We have compared both the original BitSeq algorithm and our new method with the majority of available methods for transcript expression estimation and conclude that BitSeqVB is highly competitive both in terms of expression estimation and run-time.
Expression estimation and group comparisons were done with Array Analyzer.
Availability: The implementation of the transcriptome expression estimation and differential expression analysis, BitSeq, has been written in C++ and Python.
MP, MM, PF, and AF analysed the RNA-Seq sequences by mapping, gene expression estimation, and statistical analysis.
The BitSeq analysis pipeline consists of two main stages: transcript expression estimation and DE assessment (Fig. 1).
In light of these considerations we have developed ArrayExpressHTS, an automated R/Bioconductor-based pipeline for pre-processing, expression estimation and data quality assessment of RNA-seq datasets.
Summary: We present an R based pipeline, ArrayExpressHTS, for pre-processing, expression estimation and data quality assessment of high-throughput sequencing transcriptional profiling (RNA-seq) datasets.
For gene expression estimation and differential expression analysis, the programs Cufflinks and Cuffdiff version 2.0.2 were used with the following options '-u —max-bundle-frags 1000000 —no-effective-length-correction —compatible-hits-norm' (http://www.ncbi.nlm.nih.gov/pubmed/23222703).nih.gov/pubmed/23222703
This association of genes and sequences between soybean and A. thaliana can serve several functions, including identification of gene-clusters within soybean, identification of potentially syntenic chromosomal regions, soybean expression estimation and single nucleotide polymorphism detection within soybean sequences.
We performed experiments evaluating both gene expression estimation accuracy as well as DE analysis precision.
