Biomedical subjects

Raja Mazumder

Publications and source records attributed to Raja Mazumder.

7 recordsLinked to original sources

PubMed2025

The Data Distillery: A Graph Framework for Semantic Integration and Querying of Biomedical Data.

The Data Distillery Knowledge Graph (DDKG) is a framework for semantic integration and querying of biomedical data across domains. Built for the NIH Common Fund Data Ecosystem, it supports translational research by linking clinical and experimental datasets in a unified graph model. Clinical standards such as ICD-10, SNOMED, and DrugBank are integrated through UMLS, while genomics and basic science data are structured using ontologies and standards such as HPO, GENCODE, Ensembl, STRING, and ClinVar. The DDKG uses a property graph architecture based on the UBKG infrastructure and supports ontology-based ingestion, identifier normalization, and graph-native querying. The system is modular and can be extended with new datasets or schema modules. We demonstrate its utility for informatics queries across eight use cases, including regulatory variant analysis, tissue-specific expression, biomarker discovery, and cross-species variant prioritization. The DDKG is accessible via a public interface, a programmatic API, and downloadable builds for local use.

Journal Article↗

PubMed2003

The COG database: an updated version includes eukaryotes.

BACKGROUND: The availability of multiple, essentially complete genome sequences of prokaryotes and eukaryotes spurred both the demand and the opportunity for the construction of an evolutionary classification of genes from these genomes. Such a classification system based on orthologous relationships between genes appears to be a natural framework for comparative genomics and should facilitate both functional annotation of genomes and large-scale evolutionary studies. RESULTS: We describe here a major update of the previously developed system for delineation of Clusters of Orthologous Groups of proteins (COGs) from the sequenced genomes of prokaryotes and unicellular eukaryotes and the construction of clusters of predicted orthologs for 7 eukaryotic genomes, which we named KOGs after eukaryotic orthologous groups. The COG collection currently consists of 138,458 proteins, which form 4873 COGs and comprise 75% of the 185,505 (predicted) proteins encoded in 66 genomes of unicellular organisms. The eukaryotic orthologous groups (KOGs) include proteins from 7 eukaryotic genomes: three animals (the nematode Caenorhabditis elegans, the fruit fly Drosophila melanogaster and Homo sapiens), one plant, Arabidopsis thaliana, two fungi (Saccharomyces cerevisiae and Schizosaccharomyces pombe), and the intracellular microsporidian parasite Encephalitozoon cuniculi. The current KOG set consists of 4852 clusters of orthologs, which include 59,838 proteins, or approximately 54% of the analyzed eukaryotic 110,655 gene products. Compared to the coverage of the prokaryotic genomes with COGs, a considerably smaller fraction of eukaryotic genes could be included into the KOGs; addition of new eukaryotic genomes is expected to result in substantial increase in the coverage of eukaryotic genomes with KOGs. Examination of the phyletic patterns of KOGs reveals a conserved core represented in all analyzed species and consisting of approximately 20% of the KOG set. This conserved portion of the KOG set is much greater than the ubiquitous portion of the COG set (approximately 1% of the COGs). In part, this difference is probably due to the small number of included eukaryotic genomes, but it could also reflect the relative compactness of eukaryotes as a clade and the greater evolutionary stability of eukaryotic genomes. CONCLUSION: The updated collection of orthologous protein sets for prokaryotes and eukaryotes is expected to be a useful platform for functional annotation of newly sequenced genomes, including those of complex eukaryotes, and genome-wide evolutionary studies.

Animals↗

PubMed2003

MMDB: Entrez's 3D-structure database.

Three-dimensional structures are now known within most protein families and it is likely, when searching a sequence database, that one will identify a homolog of known structure. The goal of Entrez's 3D-structure database is to make structure information and the functional annotation it can provide easily accessible to molecular biologists. To this end, Entrez's search engine provides several powerful features: (i) links between databases, for example between a protein's sequence and structure; (ii) pre-computed sequence and structure neighbors; and (iii) structure and sequence/structure alignment visualization. Here, we focus on a new feature of Entrez's Molecular Modeling Database (MMDB): Graphical summaries of the biological annotation available for each 3D structure, based on the results of automated comparative analysis. MMDB is available at: http://www.ncbi.nlm.nih.gov/Entrez/structure.html.

Animals↗

PubMed2003

CDD: a curated Entrez database of conserved domain alignments.

