1. Introduction To Next Generation Sequencing (NGS) Data Analysis
Jenny Wu
Genomics High Throughput Facility
UCI

2. Outline Goals : Practical guide to NGS data processing
Bioinformatics in NGS data analysis
Basics: terminology, data formats, general workflow etc.
Data Analysis Pipeline
Sequence QC and preprocessing
Downloading reference sequences
Sequence mapping
Downstream analysis workflow and software
RNA-Seq data analysis
Concepts: spliced alignment, normalization, coverage, differential expression.
Tuxedo suite: Popular RNA-Seq pipeline
Data visualization with Genome Browsers.
RNA-Seq pipeline software: Galaxy vs. shell scripting
Downstream Pathway analysis
ChIP-Seq data analysis workflow and software
NGS bioinformatics resources
Summary

3. Why Next Generation Sequencing
One can generate hundreds of millions of short sequences (up to 250bp) in a single run in a short period of time with low per base cost.
Illumina/Solexa GA II, HiSeq 2500, 3000,X
Roche/454 FLX, Titanium
Life Technologies/Applied Biosystems SOLiD
(200MX8)=1.6 billion DNA fragments can be sequenced in parallel in a single run, to produce a total of 320Gbp(HiSeq 2000)
200M*300/3G=20X
Reviews: Michael Metzker (2010) Nature Reviews Genetics 11:31
Quail et al (2012) BMC Genomics Jul 24;13:341.

4. Why Bioinformatics
Informatics
(wall.hms.harvard.edu)

5. Bioinformatics Challenges in NGS Data Analysis
“Big Data” (thousands of millions of lines long)
Can’t do ‘business as usual’ with familiar tools
Impossible memory usage and execution time
Manage, analyze, store, transfer and archive huge files
Need for powerful computers and expertise
Informatics groups must manage compute clusters
New algorithms and software are required and often time they are open source Unix/Linux based.
Collaboration of IT experts, bioinformaticians and biologists

6. Basic NGS Workflow
Olson et al.

7. NGS Data Analysis Overview
Olson et al.

8. Outline Goals : Practical guide to NGS data processing
Bioinformatics in NGS data analysis
Basics: terminology, data formats, general workflow etc.
Data Analysis Pipeline
Sequence QC and preprocessing
Downloading reference sequences: query NCBI, UCSC databases.
Sequence mapping
Downstream analysis workflow and software
RNA-Seq data analysis
Concepts: spliced alignment, normalization, coverage, differential expression.
Tuxedo suite: Tophat, Cufflinks and cummeRbund
Data visualization with Genome Browsers.
RNA-Seq pipeline software: Galaxy vs. shell scripting
ChIP-Seq data analysis workflow and software
NGS bioinformatics resources
Summary

9. Terminology Data analysis: Experimental Design:
Coverage (sequencing depth): The number of nucleotides from reads that are mapped to a given position.
Paired-End Sequencing: Both end of the DNA fragment is sequenced, allowing highly precise alignment.
Multiplexed Sequencing: "barcode" sequences are added to each sample so they can be distinguished in order to sequence large number of samples on one lane.
Data analysis:
Quality Score: Each called base comes with a quality score which measures the probability of base call error.
Mapping: Align reads to reference to identify their origin.
Assembly: Merging of fragments of DNA in order to reconstruct the original sequence.
Duplicate reads: Reads that are identical. Can be identified after mapping.
Multi-reads: Reads that can be mapped to multiple locations equally well.

10. What does the data look like? Common NGS Data Formats
For a full list, go to

11. File Formats Reference sequences, reads: Alignments:
FASTA
FASTQ (FASTA with quality scores)
Alignments:
SAM (Sequence Alignment Mapping)
BAM (Binary version of SAM)
Features, annotation, scores:
GFF3/GTF(General Feature Format)
BED/BigBed
WIG/BigWig

