Biomedical subjects

Michael A Siani-Rose

Publications and source records attributed to Michael A Siani-Rose.

4 recordsLinked to original sources

PubMed2003

GPCR-GRAPA-LIB--a refined library of hidden Markov Models for annotating GPCRs.

GPCR-GRAPA-LIB is a library of HMMs describing G protein coupled receptor families. These families are initially defined by class of receptor ligand, with divergent families divided into subfamilies using phylogenic analysis and knowledge of GPCR function. Protein sequences are applied to the models with the GRAPA curve-based selection criteria. RefSeq sequences for Homo sapiens, Drosophila melanogaster, and Caenorhabditis elegans have been annotated using this approach.

Algorithms↗

PubMed2003

NetAffx: Affymetrix probesets and annotations.

NetAffx (http://www.affymetrix.com) details and annotates probesets on Affymetrix GeneChip microarrays. These annotations include (i) static information specific to the probeset composition; (ii) sequence annotations extracted from public databases; and (iii) protein sequence-level annotations derived from public domain programs, as well as libraries of hidden Markov models (HMMs) developed at Affymetrix. For each probeset, NetAffx lists the probe sequences, and the consensus sequence interrogated by the probes; for the larger chip sets, interactive maps display this sequence data in genomic context. Sequence annotations include Gene Ontology (GO) terms and depiction of GO graph relationships; predicted protein domains and motifs; orthologous sequences; links to relevant pathways; and links to public databases including UniGene, LocusLink, SWISS-PROT and OMIM.

Animals↗

PubMed2003

Gene structure-based splice variant deconvolution using a microarray platform.

MOTIVATION: Alternative splicing allows a single gene to generate multiple mRNAs, which can be translated into functionally and structurally diverse proteins. One gene can have multiple variants coexisting at different concentrations. Estimating the relative abundance of each variant is important for the study of underlying biological function. Microarrays are standard tools that measure gene expression. But most design and analysis has not accounted for splice variants. Thus splice variant-specific chip designs and analysis algorithms are needed for accurate gene expression profiling. RESULTS: Inspired by Li and Wong (2001), we developed a gene structure-based algorithm to determine the relative abundance of known splice variants. Probe intensities are modeled across multiple experiments using gene structures as constraints. Model parameters are obtained through a maximum likelihood estimation (MLE) process/framework. The algorithm produces the relative concentration of each variant, as well as an affinity term associated with each probe. Validation of the algorithm is performed by a set of controlled spike experiments as well as endogenous tissue samples using a human splice variant array.

Algorithms↗

PubMed2002

Structure-based comparison of four eukaryotic genomes.

The field of comparative genomics allows us to elucidate the molecular mechanisms necessary for the machinery of an organism by contrasting its genome against those of other organisms. In this paper, we contrast the genome of homo sapiens against C. Elegans, Drosophila melanogaster, and S. cerevisiae to gain insights on what structural domains are present in each organism. Previous work has assessed this using sequence-based homology recognition systems such as Pfam [1] and Interpro [2]. Here, we pursue a structure-based assessment, analyzing genomes according to domains in the SCOP structural domain dictionary. Compared to other eukaryotic genomes, we observe additional domains in the human genome relating to signal transduction, immune response, transport, and certain enzymes. Compared to the metazoan genomes, the yeast genome shows an absence of domains relating to immune response, cell-cell interactions, and cell signaling.

Animals↗