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Samir K. Brahmachari

dblp:63/1827 · DBLP profile ↗
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5ranked-venue papers
0as 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 · 5

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
3 papers
Bioinformatics and computational biology · 100%

Topics — the 6 heaviest of 7, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology
genomics
0.122004
Recognition and analysis of protein-coding genes in severe acute respiratory syndrome associated coronavirus · Bioinform. 2004
Alu repeat analysis in the complete human genome: trends and variations with respect to genomic composition · Bioinform. 2004
Bioinformatics and computational biology › genome annotation
gene prediction
0.012004
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
0.012004
Recognition and analysis of protein-coding genes in severe acute respiratory syndrome associated coronavirus · Bioinform. 2004
Bioinformatics and computational biology › genomics
transposable elements
0.012004
Alu repeat analysis in the complete human genome: trends and variations with respect to genomic composition · Bioinform. 2004
Bioinformatics and computational biology
protein function prediction
0.012004
CoPS: Comprehensive Peptide Signature Database · Bioinform. 2004
Bioinformatics and computational biology › microbiology
virology
0.012004
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

statistical correlation analysis · 0.0sequence motif analysis · 0.0genedecipher · 0.0
YearPublicationVenuePosition
2009 dbSMR: a novel resource of genome-wide SNPs affecting microRNA mediated regulation
abstract
BACKGROUND: MicroRNAs (miRNAs) regulate several biological processes through post-transcriptional gene silencing. The efficiency of binding of miRNAs to target transcripts depends on the sequence as well as intramolecular structure of the transcript. Single Nucleotide Polymorphisms (SNPs) can contribute to alterations in the structure of regions flanking them, thereby influencing the accessibility for miRNA binding. DESCRIPTION: The entire human genome was analyzed for SNPs in and around predicted miRNA target sites. Polymorphisms within 200 nucleotides that could alter the intramolecular structure at the target site, thereby altering regulation were annotated. Collated information was ported in a MySQL database with a user-friendly interface accessible through the URL: (http://miracle.igib.res.in/dbSMR). CONCLUSION: The database has a user-friendly interface where the information can be queried using either the gene name, microRNA name, polymorphism ID or transcript ID. Combination queries using 'AND' or 'OR' is also possible along with specifying the degree of change of intramolecular bonding with and without the polymorphism. Such a resource would enable researchers address questions like the role of regulatory SNPs in the 3' UTRs and population specific regulatory modulations in the context of microRNA targets.
Manoj Hariharan, Vinod Scaria, Samir K. Brahmachari
BMC Bioinform.3
2005 Comparative analysis of chromatin landscape in regulatory regions of human housekeeping and tissue specific genes
abstract
BACKGROUND: Global regulatory mechanisms involving chromatin assembly and remodelling in the promoter regions of genes is implicated in eukaryotic transcription control especially for genes subjected to spatial and temporal regulation. The potential to utilise global regulatory mechanisms for controlling gene expression might depend upon the architecture of the chromatin in and around the gene. In-silico analysis can yield important insights into this aspect, facilitating comparison of two or more classes of genes comprising of a large number of genes within each group. RESULTS: In the present study, we carried out a comparative analysis of chromatin characteristics in terms of the scaffold/matrix attachment regions, nucleosome formation potential and the occurrence of repetitive sequences, in the upstream regulatory regions of housekeeping and tissue specific genes. Our data show that putative scaffold/matrix attachment regions are more abundant and nucleosome formation potential is higher in the 5' regions of tissue specific genes as compared to the housekeeping genes. CONCLUSION: The differences in the chromatin features between the two groups of genes indicate the involvement of chromatin organisation in the control of gene expression. The presence of global regulatory mechanisms mediated through chromatin organisation can decrease the burden of invoking gene specific regulators for maintenance of the active/silenced state of gene expression. This could partially explain the lower number of genes estimated in the human genome.
Mythily Ganapathi, Pragya Srivastava, Sushanta Kumar Das Sutar, Dipayan Dasgupta, Gajinder Pal Singh, Vani Brahmachari, Samir K. Brahmachari
BMC Bioinform.8
2004 Alu repeat analysis in the complete human genome: trends and variations with respect to genomic composition
abstract
MOTIVATION: Transposon-derived Alu repeats are exclusively associated with primate genomes. They have gained considerable importance in the recent times with evidence of their involvement in various aspects of gene regulation, e.g. alternative splicing, nucleosome positioning, CpG methylation, binding sites for transcription factors and hormone receptors, etc. The objective of this study is to investigate the factors that influence the distribution of Alu repeat elements in the human genome. Such analysis is expected to yield insights into various aspects of gene regulation in primates. RESULTS: Analysis of Alu repeat distribution for the human genome build 32 (released in January 2003) reveals that they occupy nearly one-tenth portion of the sequenced regions. Huge variations in Alu frequencies were seen across the genome with chromosome 19 being the most and chromosome Y being the least Alu dense chromosomes. The highlights of the analysis are as follows: (1). three-fourth of the total genes in the genome are associated with Alus. (2). Alu density is higher in genes as compared with intergenic regions in all the chromosomes except 19 and 22. (3). Alu density in human genome is highly correlated with GC content, gene density and intron density with GC content being major deterministic factor compared with other two. (4). Alu densities were correlated more with gene density than intron density indicating the insertion of Alus in untranslated regions of exons.
Deepak Grover, Mitali Mukerji, Pankaj Bhatnagar, Krishnamoorthy Kannan, Samir K. Brahmachari
Bioinform.5
2004 CoPS: Comprehensive Peptide Signature Database
abstract
UNLABELLED: We present the development of a Comprehensive database of 12 076 invariant Peptide Signatures (CoPS) derived from 52 bacterial genomes with a minimum occurrence in at least seven organisms. These peptides were observed in functionally similar proteins and are distributed over nearly 1250 different functional proteins. The database provides function, structure and occurrence in biochemical pathways of the proteins containing these signature peptides. It houses additional information on the signature peptides, such as identical match in other motif/pattern (e.g. PROSITE, BLOCKS, PRINTS and Pfam) databases and the database of interacting proteins, human proteome and mutation effect on these signature peptides. There is a wide applicability of this database in the identification of critical functional residues in proteins. The database also facilitates the identification of folding nucleus/structural determinants in proteins and functional assignment to yet unknown proteins. We demonstrate functional assignment to 2605 hypothetical proteins in bacterial genomes and 112 unknown proteins in human using this database. AVAILABILITY: The database can be freely accessed through the following URL: http://203.195.151.46/copsv2/index.html or http://203.90.127.70/copsv2/index.html
Tulika Prakash, Mamta Khandelwal, Dipayan Dasgupta, Debasis Dash, Samir K. Brahmachari
Bioinform.5
2004 Recognition and analysis of protein-coding genes in severe acute respiratory syndrome associated coronavirus
abstract
MOTIVATION: 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.5