{"id":34896395,"url":"https://github.com/ay-lab/chipline","last_synced_at":"2025-12-26T07:04:55.644Z","repository":{"id":112915464,"uuid":"146024184","full_name":"ay-lab/ChIPLine","owner":"ay-lab","description":"ChIP-seq analysis pipeline","archived":false,"fork":false,"pushed_at":"2024-08-15T18:42:24.000Z","size":96,"stargazers_count":9,"open_issues_count":1,"forks_count":6,"subscribers_count":3,"default_branch":"master","last_synced_at":"2025-09-09T13:28:49.888Z","etag":null,"topics":["chip-seq","idr","macs2","qc"],"latest_commit_sha":null,"homepage":null,"language":"Shell","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/ay-lab.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null,"notice":null,"maintainers":null,"copyright":null,"agents":null,"dco":null,"cla":null}},"created_at":"2018-08-24T18:04:31.000Z","updated_at":"2024-10-28T17:04:09.000Z","dependencies_parsed_at":null,"dependency_job_id":"6e45b756-c547-4df0-9d91-3d452a609f22","html_url":"https://github.com/ay-lab/ChIPLine","commit_stats":null,"previous_names":[],"tags_count":2,"template":false,"template_full_name":null,"purl":"pkg:github/ay-lab/ChIPLine","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ay-lab%2FChIPLine","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ay-lab%2FChIPLine/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ay-lab%2FChIPLine/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ay-lab%2FChIPLine/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/ay-lab","download_url":"https://codeload.github.com/ay-lab/ChIPLine/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ay-lab%2FChIPLine/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":28048026,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","status":"online","status_checked_at":"2025-12-26T02:00:06.189Z","response_time":55,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["chip-seq","idr","macs2","qc"],"created_at":"2025-12-26T07:03:35.481Z","updated_at":"2025-12-26T07:04:55.629Z","avatar_url":"https://github.com/ay-lab.png","language":"Shell","funding_links":[],"categories":[],"sub_categories":[],"readme":"# ChIPLine - a pipeline for ChIP-seq analysis\n\nDevelopers\n----------\n\nDevloped by : Sourya Bhattacharyya\n\nSupervisors: Dr. Ferhat Ay and Dr. Pandurangan Vijayanand\n\nLa Jolla Institute for Allergy and Immunology\n\nLa Jolla, San Diego, CA 92037, USA\n\n\n#######################\n\nChIPLine is a pipeline to analysis ChIP-seq data, starting from input Fastq/BAM files and generating alignment summary, various quality statistics, peak calling, and BigWig formatted tracks ready for visualization in UCSC genome browser. It also performs IDR analysis between a set of peak \nfiles or even a set of BAM alignment files (in which case, peaks are estimated first) \ncorresponding to a set of biological or technical ChIP-seq replicates.\n\n#######################\n\nTheory\n----------\n\nUser can check the following papers or links for understanding ChIP-seq QCs:\n\n1) https://github.com/crazyhottommy/ChIP-seq-analysis  (very useful)\n\n2) https://www.encodeproject.org/data-standards/terms/#library\n\n3) https://www.biostars.org/p/205576/\n\n4) https://sites.google.com/site/anshulkundaje/projects/idr#TOC-Latest-pipeline (for IDR analysis)\n\nRequired packages for executing basic ChIP-seq pipeline\n-------------------------------------------------------\n\nWhen executing basic ChIP-seq pipeline, user should install following \npackages / libraries in the system:\n\n1) Bowtie2 (we have used version 2.3.3.1) http://bowtie-bio.sourceforge.net/bowtie2/index.shtml\n\n2) samtools (we have used version 1.6) http://samtools.sourceforge.net/\n\n3) PICARD tools (we have used 