Next generation sequencing (NGS)- RNA sequencing

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1 Next generation sequencing (NGS)- RNA sequencing Vijayachitra Modhukur BIIT 1

2 NGS lectures Genomics Transcriptomics Epigenomics Protomics 2

3 NGS lectures Genomics Transcriptomics Epigenomics Protomics 3

4 Recap 4

5 Sequencing 5

6 Different generations sequencing 6

7 Second generation sequencing 7

8 NGS platforms 454 Solexa/Illumina SOLiD (ABI) Bp per run 400 Mb 2-3 Gb 3-6 Gb Read length bp (70-100) bp bp run time 10 hr 2.5 days 5 days Download 20 min 27 hr (44 min) ~1 day Analysis 2-5 hr 2 days 2-3 days Files Gb 1T 1 T 8

9 Massive amount of sequenced data 9

10 De novo alignment Reference alignment Sequence alignment 10

11 Short read mapping (Denovo) - ssp Let f1,f2 fk be the words in Σ*. We want to find shortest substring g εσ* such that fi is the substring of g Example: Lets say we have set of strings f1 = ACGTA, f2 = CTTGA, f3 = ACTT, f4 = GTAAC Find the shortest common superstring of these 4 string 11

12 Reference alignment Find locations where short read is identical to reference genome 12

13 NGS Analysis 13

14 Data analysis cpu/memory intensive 14

15 Quality scores Each base from a sequencer comes with a quality score Base-calling error probabilities Phred quality score Q = 10 log10 P higher quality score indicates a smaller probability of error 15

16 Quality scores 16

17 File formats 17

18 fastq Raw data 18

19 Reference assembly Spaced seed BWT Alignment methods Denovo assembly Greedy Assemblers Graph based Overlap layout consensus Graph based Debruign graph 19

20 RNA sequencing 20

21 Transcription 21

22 RNA world hypothesis 22

23 What is RNA-seq? Use of high-throughput sequencing technologies to assess the RNA content of a sample. 23 slides from Halisha Holloway

24 RNA-seq vs microarray 24 RNA-seq ID novel genes, transcripts, & exons Greater dynamic range Less bias due to genetic variation Repeatable No species-specific primer/probe design More accurate relative to qpcr Many more applications Microarray Well vetted QC and analysis methods Well characterized biases Quick turnaround from established core facilities Currently less expensive

25 RNA-Seq vs microarray 25

26 Why do an RNA-seq experiment? Detect differential expression Assess allele-specific expression Quantify alternative transcript usage Discover novel genes/transcripts, gene fusions Profile transcriptome Ribosome profiling to measure translation 26

27 Why do an RNA-seq experiment? Detect differential expression Assess allele-specific expression Quantify alternative transcript usage Discover novel genes/transcripts, gene fusions Profile transcriptome Ribosome profiling to measure translation 27 Skelly et al. 2011

28 Why do an RNA-seq experiment? Detect differential expression Assess allele-specific expression Quantify alternative transcript usage Discover novel genes/transcripts, gene fusions Profile transcriptome Ribosome profiling to measure translation 28

29 Why do an RNA-seq experiment? Detect differential expression Assess allele-specific expression Quantify alternative transcript usage Discover novel genes/transcripts, gene fusions Profile transcriptome Ribosome profiling to measure translation 29

30 Why do an RNA-seq experiment? Detect differential expression Assess allele-specific expression Quantify alternative transcript usage Pluripotent Stem Cell Cardiogenic Mesoderm Cardiac Precursors Cardiomyocytes Discover novel genes/transcripts, gene fusions Profile transcriptome Ribosome profiling to measure translation 30

31 Why do an RNA-seq experiment? Detect differential expression Assess allele-specific expression Quantify alternative transcript usage Discover novel genes/transcripts, gene fusions Profile transcriptome Ribosome profiling to measure translation 31

32 RNA-seq protocol 32

33 RNA-seq protocol Sample RNA Amplified cdna cdna fragments reads reverse transcription + PCR fragmentation sequencing machine CCTTCNCACTTCGTTTCCCAC TTTTTNCAGAGTTTTTTCTTG GAACANTCCAACGCTTGGTGA GGAAANAAGACCCTGTTGAGC CCCGGNGATCCGCTGGGACAA GCAGCATATTGATAGATAACT CTAGCTACGCGTACGCGATCG CATCTAGCATCGCGTTGCGTT CCCGCGCGCTTAGGCTACTCG TCACACATCTCTAGCTAGCAT CATGCTAGCTATGCCTATCTA CACCCCGGGGATATATAGGAT 33

