How do alignment programs perform on sequencing data with varying qualities and from repetitive regions?
BACKGROUND
Next-generation sequencing technologies generate a significant number of short reads that are utilized to address a variety of biological questions. However, quite often, sequencing reads tend to have low quality at the 3' end and are generated from the repetitive regions of a genome. 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 above issues 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 concordance between any pair of aligners (for real sequencing data without known truth) and the accuracy of simulated read alignment.


RESULTS
Our results show that, for sequencing data with reads that have relatively good quality or that have had low quality bases trimmed off, all four alignment programs perform similarly. We have also demonstrated that trimming off low quality ends markedly increases the number of aligned reads and improves the consistency among different aligners as well, especially for low quality data. However, Novoalign is more sensitive to the improvement of data quality. Trimming off low quality ends significantly increases the concordance between Novoalign and other aligners. As for aligning reads from repetitive regions, our simulation data show that reads from repetitive regions tend to be aligned incorrectly, and suppressing reads with multiple hits can improve alignment accuracy.


CONCLUSIONS
This study provides a systematic comparison of commonly used alignment algorithms in the context of sequencing data with varying qualities and from repetitive regions. Our approach can be applied to different sequencing data sets generated from different platforms. It can also be utilized to study the performance of other alignment programs.
Department of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, OH, 44106, USA. ssun5211@yahoo.com.
Xiaoqing
X
Yu
Kishore
K
Guda
Joseph
J
Willis
Martina
M
Veigl
Zhenghe
Z
Wang
Sanford
S
Markowitz
Mark D
MD
Adams
Shuying
S
Sun