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Sarah Sheppard

dblp:135/5867 · DBLP profile ↗
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2ranked-venue papers
2as 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 · 2 · 2 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 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology › RNA biology › RNA processing
polyadenylation site prediction
0.212013
Accurate identification of polyadenylation sites from 3′ end deep sequencing using a naïve Bayes classifier · Bioinform. 2013
Bioinformatics and computational biology
sequence analysis
0.212013
Accurate identification of polyadenylation sites from 3′ end deep sequencing using a naïve Bayes classifier · Bioinform. 2013

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

naïve bayes classifier · 0.2
YearPublicationVenuePosition
2014 Accurate identification of polyadenylation sites from 3′ end deep sequencing using a naïve Bayes classifier
abstract
Vol. 29, No. 20, 2013, pp. 2564–2571 doi:10.1093/bioinformatics/btt446 The publishers regret that the corresponding author details for this article should appear as follows: Contact:[email protected]; [email protected]
Sarah Sheppard, Nathan D. Lawson, Lihua Julie Zhu
Bioinform.1
2013 Accurate identification of polyadenylation sites from 3′ end deep sequencing using a naïve Bayes classifier
abstract
MOTIVATION: 3' end processing is important for transcription termination, mRNA stability and regulation of gene expression. To identify 3' ends, most techniques use an oligo-dT primer to construct deep sequencing libraries. However, this approach can lead to identification of artifactual polyadenylation sites due to internal priming in homopolymeric stretches of adenines. Although heuristic filters have been applied in these cases, they typically result in a high proportion of both false-positive and -negative classifications. Therefore, there is a need to develop improved algorithms to better identify mis-priming events in oligo-dT primed sequences. RESULTS: By analyzing sequence features flanking 3' ends derived from oligo-dT-based sequencing, we developed a naïve Bayes classifier to classify them as true or false/internally primed. The resulting algorithm is highly accurate, outperforms previous heuristic filters and facilitates identification of novel polyadenylation sites.
Sarah Sheppard, Nathan D. Lawson, Lihua Julie Zhu
Bioinform.1