epic2 is an ultraperformant reimplementation of SICER. It focuses on speed, low memory overhead and ease of use.
It also contains a reimplementation of the SICER-df scripts for differential enrichment and a script to create many kinds of bigwigs from your data.
epic2 efficiently finds diffuse domains in ChIP-seq data (Bioinformatics, 2019)
See CHANGELOG.txt in this directory.
We are extremely responsive to bugs, issues and installation problems. We are proud to say that epic was 20% more downloaded on PyPI than MACS2 even, and we believe that was (among other things) because it was easy to install and use. We wish to make epic2 similarly easy to install and use, so please report any issues you have.
- easy to install and use
- reads sam, single-end bam, bed and bedpe (.gz)
- extremely fast
- very low memory requirements
- works both with and without input
- metadata for ~80 UCSC genomes built in
- easily use custom genomes and assemblies with --chromsizes and --effective-genome-fraction args
- differential enrichment for WT vs. KO works with and without input (epic2-df)
- fixes two bugs in the original SICER and one bug in epic
- create many types of useful bigwigs for visualization in genome browsers
pip install epic2
epic2 --example # or -ex
# Treatment: /mnt/work/endrebak/software/anaconda/lib/python3.6/site-packages/epic2-0.0.13-py3.6-linux-x86_64.egg/epic2/examples/test.bed.gz
# Control: /mnt/work/endrebak/software/anaconda/lib/python3.6/site-packages/epic2-0.0.13-py3.6-linux-x86_64.egg/epic2/examples/control.bed.gz
# Example command: epic2 -t /mnt/work/endrebak/software/anaconda/lib/python3.6/site-packages/epic2-0.0.13-py3.6-linux-x86_64.egg/epic2/examples/test.bed.gz -c /mnt/work/endrebak/software/anaconda/lib/python3.6/site-packages/epic2-0.0.13-py3.6-linux-x86_64.egg/epic2/examples/control.bed.gz > deleteme.txt
epic2 -t /mnt/work/endrebak/software/anaconda/lib/python3.6/site-packages/epic2-0.0.13-py3.6-linux-x86_64.egg/epic2/examples/test.bed.gz \
-c /mnt/work/endrebak/software/anaconda/lib/python3.6/site-packages/epic2-0.0.13-py3.6-linux-x86_64.egg/epic2/examples/control.bed.gz \
> deleteme.txt
head -3 deleteme.txt
# Chromosome Start End PValue Score Strand ChIPCount InputCount FDR log2FoldChange
# chr1 23568400 23568599 8.184732752658519e-11 1000.0 . 2 0 6.319375023267071e-10 11.307485580444336
# chr1 26401200 26401399 8.184732752658519e-11 1000.0 . 2 0 6.319375023267071e-10 11.307485580444336
epic2-df -ex
# Knockout: /mnt/work/endrebak/software/anaconda/lib/python3.6/site-packages/epic2-0.0.20-py3.6-linux-x86_64.egg/epic2/examples/test.bed.gz
# Wildtype: /mnt/work/endrebak/software/anaconda/lib/python3.6/site-packages/epic2-0.0.20-py3.6-linux-x86_64.egg/epic2/examples/control.bed.gz
# Example command: epic2-df -tk /mnt/work/endrebak/software/anaconda/lib/python3.6/site-packages/epic2-0.0.20-py3.6-linux-x86_64.egg/epic2/examples/test.bed.gz -tw /mnt/work/endrebak/software/anaconda/lib/python3.6/site-packages/epic2-0.0.20-py3.6-linux-x86_64.egg/epic2/examples/control.bed.gz -ok deleteme_ko.txt -ow deleteme_wt.txt > deleteme.txt
The installation currently requires bioconda htslib to be installed for setup.py to find the appropriate headers. I will update the install script with more ways to include the headers.
First you need to install bioconda:
wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh
bash Miniconda3-latest-Linux-x86_64.sh -b -p $HOME/miniconda
export PATH="$HOME/miniconda/bin:$PATH"
Linux:
conda install -c bioconda epic2
macOS:
pip install Cython
pip install pysam
pip install epic2
usage: epic2 [-h] --treatment TREATMENT [TREATMENT ...]
