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Here is a short evaluation I wrote for f1000. It briefs a recent paper in Science titled “Differential arginylation of actin isoforms is regulated by coding sequence-dependent degradation” by Zhang et al.

Although the discovery that there are multiple codons for each amino acid was made decades ago, still little is known about whether those extra codons are redundant or not. Because the AT content of genomic DNA varies widely from 30-70% on the coding regions across species (see ref {1}, on which Jike Cui is an author), codons fitting the background AT content could probably be favored. While that is a reasonable hypothesis for the different usages of codons coding for the same amino acid, recent reports and reviews shed much brighter light on this topic {2,3}.

Mammalian cytoskeletal proteins beta- and gamma-actin share 98% amino acid identity; however, they have very distinct roles in cells. The reason is that beta-actin is arginylated, whereas gamma-actin is not. The question is that, if the 2% difference in amino acid sequence cannot explain why one undergoes the post-translational modification and the other does not, then what does? Here, Zhang et al. found that identical amino acid segments between the two actins have different codons, which leads to a different speed of translation and that the different speed results in the distinction in arginylation.

This discovery provides a new angle to study the mechanisms of co- and post-translational modification, and it will also have an impact on understanding gene evolution.

References: {1} Cui et al. Proc Natl Acad Sci USA 2009, 106:13421-6 [PMID:19666543]. {2} Weygand-Durasevic and Ibba, Science 2010, 329:1473-4 [PMID:20847254]. {3} Baker M, Nat Methods 2010, 7:874 [PMID:21049579].

A study published in PLoS ONE conducted CNV analysis on Autism patients [van der Zwaag et al., 2009]. While there are other similar analyses, the noteworthy points in this one are:

1. In addition to the control group, the patients are divided into two groups, complex-autism group where patients have both autism and other neurological disorders, and non-complex-autism group.
2. Significant CNVs were inferred between the non-complex and control group.
3. Among genes located in those CNVs, there is an over-representation of those involved in glycosylation.
4. Similar findings are also present in autism related loci published by others.

Their conclusion is that there is an over-representation of genes related to glycobiology in those CNV regions. It is well known that mutations in genes for glycosylation can cause severe congenital disorders. Their research suggests that dosage alterations in these genes could contribute to the phenotypic anomalies, such as autism.

van der Zwaag B, Franke L, Poot M, Hochstenbach R, Spierenburg HA, et al. (2009) Gene-Network Analysis Identifies Susceptibility Genes Related to Glycobiology in Autism. PLoS ONE 4(5): e5324.

We are looking for a motivated, energetic hardworker to help keep our science rolling smoothly. You may read more and apply here.

We just published a new paper on how to use the cloud for high performance computing problems. You may view it here here. Briefly, we created a model to estimate cloud runtime based on the size and complexity of the genomes being compared that determines in advance the optimal order of the jobs to be submitted. Using that, we computed orthologous relationships for 245,323 genome-to-genome comparisons on Amazon’s computing cloud, a computation that required just over 200 hours and cost $8,000 USD, at least 40% less than expected under a strategy in which genome comparisons were submitted to the cloud randomly with respect to runtime. Our cost savings projections were based on a model that not only demonstrates the optimal strategy for deploying RSD to the cloud, but also finds the optimal cluster size to minimize waste and maximize usage. Our cost-reduction model is readily adaptable for other comparative genomics tools and potentially of significant benefit to labs seeking to take advantage of the cloud as an alternative to local computing infrastructure.