BOSS: a novel scaffolding algorithm based on an optimized scaffold graph

While aiming to determine orientations and orders of fragmented contigs, scaffolding is an essential step of assembly pipelines and can make assembly results more complete. Most existing scaffolding tools adopt scaffold graph approaches. However, due to repetitive regions in genome, sequencing error...

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Bibliographic Details
Published in:Bioinformatics (Oxford, England) Vol. 33; no. 2; pp. 169 - 176
Main Authors: Luo, Junwei, Wang, Jianxin, Zhang, Zhen, Li, Min, Wu, Fang-Xiang
Format: Journal Article
Language:English
Published: England 15.01.2017
Subjects:
Algorithms
Genome
Humans
Plasmodium falciparum - genetics
Repetitive Sequences, Nucleic Acid
Rhodobacter sphaeroides - genetics
Sequence Analysis, DNA - methods
Software
Staphylococcus aureus - genetics
ISSN:1367-4803, 1367-4811, 1367-4811
Online Access:Get full text
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Summary:While aiming to determine orientations and orders of fragmented contigs, scaffolding is an essential step of assembly pipelines and can make assembly results more complete. Most existing scaffolding tools adopt scaffold graph approaches. However, due to repetitive regions in genome, sequencing errors and uneven sequencing depth, constructing an accurate scaffold graph is still a challenge task. In this paper, we present a novel algorithm (called BOSS), which employs paired reads for scaffolding. To construct a scaffold graph, BOSS utilizes the distribution of insert size to decide whether an edge between two vertices (contigs) should be added and how an edge should be weighed. Moreover, BOSS adopts an iterative strategy to detect spurious edges whose removal can guarantee no contradictions in the scaffold graph. Based on the scaffold graph constructed, BOSS employs a heuristic algorithm to sort vertices (contigs) and then generates scaffolds. The experimental results demonstrate that BOSS produces more satisfactory scaffolds, compared with other popular scaffolding tools on real sequencing data of four genomes. BOSS is publicly available for download at https://github.com/bioinfomaticsCSU/BOSS CONTACT: jxwang@mail.csu.edu.cnSupplementary information: Supplementary data are available at Bioinformatics online.
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ISSN:1367-4803
1367-4811
1367-4811
DOI:10.1093/bioinformatics/btw597