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PubMed2003

Bioinformatics.

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Cells↗
PubMed2005

Biomedical informatics methods in pharmacogenomics.

Pharmacogenomics is the study of the genetic basis of individual variation in response to therapeutic agents. Pharmacogenomics may potentially affect on every step of health care and every drug treatment protocol. The optimal approach to pharmacogenomics in hypertension requires the integration of different disciplines, in which biomedical informatics plays an essential role. This chapter describes biomedical informatics methods used in dealing with key issues in pharmacogenomics. These key issues include the association between structure and function, the interaction between gene and drug, and the correlation between genotype and phenotype. Heterogeneous resources, including web sites, databases, and software analysis tools, are selected, organized, and integrated in practical methods to support these studies. Bioinformatics methods described in this chapter include genetic sequence searching, comparison, structural modeling, functional analysis, and systems biology studies, with emphasis on single-nucleotide polymorphism (SNP) analysis. Medical informatics methods such as disease and drug information and clinical terminology are also embraced in this chapter. This combination of both biological and medical informatics provides comprehensive methodologies to resolve complex problems in pharmacogenomics.

Computational Biology↗
PubMed2006

A predictive model for identifying proteins by a single peptide match.

MOTIVATION: Tandem mass-spectrometry of trypsin digests, followed by database searching, is one of the most popular approaches in high-throughput proteomics studies. Peptides are considered identified if they pass certain scoring thresholds. To avoid false positive protein identification, > or = 2 unique peptides identified within a single protein are generally recommended. Still, in a typical high-throughput experiment, hundreds of proteins are identified only by a single peptide. We introduce here a method for distinguishing between true and false identifications among single-hit proteins. The approach is based on randomized database searching and usage of logistic regression models with cross-validation. This approach is implemented to analyze three bacterial samples enabling recovery 68-98% of the correct single-hit proteins with an error rate of < 2%. This results in a 22-65% increase in number of identified proteins. Identifying true single-hit proteins will lead to discovering many crucial regulators, biomarkers and other low abundance proteins. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Algorithms↗
PubMed2025

Analysis of differentially expressed genes in schizophrenia based on bioinformatics and corresponding mRNA expression levels.

OBJECTIVE: This study aimed to use bioinformatics analysis to identify differentially expressed genes (DEGs) involved in the pathogenesis of schizophrenia and validate their mRNA expression levels through real-time quantitative PCR (qPCR). MATERIAL/METHODS: Datasets from the publicly available Gene Expression Omnibus (GEO) database were analyzed using R software to identify DEGs. Functional enrichment analyses, including Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, were conducted. A protein-protein interaction (PPI) network was constructed using Cytoscape software to identify key genes with notable expression changes. The expression levels of these key genes were subsequently validated in schizophrenia patients using qPCR to assess potential susceptibility genes. RESULTS: In total, 813 DEGs were identified, with six key genes highlighted through GO analysis and PPI network screening. Among these, HDAC1, UBA52, and FYN demonstrated statistically significant differences in mRNA expression between schizophrenia patients and healthy controls (P&#xa0;<&#xa0;0.05). CONCLUSIONS: This study identified several DEGs potentially linked to the pathogenesis of schizophrenia, suggesting that HDAC1, UBA52, and FYN could serve as candidate susceptibility genes and diagnostic biomarkers. These findings provide new insights and directions for future schizophrenia research.

Humans↗
PubMed2000

Rice at the forefront of plant genome informatics.

As rice genomicsdata continue to accumulate at a rapid rate, are becoming more valuable to warehouse and access large and rigorous data sets. This article gives an overview of available resources on rice bioinformatics and their role in elucidating and propagating biological and genomic information in rice. Of particular focus here is the informatics infrastructure developed at the Rice Genome Research Program (RGP) following an extensive rice genome analysis. The database named INE (Integrated Rice Genome Explorer) integrates the genetic and physical mapping information with the genome sequence being generated in collaboration with the International Rice Genome Sequencing Project (IRGSP). Database links are initially evaluated using an interoperable query tool to explore and compare data across the rice and maize genome databases and potential application to multiple crop database querying. A proposed logistics for interlinking these resources is presented to integrate, manipulate and analyze information on the rice genome. One of the biggest challenges of rice bioinformatics lies in the emerging role of rice as a model system among grass crop species. In view of the importance of comparative genetics in the formulation of new knowledge on plant genomes and genes, comparative bioinformatics remains an essential strategy to gain new insights on the needs and expectations on rice genomics.

