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Kevin C. O'Connor

dblp:147/7117 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2014
—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%

Topics — the 2 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology › sequence analysis › sequencing data processing
high-throughput sequencing data processing
0.212014
pRESTO: a toolkit for processing high-throughput sequencing raw reads of lymphocyte receptor repertoires · Bioinform. 2014
Bioinformatics and computational biology
immunoinformatics
0.212014
pRESTO: a toolkit for processing high-throughput sequencing raw reads of lymphocyte receptor repertoires · Bioinform. 2014

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

sequence annotation · 0.2parallelization · 0.2error correction · 0.2
YearPublicationVenuePosition
2014 pRESTO: a toolkit for processing high-throughput sequencing raw reads of lymphocyte receptor repertoires
abstract
UNLABELLED: Driven by dramatic technological improvements, large-scale characterization of lymphocyte receptor repertoires via high-throughput sequencing is now feasible. Although promising, the high germline and somatic diversity, especially of B-cell immunoglobulin repertoires, presents challenges for analysis requiring the development of specialized computational pipelines. We developed the REpertoire Sequencing TOolkit (pRESTO) for processing reads from high-throughput lymphocyte receptor studies. pRESTO processes raw sequences to produce error-corrected, sorted and annotated sequence sets, along with a wealth of metrics at each step. The toolkit supports multiplexed primer pools, single- or paired-end reads and emerging technologies that use single-molecule identifiers. pRESTO has been tested on data generated from Roche and Illumina platforms. It has a built-in capacity to parallelize the work between available processors and is able to efficiently process millions of sequences generated by typical high-throughput projects. AVAILABILITY AND IMPLEMENTATION: pRESTO is freely available for academic use. The software package and detailed tutorials may be downloaded from http://clip.med.yale.edu/presto.
Jason A. Vander Heiden, Gur Yaari, Mohamed Uduman, Joel N. H. Stern, Kevin C. O'Connor, David A. Hafler, Francois Vigneault, Steven H. Kleinstein
Bioinform.5