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

2. 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
Example: RNA-Seq analysis with Tuxedo protocol
Summary and future plan

3. Why Next Generation Sequencing
One can sequence hundreds of millions of short sequences (35bp-120bp) in a single run in a short period of time with low per base cost.
Illumina/Solexa GA II / HiSeq 2000, 2500
Life Technologies/Applied Biosystems SOLiD
Roche/454 FLX, Titanium
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
VERY large text files (tens 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, bioinformaticians and biologists

6. Basic NGS Workflow

7. NGS Data Analysis Overview
Olson et al.

8. Outline Goals Bioinformatics Challenges in NGS data analysis
Basics: terminology, data file formats, general workflow
Analysis Pipeline
Sequence QC and preprocessing
Obtaining and preparing reference
Sequence mapping
Downstream analysis workflow and software
RNA-Seq analysis with Tuxedo protocol
Summary and future plan

9. Terminology
Coverage (depth): The number of nucleotides from reads that are mapped to a given position.
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 its origin.
Assembly: Merging of fragments of DNA in order to reconstruct the original sequence.
Duplicate reads: Reads that are identical.
Multi-reads: Reads that can be mapped to multiple locations equally well.

10. What does the data look like? Common NGS Data Formats

11. FASTA Format (Reference Seq)

12. FASTQ Format (reads)

13. FASTQ Format (Illumina Example)
@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 Record
Header
Read Bases
Separator
(with optional repeated header)
Read Quality Scores
Flow Cell ID
Lane
Tile
Tile Coordinates
Barcode
NOTE: for paired-end runs, there is a second file
with one-to-one corresponding headers and reads.
(Passarelli, 2012)

14. Outline Goals Bioinformatics Challenges in NGS data analysis
Basics: terminology, data file formats, general workflow,
Analysis Pipeline
Sequence QC and preprocessing
Obtaining and preparing reference
Sequence mapping
Downstream analysis workflow and software
RNA-Seq analysis with Tuxedo protocol
Summary and future plan

15. Data Analysis Pipeline
Raw reads
Read QC and preprocessing
Read Mapping
Analysis-ready
reads
FASTQ
FASTQC, FASTX-toolkit, PRINSEQ
Local realignment, base quality recalibration
SAM/BAM
Mapped reads
Visualization (IGV, USCS GB)
Bowtie, BWA, MAQ
Collecting reference sequences and annotation
FASTA GTF/GFF
Data
Task
File Format
Software
Whole Genome Sequencing:
Variant calling, annotation
RNA-Seq:
Transcript assembly, quantification
ChIP-Seq :
Peak Calling
Methyl-Seq:
Methylation calling ……

16. Why QC? Sequencing runs cost money
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? $: fastqc s_1_1.fastq;
available on HPC Tutorial :

18. Outline Goals Bioinformatics Challenges in NGS data analysis
Basics: terminology, data file formats, general workflow,
Analysis Pipeline
Sequence QC and preprocessing
Obtaining and preparing reference
Sequence mapping
Downstream analysis workflow and software
RNA-Seq analysis with Tuxedo protocol
Summary and future plan

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

20. Getting reference sequences

21. Getting Reference Annotation

22. Outline Goals Bioinformatics Challenges in NGS data analysis
Basics: terminology, data file formats, general workflow,
Analysis Pipeline
Sequence QC and preprocessing
Obtaining and preparing reference
Sequence mapping
Downstream analysis workflow and software
RNA-Seq analysis with Tuxedo protocol
Summary and future plan

23. Sequence Mapping Challenges
Alignment (Mapping) is the first steps once read sequences 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.

24. Short Read Alignment
Olson et al.

25. Short Read Alignment Software

26. Short Reads Mapping Software

27. How to choose an aligner?
There are many aligners and they vary a lot in performance (accuracy, memory usage, speed, 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.

