Go Back   SEQanswers > Literature Watch

Similar Threads
Thread Thread Starter Forum Replies Last Post
ChIP-Seq: Analyzing ChIP-seq Data: Preprocessing, Normalization, Differential Identif Newsbot! Literature Watch 0 12-02-2011 05:51 AM
ChIP-Seq Data Analysis and Normalization snape_ar Bioinformatics 0 10-25-2011 01:16 PM
Free Partek RNA-Seq & ChIP-Seq Data Analysis Workshops Events / Conferences 0 04-05-2011 08:28 AM
ChIP-seq duplicate reads/ Poisson distribution kathrin Bioinformatics 4 08-26-2010 01:23 PM
PubMed: A signal-noise model for significance analysis of ChIP-seq with negative cont Newsbot! Literature Watch 0 05-09-2010 08:00 PM

Thread Tools
Old 03-10-2011, 03:00 AM   #1
RSS Posting Maniac

Join Date: Feb 2008
Posts: 1,443
Default ChIP-Seq: The Poisson Margin Test for Normalization-Free Significance Analysis of NGS

Syndicated from PubMed RSS Feeds

The Poisson Margin Test for Normalization-Free Significance Analysis of NGS Data.

J Comput Biol. 2011 Mar;18(3):391-400

Authors: Kowalczyk A, Bedo J, Conway T, Beresford-Smith B

Abstract The current methods for the determination of the statistical significance of peaks and regions in next generation sequencing (NGS) data require an explicit normalization step to compensate for (global or local) imbalances in the sizes of sequenced and mapped libraries. There are no canonical methods for performing such compensations; hence, a number of different procedures serving this goal in different ways can be found in the literature. Unfortunately, the normalization has a significant impact on the final results. Different methods yield very different numbers of detected "significant peaks" even in the simplest scenario of ChIP-Seq experiments that compare the enrichment in a single sample relative to a matching control. This becomes an even more acute issue in the more general case of the comparison of multiple samples, where a number of arbitrary design choices will be required in the data analysis stage, each option resulting in possibly (significantly) different outcomes. In this article, we investigate a principled statistical procedure that eliminates the need for a normalization step. We outline its basic properties, in particular the scaling upon depth of sequencing. For the sake of illustration and comparison, we report the results of re-analyzing a ChIP-Seq experiment for transcription factor binding site detection. In order to quantify the differences between outcomes, we use a novel method based on the accuracy of in silico prediction by support vector machine (SVM) models trained on part of the genome and tested on the remainder. See Kowalczyk et al. ( 2009 ) for supplementary material.

PMID: 21385042 [PubMed - in process]

Newsbot! is offline   Reply With Quote

Thread Tools

Posting Rules
You may not post new threads
You may not post replies
You may not post attachments
You may not edit your posts

BB code is On
Smilies are On
[IMG] code is On
HTML code is Off

All times are GMT -8. The time now is 06:29 AM.

Powered by vBulletin® Version 3.8.9
Copyright ©2000 - 2020, vBulletin Solutions, Inc.
Single Sign On provided by vBSSO