- The most crucial step in data processing from high-throughput sequencing applications is the accurate and sensitive alignment of the sequencing reads to reference genomes or transcriptomes. The accurate detection of insertions and deletions (indels) and errors introduced by the sequencing platform or by misreading of modified nucleotides is essential for the quantitative processing of the RNA-based sequencing (RNA-Seq) datasets and for the identification of genetic variations and modification patterns. We developed a new, fast and accurate algorithm for nucleic acid sequence analysis, FANSe, 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 implementsThe most crucial step in data processing from high-throughput sequencing applications is the accurate and sensitive alignment of the sequencing reads to reference genomes or transcriptomes. The accurate detection of insertions and deletions (indels) and errors introduced by the sequencing platform or by misreading of modified nucleotides is essential for the quantitative processing of the RNA-based sequencing (RNA-Seq) datasets and for the identification of genetic variations and modification patterns. We developed a new, fast and accurate algorithm for nucleic acid sequence analysis, FANSe, 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.…
