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Model Based Analysis for ChIP-Seq data

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With the improvement of sequencing techniques, chromatinimmunoprecipitation followed by high throughput sequencing (ChIP-Seq)is getting popular to study genome-wide protein-DNA interactions. Toaddress the lack of powerful ChIP-Seq analysis method, we presentedthe Model-based Analysis of ChIP-Seq (MACS), foridentifying transcript factor binding sites. MACS captures theinfluence of genome complexity to evaluate the significance ofenriched ChIP regions and MACS improves the spatial resolution ofbinding sites through combining the information of both sequencing tagposition and orientation. MACS can be easily used for ChIP-Seq dataalone, or with a control sample with the increase ofspecificity. Moreover, as a general peak-caller, MACS can also beapplied to any 'DNA enrichment assays' if the question to be asked issimply: where we can find significant reads coverage than the randombackground.

Recent Changes for MACS (




Please check the file 'INSTALL.md' in the distribution.


Example for regular peak calling: macs2 callpeak -t ChIP.bam -c Control.bam -f BAM -g hs -n test -B -q 0.01

Example for broad peak calling: macs2 callpeak -t ChIP.bam -c Control.bam --broad -g hs --broad-cutoff 0.1

There are twelve functions available in MAC2S serving as sub-commands.

callpeakMain MACS2 Function to call peaks from alignment results.
bdgpeakcallCall peaks from bedGraph output.
bdgbroadcallCall broad peaks from bedGraph output.
bdgcmpComparing two signal tracks in bedGraph format.
bdgoptOperate the score column of bedGraph file.
cmbrepsCombine BEDGraphs of scores from replicates.
bdgdiffDifferential peak detection based on paired four bedGraph files.
filterdupRemove duplicate reads, then save in BED/BEDPE format.
predictdPredict d or fragment size from alignment results.
pileupPileup aligned reads (single-end) or fragments (paired-end)
randsampleRandomly choose a number/percentage of total reads.
refinepeakTake raw reads alignment, refine peak summits.

We only cover callpeak subcommand in this document. Please usemacs2 COMMAND -h to see the detail description for each option ofeach subcommand.

Call peaks

This is the main function in MACS2. It can be invoked by macs2 callpeak . If you type this command with -h, you will see a fulldescription of command-line options. Here we only list the essentials.

Essential Options

-t/--treatment FILENAME

This is the only REQUIRED parameter for MACS. The file can be in anysupported format -- see detail in the --format option. If you havemore than one alignment file, you can specify them as -t A B C. MACSwill pool up all these files together.


The control, genomic input or mock IP data file. Please follow thesame direction as for -t/--treatment.


The name string of the experiment. MACS will use this string NAME tocreate output files like NAME_peaks.xls, NAME_negative_peaks.xls,NAME_peaks.bed , NAME_summits.bed, NAME_model.r and so on. Soplease avoid any confliction between these filenames and your existingfiles.


MACS2 will save all output files into the specified folder for thisoption. A new folder will be created if necessary.

-f/--format FORMAT

Format of tag file can be ELAND, BED, ELANDMULTI, ELANDEXPORT,SAM, BAM, BOWTIE, BAMPE, or BEDPE. Default is AUTO whichwill allow MACS to decide the format automatically. AUTO is alsouseful when you combine different formats of files. Note that MACScan't detect BAMPE or BEDPE format with AUTO, and you have toimplicitly specify the format for BAMPE and BEDPE.

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Nowadays, the most common formats are BED or BAM (includingBEDPE and BAMPE). Our recommendation is to convert your data toBED or BAM first.

Also, MACS2 can detect and read gzipped file. For example, .bed.gzfile can be directly used without being uncompressed with --format BED.

Here are detailed explanation of the recommanded formats:


The BED format can be found at UCSC genome browserwebsite.

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The essential columns in BED format input are the 1st columnchromosome name, the 2nd start position, the 3rd end position,and the 6th, strand.

Note that, for BED format, the 6th column of strand information isrequired by MACS. And please pay attention that the coordinates in BEDformat are zero-based and half-open. See more detail atUCSC site.


If the format is BAM/SAM, please check the definition in(http://samtools.sourceforge.net/samtools.shtml). If the BAM file isgenerated for paired-end data, MACS will only keep the left mate(5'end) tag. However, when format BAMPE is specified, MACS will use thereal fragments inferred from alignment results for reads pileup.


