1. Experimental validation

2. Integration of transcriptome and genome sequencing uncovers functional variation in human populations Tuuli Lappalainen et al. Geuvadis Analysis Group Meeting II July 11, 2012, Barcelona

3. 1. How to do RNAseq in a distributed setting? What are the most important covariates in RNAseq? What kind of factors affect replication? What causes lab effects? -> companion paper? 1. How do different features of the transcriptome vary in populations? mRNA levels, different types of splicing, N-TARs, conjoined genes, … 2. How does genome variation affect transcriptome variation? catalogue of tQTLs functional mechanisms of regulatory variants frequency spectra of variants, genome-wide view to regulatory variation loss-of-function -> companion paper 3. Data availability and visualization Integrating everything into a good story! “…and then we looked at X just because we could…”

4. Basics Material and methods it will be a lot easier if we keep on writing these to the wiki during the analysis Basic descriptive stats of the data and things that we measure

5. How to do RNAseq Main paper: data is good. Detailed descriptions in the companion Rank correlation to measure sample similarity Micha´s script, PCA Technical covariates of quantifications What are the most important covariates in RNAseq? How do different QC measures correlate with each other, and what are the diagnostic measures of different problems? What kind of factors affect replication rate of samples and genes? What causes lab effects? Companion paper: coordinated by Peter, contributions from Olof, Jonas, Tuuli, Micha, Marc

6. How does the transcriptome vary? Descriptions of different types of transcriptome characteristics – what ever stats/descriptions make most sense and are most interesting for each type quantitative and qualitative mRNA variation, differential expression Splicing events N-TARs fusion genes RNA editing miRNA levels Novel miRNAs How does miRNA expression regulate mRNA levels? Focus on variation and on the gain of sequencing populations Annotation bias: Do we see that rare and non-European features are underrepresented in the existing annotations?

7. How does genetic variation link to transcriptome variation? Transcriptome QTLs common variants (also repeat genotypes) integrating different features of transcriptome variation in the same model miRNA variants > mRNA Rare regulatory variants ASE approach Transcriptome effects of loss-of-function variation validating predicted LOF variants compensatory mechanisms better predictions of NMD and splicing unannotated LOF effects – novel splice variants

8. How do tQTLs affect gene expression? Are we finding causal regulatory variants? Functional annotations of tQTLs Different types of genetic variants: SNPs, indels, SVs

9. Genome-wide landscape of regulatory variation Partitioning individual variation in allelic expression to rare and common variants How much of transcriptome variation can we explain by the tQTLs that we discover? Do genes with different conservation / ontology have different amount of (genetic) transcriptome variation?

10. Data availability and visualization Accessibility will bring citations Data files that we’ll make available: bams, uantification of genes, transcrfipts, exons, junctions, introns. ASE results? tQTLs? miRNA fastqs and quantifications Browser how to display data from 464 samples? 5-number summary tracks and data available for all individual values? RNAseq coverage quantitative track Quantifications of exons, junctions… ? tQTL p-values quantitative track ASE allelic ratios as a quantitative track proportion of individuals with significant bias