Biomedical subjects

M T Hallett

Publications and source records attributed to M T Hallett.

5 recordsLinked to original sources

PubMed2006

Predicting the subcellular localization of viral proteins within a mammalian host cell.

BACKGROUND: The bioinformatic prediction of protein subcellular localization has been extensively studied for prokaryotic and eukaryotic organisms. However, this is not the case for viruses whose proteins are often involved in extensive interactions at various subcellular localizations with host proteins. RESULTS: Here, we investigate the extent of utilization of human cellular localization mechanisms by viral proteins and we demonstrate that appropriate eukaryotic subcellular localization predictors can be used to predict viral protein localization within the host cell. CONCLUSION: Such predictions provide a method to rapidly annotate viral proteomes with subcellular localization information. They are likely to have widespread applications both in the study of the functions of viral proteins in the host cell and in the design of antiviral drugs.

Computational Biology↗

PubMed2005

Murine candidate bleomycin induced pulmonary fibrosis susceptibility genes identified by gene expression and sequence analysis of linkage regions.

BACKGROUND: Pulmonary fibrosis is a complex disease for which the predisposing genetic variants remain unknown. In a prior study, susceptibility to bleomycin induced pulmonary fibrosis was mapped to loci Blmpf1 and Blmpf2 on chromosomes 17 and 11, respectively, in a C57BL/6J (B6, susceptible) and C3Hf/KAM (C3H, resistant) mouse cross. METHODS: Herein, the genetic basis of bleomycin induced pulmonary fibrosis was investigated in an approach combining gene expression and sequencing data with previously mapped linkage intervals. RESULTS: In this study, gene expression analysis with microarrays revealed 1892 genes or ESTs (expressed sequence tags) to be differentially expressed between bleomycin treated B6 and C3H mice and 67 of these genetic elements map to Blmpf1 or Blmpf2. This group included genes involved in an oxidative stress response, in apoptosis, and in immune regulation. A comparison of the B6 and C3H sequence, for Blmpf1 and Blmpf2, made using the NCBI database and available C3H sequence, revealed approximately 35% of the genes in these regions contain non-synonymous coding sequence changes. An assessment of genotype/phenotype correlation among other inbred strains revealed 36% of these B6/C3H sequence variations predict for the known bleomycin induced fibrosis susceptibility of the DBA (susceptible) and A/J (resistant) mouse strains. CONCLUSIONS: Combining genomics approaches of differential gene expression and sequence variation potentially identifies approximately 5% the linked genes as fibrosis susceptibility candidate genes in this mouse cross.

Animals↗

PubMed2000

Darwin v. 2.0: an interpreted computer language for the biosciences.

MOTIVATION: We announce the availability of the second release of Darwin v. 2.0, an interpreted computer language especially tailored to researchers in the biosciences. The system is a general tool applicable to a wide range of problems. RESULTS: This second release improves Darwin version 1.6 in several ways: it now contains (1) a larger set of libraries touching most of the classical problems from computational biology (pairwise alignment, all versus all alignments, tree construction, multiple sequence alignment), (2) an expanded set of general purpose algorithms (search algorithms for discrete problems, matrix decomposition routines, complex/long integer arithmetic operations), (3) an improved language with a cleaner syntax, (4) better on-line help, and (5) a number of fixes to user-reported bugs. AVAILABILITY: Darwin is made available for most operating systems free of char ge from the Computational Biochemistry Research Group (CBRG), reachable at http://chrg.inf.ethz.ch. CONTACT: darwin@inf.ethz.ch

Algorithms↗

PubMed2000

A cross-comparison of a large dataset of genes.

SUMMARY: We make available a large cross-comparison for 16 of the completely sequenced genomes and additional eukaryotic genes. The alignments were performed at the protein level using liberal similarity bounds in order to capture as many significant alignments as possible. This dataset will be updated as new genomes become available.

Animals↗

PubMed1995

Parameterized complexity analysis in computational biology.

Many computational problems in biology involve parameters for which a small range of values cover important applications. We argue that for many problems in this setting, parameterized computational complexity rather than NP-completeness is the appropriate tool for studying apparent intractability. At issue in the theory of parameterized complexity is whether a problem can be solved in time O(n alpha) for each fixed parameter value, where alpha is a constant independent of the parameter. In addition to surveying this complexity framework, we describe a new result for the Longest Common Subsequence problem. In particular, we show that the problem is hard for W[t] for all t when parameterized by the number of strings and the size of the alphabet. Lower bounds on the complexity of this basic combinatorial problem imply lower bounds on more general sequence alignment and consensus discovery problems. We also describe a number of open problems pertaining to the parameterized complexity of problems in computational biology where small parameter values are important.

Algorithms↗