Konnector v2.0: pseudo-long reads from paired-end sequencing data. Issue 3 (December 2015)

• Record Type: Journal Article
• Authors: Vandervalk, Benjamin; Yang, Chen; Xue, Zhuyi; Raghavan, Karthika; Chu, Justin; Mohamadi, Hamid; Jackman, Shaun; Chiu, Readman; Warren, René; Birol, Inanç
• Is Part Of: BMC medical genomics. Volume 8:Issue 3(2015)
• Page Start: 1
• Page End: 10
• Publication Date: 2015-12
• Subjects: Bloom filter -- de Bruijn graph -- paired-end sequencing -- de novo -- genome assembly; Medical genetics -- Periodicals; Genomics -- Periodicals; 616.042
• DOI: 10.1186/1755-8794-8-S3-S1
• Languages: English
• ISSNs: 1755-8794

Abstract

Abstract Background Reading the nucleotides from two ends of a DNA fragment is called paired-end tag (PET) sequencing. When the fragment length is longer than the combined read length, there remains a gap of unsequenced nucleotides between read pairs. If the target in such experiments is sequenced at a level to provide redundant coverage, it may be possible to bridge these gaps using bioinformatics methods. Konnector is a localde novo assembly tool that addresses this problem. Here we report on version 2.0 of our tool. Results Konnector uses a probabilistic and memory-efficient data structure called Bloom filter to represent a k-mer spectrum - all possible sequences of length k in an input file, such as the collection of reads in a PET sequencing experiment. It performs look-ups to this data structure to construct an implicit de Bruijn graph, which describes (k-1) base pair overlaps between adjacent k-mers. It traverses this graph to bridge the gap between a given pair of flanking sequences. Conclusions Here we report the performance of Konnector v2.0 on simulated and experimental datasets, and compare it against other tools with similar functionality. We note that, representing k-mers with 1.5 bytes of memory on average, Konnector can scale to very large genomes. With our parallel implementation, it can also process over a billion bases on commodity hardware.