Transcriptome Analysis

Transcription

1 Transcriptome Analysis Data Preprocessing Sample Preparation Illumina Sequencing Demultiplexing Raw FastQ Reference Genome (fasta) Reference Annotation (GTF) Reference Genome Analysis Tophat Accepted hits (BAM) Cufflinks Merged Annotation (GTF) Cuffdiff Normalized counts DEGs Cummerbund Data Cleaning Cleaned FastQ Differential Expression HTSeq Raw Counts DeSeq2/EdgeR DEGs FastQC Trinity de novo Assembly (fasta) RSEM De novo transcriptome assembly Pathway analysis GO analysis

2 Gene Expression Gene Expression Steps in analysis Experimental design Samples Controls Replicates, biological and technical RNA extraction & hybridization Data preprocessing Contaminant removal Quality trimming Initial data processing Normalization Replicate merging Comparison of samples and controls -> differentially expressed genes Data interpretation Clustering of genes and/or experimental conditions Finding the meaning of clusters

3 Gene Expression Kinds of experiments Defined treatment defined genotype Laboratory strain Defined mutant uncharacterized genotype Segregating cross Ill-defined treatment (e.g., field grown or collected) known genotype mixed or unknown genotype Mixed samples Whole body Pooled individuals Time Course

4 Gene Expression Analysis Variables Tissues (or even cultured cells) are not uniform Organs and tissues are made of multiple cell types Individual cells differ due to cell cycle, age, and other factors Condition variability controlled laboratory conditions often have important variation Temperature Light Vibration Neighborhood Individual variability genotypically identical individuals show differences due to developmental and environmental differences. Differences may be due to parent or grandparent effects Time variability Diurnal variation Seasonal variation

5 Gene Expression Analysis How to deal with variation Carefully thought out experimental design to minimize Replicates Technical replicates control for variation in your procedure. Very important for microarrays where technical variation is very large Biological replicates controls for biological variation such as growth effects Internal standards Implicit housekeeping genes Explicit spiked in RNA Universal reference (synthetic reference)

6 Gene Expression Controls Meaningful biological control Question 1: Genes that respond differently between the Treatment and the Control. Question 2: Genes that responded similarly across two or more treatments relative to control. Use of universal reference. Question: To discover tumor subtypes T1 T2 T3 T4 T5 T 2 T n-1 T n T 1 T 3 C Ref

7 Gene Expression Experimental Design Replicates Biological replicates independent samples not splits of one sample 3 minimum, but more is better Pooled or not pooled Why pool? Insufficient sample (consider amplification) Excess variability (can t estimate variability if you pool) Avoid pooling if If goal of study is to test for differential expression If goal of study requires individual s information Technical replicates Splits of samples to account for variation in analytical process Used to assess measurement precision Dye swap for two sample (cdna array) experiments (microarray only)

8 Gene Expression Analysis Assumptions Most gene expression experiments assume most genes do not change i.e., they show random variation Only a few genes have significant changes in expression If many or all genes are changing analysis is very difficult Requires internal or external standard, or synthetic reference Synechococcus Sp. Strain PCC7002

9 Gene Expression Analysis RNASeq gene expression by sequencing Prepare desired RNA PolyA+ or capped messenger RNA Remove rrna with RiboZero Small RNA (microrna) Other mitochondrial or ribosomal Fragment Attach adapters Sequence using next-gen sequencing Map reads to reference genome WGS annotated reference De novo transcriptome Number of reads from a gene is proportional to the expression level Gene expression varies over at least 5 orders of magnitude Partially spliced RNA is present Contaminants may be present Library size is a big effect

10 Gene Expression Analysis RNAseq Next Generation sequencing technology 25,000, ,000,000 short reads (~ bases) Mapping to genome BLAST type searches of 10 million queries against human genome can take days (however, Blast itself is too slow) Fast mapping methods based on the Burrows-Wheeler transform are generally used: BBMap, Bowtie, BWA, etc.

11 Transcriptome Analysis Data Preprocessing Sample Preparation Illumina Sequencing Demultiplexing Raw FastQ Reference Genome (fasta) Reference Annotation (GTF) Reference Genome Analysis Tophat Accepted hits (BAM) Cufflinks Merged Annotation (GTF) Cuffdiff Normalized counts DEGs Cummerbund Data Cleaning Cleaned FastQ Differential Expression HTSeq Raw Counts DeSeq2/EdgeR DEGs FastQC Trinity de novo Assembly (fasta) RSEM De novo transcriptome assembly Pathway analysis GO analysis

12 Transcriptome analysis Reference genome based Tuxedo Suite Bowtie Tophat Cufflinks Cuffmerge Cuffdiff

13 Transcriptome analysis Read Mapping/Alignment - Transcripts Transcript mapping is harder reads are discontinuous with respect to genome multiple isoforms

14 Transcriptome analysis Tophat

15 Tophat Aligns RNA-Seq reads to reference genome taking introns into account Map reads to genome with bowtie2 continuously mapped reads -> exons discontinuous reads -> possible junction fragments

16 Transcriptome analysis Cuffmerge combine information from replicate samples reference annotation

17 Transcriptome analysis Cuffdiff How do you count reads vs isoforms?

18 Transcriptome analysis Cuffdiff Different versions of Cuffdiff have given dramatically different results. Many prefer to use other methods

19 Transcriptome Analysis Data Preprocessing Sample Preparation Illumina Sequencing Demultiplexing Raw FastQ Reference Genome (fasta) Reference Annotation (GTF) Reference Genome Analysis Tophat Accepted hits (BAM) Cufflinks Merged Annotation (GTF) Cuffdiff Normalized counts DEGs Cummerbund Data Cleaning Cleaned FastQ Differential Expression HTSeq Raw Counts DeSeq2/EdgeR DEGs FastQC Trinity de novo Assembly (fasta) RSEM De novo transcriptome assembly Pathway analysis GO analysis

20 De Novo Transcriptome Assembly De Bruijn based assemblers Among others Velvet (Oases) ABySS (trans-abyss) ALLPATHS SOAP denovo (SOAPdenovo-trans) Minia Trinity Bridger Fig 3. Flicek & Birney, 2009

21 De Novo Transcriptome Assembly Practical Issues Many methods use a kmer approach, what k should you use? large k give more unique matches large k misses more overlaps due to errors/snps Should you use a metaassembly? What method/program should you use

22 De Novo Transcriptome Assembly Assembly Quality 50 base illumina reads Transcripts mapped to Genome (S. Pombe) Number of reconstructed protein coding genes. Zhao et al., Optimizing de novo transcriptome assembly from shortread RNA-Seq data: a comparative study, BMC bioinformatics 12 (suppl 14): 52, 2011

23 De Novo Transcriptome Assembly Recovery vs expression level

24 De Novo Transcriptome Assembly Effect of program choice

25 De Novo Transcriptome Assembly Effect of program

26 De Novo Transcriptome Assembly Effect of Program Read alignment

27 De Novo Transcriptome Assembly Effect of program - DEGs

28 De Novo Transcriptome Assembly Effect of Program - DEGs