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RNAseq Applications in Genome Studies

RNAseq Applications in Genome Studies. Alexander Kanapin, PhD Wellcome Trust Centre for Human Genetics, University of Oxford. RNAseq Protocols. Next generation sequencing protocol cDNA, not RNA sequencing Types of libraries available: Total RNA sequencing polyA+ RNA sequencing

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RNAseq Applications in Genome Studies

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  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 • Ribosomal depletion

  3. Genome Study Applications • transcriptome analysis • identifying new transcribed regions • expression profiling • Resequencing to find genetic polymorphisms: • SNPs, micro-indels • CNVs

  4. cDNA Synthesis

  5. Sequencing details • Standard sequencing • polyA/total RNA • Size slection • Primers and adapters • Single- and paired-end sequencing • Strand-specific sequencing • Beta version • Sequencing only + or – strand • Mostly paired-end

  6. 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

  7. Array vs RNAseq (2)

  8. A bit of statistics • Short reads distribution • Poisson • Negative binomial • Normal • Expression values normalization • FPKM • Normalized reads number • VST (variance stabilized transformation) • Differential expression analysis • Replicates vs non-replicates

  9. Analysis Dataflow Illumina Pipeline (FASTQ) Alignment (BAM) FASTX Toolkit (FASTQ/FASTA) Expression profiles/RNA abundance SNP analysis Splice variants

  10. Software • Short reads aligners • Stampy, BWA, Novoalign, Bowtie,… • Data preprocessing (reads statistics, adapter clipping, formats conversion, read counters) • Fastx toolkit • Htseq • MISO • samtools • Expression studies • Cufflinks package • RSEQtools • R packages (DESeq, edgeR, baySeq, DEGseq, Genominator) • Alternative splicing • Cufflinks • Augustus • Commercial software • Partek • CLCBio

  11. FASTQ: Sequence Data • “FASTA with Qualities” @HWI-EAS225:3:1:2:854#0/1 GGGGGGAAGTCGGCAAAATAGATCCGTAACTTCGGG +HWI-EAS225:3:1:2:854#0/1 a`abbbbabaabbababb^`[aaa`_N]b^ab^``a @HWI-EAS225:3:1:2:1595#0/1 GGGAAGATCTCAAAAACAGAAGTAAAACATCGAACG +HWI-EAS225:3:1:2:1595#0/1 a`abbbababbbabbbbbbabb`aaababab\aa_`

  12. SAM(BAM): Alignment Data

  13. 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. 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.

  14. Cufflinks package • http://cufflinks.cbcb.umd.edu/ • Cufflinks: • Expression values calculation • Transcripts de novo assembly • Cuffcompare: • Transcripts comparison (de novo/genome annotation) • Cuffdiff: • Differential expression analysis

  15. Cufflinks (Expression analysis) gene_id bundle_id chr left right FPKM FPKM_conf_lo FPKM_conf_hi status ENSG00000236743 31390 chr1 459655 461954 0 0 0 OK ENSG00000248149 31391 chr1 465693 688071 787.12 731.009 843.232 OK ENSG00000236679 31391 chr1 470906 471368 0 0 0 OK ENSG00000231709 31391 chr1 521368 523833 0 0 0 OK ENSG00000235146 31391 chr1 523008 530148 0 0 0 OK ENSG00000239664 31391 chr1 529832 532878 0 0 0 OK ENSG00000230021 31391 chr1 536815 659930 2.53932 0 5.72637 OK ENSG00000229376 31391 chr1 657464 660287 0 0 0 OK ENSG00000223659 31391 chr1 562756 564390 0 0 0 OK ENSG00000225972 31391 chr1 564441 564813 96.9279 77.2375 116.618 OK ENSG00000243329 31391 chr1 564878 564950 0 0 0 OK ENSG00000240155 31391 chr1 564951 565019 0 0 0 OK

  16. 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 ENSG00000000003 TSPAN6 chrX:99883666-99894988 q1 q2 NOTEST 0 0 0 0 1 no ENSG00000000005 TNMD chrX:99839798-99854882 q1 q2 NOTEST 0 0 0 0 1 no ENSG00000000419 DPM1 chr20:49551403-49575092 q1 q2 NOTEST 15.0775 23.8627 0.459116 -1.39556 0.162848 no ENSG00000000457 SCYL3 chr1:169631244-169863408 q1 q2 OK 32.5626 16.5208 -0.678541 15.8186 0 yes

  17. Cufflinks: Alternative splicing trans_id bundle_id chr left right FPKM FMI frac FPKM_conf_lo FPKM_conf_hi coverage length effective_length status ENST00000503254 31391 chr1 465693 688071 787.12 1 1 731.009 843.232 124.849 1509 440.26 OK ENST00000458203 31391 chr1 470906 471368 0 0 0 0 0 0 462 440.005 OK ENST00000417636 31391 chr1 521368 523833 0 0 0 0 0 0 842 842 OK ENST00000423796 31391 chr1 523008 530148 0 0 0 0 0 0 607 607 OK ENST00000450696 31391 chr1 523047 529954 0 0 0 0 0 0 402 402 OK ENST00000440196 31391 chr1 529832 530595 0 0 0 0 0 0 437 437 OK ENST00000357876 31391 chr1 529838 532878 0 0 0 0 0 0 498 498 OK ENST00000440200 31391 chr1 536815 655580 2.53932 1 1 0 5.72637 0.185236 413 413 OK ENST00000441245 31391 chr1 637315 655530 0 0 0 0 0 0 629 629 OK ENST00000419394 31391 chr1 639064 655574 0 0 0 0 0 0 480 480 OK ENST00000448605 31391 chr1 639064 655580 0 0 0 0 0 0 274 274 OK ENST00000414688 31391 chr1 646721 655580 0 0 0 0 0 0 750 750 OK ENST00000447954 31391 chr1 655437 659930 0 0 0 0 0 0 336 336 OK ENST00000440782 31391 chr1 657464 660287 0 0 0 0 0 0 2823 2823 OK ENST00000452176 31391 chr1 562756 564390 0 0 0 0 0 0 802 802 OK ENST00000416931 31391 chr1 564441 564813 96.9279 1 1 77.2375 116.618 21.1488 372 372 OK ENST00000485393 31391 chr1 564878 564950 0 0 0 0 0 0 72 72 OK ENST00000482877 31391 chr1 564951 565019 0 0 0 0 0 0 68 68 OK

