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Chenna Ramu

dblp:62/2701 · DBLP profile ↗
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4ranked-venue papers
2as first author
0since 2021 · last 2009
—ORCID · none

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

Applied, interdisciplinary, general and emerging computing · 4 · 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
4 papers
Bioinformatics and computational biology · 100%

Topics — the 7 heaviest of 8, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology › sequence analysis
motif discovery
0.112009
KEPE - a motif frequently superimposed on sumoylation sites in metazoan chromatin proteins and transcription factors · Bioinform. 2009
Bioinformatics and computational biology
sequence analysis
0.112009
KEPE - a motif frequently superimposed on sumoylation sites in metazoan chromatin proteins and transcription factors · Bioinform. 2009
Bioinformatics and computational biology
protein function prediction
0.112008
Discovery of candidate KEN-box motifs using Cell Cycle keyword enrichment combined with native disorder prediction and motif conservation · Bioinform. 2008
Bioinformatics and computational biology › sequence analysis › motif discovery
short linear motif discovery
0.112008
Discovery of candidate KEN-box motifs using Cell Cycle keyword enrichment combined with native disorder prediction and motif conservation · Bioinform. 2008
Bioinformatics and computational biology › biological database
biological database search
0.012001
SIR: a simple indexing and retrieval system for biological flat file databases · Bioinform. 2001
Bioinformatics and computational biology › protein structure prediction
protein disorder prediction
0.012008
Discovery of candidate KEN-box motifs using Cell Cycle keyword enrichment combined with native disorder prediction and motif conservation · Bioinform. 2008
Bioinformatics and computational biology
protein structure prediction
0.012008
Discovery of candidate KEN-box motifs using Cell Cycle keyword enrichment combined with native disorder prediction and motif conservation · Bioinform. 2008

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

regular expression search · 0.1native disorder prediction · 0.1motif conservation analysis · 0.1keyword enrichment · 0.1indexing · 0.0object-oriented programming · 0.0
YearPublicationVenuePosition
2009 KEPE - a motif frequently superimposed on sumoylation sites in metazoan chromatin proteins and transcription factors
abstract
MOTIVATION: We noted that the sumoylation site in C/EBP homologues is conserved beyond the canonical consensus sequence for sumoylation. Therefore, we investigated whether this pattern might define a more general protein motif. RESULTS: We undertook a survey of the human proteome using a regular expression based on the C/EBP motif. This revealed significant enrichment of the motif using different Gene Ontology terms (e.g. 'transcription') that pertain to the nucleus. When considering requirements for the motif to be functional (evolutionary conservation, structural accessibility of the motif and proper cell localization of the protein), more than 130 human proteins were retrieved from the UniProt/Swiss-Prot database. These candidates were particularly enriched in transcription factors, including FOS, JUN, Hif-1alpha, MLL2 and members of the KLF, MAF and NFATC families; chromatin modifiers like CHD-8, HDAC4 and DNA Top1; and the transcriptional regulatory kinases HIPK1 and HIPK2. The KEPEmotif appears to be restricted to the metazoan lineage and has three length variants-short, medium and long-which do not appear to interchange.
Francesca Diella, Sophie Chabanis-Davidson, Katja Luck, Claudia Chica, Chenna Ramu, Claus Nerlov, Toby J. Gibson
Bioinform.5
2008 Discovery of candidate KEN-box motifs using Cell Cycle keyword enrichment combined with native disorder prediction and motif conservation
abstract
MOTIVATION: KEN-box-mediated target selection is one of the mechanisms used in the proteasomal destruction of mitotic cell cycle proteins via the APC/C complex. While annotating the Eukaryotic Linear Motif resource (ELM, http://elm.eu.org/), we found that KEN motifs were significantly enriched in human protein entries with cell cycle keywords in the UniProt/Swiss-Prot database-implying that KEN-boxes might be more common than reported. RESULTS: Matches to short linear motifs in protein database searches are not, per se, significant. KEN-box enrichment with cell cycle Gene Ontology terms suggests that collectively these motifs are functional but does not prove that any given instance is so. Candidates were surveyed for native disorder prediction using GlobPlot and IUPred and for motif conservation in homologues. Among >25 strong new candidates, the most notable are human HIPK2, CHFR, CDC27, Dab2, Upf2, kinesin Eg5, DNA Topoisomerase 1 and yeast Cdc5 and Swi5. A similar number of weaker candidates were present. These proteins have yet to be tested for APC/C targeted destruction, providing potential new avenues of research.
Sushama Michael, Gilles Travé, Chenna Ramu, Claudia Chica, Toby J. Gibson
Bioinform.3
2001 SIR: a simple indexing and retrieval system for biological flat file databases
abstract
SUMMARY: SIR is a Simple Indexing and Retrieval tool for indexing and searching biological flat file databases. SIR is a cross-platform solution entirely written in Python. Since the package is very small and installation is trivial, this would be an ideal solution for database providers to provide a custom retrieval tool to access them. AVAILABILITY: The modules will be made available at http://www.EMBLHeidelberg.de/~chenna/PySAT/sir.html
Chenna Ramu
Bioinform.1
2000 Object-oriented parsing of biological databases with Python
abstract
Abstract Motivation: While database activities in the biological area are increasing rapidly, rather little is done in the area of parsing them in a simple and object-oriented way. Results: We present here an elegant, simple yet powerful way of parsing biological flat-file databases. We have taken EMBL, SWISSPROT and GENBANK as examples. EMBL and SWISS-PROT do not differ much in the format structure. GENBANK has a very different format structure than EMBL and SWISS-PROT. Extracting the desired fields in an entry (for example a sub-sequence with an associated feature) for later analysis is a constant need in the biological sequence-analysis community: this is illustrated with tools to make new splice-site databases. The interface to the parser is abstract in the sense that the access to all the databases is independent from their different formats, since parsing instructions are hidden. Availability: The modules are available at http://shag.embl-heidelberg.de:8000/Biopy/ Contact: [email protected] Supplementary information: http://shag.embl-heidelberg.de:8000/Biopy/ * To whom correspondence should be addressed.
Chenna Ramu, Christine Gemünd, Toby J. Gibson
Bioinform.1