Differential gene expression analysis using RNA-seq
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1 Differential gene expression analysis using RNA-seq Applied Bioinformatics Core, August 2017 Friederike Dündar with Luce Skrabanek & Ceyda Durmaz
2 Day 4 overview (brief) theoretical background for DE analysis DE analysis using DESeq2 exploring the results suggested: DE analysis with edger and/or limma
3 DIFFERENTIAL GENE EXPRESSION
4 Bioinformatics workflow of RNA-seq analysis Descriptive plots FASTQC RSeQC Images.tif Raw reads.fastq Aligned reads.sam/.bam Read count table.txt Normalized read count table.robj Base calling & demultiplexing Bustard/RTA/OLB, CASAVA Mapping STAR Counting HTSeq, featurecounts Normalizing DESeq2, edger List of fold changes & statistical values.robj,.txt Downstream analyses on DE genes
5 Bioinformatics workflow of RNA-seq analysis Descriptive plots FASTQC RSeQC Images.tif Raw reads.fastq Aligned reads.sam/.bam Read count table.txt Normalized read count table.robj List of fold changes & statistical values.robj,.txt Base calling & demultiplexing Bustard/RTA/OLB, CASAVA Mapping STAR Counting HTSeq, featurecounts Normalizing DESeq2, edger DE test & multiple testing correction DESeq2, edger, limma Downstream analyses on DE genes
6 Read count table
7 Garber et al. (2011) Nature Methods, 8(6), doi: /nmeth.1613 DE basics 1. Estimate magnitude of DE taking into account differences in sequencing depth, technical, and biological read count variability. logfc 2. Estimate the significance of the difference accounting for performing thousands of tests. (adjusted) p-value 1 test per gene! H0: no difference in the read distribution between two conditions
8 Modeling read counts (DESeq) K ij NB(µ ij, i ) read counts for gene i and sample j fitted mean gene-specific dispersion parameter (fitted towards the library size factor µ ij = s j q ij average dispersion) expression value estimate log 2 (q ij )=x j. moderated log-fold change for gene i i model matrix column for sample j
9 From read counts to DE average norm. count standard error estimate for the logfc
10 What next? Do your results make sense? Are the results robust? do multiple tools agree on the majority of the genes? are the fold changes strong enough to explain the phenotype you are seeing? have other experiments yielded similar results? Downstream analyses: mostly exploratory How to decide which tool(s) to use? function/content of original publication code maintained? well documented? used by others? efficient?
11 RNACocktail tries to implement all (current!) best performers for various RNA-seq analyses Sahraeian et al. (2017). Nat Comm, 8(1), 59.
12 Where to get help and inspiration? bioconductor.org/help/workflows biostars.org seqanswers.com stackoverflow.com F100Research Software Tool Articles mailing lists/github issues of the individual tools WALK-IN WCM: Thursdays, 1:30 3 pm, LC-504 (1300 York MSKCC: research-advantage/corefacilities/bioinformatics abc.med.cornell.edu supplemental material of publications based on HTS data
13 Everything s connected Sample type & quality Low input? Degraded? Experimental design Controls No. of replicates Randomization Library preparation Poly-A enrichment vs. ribo minus Strand information Biological question Expression quantification Alternative splicing De novo assembly needed mrnas, small RNAs. Sequencing Read length PE vs. SR Sequencing errors Bioinformatics Aligner Annotation Normalization DE analysis strategy
Differential gene expression analysis using RNA-seq
https://abc.med.cornell.edu/ Differential gene expression analysis using RNA-seq Applied Bioinformatics Core, August 2017 Friederike Dündar with Luce Skrabanek & Ceyda Durmaz Day 3 QC of aligned reads
Differential gene expression analysis using RNA-seq
https://abc.med.cornell.edu/ Differential gene expression analysis using RNA-seq Applied Bioinformatics Core, March 2018 Friederike Dündar with Luce Skrabanek & Paul Zumbo Day 1: Introduction into high-throughput
Differential gene expression analysis using RNA-seq
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