The Conserved Domain Database (CDD) is now indexed as a separate database within the Entrez system and linked to other Entrez databases such as MEDLINE(R). This allows users to search for domain types by name, for example, or to view the domain architecture of any protein in Entrez's sequence database. CDD can be accessed on the WorldWideWeb at http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=cdd. Users may also employ the CD-Search service to identify conserved domains in new sequences, at http://www.ncbi.nlm.nih.gov/Structure/cdd/wrpsb.cgi. CD-Search results, and pre-computed links from Entrez's protein database, are calculated using the RPS-BLAST algorithm and Position Specific Score Matrices (PSSMs) derived from CDD alignments. CD-Searches are also run by default for protein-protein queries submitted to BLAST(R) at http://www.ncbi.nlm.nih.gov/BLAST. CDD mirrors the publicly available domain alignment collections SMART and PFAM, and now also contains alignment models curated at NCBI. Structure information is used to identify the core substructure likely to be present in all family members, and to produce sequence alignments consistent with structure conservation. This alignment model allows NCBI curators to annotate 'columns' corresponding to functional sites conserved among family members.

Amino Acid Sequence↗

PubMed2002

Detection of novel members, structure-function analysis and evolutionary classification of the 2H phosphoesterase superfamily.

2',3' Cyclic nucleotide phosphodiesterases are enzymes that catalyze at least two distinct steps in the splicing of tRNA introns in eukaryotes. Recently, the biochemistry and structure of these enzymes, from yeast and the plant Arabidopsis thaliana, have been extensively studied. They were found to share a common active site, characterized by two conserved histidines, with the bacterial tRNA-ligating enzyme LigT and the vertebrate myelin-associated 2',3' phosphodiesterases. Using sensitive sequence profile analysis methods, we show that these enzymes define a large superfamily of predicted phosphoesterases with two conserved histidines (hence 2H phosphoesterase superfamily). We identify several new families of 2H phosphoesterases and present a complete evolutionary classification of this superfamily. We also carry out a structure- function analysis of these proteins and present evidence for diverse interactions for different families, within this superfamily, with RNA substrates and protein partners. In particular, we show that eukaryotes contain two ancient families of these proteins that might be involved in RNA processing, transcriptional co-activation and post-transcriptional gene silencing. Another eukaryotic family restricted to vertebrates and insects is combined with UBA and SH3 domains suggesting a role in signal transduction. We detect these phosphoesterase modules in polyproteins of certain retroviruses, rotaviruses and coronaviruses, where they could function in capping and processing of viral RNAs. Furthermore, we present evidence for multiple families of 2H phosphoesterases in bacteria, which might be involved in the processing of small molecules with the 2',3' cyclic phosphoester linkages. The evolutionary analysis suggests that the 2H domain emerged through a duplication of a simple structural unit containing a single catalytic histidine prior to the last common ancestor of all life forms. Initially, this domain appears to have been involved in RNA processing and it appears to have been recruited to perform various other functions in later stages of evolution.

2',3'-Cyclic-Nucleotide Phosphodiesterases↗

PubMed2002

CoreGenes: a computational tool for identifying and cataloging "core" genes in a set of small genomes.

BACKGROUND: Improvements in DNA sequencing technology and methodology have led to the rapid expansion of databases comprising DNA sequence, gene and genome data. Lower operational costs and heightened interest resulting from initial intriguing novel discoveries from genomics are also contributing to the accumulation of these data sets. A major challenge is to analyze and to mine data from these databases, especially whole genomes. There is a need for computational tools that look globally at genomes for data mining. RESULTS: CoreGenes is a global JAVA-based interactive data mining tool that identifies and catalogs a "core" set of genes from two to five small whole genomes simultaneously. CoreGenes performs hierarchical and iterative BLASTP analyses using one genome as a reference and another as a query. Subsequent query genomes are compared against each newly generated "consensus." These iterations lead to a matrix comprising related genes from this set of genomes, e. g., viruses, mitochondria and chloroplasts. Currently the software is limited to small genomes on the order of 330 kilobases or less. CONCLUSION: A computational tool CoreGenes has been developed to analyze small whole genomes globally. BLAST score-related and putatively essential "core" gene data are displayed as a table with links to GenBank for further data on the genes of interest. This web resource is available at http://pumpkins.ib3.gmu.edu:8080/CoreGenes or http://www.bif.atcc.org/CoreGenes.

Algorithms↗

PubMed2002

Trends in protein evolution inferred from sequence and structure analysis.

Complementary developments in comparative genomics, protein structure determination and in-depth comparison of protein sequences and structures have provided a better understanding of the prevailing trends in the emergence and diversification of protein domains. The investigation of deep relationships among different classes of proteins involved in key cellular functions, such as nucleic acid polymerases and other nucleotide-dependent enzymes, indicates that a substantial set of diverse protein domains evolved within the primordial, ribozyme-dominated RNA world.

Evolution, Molecular↗