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This Week in Genome Biology: Sep 22, 2010

In Genome Biology this week, an international research team reports a "genome-wide analysis of mRNA decay patterns during early Drosophila development." Using a timed collection of fly embryos and unfertilized eggs, the team performed microarray analysis to determine the mRNA degradation patterns throughout early development. "Our work studies the kinetics of mRNA decay, the contributions of maternally- and zygotically-encoded factors to mRNA degradation, and the ways in which mRNA decay profiles relate to gene function, mRNA localization patterns, translation rates and protein turnover," the authors write, adding their suggestion that "several proteins and miRNAs [are] developmental regulators of mRNA decay."

Another paper appearing online in Genome Biology this week details "a statistical method for the characterization of genomic aberrations in single nucleotide polymorphism microarray data acquired from cancer genomes." The University of Oxford's Christopher Yau and his colleagues have developed an approach that utilizes a Bayesian framework to "model the joint effect of polyploidy, normal DNA contamination, and intra-tumor heterogeneity," they write.

In a paper published online in advance, a University College Dublin-led team reports the first Irish human genome. In their analysis of the Irish genome, the researchers identified "variants that may be specific to this population," though they suggest that "future re-sequencing studies and ... the imputation of Irish haplotypes using data from the current Human Genome Diversity Cell Line Panel" are avenues for further research. Check out Daily Scan's round-up of reactions to this paper, here.

In a methods paper published in Genome Biology online in advance, investigators at the Memorial Sloan-Kettering Cancer Center and Columbia University describe MirSVR, "a new machine learning method for ranking microRNA target sites by a down-regulation score" that is capable of predicting functional non-conserved and non-canonical sites. The algorithm behind MirSVR, the team writes, "trains a regression model on sequence and contextual features extracted from miRanda-predicted target sites." The team shows that miRanda-mirSVR is an effective tool for identifying target genes and "predicting the extent of their downregulation at the mRNA or protein levels."