2016 Journal Clubs

01 Heterogeneity: From bulk tissue to single cell

Hosted by Yuchao Jiang (University of North Carolina at Chapel Hill)
Heterogeneity in various features (e.g., genotype, gene expression and phenotype) prevails not only between individuals within a population but also between cells within each individual, and even between cells of the same cell type from the same tissue. We will discuss recently published methods to study heterogeneity from two different perspectives -- bulk-tissue sequencing and single-cell DNA/RNA sequencing. Emphasis will be placed on assessing intratumor heterogeneity in cancer genomics.

Potential papers to discuss (other suggestions are welcome):
1. Wang, Yong, et al. "Clonal evolution in breast cancer revealed by single nucleus genome sequencing." Nature 512.7513 (2014): 155-160.
2. Patel, Anoop P., et al. "Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma." Science 344.6190 (2014): 1396-1401.
3. Deshwar, Amit G., et al. "PhyloWGS: reconstructing subclonal composition and evolution from whole-genome sequencing of tumors." Genome biology16.1 (2015): 1.

02 Understanding topic models (e.g. STRUCTURE, ADMIXTURE) as a matrix factorization problem

Hosted by Hussein Al-Asadi (University of Chicago)
Sample papers:
1. Arora, Sanjeev, Rong Ge, and Ankur Moitra. "Learning topic models--going beyond SVD." In Foundations of Computer Science (FOCS), 2012 IEEE 53rd Annual Symposium on, pp. 1-10. IEEE, 2012. https://arxiv.org/abs/1204.1956
2. Engelhardt, Barbara E., and Matthew Stephens. "Analysis of population structure: a unifying framework and novel methods based on sparse factor analysis." PLoS Genet 6, no. 9 (2010): e1001117. http://journals.plos.org/plosgenetics/article?id=10.1371/journal.pgen.1001117

03 PCA in genetics: Theory and practice

Hosted by Elior Rahmani, Regev Schweiger, and Liat Shenhav (Tel Aviv University)
Principal component analysis (PCA) and related methods are commonly used in genetic analyses to account for population structure and other confounders. We will review the theoretical foundations for its use in genetics, survey some relevant extensions and study present large-scale applications. Participants are enthusiastically encouraged to suggest relevant papers, and several volunteers will be asked to present a relevant paper.

Sample papers:
1. Patterson, Nick, Alkes L. Price, and David Reich. "Population structure and eigenanalysis." PLoS genet 2, no. 12 (2006): e190.
http://journals.plos.org/plosgenetics/article?id=10.1371/journal.pgen.0020190

2. McVean, Gil. "A genealogical interpretation of principal components analysis." PLoS Genet 5, no. 10 (2009): e1000686.
http://journals.plos.org/plosgenetics/article?id=10.1371/journal.pgen.1000686

3. Johnstone, Iain M., and Arthur Yu Lu. "On consistency and sparsity for principal components analysis in high dimensions." Journal of the American Statistical Association (2012).
http://amstat.tandfonline.com/doi/abs/10.1198/jasa.2009.0121

4. Popescu, Andrei-Alin, Andrea L. Harper, Martin Trick, Ian Bancroft, and Katharina T. Huber. "A novel and fast approach for population structure inference using kernel-PCA and optimization." Genetics 198, no. 4 (2014): 1421-1431.
http://www.genetics.org/content/198/4/1421

5. Galinsky, Kevin J., Gaurav Bhatia, Po-Ru Loh, Stoyan Georgiev, Sayan Mukherjee, Nick J. Patterson, and Alkes L. Price. "Fast principal-component analysis reveals convergent evolution of ADH1B in Europe and East Asia." The American Journal of Human Genetics 98, no. 3 (2016): 456-472.
http://www.sciencedirect.com/science/article/pii/S0002929716000033

04 Transcriptome-wide association studies

Hosted by Nicholas Mancuso and Huwenbo Shi (University of California, Los Angeles)
This journal club will focus on methodologies to find associations between transcript levels and complex disease in large cohorts. This gene-based approach is collectively known as transcriptome-wide association study (TWAS).

