RNAseq Applications in Genome Studies. Alexander Kanapin, PhD Wellcome Trust Centre for Human Genetics, University of Oxford

Transcription

1 RNAseq Applications in Genome Studies Alexander Kanapin, PhD Wellcome Trust Centre for Human Genetics, University of Oxford

2 RNAseq Protocols } Next generation sequencing protocol } cdna, not RNA sequencing } Types of libraries available: } Total RNA sequencing } polya+ RNA sequencing } Small RNA sequencing } Special protocols: } DSN treatment } Ribominus } SMARTer: Ultra Low RNA sequencing protocol } Strand-specific sequencing } Sequencing only + or strand } Mostly paired-end

3 Genome Study Applications } transcriptome analysis } identifying new transcribed regions } expression profiling } alternative splicing studies } resequencing to find genetic polymorphisms: } SNPs, micro-indels } CNVs

4 cdna Synthesis

5 Arrays vs RNAseq (1) } Correlation of fold change between arrays and RNAseq is similar to correlation between array platforms (0.73) } Technical replicates are almost identical, no need to run } Extra analysis: prediction of alternative splicing, SNPs } Low- and high-expressed genes do not match

6 Array vs RNAseq (2)

7 Data processing and analysis } Alignment } Splice-aware } Reads counting/preprocessing } Adaptor trimming } Counting } Overlapping genes } Strand specific sequencing protocols } Sanity checks } Expression studies } Differential expression } Alternative splicing } GO and pathway analysis

8 Dataflow and Formats Illumina Pipeline (FASTQ) Alignment (BAM) Preprocessing (FASTQ/ FASTA) Expression profiles/ RNA abundance (BED,GTF) Splice variants (GTF) SNP analysis (VCF)

9 Software } } } } } } Short reads aligners } TopHat, STAR Data preprocessing (reads statistics, adapter clipping, formats conversion, read counters) } Fastx toolkit } Htseq } samtools Expression studies } Cufflinks, cuffdiff, cuffcompare } RSEQtools } R packages (DESeq, edger, bayseq, DEGseq, Genominator) Alternative splicing } Cufflinks } MISO } Augustus Downstream analysis } GOSeq } GOStats } SPIA Commercial software } Partek } CLCBio

10 RNASeq alignment } TopHat } University of Maryland ( manual.shtml) } Python wrapper around bowtie aligner } Identifies exons without reference database } Assisted or de novo transcripts assembly } STAR } CSHL ( } Used by ENCODE project as RNASeq aligner } Unbiased detection of splice junctions } Arbitrary large intron length } Heuristic non-exhaustive algorithm

11 FASTQ: Sequence Data } FASTA with Qualities } PHREQ quality score (probability that the corresponding base call is incorrect) with +33 or +64 offset, recorded as an ASCII GGGGGGAAGTCGGCAAAATAGATCCGTAACTTCGGG! +HWI-EAS225:3:1:2:854#0/1! a`abbbbabaabbababb^`[aaa`_n]b^ab^``a!

12 SAM(BAM): Alignment Data Read ID Bitwise Insert flag Chr Pos MapQ CIGAR Mate ref Mate pos size Sequence Scores Extra tags S35_42763_ 4 0 X M * 0 0 CACACGATTCTCAAAGGT IIIIIIIIIIIIIIIIII XA:i:0

13 Statistics and Algorithms } Аim: to detect changes between experimental conditions of interest that are significantly larger than the technical and biological variability among replicates. } Short reads distribution } Poisson } Negative binomial } Normal } Expression values normalization } FPKM } Normalized reads number } VST (variance stabilized transformation)

14 FPKM (RPKM): Expression Values } Fragments Reads Per Kilobase of exon model per Million mapped fragments } Nat Methods. 2008, Mapping and quantifying mammalian transcriptomes by RNA-Seq. Mortazavi A et al. FPKM =10 9 " C NL C= the number of reads mapped onto the gene's exons N= total number of reads in the experiment L= the sum of the exons in base pairs.

15 Read counts } HTSeq-count } } Python script producing raw read counts using sorted sam files } BEDTools } } coveragebed computes both the depth and breadth of coverage of features in file A across the features in file B.

