Work led by the University of Toronto's Corey Nislow and Andrew Smith used deep sequencing to perform "genome-scale fitness profiling to evaluate yeast strain collections in parallel," they report in this week's early online edition of Genome Research. The method, called Barcode analysis by Sequencing, or Bar-seq, did better than the current benchmark barcode microarray assay when it came to both dynamic range and throughput, they found. Applying their method to re-sequence the entire collection of deleted genes in yeast, they found about 20 percent of the barcodes and common primers to be different from what was expected. "Together, this new assay and analysis routine provide a deep-sequencing-based toolkit for identifying gene-environment interactions on a genome-wide scale."
Researchers at McGill University and Genome Quebec Innovation Centre have partnered up to create a probabilistic approach for SNP discovery. Using a machine learning method to resequence heterozygous sites that produces a "heterozygosity score for each chromosomal position," they were able to call sites as heterozygous or homozygous with 98.5 percent accuracy in a genotype set from the HapMap. Comparing probabilistic heterozygote detection, or ProbHD, to high-coverage sequencing data from the 1,000 Genomes Project, they found an overall agreement for genotype calls of 99.9 percent and close to 90 percent agreement for heterozygote calls.
A group of scientists led in part by Eric Green and other members of the NIH, NHGRI, NISC, and NHLBI has begun the ClinSeq Project, which is a pilot effort to see how whole genome sequencing would fare as a tool for clinical research. In the first phase of ClinSeq, they will enroll about 1,000 participants and initially will sequence 300 to 400 genes thought to be implicated in atherosclerosis. This paper, they say in the abstract, presents "the general considerations in designing ClinSeq, preliminary results based on the generation of an initial 826 Mb of sequence data, the findings for several genes that serve as positive controls for the project, and our views about the potential implications of ClinSeq."
Because evidence-based gene builders require "transcriptional evidence" in the form of proteins, cDNAs, or ESTs, existing programs work less and less effectively on plants, where the rate of genome sequencing is increasingly much greater than that of cDNAs and ESTs. Work from Lincoln Stein's lab led by Chengzhi Liang has developed an evidence-based gene build system, called the Gramene pipeline, that can use transcriptional evidence across related plant species. Using the already annotated genomes of A. thaliana and O. sativa, they show that the cross-species ESTs can be used for gene predictions.