Recent Changes for MACS (2.1.2)

2.1.2

* New features

1) Added missing BEDPE support. And enable the support for BAMPE
and BEDPE formats in 'pileup', 'filterdup' and 'randsample'
subcommands. When format is BAMPE or BEDPE, The 'pileup' command
will pile up the whole fragment defined by mapping locations of
the left end and right end of each read pair. Thank @purcaro

2) Added options to callpeak command for tweaking max-gap and
min-len during peak calling. Thank @jsh58!

3) The callpeak option "--to-large" option is replaced with
"--scale-to large".

4) The randsample option "-t" has been replaced with "-i".

* Bug fixes

1) Fixed memory issue related to #122 and #146

2) Fixed a bug caused by a typo. Related to #249, Thank @shengqh

3) Fixed a bug while setting commandline qvalue cutoff.

4) Better describe the 5th column of narrowPeak. Thank @alexbarrera

5) Fixed the calculation of average fragment length for paired-end
data. Thank @jsh58

6) Fixed bugs caused by khash while computing p/q-value and log
likelihood ratios. Thank @jsh58

7) More spelling tweaks in source code. Thank @mr-c

2.1.1

* Retire the tag:rc. 

* Fixed spelling. Merged pull request #120. Thank @mr-c!

* Change filtering criteria for reading BAM/SAM files

Related to callpeak and filterdup commands. Now the
reads/alignments flagged with 1028 or 'PCR/Optical duplicate' will
still be read although MACS2 may decide them as duplicates
later. Related to old issue #33. Sorry I forgot to address it for
years!

README for MACS (2.1.2)

Introduction

With the improvement of sequencing techniques, chromatin immunoprecipitation followed by high throughput sequencing (ChIP-Seq) is getting popular to study genome-wide protein-DNA interactions. To address the lack of powerful ChIP-Seq analysis method, we present a novel algorithm, named Model-based Analysis of ChIP-Seq (MACS), for identifying transcript factor binding sites. MACS captures the influence of genome complexity to evaluate the significance of enriched ChIP regions, and MACS improves the spatial resolution of binding sites through combining the information of both sequencing tag position and orientation. MACS can be easily used for ChIP-Seq data alone, or with control sample with the increase of specificity.

Install

Please check the file 'INSTALL' in the distribution.

Usage

`macs2 [-h] [--version]  {callpeak,filterdup,bdgpeakcall,bdgcmp,randsample,bdgdiff,bdgbroadcall}`

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 seven major functions available in MACS serving as sub-commands.

Subcommand	Description
callpeak	Main MACS2 Function to call peaksfrom alignment results.
bdgpeakcall	Call peaks from bedGraph output.
bdgbroadcall	Call broad peaks from bedGraph output.
bdgcmp	Comparing two signal tracks in bedGraph format.
bdgopt	Operate the score column of bedGraph file.
cmbreps	Combine BEDGraphs of scores from replicates.
bdgdiff	Differential peak detection based on paired four bedgraph files.
filterdup	Remove duplicate reads, then save in BED/BEDPE format.
predictd	Predict d or fragment size from alignment results.
pileup	Pileup aligned reads (single end) or fragments (paired-end)
randsample	Randomly choose a number/percentage of total reads.
refinepeak	Take raw reads alignment, refine peak summits.

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

Call peaks

This is the main function in MACS2. It can be invoked by 'macs2 callpeak' command. If you type this command without parameters, you will see a full description of commandline options. Here we only list the essential options.

Essential Options

-t/--treatment FILENAME

This is the only REQUIRED parameter for MACS. File can be in any supported format specified by --format option. Check --format for detail. If you have more than one alignment files, you can specify them as -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 to create output files like NAME_peaks.xls, NAME_negative_peaks.xls, NAME_peaks.bed , NAME_summits.bed, NAME_model.r and so on. So please avoid any confliction between these filenames and your existing files.

--outdir

MACS2 will save all output files into speficied folder for this option.

-f/--format FORMAT

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

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

BED

The BED format can be found at UCSC genome browser website.

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

BAM/SAM

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

BEDPE or BAMPE

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

The BAMPE format is just BAM format containing paired-end alignment information, 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 chromosome name, left and right position of the fragment from Paired-end sequencing. Please note, this is NOT the same format used by BEDTOOLS, and BEDTOOLS version of BEDPE is actually not in a standard BED format.

