11 recount workflow: Accessing over 70,000 human RNA-seq samples with Bioconductor Leonardo Collado-Torres @fellgernon sites.google.com/view/lalresearchgroup/our-weekly-webinar speakerdeck.com/lcolladotor/recount-webinar April 17, 2018

Reference genome Reads f1000research.com/articles/6-1558/v1

f1000research.com/articles/6-1558/v1

GTEx TCGA slide adapted from Shannon Ellis

SRA

http://rail.bio/ Slide adapted from Ben Langmead

http://blogs.citrix.com/2012/10/17/announcing-general-availability-of-sharefile-with-storagezones/

https://jhubiostatistics.shinyapps.io/recount/

jx 1 jx 2 jx 3 jx 4 jx 5 jx 6 Coverage Reads Gene Isoform 1 Isoform 2 Potential isoform 3 exon 1 exon 2 exon 3 exon 4 Expressed region 1: potential exon 5 f1000research.com/articles/6-1558/v1

f1000research.com/articles/6-1558/v1

exon 1 exon 2 exon 3 f1000research.com/articles/6-1558/v1

disjoint exon 1 disjoint exon 2 disjoint exon 3 f1000research.com/articles/6-1558/v1

f1000research.com/articles/6-1558/v1

f1000research.com/articles/6-1558/v1

> library('recount') > download_study( 'ERP001942', type='rse-gene') > load(file.path('ERP001942 ', 'rse_gene.Rdata')) > rse <- scale_counts(rse_gene) github.com/leekgroup/recount-analyses/

>library('recount') > download_study('SRP029880', type='rse-gene') > download_study('SRP059039', type='rse-gene') > load(file.path('SRP029880 ', 'rse_gene.Rdata')) > load(file.path('SRP059039', 'rse_gene.Rdata')) > mdat <- do.call(cbind, dat) github.com/leekgroup/recount-analyses/

Collado Torres et al. Nat. Biotech 2017

slide adapted from Kai Kammers Can combine with genotype data to identify eQTLs

biorxiv.org/content/early/2018/01/12/247346

http://www.rna-seqblog.com/ Can we use expression data to predict tissue? slide adapted from Shannon Ellis

Number of Regions 589 589 589 589 Number of Samples (N) 4,769 4,769 7,193 8,951 97.3% 96.5% 71.9% 50.6% Tissue prediction is accurate across data sets doi.org/10.1093/nar/gky102

Number of Regions 589 589 589 589 589 Number of Samples (N) 4,769 4,769 613 6,579 8,951 97.3% 96.5% 91.0% 70.2% Prediction is more accurate in healthy tissue 50.6% doi.org/10.1093/nar/gky102

> library('recount') > download_study( 'ERP001942', type='rse-gene') > load(file.path('ERP001942 ', 'rse_gene.Rdata')) > rse <- scale_counts(rse_gene) > rse_with_pred <- add_predictions(rse_gene) github.com/leekgroup/recount-analyses/

Ashkaun Razmara, in prep.

expression data for ~70,000 human samples samples phenotypes ? GTEx N=9,962 TCGA N=11,284 SRA N=49,848 samples expression estimates gene exon junctions ERs Answer meaningful questions about human biology and expression sex tissue M Blood F Heart F Liver slide adapted from Shannon Ellis

Code Example: research.libd.org/recount-brain/example_PMI/example_PMI.html research.libd.org/recount-brain/example_PMI/example_PMI.Rmd Replicates part of the GTEx PMI paper by Ferreira et al. doi.org/10.1038/s41467-017-02772-x Ashkaun Razmara, in prep.

slide adapted from Jeff Leek

The recount2 team Hopkins Kai Kammers Shannon Ellis Margaret Taub Kasper Hansen Jeff Leek Ben Langmead OHSU Abhinav Nellore LIBD Leonardo Collado-Torres Andrew Jaffe recount-brain Ashkaun Razmara Funding and hosting NIH R01 GM105705 NIH 1R21MH109956 CONACyT 351535 AWS in Education Seven Bridges IDIES SciServer

expression data for ~70,000 human samples (Multiple) Postdoc positions available to - develop methods to process and analyze data from recount2 - use recount2 to address specific biological questions This project involves the Hansen, Leek, Langmead and Battle labs at JHU Contact: Kasper D. Hansen ([email protected] | www.hansenlab.org) Code for making a 5 min video on recount2: github.com/lcolladotor/biopeerprize2018