Chromosome Mapping↗
PubMed2003

SIRW: A web server for the Simple Indexing and Retrieval System that combines sequence motif searches with keyword searches.

SIRW (http://sirw.embl.de/) is a World Wide Web interface to the Simple Indexing and Retrieval System (SIR) that is capable of parsing and indexing various flat file databases. In addition it provides a framework for doing sequence analysis (e.g. motif pattern searches) for selected biological sequences through keyword search. SIRW is an ideal tool for the bioinformatics community for searching as well as analyzing biological sequences of interest.

Abstracting and Indexing↗
PubMed2006

SNP-PHAGE--High throughput SNP discovery pipeline.

BACKGROUND: Single nucleotide polymorphisms (SNPs) as defined here are single base sequence changes or short insertion/deletions between or within individuals of a given species. As a result of their abundance and the availability of high throughput analysis technologies SNP markers have begun to replace other traditional markers such as restriction fragment length polymorphisms (RFLPs), amplified fragment length polymorphisms (AFLPs) and simple sequence repeats (SSRs or microsatellite) markers for fine mapping and association studies in several species. For SNP discovery from chromatogram data, several bioinformatics programs have to be combined to generate an analysis pipeline. Results have to be stored in a relational database to facilitate interrogation through queries or to generate data for further analyses such as determination of linkage disequilibrium and identification of common haplotypes. Although these tasks are routinely performed by several groups, an integrated open source SNP discovery pipeline that can be easily adapted by new groups interested in SNP marker development is currently unavailable. RESULTS: We developed SNP-PHAGE (SNP discovery Pipeline with additional features for identification of common haplotypes within a sequence tagged site (Haplotype Analysis) and GenBank (-dbSNP) submissions. This tool was applied for analyzing sequence traces from diverse soybean genotypes to discover over 10,000 SNPs. This package was developed on UNIX/Linux platform, written in Perl and uses a MySQL database. Scripts to generate a user-friendly web interface are also provided with common queries for preliminary data analysis. A machine learning tool developed by this group for increasing the efficiency of SNP discovery is integrated as a part of this package as an optional feature. The SNP-PHAGE package is being made available open source at http://bfgl.anri.barc.usda.gov/ML/snp-phage/. CONCLUSION: SNP-PHAGE provides a bioinformatics solution for high throughput SNP discovery, identification of common haplotypes within an amplicon, and GenBank (dbSNP) submissions. SNP selection and visualization are aided through a user-friendly web interface. This tool is useful for analyzing sequence tagged sites (STSs) of genomic sequences, and this software can serve as a starting point for groups interested in developing SNP markers.

Base Sequence↗
PubMed2005

A functional genomics approach to hematopoietic stem cell regulation.

Elucidation of the molecular mechanisms that are responsible for regulating the most basic properties of stem cells, self-renewal, and differentiation remains a major challenge in hematopoietic stem cell biology. We have taken a functional genomics approach towards revealing these mechanisms. Previous studies of the fetal liver genetic program led to the development of Stem Cell Database (SCDb, http://stemcell.princeton.edu), a resource for the stem cell community. These studies have been expanded to include the microenvironmental component of hematopoiesis and are the focus herein. In our efforts to study the microenvironmental component we have identified a stromal cell line, AFT024, which serves as a surrogate stem cell niche. The line provides a milieu that facilitates the maintenance of transplantable mouse and human stem cells as well as the generation of large populations of committed progenitors. In a manner mirroring the work done with the SCDb, we provide an online resource, Stromal Cell Database, StroCDB (http://stromalcell.princeton.edu), that is a compendium of information and data derived from biological and molecular studies of this surrogate niche. These include bioinformatic analyses of over 6000 clones derived from a subtracted library enriched for messages expressed in AFT024 as well as data derived from custom expression arrays developed from this library. Herein we describe these efforts and provide a guide for navigating the database and mining the information contained within.

Animals↗