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A special mode will be triggered while the format is specified asBAMPE or BEDPE. In this way, MACS2 will process the BAM or BEDfiles as paired-end data. Instead of building a bimodal distributionof plus and minus strand reads to predict fragment size, MACS2 willuse actual insert sizes of pairs of reads to build fragment pileup.

The BAMPE format is just a BAM format containing paired-end alignmentinformation, such as those from BWA or BOWTIE.

The BEDPE format is a simplified and more flexible BED format,which only contains the first three columns defining the chromosomename, left and right position of the fragment from Paired-endsequencing. Please note, this is NOT the same format used byBEDTOOLS, and the BEDTOOLS version of BEDPE is actually not in astandard BED format. You can use MACS2 subcommand randsample toconvert a BAM file containing paired-end information to a BEDPEformat file:


PLEASE assign this parameter to fit your needs!

It's the mappable genome size or effective genome size which isdefined as the genome size which can be sequenced. Because of therepetitive features on the chromosomes, the actual mappable genomesize will be smaller than the original size, about 90% or 70% of thegenome size. The default hs -- 2.7e9 is recommended for humangenome. Here are all precompiled parameters for effective genome size:

  • hs: 2.7e9
  • mm: 1.87e9
  • ce: 9e7
  • dm: 1.2e8

Users may want to use k-mer tools to simulate mapping of Xbps longreads to target genome, and to find the ideal effective genomesize. However, usually by taking away the simple repeats and Ns fromthe total genome, one can get an approximate number of effectivegenome size. A slight difference in the number won't cause a bigdifference of peak calls, because this number is used to estimate agenome-wide noise level which is usually the least significant onecompared with the local biases modeled by MACS.


The size of sequencing tags. If you don't specify it, MACS will try touse the first 10 sequences from your input treatment file to determinethe tag size. Specifying it will override the automatically determinedtag size.


The q-value (minimum FDR) cutoff to call significant regions. Defaultis 0.05. For broad marks, you can try 0.05 as the cutoff. Q-values arecalculated from p-values using the Benjamini-Hochberg procedure.


The p-value cutoff. If -p is specified, MACS2 will use p-value insteadof q-value.

--min-length, --max-gap

These two options can be used to fine-tune the peak calling behaviorby specifying the minimum length of a called peak and the maximumallowed a gap between two nearby regions to be merged. In other words,a called peak has to be longer than min-length, and if the distancebetween two nearby peaks is smaller than max-gap then they will bemerged as one. If they are not set, MACS2 will set the DEFAULT valuefor min-length as the predicted fragment size d, and the DEFAULTvalue for max-gap as the detected read length. Note, if you set amin-length value smaller than the fragment size, it may have NOeffect on the result. For broad peak calling with --broad optionset, the DEFAULT max-gap for merging nearby stronger peaks will bethe same as narrow peak calling, and 4 times of the max-gap will beused to merge nearby weaker (broad) peaks. You can also use--cutoff-analysis option with the default setting, and check thecolumn avelpeak under different cutoff values to decide a reasonablemin-length value.

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With this flag on, MACS will use the background lambda as locallambda. This means MACS will not consider the local bias at peakcandidate regions.

--slocal, --llocal

These two parameters control which two levels of regions will bechecked around the peak regions to calculate the maximum lambda aslocal lambda. By default, MACS considers 1000bp for small localregion(--slocal), and 10000bps for large local region(--llocal)which captures the bias from a long-range effect like an openchromatin domain. You can tweak these according to yourproject. Remember that if the region is set too small, a sharp spikein the input data may kill a significant peak.


While on, MACS will bypass building the shifting model.


While --nomodel is set, MACS uses this parameter to extend reads in5'->3' direction to fix-sized fragments. For example, if the size ofthe binding region for your transcription factor is 200 bp, and youwant to bypass the model building by MACS, this parameter can be setas 200. This option is only valid when --nomodel is set or when MACSfails to build model and --fix-bimodal is on.


