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Meraculous: De Novo Genome Assembly with Short Paired-End Reads

Overview of attention for article published in PLOS ONE, August 2011
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Title
Meraculous: De Novo Genome Assembly with Short Paired-End Reads
Published in
PLOS ONE, August 2011
DOI 10.1371/journal.pone.0023501
Pubmed ID
Authors

Jarrod A. Chapman, Isaac Ho, Sirisha Sunkara, Shujun Luo, Gary P. Schroth, Daniel S. Rokhsar

Abstract

We describe a new algorithm, meraculous, for whole genome assembly of deep paired-end short reads, and apply it to the assembly of a dataset of paired 75-bp Illumina reads derived from the 15.4 megabase genome of the haploid yeast Pichia stipitis. More than 95% of the genome is recovered, with no errors; half the assembled sequence is in contigs longer than 101 kilobases and in scaffolds longer than 269 kilobases. Incorporating fosmid ends recovers entire chromosomes. Meraculous relies on an efficient and conservative traversal of the subgraph of the k-mer (deBruijn) graph of oligonucleotides with unique high quality extensions in the dataset, avoiding an explicit error correction step as used in other short-read assemblers. A novel memory-efficient hashing scheme is introduced. The resulting contigs are ordered and oriented using paired reads separated by ∼280 bp or ∼3.2 kbp, and many gaps between contigs can be closed using paired-end placements. Practical issues with the dataset are described, and prospects for assembling larger genomes are discussed.

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Mendeley readers

The data shown below were compiled from readership statistics for 327 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 8 2%
Brazil 7 2%
Norway 3 <1%
Germany 3 <1%
France 2 <1%
Netherlands 2 <1%
Spain 2 <1%
Sweden 2 <1%
United Kingdom 2 <1%
Other 8 2%
Unknown 288 88%

Demographic breakdown

Readers by professional status Count As %
Researcher 85 26%
Student > Ph. D. Student 74 23%
Student > Master 41 13%
Student > Doctoral Student 18 6%
Student > Bachelor 16 5%
Other 55 17%
Unknown 38 12%
Readers by discipline Count As %
Agricultural and Biological Sciences 163 50%
Biochemistry, Genetics and Molecular Biology 55 17%
Computer Science 39 12%
Environmental Science 5 2%
Engineering 3 <1%
Other 20 6%
Unknown 42 13%