[--control CONTROL [CONTROL ...]] [--genome GENOME]
[--keep-duplicates] [--original-algorithm] [--bin-size BIN_SIZE]
[--gaps-allowed GAPS_ALLOWED] [--fragment-size FRAGMENT_SIZE]
[--false-discovery-rate-cutoff FALSE_DISCOVERY_RATE_CUTOFF]
[--effective-genome-fraction EFFECTIVE_GENOME_FRACTION]
[--chromsizes CHROMSIZES] [--e-value E_VALUE]
[--required-flag REQUIRED_FLAG] [--filter-flag FILTER_FLAG]
[--mapq MAPQ] [--autodetect-chroms]
[--discard-chromosomes-pattern DISCARD_CHROMOSOMES_PATTERN]
[--experimental-statistics] [--output OUTPUT] [--quiet]
[--example] [--version]
epic2, version: 0.0.26 (Visit github.com/endrebak/epic2 for examples and help.
Run epic2-example for a simple example command.)
optional arguments:
-h, --help show this help message and exit
--treatment TREATMENT [TREATMENT ...], -t TREATMENT [TREATMENT ...]
Treatment (pull-down) file(s) in one of these formats:
bed, bedpe, bed.gz, bedpe.gz or (single-end) bam, sam.
The --guess-bampe flag enables optional support for
(paired-end) bampe and sampe formats.
Mixing file formats is allowed.
--control CONTROL [CONTROL ...], -c CONTROL [CONTROL ...]
Control (input) file(s) in one of these formats:
bed, bedpe, bed.gz, bedpe.gz or (single-end) bam, sam.
The --guess-bampe flag enables optional support for
(paired-end) bampe and sampe formats.
Mixing file formats is allowed.
--genome GENOME, -gn GENOME
Which genome to analyze. Default: hg19. If
--chromsizes and --egf flag is given, --genome is not
required.
--keep-duplicates, -kd
Keep reads mapping to the same position on the same
strand within a library. Default: False.
--original-algorithm, -oa
Use the original SICER algorithm, without the epic2
fix. This will use all reads in your files to compute
the p-values, including those falling outside the
genome boundaries.
--bin-size BIN_SIZE, -bin BIN_SIZE
Size of the windows to scan the genome. BIN-SIZE is
the smallest possible island. Default 200.
--gaps-allowed GAPS_ALLOWED, -g GAPS_ALLOWED
This number is multiplied by the window size to
determine the number of gaps (ineligible windows)
allowed between two eligible windows. Must be an
integer. Default: 3.
--fragment-size FRAGMENT_SIZE, -fs FRAGMENT_SIZE
(Single end reads only) Size of the sequenced
fragment. Each read is extended half the fragment size
from the 5' end. Default 150 (i.e. extend by 75).
--false-discovery-rate-cutoff FALSE_DISCOVERY_RATE_CUTOFF, -fdr FALSE_DISCOVERY_RATE_CUTOFF
Remove all islands with an FDR above cutoff. Default
0.05.
--effective-genome-fraction EFFECTIVE_GENOME_FRACTION, -egf EFFECTIVE_GENOME_FRACTION
Use a different effective genome fraction than the one
included in epic2. The default value depends on the
genome and readlength, but is a number between 0 and
1.
--chromsizes CHROMSIZES, -cs CHROMSIZES
Set the chromosome lengths yourself in a file with two
columns: chromosome names and sizes. Useful to analyze
custom genomes, assemblies or simulated data. Only
chromosomes included in the file will be analyzed.
--e-value E_VALUE, -e E_VALUE
The E-value controls the genome-wide error rate of
identified islands under the random background
assumption. Should be used when not using a control
library. Default: 1000.
--required-flag REQUIRED_FLAG, -f REQUIRED_FLAG
(bampe/bam only.) Keep reads with these bits set in flag.
Same as `samtools view -f`. Default 0
--filter-flag FILTER_FLAG, -F FILTER_FLAG
(bampe/bam only.) Discard reads with these bits set in flag.
Same as `samtools view -F`. Default 1540 (hex: 0x604).
See https://broadinstitute.github.io/picard/explain-
flags.html for more info.
--mapq MAPQ, -m MAPQ (bampe/bam only.) Discard reads with mapping quality lower
than this. Default 5.
--autodetect-chroms, -a
(bampe/bam only.) Autodetect chromosomes from bam file. Use
with --discard-chromosomes flag to avoid non-canonical
chromosomes.
--discard-chromosomes-pattern DISCARD_CHROMOSOMES_PATTERN, -d DISCARD_CHROMOSOMES_PATTERN
(bampe/bam only.) Discard reads from chromosomes matching
this pattern. Default '_'. Note that if you are not
interested in the results from non-canonical
chromosomes, you should ensure they are removed with
this flag, otherwise they will make the statistical
analysis too stringent.
--experimental-statistics
(advanced): Use a sligthly modified way to compute the
statistics that avoids a bug in the original SICER on
large datasets. Only use if you get an error.
--guess-bampe
Autodetect bampe file format based on flags from the first
100 reads. If all of them are paired, then the format is
bampe. Only properly paired reads are processed by default
(0x1 and 0x2 samtools flags).