Note, this is NOT the legacy --shiftsize option which is replaced by--extsize! You can set an arbitrary shift in bp here. Please Usediscretion while setting it other than the default value (0). When--nomodel is set, MACS will use this value to move cutting ends (5')then apply --extsize from 5' to 3' direction to extend them tofragments. When this value is negative, ends will be moved toward3'->5' direction, otherwise 5'->3' direction. Recommended to keep itas default 0 for ChIP-Seq datasets, or -1 * half of EXTSIZE togetherwith --extsize option for detecting enriched cutting loci such ascertain DNAseI-Seq datasets. Note, you can't set values other than 0if the format is BAMPE or BEDPE for paired-end data. The default is 0.

Here are some examples for combining --shift and --extsize:

  1. To find enriched cutting sites such as some DNAse-Seq datasets. Inthis case, all 5' ends of sequenced reads should be extended in bothdirections to smooth the pileup signals. If the wanted smoothingwindow is 200bps, then use --nomodel --shift -100 --extsize 200.

  2. For certain nucleosome-seq data, we need to pile up the centers ofnucleosomes using a half-nucleosome size for wavelet analysis(e.g. NPS algorithm). Since the DNA wrapped on nucleosome is about147bps, this option can be used: --nomodel --shift 37 --extsize 73.


It controls the MACS behavior towards duplicate tags at the exact samelocation -- the same coordination and the same strand. The defaultauto option makes MACS calculate the maximum tags at the exact samelocation based on binomial distribution using 1e-5 as p-value cutoff;and the all option keeps every tag. If an integer is given, at mostthis number of tags will be kept at the same location. The default isto keep one tag at the same location. Default: 1


When this flag is on, MACS will try to composite broad regions inBED12 ( a gene-model-like format ) by putting nearby highly enrichedregions into a broad region with loose cutoff. The broad region iscontrolled by another cutoff through --broad-cutoff. Please notethat, the max-gap value for merging nearby weaker/broad peaks is 4times of max-gap for merging nearby stronger peaks. The later onecan be controlled by --max-gap option, and by default it is theaverage fragment/insertion length in the PE data. DEFAULT: False


Cutoff for the broad region. This option is not available unless--broad is set. If -p is set, this is a p-value cutoff, otherwise,it's a q-value cutoff. DEFAULT: 0.1

--scale-to <large small>

When set to large, linearly scale the smaller dataset to the samedepth as the larger dataset. By default or being set as small, thelarger dataset will be scaled towards the smaller dataset. Beware, toscale up small data would cause more false positives.


If this flag is on, MACS will store the fragment pileup, controllambda in bedGraph files. The bedGraph files will be stored in thecurrent directory named NAME_treat_pileup.bdg for treatment data,NAME_control_lambda.bdg for local lambda values from control.


MACS will now reanalyze the shape of signal profile (p or q-scoredepending on the cutoff setting) to deconvolve subpeaks within eachpeak called from the general procedure. It's highly recommended todetect adjacent binding events. While used, the output subpeaks of abig peak region will have the same peak boundaries, and differentscores and peak summit positions.


MACS uses a buffer size for incrementally increasing internal arraysize to store reads alignment information for each chromosome orcontig. To increase the buffer size, MACS can run faster but willwaste more memory if certain chromosome/contig only has very fewreads. In most cases, the default value 100000 works fine. However, ifthere are a large number of chromosomes/contigs in your alignment andreads per chromosome/contigs are few, it's recommended to specify asmaller buffer size in order to decrease memory usage (but it willtake longer time to read alignment files). Minimum memory requestedfor reading an alignment file is about # of CHROMOSOME * BUFFER_SIZE *8 Bytes. DEFAULT: 100000

Output files

  1. NAME_peaks.xls is a tabular file which contains information aboutcalled peaks. You can open it in excel and sort/filter using excelfunctions. Information include:

    • chromosome name
    • start position of peak
    • end position of peak
    • length of peak region
    • absolute peak summit position
    • pileup height at peak summit
    • -log10(pvalue) for the peak summit (e.g. pvalue =1e-10, thenthis value should be 10)
    • fold enrichment for this peak summit against random Poissondistribution with local lambda,
    • -log10(qvalue) at peak summit

    Coordinates in XLS is 1-based which is different from BEDformat. When --broad is enabled for broad peak calling, thepileup, p-value, q-value, and fold change in the XLS file will bethe mean value across the entire peak region, since peak summitwon't be called in broad peak calling mode.

