RNAseq Applications in Genome StudiesAlexander 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 Small RNA sequencing

Special protocols: DSN treatment Ribosomal depletion

Genome Study Applications transcriptome analysis identifying new transcribed regions expression profiling Resequencing to find genetic polymorphisms:

SNPs, micro-indels CNVs

cDNA Synthesis

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

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

Array vs RNAseq (2)

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

Analysis Dataflow

Illumina Pipeline(FASTQ)

Alignment (BAM)

Expression

profiles/RNA

abundance

Splice variants

SNP analysis

FASTX Toolkit

(FASTQ/FASTA)

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

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_`

SAM(BAM): Alignment Data

Read IDBitwise flag Chr Pos MapQ CIGAR

Mate ref Mate pos

Insert size Sequence Scores

Extra tags

S35_42763_4 0X 15401991 25518M * 0 0CACACGATTCTCAAAGGT IIIIIIIIIIIIIIIIII XA:i:0

FPKM (RPKM): Expression Values

C= the number of reads mapped onto the gene's exonsN= total number of reads in the experimentL= the sum of the exons in base pairs.€

FPKM =109 ×C

NL

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

Cufflinks (Expression analysis)

gene_id bundle_id chr left right FPKM FPKM_conf_lo FPKM_conf_hi statusENSG00000236743 31390 chr1 459655 461954 0 0 0 OKENSG00000248149 31391 chr1 465693 688071 787.12 731.009 843.232 OKENSG00000236679 31391 chr1 470906 471368 0 0 0 OKENSG00000231709 31391 chr1 521368 523833 0 0 0 OKENSG00000235146 31391 chr1 523008 530148 0 0 0 OKENSG00000239664 31391 chr1 529832 532878 0 0 0 OKENSG00000230021 31391 chr1 536815 659930 2.53932 0 5.72637 OKENSG00000229376 31391 chr1 657464 660287 0 0 0 OKENSG00000223659 31391 chr1 562756 564390 0 0 0 OKENSG00000225972 31391 chr1 564441 564813 96.9279 77.2375 116.618 OKENSG00000243329 31391 chr1 564878 564950 0 0 0 OKENSG00000240155 31391 chr1 564951 565019 0 0 0 OK

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 significantENSG00000000003 TSPAN6chrX:99883666-99894988 q1 q2 NOTEST 0 0 0 0 1 noENSG00000000005 TNMD chrX:99839798-99854882 q1 q2 NOTEST 0 0 0 0 1 noENSG00000000419 DPM1 chr20:49551403-49575092 q1 q2 NOTEST 15.0775 23.8627 0.459116 -1.39556 0.162848 noENSG00000000457 SCYL3 chr1:169631244-169863408 q1 q2 OK 32.5626 16.5208 -0.678541 15.8186 0 yes

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 OKENST00000458203 31391 chr1 470906 471368 0 0 0 0 0 0 462 440.005 OKENST00000417636 31391 chr1 521368 523833 0 0 0 0 0 0 842 842 OKENST00000423796 31391 chr1 523008 530148 0 0 0 0 0 0 607 607 OKENST00000450696 31391 chr1 523047 529954 0 0 0 0 0 0 402 402 OKENST00000440196 31391 chr1 529832 530595 0 0 0 0 0 0 437 437 OKENST00000357876 31391 chr1 529838 532878 0 0 0 0 0 0 498 498 OKENST00000440200 31391 chr1 536815 655580 2.53932 1 1 0 5.72637 0.185236 413 413 OKENST00000441245 31391 chr1 637315 655530 0 0 0 0 0 0 629 629 OKENST00000419394 31391 chr1 639064 655574 0 0 0 0 0 0 480 480 OKENST00000448605 31391 chr1 639064 655580 0 0 0 0 0 0 274 274 OKENST00000414688 31391 chr1 646721 655580 0 0 0 0 0 0 750 750 OKENST00000447954 31391 chr1 655437 659930 0 0 0 0 0 0 336 336 OKENST00000440782 31391 chr1 657464 660287 0 0 0 0 0 0 2823 2823 OKENST00000452176 31391 chr1 562756 564390 0 0 0 0 0 0 802 802 OKENST00000416931 31391 chr1 564441 564813 96.9279 1 1 77.2375 116.618 21.1488 372 372 OKENST00000485393 31391 chr1 564878 564950 0 0 0 0 0 0 72 72 OKENST00000482877 31391 chr1 564951 565019 0 0 0 0 0 0 68 68 OK

