefiles
module load Nonpareil

VG-Flow

Website: https://bitbucket.org/jbaaijens/vg-flow

Reference:

Short description: VG-Flow uses a de novo approach that enables full-length haplotype reconstruction from pre-assembled contigs of complex mixed samples.

Importance notice: This requires a FREE Gurobi academic license: https://user.gurobi.com/download/licenses/free-academic

Singularity use:

module load singularity/current
module use /fs/project/PAS1117/modulefiles
module load singularityImages

# If you haven't YET installed a license key
vg-flow.sif grbgetkey <long-license-key-that-was-generated-at-sign-up>

# Create the variation graph
vg-flow.sif build_graph_msga.py -f example/forward.fastq -r example/reverse.fastq -c example/input.fasta -vg vg -t 4

# Build the haplotypes
vg-flow.sif vg-flow.py -m 10 -c 20 node_abundance.txt contig_graph.final.gfa

Note: This was installed prior to tool updates

PEAR

Website: https://cme.h-its.org/exelixis/web/software/pear/

Reference:

Short description: PEAR is an ultrafast, memory-efficient and highly accurate pair-end read merger. It is fully parallelized and can run with as low as just a few kilobytes of memory.

PEAR evaluates all possible paired-end read overlaps and without requiring the target fragment size as input. In addition, it implements a statistical test for minimizing false-positive results. Together with a highly optimized implementation, it can merge millions of paired end reads within a couple of minutes on a standard desktop computer.

Module use:

module use /fs/project/PAS1117/modulefiles
module load PEAR/0.9.11

Bioscripts-2.7 and Bioscripts

Website: https://github.com/christophertbrown/bioscripts27, https://github.com/christophertbrown/bioscripts

Reference:

Short description: Useful scripts for working with genomics and sequencing data

Module use:

module use /fs/project/PAS1117/modulefiles
module load bioscripts/bioscripts27
# OR
module load bioscripts/bioscripts3

MMSeqs2

Website: https://github.com/soedinglab/MMseqs2

Reference: Steinegger, M. & Söding, J. MMseqs2 enables sensitive protein sequence searching for the analysis of massive data sets. Nat. Biotechnol. 35, 2–4 (2017).

Short description: MMseqs2 (Many-against-Many sequence searching) is a software suite to search and cluster huge protein and nucleotide sequence sets. MMseqs2 is open source GPL-licensed software implemented in C++ for Linux, MacOS, and (as beta version, via cygwin) Windows. The software is designed to run on multiple cores and servers and exhibits very good scalability. MMseqs2 can run 10000 times faster than BLAST. At 100 times its speed it achieves almost the same sensitivity. It can perform profile searches with the same sensitivity as PSI-BLAST at over 400 times its speed.

Module use:

module use /fs/project/PAS1117/modulefiles
module load MMseqs2

CD-HIT

Website: https://github.com/weizhongli/cdhit, http://cd-hit.org

Reference: Fu, L., Niu, B., Zhu, Z., Wu, S. & Li, W. CD-HIT: accelerated for clustering the next-generation sequencing data. Bioinformatics 28, 3150–3152 (2012).

Short description: CD-HIT is a very widely used program for clustering and comparing protein or nucleotide sequences

Module use:

module use /fs/project/PAS1117/modulefiles
module load cdhit/4.6.1

Singularity use:

module load singularity/current

# For eMicro users
singularity run /users/PAS1117/osu9664/eMicro-Apps/CD-HIT-4.8.1.sif

By default, the Singularity/Apptainer container uses the “cd-hit” program. If you want to use the other cd-hit tools, use exec

singularity exec /users/PAS1117/osu9664/eMicro-Apps/cd-hit
singularity exec /users/PAS1117/osu9664/eMicro-Apps/cd-hit-2d
singularity exec /users/PAS1117/osu9664/eMicro-Apps/cd-hit-est
singularity exec /users/PAS1117/osu9664/eMicro-Apps/cd-hit-454
singularity exec /users/PAS1117/osu9664/eMicro-Apps/cd-hit-dup
...

BLAST+

Website:

Reference:

Short description:

Module use:

module use /fs/project/PAS1117/modulefiles
module load blast/2.8.1+

Clust

Website: https://github.com/BaselAbujamous/clust

Reference: Abu-Jamous, B. & Kelly, S. Clust: automatic extraction of optimal co-expressed gene clusters from gene expression data. Genome Biol. 19, 172 (2018).

Short description: Optimised consensus clustering of one or more heterogeneous datasets.

Module use:

module use /fs/project/PAS1117/modulefiles
module load clust/1.8.9

Singularity use:

module load singularity/current

# For PAS1117 users
module use /fs/project/PAS1117/modulefiles
module load singularityImages
clust-1.8.9.img --help

Bowtie2

Website: http://bowtie-bio.sourceforge.net/bowtie2/index.shtml

Reference: Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–9 (2012).

Short description: Bowtie 2 is an ultrafast and memory-efficient tool for aligning sequencing reads to long reference sequences. It is particularly good at aligning reads of about 50 up to 100s or 1,000s of characters, and particularly good at aligning to relatively long (e.g. mammalian) genomes. Bowtie 2 indexes the genome with an FM Index to keep its memory footprint small: for the human genome, its memory footprint is typically around 3.2 GB. Bowtie 2 supports gapped, local, and paired-end alignment modes.

Module use:

module use /fs/project/PAS1117/modulefiles
module load bowtie2/2.4.1

Note: We have A LOT of bowtie2 versions, be aware that they may be updated more frequently than this site!

Jellyfish

Website: http://www.genome.umd.edu/jellyfish.html

Reference: Marcais, G. & Kingsford, C. A fast, lock-free approach for efficient parallel counting of occurrences of k-mers. Bioinformatics 27, 764–770 (2011).

Short description: Jellyfish is a tool for fast, memory-efficient counting of k-mers in DNA. A k-mer is a substring of length k, and counting the occurrences of all such substrings is a central step in many analyses of DNA sequence. JELLYFISH can count k-mers quickly by using an efficient encoding of a hash table and by exploiting the “compare-and-swap” CPU instruction to increase parallelism.

Module use:

module use /fs/project/PAS1117/modulefiles
module load jellyfish/2.2.10

WIsH

Website: https://github.com/soedinglab/WIsH

Reference: Galiez, C., Siebert, M., Enault, F., Vincent, J. & Söding, J. WIsH: Who is the host? Predicting prokaryotic hosts from metagenomic phage contigs. Bioinformatics 1–2 (2017). doi:10.1093/bioinformatics/btx383

Short description: WIsH can identify bacterial hosts from metagenomic data, keeping good accuracy even on smaller contigs.

Module use:

module use /fs/project/PAS1117/modulefiles
module load WiSH

# Taken from the website
WIsH -c build -g prokaryoteGenomesDir -m modelDir
WIsH -c predict -g phageContigsDir -m modelDir -r outputResultDir -b 1

Singularity use:

module load singularity/current
singularity run /users/PAS1117/osu9664/eMicro-Apps/WIsH-1.0.0.sif

iPHoP

Website: https://bitbucket.org/srouxjgi/iphop

Reference: Roux, S., Camargo, A.P., Coutinho, F.H., Dabdoub, S.M., Dutilh, B.E., Nayfach, S. and Tritt, A., 2023. iPHoP: An integrated machine learning framework to maximize host prediction for metagenome-derived viruses of archaea and bacteria. PLoS biology, 21(4), p.e3002083. https://doi.org/10.1371/journal.pbio.3002083.

Short description: iPHoP stands for integrated Phage H**ost **Prediction. It is an automated command-line pipeline for predicting host genus of novel bacteriophages and archaeoviruses based on their genome sequences.

Module use:

module use /fs/project/PAS1117/modulefiles
module load iPHoP/1.0.0

# Taken from the website
# Download test file
wget https://bitbucket.org/srouxjgi/iphop/raw/7319bbb6d3c4a8dac5e4bd613847c40ad99e8a73/test/test_input_phages.fna

# Run with TEST database
iphop predict --fa_file test_input_phages.fna --db_dir $DB_TEST --out_dir iphop_test_results

# Alternatively, run with FULL database
iphop predict --fa_file test_input_phages.fna --db_dir $DB --out_dir iphop_test_results
### Welcome to iPHoP ###
Looks like everything is now set up, we will first clean up the input file, and then we will start the host prediction steps themselves
[1/1/Run] Running blastn against genomes...
[1/3/Run] Get relevant blast matches...
[2/1/Run] Running blastn against CRISPR...
[2/2/Run] Get relevant crispr matches...
[3/1/Run] Running WIsH...
[3/2/Run] Get relevant WIsH hits...
[4/1/Run] Running VHM s2 similarities...
[4/2/Run] Get relevant VHM hits...
[5/1/Run] Running PHP...
[5/2/Run] Get relevant PHP hits...
[6/1/Run] Running RaFAH...
[6/2/Run] Get relevant RaFAH scores...
[6.1/1/Run] Running Diamond comparison to RaFAH references...
[5/2/Run] Get AAI distance to RaFAH refs...
write
[7] Aggregating all results and formatting for TensorFlow...