2.7.1 version) https://broadinstitute.github.io/picard/\n\n4) Package phantompeakqualtools (Developed by Kundaje et al., for analyzing ChIP-seq quality) https://code.google.com/archive/p/phantompeakqualtools/\n\n5) Utilities \"bedGraphToBigWig\", \"bedSort\", \"bigBedToBed\", \"hubCheck\" and \"fetchChromSizes\" \ndownloaded from UCSC repository. Executables corresponding to the linux system, \nfor example, is provided in this link: http://hgdownload.soe.ucsc.edu/admin/exe/linux.x86_64/\n\n6) deepTools (we have used version 3.5.1; requires Python 3.7.12) https://deeptools.readthedocs.io/en/develop/\n\n7) MACS2 (we have used version 2.2.9.1; requires Python 3.7.12) https://github.com/taoliu/MACS\n\n8) HOMER (we recommend using the latest version) http://homer.ucsd.edu/homer/\n\n9) R environment (we have used 3.4.3)\n\nUser should include the PATH of above mentioned libraries / packages inside their SYSTEM PATH variable. \nSome of these PATHS are also to be mentioned in a separate configuration file (mentioned below).\n\n\nRequired packages for executing IDR code\n------------------------------------------\n\nIn addition, when user requires to execute the IDR code, \nfollowing packages / libraries are to be installed in the system:\n\n1) sambamba (we have used version 0.6.7) http://lomereiter.github.io/sambamba/\n\n2) The package IDRCode (available in https://drive.google.com/file/d/0B_ssVVyXv8ZSX3luT0xhV3ZQNWc/view?usp=sharing). Unzip the archieve and store in convenient location. Path of this \narchieve is to be provided for executing IDR code.\n\n\nPackages to be installed for peak analysis\n----------------------------------------------\n\nThe package phantompeakqualtools (https://github.com/kundajelab/phantompeakqualtools) to be installed. You may check this webpage for installing this package along with its dependencies.\n\ngit clone https://github.com/kundajelab/phantompeakqualtools\n\nThen, install the R packages: snow (for parallel processing), snowfall, bitops, caTools, spp\n\nAlso install the bioconductor package Rsamtools\n\n\nExecution of basic ChIP-seq pipeline\n------------------------------------\n\nCurrent package includes a sample script file \"pipeline_exec.sh\". It conains sample commands required to \ninvoke the main executable named \"pipeline.sh\", which is provided within the folder \"bin\".\n\nIn general, ChIP-seq pipeline (the executable \"pipeline.sh\") involves following command line options:\n\nOptions:\n\nMandatory parameters:\n\n\t-C  ConfigFile\t\t    \n\t             A configuration file to be separately provided. Mandatory parameter. \n\t             Current package includes a sample configuration file named \"configfile\". \n\t             Details of the entries in this file are mentioned later.\n\t              \n\t-f  FASTQ1          \n\t            Read 1 (or forward strand) of paired-end sequencing data  [.fq|.gz|.bz2]. \n\t\tOr, even an aligned genome (.bam file) can be provided.\n\t        \n\t-r  FASTQ2          \n\t            R2 of pair-end sequencing data [.fq|.gz|.bz2]. If not provided, and the -f parameter \n\t            is not a BAM file, the input is assumed to be single ended.\n\t          \n\t-n  PREFIX           \n\t            Prefix string of output files. For example, -n \"TEST\" means that the \n\t            output filenames start with the string \"TEST\".\n\n\t-g  BOWTIE2_GENOME   \n\t            Bowtie2 indexed reference genome. Basically, the folder containing \n\t            the bwt2 indices are to be provided. \n\t            Mandatory parameter if user provides fastq files as input (-f and -r options).\n\t\t\t\tIf user provides .bam files as input (-f option) then no need to provide this value.