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This Week in Genome Biology: Mar 18, 2009

Two abstracts published in the past two weeks point to new softwares. One, a short-read aligner called Bowtie, comes from Steven Salzberg's lab at the University of Maryland. Being ultrafast, Bowties uses Burrows-Wheeler indexing and can align more than 25 million human genome reads per CPU hour with a memory footprint of approximately 1.3 gigabytes. It's open source and available here. Another software, TFCat, is a catalog of mouse and human transcription factors based on a core collection of annotations obtained by expert review of the scientific literature. Development was led by McGill's Robert Sladek. The collection includes proven and homology-based candidate TFs, says the abstract, can be found online here.

In an article from two weeks ago, scientists performed identical kinome RNAi screens in six different Drosophila cell lines to pinpoint regulators of cell morphology. They identified generic and cell-type specific regulators, including mnb/DYRK1A, in the regulation of protrusion morphology in CNS-derived cell lines. "This analysis reveals the importance of using different cell types to gain a thorough understanding of gene function across the genome and, in the case of kinases, the difficulties of using the differential gene expression to predict function," they say in the abstract.

A review talks about how protein isoforms with PTMs are common in red blood cell stage of the malaria parasite, while another paper studies origin recognition complex (ORC) proteins. The proteins were first discovered as a six-subunit assembly in budding yeast that promotes the initiation of DNA replication. Using RNAi knockdown, they found that the ORC proteins play a role in mitosis and cytokinesis in metazoan cells. They also found that these proteins interact with heterochromatin factors like Sir1 in budding yeast and HP1 in higher eukaryotes to induce epigenetic gene silencing.

In a methods paper, researchers present a new machine learning-based method for the identification of metabolic pathways related to specific phenotypes in multiple microbial genomes. In applying their method to 266 genomes, they were able to make testable hypotheses such as the link between the potential of microorganisms to cause periodontal disease and their ability to degrade histidine.