Evidence map›Paper›PMID 29511353›Full record

ArticleEvolutionary bioinformatics online2018

A Comprehensive Study of De Novo Genome Assemblers: Current Challenges and Future Prospective.

Abdul Rafay Khan, Muhammad Tariq Pervez, Masroor Ellahi Babar, Nasir Naveed, Muhammad Shoaib

Abstract read
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Article in Evolutionary bioinformatics online, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.

0numbers the graph read from it
0cells of the map it votes in
24citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

24 citing papers in PubMed.

  1. International journal of systematic and evolutionary microbiology · 2025
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  6. The Bioinformatic Applications of Hi-C and Linked Reads.Genomics, proteomics & bioinformatics · 2024
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Abdul Rafay KhanDepartment of Bioinformatics and Computational Biology, Virtual University of Pakistan, Lahore, Pakistan.
Muhammad Tariq PervezDepartment of Bioinformatics and Computational Biology, Virtual University of Pakistan, Lahore, Pakistan.
Masroor Ellahi BabarDepartment of Biotechnology, Virtual University of Pakistan, Lahore, Pakistan.
Nasir NaveedDepartment of Computer Science, Virtual University of Pakistan, Lahore, Pakistan.
Muhammad ShoaibDepartment of Computer Science and Engineering, University of Engineering and Technology, Lahore, Pakistan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundCurrent 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.

resultsResults 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.

conclusionsOur 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.

Indexed as

bps (base pairs)DBG (de Bruijn graph)ENA (European Nucleotide Archive)NGS (next-generation sequencing)OLC (overlap layout consensus)

Identifiers

PMID29511353
PMCPMC5826002

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.