\n\n\t-d  OutDir \t\t\t  \n\t            Output directory which will contain all the results.\n\n\t-c  CONTROLBAM\t\t \n\t         \tControl file(s) used for peak calling using MACS2. One or more \n\t\t\t\talignment files can be provided to be used \n\t\t\t\tas a control. It may not be specified at all, in which \n\t\t\t\tcase MACS2 operates without any control. \n\t\t\t\tControl file can be either in BAM or in  (tagalign.gz) format. \n\t\t\t\tIf multiple control files are provided, user needs to ensure that all of the \n\t\t\t\tcontrol files follow the same format (i.e. either all BAM or all TAGAlign).\n\t\t\t\tExample: -c control1.bam -c control2.bam puts two control files for using in MACS2.\n\t\t\n\t\t\t\tConversion from BAM to TagAlign.gz format can be done using the script \"TagAlign.sh\" \n\t\t\t\tprovided within the folder \"bin\".\n\t\t\n\t-w \tBigWigGenome\t \n\t\t\t\tReference genome which is used to convert BAM file to a BigWig file. \n\t\t\t\tUsed for visualization track creation purpose. \n\t\t\t\tIf -g option is enabled (i.e. the Bowtie2 index genome is provided) \n\t\t\t\tthen this option is not required. \n\t\t\t\tOtherwise, this is a mandatory parameter. Allowed values are 'hg19' \n\t\t\t\t(default), 'mm9', 'hg38', and 'mm10'.\n\n\t-T  Tagmentation\t \n\t\t\t\tIf 1, means that Tagmentation was used for ChIP file creation. \n\t\t\t\tThen, forward and reverse strands \n\t\t\t\tof the current ChIP signal are shifted by the transposon \n\t\t\t\tlength, and a tagAlign file is generated. \n\t\t\t\tPeaks are called from this tagAlign file. Similar to the ATAC seq principle. \n\t\t\t\tApplicable for the ChIPMentation technique (Christian Schmidl et. al., \n\t\t\t\tChIPmentation: fast, robust, low-input ChIP-seq for histones and transcription factors, \n\t\t\t\tNature Methods volume 12, pages 963–965, 2015). Default value of this parameter is 0.\t\t\t\n\t\t\n\t-D  DEBUG_TXT\t\t \n\t\t\t\tBinary variable. If 1 (recommended), different statistics corresponding to \n\t\t\t\tquality metrics and reads are printed. Useful when a summary of a large set \n\t\t\t\tof ChIP-seq samples are to be generated.\n\t\t\n\t-q  MAPQ_THR\t\t \n\t\t\t\tQuality value threshold, below which the mapped reads are removed (Default 30).\n\t\t\n\t-p  PEAKCALLGENOMESIZE \n\t\t\t\tgenome size parameter for MACS2 peak calling (\"hs\", \"mm\", \"ce\", \"dm\": default \"hs\")\n\nOptional parameters:\n\n\t-O \tOverwrite\t\t \n\t\t\t\tBinary variable. If 1, overwrites the existing files (if any). Default = 0.\n\t\t\t\t\t\t \n\t-t  NUMTHREADS              \n\t\t\t\tNumber of sorting, Bowtie2 mapping THREADS [Default = 1]. For parallel processing of Bowtie2, \n\t\t\t\tuser should specify \u003e 1 value such as 4 ot 8.\n\t\t\n\t-m  MAX_MEM          \n\t\t\t\tSet max memory used for PICARD duplication removal [Default = 8G].\n\t\t\n\t-a  ALIGNVALIDMAX\t \n\t\t\t\tSet the number of (max) valid alignments which will be searched [Default = 4] \n\t\t\t\tfor Bowtie2.\n\t\t\n\t-l  MAXFRAGLEN \t\t \n\t\t\t\tSet the maximum fragment length to be used for Bowtie2 alignment [Default = 2000]\n\t\t\t\n\nEntries in the configuration file (first parameter)\n---------------------------------------------------\n\nThe configuration file follows the format parameter=value\n\nAnd is to be filled with the following entries:\n\n\tsppexec=\n\t\texecutable of SPP R code (from the package phantompeakqualtools)\n\t\tafter installation. \n\t\tFor example, /home/sourya/packages/phantompeakqualtools/run_spp.R.