--output OUTPUT, -o OUTPUT
File to write results to. Default: stdout.
--quiet, -q Do not write output messages to stderr.
--example, -ex Show the paths of the example data and an example
command.
--version, -v show program's version number and exit
When used with a background library, epic2 produces the following bed6-compatible file:
Chromosome Start End PValue Score Strand ChIPCount InputCount FDR log2FoldChange
chr1 23568400 23568599 8.184732752658519e-11 1000.0 . 2 0 6.319375023267071e-10 11.307485580444336
chr1 26401200 26401399 8.184732752658519e-11 1000.0 . 2 0 6.319375023267071e-10 11.307485580444336
...
This is the meaning of the columns:
| Column | Description |
|---|---|
| PValue | Poisson-computed PValue based on the number of ChIP count vs. library-size normalized Input count in the region |
| Score | Log2FC * 100 (capped at 1000). Regions with a larger relative ChIP vs. Input count will show as darker in the UCSC genome browser |
| ChIPCount | The number of ChIP counts in the region (also including counts from windows with a count below the cutoff) |
| InputCount | The number of Input counts in the region |
| FDR | Benjamini-Hochberg correction of the p-values |
| log2FoldChange | Log2 of the region ChIP count vs. the library-size corrected region Input count |
When used without a background library, epic2 produces the following bed6-compatible file:
Chromosome Start End ChIPCount Score Strand
chr1 23568400 23568599 2 14.983145713806152 .
chr1 26401200 26401399 2 14.983145713806152 .
...
Here Score is merely the score the island got (SICER internal really).
The output from epic2-df contains one row for each region tested for differential enrichment between the two conditions:
Chromosome Start End KO WT FC_KO FC_WT P_KO P_WT FDR_KO FDR_WT
chr1 23568400 23568599 2 0 2.8140145395799676 0.3553641908862576 0.09285120415065423 1.0 0.23131352612970002 1.0
chr1 26401200 26401399 2 0 2.8140145395799676 0.3553641908862576 0.09285120415065423 1.0 0.23131352612970002 1.0
...
KO and WT is the number of reads from the KO and WT conditions in the region. FC_KO is just
scaling_factor = sum_ko / sum_wt
((KO + 1) / (WT + 1)) * scaling_factor
while FC_WT is
scaling_factor = sum_ko / sum_wt
((WT + 1) / (KO + 1)) / scaling_factor
so a FC_KO > 1 means that the knockout condition is overrepresented in the region.
The FDR_KO and FDR_WT tells whether the overrepresentation actually is statistically significant after controlling for multiple testing.
- How are paired-end read handled?
Paired-end reads (bedpe format only) are handled automatically. They are turned into a regular read by taking the leftmost end of the leftmost mate and the rightmost end of the rightmost mate. If two intervals have these two coordinates in common and are on the same chromosome/strand, they are considered a duplicate. Instead of extending from the 5'-end, the midpoint is used when counting reads in bins.
To enable bampe format support, use the --guess-bampe flag.
- Can I be sure that epic2 and SICER give the exact same results?
See https://github.com/endrebak/epic2_supplementaries/tree/master/workflows/show_same_results
- Can you mix the input filetypes? I.e. use bam and bed together?
Yes. However, note that the chromosomes are often named differently in bam and bed files.
- Can epic2 read from bash process substitutions?
No. As epic2 sniffs the readlengths and file formats the lines used to do this are lost if using process substitution. See https://unix.stackexchange.com/q/164107/26674
- How do I cite epic2?
# bibtex format
@article{10.1093/bioinformatics/btz232,
author = {Sætrom, Pål and Stovner, Endre Bakken},
title = "{epic2 efficiently finds diffuse domains in ChIP-seq data}",
year = {2019},
month = {03},
abstract = "{Data from chromatin immunoprecipitation (ChIP) followed by high throughput sequencing (ChIP-seq) generally contain either narrow peaks or broad and diffusely enriched domains. The SICER ChIP-seq caller has proven adept at finding diffuse domains in ChIP-seq data, but it is slow, requires much memory, needs manual installation steps and is hard to use. epic2 is a complete rewrite of SICER that is focused on speed, low memory overhead and ease-of-use.The MIT-licensed code is available at https://github.com/biocore-ntnu/epic2}",
doi = {10.1093/bioinformatics/btz232},
url = {https://doi.org/10.1093/bioinformatics/btz232},
eprint = {http://oup.prod.sis.lan/bioinformatics/advance-article-pdf/doi/10.1093/bioinformatics/btz232/28377147/btz232.pdf},
}
