README

README for AREM 1.0.1, based on MACS 1.4.0rc2
Time-stamp: <2011-03-01 18:21:42 Jake Biesinger>

* Introduction

High-throughput sequencing coupled to chromatin immuno-
precipitation (ChIP-Seq) is widely used in characterizing genome-wide
binding patterns of transcription factors, cofactors, chromatin modifiers,
and other DNA binding proteins. A key step in ChIP-Seq data analysis
is to map short reads from high-throughput sequencing to a reference
genome and identify peak regions enriched with short reads. Although
several methods have been proposed for ChIP-Seq analysis, most ex-
isting methods only consider reads that can be uniquely placed in the
reference genome, and therefore have low power for detecting peaks lo-
cated within repeat sequences. Here we introduce a probabilistic ap-
proach for ChIP-Seq data analysis which utilizes all reads, providing a
truly genome-wide view of binding patterns. Reads are modeled using a
mixture model corresponding to K enriched regions and a null genomic
background. We use maximum likelihood to estimate the locations of the
enriched regions, and implement an expectation-maximization (E-M) al-
gorithm, called AREM, to update the alignment probabilities of each
read to different genomic locations.

For additional information, see our paper in RECOMB 2011 or visit our website:
http://cbcl.ics.uci.edu/AREM

AREM is based on the popular MACS peak caller, as described below:

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.

The original MACS package is available at: http://liulab.dfci.harvard.edu/MACS/

* Install

Please check the file 'INSTALL' in the distribution.

* Usage

Usage: arem <-t tfile> [-n name] [-g genomesize] [options]

Example: arem -t ChIP.bam -c Control.bam -f BAM -g h -n test -w --call-subpeaks


arem -- Aligning Reads by Expectation-Maximization, based on Model-based Analysis for ChIP-Sequencing (MACS)