\n\n\tpicardexec=\n\t\tPath of Picard tool executable\n\t\tExample: /home/sourya/packages/picard-tools/picard-tools-2.7.1/picard.jar\n\n\tHOMERPath=\n\t\tPath of HOMER (after installation)\n\t\tExample: /home/sourya/packages/HOMER/bin/\n\n\tDeepToolsDir=\n\t\tPath of deepTools executable\n\t\tExample: /home/sourya/packages/deepTools/deepTools2.0/bin/\n\n\tRPackageExec=\n\t\tInstalled R package directory.\n\t\tExample: /home/sourya/R-3.4.3/bin/Rscript\n\t\tIf left as blank, default Rscript installed in the system will be invoked.\n\n\tNarrowPeakASFile=\n\t\tfile (SQL) required to convert the narrowPeak file to the bigBed format\n\t\tDownload the file from this link (and save):\n\t\thttps://genome-source.gi.ucsc.edu/gitlist/kent.git/blob/master/src/hg/lib/encode/narrowPeak.as\n\t\tSpecify the location of this downloaded file:\n\t\tExample: /home/sourya/genomes/chrsize/narrowPeak.as\n\n\tBigNarrowPeakASFile=\n\t\tfile (SQL) required to convert the bignarrowPeak file to the bigBed format\n\t\tDownload the file from this link (and save):\n\t\thttps://genome.ucsc.edu/goldenPath/help/examples/bigNarrowPeak.as\n\t\tSpecify the location of this downloaded file:\n\t\tExample: /home/sourya/genomes/chrsize/bigNarrowPeak.as\n\t\t\n\tBroadPeakASFile=\n\t\tfile (SQL) required to convert the broadPeak file to the bigBed format\n\t\tDownload the file from this link (and save):\n\t\thttps://genome-source.gi.ucsc.edu/gitlist/kent.git/blob/master/src/hg/lib/encode/broadPeak.as\n\t\tSpecify the location of this downloaded file:\n\t\tExample: /home/sourya/genomes/chrsize/broadPeak.as\n\t\t\n\tRefChrSizeFile=\n\t\tfiles containing chromosome size information\n\t\ttwo column file storing the size of individual chromosomes\n\t\tDownloaded from the link (depends on the reference Chromosome employed):\n\t\tFor example, the hg38.chrom.sizes file for the hg38 database is located at \n\t\thttp://hgdownload.soe.ucsc.edu/goldenPath/hg38/bigZips/hg38.chrom.sizes.\n\t\tAlternatively, Use the \"fetchChromSizes\" script from the UCSC repository \n\t\tto get the appropriate chromosome size file.\n\t\tSpecify the location of this downloaded file:\n\t\tExample: /home/sourya/genomes/chrsize/hg38.chrom.sizes\n\t\t\n\tRefChrFastaFile=\n\t\tFasta file of the reference Chromosome.\n\t\tCan be downloaded from the link:\n\t\thttp://hgdownload.cse.ucsc.edu/downloads.html\n\t\tExample: /home/sourya/genomes/Complete_Genome/hg38/hg38.fa\n\t\t\n\tRefChrAnnotFile=\n\t\tfile containing reference UCSC annotation (.gtf format) \n\t\tcorresponding to the reference Chromosome.\n\t\tCan be downloaded from the link:\n\t\thttp://hgdownload.cse.ucsc.edu/downloads.html\n\t\tExample: /home/sourya/genomes/Annotation/hg38/UCSC/hg38_UCSC_Annotation.gtf\n\nDescribing output of ChIP-seq analysis\n-----------------------------------------\n\nWithin the folder \"OutDir\" (base directory containing all the outputs of \ncurrent ChIP-seq analysis, following files (f) and folders (F) exist):\n\n\tF1: Alignment_MAPQ${MAPQ_THR}\n\n\t\tf1: UniqMappedRead.bam\n\t\t\tInitial alignment after unique mapping.\n\t\tf2: Bowtie2_del_Random.bam\n\t\t\tAlignment after deleting random reads.\n\t\tf3: Bowtie2_del_Mitch.bam: \n\t\t\tAfter deleting mitochondrial reads.\t\n\t\tf4: ${PREFIX}.align.sort.MAPQ${MAPQ_THR}.bam\n\t\t\tSorted, and MAPQ thresholded alignment.\n\t\tf5: ${PREFIX}.align.sort.MAPQ${MAPQ_THR}.bam.bai\n\t\t\tCorresponding index.\n\t\tf6: ${PREFIX}.align.sort.MAPQ${MAPQ_THR}.rmdup.bam\n\t\t\tDe-duplicated alignment (used for subsequent operations)\n\t\tf7: ${PREFIX}.align.sort.MAPQ${MAPQ_THR}.picard_metrics.txt\n\t\t\tCorresponding PICARD metrics log file.\n\t\t\t\n\tF2: Out_BigWig\n\t\tf1: ${PREFIX}.bw \n\t\t\tbigwig file for track visualization.\n\n\tF3: Out_BigWig_NormCov:\n\t\tf1: ${PREFIX}_NormCov.bw\n\t\t\tbigwig file for track visualization (after normalizing the coverage).