RNA-Seq / ChIP-Seq Analysis Workflow

RNA-Seq / ChIP-Seq Data Analysis Workshop10 September 2012CSC, Helsinki

Nicolas Delhomme

Outline

• Introducing ourselves

• Introduction to the topic• The technology• Technologies• Field of application• Issues

• Analyses Workflows• RNA-Seq• ChIP-Seq

2

Ângela Gonçalves, Bioinformatician

• Computer scientist by training

• Master thesis in artificial intelligence at the University of Coimbra, Portugal

• Trainee at the European Space Agency’s outstation in Italy

• PhD at the European Bioinformatics Institute (EMBL-Cambridge)

• analysing RNA-seq for the study of gene expression regulation in mice

•• author of the ArrayExpressHTS package in Bioconductor - a pipeline

for analysing RNA-seq data

3

Myself

• Nicolas Delhomme: Bio-informatician/Bio-statistician• Geneticist by training

• Master Thesis in Bioinformatics (2001), 6 months internship at LION Bioscience - a biotech company, Heidelberg, Germany

• Worked as a software engineer at LION Bioscience, HD until 2004

• PhD Thesis at the DKFZ (German Cancer Research Center), HD, until 2009 on integrative and comparative analysis of micro-array data

4

• Technical Officer at the EMBL (European Molecular Biology Laboratory), HD, until 2012• establishing a pipeline for Next-Generation Sequencing data (from

the sequencer to the analysis (Galaxy))• developing tools for Galaxy and Bioconductor (package

easyRNASeq, manuscript in press)• performing analyses on RNA-Seq, ChIP-Seq and DNA-Seq data• assembling de-novo genome (yeast) and transcriptome (fruitfly)

• Post-doc at the UPSC (Umeå Plant Science Center), Umeå, Sweden• Working on RNA-Seq and assembly of one of the largest genome

(Spruce, 20GB)

5

Technologies

• Platforms• Roche 454• Illumina Genome Analyzer• ABI SOLiD

• 3rd Generation:• PacBio, Ion Torrent, Complete Genomics, Oxford Nanopore, ...

• Most data currently comes from Illumina Genome Analyzer or HiSeq machines• most tools are developed for that platform

6

Important Issues

• New technology

• “expensive”• no or too few replicates

• experimental design and protocols• unknown/not understood biases• sample preparation artifacts

• data properties not fully understood• Poisson distribution• Negative Binomial distribution

• pre-processing in its infancy• alignment biases

7

Typical NGS experiments

• ChIP-seq• Transcription Factor Binding Site, very localized• Histone modifications: very variable

• RNA-seq• Annotation based• Alternative splicing• de-novo Transcriptome• Absolute versus Differential Expression

• Sequencing• Re-sequencing• de-novo sequencing• Metagenomics

8

A typical workflow

• Quality Assessment

• Read alignment

• Pre-processing

• Getting annotation of interest

• Determining the count table / peaks

• Exporting/Visualizing the results

9

Quality Assessment (day 1: Nicolas and Ângela)

• number of reads• percentage ATGC• quality per cycle• quality variation per cycle• base per cycle• tile performance• tile quality• alignment quality

10

Sequence alignment (day 1: Olli-Pekka and Kui)

• Two main approaches:

• based on hash table• spaced seeds

• based on suffix/prefix tries• Burrows-Wheeler transform (BWT)

• Reviewed in Li and Homer: A survey of sequence alignment algorithms for next-generation sequencing. Briefings in Bioinformatics (2010)

11

Aligners c’ed

• 20 aligners published in the last 2 years

• Most deal with short reads

• some of those with ABI specific “color-space”

• A large scale study comparing them is underway:• GSNAP: http://research-pub.gene.com/gmap/ is the most efficient so

far (personal communication, Paul Bertone, EBI)

12

Recent developments• gapped alignment• Recent aligners are able to perform gapped alignments• small indels• no splicing events with large introns

• BWA, Novoalign

• bisulfite sequencing• unmethylated C are converted to T (G complement converted

to A)• 2 references• one with all C converted to T• one with all G converted to A• C-T mismatch or G-A mismatch are ignored• results from both alignments are combined

13

Formats

• export• ID run lane tile x y PE mate seq qual alignment info chastity• HWI-EAS225 37 3 1 1080 935 0 1 NAGAGCAAC... BBBBBBBBBB... NM N• HWI-EAS225 37 3 1 1218 936 0 1 NGCTGCATT... BBBBBBBBBB... chr5 6827036 F A30C44 39 Y

• fastq• first line:@ID; second line: sequence; third line:+(ID); forth line: quality (different encodings)• @HWI-ST169_0186:4:1:1369:1932#0/1• NCCTAACGACGTTTGGTCAGTTCCATCAACATCATAGCC...• +HWI-ST169_0186:4:1:1369:1932#0/1• BUWUX[WVWVccccc_\ccccccccccacccc_cc^V^^V[[][[^^X^B...

