SICER specifications

Unique identifier:
OMICS_00461
Software type:
Package/Module
Restrictions to use:
None
Programming languages:
Python
Version:
1.1
Requirements:
Python compiler, numpy, scipy
Name:
spatial clustering approach for the identification of ChIP-enriched regions
Interface:
Command line interface
Operating system:
Unix/Linux
Computer skills:
Advanced
Stability:
Stable
Maintained:
Yes

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SICER distribution

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SICER specifications

Unique identifier:
OMICS_00461
Interface:
Web user interface
Input data:
Some reads.
Computer skills:
Basic
Free trial:
Yes
Maintained:
Yes
Name:
spatial clustering approach for the identification of ChIP-enriched regions
Restrictions to use:
License purchase required
Output format:
BED
Stability:
Stable
Registration required:
Yes

SICER support

Maintainer

  • Weiqun Peng <>
  • Weiqun Peng <>

Additional information

The web application version is part of the Genomatix Genome Analyzer. http://www.genomatix.de/online_help/help_regionminer/sicer.html

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Credits

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Publications

Institution(s)

Department of Physics, The George Washington University, Washington, DC, USA; Laboratory of Molecular Immunology, National Heart Lung and Blood Institute, NIH, Bethesda, MD, USA

Funding source(s)

Supported by University Facilitating Fund; the National Science Foundation (DMR0313129); Intramural Research Program for the National Heart Lung and blood Institute; National Institute of Health.

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2 user reviews

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2 user reviews

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Miklós Laczik's avatar image Miklós Laczik's country flag

Miklós Laczik

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SICER is a peak calling tool optimized for diffuse signals, primarily the broad enrichments in ChIP-seq experiments in histone marks. It is extremely efficient for such data, with the three key parameters (window size, gap size and significance cutoff) you can detect the characteristic peaks of any histone mark, although it can take a bit of experimenting to find the right settings; still I rather recommend finding the parameters that fits your data (most importantly, your histone mark, as most of them generate a certain type of enrichment characteristic to that mark), than trying to find a setting that fits all types of data. In the world of histone marks I don't believe there is a "one size fits all" group of parameters. SICER can work both with and without a control dataset.

However, though this is currently my peak caller of choice for histone marks, it is not a flawless tool. I think it was not very well written, it uses shell scripts with positional parameters - could have been much nicer with pytho, especially considering that other parts are written in python - feels like a bit rushed shortcut. Also, the files need some manual editing (e.g. before you first run the scripts, or when you add a new genome assembly), and the documentation is also lacking. It only accepts BED files as input (feels a bit strange considering that the SAM/BAM format has been quite standard for storing NGS alignments for a long time), and it fails to produce BED files (or any other standard format) for peak files; instead it generates a bunch of text files in SICER-specific formats, which you need to manually edit if you want to convert them to e.g. a BED file. Also, it produces sometimes mysterious bugs, like individual steps of the peak calling process cannot be rerun (despite the manual says so, it doesn't work), or with some settings and data the software quits with a "list index out of range" error, when it successfully finished the analysis under the same circumstances before...
Probably the biggest drawback is that it seems it is not maintained anymore, the last release came out in 2011, the mailing list is quite inactive... It's a pity, because despite all of the above (with the right settings) it still outperforms most peak callers when it comes to histone marks and ChIP-seq, which is amazing, so I think it deserves the 4 stars depite all its faults. But I guess there is no chance to fix the bugs or add some new features or just keep it up to date (I think some bugs like the last one in the above paragraph is related to switching to a newer version of python, or scipy/numpy... Several years ago I never had that problem). Which forces me to eventually look for a replacement for SICER.

Fabien Pichon's avatar image Fabien Pichon's country flag

Fabien Pichon

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Ideal for broad peaks like H3K27me3 and H3K36me3. Where MACS will truncate peaks in smaller fragments, generating dozens of small peaks, the strenght of SICER is that you can take gaps into account. You just have to perform various tests to choose a good combination between gap size and window size. Typically, I used a window of 500nt and a gap of 3500nt for a good compromise between sensitivity and specificity.

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