BOWTIE

If the format is BOWTIE, you need to provide the ASCII bowtie output file with the suffix '.map'. Please note that, you need to make sure that in the bowtie output, you only keep one location for one read. Check the bowtie manual for detail if you want at (http://bowtie-bio.sourceforge.net/manual.shtml)

Here is the definition for Bowtie output in ASCII characters I copied from the above webpage:

1. Name of read that aligned
2. Orientation of read in the alignment, '-' for reverse complement, '+' otherwise
3. Name of reference sequence where alignment occurs, or ordinal ID if no name was provided
4. 0-based offset into the forward reference strand where leftmost character of the alignment occurs
5. Read sequence (reverse-complemented if orientation is -)
6. ASCII-encoded read qualities (reversed if orientation is -). The encoded quality values are on the Phred scale and the encoding is ASCII-offset by 33 (ASCII char !).
7. Number of other instances where the same read aligns against the same reference characters as were aligned against in this alignment. This is not the number of other places the read aligns with the same number of mismatches. The number in this column is generally not a good proxy for that number (e.g., the number in this column may be '0' while the number of other alignments with the same number of mismatches might be large). This column was previously described as "Reserved".
8. Comma-separated list of mismatch descriptors. If there are no mismatches in the alignment, this field is empty. A single descriptor has the format offset:reference-base>read-base. The offset is expressed as a 0-based offset from the high-quality (5') end of the read.

ELAND

If the format is ELAND, the file must be ELAND result output file, each line MUST represents only ONE tag, with fields of:

1. Sequence name (derived from file name and line number if format is not Fasta)
2. Sequence
3. Type of match:

• NM: no match found.
• QC: no matching done: QC failure (too many Ns basically).
• RM: no matching done: repeat masked (may be seen if repeatFile.txt was specified).
• U0: Best match found was a unique exact match.
• U1: Best match found was a unique 1-error match.
• U2: Best match found was a unique 2-error match.
• R0: Multiple exact matches found.
• R1: Multiple 1-error matches found, no exact matches.
• R2: Multiple 2-error matches found, no exact or 1-error matches.

4. Number of exact matches found.
5. Number of 1-error matches found.
6. Number of 2-error matches found.
   Rest of fields are only seen if a unique best match was found (i.e. the match code in field 3 begins with "U").
7. Genome file in which match was found.
8. Position of match (bases in file are numbered starting at 1).
9. Direction of match (F=forward strand, R=reverse).
10. How N characters in read were interpreted: ("."=not applicable, "D"=deletion, "I"=insertion). Rest of fields are only seen in the case of a unique inexact match (i.e. the match code was U1 or U2).
11. Position and type of first substitution error (e.g. 12A: base 12 was A, not whatever is was in read).
12. Position and type of first substitution error, as above.

ELANDMULTI

If the format is ELANDMULTI, the file must be ELAND output file from multiple-match mode, each line MUST represents only ONE tag, with fields of:

1. Sequence name
2. Sequence
3. Either NM, QC, RM (as described above) or the following:
4. x:y:z where x, y, and z are the number of exact, single-error, and 2-error matches found
5. Blank, if no matches found or if too many matches found, or the following: BAC_plus_vector.fa:163022R1,170128F2,E_coli.fa:3909847R1 This says there are two matches to BAC_plus_vector.fa: one in the reverse direction starting at position 160322 with one error, one in the forward direction starting at position 170128 with two errors. There is also a single-error match to E_coli.fa.

Notes

1. For BED format, the 6th column of strand information is required by MACS. And please pay attention that the coordinates in BED format is zero-based and half-open (http://genome.ucsc.edu/FAQ/FAQtracks#tracks1).

2. For plain ELAND format, only matches with match type U0, U1 or U2 is accepted by MACS, i.e. only the unique match for a sequence with less than 3 errors is involed in calculation. If multiple hits of a single tag are included in your raw ELAND file, please remove the redundancy to keep the best hit for that sequencing tag.

3. ELAND export format support sometimes may not work on your datasets, because people may mislabel the 11th and 12th column. MACS uses 11th column as the sequence name which should be the chromosome names.

-g/--gsize

PLEASE assign this parameter to fit your needs!

It's the mappable genome size or effective genome size which is defined as the genome size which can be sequenced. Because of the repetitive features on the chromsomes, the actual mappable genome size will be smaller than the original size, about 90% or 70% of the genome size. The default hs -- 2.7e9 is recommended for UCSC human hg18 assembly. Here are all precompiled parameters for effective genome size:

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

-s/--tsize

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

-q/--qvalue

The qvalue (minimum FDR) cutoff to call significant regions. Default is 0.05. For broad marks, you can try 0.05 as cutoff. Q-values are calculated from p-values using Benjamini-Hochberg procedure.

-p/--pvalue

The pvalue cutoff. If -p is specified, MACS2 will use pvalue instead of qvalue.

--nolambda

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

--slocal, --llocal

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

--nomodel

While on, MACS will bypass building the shifting model.

--extsize

While '--nomodel' is set, MACS uses this parameter to extend reads in 5'->3' direction to fix-sized fragments. For example, if the size of binding region for your transcription factor is 200 bp, and you want to bypass the model building by MACS, this parameter can be set as 200. This option is only valid when --nomodel is set or when MACS fails 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 Use discretion while setting it other than 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 to fragments. When this value is negative, ends will be moved toward 3'->5' direction, otherwise 5'->3' direction. Recommended to keep it as default 0 for ChIP-Seq datasets, or -1 * half of EXTSIZE together with --extsize option for detecting enriched cutting loci such as certain DNAseI-Seq datasets. Note, you can't set values other than 0 if format is BAMPE or BEDPE for paired-end data. Default is 0.