28. Outline Goals Bioinformatics Challenges in NGS data analysis
Basics: terminology, data file formats, general workflow,
Analysis Pipeline
Sequence QC and preprocessing
Obtaining and preparing reference
Sequence mapping
Downstream analysis workflow and software
RNA-Seq analysis with Tuxedo protocol
Summary and future plan

29. NGS Applications and Analysis Strategy
Name
Nucleic acid population
Brief analysis strategy
RNA-Seq
RNA (may be poly-A mRNA or total RNA)
Alignment of reads to “genes”; variations for detecting splice junctions and quantifying abundance
Small RNA sequencing
Small RNA (often miRNA)
Alignment of reads to small RNA references (e.g. miRbase), then to the genome; quantify abundance
ChIP-Seq
DNA bound to protein, captured via antibody (ChIP = Chromatin ImmunoPrecipitation)
Align reads to reference genome, identify peaks & motifs
RIP-Seq
RNA bound to protein, captured via antibody (RIP = RNA ImmunoPrecipitation)
Align reads to reference genome and/or “genes”, identify peaks and motifs
Methylation Analysis
Select methylated genomic DNA regions, or convert methylated nucleotides to alternate forms
Align reads to reference and either identify peaks or regions of methylation
SNP calling/ discovery
All or some genomic DNA or RNA
Either align reads to reference and identify statistically significant SNPs, or compare multiple samples to each other to identify SNPs
Structural Variation Analysis
Genomic DNA, with two reads (mate-pair reads) per DNA template
Align mate-pairs to reference sequence and interpret structural variants
de novo Sequencing
Genomic DNA (possibly with external data e.g. cDNA, genomes of closely related species, etc.)
Piece-together reads to assemble contigs, scaffolds, and (ideally) whole-genome sequence
Metagenomics
Entire RNA or DNA from a (usually microbial) community
Phylogenetic analysis of sequences
(Hunicke-Smith et al, 2010)

30. Application Specific Software
Mapped reads
Whole Genome Sequencing, Exome Sequencing
RNA-Seq:
Transcriptome analysis
ChIP-Seq :
Protein DNA binding site,
Methyl-Seq:
Methylation pattern analysis ……
Peak Identification
Variant Calling: SNPs, InDels
1: Transcriptome assembly
2. Abundance quantification
3. Differential expression and regulation
Methylation calling
ssahaSNP, Samtools, PyroBayes
Tophat, STAR, Cufflinks, edgeR,
MACS, AREM, PeakSeq
Bismark, BS Seeker

31. Outline Goals Bioinformatics Challenges in NGS data analysis
Basics: terminology, data file formats, general workflow,
Analysis Pipeline
Sequence QC and preprocessing
Obtaining and preparing reference
Sequence mapping
Downstream analysis workflow and software
RNA-Seq analysis with Tuxedo protocol
Summary and future plan

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

33. 1. Spliced Alignment: Tophat
Tophat : a spliced short read aligner for RNA-seq.
$ tophat -p 8 -G genes.gtf -o C1_R1_thout genome C1_R1_1.fq C1_R1_2.fq
$ tophat -p 8 -G genes.gtf -o C1_R2_thout genome C1_R2_1.fq C1_R2_2.fq
$ tophat -p 8 -G genes.gtf -o C2_R1_thout genome C2_R1_1.fq C2_R1_2.fq
$ tophat -p 8 -G genes.gtf -o C2_R2_thout genome C2_R2_1.fq C2_R2_2.fq

34. 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

35. 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

36. 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

37. Integrative Genomics Viewer (IGV)

38. Visualizing RNA-seq mapping with IGV
Specify range or tem in search box
Click on ruler
Click and drag
Use scroll bar
Use keyboard:
Arrow keys, Page up Page down, Home, End
Neilsen, C.B., et al. Visualizing Genomes: techniques and challenges Nature Methods 7:S5‐S15 (2010)

39. 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 with Tuxedo suite
Thank you!