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BioHPC Cloud:
: User Guide

 


BioHPC Cloud Software

There is 928 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, ant, 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, barcode_splitter, BarNone, Basset, BayeScan, Bayescenv, baypass, bazel, BBMap/BBTools, BCFtools, bcl2fastq, BCP, Beagle, Beast2, bedops, BEDtools, bfc, bgc, bgen, bigQF, bigWig, bioawk, biobakery, biobambam, Bioconductor, biom-format, BioPerl, BioPython, Birdsuite, 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, BSseeker2, BUSCO, BWA, bwa-mem2, bwa-meth, bwtool, cactus, CAFE, canu, CAP3, caper, CarveMe, catch, cBar, CBSU RNAseq, CCMetagen, CCTpack, cd-hit, cdbfasta, CEGMA, CellRanger, cellranger-arc, cellranger-atac, cellranger-dna, centrifuge, centroFlye, CFM-ID, CFSAN SNP pipeline, CheckM, chimera, chip-seq-pipeline, chromosomer, Circlator, Circos, Circuitscape, CITE-seq-Count, ClermonTyping, clues, CLUMPP, clust, Clustal Omega, CLUSTALW, Cluster, cmake, CMSeq, CNVnator, compat, CONCOCT, Conda, Cooler, copyNumberDiff, cortex_var, CoverM, 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, elai, 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, fastsimcoal2, fastspar, fastStructure, FastTree, FASTX, feems, feh, FFmpeg, fgbio, fineRADstructure, fineSTRUCTURE, FIt-SNE, flash, flash2, flexbar, Flexible Adapter Remover, Flye, FMAP, FragGeneScan, FragGeneScan, freebayes, FSA, funannotate, FunGene Pipeline, G-PhoCS, GADMA, GAEMR, Galaxy in Docker, Galaxy Server, GATK, gatk4, gatk4amplicon.py, gblastn, Gblocks, GBRS, gcc, GCTA, GDAL, gdc-client, GEM library, GEMMA, GENECONV, geneid, GeneMark, GeneMarker, Genome STRiP, GenomeMapper, GenomeStudio (Illumina), GenomeThreader, genometools, GenomicConsensus, genozip, gensim, GEOS, germline, gerp++, GET_PHYLOMARKERS, GffCompare, gffread, giggle, git, glactools, GlimmerHMM, GMAP/GSNAP, GNU Compilers, GNU parallel, go-perl, GO2MSIG, GoShifter, gradle-4.4, graftM, grammy, GraPhlAn, graphtyper, graphviz, GRiD, Grinder, GROMACS, GroopM, 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, https://github.com/CVUA-RRW/RRW-PrimerBLAST, humann, HUMAnN2, hyperopt, HyPhy, hyphy-analyses, iAssembler, IBDLD, idba, IDBA-UD, IDP-denovo, idr, idseq, IgBLAST, IGoR, IGV, IMa2, IMa2p, IMAGE, ImageJ, ImageMagick, Immcantation, impute2, IMSA-A, INDELseek, infernal, Infomap, inStrain, 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, KrakenTools, KronaTools, 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, maffilter, MAFFT, mafTools, MAGeCK, MAGeCK-VISPR, Magic-BLAST, magick, MAKER, mapDamage, MAQ, MARS, MASH, mashtree, Mashtree, MaSuRCA, MATLAB, Matlab_runtime, Mauve, MaxBin, MaxQuant, McClintock, mccortex, mcl, MCscan, MCScanX, medusa, megahit, MeGAMerge, MEGAN, MELT, MEME Suite, MERLIN, MetaBAT, MetaCache, MetaCRAST, metaCRISPR, MetAMOS, MetaPathways, MetaPhlAn, metaron, MetaVelvet, MetaVelvet-SL, methpipe, mfeprimer, 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, mummer2circos, muscle, MUSIC, Mutation-Simulator, muTect, MZmine, nag-compiler, nanofilt, NanoPlot, Nanopolish, nanovar, 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, numactl, nvidia-docker, nvtop, Oases, OBITools, Octave, OMA, Oneflux, openmpi, openssl, OrthoFinder, orthologr, Orthomcl, pacbio, PacBioTestData, PAGIT, pal2nal, paleomix, PAML, panaroo, pandas, pandaseq, pandoc, PanPhlAn, Panseq, Parsnp, PASA, PASTEC, PAUP*, pb-assembly, pbalign, pbbam, pbh5tools, PBJelly, pblat, pbmm2, PBSuite, pbsv, 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, PICRUSt2, pigz, Pilon, Pindel, piPipes, PIQ, PlasFlow, platanus, Platypus, plink, plink2, Plotly, Point Cloud Library, popbam, PopCOGenT, PopLDdecay, Porechop, poretools, portcullis, POUTINE, pplacer, PRANK, preseq, primalscheme, primer3, PrimerBLAST, PrimerPooler, 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, pyseer, PySnpTools, python, PyTorch, PyVCF, QIIME, QIIME2, QTCAT, Quake, Qualimap, QuantiSNP2, QUAST, quickmerge, QUMA, R, RACA, racon, rad_haplotyper, 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, Ribotaper, ripgrep, rJava, rMATS, RNAMMER, rnaQUAST, Rnightlights, Roary, Rockhopper, rohan, 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, SEACR, 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, Struo2, stylegan2-ada-pytorch, subread, supernova, SURPI, sutta, SV-plaudit, SVDetect, SVseq2, svtools, svtyper, SWAMP, sweed, SweepFinder, SweepFinder2, sweepsims, tabix, Taiji, Tandem Repeats Finder (TRF), tardis, TargetP, TASSEL 3, TASSEL 4, TASSEL 5, tbl2asn, tcoffee, TensorFlow, TEToolkit, TEtranscripts, texlive, TFEA, tfTarget, thermonucleotideBLAST, 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, unitig-caller, unrar, usearch, vamb, Variant Effect Predictor, VarScan, VCF-kit, vcf2diploid, vcfCooker, vcflib, vcftools, vdjtools, Velvet, vep, VESPA, vg, ViennaRNA, VIP, viral-ngs, virmap, VirSorter, VirusDetect, VirusFinder 2, vispr, VizBin, vmatch, vsearch, vt, WASP, wget, wgs-assembler (Celera), windowmasker, Wise2 (Genewise), wombat, Xander_assembler, xpclr, yaha