...
...
...

[9/1.1] Preparing data for aggregated score ...
[9/1.2] Run classifier for aggregated score ...
[INFO kernel.cc:1153] Loading model from path
[INFO decision_forest.cc:617] Model loaded with 500 root(s), 689556 node(s), and 30 input feature(s).
[INFO abstract_model.cc:1063] Engine "RandomForestOptPred" built
[INFO kernel.cc:1001] Use fast generic engine
[9/2] Combining all results (Blast, CRISPR, iPHoP, and RaFAH) in a single file: iphop_test_results_2nd/Wdir/All_combined_scores.csv
[10/1] Preparing the detailed output...
[10/2] Preparing the iPHoP-only result file, linking viruses to individual genomes (iphop_test_results_2nd/Host_prediction_to_genome_m90.csv) ...
[10/3] Preparing the combined iPHoP / RaFAH output summarized at the genus rank (iphop_test_results_2nd/Host_prediction_to_genus_m90.csv) ...

Notes: Both a $DB and $TB_TEST system variable exist, depending on if you’re testing your dataset, or you wish to run the full database.

Singularity use: (Experimental!)

module use singularity/current
singularity run /users/PAS1117/osu9664/eMicro-Apps/iPHoP-1.1.0.sif

PICRUSt

Website: https://github.com/picrust/picrust, http://picrust.github.io/picrust/index.html

Reference: Langille, M. G. I. et al. Predictive functional profiling of microbial communities using 16S rRNA marker gene sequences. Nat. Biotechnol. 31, 814–821 (2013).

Short description: PICRUSt (pronounced “pie crust”) is a bioinformatics software package designed to predict metagenome functional content from marker gene (e.g., 16S rRNA) surveys and full genomes.

Module use:

module use /fs/project/PAS1117/modulefiles
module load PICRUSt/1.1.3

RDP Classifier

Website: https://github.com/rdpstaff/classifier, https://rdp.cme.msu.edu/classifier/classifier.jsp

Reference: Wang, Q., Garrity, G. M., Tiedje, J. M. & Cole, J. R. Naïve Bayesian Classifier for Rapid Assignment of rRNA Sequences into the New Bacterial Taxonomy. Appl. Environ. Microbiol. 73, 5261–5267 (2007).

Short description: The RDP Classifier is a naive Bayesian classifier which was developed to provide rapid taxonomic placement based on rRNA sequence data. The RDP Classifier can rapidly and accurately classify bacterial and archaeal 16s rRNA sequences, and Fungal LSU sequences. It provides taxonomic assignments from domain to genus, with confidence estimates for each assignment. The RDP Classifier likely can be adapted to additional phylogenetically coherent bacterial taxonomies.

Module use:

module use /fs/project/PAS1117/modulefiles
module load rdp_classifier/2.3

MASH

Website: https://github.com/marbl/Mash

Reference: Ondov, B. D. et al. Mash: fast genome and metagenome distance estimation using MinHash. Genome Biol. 17, 132 (2016).

Short description: Fast genome and metagenome distance estimation using MinHash

Module use:

module use /fs/project/PAS1117/modulefiles
module load MASH/1.1.1

MUMmer

Website: http://mummer.sourceforge.net/

Reference: Kurtz, S. et al. Versatile and open software for comparing large genomes. Genome Biol. 5, 12 (2004).

Short description: MUMmer is a system for rapidly aligning entire genomes, whether in complete or draft form. For example, MUMmer 3.0 can find all 20-basepair or longer exact matches between a pair of 5-megabase genomes in 13.7 seconds, using 78 MB of memory, on a 2.4 GHz Linux desktop computer. MUMmer can also align incomplete genomes; it can easily handle the 100s or 1000s of contigs from a shotgun sequencing project, and will align them to another set of contigs or a genome using the NUCmer program included with the system. If the species are too divergent for a DNA sequence alignment to detect similarity, then the PROmer program can generate alignments based upon the six-frame translations of both input sequences

Module use:

module use /fs/project/PAS1117/modulefiles
module load MUMmer/3.23

HMMER3

Website: http://hmmer.org/

Reference: Eddy, S. R. Accelerated Profile HMM Searches. PLoS Comput. Biol. 7, e1002195 (2011).

