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Introduction

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.

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3. MACS can be applied to scenarios other than calling enriched regions from ChIP-seq data. MACS-1.4.2 and older versions can be used to identify differential read-enriched regions from two ChIP-seq samples by viewing one of the samples as a control, so that peak regions correspond to more enriched regions in the other sample.

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Recent Changes for MACS (2.2.4)

2.2.4

2.1.4

Install

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

Usage

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.

We only cover callpeak module in this document. Please use macs2 COMMAND -h to see the detail description for each option of eachmodule.

Call peaks

This is the main function in MACS2. It can be invoked by 'macs2callpeak' command. If you type this command without parameters, youwill see a full description of command-line options. Here we only listthe essential options.

Essential Options

-t/--treatment FILENAME

This is the only REQUIRED parameter for MACS. The file can be in anysupported format specified by --format option. Check --format fordetail. If you have more than one alignment file, you can specify themas -t A B C. MACS will pool up all these files together.

-c/--control

The control or mock data file. Please follow the same direction as for-t/--treatment.

-n/--name

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.

--outdir

MACS2 will save all output files into the specified folder for thisoption.

-f/--format FORMAT

Format of tag file can be ELAND, BED, ELANDMULTI, ELANDEXPORT,ELANDMULTIPET (for pair-end tags), SAM, BAM, BOWTIE, BAMPEor BEDPE. Default is AUTO which will allow MACS to decide theformat automatically. AUTO is also useful when you combine differentformats of files. Note that MACS can't detect BAMPE or BEDPEformat with AUTO, and you have to implicitly specify the format forBAMPE and BEDPE.

Nowadays, the most common formats are BED or BAM/SAM.

BED

The BED format can be found at UCSC genome browserwebsite.

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(http://genome.ucsc.edu/FAQ/FAQtracks#tracks1).

BAM/SAM

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.

BEDPE or BAMPE

A special mode will be triggered while the format is specified as'BAMPE' 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, whichonly contains the first three columns defining the chromosome name,left and right position of the fragment from Paired-endsequencing. Please note, this is NOT the same format used by BEDTOOLS,and the BEDTOOLS version of BEDPE is actually not in a standard BEDformat. You can use MACS2 subcommand randsample to convert a BAMfile containing paired-end information to a BEDPE format file:

-g/--gsize

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.

-s/--tsize

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.

-q/--qvalue

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.

-p/--pvalue

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 anotherword, a called peak has to be longer than min-length, and if thedistance between two nearby peaks is smaller than max-gap then theywill be merged as one. If they are not set, MACS2 will set the DEFAULTvalue for min-length as the predicted fragment size d, and theDEFAULT value for max-gap as the detected read length. Note, if youset a min-length value smaller than the fragment size, it may haveNO effect on the result. For BROAD peak calling, try to set a largevalue such as 500bps. You can also use '--cutoff-analysis' option withthe default setting, and check the column 'avelpeak' under differentcutoff values to decide a reasonable min-length value.

--nolambda

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.

--nomodel

While on, MACS will bypass building the shifting model.

--extsize

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.

--shift

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.

--keep-dup

It controls the MACS behavior towards duplicate tags at the exact samelocation -- the same coordination and the same strand. The default'auto' 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 tags. If an integer is given, atmost this number of tags will be kept at the same location. Thedefault is to keep one tag at the same location. Default: 1

--broad

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. The maximumlength of broad region length is 4 times of d from MACS. DEFAULT:False

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--broad-cutoff

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 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.

-B/--bdg

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.

--call-summits

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.

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--buffer-size

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.

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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.

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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