ALBERT

Beschreibung: Background: Next-generation sequencing technologies generate a significant number of short reads that areutilized to address a variety of biological questions. However, quite often, sequencing readstend to have low quality at the 3' end and are generated from the repetitive regions of agenome. It is unclear how different alignment programs perform under these different cases.In order to investigate this question, we use both real data and simulated data with the aboveissues to evaluate the performance of four commonly used algorithms: SOAP2, Bowtie,BWA, and Novoalign. Methods: The performance of different alignment algorithms are measured in terms of concordancebetween any pair of aligners (for real sequencing data without known truth) and the accuracyof simulated read alignment. Results: Our results show that, for sequencing data with reads that have relatively good quality or thathave had low quality bases trimmed off, all four alignment programs perform similarly. Wehave also demonstrated that trimming off low quality ends markedly increases the number ofaligned reads and improves the consistency among different aligners as well, especially forlow quality data. However, Novoalign is more sensitive to the improvement of data quality.Trimming off low quality ends significantly increases the concordance between Novoalignand other aligners. As for aligning reads from repetitive regions, our simulation data showthat reads from repetitive regions tend to be aligned incorrectly, and suppressing reads withmultiple hits can improve alignment accuracy. Conclusions: This study provides a systematic comparison of commonly used alignment algorithms in thecontext of sequencing data with varying qualities and from repetitive regions. Our approachcan be applied to different sequencing data sets generated from different platforms. It canalso be utilized to study the performance of other alignment programs.