34 RNA-seq CCTTCNCACTTCGTTTCCCACTTAGCGATAATTTG +HWUSI-EAS1789_0001:3:2:1708:1305#0/1 TTTTTNCAGAGTTTTTTCTTGAACTGGAAATTTTT +HWUSI-EAS1789_0001:3:2:2062:1304#0/1 a GAACANTCCAACGCTTGGTGAATTCTGCTTCACAA +HWUSI-EAS1789_0001:3:2:3194:1303#0/1 ZZ[[VBZZY][TWQQZ\ZS\[ZZXV GGAAANAAGACCCTGTTGAGCTTGACTCTAGTCTG +HWUSI-EAS1789_0001:3:2:3716:1304#0/1 CCCGGNGATCCGCTGGGACAAGCAGCATATTGATA +HWUSI-EAS1789_0001:3:2:5000:1304#0/1 aaaaabeeeeffffehhhhhhggdhhhhahhhadh?? 34

35 Coverage Coverage = Number of sequenced reads/size of the original genome The number of sequenced reads = Number of reads length of the reads 35

36 Some things to consider in experimental design 36

37 Plan it well Experimental design Biological replicates Reference genome? Good gene annotation? Read depth Read length Paired vs. single-end Biological variation Technical variation 37

38 Experimental design Biological replicates Reference genome? Good gene annotation? Read depth Read length Paired vs. single-end Plan it well 38

39 Plan it well Experimental design Biological replicates Reference genome? Good gene annotation? Read depth Read length Paired vs. single-end Fraction of transcripts with non zero FPKM (relative to 100%) Robustness of transcript identification as input data are removed 10% 5% 2% 1% 0.1% Cufflinks USeq-DESeq Fraction of total number of reads in jackknifed data set 39

40 How much data do we need? ~15-20K genes expressed in a tissue cell line. Genes are on average 3KB For 1x coverage using 100 bp reads, would need 600K sequence reads In reality, we need MUCH higher coverage to accurately estimate gene expression levels million reads 40

41 Plan it well Experimental design Biological replicates Reference genome? Good gene annotation? Read depth Read length Paired vs. single-end Uniq seq = 4read length Read length Unique seq x x x10 60 ~60 million coding bases in vertebrate genome 41

42 Experimental design Biological replicates Reference genome? Good gene annotation? Read depth Barcoding Read length Paired vs. single-end Plan it well 42

43 Power of paired-end reads Huge impact on read mapping Pairs give two locations to determine whether read is unique Critical for estimating transcript-level abundance Increases number of splice junction spanning reads 43

44 Comparison of two designs for testing differential expression between treatments A and B. Treatment A is denoted by red tones and treatment B by blue tones. Auer P L, and Doerge R W Genetics 2010;185: Copyright 2010 by the Genetics Society of America

45 RNA-seq pipeline 45

46 Typical RNA-seq experiment 46

47 RNA-seq informatics workflow 1. Qc and genome mapping 2. Splice junction fragments 3. Predict novel junctions/ exons 4. Counts 5. Normalize 6. Differential expression 7. Gene lists 47

48 Quality control 48

49 QC: Raw Data Sequence call quality 49

50 Sequence bias QC: Raw Data 50

51 Duplication level QC: Raw Data 51

52 Mapping 52

53 Mapping Align read to the genome Simple for genomic sequences Difficult for transcripts with splice junction 53

54 Junction reads 54

55 Tophat-pipeline 55

56 Alternative splicing 56

57 Alternative splicing 57

58 Cuff-links 58

59 RNA-seq complete pipeline 59

60 RNA seq-summarization 60

61 Normalization aims Comparable across features (genes, isoforms etc.,) Comparable across different samples (libraries) Between samples (libraries) Within sampes(libraries) Easily interprettable 61

62 Within library normalization Allows quantification of expression levels of each gene relative to each other s gene with in the library Longer transcripts have higher read counts( with same expression level) Widely used : RPKM (Reads per Kilobase per Million Base) 62

63 No.of mapped reads =3 lenth of transcript=300 bp Total no. of reads =10,000 RPKM-example RPK = 3/(300/1000) = 3/0.3 = 10 RPKM = 10 / (10,000/1,000,000) = 10/ 0.01 = 1000 RPKM =

64 Between library normalization Adjust by total number of reads in the library Smaller number of highly expressed genes can consume significant amount of sequences Solution: scaling factor Scaling the number of reads in a library to a common value Quantile normalization 64

