BioHPC Cloud Software
There are 1266 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 Slicer, 3d-dna, 454 gsAssembler or gsMapper, 7zip, a5, ABRicate, ABruijn, ABySS, AdapterRemoval, adephylo, Admixtools, Admixture, AF_unmasked, AFProfile, AGAT, agrep, albacore, Alder, AliTV-Perl interface, AlleleSeq, ALLMAPS, ALLPATHS-LG, Alphafold, Alphafold3, alphapickle, Alphapulldown, AlphScore, Amber, AMOS, AMPHORA, amplicon.py, AMRFinder, analysis, ANGSD, AnnotaPipeline, Annovar, ant, antiSMASH, anvio, apollo, arcs, ARGweaver, aria2, ariba, Arlequin, ART, ASEQ, aspera, assembly-stats, aster, ASTRAL, atac-seq-pipeline, ataqv, athena_meta, ATLAS, Atlas-Link, ATLAS_GapFill, atom, ATSAS, Augustus, autocycler, AWS command line interface, AWS v2 Command Line Interface, axe, axel, BA3, BactSNP, bakta, BAMdash, bamm, bamsnap, bamsurgeon, bamtools, bamUtil, barcode_splitter, BarNone, Basset, BayeScan, Bayescenv, bayesR, baypass, bazel, BBMap/BBTools, BCFtools, BCL convert, bcl2fastq, BCP, bdbag, Beagle, beagle-lib, BEAST, BEAST X, Beast2, bed2diffs, bedops, BEDtools, bettercallsal, bfc, bgc, bgen, bicycle, BiG-SCAPE, bigQF, bigtools, bigWig, bioawk, biobakery, biobambam, Bioconductor, biom-format, BioPerl, BioPython, Birdsuite, biscuit, Bismark, Blackbird, blasr, BLAST, BLAST_to_BED, blast2go, BLAT, BlobToolKit, BLUPF90, BMGE, bmtagger, bonito, Boost, Bowtie, Bowtie2, BPGA, Bracken, BRAKER, BRAT-NextGen, BRBseqTools, BreedingSchemeLanguage, breseq, brocc, BSBolt, bsmap, BSseeker2, btyper3, BUSCO, BUSCO Phylogenomics, BWA, bwa-mem2, bwa-meth, bwtool, cactus, CAFE, CAFE5, caffe, cagee, canu, Canvas, CAP3, caper, CarveMe, catch, cBar, CBSU RNAseq, CCMetagen, CCTpack, cd-hit, cdbfasta, cdo, CEGMA, CellRanger, cellranger-arc, cellranger-atac, cellranger-dna, centrifuge, centrifuger, centroFlye, CFM-ID, CFSAN SNP pipeline, CheckM, CheckM2, chimera, ChimeraTE, chimerax, chip-seq-pipeline, chromeister, ChromHMM, chromosomer, Circlator, Circos, Circuitscape, CITE-seq-Count, ClermonTyping, clues, CLUMPP, clust, Clustal Omega, CLUSTALW, Cluster, cmake, CMSeq, CNVnator, coidb, coinfinder, colabfold, CombFold, Comparative-Annotation-Toolkit, compat, CONCOCT, Conda, Conform-gt, Cooler, cooltools, copyNumberDiff, cortex_var, CoverM, crabs, CRISPRCasFinder, CRISPResso, crispron, Cromwell, CrossMap, CRT, cuda, Cufflinks, curatedMetagenomicDataTerminal, cutadapt, cuteFC, cuteSV, Cytoscape, dadi, dadi-1.6.3_modif, dadi-cli, danpos, DAS_Tool, dashing, DBSCAN-SWA, dDocent, DeconSeq, Deepbinner, deeplasmid, DeepTE, deepTools, Deepvariant, defusion, degenotate, delly, DESMAN, destruct, DETONATE, dfast, diamond, dipcall, diploSHIC, discoal, Discovar, Discovar de novo, distruct, DiTASiC, DIYABC, dmtcp, dnmtools, Docker, dorado, DRAM, dREG, dREG.HD, drep, Drop-seq, dropEst, dropSeqPipe, dsk, dssat, Dsuite, dTOX, duphold, DWGSIM, dynare, ea-utils, earlgrey, ecCodes, ecopcr, ecoPrimers, ectyper, EDGE, edirect, EDTA, eems, EgaCryptor, EGAD, eggnog-mapper, EIGENSOFT, elai, ElMaven, emacs, EMBLmyGFF3, EMBOSS, EMIRGE, Empress, enfuse, EnTAP, entropy, epa-ng, ephem, epic2, ermineJ, ete3, EukDetect, EukRep, EVE, EVM, exabayes, exonerate, ExpansionHunterDenovo-v0.8.0, eXpress, FALCON, FALCON_unzip, Fast-GBS, fasta, FastAAI, FastANI, fastcluster, fastGEAR, FASTK, FastME, FastML, fastp, FastQ Screen, fastq-multx-1.4.3, fastq_demux, fastq_pair, fastq_species_detector, FastQC, fastqsplitter, fastsimcoal2, fastspar, fastStructure, FastTree, FASTX, fcs, FEELnc, feems, feh, FFmpeg, fgbio, ficle, figaro, Fiji, Filtlong, fineRADstructure, fineSTRUCTURE, FIt-SNE, FlaGs2, flash, flash2, flexbar, Flexible Adapter Remover, Flye, FMAP, FragGeneScan, FragGeneScan, FRANz, freebayes, FSA, funannotate, FunGene Pipeline, FunOMIC, G-PhoCS, g4predict, GADMA, GAEMR, Galaxy, Galaxy in Docker, GATK, gatk4, gatk4amplicon.py, gblastn, Gblocks, GBRS, gcc, GCTA, GDAL, gdc-client, gem, GEM library, GEMMA, GeMoMa, GENECONV, geneid, GeneMark, GeneRax, Genespace, genomad, Genome STRiP, Genome Workbench, GenomeMapper, Genomescope, GenomeThreader, genometools, GenomicConsensus, genozip, gensim, GEOS, germline, gerp++, GET_PHYLOMARKERS, GetOrganelle, gfastats, gfaviz, GffCompare, gffread, giggle, git, glactools, GlimmerHMM, GLIMPSE, GLnexus, Globus connect personal, GMAP/GSNAP, gmx_MMPBSA, GNU Compilers, GNU parallel, go-perl, GO2MSIG, GONE, GoShifter, gradle, graftM, grammy, GraPhlAn, graphtyper, graphviz, greenhill, GRiD, gridss, Grinder, grocsvs, GROMACS, GroopM, GSEA, gsort, GTDB-Tk, GTFtools, Gubbins, gunc, GUPPY, gvcftools, hail, hal, HapCompass, HAPCUT, HAPCUT2, hapflk, HaploMerger, Haplomerger2, haplostrips, HaploSync, happ, HapSeq2, harpy, HarvestTools, haslr, hdf5, helixer, hget, hh-suite, HiC-Pro, hic_qc, HiCExplorer, HiFiAdapterFilt, hifiasm, hificnv, HiPhase, HISAT2, HMMER, Homer, HOTSPOT, HTSeq, htslib, https://github.com/CVUA-RRW/RRW-PrimerBLAST, https://github.com/ksahlin/strobealign, hugin, humann, HUMAnN2, hybpiper, hyde, HyLiTE, Hyper-Gen, hyperopt, HyPhy, hyphy-analyses, iAssembler, IBDLD, IBDNe, IBDseq, idba, IDBA-UD, idemux, IDP-denovo, idr, idseq, IgBLAST, IGoR, IGV, IMa2, IMa2p, IMAGE, ImageJ, ImageMagick, Immcantation, impute2, impute5, IMSA-A, INDELseek, infernal, Infomap, inspector, inStrain, inStrain_lite, InStruct, Intel MKL, InteMAP, InterProScan, ipyrad, IQ-TREE, iRep, IRMA, isoseq, itsx, iva, ivar, JaBbA, jags, Jane, java, jbrowse, JCVI, jellyfish, jsalignon/cactus, juicer, julia, jupyter, jupyterlab, kaiju, kallisto, Kent Utilities, keras, khmer, kineticsTools, kinfin, king, kma, KMC, KmerFinder, KmerGenie, kneaddata, kraken, KrakenTools, KronaTools, kSNP, kWIP, LACHESIS, lammps, LAPACK, lapels, LAST, lastz, lcMLkin, LDAK, LDBlockShow, LDhat, LeafCutter, leeHom, lep-anchor, Lep-MAP3, LEVIATHAN, lftp, Liftoff, lifton, Lighter, LinkedSV, LINKS, localcolabfold, LocARNA, LocusZoom, lofreq, longranger, Loupe, LS-GKM, LTR_retriever, LUCY, LUCY2, LUMPY, lyve-SET, m6anet, Macaulay2, MACE, MACS, MaCS simulator, MACS2, macs3, maffilter, MAFFT, mafTools, MAGeCK, MAGeCK-VISPR, Magic-BLAST, magick, MAGScoT, MAKER, manta, mapDamage, mapquik, MAQ, MARS, MASH, mashtree, Mashtree, MaSuRCA, MATLAB, Matlab_runtime, Mauve, MaxBin, MaxQuant, McClintock, mccortex, mcl, MCscan, MCScanX, mdust, medaka, medusa, medusa2, megahit, MeGAMerge, MEGAN, MELT, MEME