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BioHPC Cloud:
: User Guide

 


BioHPC Cloud Software

There is 859 software titles installed in BioHPC Cloud. The sofware is available on all machines (unless stated otherwise in notes), complete list of programs is below, please click on a title to see details and instructions. Tabular list of software is available here

Please read details and instructions before running any program, it may contain important information on how to properly use the software in BioHPC Cloud.

3d-dna, 454 gsAssembler or gsMapper, a5, ABRicate, ABruijn, ABySS, AdapterRemoval, adephylo, Admixtools, Admixture, agrep, albacore, Alder, AlleleSeq, ALLMAPS, ALLPATHS-LG, Alphafold, AMOS, AMPHORA, amplicon.py, AMRFinder, analysis, ANGSD, Annovar, antiSMASH, anvio, apollo, arcs, ARGweaver, Arlequin, ART, aspera, assembly-stats, ASTRAL, atac-seq-pipeline, ataqv, athena_meta, ATLAS, Atlas-Link, ATLAS_GapFill, atom, ATSAS, Augustus, AWS command line interface, AWS v2 Command Line Interface, axe, axel, BactSNP, bakta, bam2fastx, bamtools, bamUtil, BarNone, Basset, BayeScan, Bayescenv, baypass, BBmap, BCFtools, bcl2fastq, BCP, Beagle, Beast2, bedops, BEDtools, bfc, bgc, bgen, bigQF, bigWig, bioawk, biobambam, Bioconductor, biom-format, BioPerl, BioPython, Birdsuite, Bismark, blasr, BLAST, BLAST_to_BED, blast2go, BLAT, BlobToolKit, BLUPF90, BMGE, bmtagger, Boost, Bowtie, Bowtie2, BPGA, Bracken, BRAKER, BRAT-NextGen, BreedingSchemeLanguage, breseq, brocc, BSseeker2, BUSCO, BWA, bwa-meth, cactus, CAFE, canu, CAP3, caper, CarveMe, catch, cBar, CBSU RNAseq, CCTpack, cd-hit, cdbfasta, CEGMA, CellRanger, cellranger-arc, cellranger-atac, cellranger-dna, centrifuge, centroFlye, CFM-ID, CFSAN SNP pipeline, CheckM, chimera, chromosomer, Circlator, Circos, Circuitscape, CITE-seq-Count, clues, CLUMPP, clust, Clustal Omega, CLUSTALW, Cluster, cmake, CNVnator, compat, CONCOCT, Conda, Cooler, copyNumberDiff, cortex_var, CRISPRCasFinder, CRISPResso, CrossMap, CRT, cuda, Cufflinks, cutadapt, cuteSV, dadi, dadi-1.6.3_modif, danpos, dDocent, DeconSeq, Deepbinner, DeepTE, deepTools, defusion, delly, DESMAN, destruct, DETONATE, diamond, diploSHIC, discoal, Discovar, Discovar de novo, distruct, DiTASiC, DIYABC, Docker, dREG, dREG.HD, drep, drive, Drop-seq, dropEst, dropSeqPipe, dsk, dssat, Dsuite, dTOX, duphold, dynare, ea-utils, ecopcr, ecoPrimers, ectyper, EDGE, edirect, EDTA, eems, EgaCryptor, EGAD, EIGENSOFT, EMBOSS, EMIRGE, Empress, entropy, epa-ng, ephem, epic2, ermineJ, ete3, EVM, exabayes, exonerate, ExpansionHunterDenovo-v0.8.0, eXpress, FALCON, FALCON_unzip, Fast-GBS, fasta, FastANI, fastcluster, FastME, FastML, fastp, FastQ Screen, fastq_pair, fastq_species_detector, FastQC, fastqsplitter, fastsimcoal26, fastStructure, FastTree, FASTX, feh, FFmpeg, fineRADstructure, fineSTRUCTURE, FIt-SNE, flash, flash2, flexbar, Flexible Adapter Remover, Flye, FMAP, FragGeneScan, FragGeneScan, freebayes, FSA, FunGene