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Eric J. Duncavage

dblp:41/8712 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2010
—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 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology › genomics › structural variation
structural variant detection
0.112010
SLOPE: a quick and accurate method for locating non-SNP structural variation from targeted next-generation sequence data · Bioinform. 2010
Bioinformatics and computational biology › sequence analysis
targeted sequencing
0.112010
SLOPE: a quick and accurate method for locating non-SNP structural variation from targeted next-generation sequence data · Bioinform. 2010
Bioinformatics and computational biology › cancer genomics › chromosomal aberration detection
translocation detection
0.112010
SLOPE: a quick and accurate method for locating non-SNP structural variation from targeted next-generation sequence data · Bioinform. 2010

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

short-read analysis · 0.1
YearPublicationVenuePosition
2010 SLOPE: a quick and accurate method for locating non-SNP structural variation from targeted next-generation sequence data
abstract
MOTIVATION: Targeted 'deep' sequencing of specific genes or regions is of great interest in clinical cancer diagnostics where some sequence variants, particularly translocations and indels, have known prognostic or diagnostic significance. In this setting, it is unnecessary to sequence an entire genome, and target capture methods can be applied to limit sequencing to important regions, thereby reducing costs and the time required to complete testing. Existing 'next-gen' sequencing analysis packages are optimized for efficiency in whole-genome studies and are unable to benefit from the particular structure of targeted sequence data. RESULTS: We developed SLOPE to detect structural variants from targeted short-DNA reads. We use both real and simulated data to demonstrate SLOPE's ability to rapidly detect insertion/deletion events of various sizes as well as translocations and viral integration sites with high sensitivity and low false discovery rate. AVAILABILITY: Binary code available at http://www-genepi.med.utah.edu/suppl/SLOPE/index.html
Haley J. Abel, Eric J. Duncavage, Nils Becker, Jon R. Armstrong, Vincent J. Magrini, John D. Pfeifer
Bioinform.2