Suite, MERLIN, merqury, meryl, MetaBAT, MetaBinner, MetaboAnalystR, MetaCache, MetaCRAST, metaCRISPR, metamaps, MetAMOS, MetaPathways, MetaPhlAn, metapop, metaron, MetaVelvet, MetaVelvet-SL, metaWRAP, methbat, methpipe, mfeprimer, MGmapper, MicrobeAnnotator, microtrait, MIDAS, MiFish, Migrate-n, mikado, MinCED, minigraph, Minimac3, Minimac4, minimap2, miniprot, mira, miRDeep2, mirge3, miRquant, MISO, MITE-Hunter, MITE-Tracker, MITObim, MitoFinder, mitohelper, MitoHiFi, mity, MiXCR, MixMapper, MKTest, mlift, mlst, MMAP, MMSEQ, MMseqs2, MMTK, MobileElementFinder, modeltest, MODIStsp-2.0.5, module, moments, momi, MoMI-G, mongo, mono, monocle3, morphographx, mosdepth, mothur, MrBayes, mrcanavar, mrsFAST, msdial, msld, MSMC, msprime, MSR-CA Genome Assembler, msstats, MSTMap, mugsy, MultiQC, multiz-tba, MUMandCo, MUMmer, mummer2circos, muscle, MUSIC, Mutation-Simulator, muTect, myte, MZmine, nag-compiler, namfinder, nanocompore, nanofilt, NanoPlot, Nanopolish, nanovar, ncbi_datasets, ncftp, ncl, NECAT, Nemo, Netbeans, NEURON, new_fugue, Nextflow, NextGenMap, NextPolish2, nf-core/rnaseq, ngmlr, NGS_data_processing, NGSadmix, ngsDist, ngsF, ngsLD, NGSNGS, ngsplot, NgsRelate, ngsTools, NGSUtils, NINJA, NLR-Annotator, NLR-Parser, NLRtracker, Novoalign, NovoalignCS, nQuire, NRSA, ntSynt, NuDup, numactl, nvidia-docker, nvtop, Oases, OBITools, Octave, odgi, OMA, Oneflux, OpenBLAS, openmpi, openslide, openssl, ORFeus, orthodb-clades, OrthoFinder, orthologr, Orthomcl, pacbio, PacBioTestData, PAGIT, pairtools, pal2nal, paleomix, PAML, panacus, panaroo, pandas, pandaseq, pandoc, pangene, PanPhlAn, PanPhlAn_pangenome_exporter, Panseq, pantools, Parsnp, PASA, PASTEC, PAUP*, pauvre, pb-assembly, pb-CpG-tools, pbalign, pbbam, pbh5tools, PBJelly, pblat, pbmm2, PBSuite, pbsv, pbtk, PCAngsd, pcre, pcre2, PeakRanger, PeakSplitter, PEAR, PEER, PennCNV, peppro, PERL, PfamScan, pgap, PGDSpider, ph5tools, Phage_Finder, pharokka, phasedibd, PHAST, phenopath, Phobius, PHRAPL, phykit, PHYLIP, PhyloCSF, phyloFlash, phylonet, phylophlan*, PhyloPhlAn2, phylophlan3, phyluce, PhyML, phyx, Picard, PICRUSt2, pigz, Pilon, Pindel, piPipes, PIQ, piranha, pixy, PlasFlow, platanus, Platypus, plink, plink2, Plotly, plotsr, plumed, pocp, Point Cloud Library, popbam, PopCOGenT, PopLDdecay, Porechop, poretools, portcullis, POUTINE, pplacer, PRANK, preseq, pretext-suite, primalscheme, primer3, PrimerBLAST, PrimerPooler, prinseq, prodigal, progenomics, progressiveCactus, PROJ, prokka, Proseq2, ProtExcluder, protolite, PSASS, psmc, psutil, pullseq, purge_dups, pyani, PyCogent, pycoQC, pyfaidx, pyGenomeTracks, PyMC, pymol-open-source, pyopencl, pypy, pyRAD, pyrho, Pyro4, pyseer, PySnpTools, python, PyTorch, PyVCF, qapa, qcat, QIIME, QIIME2, QTCAT, Quake, Qualimap, QuantiSNP2, QUAST, quickmerge, QUMA, QuPath, R, RACA, racon, rad_haplotyper, RADIS, RadSex, RagTag, rapt, RAPTR-SV, RATT, raven, RAxML, raxml-ng, Ray, rck, rclone, Rcorrector, RDP Classifier, REAGO, REAPR, Rebaler, reCOGnizer, Red, ReferenceSeeker, regenie, regtools, Relate, RelocaTE2, Repbase, RepeatMasker, RepeatModeler, RERconverge, ReSeq, resistify, RevBayes, RFdiffusion, RFMix, RGAAT, rgdal, RGI, Rgtsvm, Ribotaper, ripgrep, rJava, rMATS, RNAMMER, rnaQUAST, Rnightlights, roadies, Roary, Rockhopper, rohan, RoseTTAFold-All-Atom, RoseTTAFold2NA, rphast, Rqtl, Rqtl2, RSAT, rseg, RSEM, RSeQC, RStudio, rtfbs_db, ruby, run_dbcan, rv-tdt, sabre, SaguaroGW, salmon, SALSA, Sambamba, samblaster, sample, SampleTracker, samplot, samtabix, Samtools, Satsuma, Satsuma2, sawfish, SCALE, scanorama, SCE-VCF, scikit-learn, Scoary, scoary-2, scTE, scythe, seaborn, SEACR, SecretomeP, segul, self-assembling-manifold, selscan, seqfu, seqkit, SeqPrep, SeqSero2, seqtk, SequelTools, sequenceTubeMap, Seurat, sf, sgrep, sgrep