65 Differential expression List genes changed significantly in abundace across different experimental conditions Not same as microarrays, since not log transformed If reads independently sampled from population, reads would follow multinomial distribution appx by Poisson Pr(X = k) =λ k e -k /k! 65

66 Several tools for differential expression Differential expression maximization (RSEM) Cuffdiff 29 Uses isoform levels in analysis Identifying differentially DegSeq 79 Uses a normal distribution expressed genes or transcript isoforms EdgeR 77 Differential Expression analysis of count data (DESeq) 78 Myrna 75 Cloud-based permutation method Read alignments and transcript models 66

67 Analysis of differentially expressed gene list 67

Gene ontology analysis Nucleic Acids Research, 2007 3 (A) (B) 68 Figure 1. (A) A typical user input and output scenario of g:profiler.

68 Gene ontology analysis Nucleic Acids Research, (A) (B) 68 Figure 1. (A) A typical user input and output scenario of g:profiler. User inserts a set of genes in the main text window and optionally adjusts query parameters. Results are provided either graphically or in textual format. Genes are presented in columns, and significant functional categories in rows. The analysis of an ordered list shows the length of the most significant query head. GO annotation evidence codes are coloured like a heat map, showing the strength of evidence between a gene and GO term. The legend is provided at the top of the page. It is displayed when the user clicks on the tree icon on the results page. The g:orth, g:convert and G:Sorter tools are directly linked to relevant genes from the current query. Additional examples are available in Supplementary Data. (B) Hierarchical relations between the resulting GO categories can be browsed by clicking on corresponding icons.

69 epithelium development Arrhythmogenic right ventricular... extracellular matrix organization Glucose metabolism Huntington's disease response to inorganic substance cell junction assembly protein N linked glycosylation v... intracellular protein transport protein N linked glycosylation DNA dependent transcription, ter... Leukocyte transendothelial migra... induction of apoptosis positive regulation of leukocyte... negative regulation of programme... Natural killer cell mediated cyt... intracellular protein kinase cas... Hematopoietic cell lineage Chemokine signaling pathway protein complex subunit organiza... regulation of protein kinase act... vesicle mediated transport Dopaminergic synapse Retrograde endocannabinoid signa... cytoskeleton organization Glutamatergic synapse synapse organization Opioid Signalling purine nucleoside triphosphate m... Calcium signaling pathway regulation of small GTPase media... negative regulation of cellular... regulation of cellular localization actin filament based process regulation of transporter activity Gastric acid secretion regulation of cell morphogenesis... secretion by cell Salivary secretion cognition GABAergic synapse transmembrane receptor protein t... generation of a signal involved... ion transmembrane transport Long term potentiation GTP catabolic process Morphine addiction positive regulation of cellular... Gene ontology Gosummaries A cell.line VS brain B G1 > G2: 2168 G1 < G2: 2132 D Tissue brain cell line C hematopoietic system muscle spindle organization chromosome organization interspecies interaction between... viral reproduction RNA processing DNA replication translation anaphase promoting complex depen... chromosome segregation nuclear division mitotic cell cycle cell cycle phase cell cycle checkpoint establishment of organelle local... regulation of mitosis DNA conformation change p53 signaling pathway protein complex subunit organiza... Cell Cycle Checkpoints cellular component biogenesis at... RNA transport regulation of cellular amino aci... DNA Replication ncrna metabolic process cellular macromolecular complex... positive regulation of ligase ac... negative regulation of ubiquitin... positive regulation of protein u... DNA recombination Cell Cycle, Mitotic Cell cycle response to DNA damage stimulus cell division mrna metabolic process regulation of ubiquitin protein... DNA damage response, signal tran... ion transport regulation of nervous system dev... behavior central nervous system development neuron projection morphogenesis neuron development multicellular organismal signaling neuron projection development regulation of synaptic transmission regulation of membrane potential axon guidance regulation of neuron differentia... neurotransmitter transport E Enrichment P value muscle VS hematopoietic.system G1 > G2: 1527 G1 < G2: 1159 cell morphogenesis involved in d... acetyl CoA metabolic process actin filament based process taxis wound healing Cardiac muscle contraction circulatory system process Focal adhesion Glucose Regulation of Insulin Se... energy derivation by oxidation o... Alzheimer's disease cell adhesion enzyme linked receptor protein s... ECM receptor interaction muscle structure development cardiovascular system development muscle system process generation of precursor metaboli... muscle tissue development Oxidative phosphorylation Hypertrophic cardiomyopathy (HCM) regulation of anatomical structu... tissue morphogenesis anatomical structure formation i... cell migration Parkinson's disease glucose metabolic process Dilated cardiomyopathy organ morphogenesis regulation of system process response to endogenous stimulus regulation of cell migration neuron projection morphogenesis positive regulation of cytokine... integrin mediated signaling pathway positive regulation of protein m... positive regulation of catalytic... regulation of hydrolase activity blood coagulation cell chemotaxis innate immune response response to other organism cell adhesion regulation of defense response inflammatory response hemopoiesis positive regulation of immune sy... cell activation regulation of immune response immune effector process response to cytokine stimulus interspecies interaction between... leukocyte migration actin polymerization or depolyme... positive regulation of lymphocyt... cytokine production lymphocyte proliferation hemostasis adaptive immune response regulation of protein phosphoryl... positive regulation of cytokine... peptidyl tyrosine phosphorylation vesicle mediated transport 69

