June 18th, Susan Holmes: Using the data, all the data

Susan Holmes

Susan Holmes

UNFORTUNATELY THIS TALK IS CANCELLED

Trained in the French school of Data Analysis in Montpellier, Susan Holmes has been working in non parametric multivariate statistics applied to Biology since 1985. She has taught at MIT, Harvard and was an Associate Professor of Biometry at Cornell before moving to Stanford in 1998. She teaches the Thinking Matters class: Breaking Codes and Finding Patterns, and likes working on big messy data sets, mostly from the areas of Immunology, Cancer Biology and Microbial Ecology. Her theoretical interests include applied probability, Graph Limit Theory and the topology of the space of Phylogenetic Trees.

Talk: Using the data, all the data

Study of microbiome census data together with covarying tables such as Mass Spectroscopy and Metagenomic data poses statistical and computational challenges linked to heterogeneity of the data structures and data sources. We will give some examples of the problem of irreproducibility in the analysis of such data.

This contains joint work with Joey McMurdie and David Relman and his team.

Seminar details

Wednesday June 18th, 2014
1:15 PM Seminar
Location: Clark Auditorium

June 11th, Maude David: Cross-referencing analysis of autism to identify novel genes and pathways

Maude David

Maude David

Maude joined the Wall lab in January 2014 and namely studies the gut microbiome of children with ASD (Autism Spectra Disorder) utilizing a large scale, crowd sourced clinical trial approach. Her expertise are in microbiology, bioinformatics and biochemistry, using and integrating metagenomics, metatranscriptomics and metaproteomics to understand microbial community functions. She received her PhD in December 2009 from the Ecole Centrale de Lyon, University of Lyon, France, with Prof. T.M. Vogel, on the origin of the dehalogenases and bioremediation of chlorinated solvent. Her grad-school work focused on the bacterial adaptation to chlorinated compounds at the genome (evolution mechanisms) and community (bioremediation) level. After graduation, she became a post-doctoral fellow at Lawrence Berkeley National Laboratory with Prof. Janet Jansson. Her work looked at the impact of climate change on soil microbial ecology and specifically at how altered precipitation affect carbon cycle using meta-“omics” analysis of microbial carbon cycling responses.

Talk:  Cross-referencing analysis of autism to identify novel genes and pathways

Autism Spectrum Disorder (ASD) afflicts one out of 88 people. While the causes of ASD are only partially understood, the disease exhibits an important genetic component, with high heritability and familial clustering.

In order to identify potential candidate genes underlying ASD, we performed two analyses in parallel.

First, we identified rare and de-novo gene variants that appear only in a population with ASD. We took advantage of whole-genome sequencing (WGS), as a tool for identifying ASD risk genes as well as unreported mutations in known loci, and applied it to the genomes of 32 trios with ASD sequenced in a previous study (Jiang et al., 2013). To do so, we developed and used a novel software: COSMOS, which allowed us to rapidly annotate and analyze this dataset (Gafni et al., submitted). We identified rare, previously overlooked ASD-related gene variants by comparing our annotated dataset with SNPs reported in the 1000 Genomes Project. (1000 Genomes Project Consortium et al., 2012).

In parallel with this study, we used the NeuroSynth database to extract brain loci relevant for a set of psychologically relevant terms. We then extracted gene expression values from GEO database. We performed a Gene Set Enrichment Analysis (GSEA) (Subramanian et al., 2005) and found several genes associated with autism.

Cross-referencing these two methods allowed us to identify several pathways common to both analyses. We analyzed the potential impact of these candidate genes on the physiology of the patient by mining the KEGG database and identifying the affected pathways. Our study identified novel genes such as multiple genes involved in oocyte meiosis and thyroid hormone synthesis, as well as targets previously implicated in ASD like RNA transport or MAPK family of enzymes. This integrative approach gives us novel insights into the genetic variant most likely to be involved in ASD.

References

1000 Genomes Project Consortium, Abecasis, G. R., Auton, A., Brooks, L. D., DePristo, M. A., Durbin, R. M., et al. (2012). An integrated map of genetic variation from 1,092 human genomes. Nature, 491(7422), 56–65. doi:10.1038/nature11632

Jiang, Y.-H., Yuen, R. K. C., Jin, X., Wang, M., Chen, N., Wu, X., et al. (2013). Detection of clinically relevant genetic variants in autism spectrum disorder by whole-genome sequencing. American Journal of Human Genetics, 93(2), 249–263. doi:10.1016/j.ajhg.2013.06.012

Subramanian, A., Tamayo, P., Mootha, V. K., Mukherjee, S., Ebert, B. L., Gillette, M. A., et al. (2005). Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proceedings of the National Academy of Sciences of the United States of America, 102(43), 15545–15550. doi:10.1073/pnas.0506580102

Seminar details

Wednesday June 11th, 2014
1:15 PM Seminar
Location: Clark Auditorium