In-depth query of large genomes using tiling arrays

The HLA genomic loci map: expression, interaction, diversity and disease

The human leukocyte antigen (HLA) super-locus is a genomic region in the chromosomal position 6p21 that encodes the six classical transplantation HLA genes and at least 132 protein coding genes that have important roles in the regulation of the immune system as well as some other fundamental molecular and cellular processes. This small segment of the human genome has been associated with more than 100 different diseases, including common diseases, such as diabetes, rheumatoid arthritis, psoriasis, asthma and various other autoimmune disorders. The first complete and continuous HLA 3.6 Mb genomic sequence was reported in 1999 with the annotation of 224 gene loci, including coding and non-coding genes that were reviewed extensively in 2004. In this review, we present (1) an updated list of all the HLA gene symbols, gene names, expression status, Online Mendelian Inheritance in Man (OMIM) numbers, including new genes, and latest changes to gene names and symbols, (2) a regional analysis of the extended class I, class I, class III, class II and extended class II subregions, (3) a summary of the interspersed repeats (retrotransposons and transposons), (4) examples of the sequence diversity between different HLA haplotypes, (5) intra- and extra-HLA gene interactions and (6) some of the HLA gene expression profiles and HLA genes associated with autoimmune and infectious diseases. Overall, the degrees and types of HLA super-locus coordinated gene expression profiles and gene variations have yet to be fully elucidated, integrated and defined for the processes involved with normal cellular and tissue physiology, inflammatory and immune responses, and autoimmune and infectious diseases.

NASA Radiation Biomarker Workshop, September 27-28, 2007

A summary is provided of presentations and discussions at the NASA Radiation Biomarker Workshop held September 27-28, 2007 at NASA Ames Research Center in Mountain View, CA. Invited speakers were distinguished scientists representing key sectors of the radiation research community. Speakers addressed recent developments in the biomarker and biotechnology fields that may provide new opportunities for health-related assessment of radiation-exposed individuals, including those exposed during long-duration space travel. Topics discussed included the space radiation environment, biomarkers of radiation sensitivity and individual susceptibility, molecular signatures of low-dose responses, multivariate analysis of gene expression, biomarkers in biodefense, biomarkers in radiation oncology, biomarkers and triage after large-scale radiological incidents, integrated and multiple biomarker approaches, advances in whole-genome tiling arrays, advances in mass spectrometry proteomics, radiation biodosimetry for estimation of cancer risk in a rat skin model, and confounding factors. A summary of conclusions is provided at the end of the report.

A machine-learning approach to combined evidence validation of genome assemblies

MOTIVATION

While it is common to refer to 'the genome sequence' as if it were a single, complete and contiguous DNA string, it is in fact an assembly of millions of small, partially overlapping DNA fragments. Sophisticated computer algorithms (assemblers and scaffolders) merge these DNA fragments into contigs, and place these contigs into sequence scaffolds using the paired-end sequences derived from large-insert DNA libraries. Each step in this automated process is susceptible to producing errors; hence, the resulting draft assembly represents (in practice) only a likely assembly that requires further validation. Knowing which parts of the draft assembly are likely free of errors is critical if researchers are to draw reliable conclusions from the assembled sequence data.

RESULTS

We develop a machine-learning method to detect assembly errors in sequence assemblies. Several in silico measures for assembly validation have been proposed by various researchers. Using three benchmarking Drosophila draft genomes, we evaluate these techniques along with some new measures that we propose, including the good-minus-bad coverage (GMB), the good-to-bad-ratio (RGB), the average Z-score (AZ) and the average absolute Z-score (ASZ). Our results show that the GMB measure performs better than the others in both its sensitivity and its specificity for assembly error detection. Nevertheless, no single method performs sufficiently well to reliably detect genomic regions requiring attention for further experimental verification. To utilize the advantages of all these measures, we develop a novel machine learning approach that combines these individual measures to achieve a higher prediction accuracy (i.e. greater than 90%). Our combined evidence approach avoids the difficult and often ad hoc selection of many parameters the individual measures require, and significantly improves the overall precisions on the benchmarking data sets.