Demonstration venue · read-only. Every page can be browsed; the buttons that would change it are switched off. Create an account to run TaxoReview on your own data. A simulation from public publication records: the people listed were not invited and did not agree to review.
? Tutorial for this page

Elizabeth C. Reuman

dblp:40/8886 · DBLP profile ↗
← Back
1ranked-venue papers
1as first author
0since 2021 · last 2010
—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 › high-throughput sequencing data analysis
deep sequencing data analysis
0.112010
A classification model for G-to-A hypermutation in hepatitis B virus ultra-deep pyrosequencing reads · Bioinform. 2010
Bioinformatics and computational biology
sequence analysis
0.112010
A classification model for G-to-A hypermutation in hepatitis B virus ultra-deep pyrosequencing reads · Bioinform. 2010
Bioinformatics and computational biology › genomics
viral genomics
0.112010
A classification model for G-to-A hypermutation in hepatitis B virus ultra-deep pyrosequencing reads · Bioinform. 2010

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

lethal mutation prediction · 0.1classification models · 0.1
YearPublicationVenuePosition
2010 A classification model for G-to-A hypermutation in hepatitis B virus ultra-deep pyrosequencing reads
abstract
MOTIVATION: G → A hypermutation is an innate antiviral defense mechanism, mediated by host enzymes, which leads to the mutational impairment of viruses. Sensitive and specific identification of host-mediated G → A hypermutation is a novel sequence analysis challenge, particularly for viral deep sequencing studies. For example, two of the most common hepatitis B virus (HBV) reverse transcriptase (RT) drug-resistance mutations, A181T and M204I, arise from G → A changes and are routinely detected as low-abundance variants in nearly all HBV deep sequencing samples. RESULTS: We developed a classification model using measures of G → A excess and predicted indicators of lethal mutation and applied this model to 325 920 unique deep sequencing reads from plasma virus samples from 45 drug treatment-naïve HBV-infected individuals. The 2.9% of sequence reads that were classified as hypermutated by our model included most of the reads with A181T and/or M204I, indicating the usefulness of this model for distinguishing viral adaptive changes from host-mediated viral editing. AVAILABILITY: Source code and sequence data are available at http://hivdb.stanford.edu/pages/resources.html. CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Elizabeth C. Reuman, Severine Margeridon-Thermet, Harrison B. Caudill, Tommy F. Liu, Katyna Borroto-Esoda, Evguenia S. Svarovskaia, Susan P. Holmes, Robert W. Shafer
Bioinform.1