12. FASTA Format (Reference Seq)

13. FASTQ Format (Illumina Example)
Flow Cell ID
Lane
Tile
Tile Coordinates
Barcode
Read Record
Header
@DJG84KN1:272:D17DBACXX:2:1101:12432:5554 1:N:0:AGTCAA
CAGGAGTCTTCGTACTGCTTCTCGGCCTCAGCCTGATCAGTCACACCGTT +
BCCFFFDFHHHHHIJJIJJJJJJJIJJJJJJJJJJIJJJJJJJJJIJJJJ
@DJG84KN1:272:D17DBACXX:2:1101:12454:5610 1:N:0:AG
AAAACTCTTACTACATCAGTATGGCTTTTAAAACCTCTGTTTGGAGCCAG
@DJG84KN1:272:D17DBACXX:2:1101:12438:5704 1:N:0:AG
CCTCCTGCTTAAAACCCAAAAGGTCAGAAGGATCGTGAGGCCCCGCTTTC
@DJG84KN1:272:D17DBACXX:2:1101:12340:5711 1:N:0:AG
GAAGATTTATAGGTAGAGGCGACAAACCTACCGAGCCTGGTGATAGCTGG
CCCFFFFFHHHHHGGIJJJIJJJJJJIJJIJJJJJGIJJJHIIJJJIJJJ
Read Bases
Separator
(with optional repeated header)
Read Quality Scores
NOTE: for paired-end runs, there is a second file
with one-to-one corresponding headers and reads.
(Passarelli, 2012)

14. Outline Goals : Practical guide to NGS data processing
Bioinformatics in NGS data analysis
Basics: terminology, data formats, general workflow etc.
Data Analysis Pipeline
Sequence QC and preprocessing
Downloading reference sequences: query NCBI, UCSC databases.
Sequence mapping
Downstream analysis workflow and software
RNA-Seq data analysis
Concepts: spliced alignment, normalization, coverage, differential expression.
Tuxedo suite: Tophat, Cufflinks and cummeRbund
Data visualization with Genome Browsers.
RNA-Seq pipeline software: Galaxy vs. shell scripting
ChIP-Seq data analysis workflow and software
Scripting Languages and bioinformatics resources
Summary

15. General Data Pipeline

16. Why QC? Sequencing runs cost money Data analysis costs money and time
Consequences of not assessing the Data
Sequencing a poor library on multiple runs – throwing money away!
Data analysis costs money and time
Cost of analyzing data, CPU time $$
Cost of storing raw sequence data $$$
Hours of analysis could be wasted $$$$
Downstream analysis can be incorrect.

17. How to QC? $ module load fastqc $ fastqc s_1_1.fastq;
available on HPC Tutorial :

18. FastQC: Example

19. Outline Goals : Practical guide to NGS data processing
Bioinformatics in NGS data analysis
Basics: terminology, data formats, general workflow etc.
Data Analysis Pipeline
Sequence QC and preprocessing
Downloading reference sequences
Sequence mapping
Downstream analysis workflow and software
RNA-Seq data analysis
Concepts: spliced alignment, normalization, coverage, differential expression.
Tuxedo suite: Tophat, Cufflinks and cummeRbund
Data visualization with Genome Browsers.
RNA-Seq pipeline software: Galaxy vs. shell scripting
ChIP-Seq data analysis workflow and software
Scripting Languages and bioinformatics resources
Summary

20. Premade Genome Sequence Indexes and Annotation

21. The UCSC Genome Browser Homepage
General information
Get genome annotation here!
Get reference sequences here!
Specific information—
new features, current status, etc.

22. Downloading Reference Sequences

23. Downloading Reference Annotation

24. Outline Goals : Practical guide to NGS data processing
Bioinformatics in NGS data analysis
Basics: terminology, data formats, general workflow etc.
Data Analysis Pipeline
Sequence QC and preprocessing
Downloading reference sequences: query NCBI, UCSC databases.
Sequence mapping
Downstream analysis workflow and software
RNA-Seq data analysis
Concepts: spliced alignment, normalization, coverage, differential expression.
Tuxedo suite: Tophat, Cufflinks and cummeRbund
Data visualization with Genome Browsers.
RNA-Seq pipeline software: Galaxy vs. shell scripting
ChIP-Seq data analysis workflow and software
Scripting Languages and bioinformatics resources
Summary

25. Sequence Mapping Challenges
Alignment (Mapping) is often the first step once analysis-read reads are obtained.
The task: to align sequencing reads against a known reference.
Difficulties: high volume of data, size of reference genome, computation time, read length constraints, ambiguity caused by repeats and sequencing errors.

