A comprehensive analysis of RNA-Seq data uses a wide range of different tools and algorithms, which are normally limited to R users only. While several tools and advanced analysis pipelines are available, some require programming skills and others lack the ...
Read More »Best practices and appropriate workflows to analyse alternative and differential splicing
Over the last few years, RNA-seq has been used to study alterations in alternative splicing related to several diseases. Bioinformatics workflows used to perform these studies can be divided into two groups, those finding changes in the absolute isoform expression ...
Read More »RNA-Seq analysis is easy as 1-2-3
The ability to easily and efficiently analyse RNA-sequencing data is a key strength of the Bioconductor project. Starting with counts summarised at the gene-level, a typical analysis involves pre-processing, exploratory data analysis, differential expression testing and pathway analysis with the ...
Read More »RNA-seq analysis is easy as 1-2-3 with limma, Glimma and edgeR
The ability to easily and efficiently analyse RNA-sequencing data is a key strength of the Bioconductor project. Starting with counts summarised at the gene-level, a typical analysis involves pre-processing, exploratory data analysis, differential expression testing and pathway analysis with the ...
Read More »Published reference genomes should be re-annotated before use as references for RNA-Seq experiments
RNA-seq based on short reads generated by next generation sequencing technologies has become the main approach to study differential gene expression. Until now, the main applications of this technique have been to study the variation of gene expression in a ...
Read More »Microarray analysis workhorse limma – now capable of differential expression and differential splicing analyses of RNA-Seq data
limma is an R/Bioconductor software package that provides an integrated solution for analysing data from gene expression experiments. It contains rich features for handling complex experimental designs and for information borrowing to overcome the problem of small sample sizes. Over ...
Read More »The characteristic direction: a geometrical approach to identify differentially expressed genes
Identifying differentially expressed genes (DEG) is a fundamental step in studies that perform genome wide expression profiling. Typically, DEG are identified by univariate approaches such as Significance Analysis of Microarrays (SAM) or Linear Models for Microarray Data (LIMMA) for processing ...
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