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

Eric Olo Ndela

dblp:425/2847 · DBLP profile ↗
← Back
1ranked-venue papers
1as first author
1since 2021 · last 2023
0000-0002-4241-1161ORCID · reported

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

Applied, interdisciplinary, general and emerging computing · 1 · 1 first-author · 1 since 2021

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 › genome annotation
coding region prediction
0.712023
CodingDiv: analyzing SNP-level microdiversity to discriminate between coding and noncoding regions in viral genomes · Bioinform. 2023
Bioinformatics and computational biology › genome annotation
gene prediction
0.712023
CodingDiv: analyzing SNP-level microdiversity to discriminate between coding and noncoding regions in viral genomes · Bioinform. 2023
Bioinformatics and computational biology › genomics
viral genomics
0.712023
CodingDiv: analyzing SNP-level microdiversity to discriminate between coding and noncoding regions in viral genomes · Bioinform. 2023

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

metagenomic read analysis · 0.7
YearPublicationVenuePosition
2023 CodingDiv: analyzing SNP-level microdiversity to discriminate between coding and noncoding regions in viral genomes
abstract
SUMMARY: Viral genes, that are frequently small genes and/or with large overlaps, are still difficult to predict accurately. To help predict all genes in viral genomes, we provide CodingDiv that detects SNP-level microdiversity of all potential coding regions, using metagenomic reads and/or similar sequences from external databases. Protein coding regions can then be identified as the ones containing more synonymous SNPs than unfavorable nonsynonymous substitutions SNPs. AVAILABILITY AND IMPLEMENTATION: CodingDiv is released under the GPL license. Source code is available at https://github.com/ericolo/codingDiv. The software can be installed and used through a docker container.
Eric Olo Ndela, François Enault
Bioinform.1