PubMed Health⌕ Search

Biomedical subjects

Sean Martin

Publications and source records attributed to Sean Martin.

4 recordsLinked to original sources
PubMed2005

The impact of Life Science Identifier on informatics data.

Since the Life Science Identifier (LSID) data identification and access standard made its official debut in late 2004, several organizations have begun to use LSIDs to simplify the methods used to uniquely name, reference and retrieve distributed data objects and concepts. In this review, the authors build on introductory work that describes the LSID standard by documenting how five early adopters have incorporated the standard into their technology infrastructure and by outlining several common misconceptions and difficulties related to LSID use, including the impact of the byte identity requirement for LSID-identified objects and the opacity recommendation for use of the LSID syntax. The review describes several shortcomings of the LSID standard, such as the lack of a specific metadata standard, along with solutions that could be addressed in future revisions of the specification.

Computational Biology↗
PubMed2004

Identification of novel highly expressed genes in pancreatic ductal adenocarcinomas through a bioinformatics analysis of expressed sequence tags.

In most microarray experiments, a significant fraction of the differentially expressed mRNAs identified correspond to expressed sequence tags (ESTs) and are generally discarded from further analyses. We used careful bioinformatics analyses to characterize those ESTs that were found to be highly overexpressed in a series of pancreatic adenocarcinomas. cDNA was prepared from 60 non-neoplastic samples (normal pancreas [n = 20], normal colon [n = 10], or normal duodenal mucosal [n = 30]) and from 64 pancreatic cancers (resected cancers [n = 50] or cancer cell lines [n = 14]) and hybridized to the complete Affymetrix Human Genome U133 GeneChip(R) set (arrays U133A and B) for simultaneous analysis of 45,000 fragments corresponding to 33,000 known genes and 6,000 ESTs. The GeneExpress(R) software system Fold Change Analysis Tool was used and 60 ESTs were identified that were expressed at levels at least 3-fold greater in the pancreatic cancers as compared to normal tissues. Searches against the human genomic sequence and comparative genomic analysis of human and mouse genomes was carried out using basic local alignment search tools (BLAST), BLASTN, and BLASTX, for identifying protein coding genes corresponding to the ESTs. Subsequently, in order to pick the most relevant candidate genes for a more detailed analysis, we looked for domains/motifs in the open reading frames using SMART and Pfam programs. We were able to definitively map 43 of the 60 ESTs to known or novel genes, and 15 of the ESTs could be localized in close proximity to a gene in the human genome although we were unable to establish that the EST was indeed derived from those genes. The differential expression of a subset of genes was confirmed at the protein level by immunohistochemical labeling of tissue microarrays (inhibin beta A [INHBA] and CD29) and/or at the transcript level by RT-PCR (INHBA, AKAP12, ELK3, FOXQ1, EIF5A2, and EFNA5). We conclude that bioinformatics tools can be used to characterize differentially overexpressed ESTs, and that some of these ESTs may represent diagnostically and therapeutically useful targets that might be missed using data solely from currently annotated databases.

Adenocarcinoma↗
PubMed2004

Globally distributed object identification for biological knowledgebases.

The World-Wide Web provides a globally distributed communication framework that is essential for almost all scientific collaboration, including bioinformatics. However, several limits and inadequacies have become apparent, one of which is the inability to programmatically identify locally named objects that may be widely distributed over the network. This shortcoming limits our ability to integrate multiple knowledgebases, each of which gives partial information of a shared domain, as is commonly seen in bioinformatics. The Life Science Identifier (LSID) and LSID Resolution System (LSRS) provide simple and elegant solutions to this problem, based on the extension of existing internet technologies. LSID and LSRS are consistent with next-generation semantic web and semantic grid approaches. This article describes the syntax, operations, infrastructure compatibility considerations, use cases and potential future applications of LSID and LSRS. We see the adoption of these methods as important steps toward simpler, more elegant and more reliable integration of the world's biological knowledgebases, and as facilitating stronger global collaboration in biology.

Animals↗