https://github.com/genometric/mspc
Using combined evidence from replicates to evaluate ChIP-seq peaks
https://github.com/genometric/mspc
analysis chip-seq enriched-regions genome-analysis mspc next-generation-sequencing ngs-analysis overlapping-peaks peak peaks
Last synced: about 1 year ago
JSON representation
Using combined evidence from replicates to evaluate ChIP-seq peaks
- Host: GitHub
- URL: https://github.com/genometric/mspc
- Owner: Genometric
- License: gpl-3.0
- Created: 2017-07-25T00:22:22.000Z (almost 9 years ago)
- Default Branch: dev
- Last Pushed: 2025-04-08T02:04:01.000Z (about 1 year ago)
- Last Synced: 2025-04-12T20:54:04.892Z (about 1 year ago)
- Topics: analysis, chip-seq, enriched-regions, genome-analysis, mspc, next-generation-sequencing, ngs-analysis, overlapping-peaks, peak, peaks
- Language: C#
- Homepage: https://genometric.github.io/MSPC/
- Size: 21.9 MB
- Stars: 20
- Watchers: 3
- Forks: 10
- Open Issues: 7
-
Metadata Files:
- Readme: README.md
- License: license.md
- Citation: CITATION.cff
Awesome Lists containing this project
README
Quick Start |
Documentation |
Download |
Publication
## About
The analysis of ChIP-seq samples outputs a number of enriched regions,
each indicating a protein-DNA interaction or a specific chromatin
modification. Enriched regions (commonly known as "peaks") are called
when the read distribution is significantly different from the background
and its corresponding significance measure (p-value) is below a
user-defined threshold.
When replicate samples are analysed, overlapping enriched regions are
expected. This repeated evidence can therefore be used to locally lower
the minimum significance required to accept a peak. Here, we propose a
method for joint analysis of weak peaks.
Given a set of peaks from (biological or technical) replicates, the method
combines the p-values of overlapping enriched regions: users can choose a
threshold on the combined significance of overlapping peaks and set a
minimum number of replicates where the overlapping peaks should be present.
The method allows the "rescue" of weak peaks occuring in more than one
replicate and outputs a new set of enriched regions for each replicate.
In general, the method groups enriched regions as
[_background_](https://genometric.github.io/MSPC/docs/method/sets#background),
[_weak_](https://genometric.github.io/MSPC/docs/method/sets#weak),
or [_stringent_](https://genometric.github.io/MSPC/docs/method/sets#stringent)
based on user-defined
[weak](https://genometric.github.io/MSPC/docs/cli/args#weak-threshold)
and [stringency thresholds](https://genometric.github.io/MSPC/docs/cli/args#stringency-threshold).
The method then [_confirms_](https://genometric.github.io/MSPC/docs/method/sets#confirmed)
or [_discards_](https://genometric.github.io/MSPC/docs/method/sets#discarded)
the _weak_ and _stringent_ enriched regions if their combined stringency is at least as significant
as a [user-defined threshold](https://genometric.github.io/MSPC/docs/cli/args#gamma).
The method then performs a multiple testing correction on
_confirmed_ enriched regions at
[a user-defined false-discovery rate](https://genometric.github.io/MSPC/docs/cli/args#alpha),
identifying
[true-positive](https://genometric.github.io/MSPC/docs/method/sets#truepositive) and
[false-positive](https://genometric.github.io/MSPC/docs/method/sets#falsepositive)
regions. See the following figure as an example, and you may refer to
[MSPC publications](https://genometric.github.io/MSPC/publications),
[slides on slideshare](http://www.slideshare.net/jalilivahid/mspc-50694133),
or [documentation](https://genometric.github.io/MSPC/docs/method/about)
page for more details.
## Download and Run
- #### [__Quick Start__: _download, install, and run a demo use-case_](https://genometric.github.io/MSPC/docs/quick_start);
- #### [__Install__: _details on different installation options_](https://genometric.github.io/MSPC/docs/installation);
- #### [__Bioconductor R package__:](https://bioconductor.org/packages/release/bioc/html/rmspc.html) [Bioconductor user guide with examples on installing and using it in R](https://bioconductor.org/packages/release/bioc/vignettes/rmspc/inst/doc/rmpsc.html).
MSPC is distributed as a cross-platform console application, a .NET library,
and a Bioconductor R package.