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

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

2. For certain nucleosome-seq data, we need to pileup the centers of nucleosomes using a half-nucleosome size for wavelet analysis (e.g. NPS algorithm). Since the DNA wrapped on nucleosome is about 147bps, this option can be used: --nomodel --shift 37 --extsize 73.

--keep-dup

It controls the MACS behavior towards duplicate tags at the exact same location -- the same coordination and the same strand. The default 'auto' option makes MACS calculate the maximum tags at the exact same location based on binomal distribution using 1e-5 as pvalue cutoff; and the 'all' option keeps every tags. If an integer is given, at most this number of tags will be kept at the same location. The default is to keep one tag at the same location. Default: 1

--broad

When this flag is on, MACS will try to composite broad regions in BED12 ( a gene-model-like format ) by putting nearby highly enriched regions into a broad region with loose cutoff. The broad region is controlled by another cutoff through --broad-cutoff. The maximum length of broad region length is 4 times of d from MACS. DEFAULT: False

--broad-cutoff

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

--scale-to <large|small>

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

-B/--bdg

If this flag is on, MACS will store the fragment pileup, control lambda, -log10pvalue and -log10qvalue scores in bedGraph files. The bedGraph files will be stored in current directory named NAME_treat_pileup.bdg for treatment data, NAME_control_lambda.bdg for local lambda values from control, NAME_treat_pvalue.bdg for Poisson pvalue scores (in -log10(pvalue) form), and NAME_treat_qvalue.bdg for q-value scores from Benjamini–Hochberg–Yekutieli procedure.

--call-summits

MACS will now reanalyze the shape of signal profile (p or q-score depending on cutoff setting) to deconvolve subpeaks within each peak called from general procedure. It's highly recommended to detect adjacent binding events. While used, the output subpeaks of a big peak region will have the same peak boundaries, and different scores and peak summit positions.

Output files

1. NAME_peaks.xls is a tabular file which contains information about called peaks. You can open it in excel and sort/filter using excel functions. 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, then this value should be 10)
   • fold enrichment for this peak summit against random Poisson distribution with local lambda, -log10(qvalue) at peak summit

   Coordinates in XLS is 1-based which is different with BED format.

2. NAME_peaks.narrowPeak is BED6+4 format file which contains the peak locations together with peak summit, pvalue and qvalue. You can load it to UCSC genome browser. Definition of some specific columns are:

   • 5th: integer score for display calculated as int(-10*log10qvalue). Please note that currently this value might be out of the [0-1000] range defined in UCSC Encode narrowPeak format
   • 7th: fold-change
   • 8th: -log10pvalue
   • 9th: -log10qvalue
   • 10th: relative summit position to peak start

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

3. NAME_summits.bed is in BED format, which contains the peak summits locations for every peaks. The 5th column in this file is -log10pvalue the same as NAME_peaks.bed. If you want to find the motifs at the binding sites, this file is recommended. The file can be loaded directly to UCSC genome browser. Remove the beginning track line if you want to analyze it by other tools.

4. NAME_peaks.broadPeak is in BED6+3 format which is similar to narrowPeak file, except for missing the 10th column for annotating peak summits.

5. NAME_peaks.gappedPeak is in BED12+3 format which contains both the broad region and narrow peaks. The 5th column is 10*-log10qvalue, to be more compatible to show grey levels on UCSC browser. Tht 7th is the start of the first narrow peak in the region, and the 8th column 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 you want. The 10th column tells how many blocks including the starting 1bp and ending 1bp of broad regions. The 11th column shows the length of each blocks, and 12th for the starts of each blocks. 13th: fold-change, 14th: -log10pvalue, 15th: -log10qvalue. The file can be loaded directly to UCSC genome browser.

6. NAME_model.r is an R script which you can use to produce a PDF image about 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 current directory. Note, R is required to draw this figure.

7. The .bdg files are in bedGraph format which can be imported to UCSC genome browser or be converted into even smaller bigWig files. There are two kinds of bdg files, one for treatment and the other one for control.

Other useful links

• Cistrome
• bedTools
• UCSC toolkits

Tips of fine-tuning peak calling

Check the three scripts within MACSv2 package:

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

2. bdgpeakcall can be used on *_treat_pvalue.bdg or the file generated from bdgcmp or bedGraph file from other resources to call peaks with given cutoff, maximum-gap between nearby mergable peaks and minimum length of peak. bdgbroadcall works similarly to bdgpeakcall, however it will output _broad_peaks.bed in BED12 format.

3. Differential calling tool -- bdgdiff, can be used on 4 bedgraph files which are scores between treatment 1 and control 1, treatment 2 and control 2, treatment 1 and treatment 2, treatment 2 and treatment 1. It will output the consistent and unique sites according to parameter settings for minimum length, maximum gap and cutoff.

4. You can combine subcommands to do a step-by-step peak calling. Read detail at MACS2 wikipage