26. How to choose an aligner?
There are many short read aligners and they vary a lot in performance(accuracy, memory usage, speed and flexibility etc).
Factors to consider : application, platform, read length, downstream analysis, etc.
Constant trade off between speed and sensitivity (e.g. MAQ vs. Bowtie).
Guaranteed high accuracy will take longer.
Popular choices: Bowtie, BWA, Tophat, STAR.

27. Outline Goals : Practical guide to NGS data processing
Bioinformatics in NGS data analysis
Basics: terminology, data formats, general workflow etc.
Data Analysis Pipeline
Sequence QC and preprocessing
Downloading reference sequences: query NCBI, UCSC databases.
Sequence mapping
Downstream analysis workflow and software
RNA-Seq data analysis
Concepts: spliced alignment, normalization, coverage, differential expression.
Tuxedo suite: Tophat, Cufflinks and cummeRbund
Data visualization with Genome Browsers.
RNA-Seq pipeline software: Galaxy vs. shell scripting
ChIP-Seq data analysis workflow and software
Scripting Languages and bioinformatics resources
Summary

28. Application Specific Software

29. Outline Goals : Practical guide to NGS data processing
Bioinformatics in NGS data analysis
Basics: terminology, data formats, general workflow etc.
Data Analysis Pipeline
Sequence QC and preprocessing
Downloading reference sequences: query NCBI, UCSC databases.
Sequence mapping
Downstream analysis workflow and software
RNA-Seq data analysis
Concepts: spliced alignment, normalization, coverage, differential expression.
Tuxedo suite: Tophat, Cufflinks and cummeRbund
Data visualization with Genome Browsers.
RNA-Seq pipeline software: Galaxy vs. shell scripting
ChIP-Seq data analysis workflow and software
Scripting Languages and bioinformatics resources
Summary

31. Two Major Approaches DESeq2, EdgeR, DEXSeq…
 1. Gene or Exon level differential expression (DE):
DESeq2, EdgeR, DEXSeq…
2. Transcripts assembly :
Trinity, Velvet-Oasis, TransABySS, Cufflinks, Scripture…

32. RNA-Seq Pipeline for DE

33. RNA-Seq: Spliced Alignment
Some reads will span two different exons
Need long enough reads to be able to reliably map both sides
Use a splice aware aligner!
“Systematic evaluation of spliced alignment programs for RNA-seq data”
Nature Methods, 2013

34. How much sequence do I need?
Oversimplified answer:20-50M PE/sample (Human/Mouse)
Depends on:
Size and complexity of transcriptome.
Goal of experiment: DE, transcript discovery.
Tissue type, library type, RNA quality, read length, single-end…

35. RNA-Seq: Coverage Coverage in RNA-Seq is highly non-uniform
Within a single exon, there are regions with high coverage and regions with zero coverage.
They change when the library preparation protocol is changed.
The binding preferences of random hexamer primers explain them only partially.
We simply hope that this averages out over the whole transcript !

36. RNA-Seq: Normalization
Gene-length bias
• Differential expression of longer genes is more significant because long genes yield more reads
RNA-Seq normalization methods:
Scaling factor based: Total count, upper quartile, median, DESeq, TMM in edgeR
Quantile, RPKM (cufflinks)
ERCC
Normalize by gene length and by number of reads mapped, e.g. RPKM/FPKM (reads/fragments per kilo bases per million mapped reads)

37. RNA-Seq: Differential Expression
 Discrete vs. Continuous data:
 Microarray florescence intensity data: continuous
Modeled using normal distribution  
RNA-Seq read count data: discrete
 Modeled using negative binomial distribution
Microarray software can NOT be directly used to analyze RNA-Seq data!