sorted_grep, SHAPEIT, SHAPEIT4, SHAPEIT5, shasta, Shiny, shoelaces, shore, SHOREmap, shortBRED, SHRiMP, SICER2, sickle, sift4g, SignalP, SimPhy, simsapiper, simuPOP, sina, SINGER, singularity, sinto, sirius, sistr_cmd, skani, SKESA, skewer, SLiM, SLURM, smap, smash, smcpp, smoove, SMRT Analysis, SMRT LINK, smudgeplot, snakemake, snap, SnapATAC, snapatac2, SNAPP, SnapTools, snATAC, SNeP, Sniffles, snippy, snp-sites, snpArcher, SnpEff, SNPgenie, SNPhylo, SNPsplit, SNVPhyl, SOAP2, SOAPdenovo, SOAPdenovo-Trans, SOAPdenovo2, SoloTE, SomaticSniper, songbird, sorted_grep, spaceranger, SPAdes, SPALN, SparCC, sparsehash, SPARTA, speedseq, split-fasta, SQANTI3, sqlite, SqueezeMeta, SQuIRE, SRA Toolkit, srst2, ssantichaivekin/empress, stacks, Stacks 2, stairway-plot, stampy, STAR, staramr, Starcode, statmodels, stellarscope, STITCH, STPGA, StrainPhlAn, strawberry, Strelka, stringMLST, StringTie, STRUCTURE, Structure_threader, Struo2, stylegan2-ada-pytorch, subread, sumatra, supernova, suppa, SURPI, surpyvor, SURVIVOR, sutta, SV-plaudit, SVaBA, SVclone, SVDetect, svengine, SVseq2, svtools, svtyper, svviz2, SWAMP, sweed, SweepFinder, SweepFinder2, sweepsims, swiss2fasta.py, sword, syri, tabix, TAGADA, tagdust, Taiji, tama, Tandem Repeats Finder (TRF), tardis, TargetP, TASSEL 3, TASSEL 4, TASSEL 5, tax_myPHAGE, tbl2asn, tcoffee, TE-Aid, telescope, TELR, TensorFlow, TEToolkit, TEtranscripts, texlive, TFEA, tfmodisco, tfTarget, thermonucleotideBLAST, ThermoRawFileParser, TMHMM, tmux, Tomahawk, TopHat, Torch, traitRate, Trans-Proteomic Pipeline (TPP), TransComb, TransDecoder, TRANSIT, transrate, TRAP, tree, treeCl, treemix, treePL, Trim Galore!, trimal, trimmomatic, Trinity, Trinotate, TrioCNV2, tRNAscan-SE, Trycycler, twisst2, UBCG2, UCSC Kent utilities, ullar, ultra, ultraplex, UMAP, UMI-tools, umi-transfer, UMIScripts, Unicycler, UniRep, unitig-caller, unrar, usearch, VALET, valor, vamb, variabel, Variant Effect Predictor, VarScan, VCF-kit, vcf2diploid, VCF2PCACluster, vcf2phylip, vcfCooker, vcflib, vcftools, vdjtools, Velvet, vep, verkko, VESPA, vg, Vicuna, ViennaRNA, VIP, viral-ngs, virmap, VirSorter, VirusDetect, VirusFinder 2, visidata, vispr, VizBin, vmatch, vscode, vsearch, vSNP3, vt, WASP, webin-cli, wget, wgs-assembler (Celera), WGSassign, What_the_Phage, wiggletools, windowmasker, wine, Winnowmap, Wise2 (Genewise), wombat, Xander_assembler, xpclr, yaha, yahs, yap
Details for Alphafold (If the copy-pasted commands do not work, use this tool to remove unwanted characters)
| Name: | Alphafold |
| Version: | 2.3.2 |
| OS: | Linux |
| About: | Protein structure prediction. |
| Added: | 7/27/2021 5:47:44 PM |
| Updated: | 7/8/2023 9:00:01 AM |
| Link: | https://github.com/deepmind/alphafold |
| Notes: | Alphafold is currently installed on the rental GPU machines. To use this software, you need to reserve time on one of these machines. (v2.3.2 is available on cbsugpu03-05,07. v2.2.2 is available on cbsugpu02)
* Run time of alphafold: cbsugpu07 is the fastest, cbsugpu-5-6 in between cbsugpu04 is the slowest.
