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

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Reference genome Reads

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GTE TCGA slide adapted from Shannon Elli

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SRA

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Slide adapted from Ben Langmead

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

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

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

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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

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exon 1 exon 2 exon 3

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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/

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slide adapted from Jeff Leek

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>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/

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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

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Collado-Torres et al, NAR, 2017

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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

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Jaffe et al, Nat. Neuroscience, 2015

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BrainSpan data Jaffe et al, Nat. Neuroscience, 2015

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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

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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

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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

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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

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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

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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

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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

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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

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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

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

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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

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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

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

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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

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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

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> 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/

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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

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

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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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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

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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.

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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

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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)