Peak-Finder Meta Server for ChIP-Seq Data Analysis, User Manual
Contents
1. 0 F 50 L 100 w 50 HPEAK ERANGE CISGENOME SEQSITE F FINDPEAKS dist_ type 1 wig_step_ size 10 Peakfinder s path related to PeakFinders directory which is parent directory of this file and the included peak finders SISSR PATH sissrs_ v1 4 sissrs pl CISGENOME PATH cisGenome 2 0 FINDPEAKS PATH findpeaks HPEAK PATH HPeak HPeak 1 1 HPeak pl SEQSITE PATH SeqSite1 0 ERANGE PATH Erange commoncode I1f you alreay have installed macs on yor system then change the line below with MACS PATH macs MACS PATH MACS 1 3 7 1 lib modefied macs Bibliography 1 2 3 4 5 6 7 8 Rye M B Si jtrom P Drabli js F A manually curated ChIP seq benchmark demonstrates room for improvement in current peak finder programs Nucleic Acid Res Mar 39 4 e25 Quinlan A R and Hall I M 2010 BEDTools a flexible suite of utilities for comparing genomic features Bioinformatics 26 6 pp 8411 j842 Li H Handsaker B Wysoker A Fennell T Ruan J Homer N Marth G Abecasis G Durbin R and 1000 Genome Project Data Pro cessing Subgroup 2009 The Sequence alignment map SAM format and SAMtools Bioinformatics 25 2078 9 PMID 19505943 Anthony P Fejes Gordon Robertson Mikhail Bilenky Richard Varhol Matthew Bainbridge Steven J M Jones FindPeaks 3 1 a tool for iden tifying areas of enrichment from massively parallel short read sequenc ing technology Bioinforma2. Peak Finder Meta Server PFMS User Manual Computational amp Systems Biology ICM Uppsala University http www icm uu se 1 1 Overview PFMS is a free software application which identifies genome wide transcrip tion factor binding sites from CHIP Seq data PFMS combines identified sites from seven different peak finders after making a scoring based compar ison it gives the highest ranked overlapped peaks 1 2 Download PFMS PFMS is a free software implemented in Python and intended to be used for the purpose of academic research in the Bioinformatics and Genomic areas The latest version of PFMS is freely available under GNU public license and it can be obtained via http bioinf icm uu se pfms 1 3 Supplementary Material Users are expected to have their own datasets and or background data but if you would like to get the datasets that have been used to evaluate PFMS please refer to the following web page I http tare medisin ntnu no chipseqbenchmark downloads ChIPSeq_files_ in_bed_format The results of PFMS for the datasets above are provided in the supple mantary material on http bioinf icm uu se pfms results php 1 4 Installation PFMS 1 3 has been implemented and tested on Unix based systems Win dows users are encouraged to try it using 1 Please make sure the following software tools are installed e Python 2 6 or higher required to use PFMS e GCC or C compiler Some of the peak finders impl
3. all_ chr is used min size lt number gt Minimum file size in KB of a peak finder result in order to be included in the comparison default is 1 all_chr Executes PFMS for each chromosome in a given dataset and combines the results this is the default mode findpeaks Detects the binding sites using Findpeaks 4 cisgenome Detects the binding sites using CisGenome 5 macs Detects the binding sites using MACS 6 6 hpeak Detects the binding sites using HPeak 7 can be used with no presence of control data erange Detects the binding sites using Erange 8 can be used only with BED comparison approach sissr Detects the binding sites using SISSRs 9 can be used with BED comparison approach seqsite Detects the binding sites using SeqSite IO can be used only with BED comparison approach help Prints a usage message with a list of the implemented options 1 5 3 Output Visualization The identified transcription factor binding sites peaks can be visualized using UCSC genome browser integrated genome browser IGB or any other browser that supports either BED or WIG format The results can be found under output_label_ Results directory The iden tified with PFMS are stored either in output_label_ Results bed wig or output_label_ Results bed wig when all_ chr was used That is beside the result of each peak finder for each chromosome that will be stored sepa rately whe
4. bed 5 normal_shift xtless number gt is mean value of the scores after nor mal normalization if the original distribution of the scores calculated from the individual peakfinders was normal default value is 3 quantile lt number gt normalizes the peak scores using quantile method the default method with value of 75 normal normalizes the peak scores normal method only with bed average normalizes the peak scores using average method rank normalizes the peak scores using rank method i e assumes that all used peak finders return similar fraction of FPs only with bed top rank normalizes peak scores using top rank method i e assumes that peaks from peak finders returning small number of peaks are more reliable only with bed parallel Forces PFMS to execute the peak finders in parallel it s the default if more than two processors are available sequential Forces PFMS to execute the peak finders Sequentially it s the default mode when less two processors are available or the Python 2 6 or higher is not available max cpu_use lt number gt Sets the maximum number of processors to be used by PFMS default is 6 min cpu lt number gt PFMS is running in parallel mode if minimum number of processors CPU was available on the system default is 2 store_ results Keeps the original files generated by the peak finders plus results of the spitted chromosomes when
