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A Practical Comparison of De Novo Genome Assembly Software Tools for Next-Generation Sequencing Technologies

Overview of attention for article published in PLOS ONE, March 2011
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Title
A Practical Comparison of De Novo Genome Assembly Software Tools for Next-Generation Sequencing Technologies
Published in
PLOS ONE, March 2011
DOI 10.1371/journal.pone.0017915
Pubmed ID
Authors

Wenyu Zhang, Jiajia Chen, Yang Yang, Yifei Tang, Jing Shang, Bairong Shen

Abstract

The advent of next-generation sequencing technologies is accompanied with the development of many whole-genome sequence assembly methods and software, especially for de novo fragment assembly. Due to the poor knowledge about the applicability and performance of these software tools, choosing a befitting assembler becomes a tough task. Here, we provide the information of adaptivity for each program, then above all, compare the performance of eight distinct tools against eight groups of simulated datasets from Solexa sequencing platform. Considering the computational time, maximum random access memory (RAM) occupancy, assembly accuracy and integrity, our study indicate that string-based assemblers, overlap-layout-consensus (OLC) assemblers are well-suited for very short reads and longer reads of small genomes respectively. For large datasets of more than hundred millions of short reads, De Bruijn graph-based assemblers would be more appropriate. In terms of software implementation, string-based assemblers are superior to graph-based ones, of which SOAPdenovo is complex for the creation of configuration file. Our comparison study will assist researchers in selecting a well-suited assembler and offer essential information for the improvement of existing assemblers or the developing of novel assemblers.

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Geographical breakdown

Country Count As %
United States 40 4%
United Kingdom 17 2%
Brazil 15 1%
France 14 1%
Germany 9 <1%
Spain 9 <1%
Netherlands 8 <1%
Canada 6 <1%
Sweden 6 <1%
Other 49 4%
Unknown 960 85%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 291 26%
Researcher 288 25%
Student > Master 174 15%
Student > Bachelor 77 7%
Other 60 5%
Other 173 15%
Unknown 70 6%
Readers by discipline Count As %
Agricultural and Biological Sciences 708 62%
Biochemistry, Genetics and Molecular Biology 138 12%
Computer Science 97 9%
Engineering 17 2%
Medicine and Dentistry 14 1%
Other 73 6%
Unknown 86 8%