Kevin R. Shieh

dblp:166/9731 · DBLP profile ↗
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1ranked-venue papers
1as first author
0since 2021 · last 2020
—ORCID · none

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

Applied, interdisciplinary, general and emerging computing · 1 · 1 first-author

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 › sequence analysis
aptamer analysis
0.412020
AptCompare: optimized de novo motif discovery of RNA aptamers via HTS-SELEX · Bioinform. 2020
Bioinformatics and computational biology › sequence analysis
high-throughput sequencing data analysis
0.412020
AptCompare: optimized de novo motif discovery of RNA aptamers via HTS-SELEX · Bioinform. 2020
Bioinformatics and computational biology › sequence analysis
motif discovery
0.412020
AptCompare: optimized de novo motif discovery of RNA aptamers via HTS-SELEX · Bioinform. 2020

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

motif detection algorithms · 0.4consensus ranking · 0.4
YearPublicationVenuePosition
2020 AptCompare: optimized de novo motif discovery of RNA aptamers via HTS-SELEX
abstract
SUMMARY: High-throughput sequencing can enhance the analysis of aptamer libraries generated by the Systematic Evolution of Ligands by EXponential enrichment. Robust analysis of the resulting sequenced rounds is best implemented by determining a ranked consensus of reads following the processing by multiple aptamer detection algorithms. While several such approaches have been developed to this end, their installation and implementation is problematic. We developed AptCompare, a cross-platform program that combines six of the most widely used analytical approaches for the identification of RNA aptamer motifs and uses a simple weighted ranking to order the candidate aptamers, all driven within the same GUI-enabled environment. We demonstrate AptCompare's performance by identifying the top-ranked candidate aptamers from a previously published selection experiment in our laboratory, with follow-up bench assays demonstrating good correspondence between the sequences' rankings and their binding affinities. AVAILABILITY AND IMPLEMENTATION: The source code and pre-built virtual machine images are freely available at https://bitbucket.org/shiehk/aptcompare. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Kevin R. Shieh, Christina Kratschmer, Keith E. Maier, John M. Greally, Matthew Levy, Aaron Golden
Bioinform.1