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C. E. Penrod

dblp:185/8577 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2016
—ORCID · none

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%
Theoretical computer science
1 paper
Algorithms and data structures · 100%

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

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology
sequence analysis
0.212016
SA-SSR: a suffix array-based algorithm for exhaustive and efficient SSR discovery in large genetic sequences · Bioinform. 2016
Bioinformatics and computational biology › genomics › repetitive DNA analysis
simple sequence repeat detection
0.212016
SA-SSR: a suffix array-based algorithm for exhaustive and efficient SSR discovery in large genetic sequences · Bioinform. 2016
Algorithms and data structures › sequence algorithms
string algorithms
0.212016
SA-SSR: a suffix array-based algorithm for exhaustive and efficient SSR discovery in large genetic sequences · Bioinform. 2016
Algorithms and data structures › sequence algorithms › string algorithms › string indexing
suffix array
0.212016
SA-SSR: a suffix array-based algorithm for exhaustive and efficient SSR discovery in large genetic sequences · Bioinform. 2016

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

longest common prefix array · 0.5
YearPublicationVenuePosition
2016 SA-SSR: a suffix array-based algorithm for exhaustive and efficient SSR discovery in large genetic sequences
abstract
UNLABELLED: Simple Sequence Repeats (SSRs) are used to address a variety of research questions in a variety of fields (e.g. population genetics, phylogenetics, forensics, etc.), due to their high mutability within and between species. Here, we present an innovative algorithm, SA-SSR, based on suffix and longest common prefix arrays for efficiently detecting SSRs in large sets of sequences. Existing SSR detection applications are hampered by one or more limitations (i.e. speed, accuracy, ease-of-use, etc.). Our algorithm addresses these challenges while being the most comprehensive and correct SSR detection software available. SA-SSR is 100% accurate and detected >1000 more SSRs than the second best algorithm, while offering greater control to the user than any existing software. AVAILABILITY AND IMPLEMENTATION: SA-SSR is freely available at http://github.com/ridgelab/SA-SSR CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Brandon D. Pickett, S. M. Karlinsey, C. E. Penrod, Michael J. Cormier, Mark T. W. Ebbert, Dennis K. Shiozawa, C. J. Whipple, Perry G. Ridge
Bioinform.3