  2. NAME_peaks.narrowPeak is BED6+4 format file which contains thepeak locations together with peak summit, p-value, and q-value. Youcan load it to the UCSC genome browser. Definition of some specificcolumns are:

    • 5th: integer score for display. It's calculated asint(-10*log10pvalue) or int(-10*log10qvalue) depending onwhether -p (pvalue) or -q (qvalue) is used as scorecutoff. Please note that currently this value might be out of the[0-1000] range defined in UCSC ENCODE narrowPeakformat. Youcan let the value saturated at 1000 (i.e. p/q-value = 10^-100) byusing the following 1-liner awk: awk -v OFS='t' '{$5=$5>1000?1000:$5} {print}' NAME_peaks.narrowPeak
    • 7th: fold-change at peak summit
    • 8th: -log10pvalue at peak summit
    • 9th: -log10qvalue at peak summit
    • 10th: relative summit position to peak start

    The file can be loaded directly to the UCSC genome browser. Removethe beginning track line if you want to analyze it by other tools.

  3. NAME_summits.bed is in BED format, which contains the peaksummits locations for every peak. The 5th column in this file isthe same as what is in the narrowPeak file. If you want to findthe motifs at the binding sites, this file is recommended. The filecan be loaded directly to the UCSC genome browser. Remove thebeginning track line if you want to analyze it by other tools.

  4. NAME_peaks.broadPeak is in BED6+3 format which is similar tonarrowPeak file, except for missing the 10th column forannotating peak summits. This file and the gappedPeak file willonly be available when --broad is enabled. Since in the broadpeak calling mode, the peak summit won't be called, the values inthe 5th, and 7-9th columns are the mean value across all positionsin the peak region. Refer to narrowPeak if you want to fix thevalue issue in the 5th column.

  5. NAME_peaks.gappedPeak is in BED12+3 format which contains boththe broad region and narrow peaks. The 5th column is the score forshowing grey levels on the UCSC browser as in narrowPeak. The 7this the start of the first narrow peak in the region, and the 8thcolumn is the end. The 9th column should be RGB color key, however,we keep 0 here to use the default color, so change it if youwant. The 10th column tells how many blocks including the starting1bp and ending 1bp of broad regions. The 11th column shows thelength of each block and 12th for the start of each block. 13th:fold-change, 14th: -log10pvalue, 15th: -log10qvalue. The file canbe loaded directly to the UCSC genome browser. Refer tonarrowPeak if you want to fix the value issue in the 5th column.

  6. NAME_model.r is an R script which you can use to produce a PDFimage of the model based on your data. Load it to R by:

    $ Rscript NAME_model.r

    Then a pdf file NAME_model.pdf will be generated in your currentdirectory. Note, R is required to draw this figure.

  7. The NAME_treat_pileup.bdg and NAME_control_lambda.bdg files arein bedGraph format which can be imported to the UCSC genome browseror be converted into even smaller bigWig files. TheNAME_treat_pielup.bdg contains the pileup signals (normalizedaccording to --scale-to option) from ChIP/treatment sample. TheNAME_control_lambda.bdg contains local biases estimated for eachgenomic location from the control sample, or from treatment samplewhen the control sample is absent. The subcommand bdgcmp can beused to compare these two files and make a bedGraph file of scoressuch as p-value, q-value, log-likelihood, and log fold changes.

Other useful links

Tips of fine-tuning peak calling

There are several subcommands within MACSv2 package to fine-tune orcustomize your analysis:

  1. bdgcmp can be used on *_treat_pileup.bdg and*_control_lambda.bdg or bedGraph files from other resources tocalculate the score track.

  2. bdgpeakcall can be used on *_treat_pvalue.bdg or the filegenerated from bdgcmp or bedGraph file from other resources to callpeaks with given cutoff, maximum-gap between nearby mergeable peaksand a minimum length of peak. bdgbroadcall works similarly tobdgpeakcall, however, it will output _broad_peaks.bed in BED12format.

  3. Differential calling tool -- bdgdiff, can be used on 4 bedGraphfiles which are scores between treatment 1 and control 1, treatment2 and control 2, treatment 1 and treatment 2, treatment 2 andtreatment 1. It will output consistent and unique sites accordingto parameter settings for minimum length, the maximum gap andcutoff.

  4. You can combine subcommands to do a step-by-step peak calling. Readdetail at MACS2wikipage

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