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Matthew D. MacManes

dblp:209/7840 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2017
—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 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology › metagenomics
functional profiling
0.312017
PALADIN: protein alignment for functional profiling whole metagenome shotgun data · Bioinform. 2017
Bioinformatics and computational biology
metagenomics
0.312017
PALADIN: protein alignment for functional profiling whole metagenome shotgun data · Bioinform. 2017
Bioinformatics and computational biology › protein sequence analysis
protein alignment
0.312017
PALADIN: protein alignment for functional profiling whole metagenome shotgun data · Bioinform. 2017
Bioinformatics and computational biology
sequence alignment
0.312017
PALADIN: protein alignment for functional profiling whole metagenome shotgun data · Bioinform. 2017

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

burrows-wheeler alignment · 0.3
YearPublicationVenuePosition
2017 PALADIN: protein alignment for functional profiling whole metagenome shotgun data
abstract
MOTIVATION: Whole metagenome shotgun sequencing is a powerful approach for assaying the functional potential of microbial communities. We currently lack tools that efficiently and accurately align DNA reads against protein references, the technique necessary for constructing a functional profile. Here, we present PALADIN-a novel modification of the Burrows-Wheeler Aligner that provides accurate alignment, robust reporting capabilities and orders-of-magnitude improved efficiency by directly mapping in protein space. RESULTS: We compared the accuracy and efficiency of PALADIN against existing tools that employ nucleotide or protein alignment algorithms. Using simulated reads, PALADIN consistently outperformed the popular DNA read mappers BWA and NovoAlign in detected proteins, percentage of reads mapped and ontological similarity. We also compared PALADIN against four existing protein alignment tools: BLASTX, RAPSearch2, DIAMOND and Lambda, using empirically obtained reads. PALADIN yielded results seven times faster than the best performing alternative, DIAMOND and nearly 8000 times faster than BLASTX. PALADIN's accuracy was comparable to all tested solutions. AVAILABILITY AND IMPLEMENTATION: PALADIN was implemented in C, and its source code and documentation are available at https://github.com/twestbrookunh/paladin. CONTACT: [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Anthony Westbrook, Jordan Ramsdell, Taruna Schuelke, Louisa Normington, R. Daniel Bergeron, William Kelley Thomas, Matthew D. MacManes
Bioinform.7