5. du cn seqsite SISSRs v1 4 http sissrs rajajothi com 9 Table 1 1 Peak Finders included in PEMS 1 6 1 Customizing Peak Finder s Parameters PFMS comes with a configuration file which is used to customize the optional parameters of each peak finder If you have installed PFMS on the system directory the first installation type you should be able to locate pfms conf file in a directory called Peak Finders in one of the following places usr local Unix bassed systems with standard python installation usr Unix bassed systems with non standard python installation C Python Windows systems But PFMS is used under the original source directory then the pfms conf file should exist in PFMS 1 3 PeakFinders directory Configuration File Style The configuration file is divided into two sections 1 Peak finder parameters List of optional parameters for each peak finder can be stated in a single line followed by the the peak finder s name and a colon please consider the peak finder s usage options 2 Peak finders paths related to the PeakFinders directory This is par ticularly useful to upgrade a peak finder to a newer version as far as 8 the new version has the same directory structure and input format as it s current version or to force PFMS to look for a specific peak finder in a different location Below is the default content of pfms conf MACS gsize 1000000000 SISSR s 308000000
6. emented in C e Perl Required to use SISSr peak finder e JRE 1 6 Required to use FindPeaks peak finder and handling datasets containing reads of more than one chromosome Note a BEDTools 2 and samtools 3 are also installed with PFMS Since they are used for converting BAM amp SAM to BED format b On most of the UNIX based systems including Mac OS X and Linux Python C compiler and Perl are installed by default c Without Perl and or JRE1 6 the PFMS would still work but you will not be able to use SISSRs and or FindPeaks After downloading the compressed source distribution extract it eg with unzip unzip q PFMS 1 3 zip Then navigate to the extracted directory cd directory _path PFMS 1 3 Installing PFMS with root access The following command installs the Python modules to Python s standard location and the supported Peak finders to Python s prefix directory hint you need to have root access to perform this type installation sudo python setup py install Installing PFMS by Normal users If You don t have root access to perform the previous installation you can still use PFMS within the the extracted directory aka PFMS 1 3 in order to run the meta server since everything will be installed there python setup py install normal In order to remove PFMS navigate into the extracted directory and type sudo python setup py remove 1 5 PFMS Usage In order to execute PFMS with it s default settin
7. gs use on of the following commands based on the installation type PFMetaserver i lt input_file gt lt o output_label gt Options Note If PFMS is used with the normal user mode without system installa tion then PFMetaserver in the above command needs to be replaced with python PFMetaserver py as follows python PFMetaserver py i lt input_file gt o lt output_label gt Options 1 5 1 PEMS Settings Data Set Handling by default the given input file is splitted per chromo some using FindPeaks split tool 4 and the reads of each chromosome are stored in a separated file Then the reads of each chromosome are processed under PFMS Alternatively a single chromosome from the input dataset can be handled by adding chr option to the list of the options This option forces PFMS to process the reads from the spec ified chromosome only and ignores the rest After identifying peaks for each chromosome individually it combines all the results to a single output file In addition to the list of peaks obtained by PFMS for the whole dataset the peaks identified for each chromosome and the results obtained from the selected peak finders can optionally be kept using store_ results option Peak Selection BED or WIG The peaks detected by the selected peak finders are combined and unified into either BED or WIG format based on user s preference e BED Mode voting the default or minFP or minFN When BED opti
8. ite FindPeaks and HPeak While only MACS CisGenome and FindPeaks can be used with WIG format The default and recommended peak finder are MACS CisGenome and SISSRs Normalization rank or normal or quantile lt number gt or average BED format When either MinFP or minFN peak selection method is selected the peak scores identified by the individual peak finders are normalized using one of the following methods Normal normal based on normal distribution Quantile quantile lt number gt based on a given quantile value Average average based on average value of the scores Rank rank based on peak ranking Top rank top_ rank based on the peak ranking WIG format either quantile or average can be used for normalization The default normalization method used in PFMS is quantile 75 Execution Mode When more than two processors are available on the tar get machine PFMS makes a process pool to execute each peak finder in a single process and combines the results Use max_ cpu option to restrict cpu usage by PFMS or sequential to force sequential process ing 1 5 2 Command line options The following is a list of the available features and options Hence The parameters enclosed between square brackets are optional i input file Input data file path The standard 6 column BED BAM and SMA file formats are accepted o output label Used to label the output directory and file names control co