Short description: HMMER is used for searching sequence databases for sequence homologs, and for making sequence alignments. It implements methods using probabilistic models called profile hidden Markov models (profile HMMs).

Module use:

module use /fs/project/PAS1117/modulefiles
module load hmmer3/3.1b2

MUSCLE

Website: http://www.drive5.com/muscle/

Reference: Edgar, R. C. MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acid Res. 32, 1792–1797 (2004).

Short description: MUSCLE is one of the best-performing multiple alignment programs according to published benchmark tests, with accuracy and speed that are consistently better than CLUSTALW. MUSCLE can align hundreds of sequences in seconds.

Module use:

module use /fs/project/PAS1117/modulefiles
module load muscle/3.8.31

tRNA-Scan SE

Website: https://github.com/UCSC-LoweLab/tRNAscan-SE

Reference: Lowe, T. M. & Eddy, S. R. tRNAscan-SE: A Program for Improved Detection of Transfer RNA Genes in Genomic Sequence. Nucleic Acids Res. 25, 0955–0964 (1997).

Short description: We describe a program, tRNAscan-SE, which identifies 99–100% of transfer RNA genes in DNA sequence while giving less than one false positive per 15 gigabases.

Module use:

module use /fs/project/PAS1117/modulefiles
module load tRNAscan-SE/1.23

MCL

Website: https://micans.org/mcl/

Reference: Enright, a J., Van Dongen, S. & Ouzounis, C. a. An efficient algorithm for large-scale detection of protein families. Nucleic Acids Res. 30, 1575–84 (2002).

Short description: The MCL algorithm is short for the Markov Cluster Algorithm, a fast and scalable unsupervised cluster algorithm for graphs (also known as networks) based on simulation of (stochastic) flow in graphs.

Module use:

module use /fs/project/PAS1117/modulefiles
module load mcl/14.137

BAMM

Website: http://bamm-project.org/index.html

Website: https://github.com/macroevolution/bamm

Reference: Rabosky, D. L. Automatic Detection of Key Innovations, Rate Shifts, and Diversity-Dependence on Phylogenetic Trees. PLoS One 9, e89543 (2014).

Short description: BAMM (Bayesian analysis of macroevolutionary mixtures) is a program for modeling complex dynamics of speciation, extinction, and trait evolution on phylogenetic trees. The program is oriented entirely towards detecting and quantifying heterogeneity in evolutionary rates.

Singularity use:

module load singularity/current

# For PAS1117 users
module use /fs/project/PAS1117/modulefiles
module load singularityImages
BAMM-2.5.0.sif --help

# For eMicro
singularity run /users/PAS1117/osu9664/eMicro-Apps/BAMM-2.5.0.sif

Astral

Website: https://github.com/smirarab/ASTRAL

Reference: Zhang, C., Rabiee, M., Sayyari, E. & Mirarab, S. ASTRAL-III: polynomial time species tree reconstruction from partially resolved gene trees. BMC Bioinformatics 19, 153 (2018).

Short description: ASTRAL is a tool for estimating an unrooted species tree given a set of unrooted gene trees.

Singularity use:

module load singularity/current

# For PAS1117 users
module use /fs/project/PAS1117/modulefiles
module load singularityImages
Astral-5.7.8.sif

# For eMicro
singularity run /users/PAS1117/osu9664/eMicro-Apps/Astral-5.7.8.sif

Seq-Gen

Website: http://tree.bio.ed.ac.uk/software/seqgen/

Website: https://github.com/rambaut/Seq-Gen

Reference:

Short description: Seq-Gen is a program that will simulate the evolution of nucleotide or amino acid sequences along a phylogeny, using common models of the substitution process.

Singularity use:

module load singularity/current

# For PAS1117 users
module use /fs/project/PAS1117/modulefiles
module load singularityImages
Seq-Gen-1.3.4.sif

# For eMicro
singularity run /users/PAS1117/osu9664/eMicro-Apps/Seq-Gen-1.3.4.sif

BioKIT

Website: https://github.com/JLSteenwyk/BioKIT

Reference: Steenwyk, J. L. et al. BioKIT: a versatile toolkit for processing and analyzing diverse types of sequence data. Genetics iyac079 (2022) doi:10.1093/genetics/iyac079.

Short description: BioKIT is a UNIX shell toolkit for processing molecular sequence data.

