BIOBASE Presentation: Assigning Functional Significance to Human Genome Variations
When: Monday, March 5, 2012 from 3:00-3:15

>> for more information

>> for more information

Previous searches for candidate retinoblastoma genes failed to identify SYK because it is not mutated in patients. But when Zhang et al  expanded their search to consider changes in epigenetic patterns and levels of gene expression, as described in the January 19, 2012 issue of Nature, they identified SYK as a gene whose expression is consistently unregulated in retinoblastoma cells compared to normal retinal cells. They further went on to demonstrate that retinoblastoma cell growth is dependent on SYK activity, and that inhibiting SYK activity leads to retinoblastoma cell death – findings which make this potential oncogene a promising new target in the treatment of this rare but debilitating disease.

Although knowing the mutational spectrum is unquestionably critical to understanding the molecular basis of inherited diseases, we found this article to be an important reminder to not lose site of the bigger picture which may include effects mediated by changes in gene expression and epigenetic patterns.

Join our FREE webinar where we discuss the advantages that ChIP-seq technologies offer over traditional binding site analysis and demonstrate a typical application of ChIP-seq through the analysis of TAL1 intervals in two haematopoietic lineages using the ExPlain™ analysis system.

Learn about:

• Application of ChIP-seq to the discovery of transcription factor binding sites and composite modules
• Strengths and weaknesses of ChIP-seq compared to traditional binding site analysis methods
• Use of the ExPlain™ analysis system for analysis of ChIP-seq data

Who should attend:

Scientists working in the life science area interested in exploring how transcription factors influence their experimental system.

WHEN:

Tuesday, February 28, 2012
5 PM Central European / 11 AM EST (Eastern Standard – US) / 8 AM PC (Pacific Standard – US)

REGISTER NOW! (Space is limited!)

BIOBASE data analysis system was used to identify key nodes in signaling networks downstream of genes targeted by the collective group of microRNAs.  The key node analysis algorithm in the ExPlain identifies downstream molecules that are connected to a maximal number of input molecules, within a specified distance.  The ExPlain method explores cell signaling networks within a specified range from each input molecule (gene/protein) to find the most proximal molecule having the maximal number of connections to the overall set of input molecules.

>> read more

Next Generation Sequencing of human genomes presents us with a challenge in the millions of variants that are identified for each individual. We need to understand which variants contribute to health outcomes, and we need to prioritize and shortlist important known and novel variants. Such variants can be then validated and serve as starting points for further research or diagnostic recommendations. This podcast will review current strategies and unique resources to characterize variants, and highlight how these can be used within Genome Trax.

You will learn:

• How to interpret data using Genome Trax
• Advantages of Genome Trax
• Genome Trax interpretations of certain variations
• Strategies used to determine mutations

>> Watch Now

It has long been recognized in model systems such as the yeast Saccharomyces cerevisiae and the nematode Caenorhabditis elegans that genetic interactions between closely related genes can mitigate the effect of a mutation in one of the members of the interacting pair, leading to a completely suppressed or partially penetrant phenotype. Recent work by Burga et al, published in the December 8, 2011 issue of Nature, provides the latest example of such a genetic interaction network in C. elegans. Their investigation of the tbx-8 and tbx-9 T-box transcription factor genes provides a clear demonstration of two genes which functionally compensate for each other, such that when the tbx-8 gene is rendered completely non-functional increased expression of the tbx-9 gene naturally compensates for its loss in a significant percentage of affected individuals.

Going a step further, the authors work demonstrates that a general chaperone buffering system, in this case measured by the expression of the daf-21 Hsp90 gene, can additionally mitigate the effects of the tbx-8 mutation. Most interesting is the author’s observation that when the genetic interaction network mechanism and the chaperone buffering mechanism are both active, the effect of the tbx-8 mutation is nearly completely suppressed – providing intriguing clues to the variable consequences of mutation in other species, including human disease.

What’s your favorite example of compensatory genes?

Booth # 428

>> for more information

Identifying and measuring the influence of the loss or gain of ESE or ESS motifs has been challenging, at least in part due to the oftentimes default assumption that mis-sense and nonsense mutations only have an impact on protein function through amino acid substitution or truncation, without considering a potential effect on splicing. Using a sampling of exonic disease-causing mis-sense and nonsense mutations from the Human Gene Mutation Database (HGMD) compared to a sampling of common exonic single nucleotide polymorphisms identified as part of the 1000 Genomes Project, and employing a combination of bioinformatic and biochemical methods, Sterne-Weiler et al have begun to shed light on these questions.

As recently reported in Genome Research, the authors found that approximately 25% of known mis-sense and nonsense disease-causing inherited mutations result in alteration of functional splicing signals, suggesting a wider role for aberrant mRNA processing in human disease than has previously been recognized.

>> read more

Join our FREE webinar where we discuss how the complexities of post-translational modification, recycling/degradation, and alternative splicing patterns of proteins affect signaling networks in ways that can impact the selection of drug targets. Using examples of proteins involved in alzheimers, epilepsy and other diseases, we will demonstrate how the use of biological databases describing inherited mutations (HGMD®) and the molecular mechanisms of signaling events (PROTEOME™) make it easier to prioritize and filter potential drug targets than relying on literature searches alone.

Learn how to:

• Identify known mutations that affect sites of post-translational modification
• Visualize how key post-translational events affect characterized pathways
• Identify proteins which participate in multiple signaling networks

Who should attend:

Researchers who are interested in the identification or characterization of drug targets and who want to make more efficient use of the published literature to identify, prioritize and filter candidates. Genetic counselors and researchers who are interested in the functional consequences/downstream effects of disease causing mutations.

WHEN:

Thursday, February 2, 2012
4 PM Central European / 10 AM EST (Eastern Standard – US) / 7 AM PC (Pacific Standard – US)

REGISTER NOW! (Space is limited!)