Go Back   SEQanswers > Literature Watch

Similar Threads
Thread Thread Starter Forum Replies Last Post
RNA-Seq: Differential expression--the next generation and beyond. Newsbot! Literature Watch 0 01-03-2012 03:40 AM
RNA-seq for differential expression Inti RNA Sequencing 0 01-17-2011 07:46 AM
RNA-Seq: From RNA-seq reads to differential expression results. Newsbot! Literature Watch 0 12-24-2010 03:13 AM
RNA-Seq: Cloud-scale RNA-sequencing differential expression analysis with Myrna. Newsbot! Literature Watch 0 08-13-2010 09:00 AM

Thread Tools
Old 09-10-2011, 03:12 AM   #1
RSS Posting Maniac

Join Date: Feb 2008
Posts: 1,443
Default RNA-Seq: Differential expression in RNA-seq: A matter of depth.

Syndicated from PubMed RSS Feeds

Differential expression in RNA-seq: A matter of depth.

Genome Res. 2011 Sep 8;

Authors: Tarazona S, García-Alcalde F, Dopazo J, Ferrer A, Conesa A

Next Generation Sequencing (NGS) technologies are revolutionizing genome research and in particular, their application to transcriptomics (RNA-seq) is increasingly being used for gene expression profiling as a replacement for microarrays. However, the properties of RNA-seq data have not been yet fully established and additional research is needed for understanding how these data respond to differential expression analysis. In this work we set out to gain insights into the characteristics of RNA-seq data analysis by studying an important parameter of this technology: the sequencing depth. We have analyzed how sequencing depth affects the detection of transcripts and their identification as differentially expressed, looking at aspects such as transcript biotype, length, expression level and fold-change. We have evaluated different algorithms available for the analysis of RNA-seq and proposed a novel approach -NOISeq- that differs from existing methods in that it is data-adaptive and non-parametric. Our results reveal that most existing methodologies suffer from a strong dependency on sequencing depth for their differential expression calls and that this results in a considerable number of false positives that increases as the number of reads grows. In contrast, our proposed method models the noise distribution from the actual data, can therefore better adapt to the size of the dataset and is more effective in controlling the rate of false discoveries. This work discusses the true potential of RNA-seq for studying regulation at low expression ranges, the noise within RNA-seq data and the issue of replication.

PMID: 21903743 [PubMed - as supplied by publisher]

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 05:42 PM.

Powered by vBulletin® Version 3.8.9
Copyright ©2000 - 2021, vBulletin Solutions, Inc.
Single Sign On provided by vBSSO