70 Pathway analysis 70

71 And many more.. 71

72 Novel genomes How do we compute RNA-seq gene expression for novel genomes? Must have complete genome sequence (or contigs). Use predicted gene models (all protein BLASTX or EST vs genome data) to create an exon map or de novo assembly of transcripts from RNA-seq data Computationally huge problem: all-against-all similarity searching and multiple overlapping transcripts. 72

73 73

74 RNA seq analysis programs Table 1 Selected list of RNA-seq analysis programs Class Category Package Notes Uses Input Read mapping Unspliced Seed methods Short-read mapping package Smith-Waterman extension Aligning reads to a aligners a (SHRiMP) 41 reference transcriptome Burrows-Wheeler transform methods Stampy 39 Bowtie 43 BWA 44 Probabilistic model Incorporates quality scores Spliced aligners Exon-first methods MapSplice 52 Works with multiple unspliced SpliceMap 50 aligners Transcriptome reconstruction Genome-guided reconstruction Genomeindependent reconstruction Expression quantification Expression quantification Differential expression TopHat 51 Uses Bowtie alignments Seed-extend methods GSNAP 53 Can use SNP databases QPALMA 54 Smith-Waterman for large gaps Aligning reads to a reference genome. Allows for the identification of novel splice junctions Exon identification G.Mor.Se Assembles exons Identifying novel transcripts Genome-guided Scripture 28 Reports all isoforms using a known reference assembly Cufflinks 29 Reports a minimal set of isoforms genome Genome-independent assembly Velvet 61 Reports all isoforms Identifying novel genes and Reads TransABySS 56 transcript isoforms without a known reference genome Gene quantification Alexa-seq 47 Quantifies using differentially included exons Enhanced read analysis of gene expression (ERANGE) 20 Quantifies using union of exons Normalization by expected uniquely mappable area (NEUMA) 82 Quantifies using unique reads Isoform quantification Cufflinks 29 Maximum likelihood estimation of MISO 33 relative isoform expression RNA-seq by expectaion maximization (RSEM) 69 Reads and reference transcriptome Reads and reference genome Alignments to reference genome Quantifying gene expression Reads and transcript models Quantifying transcript isoform expression levels Cuffdiff 29 Uses isoform levels in analysis Identifying differentially DegSeq 79 Uses a normal distribution expressed genes or EdgeR 77 transcript isoforms Differential Expression analysis of count data (DESeq) 78 Myrna 75 Cloud-based permutation method Read alignments to isoforms Read alignments and transcript models 74

75 Comparison of tools 75

76 Challenges Several sequencing technolgies Complex normalization Difficulty to achieve mappability Accurate detection of splice junction Proper summarization methods needed Most challenging for novel genomes Not many algorithms exist for denovo assembly when compared to reference assembly. 76

77 Summary RNA-seq to study RNA content Quantitative than microarrays Can be used for studying different layers of transcription several factors to be considered in experimental design Mapping, transcript assembly, summarization, differential expression and visualization are the major steps in RNA-seq Gene ontology analysis, pathway analysis, integrative study followed by systems biology are the possible proceeding steps of RNA-seq gene lists. 77

Measuring transcriptomes with RNA-Seq. BMI/CS 776 Spring 2016 Anthony Gitter

Measuring transcriptomes with RNA-Seq BMI/CS 776 www.biostat.wisc.edu/bmi776/ Spring 2016 Anthony Gitter gitter@biostat.wisc.edu Overview RNA-Seq technology The RNA-Seq quantification problem Generative