9. n store_ results is used 1 5 4 A Usage Example Assume the ChIP seq data file is named Treat bed and the control data is named Input bed both are located under the current working directory The experiment requirement goal is to find all the TFBSs in chromosome four that are identified by at least four peak finders out of six with using BED comparison approach and label the results with FoxAl_peaks In addition keep results of all the peak finders PFMetaserver i Treat bed control Input bed o FoxA_peaks macs sissr seqsite cisgenome erange hpeak min_rank 4 store_results 1 6 Included Peak Finders A list of the peak finders included in the current version of PFMS is given in the following table It s worth mentioning that some of the peak finders T probably have other useful features beside binding site detection from ChIP seq samples for instance RNA seq and downstream analysis But in the current version the main focus is on ChIP seq For more details users are recommended to consult the peak finders manual page Source MACS v1 3 7 http liulab dfci harvard edu MACS CisGenome v2 0 http www biostat jhsph edu hji cisgenome http www bcgsc ca platform bioinfo software findpeaks Findpeaks v3 1 9 2 Hpeak v1 1 E range v 2 1 http woldlab caltech edu rnaseg SeqSite v1 0 http bioinfo au tsinghua e
10. ntrol _ file Background data file path The standard 6 column BED BAM and SMA file formats are accepted chr chromosome Forces PFMS to only process the specified chromo some instead of handling all the chromosomes of the input file min_ rank lt number gt A peak is significant if it s detected by given lt number gt peak finders the default is number of selected peak find ers 2 it should be in range of the quantity of the selected peak find ers bed Input file format is BED the default value The tag file and control file have to have the same format bed bam Input file format is BAM the default value The tag file and control file have to have the same format BAM sam Input file format is SAM the default value The tag file and control file have to have the same format SAM wig Gives the detected peaks in WIG format while the default is BED please note this feature can only be used with MACS CisGenome FindPeaks and HPeak lt number gt The percentage of the identified peaks to be obtained default is 100 output percentage lt number gt The percentage of the identified peaks to be obtained default is 100 to be used only with wig option voting Uses voting mechanism for peak selection the default value not to be used with wig minFP Uses minFP for peak selection can be used only with bed minFN Uses minFN for peak selection can be used only with
11. on is used which is the default value one of the following methods will be used to candidate putative regions amongst the combined peaks In this mode the results are re ported in BED file format Voting default candidate regions are selected if they con tain peaks from more than a threshold value defined by min rank lt number gt default number of selected peak finders 2 When the threshold is set to the number of se lected peak finders only the peaks that are detected by all the peak finders will be selected 3 minFP this method minimizes the number of false positive peaks by excluding the regions that have a score smaller than the maximum score of the non candidate regions minFN this method minimizes the number of false negative peaks In addition to the regions that have min_rank a user defined value or more votes the regions that have a score larger than the minimum score amongst the candidate regions are also reported e WIG Mode When WIG option is used the combined re gions are weighted with the normalized scores of the overlapped peaks By default all the regions are reported but adding output percentage lt number gt will only report the regions that are within the specified ratio In this mode the results are reported in WIG file format Peak Finders In the case of BED mode all the integrated peak find ers can be called in PFMS these includes MACS CisGenome SIS SRs Erange SeqS
12. tics In Bioinformatics Vol 24 No 15 1 August 2008 pp 1729 1730 doi 10 1093 bioinformatics btn305 Key citeulike 3023880 Hongkai Ji Hui Jiang Wenxiu Ma David S Johnson Richard M Myers and Wing H Wong 2008 An integrated software system for analyzing ChIP chip and ChIP seq data Nature Biotechnology 26 1293 1300 doi 10 1038 nbt 1505 Zhang Y Liu T Meyer CA Eeckhoute J Johnson DS et al Model based analysis of ChIP Seq MACS Genome Biol 2008 9 R137 Qin ZS Yu J Shen J Maher CA Hu M Kalyana Sundaram S Yu J Chinnaiyan AM 2010 HPeak an HMM based algorithm for defining read enriched regions in ChIP Seq data BMC Bioinformatics 11 369 Mapping and quantifying mammalian transcriptomes by RNA Seq Ali Mortazavil Brian A Williams Kenneth McCue Lorian Schaeffer amp Bar 10 bara Wold Published online 30 May 2008 doi 10 1038 nmeth 1226 Nature Methods 5 621 628 2008 9 Jothi R Cuddapah S Barski A Cui K Zhao K Genome wide identifi cation of in vivo protein DNA binding sites from ChIP Seq data Nucleic acids research 2008 36 5221 10 Xi Wang and Xuegong Zhang Pinpointing transcription factor binding sites from ChIP seq data with SeqSite Submitted 11
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