Pipeline, G-PhoCS, GADMA, GAEMR, Galaxy in Docker, Galaxy Server, GATK, gatk4, gatk4amplicon.py, Gblocks, GBRS, gcc, GCTA, GDAL, gdc-client, GEM library, GEMMA, GENECONV, geneid, GeneMark, GeneMarker, Genome STRiP, GenomeMapper, GenomeStudio (Illumina), GenomeThreader, genometools, GenomicConsensus, gensim, GEOS, germline, gerp++, GET_PHYLOMARKERS, GffCompare, gffread, giggle, glactools, GlimmerHMM, GMAP/GSNAP, GNU Compilers, GNU parallel, go-perl, GO2MSIG, GoShifter, gradle-4.4, graftM, GraPhlAn, graphviz, GRiD, Grinder, GROMACS, GSEA, gsort, GTDB-Tk, GTFtools, Gubbins, GUPPY, hail, HapCompass, HAPCUT, HAPCUT2, hapflk, HaploMerger, Haplomerger2, haplostrips, HapSeq2, HarvestTools, haslr, hdf5, hget, hh-suite, HiC-Pro, HiCExplorer, HISAT2, HMMER, Homer, HOTSPOT, HTSeq, htslib, humann, HUMAnN2, hyperopt, HyPhy, hyphy-analyses, iAssembler, IBDLD, idba, IDBA-UD, IDP-denovo, idr, IgBLAST, IGoR, IGV, IMa2, IMa2p, IMAGE, ImageJ, ImageMagick, Immcantation, impute2, IMSA-A, INDELseek, infernal, Infomap, InStruct, Intel MKL, InteMAP, InterProScan, ipyrad, IQ-TREE, iRep, jags, Jane, java, jbrowse, JCVI, jellyfish, JoinMap, juicer, julia, jupyter, kallisto, Kent Utilities, keras, khmer, kinfin, king, KmerFinder, KmerGenie, kraken, kSNP, kWIP, LACHESIS, lammps, LAST, lastz, lcMLkin, LDAK, LeafCutter, leeHom, lep-anchor, Lep-MAP3, lftp, Liftoff, Lighter, LinkedSV, LINKS, LocARNA, LocusZoom, lofreq, longranger, LS-GKM, LTR_retriever, LUCY, LUCY2, LUMPY, lyve-SET, MACE, MACS, MaCS simulator, MACS2, MAFFT, mafTools, Magic-BLAST, magick, MAKER, mapDamage, MAQ, MARS, MASH, mashtree, Mashtree, MaSuRCA, MATLAB, Mauve, MaxBin, McClintock, mccortex, mcl, MCscan, MCScanX, medusa, megahit, MeGAMerge, MEGAN, MELT, MEME Suite, MERLIN, MetaBAT, MetaCRAST, metaCRISPR, MetAMOS, MetaPathways, MetaPhlAn, metaron, MetaVelvet, MetaVelvet-SL, MGmapper, Migrate-n, mikado, MinCED, Minimac3, Minimac4, minimap2, mira, miRDeep2, MISO (misopy), MITObim, MiXCR, MixMapper, MKTest, mlift, mlst, MMAP, MMSEQ, MMseqs2, MMTK, modeltest, MODIStsp-2.0.5, module, moments, mono, monocle3, mosdepth, mothur, MrBayes, mrsFAST, msld, MSMC, msprime, MSR-CA Genome Assembler, msstats, MSTMap, mugsy, MultiQC, multiz-tba, MUMandCo, MUMmer, muscle, MUSIC, Mutation-Simulator, muTect, MZmine, nag-compiler, nanofilt, Nanopolish, ncftp, NECAT, Nemo, Netbeans, NEURON, new_fugue, Nextflow, NextGenMap, nf-core/rnaseq, ngmlr, NGS_data_processing, NGSadmix, ngsDist, ngsF, ngsLD, NgsRelate, ngsTools, NGSUtils, NINJA, NLR-Annotator, NLR-Parser, Novoalign, NovoalignCS, nQuire, NRSA, NuDup, nvidia-docker, nvtop, Oases, OBITools, Octave, OMA, openmpi, OrthoFinder, orthologr, Orthomcl, pacbio, PacBioTestData, PAGIT, paleomix, PAML, panaroo, pandas, pandaseq, pandoc, PanPhlAn, Panseq, Parsnp, PASA, PASTEC, PAUP*, pb-assembly, pbalign, pbbam, pbh5tools, PBJelly, pblat, pbmm2, PBSuite, PCAngsd, pcre, pcre2, PeakRanger, PeakSplitter, PEAR, PEER, PennCNV, peppro, PfamScan, pgap, PGDSpider, ph5tools, Phage_Finder, PHAST, phenopath, Phobius, PHRAPL, PHYLIP, PhyloCSF, phyloFlash, phylophlan, PhyloPhlAn2, phylophlan3, PhyML, Picard, pigz, Pilon, Pindel, piPipes, PIQ, PlasFlow, platanus, Platypus, plink, plink2, Plotly, Point