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 …

DESeq: Variance estimation

SCV: the ratio of the variance at base level to the square of the base meanSolid line: biological replicates noiseDotted line: full variance scaled by size factorsShot noise: dotted minus solid

DESeq: Differential Expression

id

B cells expression

IFG expression

log2FoldChange pValue

ENSG00000000971 1.566626326 23.78874526 3.924546167 2.85599311970997e-17

ENSG00000001036 5.999081213 33.49328888 2.481058581 9.8485739442166e-13

ENSG00000001084 23.3067067 156.2725598 2.745247408 4.38856094441354e-33

ENSG00000001461 46.14566905 18.67886919 -1.304788134 2.66197080043655e-07

ENSG00000001497 68.54035056 35.87868221 -0.933826668 3.36052669642687e-05

ENSG00000001630 13.86061772 55.92825318 2.012585716 1.27410028391540e-13

ENSG00000002549 27.33856924 1096.051286 5.325233754 1.97553508993745e-133

ENSG00000002587 15.64872305 2.223202568 -2.815333625 8.43968907932538e-10

ENSG00000002834 95.68814397 272.3502328 1.509051013 8.21570437569004e-16

ENSG00000003056 63.65513823 296.6257971 2.220295194 2.92583705156055e-30

ENSG00000003400 52.02308495 117.3028844 1.173014631 4.62918844505763e-08

ENSG00000003402 154.7003657 311.1815114 1.008279739 2.59997904482726e-08

ENSG00000003756 434.3712708 180.9106662 -1.263651217 3.58591978350734e-14

ENSG00000004399 1.199584318 56.96561073 5.569484777 9.87310306834046e-40

ENSG00000004455 145.4361806 331.8994483 1.190360014 3.17246841765643e-10

ENSG00000004468 17.27590102 128.1030372 2.89047182 1.99020901042234e-33

ENSG00000004534 331.0046525 176.1290195 -0.910218864 2.28719252897662e-07

ENSG00000004799 5.425570485 18.0426855 1.733567341 1.67150844663169e-06

ENSG00000004961 15.22078545 54.5536795 1.841633697 2.76802192307592e-11

ENSG00000005020 133.1474289 248.379817 0.899523377 3.00900687072175e-06

ENSG00000005022 86.49374889 154.5210394 0.837135513 3.79777250197792e-05

ENSG00000005238 0.818439748 8.567484894 3.387923626 7.38045118427266e-07

ENSG00000005249 1.442397316 17.22208291 3.577719117 2.69990749254895e-12

ENSG00000005379 25.15059092 4.02264298 -2.644376691 2.75953193496745e-12

ENSG00000005381 0.376344415 19.36188435 5.685021995 4.99727503015434e-18

ENSG00000005436 28.46288463 11.16816604 -1.349689587 4.23389957443192e-06

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/)

IGV: Differential Expression Visualization

An Introduction to ChIP-Sequencing analysis

Linda Hughes

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.

ChIP-Seq Applications

Enables the accurate profiling of

Transcription factor binding sites Polymerases Histone modification sites DNA methylation

ChIP-Seq: The Basics

ChIP-Seq Analysis Pipeline

Sequencing

30-50 bpSequenc

es

Base Calling

Read quality

assessment

GenomeAlignme

nt

Enriched Regions

Peak Calling

Combine with gene

expression

Motif Discover

y

Visualisation with

genome browser

Differential peaks

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

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

ChIP-Seq: Peak Calling

Several ChIP-seq peak calling tools Available MACS PICS PeakSeq Cisgenome F-Seq

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.

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

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.

ChIP-Seq: Outputchr 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

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

ChIP-Seq: Need help?

http://seqanswers.com/

Good for: Publications Answering FAQ Troubleshooting Contacting the programs authors