Sparc: a sparsity-based consensus algorithm for long erroneous sequencing reads

PeerJ

article

Chengxi Ye¹  Zhanshan (Sam) Ma² 

[1] Department of Computer Science, University of Maryland, College Park;Computational Biology and Medical Ecology Lab, State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences

关键词: Consensus algorithm;    Genome assembly;    Variant discovery;    Single molecular sequencing;    Third generation sequencing technology;   

DOI  :  10.7717/peerj.2016

学科分类：社会科学、人文和艺术（综合）

来源: Inra

【 摘 要 】

Motivation. The third generation sequencing (3GS) technology generates long sequences of thousands of bases. However, its current error rates are estimated in the range of 15–40%, significantly higher than those of the prevalent next generation sequencing (NGS) technologies (less than 1%). Fundamental bioinformatics tasks such as de novo genome assembly and variant calling require high-quality sequences that need to be extracted from these long but erroneous 3GS sequences.Results. We describe a versatile and efficient linear complexity consensus algorithm Sparc to facilitate de novo genome assembly. Sparc builds a sparse k-mer graph using a collection of sequences from a targeted genomic region. The heaviest path which approximates the most likely genome sequence is searched through a sparsity-induced reweighted graph as the consensus sequence. Sparc supports using NGS and 3GS data together, which leads to significant improvements in both cost efficiency and computational efficiency. Experiments with Sparc show that our algorithm can efficiently provide high-quality consensus sequences using both PacBio and Oxford Nanopore sequencing technologies. With only 30× PacBio data, Sparc can reach a consensus with error rate <0.5%. With the more challenging Oxford Nanopore data, Sparc can also achieve similar error rate when combined with NGS data. Compared with the existing approaches, Sparc calculates the consensus with higher accuracy, and uses approximately 80% less memory and time.Availability. The source code is available for download at https://github.com/yechengxi/Sparc.

【 授权许可】

CC BY