Cloud Library, popbam, PopCOGenT, PopLDdecay, Porechop, poretools, portcullis, pplacer, PRANK, preseq, primalscheme, prinseq, prodigal, progenomics, progressiveCactus, PROJ, prokka, Proseq2, ProtExcluder, protolite, PSASS, psmc, psutil, purge_dups, pyani, PyCogent, pycoQC, pyfaidx, pyGenomeTracks, PyMC, pymol-open-source, pyopencl, pypy, pyRAD, Pyro4, PySnpTools, python, PyTorch, PyVCF, QIIME, QIIME2, QTCAT, Quake, Qualimap, QuantiSNP2, QUAST, quickmerge, QUMA, R, RACA, racon, RADIS, RadSex, RagTag, rapt, RAPTR-SV, RATT, RAxML, raxml-ng, Ray, rclone, Rcorrector, RDP Classifier, REAGO, REAPR, Red, ReferenceSeeker, regenie, Relate, RelocaTE2, Repbase, RepeatMasker, RepeatModeler, RERconverge, RFMix, RGAAT, rgdal, RGI, Rgtsvm, ripgrep, rJava, RNAMMER, rnaQUAST, Rnightlights, Roary, Rockhopper, rphast, Rqtl, Rqtl2, RSEM, RSeQC, RStudio, rtfbs_db, ruby, sabre, SaguaroGW, salmon, Sambamba, samblaster, sample, SampleTracker, samplot, samtabix, Samtools, Satsuma, Satsuma2, SCALE, scanorama, scikit-learn, Scoary, scythe, seaborn, SecretomeP, selscan, Sentieon, seqkit, SeqPrep, seqtk, Seurat, sf, sgrep, sgrep sorted_grep, SHAPEIT, SHAPEIT4, shasta, Shiny, shore, SHOREmap, shortBRED, SHRiMP, sickle, sift4g, SignalP, SimPhy, simuPOP, singularity, sinto, sistr_cmd, SKESA, skewer, SLiM, SLURM, smcpp, smoove, SMRT Analysis, SMRT LINK, snakemake, snap, SnapATAC, SNAPP, snATAC, SNeP, Sniffles, snippy, snp-sites, SnpEff, SNPgenie, SNPhylo, SNPsplit, SNVPhyl, SOAP2, SOAPdenovo, SOAPdenovo-Trans, SOAPdenovo2, SomaticSniper, sorted_grep, spaceranger, SPAdes, SPALN, SparCC, SPARTA, sqlite, SRA Toolkit, srst2, stacks, Stacks 2, stairway-plot, stampy, STAR, Starcode, statmodels, STITCH, STPGA, StrainPhlAn, strawberry, Strelka, stringMLST, StringTie, STRUCTURE, Structure_threader, subread, supernova, SURPI, sutta, SV-plaudit, SVDetect, SVseq2, svtools, svtyper, SWAMP, SweepFinder, SweepFinder2, sweepsims, tabix, Taiji, Tandem Repeats Finder (TRF), tardis, TargetP, TASSEL 3, TASSEL 4, TASSEL 5, tbl2asn, tcoffee, TensorFlow, TEToolkit, TEtranscripts, texlive, tfTarget, ThermoRawFileParser, TMHMM, tmux, Tomahawk, TopHat, Torch, traitRate, Trans-Proteomic Pipeline (TPP), TransComb, TransDecoder, TRANSIT, transrate, TRAP, treeCl, treemix, Trim Galore!, trimal, trimmomatic, Trinity, Trinotate, tRNAscan-SE, UCSC Kent utilities, UMAP, UMI-tools, Unicycler, UniRep, unrar, usearch, Variant Effect Predictor, VarScan, VCF-kit, vcf2diploid, vcfCooker, vcflib, vcftools, vdjtools, Velvet, vep, VESPA, vg, ViennaRNA, VIP, viral-ngs, virmap, VirSorter, VirusDetect, VirusFinder 2, VizBin, vmatch, vsearch, vt, WASP, wgs-assembler (Celera), Wise2 (Genewise), Xander_assembler, yaha

