Biomedical subjects

Daniel Platt

Publications and source records attributed to Daniel Platt.

5 recordsLinked to original sources

PubMed2006

Short blocks from the noncoding parts of the human genome have instances within nearly all known genes and relate to biological processes.

Using an unsupervised pattern-discovery method, we processed the human intergenic and intronic regions and catalogued all variable-length patterns with identically conserved copies and multiplicities above what is expected by chance. Among the millions of discovered patterns, we found a subset of 127,998 patterns, termed pyknons, which have additional nonoverlapping instances in the untranslated and protein-coding regions of 30,675 transcripts from 20,059 human genes. The pyknons arrange combinatorially in the untranslated and coding regions of numerous human genes where they form mosaics. Consecutive instances of pyknons in these regions show a strong bias in their relative placement, favoring distances of approximately 22 nucleotides. We also found pyknons to be enriched in a statistically significant manner in genes involved in specific processes, e.g., cell communication, transcription, regulation of transcription, signaling, transport, etc. For approximately 1/3 of the pyknons, the intergenic/intronic instances of their reverse complement lie within 380,084 nonoverlapping regions, typically 60-80 nucleotides long, which are predicted to form double-stranded, energetically stable, hairpin-shaped RNA secondary structures; additionally, the pyknons subsume approximately 40% of the known microRNA sequences, thus suggesting a possible link with posttranscriptional gene silencing and RNA interference. Cross-genome comparisons reveal that many of the pyknons have instances in the 3' UTRs of genes from other vertebrates and invertebrates where they are overrepresented in similar biological processes, as in the human genome. These unexpected findings suggest potential unique functional connections between the coding and noncoding parts of the human genome.

3' Untranslated Regions↗

PubMed2005

Data mining and clinical data repositories: Insights from a 667,000 patient data set.

Clinical repositories containing large amounts of biological, clinical, and administrative data are increasingly becoming available as health care systems integrate patient information for research and utilization objectives. To investigate the potential value of searching these databases for novel insights, we applied a new data mining approach, HealthMiner, to a large cohort of 667,000 inpatient and outpatient digital records from an academic medical system. HealthMiner approaches knowledge discovery using three unsupervised methods: CliniMiner, Predictive Analysis, and Pattern Discovery. The initial results from this study suggest that these approaches have the potential to expand research capabilities through identification of potentially novel clinical disease associations.

Clinical Chemistry Tests↗

PubMed2003

The web server of IBM's Bioinformatics and Pattern Discovery group.

We herein present and discuss the services and content which are available on the web server of IBM's Bioinformatics and Pattern Discovery group. The server is operational around the clock and provides access to a variety of methods that have been published by the group's members and collaborators. The available tools correspond to applications ranging from the discovery of patterns in streams of events and the computation of multiple sequence alignments, to the discovery of genes in nucleic acid sequences and the interactive annotation of amino acid sequences. Additionally, annotations for more than 70 archaeal, bacterial, eukaryotic and viral genomes are available on-line and can be searched interactively. The tools and code bundles can be accessed beginning at http://cbcsrv.watson.ibm.com/Tspd.html whereas the genomics annotations are available at http://cbcsrv.watson.ibm.com/Annotations/.

Computational Biology↗

PubMed2003

In silico pattern-based analysis of the human cytomegalovirus genome.

More than 200 open reading frames (ORFs) from the human cytomegalovirus genome have been reported as potentially coding for proteins. We have used two pattern-based in silico approaches to analyze this set of putative viral genes. With the help of an objective annotation method that is based on the Bio-Dictionary, a comprehensive collection of amino acid patterns that describes the currently known natural sequence space of proteins, we have reannotated all of the previously reported putative genes of the human cytomegalovirus. Also, with the help of MUSCA, a pattern-based multiple sequence alignment algorithm, we have reexamined the original human cytomegalovirus gene family definitions. Our analysis of the genome shows that many of the coded proteins comprise amino acid combinations that are unique to either the human cytomegalovirus or the larger group of herpesviruses. We have confirmed that a surprisingly large portion of the analyzed ORFs encode membrane proteins, and we have discovered a significant number of previously uncharacterized proteins that are predicted to be G-protein-coupled receptor homologues. The analysis also indicates that many of the encoded proteins undergo posttranslational modifications such as hydroxylation, phosphorylation, and glycosylation. ORFs encoding proteins with similar functional behavior appear in neighboring regions of the human cytomegalovirus genome. All of the results of the present study can be found and interactively explored online (http://cbcsrv.watson.ibm.com/virus/).

Algorithms↗

PubMed2002

Dictionary-driven protein annotation.

Computational methods seeking to automatically determine the properties (functional, structural, physicochemical, etc.) of a protein directly from the sequence have long been the focus of numerous research groups. With the advent of advanced sequencing methods and systems, the number of amino acid sequences that are being deposited in the public databases has been increasing steadily. This has in turn generated a renewed demand for automated approaches that can annotate individual sequences and complete genomes quickly, exhaustively and objectively. In this paper, we present one such approach that is centered around and exploits the Bio-Dictionary, a collection of amino acid patterns that completely covers the natural sequence space and can capture functional and structural signals that have been reused during evolution, within and across protein families. Our annotation approach also makes use of a weighted, position-specific scoring scheme that is unaffected by the over-representation of well-conserved proteins and protein fragments in the databases used. For a given query sequence, the method permits one to determine, in a single pass, the following: local and global similarities between the query and any protein already present in a public database; the likeness of the query to all available archaeal/ bacterial/eukaryotic/viral sequences in the database as a function of amino acid position within the query; the character of secondary structure of the query as a function of amino acid position within the query; the cytoplasmic, transmembrane or extracellular behavior of the query; the nature and position of binding domains, active sites, post-translationally modified sites, signal peptides, etc. In terms of performance, the proposed method is exhaustive, objective and allows for the rapid annotation of individual sequences and full genomes. Annotation examples are presented and discussed in Results, including individual queries and complete genomes that were released publicly after we built the Bio-Dictionary that is used in our experiments. Finally, we have computed the annotations of more than 70 complete genomes and made them available on the World Wide Web at http://cbcsrv.watson.ibm.com/Annotations/.

Algorithms↗