Computational analysis of bacterial RNA-Seq data

Computational analysis of bacterial RNA-Seq data

Recent advances in high-throughput RNA sequencing (RNA-seq) have enabled tremendous leaps forward in our understanding of bacterial transcriptomes. However, computational methods for analysis of bacterial transcriptome data have not kept pace with the large and growing data sets generated by RNA-seq technology. Here, we present new algorithms, specific to bacterial gene structures and transcrip...

Computational analysis of CLIP-seq data.

CLIP-seq experiments are currently the most important means for determining the binding sites of RNA binding proteins on a genome-wide level. The computational analysis can be divided into three steps. In the first pre-processing stage, raw reads have to be trimmed and mapped to the genome. This step has to be specifically adapted for each CLIP-seq protocol. The next step is peak calling, which...

Computational analysis of ChIP-seq data.

Chromatin immunoprecipitation followed by massively parallel sequencing (ChIP-seq) is a new technology to map protein-DNA interactions in a genome. The genome-wide transcription factor binding site and chromatin modification data produced by ChIP-seq provide invaluable information for studying gene regulation. This chapter reviews basic characteristics of ChIP-seq data and introduces a computat...

Comprehensive Analysis of RNA-Seq Data

An observation of circular RNAs in bacterial RNA-seq data

Nicolas Innocenti,1, 2 Hoang-Son Nguyen,3 Aymeric Fouquier d’Hérouël,4 and Erik Aurell1, 5 Department of Computational Biology, KTH Royal Institute of Technology, AlbaNova University Center, Roslagstullsbacken 17, SE-10691 Stockholm, Sweden Combient AB, Nettovägen 6, SE-17541, Järfälla, Sweden System Biology Programme, KTH Royal Institute of Technology, SE-100 44 Stockholm, Sweden Luxembourg Ce...