38. Outline Goals : Practical guide to NGS data processing
Bioinformatics in NGS data analysis
Basics: terminology, data formats, general workflow etc.
Data Analysis Pipeline
Sequence QC and preprocessing
Downloading reference sequences: query NCBI, UCSC databases.
Sequence mapping
Downstream analysis workflow and software
RNA-Seq data analysis
Concepts: spliced alignment, normalization, coverage, differential expression.
Popular RNA-Seq pipeline: Tuxedo suite, HTSeq-DESeq
Data visualization with Genome Browsers.
RNA-Seq pipeline software: Galaxy vs. shell scripting
ChIP-Seq data analysis workflow and software
Scripting Languages and bioinformatics resources
Summary

39. Popular RNA-Seq DE Pipeline
(The Tuxedo Protocol)
(The Alternative Protocol)

40. Classic RNA-Seq (Tuxedo Protocol)
Classic RNA-Seq (Tuxedo Protocol)
Spliced Read mapping
SAM/BAM
2. Transcript assembly and quantification
GTF/GFF
3. Merge assembled transcripts from multiple samples
4. Differential Expression analysis

41. Classic vs. Advanced RNA-Seq workflow

42. 1. Spliced Alignment: Tophat
$ tophat -p 8 -G genes.gtf -o C1_R1_thout ptgenome C1_R1_1.fq C1_R1_2.fq
$ tophat -p 8 -G genes.gtf -o C1_R2_thout ptgenome C1_R2_1.fq C1_R2_2.fq
$ tophat -p 8 -G genes.gtf -o C2_R1_thout ptgenome C2_R1_1.fq C2_R1_2.fq
$ tophat -p 8 -G genes.gtf -o C2_R2_thout ptgenome C2_R2_1.fq C2_R2_2.fq

43. 2.Transcript assembly and abundance quantification: Cufflinks
Cufflinks: a program that assembles aligned RNA-Seq reads into transcripts, estimates their abundances, and tests for differential expression and regulation transcriptome-wide.
$ cufflinks -p 8 -o C1_R1_clout C1_R1_thout/ accepted_hits.bam
$ cufflinks -p 8 -o C1_R2_clout C1_R2_thout/ accepted_hits.bam
$ cufflinks -p 8 -o C2_R1_clout C2_R1_thout/ accepted_hits.bam
$ cufflinks -p 8 -o C2_R2_clout C2_R2_thout/ accepted_hits.bam

44. 3. Final Transcriptome assembly: Cuffmerge
$ cuffmerge -g genes.gtf -s genome.fa -p 8 assemblies.txt
$ more assembies.txt
./C1_R1_clout/transcripts.gtf
./C1_R2_clout/transcripts.gtf
./C2_R1_clout/transcripts.gtf
./C2_R2_clout/transcripts.gtf

45. 4.Differential Expression: Cuffdiff
CuffDiff: a program that compares transcript abundance between samples.
$ cuffdiff -o diff_out -b genome.fa -p 8 –L C1,C2 -u merged_asm/merged.gtf
./C1_R1_thout/accepted_hits.bam, ./C1_R2_thout/accepted_hits.bam
./C2_R1_thout/accepted_hits.bam, ./C2_R2_thout/accepted_hits.bam

46. Cufflinks and related resources
Pachter, L. Models for transcript
quantification from RNA-Seq.arXiv preprint arXiv: (2011).
• Trapnell C, Williams BA, Pertea
G, Mortazavi AM, Kwan G, van
Baren MJ, Salzberg SL, Wold B,
Pachter L.
Transcript assembly and
quantification by RNA-Seq
reveals unannotated transcripts
and isoform switching during
cell differentiation
Nature Biotechnology doi:
/nbt.1621
• Roberts A, Trapnell C, Donaghey
J, Rinn JL, Pachter L.
Improving RNA-Seq expression
estimates by correcting for
fragment bias
Genome Biology doi: /
gb r22
• Roberts A, Pimentel H, Trapnell
C, Pachter L.
Identification of novel
transcripts in annotated
genomes using RNA-Seq
Bioinformatics doi: /
bioinformatics/btr355

47. Alternative Pipeline with HTSeq
Tophat2,
HTSeq
DESeq2/edgeR
$ htseq-count -f bam C1_R1_thout/sorted.bam -s no –o hsc/C1_R1.counts

48. HTSeq Output: Gene Count Table… …

49. DESeq2

50. Downstream Analysis Pathway and functional analysis:
Gene Ontology over representation
Gene Set Enrichment Analysis (GSEA)
Signaling Pathway Impact Analysis
Software
DAVID, GSEA, WGCNA, Blast2go, topGO..
IPA, GeneGO MetaCore

