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Steven W. Singer

dblp:176/9805 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2016
—ORCID · unresolved

Domains — the database's venue-derived domains; a paper can count in several

Applied, interdisciplinary, general and emerging computing · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Interdisciplinary, comprehensive, and emerging computing
1 paper
Bioinformatics and computational biology · 100%

Topics — the 3 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology › metagenomics › metagenome-assembled genome
metagenome-assembled genome reconstruction
0.212016
MaxBin 2.0: an automated binning algorithm to recover genomes from multiple metagenomic datasets · Bioinform. 2016
Bioinformatics and computational biology
metagenomics
0.212016
MaxBin 2.0: an automated binning algorithm to recover genomes from multiple metagenomic datasets · Bioinform. 2016
Bioinformatics and computational biology › metagenomics
metagenomic binning
0.212016
MaxBin 2.0: an automated binning algorithm to recover genomes from multiple metagenomic datasets · Bioinform. 2016

Methods — techniques the papers use, named apart from their topics

co-assembly · 0.2automated binning · 0.2
YearPublicationVenuePosition
2016 MaxBin 2.0: an automated binning algorithm to recover genomes from multiple metagenomic datasets
abstract
UNLABELLED: The recovery of genomes from metagenomic datasets is a critical step to defining the functional roles of the underlying uncultivated populations. We previously developed MaxBin, an automated binning approach for high-throughput recovery of microbial genomes from metagenomes. Here we present an expanded binning algorithm, MaxBin 2.0, which recovers genomes from co-assembly of a collection of metagenomic datasets. Tests on simulated datasets revealed that MaxBin 2.0 is highly accurate in recovering individual genomes, and the application of MaxBin 2.0 to several metagenomes from environmental samples demonstrated that it could achieve two complementary goals: recovering more bacterial genomes compared to binning a single sample as well as comparing the microbial community composition between different sampling environments. AVAILABILITY AND IMPLEMENTATION: MaxBin 2.0 is freely available at http://sourceforge.net/projects/maxbin/ under BSD license.
Blake A. Simmons, Steven W. Singer
Bioinform.3