Demo Data from EDBT 2026 / reviewers in the wild / expert
Ramakant Sharma
dblp:78/195
· DBLP profile ↗
1ranked-venue papers
1as first author
0since 2021 · last 2004
—ORCID · unresolved
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 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Bioinformatics and computational biology › genome annotation
gene prediction |
0.0 | 1 | 2004 | Recognition and analysis of protein-coding genes in severe acute respiratory syndrome associated coronavirus · Bioinform. 2004 |
Bioinformatics and computational biology
genomics |
0.0 | 1 | 2004 | Recognition and analysis of protein-coding genes in severe acute respiratory syndrome associated coronavirus · Bioinform. 2004 |
Bioinformatics and computational biology › genome annotation
protein coding region prediction |
0.0 | 1 | 2004 | Recognition and analysis of protein-coding genes in severe acute respiratory syndrome associated coronavirus · Bioinform. 2004 |
Bioinformatics and computational biology › microbiology
virology |
0.0 | 1 | 2004 | Recognition and analysis of protein-coding genes in severe acute respiratory syndrome associated coronavirus · Bioinform. 2004 |
Methods — techniques the papers use, named apart from their topics
genedecipher · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2004 | Recognition and analysis of protein-coding genes in severe acute respiratory syndrome associated coronavirusabstractMOTIVATION: The recent outbreak of severe acute respiratory syndrome (SARS) caused by SARS coronavirus (SARS-CoV) has necessitated an in-depth molecular understanding of the virus to identify new drug targets. The availability of complete genome sequence of several strains of SARS virus provides the possibility of identification of protein-coding genes and defining their functions. Computational approach to identify protein-coding genes and their putative functions will help in designing experimental protocols. RESULTS: In this paper, a novel analysis of SARS genome using gene prediction method GeneDecipher developed in our laboratory has been presented. Each of the 18 newly sequenced SARS-CoV genomes has been analyzed using GeneDecipher. In addition to polyprotein 1ab(1), polyprotein 1a and the four genes coding for major structural proteins spike (S), small envelope (E), membrane (M) and nucleocapsid (N), six to eight additional proteins have been predicted depending upon the strain analyzed. Their lengths range between 61 and 274 amino acids. Our method also suggests that polyprotein 1ab, polyprotein 1a, S, M and N are proteins of viral origin and others are of prokaryotic. Putative functions of all predicted protein-coding genes have been suggested using conserved peptides present in their open reading frames. AVAILABILITY: Detailed results of GeneDecipher analysis of all the 18 strains of SARS-CoV genomes are available at http://www.igib.res.in/sarsanalysis.html Ramakant Sharma, Jitendra Kumar Maheshwari, Tulika Prakash, Debasis Dash, Samir K. Brahmachari |
Bioinform. | 1 |