Options:
  --version             show program's version number and exit
  -h, --help            show this help message and exit.
  -t TFILE, --treatment=TFILE
                        ChIP-seq treatment files. REQUIRED. When ELANDMULTIPET
                        is selected, you must provide two files separated by
                        comma, e.g.
                        s_1_1_eland_multi.txt,s_1_2_eland_multi.txt
  -c CFILE, --control=CFILE
                        Control files. When ELANDMULTIPET is selected, you
                        must provide two files separated by comma, e.g.
                        s_2_1_eland_multi.txt,s_2_2_eland_multi.txt
  -n NAME, --name=NAME  Experiment name, which will be used to generate output
                        file names. DEFAULT: "NA"
  -f FORMAT, --format=FORMAT
                        Format of tag file, "AUTO", "BED" or "ELAND" or
                        "ELANDMULTI" or "ELANDMULTIPET" or "ELANDEXPORT" or
                        "SAM" or "BAM" or "BOWTIE". The default AUTO option
                        will let MACS decide which format the file is. Please
                        check the definition in 00README file if you choose EL
                        AND/ELANDMULTI/ELANDMULTIPET/ELANDEXPORT/SAM/BAM/BOWTI
                        E. DEFAULT: "AUTO"
  --petdist=PETDIST     Best distance between Pair-End Tags. Only available
                        when format is 'ELANDMULTIPET'. DEFAULT: 200
  -g GSIZE, --gsize=GSIZE
                        Effective genome size. It can be 1.0e+9 or 1000000000,
                        or shortcuts:'hs' for human (2.7e9), 'mm' for mouse
                        (1.87e9), 'ce' for C. elegans (9e7) and 'dm' for
                        fruitfly (1.2e8), Default:hs
  -s TSIZE, --tsize=TSIZE
                        Tag size. This will overide the auto detected tag
                        size. DEFAULT: 25
  --bw=BW               Band width. This value is only used while building the
                        shifting model. DEFAULT: 300
  -p PVALUE, --pvalue=PVALUE
                        Pvalue cutoff for peak detection. DEFAULT: 1e-5
  -m MFOLD, --mfold=MFOLD
                        Select the regions within MFOLD range of high-
                        confidence enrichment ratio against background to
                        build model. The regions must be lower than upper
                        limit, and higher than the lower limit. DEFAULT:10,30
  --nolambda            If True, MACS will use fixed background lambda as
                        local lambda for every peak region. Normally, MACS
                        calculates a dynamic local lambda to reflect the local
                        bias due to potential chromatin structure.
  --slocal=SMALLLOCAL   The small nearby region in basepairs to calculate
                        dynamic lambda. This is used to capture the bias near
                        the peak summit region. Invalid if there is no control
                        data. DEFAULT: 1000
  --llocal=LARGELOCAL   The large nearby region in basepairs to calculate
                        dynamic lambda. This is used to capture the surround
                        bias. DEFAULT: 10000.
  --off-auto            Whether turn off the auto pair model process. If not
                        set, when MACS failed to build paired model, it will
                        use the nomodel settings, the '--shiftsize' parameter
                        to shift and extend each tags. DEFAULT: False
  --nomodel             Whether or not to build the shifting model. If True,
                        MACS will not build model. by default it means
                        shifting size = 100, try to set shiftsize to change
                        it. DEFAULT: False
  --shiftsize=SHIFTSIZE
                        The arbitrary shift size in bp. When nomodel is true,
                        MACS will use this value as 1/2 of fragment size.
                        DEFAULT: 100
  --keep-dup=KEEPDUPLICATES
                        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. Default: auto
  --to-small            When set, scale the larger dataset down to the smaller
                        dataset, by default, the smaller dataset will be
                        scaled towards the larger dataset. DEFAULT: False
  -w, --wig             Whether or not to save extended fragment pileup at
                        every WIGEXTEND bps into a wiggle file. When --single-
                        profile is on, only one file for the whole genome is
                        saved. WARNING: this process is time/space consuming!!
  -B, --bdg             Whether or not to save extended fragment pileup at
                        every bp into a bedGraph file. When it's on, -w,
                        --space and --call-subpeaks will be ignored. When
                        --single-profile is on, only one file for the whole
                        genome is saved. WARNING: this process is time/space
                        consuming!!
  -S, --single-profile  When set, a single wiggle file will be saved for
                        treatment and input. Default: False
  --space=SPACE         The resoluation for saving wiggle files, by default,
                        MACS will save the raw tag count every 10 bps. Usable
                        only with '--wig' option.
  --call-subpeaks       If set, MACS will invoke Mali Salmon's PeakSplitter
                        soft through system call. If PeakSplitter can't be
                        found, an instruction will be shown for downloading
                        and installing the PeakSplitter package. -w option
                        needs to be on and -B should be off to let it work.
                        DEFAULT: False
  --verbose=VERBOSE     Set verbose level. 0: only show critical message, 1:
                        show additional warning message, 2: show process
                        information, 3: show debug messages. DEFAULT:2
  --diag                Whether or not to produce a diagnosis report. It's up
                        to 9X time consuming. Please check 00README file for
                        detail. DEFAULT: False
  --fe-min=FEMIN        For diagnostics, min fold enrichment to consider.
                        DEFAULT: 0
  --fe-max=FEMAX        For diagnostics, max fold enrichment to consider.
                        DEFAULT: maximum fold enrichment
  --fe-step=FESTEP      For diagnostics, fold enrichment step.  DEFAULT: 20
  --no-EM               Do NOT iteratively align multi-reads by E-M. Multi-
                        read probabilities will be based on quality scores or
                        uniform (if --no-quals) DEFAULT : FALSE
  --EM-converge-diff=MIN_CHANGE
                        The minimum entropy change between iterations before
                        halting E-M steps. DEFAULT : 1e-05
  --EM-min-score=MIN_SCORE
                        Minimum enrichment score. Windows below this threshold
                        will all look the same to the aligner. DEFAULT : 1.5
  --EM-max-score=MAX_SCORE
                        Maximum enrichment score. Windows above this threshold
                        will all look the same to the aligner, DEFAULT : No
                        Maximum
  --EM-show-graphs      generate diagnostic graphs for E-M. (requires
                        MATPLOTLIB). DEFAULT : FALSE
  --quality-scale=QUAL_SCALE
                        Initial alignment probabilities are determined by read
                        quality and mismatches. Each possible alignment is
                        assigned a probability from the product over all bases
                        of either 1-p(ReadError_base) when there is no
                        mismatch, or p(ReadError_base) when the called base
                        disagrees with the reference.  You may also select a
                        uniform initialization. Read quality scale is the must
                        be one of ['auto', 'sanger+33', 'illumina+64'].
                        DEFAULT : auto
  --random-multi        Convert all multi reads to unique reads by selecting
                        one alignment at random for each read. DEFAULT : False
  --no-multi            Throw away all reads that have more than one alignment
  --no-greedy-caller    Use AREM default peak caller instead of the greedy
                        caller. This normally results in wider, less enriched
                        peaks, especially with multi-reads. DEFAULT : False
  --no-map-quals        Do not use mapping probabilities as priors in each
                        update step; just use relative enrichment. DEFAULT :
                        False
  --prior-snp=PRIOR_PROB_SNP
                        Prior probability that a SNP occurs at any base in the
                        genome. DEFAULT : 0.001
  --write-read-probs    Write out all final reads, including their alignment
                        probabilities as a BED file. DEFAULT : FALSE

** Parameters:

*** -t/--treatment FILENAME

This is the only REQUIRED parameter for MACS. If the format is
ELANDMULTIPET, user must provide two treatment files separated by
comma, e.g. s_1_1_eland_multi.txt,s_1_2_eland_multi.txt.