Details for Alphafold (If the copy-pasted commands do not work, use this tool to remove unwanted characters)

Name:Alphafold
Version:2.2.2
OS:Linux
About:This package provides an implementation of the inference pipeline of AlphaFold v2.0. This is a completely new model that was entered in CASP14 and published in Nature.
Added:7/27/2021 5:47:44 PM
Updated:8/3/2022 1:22:35 PM
Link:https://github.com/deepmind/alphafold
Notes:

Alphafold is currently installed on three GPU machines: cbsugpu02, cbsugpu03, cbsugpu04 and cbsugpu05. To use this software, you need to reserve time on one of these machines. (v2.2.2 available on all servers. v2.1.0 only on cbsugpu02)

* Run time of alphafold:  cbsugpu05 is a lot faster than the other machines due to better GPU and faster disk, cbsugpu04 is the slowest. 

To run Alphafold:

1. Create directory where the program's output will be created

mkdir -p /workdir/$USER/alphafold_out

2. Copy the fasta file(s) with protein sequences to /workdir/$USER

3. Run the command (in "screen" persistent session, for most proteins it takes between 30 min to 5 hours)

#run version v2.2.2 
cd /workdir/$USER
/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)
cd /workdir/$USER
/programs/alphafold/alphafold_biohpc.sh --fasta_paths=T1050.fasta --max_template_date=2022-08-03 >& run.log

NOTES: 

  • Output will be written to subfolders of "/workdir/$USER/alphafold_out" which you created in step 1. The results files are owned by root. To claim ownership of your files, run "docker1 claim"
  • The option "max_template_date" allows you to exclude latest protein models to be used in training models.
  • 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,

4. If for any reason you need to terminate a run before it completes, you will have to stop and remove the underlying docker container as follows:
i) Run the command docker1 ps -a to find the ContainerID (this is the string in the leftmost column of the command's output in line corresponding to your alphafold container).
ii) Stop the container: docker1 stop ContainerID
iii) Remove the stopped container by command “docker1 rm ContainerID”

 

## 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:

/programs/alphafold/alphafold_biohpc.sh  --fasta_paths=prot1.fas,prot2.fas,prot3.fas --max_template_date=2022-08-20  >& 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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