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Neelanjan Mukherjee

dblp:85/6901 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2018
0000-0003-0017-1400ORCID · reported

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

Applied, interdisciplinary, general and emerging computing · 1

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 analysis › RNA bioinformatics
RNA-binding protein analysis
0.312018
SSMART: sequence-structure motif identification for RNA-binding proteins · Bioinform. 2018
Bioinformatics and computational biology › RNA biology › RNA analysis › RNA bioinformatics
RNA structure analysis
0.312018
SSMART: sequence-structure motif identification for RNA-binding proteins · Bioinform. 2018

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

degenerate alphabet · 0.3consensus string modeling · 0.3
YearPublicationVenuePosition
2018 SSMART: sequence-structure motif identification for RNA-binding proteins
abstract
Motivation: RNA-binding proteins (RBPs) regulate every aspect of RNA metabolism and function. There are hundreds of RBPs encoded in the eukaryotic genomes, and each recognize its RNA targets through a specific mixture of RNA sequence and structure properties. For most RBPs, however, only a primary sequence motif has been determined, while the structure of the binding sites is uncharacterized. Results: We developed SSMART, an RNA motif finder that simultaneously models the primary sequence and the structural properties of the RNA targets sites. The sequence-structure motifs are represented as consensus strings over a degenerate alphabet, extending the IUPAC codes for nucleotides to account for secondary structure preferences. Evaluation on synthetic data showed that SSMART is able to recover both sequence and structure motifs implanted into 3'UTR-like sequences, for various degrees of structured/unstructured binding sites. In addition, we successfully used SSMART on high-throughput in vivo and in vitro data, showing that we not only recover the known sequence motif, but also gain insight into the structural preferences of the RBP. Availability and implementation: SSMART is freely available at https://ohlerlab.mdc-berlin.de/software/SSMART_137/. Supplementary information: Supplementary data are available at Bioinformatics online.
Alina Munteanu, Neelanjan Mukherjee, Uwe Ohler
Bioinform.2