16 Sanity checks } Read counts by category } Counts distribution } Pairwise correlation Normalised count distributions Number of reads (millions) Read Assigment by Category alignment_not_unique ambiguous no_feature not_aligned too_low_aqual Ensembl genes Density WTCHG_52442_273 WTCHG_52442_274 WTCHG_52442_275 WTCHG_52442_276 WTCHG_52442_277 WTCHG_52442_ G_52442_273 G_52442_274 G_52442_275 G_52442_276 G_52442_277 G_52442_288 Log2 normalised counts

17 Cufflinks package } } Cufflinks is a program that assembles aligned RNA-Seq reads into transcripts, estimates their abundances, and tests for differential expression and regulation transcriptome-wide } Cuffcompare: } Transcripts comparison (de novo/genome annotation) } Cuffdiff: } Differential expression analysis

18 Cufflinks (Expression analysis) gene_id bundle_id chr left right FPKM FPKM_conf_lo FPKM_conf_hi status ENSG chr OK ENSG chr OK ENSG chr OK ENSG chr OK ENSG chr OK ENSG chr OK ENSG chr OK ENSG chr OK ENSG chr OK ENSG chr OK ENSG chr OK ENSG chr OK

19 Cuffdiff (differential expression) } Pairwise or time series comparison } Normal distribution of read counts } Fisher s test test_id gene locus sample_1 sample_2 status value_1 value_2 ln(fold_change) test_stat p_value significant ENSG TSPAN6 chrx: q1 q2 NOTEST no ENSG TNMD chrx: q1 q2 NOTEST no ENSG DPM1 chr20: q1 q2 NOTEST no ENSG SCYL3 chr1: q1 q2 OK yes

20 R/bioconductor Packages } Based on raw read counts per gene/transcript/genome feature (mirna) } DESeq } } Negative binomial distribution } bayseq } bayseq.html } Bayesian approach } Choice of Poisson and negative binomial distribution } edger } DEGSeq } Genominator

21 DESeq: Noise and Variance estimation squared coefficient of variation e-01 1e+01 1e+03 1e+05 base mean B IFN M NK base mean density SCV: the ratio of the variance at base level to the square of the base mean Solid line: biological replicates noise Dotted line: full variance scaled by size factors Shot noise: dotted minus solid

22 DESeq: Differential Expression e-17 ENSG e-13 ENSG e-33 ENSG e-07 ENSG e-05 ENSG e-13 ENSG ENSG e-10 ENSG e-16 ENSG e-30 ENSG e-08 ENSG e-08 ENSG e-14 ENSG e-40 ENSG e-10 ENSG e-33 ENSG e-07 ENSG e-06 ENSG e-11 ENSG e-06 ENSG e-05 ENSG e-07 ENSG e-12 ENSG e-12 ENSG e-18 ENSG e ENSG e-133 res_m_i$log2foldchange id B cells IFG expressio expressio log2foldch n n ange pvalue 1e-01 1e+01 1e+03 res_m_i$basemean 1e+05

23 Alternative splicing analysis } Cufflinks } MISO ( } probabilistic framework that quantitates the expression level of alternatively spliced genes from RNA-Seq data, and identifies differentially regulated isoforms or exons across samples } DEXSeq ( DEXSeq.html) } differential exon usage

24 Cufflinks: Alternative splicing trans_id bundle_id chr left right FPKM FMI frac FPKM_conf_lo FPKM_conf_hi coverage length effective_length status ENST chr OK ENST chr OK ENST chr OK ENST chr OK ENST chr OK ENST chr OK ENST chr OK ENST chr OK ENST chr OK ENST chr OK ENST chr OK ENST chr OK ENST chr OK ENST chr OK ENST chr OK ENST chr OK ENST chr OK ENST chr OK

25 DEXSeq } The statistical model is based on generalised linear models of the Negative Binomial family (NB- GLMs) } Exon-oriented read counts

26 Visualization: Genome Viewers } Visualize reads alignment and analysis results } Manual check of computational predictions: expression levels, alternative splicing, variants } Track-based visual presentation of data } Custom tracks upload: BAM, BED, BigWig, GTF } Web based: } Gbrowse ( } UCSC Genome Browser } Standalone } Integrated Genome Viewer ( software/igv/)

