SICER protocols

SICER specifications

Information


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

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Maintainer


  • person_outline 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

Information


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

Maintainer


  • person_outline 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

Publication for spatial clustering approach for the identification of ChIP-enriched regions

SICER IN pipelines

 (11)
2017
PMCID: 5376654
PMID: 28332497
DOI: 10.1038/ncomms14852

[…] h3k27me3, 4 libraries per lane. quality of sequencing reads was assessed using fastqc, alignment was performed using bwa v. 0.7.5a and peak calling for different histone marks was performed using sicer v1.1 (ref. 32). the average±s.d. number of total reads per mark was as follows: h3k4me3 32.5±4.0 mio, h3k27ac 41.4±4.4, h3k27me3 51.2±1.7 and input 43.4±15.6. data were visualized using […]

2017
PMCID: 5791885
PMID: 28991266
DOI: 10.1038/nsmb.3481

[…] aligned to mouse genome (mm9) using bowtie 2 version 2.0.6, with sensitive pre-setting option (-d 15 -r 2 -l 22 -i s,1,1.15) 51. to detect genomic regions enriched for multiple overlapping (peaks) sicer software version 1.1 was used to identify enriched genomic regions using the following settings: redundancy threshold= 2, window size=600, fragment size=250, effective genome fraction = 0.75, […]

2016
PMCID: 4862150
PMID: 27168766
DOI: 10.1186/s13072-016-0067-3

[…] bam file was compared to a corresponding bam file containing genomic dna sequences from the same cell line (for a list of cell lines see additional file 1: table s1a)., for peak calling, we used the sicer program, which was designed to identify broad peaks from chromatin immunoprecipitation [chip]-seq experiments against histone modifications and is efficient at identifying replication origins […]

2016
PMCID: 4862150
PMID: 27168766
DOI: 10.1186/s13072-016-0067-3

[…] program, which was designed to identify broad peaks from chromatin immunoprecipitation [chip]-seq experiments against histone modifications and is efficient at identifying replication origins [47]. sicer parameters were as follows: redundancy threshold = 2, window size = 200, fragment size = 150, gap size = 600, fdr = 0.01, p value = 0.05. sicer outputs a list of peak locations and sizes […]

2016
PMCID: 4862150
PMID: 27168766
DOI: 10.1186/s13072-016-0067-3

[…] is efficient at identifying replication origins [47]. sicer parameters were as follows: redundancy threshold = 2, window size = 200, fragment size = 150, gap size = 600, fdr = 0.01, p value = 0.05. sicer outputs a list of peak locations and sizes in a bed (browser extensible data)-formatted file that was used for further analyses. to test whether the dna preparations indeed corresponded […]

SICER 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
SICER 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.

SICER reviews

 (2)
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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

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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.