  18. R/bioconductor Packages • Based on raw read counts per gene/transcript/genome feature (miRNA) • Differential expression analysis • DESeq • http://www-huber.embl.de/users/anders/DESeq/ • Negative binomial distribution • baySeq • http://www.bioconductor.org/help/bioc-views/release/bioc/html/baySeq.html • Bayesian approach • Choice of Poisson and negative binomial distribution • edgeR • DEGSeq • Genominator • …

  19. DESeq: Variance estimation 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

  20. DESeq: Differential Expression

  21. Visualization: Genome Viewers • Web based: • Gbrowse (http://gmod.org/wiki/Gbrowse) • UCSC Genome Browser (http://genome.ucsc.edu/) • Standalone • Integrated Genome Viewer (http://www.broadinstitute.org/software/igv/)

  22. IGV: Differential Expression Visualization

  23. An Introduction to ChIP-Sequencing analysis Linda Hughes

  24. What is ChIP-Seq? • Chromatin-Immunoprecipitation (ChIP)- Sequencing • ChIP - A technique of precipitating a protein antigen out of solution using an antibody that specifically binds to the protein. • Sequencing – A technique to determine the order of nucleotide bases in a molecule of DNA. • Used in combination to study the interactions between protein and DNA.

  25. ChIP-Seq Applications Enables the accurate profiling of • Transcription factor binding sites • Polymerases • Histone modification sites • DNA methylation

  26. ChIP-Seq: The Basics

  27. ChIP-Seq Analysis Pipeline Sequencing Base Calling Read quality assessment 30-50 bp Sequences Genome Alignment Peak Calling Enriched Regions Visualisation with genome browser Differential peaks Combine with gene expression Motif Discovery

  28. ChIP-Seq: Genome Alignment • Several Aligners Available • BWA • NovoAlign • Bowtie • Currently the Sequencing analysis pipeline uses the Stampy as the default aligner for all sequencing. • All aligner output containing information about the mapping location and quality of the reads are out put in SAM format

  29. ChIP-Seq Peak Calling • The main function of peak finding programs is to predict protein binding sites • First the programs must identify clusters (or peaks) of sequence tags • The peak finding programs must determine the number of sequence tags (peak height) that constitutes “significant” enrichment likely to represent a protein binding site

  30. ChIP-Seq: Peak Calling Several ChIP-seq peak calling tools Available • MACS • PICS • PeakSeq • Cisgenome • F-Seq

  31. ChIP-Seq: Identification of Peaks • Several methods to identify peaks but they mainly fall into 2 categories: • Tag Density • Directional scoring • In the tag density method, the program searches for large clusters of overlapping sequence tags within a fixed width sliding window across the genome. • In directional scoring methods, the bimodal pattern in the strand-specific tag densities are used to identify protein binding sites.

  32. ChIP-Seq: Determination of peak significance • To account for the background signal, many methods incorporate sequence data from a control dataset. • This is usually generated from fixed chromatin or DNA immunoprecipitated with a nonspecific antibody. • Calculate false discovery rate • account the background signal in ChIP-sequence tags • Assess the significance of predicted ChIP-seq peaks

  33. ChIP-Seq: Determination of peak significance • More statistically sophisticated models developed to model the distribution of control sequence tags across the genome. • Used as a parameter to assess the significance of ChIP tag peaks • t-distribution • Poisson model • Hidden Markov model • Primarily used to assign each peak a significance metric such as a P-value FDR or posterior probability.

  34. ChIP-Seq: Output chr start end length summit tags -10*log10(pvalue) fold_enrichment FDR(%) chr1 13322611 13322934 324 101 16 58.38 6.95 73.89 chr1 14474379 14475108 730 456 63 63.73 5.98 73.81 chr1 23912933 23913336 404 155 19 57.86 8.49 73.33 chr1 24619496 24619679 184 92 44 449.34 34.00 94.12 chr1 24619857 24620057 201 100 73 780.66 56.41 100 chr1 26742705 26743590 886 252 69 132.27 7.52 69.25 chr1 26743625 26745342 1718 1422 165 141.40 4.34 70.36 chr1 33811805 33814279 2475 289 256 98.13 3.74 74.50 chr1 34516074 34517165 1092 496 206 59.13 5.22 74.42 chr1 34519503 34520082 580 334 58 53.56 4.74 70.59 chr1 34529691 34530276 586 286 40 77.33 6.12 74.63 chr1 34546832 34547631 800 311 208 233.96 5.56 73.01 chr1 34548528 34549155 628 343 39 81.43 5.75 75.15 chr1 34570690 34571225 536 267 31 98.69 7.15 74.50

  35. ChIP-Seq: Output • A list of enriched locations • Can be used: • In combination with RNA-Seq, to determine the biological function of transcription factors • Identify genes co-regulated by a common transcription factor • Identify common transcription factor binding motifs

  36. ChIP-Seq: Need help? • http://seqanswers.com/ • Good for: • Publications • Answering FAQ • Troubleshooting • Contacting the programs authors

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