* To run alphafold multimer, cbsugpu07 has 80GB of GPU RAM, cbsugpu05 have 48GB of GPU RAM. More RAM allows you to work with larger protein complex.
* The script hard coded to set the following variables.
TF_FORCE_UNIFIED_MEMORY=1
XLA_PYTHON_CLIENT_MEM_FRACTION=4.0
If you need to increase XLA_PYTHON_CLIENT_MEM_FRACTION to increase memory access, you can copy the /local/local_data/alphafold-2.3.2/run_singularity.py to your workdir, and modify the script by setting XLA_PYTHON_CLIENT_MEM_FRACTION to a bigger number.
To run Alphafold:
1. Create an output directory;
mkdir -p /workdir/$USER/alphafold_out
2. Copy your protein sequence fasta file(s) under /workdir/$USER
3. Start the command in a "screen" persistent session, from the directory /workdir/$USER (except between 30 min to 5 hours for a single protein, "multimer" mode might take up 1 to 2 days. To reduce run time, read this section about multimer setting --num_multimer_predictions_per_model=1)
- In the option "--fasta_paths=xxx", xxx must be the fasta file name only, without directories.
- To run multimer, put all protein sequences in a single fasta file, and run with option "--model_preset=multimer";
- Output will be written to subfolders of "/workdir/$USER/alphafold_out" which you created in step 1.
- The option "max_template_date" allows you to exclude latest protein models in PDB to be used for prediction.
- More options are available, as described in https://github.com/deepmind/alphafold section Running AlphaFold, starting at step 4 (ignore the previous steps as these pertain to program installtion). For fasta_paths, either use full path or launch from directory where the fasta file is located. For example,
cd /workdir/$USER
#run version v2.3.2 (on cbsugpu03, cbsugpu04, cbsugpu05, cbsugpu06)
/local/local_data/alphafold-2.3.2/run_singularity.py --fasta_paths=T1050.fasta --max_template_date=2023-07-07 >& run.log &
#run version v2.2.2.(on cbsugpu02 only)
/programs/alphafold-2.2.2/alphafold_biohpc.sh --fasta_paths=T1050.fasta --max_template_date=2022-08-03 >& run.log
#run previous version 2.1.0 (on cbsugpu02 only)
/programs/alphafold/alphafold_biohpc.sh --fasta_paths=T1050.fasta --max_template_date=2022-08-03 >& run.log
4. If for any reason you need to terminate a run before it completes, press "ctrl-c"
QC Plots, see alphapickle
To extract ptm, iptm scores from pickle file of multimer:
/programs/alphafold-2.3.2/alphafoldPickle.py alphafold_output_directory
See this page to evaluate scores.
## If you have several sequences, split them into fasta files with one sequence per file. In the command line, join these fasta files by ",". . For example:
/local/local_data/alphafold-2.3.2/run_singularity.py --fasta_paths=prot1.fas,prot2.fas,prot3.fas --max_template_date=2022-07-08 >& my.log &
## if you have a large batch of sequences to process, you might want to split the CPU and GPU parts, and run on different BioHPC machine. You can optimize the two steps separately, e.g. put the hhblits databases on NVME disks (There are some methods for separate the two steps, e.g. https://pythonrepo.com/repo/Zuricho-ParallelFold )
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