A Comprehensive Study of De Novo Genome Assemblers: Current Challenges and Future Prospective. (16th February 2018)

• Record Type: Journal Article
• Authors: Khan, Abdul Rafay; Pervez, Muhammad Tariq; Babar, Masroor Ellahi; Naveed, Nasir; Shoaib, Muhammad
• Is Part Of: Evolutionary bioinformatics online. Volume 14(2018)
• Publication Date: 2018-02-16
• Subjects: NGS (next-generation sequencing) -- DBG (de Bruijn graph) -- OLC (overlap layout consensus) -- ENA (European Nucleotide Archive) -- bps (base pairs); Bioinformatics -- Periodicals; Evolutionary computation -- Periodicals; Genetic programming (Computer science) -- Periodicals; Computational Biology; Evolution, Molecular; Bioinformatics; Electronic journals; Periodicals; Fulltext; Internet Resources; Periodicals; Periodicals; 576.8
• DOI: 10.1177/1176934318758650
• Languages: English
• ISSNs: 1176-9343

Abstract

Background: Current advancements in next-generation sequencing technology have made possible to sequence whole genome but assembling a large number of short sequence reads is still a big challenge. In this article, we present the comparative study of seven assemblers, namely, ABySS, Velvet, Edena, SGA, Ray, SSAKE, and Perga, using prokaryotic and eukaryotic paired-end as well as single-end data sets from Illumina platform. Results: Results showed that in case of single-end data sets, Velvet and ABySS outperformed in all the seven assemblers with comparatively low assembling time and high genome fraction. Velvet consumed the least amount of memory than any other assembler. In case of paired-end data sets, Velvet consumed least amount of time and produced high genome fraction after ABySS and Ray. In terms of low memory usage, SGA and Edena outperformed in all the assemblers. Ray also showed good genome fraction; however, extremely high assembling time consumed by the Ray might make it prohibitively slow on larger data sets of single and paired-end data. Conclusions: Our comparison study will provide assistance to the scientists for selecting the suitable assembler according to their data sets and will also assist the developers to upgrade or develop a new assembler for de novo assembling.