Ever wanted to know the effect of the software you use for trimming your Illumina data – this is a great bench-marking paper spotted by Luis.
Del Fabbro C, Scalabrin S, Morgante M, Giorgi FM (2013) An Extensive Evaluation of Read Trimming Effects on Illumina NGS Data Analysis. PLoS ONE 8(12): e85024. doi:10.1371/journal.pone.0085024
Next Generation Sequencing is having an extremely strong impact in biological and medical research and diagnostics, with applications ranging from gene expression quantification to genotyping and genome reconstruction. Sequencing data is often provided as raw reads which are processed prior to analysis 1 of the most used preprocessing procedures is read trimming, which aims at removing low quality portions while preserving the longest high quality part of a NGS read. In the current work, we evaluate nine different trimming algorithms in four datasets and three common NGS-based applications (RNA-Seq, SNP calling and genome assembly). Trimming is shown to increase the quality and reliability of the analysis, with concurrent gains in terms of execution time and computational resources needed.