RNA-seq is a methodology for RNA profiling based on next-generation sequencing that enables to measure and compare gene expression patterns at unprecedented resolution. Although the appealing features of this technique have promoted its application to a wide panel of transcriptomics studies, the fast-evolving nature of experimental protocols and computational tools challenges the definition of a unified RNA-seq analysis pipeline. In this review, focused on the study of differential gene expression with RNA-seq, we go through the main steps of data processing and discuss open challenges and possible solutions.
Description
Measuring differential gene expression with RNA-seq: challenges and strategies for data analysis. - PubMed - NCBI
%0 Journal Article
%1 Finotello:2015:Brief-Funct-Genomics:25240000
%A Finotello, F
%A Di Camillo, B
%D 2015
%J Brief Funct Genomics
%K DESeq2 fulltext methods review rna-seq shouldread
%N 2
%P 130-142
%R 10.1093/bfgp/elu035
%T Measuring differential gene expression with RNA-seq: challenges and strategies for data analysis
%U https://www.ncbi.nlm.nih.gov/pubmed/25240000
%V 14
%X RNA-seq is a methodology for RNA profiling based on next-generation sequencing that enables to measure and compare gene expression patterns at unprecedented resolution. Although the appealing features of this technique have promoted its application to a wide panel of transcriptomics studies, the fast-evolving nature of experimental protocols and computational tools challenges the definition of a unified RNA-seq analysis pipeline. In this review, focused on the study of differential gene expression with RNA-seq, we go through the main steps of data processing and discuss open challenges and possible solutions.
@article{Finotello:2015:Brief-Funct-Genomics:25240000,
abstract = {RNA-seq is a methodology for RNA profiling based on next-generation sequencing that enables to measure and compare gene expression patterns at unprecedented resolution. Although the appealing features of this technique have promoted its application to a wide panel of transcriptomics studies, the fast-evolving nature of experimental protocols and computational tools challenges the definition of a unified RNA-seq analysis pipeline. In this review, focused on the study of differential gene expression with RNA-seq, we go through the main steps of data processing and discuss open challenges and possible solutions.},
added-at = {2019-02-01T09:18:25.000+0100},
author = {Finotello, F and Di Camillo, B},
biburl = {https://www.bibsonomy.org/bibtex/2039210003b494b6a90727200ff0c13fe/marcsaric},
description = {Measuring differential gene expression with RNA-seq: challenges and strategies for data analysis. - PubMed - NCBI},
doi = {10.1093/bfgp/elu035},
interhash = {c95f1326299ea848f3cafce0f4f1dc44},
intrahash = {039210003b494b6a90727200ff0c13fe},
journal = {Brief Funct Genomics},
keywords = {DESeq2 fulltext methods review rna-seq shouldread},
month = mar,
number = 2,
pages = {130-142},
pmid = {25240000},
timestamp = {2019-02-01T09:32:05.000+0100},
title = {Measuring differential gene expression with RNA-seq: challenges and strategies for data analysis},
url = {https://www.ncbi.nlm.nih.gov/pubmed/25240000},
volume = 14,
year = 2015
}