27 UCSC Genome Browser } Scale chr21: RefSeq Genes Sequences SNPs Human mrnas Spliced ESTs 100 _ Layered H3K27Ac DNase Clusters Txn Factor ChIP 4 _ Mammal Cons BC _ _ Rhesus Mouse Dog Elephant Opossum Chicken X_tropicalis Zebrafish Common SNPs(137) RepeatMasker SOD1 2 kb hg19 33,033,000 33,034,000 33,035,000 33,036,000 33,037,000 33,038,000 33,039,000 33,040,000 33,041,000 UCSC Genes (RefSeq, UniProt, CCDS, Rfam, trnas & Comparative Genomics) RefSeq Genes Publications: Sequences in scientific articles Human mrnas from GenBank Human ESTs That Have Been Spliced H3K27Ac Mark (Often Found Near Active Regulatory Elements) on 7 cell lines from ENCODE Digital DNaseI Hypersensitivity Clusters in 125 cell types from ENCODE Transcription Factor ChIP-seq from ENCODE Placental Mammal Basewise Conservation by PhyloP Multiz Alignments of 46 Vertebrates Simple Nucleotide Polymorphisms (dbsnp 137) Found in >= 1% of Samples Repeating Elements by RepeatMasker

28 IGV: Differential Expression Visualization

29 Downstream analysis and bias corrections } Bias correction } RNASeqBias ( RNAseqRPackage/) } Gene length bias } GC content bias } Dinucleotide bias } GO enrichment } GOStats ( } Initially a microarray package, but can be used in RNASeq } GOSeq ( } Detects Gene Ontology and/or other user defined categories which are over/ under represented in RNA-seq data

30 Pathway analysis } SPIA ( SPIA.html) } Signaling Pathway Impact Analysis (SPIA) uses the information form a list of differentially expressed genes and their log fold changes together with signaling pathways topology, in order to identify the pathways most relevant to the condition under the study } KEGG pathways database } Human and mouse only

31 Part II: Practical demonstration } The aim of this demo is to use DESeq package for RNAseq data analysis. The dataset prodcued by a gene expression study in different types of immune cell, namely B-cells and monocytes. We have a total of 8 samples, 4 from B-cells and 4 from monocytes. } Prerequisites: } R (version > ) } Bioconductor } DESeq

32 Input data } Raw read counts prepared with htseq-count gene!075_b_cell!083_b_cell!088_b_cell!085_b_cell!085_monocyte!075_monocyte!083_monocyte!088_monocyte! ENSG !0!0!0!0!0!0!1!0! ENSG !23!12!9!12!14!4!14!12! ENSG !48!26!10!17!19!5!8!12!

33 Read and normalize data } countstable <-read.delim ("raw_counts.txt",header=true,stringsasfactors=true) } rownames( countstable ) <- countstable$gene } countstable <- countstable[, -1 ] } The next step is to create conditions vector to attribute each column to a given cell type, B for B-cells and M for monocytes: } conds <- c(rep("b",4), rep("m",4)) } Then we create main dataframe for the count data set using function newcountdataset: } cds <- newcountdataset( countstable, conds ) } And normalize the number of read counts: } cds <- estimatesizefactors(cds)

34 Estimate variance and dispersion } Finally, we estimate variance functions for the dataset: } cds <- estimatedispersions(cds, method="per-condition", sharingmode="maximum") } Now we find genes, which are differentially expressed between the two different cell types using negative binomial distribution test: } res <- nbinomtest(cds, "B", "M") } Now we plot MA diagram to estimate expression values and fold changes. Also we put a threshold for the adjusted p-value (padj field in res) as to estimate visually a scale of the differential expression: } plot( res$basemean, res$log2foldchange, log="x", pch=20, cex=.1, col = ifelse( res$padj <.0001, "red", "black" ) )

35 Significant genes } Finally, we filter out the genes with padj > and create the subset of the results for differentially expressed ones: } sig <- res[ res$padj <.001, ] } sig <- sig[ is.na(sig$pval)!= "TRUE", ] } head(sig[with(sig, order(padj)), ]) } Select 50 most significant genes for functional annotation analysis: } noquote(head(sig[with(sig, order(padj)), ]$id, 50))

36 Downstream analysis } The Database for Annotation, Visualization and Integrated Discovery (DAVID )is a powerful resource for functional annotation analysis } We are going to use it to check if there are any important functional categories describing the differentially expressed genes we detected. }