11 Reproducible RNA-seq analysis with Leonardo Collado-Torres @fellgernon #LCG2018 and

Reference genome Reads

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GTE TCGA slide adapted from Shannon Elli

SRA

Slide adapted from Ben Langmead

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

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exon 1 exon 2 exon 3

disjoint exon 1 disjoint exon 2 disjoint exon 3

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5 10 15 0 1 2 3 4 5 Genome Coverage 3 3 5 4 4 2 2 3 1 3 3 1 4 4 2 1 AUC = area under coverage = 45

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

slide adapted from Jeff Leek

>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) https://github.com/leekgroup/recount-analyses/

Collado Torres et al. Nat. Biotech 2017

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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

Collado-Torres et al, NAR, 2017

Fetal Infant Child Teen Adult 50+ 6 / group, N = 36 Discovery data Jaffe et al, Nat. Neuroscience, 2015 Postmortem Human Brain Samples Fetal Infant Child Teen Adult 50+ 6 / group, N = 36 Replication data

Jaffe et al, Nat. Neuroscience, 2015

BrainSpan data Jaffe et al, Nat. Neuroscience, 2015

expression data for ~70,000 human samples GTEx N=9,962 TCGA N=11,284 SRA N=49,848 samples expression estimates gene exon junctions ERs slide adapted from Shannon Ellis

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

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 slide adapted from Shannon Ellis

Category Frequency F 95 female 2036 Female 51 M 77 male 1240 Male 141 Total 3640 Even when information is provided, it’s not always clear… sra_meta$S ex “1 Male, 2 Female”, “2 Male, 1 Female”, “3 Female”, “DK”, “male and female” “Male (note: ….)”, “missing”, “mixed”, “mixture”, “N/A”, “Not available”, “not applicable”, “not collected”, “not determined”, “pooled male and female”, “U”, “unknown”, “Unknown” slide adapted from Shannon Ellis

SRA phenotype information is far from complete SubjectID Sex Tissue Race Age 662 0 NA female liver NA NA 662 1 NA female liver NA NA 662 2 NA female liver NA NA 662 3 NA female liver NA NA 662 4 NA female liver NA NA 662 5 NA male liver NA NA 662 6 NA male liver NA NA 662 7 NA male liver NA NA 662 8 NA male liver NA NA z z z z slide adapted from Shannon Ellis

Goal : to accurately predict critical phenotype information for all samples in recount gene, exon, exon-exon junction and expressed region RNA-Seq data SRA Sequence Read Archive N=49,848 TCGA The Cancer Genome Atlas N=11,284 GTEx Genotype Tissue Expression Project N=9,662 slide adapted from Shannon Ellis

Goal : to accurately predict critical phenotype information for all samples in recount gene, exon, exon-exon junction and expressed region RNA-Seq data SRA Sequence Read Archive N=49,848 GTEx Genotype Tissue Expression Project N=9,662 divide samples build and optimize phenotype predictor training set test accurac y of predicto r test set TCGA The Cancer Genome Atlas N=11,284 slide adapted from Shannon Ellis

Goal : to accurately predict critical phenotype information for all samples in recount gene, exon, exon-exon junction and expressed region RNA-Seq data SRA Sequence Read Archive N=49,848 GTEx Genotype Tissue Expression Project N=9,662 divide samples build and optimize phenotype predictor training set test accurac y of predicto r predict phenotypes across samples in TCGA test set TCGA The Cancer Genome Atlas N=11,284 slide adapted from Shannon Ellis

Goal : to accurately predict critical phenotype information for all samples in recount gene, exon, exon-exon junction and expressed region RNA-Seq data SRA Sequence Read Archive N=49,848 GTEx Genotype Tissue Expression Project N=9,662 divide samples build and optimize phenotype predictor training set predict phenotypes across SRA samples test accurac y of predicto r predict phenotypes across samples in TCGA test set TCGA The Cancer Genome Atlas N=11,284 slide adapted from Shannon Ellis

select_regions() Output: Coverage matrix (data.frame) Region information slide adapted from Shannon Ellis

Sex prediction is accurate across data sets Number of Regions 20 20 20 20 Number of Samples (N) 4,769 4,769 11,245 3,640 99.8 % 99.6 % 99.4 % 88.5 % slide adapted from Shannon Ellis

Sex prediction is accurate across data sets Number of Regions 20 20 20 20 Number of Samples (N) 4,769 4,769 11,245 3,640 99.8 % 99.6 % 99.4 % 88.5 % slide adapted from Shannon Ellis

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 slide adapted from Shannon Ellis

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 % slide adapted from Shannon Ellis

> 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) https://github.com/leekgroup/recount-analyses/

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

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

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

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

Sex Female Male Age/Development Fetus Child Adolescent Adult Race/Ethnicity Asian Black Hispanic White Tissue Site 1 Cerebral cortex Hippocampus Brainstem Cerebellum Tissue Site 2 Frontal lobe Temporal lobe Midbrain Basal ganglia Tissue Site 3 Dorsolateral prefrontal cortex Superior temporal gyrus Substantia nigra Caudate Hemisphere Left Right Brodmann Area 1-52 Disease Status Disease Neurological control Disease Brain tumor Alzheimer’s disease Parkinson’s disease Bipolar disorder Tumor Type Glioblastoma Astrocytoma Oligodendroglioma Ependymoma Clinical Stage 1 Grade I Grade II Grade III Grade IV Clinical Stage 2 Primary Secondary Recurrent Viability Postmortem Biopsy Preparation Frozen Thawed

Ashkaun Razmara, in prep.

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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.

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)