*** -c/--control

The control or mock data file in either BED format or any ELAND output
format specified by --format option. 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.

*** -f/--format FORMAT

Format of tag file, can be "ELAND", "BED", "ELANDMULTI",
"ELANDEXPORT", "ELANDMULTIPET" (for pair-end tags), "SAM", "BAM" or
"BOWTIE". Default is "AUTO" which will allow MACS to decide the format
automatically. Please use "AUTO" only when you combine different
formats of files.

The BED format is defined in "http://genome.ucsc.edu/FAQ/FAQformat#format1".

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. 

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.

If the data is from Pair-End sequencing. You can sepecify the format
as ELANDMULTIPET ( stands for ELAND Multiple-match Pair-End Tags),
then the --treat (and --control if needed) parameter must be two file
names separated by comma. Each file must be in ELAND multiple-match
format described above. e.g.

macs14 --format ELANDMULTIPET -t s_1_1_eland_multi.txt,s_2_1_eland_multi.txt ...

If you use ELANDMULTIPET, you may need to modify --petdist parameter.

If the format is BAM/SAM, please check the definition in
(http://samtools.sourceforge.net/samtools.shtml).  Pair-end mapping
results can be saved in a single BAM file, if so, MACS will
automatically keep the left mate(5' end) tag.

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.

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) For the experiment with several replicates, it is recommended to
concatenate several ChIP-seq treatment files into a single file. To do
this, under Unix/Mac or Cygwin (for windows OS), type:

$ cat replicate1.bed replicate2.bed replicate3.bed > all_replicates.bed

4) 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.

** --petdist=PETDIST     

Best distance between Pair-End Tags. Only available when format is
'ELANDMULTIPE'. Default is 200bps. When MACS reads mapped positions
for 5' tag and 3' tag, it will decide the best pairing for them using
this best distance parameter. A simple scoring system is used as following,

score = abs(abs(p5-p3)-200)+e5+e5

Where p5 is one of the position of 5' tag, and e5 is the
mismatch/error for this mapped position of 5' tag. p3 and e3 are for
3' tag. Then the lowest scored paring is regarded as the best
pairing. The 5' tag position of the pair is kept in model building and
peak calling.

*** -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:

-g hs   =  -g 2.7e9
-g mm   =  -g 1.87e9
-g ce   =  -g 9e7
-g dm   =  -g 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 automatic determined tag
size.

*** --bw

The band width which is used to scan the genome for model
building. You can set this parameter as the sonication fragment size
expected from wet experiment. The previous side effect on the peak
detection process has been removed. So this parameter only affects the
model building.

*** -p/--pvalue

The pvalue cutoff. Default is 1e-5.

*** -m/--mfold

This parameter is used to select the regions within MFOLD range of
high-confidence enrichment ratio against background to build
model. The regions must be lower than upper limit, and higher than the
lower limit of fold enrichment. DEFAULT:10,30 means using all regions
not too low (>10) and not too high (<30) to build paired-peaks
model. If MACS can not find more than 100 regions to build model, it
will use the --shiftsize parameter to continue the peak detection.

Check related *--off-auto* and *--shiftsize* for detail.

** --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.

** --off-auto

Whether turn off the auto paired-peak model process. If not set, when
MACS failed to build paired model, it will use the nomodel settings,
the '--shiftsize' parameter to shift and extend each tags. If set,
MACS will be terminated if paried-peak model is failed.

** --nomodel

While on, MACS will bypass building the shifting model.

** --shiftsize

While '--nomodel' is set, MACS uses this parameter to shift tags to
their midpoint. 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 100. This option is
only valid when --nomodel is set or when MACS fails to build
paired-peak model.

**  --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. Default:
auto

** --to-small 

When set scale the larger dataset down to the smaller dataset, by
default, the smaller dataset will be scaled towards the larger
dataset.

** -w/--wig

If this flag is on, MACS will store the fragment pileup in wiggle
format for every chromosome. The gzipped wiggle files will be stored
in subdirectories named NAME+'_MACS_wiggle/treat' for treatment data
and NAME+'_MACS_wiggle/control' for control data. --single-profile
option can be combined to generate a single wig file for the whole
genome.

