Biomedical subjects

William Kenworthy

Publications and source records attributed to William Kenworthy.

3 recordsLinked to original sources

PubMed2005

Gene expression profiling of Japanese psoriatic skin reveals an increased activity in molecular stress and immune response signals.

Gene expression profiling was performed on biopsies of affected and unaffected psoriatic skin and normal skin from seven Japanese patients to obtain insights into the pathways that control this disease. HUG95A Affymetrix DNA chips that contained oligonucleotide arrays of approximately 12,000 well-characterized human genes were used in the study. The statistical analysis of the Affymetrix data, based on the ranking of the Student t-test statistic, revealed a complex regulation of molecular stress and immune gene responses. The majority of the 266 induced genes in affected and unaffected psoriatic skin were involved with interferon mediation, immunity, cell adhesion, cytoskeleton restructuring, protein trafficking and degradation, RNA regulation and degradation, signalling transduction, apoptosis and atypical epidermal cellular proliferation and differentiation. The disturbances in the normal protein degradation equilibrium of skin were reflected by the significant increase in the gene expression of various protease inhibitors and proteinases, including the induced components of the ATP/ubiquitin-dependent non-lysosomal proteolytic pathway that is involved with peptide processing and presentation to T cells. Some of the up-regulated genes, such as TGM1, IVL, FABP5, CSTA and SPRR, are well-known psoriatic markers involved in atypical epidermal cellular organization and differentiation. In the comparison between the affected and unaffected psoriatic skin, the transcription factor JUNB was found at the top of the statistical rankings for the up-regulated genes in affected skin, suggesting that it has an important but as yet undefined role in psoriasis. Our gene expression data and analysis suggest that psoriasis is a chronic interferon- and T-cell-mediated immune disease of the skin where the imbalance in epidermal cellular structure, growth and differentiation arises from the molecular antiviral stress signals initiating inappropriate immune responses.

Adult↗

PubMed2003

FBSA: feature-based sequence alignment technique for very large sequences.

The ability to align pairs of very large molecular sequences is essential for a range of comparative genomic studies. However, given the complexity of genomic sequences, it has been difficult to devise a systematic method that can align - even within the same species - pairs of large sequences. Most existing approaches typically attempt to align nucleotide sequences while ignoring valuable features contained within them, eg they filter out low-complexity regions and retroelements before aligning the sequences. However, features are then added post-alignment for visualisation and analysis purposes. We argue that repetitive elements and other features (such as genes, exons and regulatory elements) should be part of the alignment process. A hierarchical approach that aligns the biologically relevant features before aligning the detailed nucleotide sequences has a number of interesting characteristics: (1) features define 'alignment anchor points' that can guide meaningful nucleotide alignment; (2) features can be weighted; (3) a hierarchical approach would identify only meaningful regions to be aligned; (4) nucleotide sequences can be described as sequences of features and non-features, providing a natural mechanism to divide the sequences for processing; and (5) computational speed is significantly faster than other approaches. In this paper, we describe and discuss a feature-based approach to aligning large genome sequences. We refer to this as 'feature-based sequence alignment'.

Algorithms↗

PubMedDate not supplied

Microarray analysis using bioinformatics analysis audit trails (BAATs).

Bioinformatics analysis plays an integrative role in genomics and functional genomics. The ability to conduct quality managed, hypothesis-driven bioinformatics analysis with the plethora of data available is mandatory. Biological interpretation of this data is dependent on versions of databases, programs and the parameters used. Thus, tracking and auditing the analyses process is important. This paper outlines what we term Bioinformatics Analysis Audit Trails (BAATs) and describes YABI, a bioinformatics environment that implements BAATs. YABI can incorporate most bioinformatics tools within the same environment, making it a valuable resource.

Computational Biology↗