Singularity use:

module load singularity/current

# For PAS1117 users
module use /fs/project/PAS1117/modulefiles
module load singularityImages
BioKIT-0.0.9.sif -h

# For eMicro
singularity run /users/PAS1117/osu9664/eMicro-Apps/BioKIT-0.0.9.sif -h

MIGRATE

Website:

Reference: Beerli, P., Ashki, H., Mashayekhi, S. & Palczewski, M. Population divergence time estimation using individual lineage label switching. G3 Genes|Genomes|Genetics 12, (2022).

Short description: Migrate estimates effective population sizes,past migration rates between n population assuming a migration matrix model with asymmetric migration rates and different subpopulation sizes, and population divergences or admixture. Migrate uses Bayesian inference to jointly estimate all parameters.

Singularity use:

module load singularity/current

# For PAS1117 users
module use /fs/project/PAS1117/modulefiles
module load singularityImages
MIGRATE-5.0.4.sif -help

# For eMicro
singularity run /users/PAS1117/osu9664/eMicro-Apps/MIGRATE-5.0.4.sif -help

Note: Though built with threads, this is a non-MPI version.

DELINEATE

Website: https://github.com/jeetsukumaran/delineate

Reference: Sukumaran, J., Holder, M. T. & Knowles, L. L. Incorporating the speciation process into species delimitation. PLOS Comput. Biol. 17, e1008924 (2021).

Short description: DELINEATE is an approach to species delimitation that incorporates an extended model of speciation to discriminate between population isolation and speciation boundaries in genomic structure. Given a tree of population lineages inferred under the classical “censored coalescent” (Rannala and Yang, 2003; Yang and Rannala 2010), now more commonly known as the “multipecies coalescent” or MSC (Degnan and Rosenberg, 2009), this package will calculate the probabilities of different organizations of the population lineages into species under the Protracted Birth Death model of (Etienne et al, 2012)

Singularity use:

module load singularity/current

# For PAS1117 users
module use /fs/project/PAS1117/modulefiles
module load singularityImages
DELINEATE-1.2.2.sif delineate-estimate --help

# For eMicro
singularity run /users/PAS1117/osu9664/eMicro-Apps/DELINEATE-1.2.2.sif delineate-estimate --help

# To run summarize
singularity run /users/PAS1117/osu9664/eMicro-Apps/DELINEATE-1.2.2.sif delineate-summarize --help

Note: To successfully run the singularity container, you must specify delineate-estimate or delineate-summarize

RANGER-DTL

Website: https://compbio.engr.uconn.edu/software/RANGER-DTL/

Reference: Bansal, M. S., Kellis, M., Kordi, M. & Kundu, S. RANGER-DTL 2.0: rigorous reconstruction of gene-family evolution by duplication, transfer and loss. Bioinformatics 34, 3214–3216 (2018).

Short description: RANGER-DTL 2.0 (short for Rapid ANalysis of Gene family Evolution using Reconciliation-DTL) is a software package for inferring gene family evolution by speciation, gene duplication, horizontal gene transfer, and gene loss. The software takes as input a gene tree (rooted or unrooted) and a rooted species tree and reconciles the two by postulating speciation, duplication, transfer, and loss events.

Singularity use:

module load singularity/current

# For PAS1117 users
module use /fs/project/PAS1117/modulefiles
module load singularityImages
RANGER-DTL-2.0.sif <name-of-program>

# For eMicro
singularity run /users/PAS1117/osu9664/eMicro-Apps/RANGER-DTL-2.0.sif <name-of-program>

Note: RANGER-DTL consists of a suite of programs, such as OptRoot, Ranger-DTL, OptResolutions-DTL, and others. Please check out the manual for additional information.

MAMMaL

Website:

Reference:

Short description: (M)ultinomial (A)pproximate (M)ixture (Ma)ximum (L)ikelihood. The main program mammal takes as input a number of classes, a sequence file and a tree and outputs estimated frequencies for classes using the methods described in Susko, Lincker and Roger (2018).

Singularity use:

module load singularity/current

# For PAS1117 users
module use /fs/project/PAS1117/modulefiles
module load singularityImages
MAMMaL-1.1.3.sif

# For eMicro
singularity run /users/PAS1117/osu9664/eMicro-Apps/MAMMaL-1.1.3.sif

Note:

PAUP*

Website: http://paup.phylosolutions.com/

Reference: Swofford, D. L. 2003. PAUP*. Phylogenetic Analysis Using Parsimony (*and Other Methods). Version 4. Sinauer Associates, Sunderland, Massachusetts.