** -B/--bdg

If this flag is on, MACS will store the fragment pileup in bedGraph
format for every chromosome. The bedGraph file is in general much
smaller than wiggle file. However, The process will take a little bit
longer than -w option, since theoratically 1bp resolution data will be
saved. The bedGraph files will be gzipped and stored in subdirectories
named NAME+'_MACS_bedGraph/treat' for treatment and
NAME+'_MACS_bedGraph/control' for control data.  --single-profile
option can be combined to generate a single bedGraph file for the
whole genome.

** -S/--single-profile (formerly --single-wig)

If this flag is on, MACS will store the fragment pileup in wiggle or
bedGraph format for the whole genome instead of for every
chromosomes. The gzipped wiggle files will be stored in subdirectories
named EXPERIMENT_NAME+'_MACS_wiggle'+'_MACS_wiggle/treat/'
+EXPERIMENT_NAME+'treat_afterfiting_all.wig.gz' or
'treat_afterfiting_all.bdg.gz' for treatment data, and
EXPERIMENT_NAME+'_MACS_wiggle'+'_MACS_wiggle/control/'
+EXPERIMENT_NAME+'control_afterfiting_all.wig.gz' or
'control_afterfiting_all.bdg.gz' for control data.

** --space=SPACE         

By default, the resoluation for saving wiggle files is 10 bps,i.e.,
MACS will save the raw tag count every 10 bps. You can change it along
with '--wig' option.

Note this option doesn't work if -B/--bdg is on.

** --call-subpeaks       

If set, MACS will invoke Mali Salmon's PeakSplitter software through
system call. If PeakSplitter can't be found, an instruction will be
shown for downloading and installing the PeakSplitter package. The
PeakSplitter can refine the MACS peaks and split the wide peaks into
smaller subpeaks. For more information, please check the following URL:

http://www.ebi.ac.uk/bertone/software/PeakSplitter_Cpp_usage.txt

Note this option doesn't work if -B/--bdg is on.

*** --verbose

If you don't want to see any message during the running of MACS, set
it to 0. But the CRITICAL messages will never be hidden. If you want
to see rich information like how many peaks are called for every
chromosome, you can set it to 3 or larger than 3.

** --diag

A diagnosis report can be generated through this option. This report
can help you get an assumption about the sequencing saturation. This
funtion is only in beta stage.

** --fe-min, --fe-max & --fe-step

For diagnostics, FEMIN and FEMAX are the minimum and maximum fold
enrichment to consider, and FESTEP is the interval of fold
enrichment. For example, "--fe-min 0 --fe-max 40 --fe-step 10" will
let MACS choose the following fold enrichment ranges to consider:
[0,10), [10,20), [20,30) and [30,40).

* 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, peak summit
 position related to the start position of peak region, number of tags
 in peak region, -10*log10(pvalue) for the peak region (e.g. pvalue
 =1e-10, then this value should be 100), fold enrichment for this
 region against random Poisson distribution with local lambda, FDR in
 percentage. Coordinates in XLS is 1-based which is different with BED
 format.

 2. NAME_peaks.bed is BED format file which contains the peak
 locations. You can load it to UCSC genome browser or Affymetrix IGB
 software.

 3. NAME_summits.bed is in BED format, which contains the peak summits
 locations for every peaks. The 5th column in this file is the summit
 height of fragment pileup. If you want to find the motifs at the
 binding sites, this file is recommended.

 4. NAME_negative_peaks.xls is a tabular file which contains
 information about negative peaks. Negative peaks are called by
 swapping the ChIP-seq and control channel.

 5. 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:

$ R --vanilla < 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.

 6. NAME_treat/control_afterfiting.wig.gz files in NAME_MACS_wiggle
 directory are wiggle format files which can be imported to UCSC
 genome browser/GMOD/Affy IGB. The .bdg.gz files are in bedGraph
 format which can also be imported to UCSC genome browser or be
 converted into even smaller bigWig files.

 7. NAME_diag.xls is the diagnosis report. First column is for various
 fold_enrichment ranges; the second column is number of peaks for that fc
 range; after 3rd columns are the percentage of peaks covered after
 sampling 90%, 80%, 70% ... and 20% of the total tags.

 8. NAME_peaks.subpeaks.bed is a text file which IS NOT in BED
 format. This file is generated by PeakSplitter
 (<http://www.ebi.ac.uk/bertone/software/PeakSplitter_Cpp_usage.txt>)
 when --call-subpeaks option is set.

* Other useful links

Cistrome web server for ChIP-chip/seq analysis: http://cistrome.org/ap/

bedTools -- a super useful toolkits for genome annotation files: http://code.google.com/p/bedtools/

UCSC toolkits: http://hgdownload.cse.ucsc.edu/admin/exe/