Biomedical subjects

Srinivasan Ramachandran

Publications and source records attributed to Srinivasan Ramachandran.

4 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

A novel complexity measure for comparative analysis of protein sequences from complete genomes.

Analysis of sequence complexities of proteins is an important step in the characterization and classification of new genomes. A new measure has been proposed to compute sequence complexity in protein sequences based on linguistic complexity. The algorithm requires a single parameter, is computationally simple and provides a framework for comparative genomic analysis. Protein sequences were classified into groups of high or low complexity based on a quantitative measure termed F(c), which is proportional to the fraction of low complexity sequence present in the protein. The algorithm was tested on sequences of 196 non-homologous proteins whose crystal structures are available at </=2.0 A resolution. Protein sequences of high complexity had 'globular' structures (95% agreement), whereas those of low complexity had non-globular structures (80% agreement). Application of this measure to proteins of unknown structure/function from different genomes revealed that the sequences of high complexity constitute the majority in all genomes (about 90% in Archaea, about 93% in Eubacteria, 89% in Saccharomyces cerevisiae and 90% in Caenorhabditis elegans). Aeropyrum pernix among Archaeae and Deinococcus radiodurans among Eubacteria have the lowest fraction of high complexity proteins (75% and 80% respectively). Further, it was observed that a few bacterial pathogens (Mycobacterium tuberculosis, Pseudomonas aeruginosa) have high fraction of low complexity proteins. The program ScanCom is available from the authors as a PERL script (UNIX system).

Algorithms↗

PubMed2002

Comparative genomics using data mining tools.

We have analysed the genomes of representatives of three kingdoms of life, namely, archaea, eubacteria and eukaryota using data mining tools based on compositional analyses of the protein sequences. The representatives chosen in this analysis were Methanococcus jannaschii, Haemophilus influenzae and Saccharomyces cerevisiae. We have identified the common and different features between the three genomes in the protein evolution patterns. M. jannaschii has been seen to have a greater number of proteins with more charged amino acids whereas S. cerevisiae has been observed to have a greater number of hydrophilic proteins. Despite the differences in intrinsic compositional characteristics between the proteins from the different genomes we have also identified certain common characteristics. We have carried out exploratory Principal Component Analysis of the multivariate data on the proteins of each organism in an effort to classify the proteins into clusters. Interestingly, we found that most of the proteins in each organism cluster closely together, but there are a few 'outliers'. We focus on the outliers for the functional investigations, which may aid in revealing any unique features of the biology of the respective organisms

Archaeal Proteins↗

PubMedDate not supplied

Characterization of a recombinant immunodiagnostic antigen (NIE) from Strongyloides stercoralis L3-stage larvae.

Due to the process of internal autoinfection, even chronic asymptomatic infections with Strongyloides stercoralis have the potential to become severe disseminated disease with fatal outcome. Intermittent and scanty larval excretion makes parasitologic diagnosis difficult. Serodiagnosis is helpful, but antigen preparation from infective larvae requires access to patients or immunosuppressed experimental animals. For these reasons, attention has turned to recombinant antigens for immunodiagnosis. A 31-kDa candidate antigen (NIE) derived from an L3 cDNA library is described in this report. Multiple alignment of the deduced amino acid sequence of NIE showed approximately 12-18% identity with various other organisms, including 17.9% of Asp1 of Ancylostoma caninum, 12.6% of Hemonchus contortus, and 17.6% of insect venom allergen 5 of yellow jacket. By ELISA, antibodies to the purified recombinant NIE antigen were demonstrated in 87.5% of 48 sera from strongyloides-infected patients and in only 6.5% of sera from presumed normal controls. Immunoreactivity of purified NIE antigen with parasite-specific IgE from sera of strongyloides-infected patients indicated its potential use as an immediate sensitivity skin test antigen. This application of the NIE antigen was supported by its capacity to trigger release of histamine upon in vitro exposure to blood from strongyloides-infected patients and its failure to produce histamine release from blood of normal controls.

Amino Acid Sequence↗