\n\n\tF4: MACS2_Default_*\n\t\tContains peaks employing MACS2 with default parameters.\n\t\t\tf1: *.narrowPeak: narowpeak formatted output with P-value threshold of 0.01\n\t\t\tf2: *.narrowPeak_Q0.05filt: peaks with Q-value threshold of 0.05\n\t\t\tf3: *.narrowPeak_Q0.01filt: peaks with Q-value threshold of 0.01\n\t\t\tf4: *.broadPeak: broadpeak formatted output with P-value threshold of 0.01\n\t\t\tf5: *.broadPeak_Q0.05filt: peaks with Q-value threshold of 0.05\n\t\t\tf6: *.broadPeak_Q0.01filt: peaks with Q-value threshold of 0.01\n\t\t\tf7: out_FRiP.txt: FRIP statistics for the narrow and broad peaks.\n\t\t\tf8: Peak_Statistics.txt: number of peaks\n\t\t\tF9: Peak_Annotate_Q*:\n\t\t\t\tFor Q-value thresholds of either 0.01 or 0.05, contains the \n\t\t\t\tHOMER based annotations.\n\t\t\t\n\t\t\tIn addition, files *.bb denote corresponding big-bd formatted peaks,\n\t\t\tuseful for USCSC track visualization.\n\t\t\t\n\tF5: MACS2_Ext_*\n\t\tContains peaks employing MACS2 with the parameters:\n\t\t\t--nomodel --nolambda --shift 0 --extsize 200\n\t\tFile structure is similar as above.\n\n\tF6: Peak_Overlap_Statistics\n\t\tOverlapping peaks corresponding to the settings F4 and F5\n\t\t\n\tf7: chipSampleMaster.tagAlign.pdf\n\t\tQuality metric display using cross correlation analysis.\n\t\t\n\tf8: out_NRF_MAPQ${MAPQ_THR}.txt\n\t\tMetric NRF\n\t\t\n\tf9: Read_Count_Stat.txt\n\t\tRead count statistics.\n\nSummarizing a list of ChIP-seq analysis\n---------------------------------------\n\nSuppose, a directory \"/home/sourya/Results\" contain within it, the following folders: \n1, 2, 3, 4, ... Each corresponds to the output results for individual ChIP-seq samples.\n\nTo get a summarized list of performance metrics for these samples, use the script \"ResSummary2.r\".\n\n\tRscript ResSummary2.r [positional_arguments]:\n\n\t1) OutBaseDir: \n\t\tDirectory under which results of all the different samples are stored\n\n\t2) BAMRead: \n\t\tBoolean variable, indicating if the BAM files were used for \n\t\tanalysis (1) or fastq (0). Default 0\n\n\t3) Tagmentation: \n\t\tBinary variable. If 1, denotes that the data samples \n\t\thave been under ChIPMentation method. Default 0.\n\t\t\t\n\t4) OldMethod:  \n\t\tUse 0 for this parameter.\n\n\t5) ControlPeak: \n\t\tVariable with value of either 0, 1 or 2. \n\t\tIf 1, control samples has been used for peak calling. \n\t\tIf 0, no control sample is used. \n\t\tIf 2, peaks using control and not using control both exist. \n\t\tDefault 0.\n\nSample execution command:\n\nRscript ResSummary2.r /home/sourya/ChIPResults/ 0 1 0 2\n\n\tWhich means that \n\t\t\n\t\tOutBaseDir=/home/sourya/ChIPResults/\n\t\t\n\t\tBAMRead=0\n\t\t\n\t\tTagmentation=1\n\t\t\n\t\tOldMethod=0\n\t\t\n\t\tControlPeak=2\n\n\n\n\nCommand for executing IDR codes\n---------------------------------\n\nCurrent pipeline supports IDR analysis between either a list of ChIP-seq peak files \nor between a list of alignment (BAM) files. In the second case, first the BAM files \nare analyzed and subsampled to contain equal number of reads (minimum number of reads \ncontained in the inputs), and subsequently, peaks are estimated from these \n(subsampled) BAM files using MACS2. These peaks are then applied for IDR analysis.\n\nThe script \"sample_IDR_Script.sh\" included within this package \nshows calling following two functions (both are included within the folder \n\"IDR_Codes\"):\n\n\t1) IDRMain.sh\n\n\t2) IDR_SubSampleBAM_Main.sh\n\n\tThe first script, IDRMain.sh, performs IDR between two or more \n\tinput peak files (we have used peaks estimated from MACS2). The parameters \n\tcorresponding to this script are as follows:\n\n\t-I  InpFile        \t \n\t\t\tA list of input peak files (obtained from MACS2 - in .narrowPeak or .narrowPeak.gz format). \n\t\t\tAt least two peak files are required. \n\t\n\t-P \tPathIDRCode\t\t \n\t\t\tPath of the IDRCode package (Kundaje et. al. after its installation). \n\t\t\tPlease check the \"Required packages\" section for the details.