SICER statistics

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Citations per year

Number of citations per year for the bioinformatics software tool SICER
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Tool usage distribution map

This map represents all the scientific publications referring to SICER per scientific context
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Associated diseases

This word cloud represents SICER usage per disease context
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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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Versioning


No version available

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 citations

 (178)
library_books

Distinct epigenetic landscapes underlie the pathobiology of pancreatic cancer subtypes

2018
Nat Commun
PMCID: 5958058
PMID: 29773832
DOI: 10.1038/s41467-018-04383-6

[…] were mapped by BWA and pairs with one or both ends uniquely mapped were retained. H3K4me3, H3K4me1, and H3K27ac peaks were called using the MACS2 software package at false discovery rate (FDR) ≤ 1%. SICER was used to identify enriched domains for H3K27me3 and H3K9me3. For data visualization, BEDTools in combination with in-house scripts were used to generate normalized tag density profile at a wi […]

library_books

Depletion of Nsd2 mediated histone H3K36 methylation impairs adipose tissue development and function

2018
Nat Commun
PMCID: 5935725
PMID: 29728617
DOI: 10.1038/s41467-018-04127-6

[…] Ultra II DNA Library Prep Kit for Illumina (NEB), following the manufacturer’s instructions. All ChIP-Seq and RNA-Seq samples were sequenced on the Illumina HiSeq 2500.ChIP-Seq data were analyzed by SICER. Raw reads were mapped to the mouse genome (mm9). H3K36me2 or H3K27me3 signal intensity at each nucleotide was calculated as read coverage, followed by scaling normalization to ensure that the a […]

library_books

Mice lacking the transcriptional regulator Bhlhe40 have enhanced neuronal excitability and impaired synaptic plasticity in the hippocampus

2018
PLoS One
PMCID: 5929507
PMID: 29715265
DOI: 10.1371/journal.pone.0196223

[…] tags from a total of 27,546,895 reads, producing 6,038 peaks. Peaks were called using either Model-based Analysis of ChIP-Seq (MACS) or Spatial Clustering for Identification of ChIP-Enriched Regions (SICER) algorithms []. The MACS default cutoff p-value was 1e-5 for narrow peaks and 1e-1 for broad peaks, and SICER default cutoff was FDR 1e-10 with gap parameter of 600 bp. […]

library_books

Histone demethylase JMJD1A coordinates acute and chronic adaptation to cold stress via thermogenic phospho switch

2018
Nat Commun
PMCID: 5908789
PMID: 29674659
DOI: 10.1038/s41467-018-03868-8

[…] erformed using TRAP, in which the algorithm ranks the known binding motifs in descending order of the enrichment in target genomic sites. The JMJD1A binding sites from ChIP-seq data were extracted by SICER using the default parameter setting: window size, 200 bp; gap size, 400 bp; E-value threshold, 100. Subsequently, the PPARγ binding sites and the PRDM16 binding sites from the ChIP-seq data were […]

call_split

Epigenetic activation during T helper 17 cell differentiation is mediated by Tripartite motif containing 28

2018
Nat Commun
PMCID: 5897371
PMID: 29651155
DOI: 10.1038/s41467-018-03852-2
call_split See protocol

[…] the sequencing data, the sequencing reads were mapped to the Mus musculus genome (version mm10) using Bowtie (version 1.0) with no more than two mismatches. After PCR duplicates removal by samtools, SICER (version 1.1) algorithm was used for peak calling, compared with each input control with FDR less than 1E-5. To accurately delineate the SE regions based on mm10, we followed the same approach p […]

library_books

Investigation into the role of the germline epigenome in the transmission of glucocorticoid programmed effects across generations

2018
Genome Biol
PMCID: 5891941
PMID: 29636086
DOI: 10.1186/s13059-018-1422-4

[…] e the area underneath the curve equal to 1. To compute the enrichment of IP over input over the gene-model we divided the IP meta-profile by the meta-profile for their respective H3 input.We utilised SICER [] to call peaks in the histone modification samples relative to their respective H3 input sample, following the author’s recommendation to call peaks in both “narrow” and “broad” modes and keep […]


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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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Desktop
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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Desktop
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.