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Model Based Analysis for ChIP-Seq data

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# 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

# 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

### 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

##### -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

##### -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
( 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

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

###### 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

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, '+'
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
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.


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

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

##### -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

* 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:

##### --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

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](

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