• SAM (BAM)• ID flag alignment info seq qual additional info• HWI-EAS225_37:3:108:4047:5812#0/1 0 ChrC 1 255 76M * 0 0 ATG... III... XA:i:1 MD:Z:44T31 NM:i:1

• BAM is the SAM compressed binary format. The specification are available there: http://samtools.sourceforge.net/SAM-1.3.pdf

14

Formats c’ed

• In May 2011, Illumina released a new version of its analysis software CASAVA 1.8.

• In that version the default output format is BAM.

• In addition, it abandons the Illumina specific quality scale (offset of 64 for the ASCII encoding) for the standard Sanger one: +33 offset.

• Note that CASAVA still generate export files to be backward compatible and that these still have a +64 offset.

15

Annotation• External• Ensembl gtf, UCSC gff files

• Bioconductor• Genome coordinate / gene (and other) relationships,•GenomicFeatures, ChIPpeakAnno

• Gene ontology / pathway

•goseq• HapMap, 1000 genomes, UCSC, SRA, ENA, GEO,

ArrayExpress

• rtracklayer , biomaRt, Rsamtools,GEOquery, SRAdb

16

Analysis (day 1-3)

• Domain specific

• ChIP-seq:

•chipseq, ChIPseqR, CSAR, BayesPeak

• Differential expression:

•DESeq, edgeR, baySeq

• RNA-seq: •Genominator

• Examples:

•EatonEtAlChIPseq, leeBamViewsq

17

RNA-Seq Workflow (day 1-2 / Nicolas and Ângela)

QA

Alignment

SNP/Indel

Diff. Exp.eQTL

Read Counts

18

•Not all that glitters is gold!!!

Bias per cycle base call

RNA-Seq DNA-SeqIllumina 20

21

Correction

• Numerous publications

• Hansen et al. Biases in Illumina transcriptome sequencing caused by random hexamer priming. Nucleic Acids Research (2010) pp.

• Zheng et al. Bias detection and correction in RNA-Sequencing data. BMC bioinformatics (2011) vol. 12 (1) pp. 290

• But no proper evaluation/comparison of these

22

Another caveat: what reference?

• How close is your sample’s genome to the published available reference one?

• Specific kind of data, such as RNA-Seq:• genome or transcriptome?• what about novel exon-exon junctions?

23

Reference modification

24

Technical artifact or amazing new biology?

• A recent paper that spilled a lot of taint:• Li et al. Widespread RNA and DNA Sequence

Differences in the Human Transcriptome. Science (2011)

• Major critics (Joe Pickrell): • http://www.genomesunzipped.org/2011/05/notes-on-the-evidence-for-extensive-rna-editing-in-humans.php

25

What they did

• Compare RNA and DNA from matched samples• observe numerous events where RNA != DNA• process known as RNA editing• known in human: • an enzyme convert A into I (Inosine) recognized as a G during

translation• another less frequently observed event frmo another enzyme: • C -> U

• BUT they observe all possible conversions!

26

What might be

• They use reads aligning uniquely to the genome.• The main point can be summarized like this: RNA

editing involves the production of two different RNA and/or protein sequences from a single DNA sequence. To infer RNA editing from the presence of two different RNA and/or protein sequences, then, one must be very sure that they derive from the same DNA sequence, rather than from two different copies of the DNA (due to, for example, paralogs or copy number variants).

27

28

Always challenge your results...

QA

Alignment

SNP/Indel

Diff. Exp.

Read Counts

!

!

samtoolsGATK

tophat/cufflinks,RSEM, edgeR, DESeq, DEXSeq

HTseqeasyRNASeq

•

29

Acknowledgments

• for the organization of the course• Ari Löytynoja• Gabriella Rustici

• for willing to participate• You

30