\n\n\t-d  OutDir \t\t \t \n\t\t\tOutput directory (absolute path preferred) which will store the IDR results.\n\n\t-n \tPREFIX \t\t\t \n\t\t\tPrefix of output files. Default 'IDR_ChIP'.\n\n\tA sample execution of this script is as follows:\n\n\t./IDRMain.sh -I peak1.narrowPeak -I peak2.narrowPeak -I peak3.narrowPeak -P /home/sourya/packages/idrCode/ -d /home/sourya/OutDir_IDR -n 'IDR_test'\n\n\n\n\tThe second script, IDR_SubSampleBAM_Main.sh, takes input of two or more BAM files, \n\testimates peaks from these BAM files, and then performs IDR analysis. The parameters \n\tcorresponding to this script are as follows:\n\n\t-I  InpFile        \t \n\t\t\tA list of input BAM files. At least two BAM files are required. \n\t\n\t-P \tPathIDRCode\t\t \n\t\t\tPath of the IDRCode package (Kundaje et. al. after its installation). \n\t\t\tPlease check the \"Required packages\" section for the details.\n\n\t-d  OutDir \t\t \t \n\t\t\tOutput directory (absolute path preferred) which will store the IDR results.\n\n\t-n \tPREFIX \t\t\t \n\t\t\tPrefix of output files. Default 'IDR_ChIP'.\n\n\t-c  CountPeak\t\t \n\t\t\tNo of peaks in both replicates that will be compared for IDR analysis.\n\t\t\tDefault 25000.\n\t\n\t-T \tTagmentation\t \n\t\t\tBinary variable. If 1, the input is a ChiPMentation data \n\t\t\twhere the TAG Align files are created by \n\t\t\tshifting the strands a bit. Default 0. \n\t\t\tTag align files are used for estimating peaks using MACS2.\n\t\n\t-C  CONTROLBAM\t\t \n\t\t\tControl file (in eiher .BAM or tagalign file in .gz format)\t\n\t\t\tused to estimate the peaks from MACS2. User may leave this field \n\t\t\tblank if no control file is available.\n\n\tA sample execution of this script is as follows:\n\n\t./IDR_SubSampleBAM_Main.sh -I inpfile1.bam -I inpfile2.bam -P /home/sourya/packages/idrCode/ -d /home/sourya/OutDir_IDR -n 'IDR_test' -c 25000 -T 1 -C control.bam\n\n\nDescribing output of IDR analysis\n----------------------------------\n\nIn the specified output directory \"OutDir\" mentioned in the IDR script, following \nfiles (f) and folders (F) exist:\n\n\tF1: Folders of the name $i$_and_$j$ where 0 \u003c= i \u003c N and 1 \u003c= j \u003c= N, where N is \n\tthe number of replicates analyzed. Individual folders contain results for \n\tpairwise IDR analysis. For example, folder 0_and_1 contain IDR analysis \n\tfor the sample 0 (first replicate) and the sample 1 (second replicate).\n\n\tf1 : \"Replicate_Names.txt\" : names of the replicate samples used for IDR analysis.\n\n\tf2: Input_Peak_Statistics.txt: number of peaks and the peak containing replicates.\n\n\tf3: IDR_Batch_Plot-plot.pdf: final IDR plot. Here individual pairs (whose results \n\t\tare stored in the above mentioned folders) are numbered 1, 2, ...\n\t\tConsideing N = 3, the number of pairs possible is also 3. Here, \n\t\tthe number 1 denotes the folder (pair) 0_and_1, \n\t\t2 denotes the folder (pair) 0_and_2, and 3 denotes the \n\t\tfolder (pair) 1_and_2.\n\n\n\n\n\n\nContact\n-----------\n\nFor any queries, please e-mail:\n\nSourya Bhattacharyya (sourya@lji.org)\n\nFerhat Ay (ferhatay@lji.org)\n\nPandurangan Vijayanand (vijay@lji.org)\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fay-lab%2Fchipline","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fay-lab%2Fchipline","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fay-lab%2Fchipline/lists"}