Details for RepeatModeler (hide)

Name:RepeatModeler
Version:2.0.1
OS:Linux
About:identify repeat in genome sequence
Added:2/14/2016 3:27:20 PM
Updated:10/15/2020 12:56:29 PM
Link:http://www.repeatmasker.org/RepeatModeler/
Download:https://github.com/Dfam-consortium/TETools
Notes:

To download and run the latest version of RepeatModeler/RepeatMaskder through Singularity

#Download latest version of dfam tetools docker image and convert it to a singularity image file;
#You can keep the image file tetools.sif in your home directory if you want to use it later.  
singularity pull tetools.sif docker://dfam/tetools:latest

#build database
./tetools.sif BuildDatabase -name myGenome myGenome.fa

#run RepeatModeler  (-LTRStruct is recommended)
./tetools.sif RepeatModeler -database myGenome  -pa 20 -LTRStruct 

 

Default repeat classification by RepeatModeler is not good (in particular, the LINE and SINE elements are quite often classified as unknown). It is not a problem if you simply want to mask your genome. But if you do care about the repeat categories, we recommend to re-do the classification step, taking advantage of Cornell library's repbase license. 

  1. Download the latest repbase database using a Cornell campus computer (if working from home, using this link ). Click "Download" , then "Current release", then at the bottom of the page, click "RepBase26.05.embl 05-21-2021" or latest to download the embl file. The file names are not easy to understand, the description are on this page. Commonly used files are "invrep.ref" for other invertebrates, "vrtrep.ref" for other vertebrates. You might want to merge multiple files with this command: "cat file1.ref file2.ref> merged.ref".  For example, "drosophila" and "other invertebrates".  Do not include "simple.ref" in your database. 
  2. Run "embl2rm.pl" to covert the embl formatted ref file into a Repeat lib file (a specially formatted fasta file).
embl2rm.pl inputFile.ref myDatabase.lib

       3. create a customized classifier database.

## create your own copy of RepeatMasker database 
./tetools.sif cp -r /opt/RepeatMasker ./

## delete the built in databae
cd RepeatMasker 
rm RepeatMasker/Libraries/RepeatMasker.lib*

## replace "myDatabase.lib" in the next command with the .lib file you produced from previous step 
cp myDatabase.lib ./RepeatMasker.lib
makeblastdb -dbtype nucl -in RepeatMasker.lib

4. Run repeatClassifier with your custom database

# when you run RepeatClassifier, copy over the RepeatMasker directory to same place as the consensi.fa file. 
# specify your RepeatMasker direcotory which include the custom db "-repeatmasker_dir ./RepeatMasker"

./tetools.sif RepeatClassifier -consensi consensi.fa -pa 8 -repeatmasker_dir ./RepeatMasker > new_consensi.fa.classified

After this step, you will have a new new_consensi.fa.classified file. You can count the number of LINE elements in the new file and compare with output from RepeatModeler.

grep "LINE" new_consensi.fa.classified | wc -l

A few more optional steps, before moving on to RepeatMasker (many protocols include optiona2, some prototols include option1). 

  1. Filter the new_consensi.fa.classified file by known proteins as described below.
  2. Merge the new_consensi.fa.classified file with the repbase based myDatabase.lib you just created (cat new_consensi.fa.classified myDatabase.lib > merged.lib)

# filter known proteins ()

We are not sure this step is needed, or make things worse, however, some bioinformatitians recommend to fiter the custom built library with known proteins. A copy of the protein database can be copied over from /shared_data/genome_db/uniref90_no_transponsase.dmnd.  In this database Transposase proteins were removed from uniref90  based on annotation line ("transposase" case insensitive match).

cp /shared_data/genome_db/uniref90_no_transponsase.dmnd ./

#run blastx against uniref90 (with transposases removed). 
/programs/diamond/diamond blastx --query consensi.fa.repbase.classified --db uniref90_no_transponsase --evalue 1e-20 --max-target-seqs 10 --outfmt 0 --out consensi_blast_results.txt


#ProtExcluder.pl can be download from https://github.com/NBISweden/ProtExcluder
#remove protein sequence + 20bp flanking sequence from the repeat file
/programs/ProtExcluder-1.2/ProtExcluder.pl -f 20 consensi_blast_results.txt  consensi.fa.repbase.classified

The output from ProtExcluder.pl is a new fasta file consensi.fa.repbase.classifiednoProtFinal. 


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