Short description: Phylogenetic Analysis Using Parsimony *and other methods

Singularity use:

module load singularity/current

# For PAS1117 users
module use /fs/project/PAS1117/modulefiles
module load singularityImages
PAUP-4a168.sif --help

# For eMicro
singularity run /users/PAS1117/osu9664/eMicro-Apps/PAUP-4a168.sif --help

TICR

Website: https://github.com/nstenz/TICR

Reference: Stenz, N. W. M., Larget, B., Baum, D. A. & Ané, C. Exploring Tree-Like and Non-Tree-Like Patterns Using Genome Sequences: An Example Using the Inbreeding Plant Species Arabidopsis thaliana (L.) Heynh. Syst. Biol. 64, 809–823 (2015).

Short description: These scripts can be utilized to perform highly parallelized concordance analyses on any given alignment, with a particular focus on very large datasets which may include dozens of taxa and may span entire chromosomes or genomes.

Singularity use:

module load singularity/current

# For PAS1117 users
module use /fs/project/PAS1117/modulefiles
module load singularityImages
TICR.sif <name-of-program>

# For eMicro
singularity run /users/PAS1117/osu9664/eMicro-Apps/TICR.sif <name-of-program>

VAMB

Website: https://github.com/RasmussenLab/vamb

Reference: Nissen, J. N. et al. Improved metagenome binning and assembly using deep variational autoencoders. Nat. Biotechnol. 39, 555–560 (2021).

Short description: Vamb is a metagenomic binner which feeds sequence composition information from a contig catalogue and co-abundance information from BAM files into a variational autoencoder and clusters the latent representation.

Singularity use:

module load singularity/current

# For PAS1117 users
module use /fs/project/PAS1117/modulefiles
module load singularityImages
VAMB-3.0.2.sif

# For eMicro
singularity run /users/PAS1117/osu9664/eMicro-Apps/VAMB-3.0.2.sif

RNAmmer

Website: https://services.healthtech.dtu.dk/service.php?RNAmmer-1.2

Reference: Lagesen, K. et al. RNAmmer: consistent and rapid annotation of ribosomal RNA genes. Nucleic Acids Res. 35, 3100–3108 (2007).

Short description: The RNAmmer 1.2 server predicts 5s/8s, 16s/18s, and 23s/28s ribosomal RNA in full genome sequences.

Singularity use:

module load singularity/current

# For PAS1117 users
module use /fs/project/PAS1117/modulefiles
module load singularityImages
RNAmmer-1.2.sif

# For eMicro
singularity run /users/PAS1117/osu9664/eMicro-Apps/RNAmmer-1.2.sif

ExpressBetaDiversity

Website: https://github.com/dparks1134/ExpressBetaDiversity

Reference: Parks, D. H. & Beiko, R. G. Measures of phylogenetic differentiation provide robust and complementary insights into microbial communities. ISME J. 7, 173–183 (2013).

Short description: Open-source software implementing the phylogenetic β-diversity measures.

Singularity use:

module load singularity/current

# For PAS1117 users
module use /fs/project/PAS1117/modulefiles
module load singularityImages
EBD-1.0.10.sif

# For eMicro
singularity run /users/PAS1117/osu9664/eMicro-Apps/EBD-1.0.10.sif

Note: Many users may find one of the EBD scripts useful: convertToEBD.py, which is used to convert from Unifrac/QIIME file formats to an EBD format.

module load singularity/current
singularity exec /users/PAS1117/osu9664/eMicro-Apps/EBD-1.0.10.sif convertToEBD.py --help

DeePhage

Website: https://github.com/shufangwu/DeePhage

Reference: Shufang Wu, Zhencheng Fang, Jie Tan, Mo Li, Congmin Xu, and Huaiqiu Zhu. DeePhage: distinguish temperate phage-derived and virulent phage-derived sequence in metavirome data using deep learning.

Short description: DeePhage is designed to identify metavirome sequences as temperate phage-derived and virulent phage-derived sequences. The program calculate a score reflecting the likelihood of each input fragment as temperate phage-derived and virulent phage-derived sequences.

Singularity use:

module load singularity/current

# For PAS1117 users
module use /fs/project/PAS1117/modulefiles
module load singularityImages
DeePhage.sif

# For eMicro
singularity run /users/PAS1117/osu9664/eMicro-Apps/DeePhage.sif

# To run with GPU enabled
module load singularity
module load cuda
singularity run --nv /users/PAS1117/osu9664/eMicro-Apps/DeePhage.sif example.fna deephage_results.csv

For CUDA, ensure that you request a GPU-enabled node with “#SBATCH –gpus-per-node=1”