51. Outline Goals : Practical guide to NGS data processing
Bioinformatics in NGS data analysis
Basics: terminology, data file formats, general workflow
Data Analysis Pipeline
Sequence QC and preprocessing
Obtaining and preparing reference
Sequence mapping
Downstream analysis workflow and software
RNA-Seq data analysis
spliced alignment, normalization, coverage, differential expression.
Tuxedo suite: Tophat/Cufflinks parameters setting, cummeRbund
Data Visualization
RNA-seq pipeline software: RobiNA, Galaxy
ChIP-Seq data analysis workflow and software
Open source pipeline software with Graphical User Interface
Summary

52. Integrative Genomics Viewer (IGV)
Available on HPC. Use ‘module load igv’ and ‘igv’

53. Visualizing RNA-Seq mapping with IGV
Integrative Genomics Viewer (IGV): high-performance genomics data visualization and exploration.Thorvaldsdóttir H et al. Brief Bioinform. 2013

54. Outline Goals : Practical guide to NGS data processing
Bioinformatics in NGS data analysis
Basics: terminology, data formats, general workflow etc.
Data Analysis Pipeline
Sequence QC and preprocessing
Downloading reference sequences: query NCBI, UCSC databases.
Sequence mapping
Downstream analysis workflow and software
RNA-Seq data analysis
Concepts: spliced alignment, normalization, coverage, differential expression.
Tuxedo suite: Tophat, Cufflinks and cummeRbund
Data visualization with Genome Browsers.
RNA-Seq pipeline software: Galaxy
ChIP-Seq data analysis workflow and software
Scripting Languages and bioinformatics resources
Summary

55. Galaxy: Web based platform for analysis of large datasets
Galaxy: A platform for interactive large-scale genome analysis: Genome Res :

56. Outline Goals : Practical guide to NGS data processing
Bioinformatics in NGS data analysis
Basics: terminology, data formats, general workflow etc.
Data Analysis Pipeline
Sequence QC and preprocessing
Downloading reference sequences: query NCBI, UCSC databases.
Sequence mapping
Downstream analysis workflow and software
RNA-Seq data analysis
Concepts: spliced alignment, normalization, coverage, differential expression.
Tuxedo suite: Tophat, Cufflinks and cummeRbund
Data visualization with Genome Browsers.
RNA-Seq pipeline software: Galaxy vs. shell scripting
ChIP-Seq data analysis workflow and software
Scripting Languages and bioinformatics resources
Summary

57. 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.

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

59. A View of ChIP-Seq Data
Typically reads (35-55bp) are quite sparsely distributed over the genome.
Controls (i.e. no pull-down by antibody) often show smaller peaks at the same locations
Rozowsky et al Nature Biotech, 2009

60. ChIP-Seq Analysis Pipeline
Sequencing
Base Calling
Read QC
Short read
Sequences
Short read
Alignment
Peak Calling
Enriched Regions
Visualization with genome browser
Differential peaks
Motif Discovery
Combine with gene expression

61. 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.
Determining the exact binding sites from short reads generated from ChIP-Seq experiments
SISSRs (Site Identification from Short Sequence Reads) (Jothi 2008)
MACS (Model-based Analysis of ChIP-Seq) (Zhang et al, 2008)

62. 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

63. Resources in NGS data analysis
Stackoverflow.com

64. Summary
NGS technologies are transforming molecular biology.
Bioinformatics analysis is a crucial part in NGS applications
Data formats, terminology, general workflow
Analysis pipeline
Software for various NGS applications
RNA-Seq and ChIP-Seq data analysis
Pathway Analysis
Data visualization
Bioinformatics resources
The current generation of DNA sequencing technologies have created massive, basepair resolution datasets that are ideally suited for systems biology studies centered on transcription. Primarily ChiP-seq, RNA-seq, Dnase-seq A new generation of tools to analyze individual datasets exist, with the integrative analysis becoming ever more critical.
 Genomics is affecting all fields of biology and will eventually move into medicine.
Thank you!