With a Genome
FANSe1 is a new, fast and accurate algorithm for nucleic acid sequence analysis with adjustable mismatch allowance settings and ability to handle indels to accurately and quantitatively map millions of reads to small or large reference genomes. It is a seed-based algorithm which uses the whole read information for mapping and high sensitivity and low ambiguity are achieved by using short and non-overlapping reads. Furthermore, FANSe uses hotspot score to prioritize the processing of highly possible matches and implements modified Smith-Watermann refinement with reduced scoring matrix to accelerate the calculation without compromising its sensitivity. The FANSe algorithm stably processes datasets from various sequencing platforms, masked or unmasked and small or large genomes. It shows a remarkable coverage of low-abundance mRNAs which is important for quantitative processing of RNA-Seq datasets.
AVAILABILITY: The FANSe algorithm is accessible at http://bioinformatics.jnu.edu.cn/software/fanse/. The web site contains a detailed tutorial and the source code for download
Without a Genome
Oases2 is a software package designed to heuristically assemble RNA-seq reads in the absence of a reference genome, across a broad spectrum of expression values and in presence of alternative isoforms. It achieves this by using an array of hash lengths, a dynamic filtering of noise, a robust resolution of alternative splicing events, and the efficient merging of multiple assemblies. It was tested on human and mouse RNA-seq data and is shown to improve significantly on the transABySS and Trinity de novo transcriptome assemblers.
AVAILABILITY: Oases is freely available under the GPL license at www.ebi.ac.uk/~zerbino/oases/
- Zhang G, Fedyunin I, Kirchner S, Xiao C, Valleriani A, Ignatova Z. (2012) FANSe: an accurate algorithm for quantitative mapping of large scale sequencing reads. Nucleic Acids Res [Epub ahead of print]. [article]
- Schulz MH, Zerbino DR, Vingron M, Birney E. (2012) Oases: Robust de novo RNA-seq assembly across the dynamic range of expression levels. Bioinformatics [Epub ahead of print]. [article]