Sample papers:
1. Gamazon, Eric R., Heather E. Wheeler, Kaanan P. Shah, Sahar V. Mozaffari, Keston Aquino-Michaels, Robert J. Carroll, Anne E. Eyler et al. "A gene-based association method for mapping traits using reference transcriptome data." Nature genetics 47, no. 9 (2015): 1091-1098.
http://www.nature.com/ng/journal/v47/n9/full/ng.3367.html

2. Gusev, Alexander, Arthur Ko, Huwenbo Shi, Gaurav Bhatia, Wonil Chung, Brenda WJH Penninx, Rick Jansen et al. "Integrative approaches for large-scale transcriptome-wide association studies." Nature genetics (2016).
http://www.nature.com/ng/journal/v48/n3/full/ng.3506.html

3. Zhu, Zhihong, Futao Zhang, Han Hu, Andrew Bakshi, Matthew R. Robinson, Joseph E. Powell, Grant W. Montgomery et al. "Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets." Nature genetics (2016).
http://www.nature.com/ng/journal/v48/n5/full/ng.3538.html

05 Antibody repertoire evolution

Hosted by Siavash Mirarab (University of California, San Diego)
Antibodies evolve high affinity to foreign proteins, creating a unique repertoire for each individual. Understanding the evolutionary dynamics of this evolutionary process has seen much interest in recent past. In this session, we read some recent papers that focus on evolutionary analyses of antibody repertoire. DISCLAIMER: the organizer is no expert in this field, and will be learning with everyone else.

Suggested papers:
1. Mccoy, C. O. et al. Quantifying evolutionary constraints on B-cell affinity maturation. Philos. Trans. R. Soc. Lond. B. Biol. Sci. 370, 20140244 (2015).
http://rstb.royalsocietypublishing.org/content/370/1676/20140244.abstract

2. Safonova, Y. et al. IgRepertoireConstructor: a novel algorithm for antibody repertoire construction and immunoproteogenomics analysis. Bioinformatics 31, i53–i61 (2015).
http://bioinformatics.oxfordjournals.org/content/31/12/i53.short

3. Briney, B., Le, K., Zhu, J. & Burton, D. R. Clonify: unseeded antibody lineage assignment from next-generation sequencing data. Sci. Rep. 6, 23901 (2016).
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4840318/

06 Leveraging functional data to understand biological mechanism

Hosted by Farhad Hormozdiari (University of California, Los Angeles), Regev Schweiger, and Elior Rahmani (Tel Aviv University)
Although GWAS era was a tremendous success to detect disease risk loci, our understanding about the biological mechanism of these loci is still very limited. Recently, with vast amount of functional data that is collected one can use these functional information to increase our knowledge about the these risk loci. The goal of this journal club is to obtain greater understanding of these functional information and how to utilize them.

Proposed papers:
-- Ernst et al. 2011 Nature
-- ENCODE Consortium 2012 Nature
-- Maurano et al. 2012 Science
-- Trynka et al. 2013 Nat Genet
-- McVicker et al. 2013 Science
-- Shlyueva et al. 2014 Nat Rev Genet
-- Pickrell 2014 AJHG

07 Microbiome and immune system

Hosted by Serghei Mangul (University of California, Los Angeles)
Nowadays sequencing allows to study both microbiome and the immune system at great resolution. Thus providing a tool to characterize the host-microbe immune interactions contributing to autoimmunity and allergies.

We will be discussing how to study the interactions of microbiome and immune system and how both are effected by genetics. Also we will be discussing the methods devoted to study the immune system and microbiome.

Suggested papers :
-Variation in Microbiome LPS Immunogenicity Contributes to Autoimmunity in Humans, http://www.nature.com/nmeth/journal/v13/n4/full/nmeth.3800.html
-Variation in the Human Immune System Is Largely Driven by Non-Heritable Influences, http://www.cell.com/cell/abstract/S0092-8674(14)01590-6

Methods to study immune system:
- Landscape of tumor-infiltrating T cell repertoire of human cancers, http://www.nature.com/ng/journal/v48/n7/full/ng.3581.html
-T cell fate and clonality inference from single-cell transcriptomes, http://www.nature.com/nmeth/journal/v13/n4/full/nmeth.3800.html

Methods to study microbiome :
- A cloud-compatible bioinformatics pipeline for ultrarapid pathogen identification from next-generation sequencing of clinical samples, http://genome.cshlp.org/content/early/2014/05/16/gr.171934.113