Introduction to Next-Generation Sequencing Data and Related Bioinformatic Analysis

Han Liang, Ph.D.Department of Bioinformatics and

Computational Biology3/25/2014 @ Rice University

Outline

• History• NGS Platforms• Applications• Bioinformatics Analysis• Challenges

Central Dogma

Sanger sequencing

• DNA is fragmented• Cloned to a plasmid

vector• Cyclic sequencing

reaction• Separation by

electrophoresis• Readout with

fluorescent tags

‘Sanger sequencing’ has been the only DNA sequencing method for 30 years but……hunger for even greater sequencing throughput

and more economical sequencing technology…NGS has the ability to process millions of sequence

reads in parallel rather than 96 at a time (1/6 of the cost)

Objections: fidelity, read length, infrastructure cost, handle large volum of data

.

• Roche/454 FLX: 2004• Illumina Solexa Genome Analyzer: 2006• Applied Biosystems SOLiDTM System: 2007• Helicos HeliscopeTM : recently available• Pacific Biosciencies SMRT: launching 2010

Quickly reduced Cost

Three Leading Sequencing Platforms

• Roche 454 • Illumina Solexa• Applied Biosystems SOLiD

The general experimental procedure

Wang et al. Nature Reviews Genetics 2009

454

bead microreactor

Maridis Annu. Rev. Genome. Human Genet. 2008

Illumina (Solexa)

Bridge amplification

Maridis Annu. Rev. Genome. Human Genet. 2008

SOLiD

color coding

Maridis Annu. Rev. Genome. Human Genet. 2008

Comparison of existing methods

Real Data – nucleotide space• Solexa@SRR002051.1 :8:1:325:773 length=33AAAGAACATTAAAGCTATATTATAAGCAAAGAT+SRR002051.1 :8:1:325:773 length=33IIIIIIIIIIIIIIIIIIIIIIIII'II@I$)-@SRR002051.2 :8:1:409:432 length=33AAGTTATGAAATTGTAATTCCAATATCGTAAGC+SRR002051.2 :8:1:409:432 length=33IIIIIIIIIIIIIIIIIIIIIIIIIIIIIII07@SRR002051.3 :8:1:488:490 length=33AATTTCTTACCATATTAGACAAGGCACTATCTT+SRR002051.3 :8:1:488:490 length=33IIIIIIIIIIIIIIIIIIIIIIIIIIIIIII&I

Real Data – color space• SOLiD Data>1_24_47_F3T1.1.23..0120230.320033300030030010022.00.0201.0201>1_24_52_F3T2.3.21..2122321.213110332101132321002.11.0111.1222>1_24_836_F3T0.2.22..2222222.010203032021102220200.01.2211.2211>1_24_1404_F3T2.3.30..2013222.222103131323012313233.22.2220.0213>1_25_202_F3T0.3213.111202312203021101111330201000313.121122211>1_25_296_F3T0.1130.100123202213120023121112113212121.013301210

Data output difference among the three platforms

• Nucleotide space vs. color space• Length of short reads 454 (400~500 bp) > SOLiD (70 bp) ~ Solexa (36~120bp)

Applications with “Digital output”

• De novo genome assembly• Genome re-sequencing• RNA-Seq (gene expression, exon-intron

structure, small RNA profiling, and mutation)• CHIP-Seq (protein-DNA interaction)• Epigenetic profiling

• Degraded state of the sample mitDNA sequencing• Nuclear genomes of ancient remains: cave bear, mommoth, Neanderthal (106

bp )

Problems: contamination modern humans and coisolation bacterial DNA

• Key part in regulating gene expression

• Chip: technique to study DNA-protein interaccions

• Recently genome-wide ChIP-based studies of DNA-protein interactions

• Readout of ChIP-derived DNA sequences onto NGS platforms

• Insights into transcription factor/histone binding sites in the human genome

• Enhance our understanding of the gene expression in the context of specific environmental stimuli

• ncRNA presence in genome difficult to predict by computational methods with high certainty because the evolutionary diversity

• Detecting expression level changes that correlate with changes in environmental factors, with disease onset and progression, complex disease set or severity

• Enhance the annotation of sequenced genomes (impact of mutations more interpretable)

• Characterizing the biodiversity found on Earth• The growing number of sequenced genomes enables us to interpret partial

sequences obtained by direct sampling of specif environmental niches.• Examples: ocean, acid mine site, soil, coral reefs, human microbiome which may

vary according to the health status of the individual

• Common variants have not yet completly explained complex disease genetics rare alleles also contribute

• Also structural variants, large and small insertions and deletions

• Accelerating biomedical research

• Enable of genome-wide patterns of methylation and how this patterns change through the course of an organism’s development.

• Enhanced potential to combine the results of different experiments, correlative analyses of genome-wide methylation, histone binding patterns and gene expression, for example.

Kahvejian et al. 2008

:Integrating Omics

mRNA expression

Alternative Splicing

microRNA expression

Protein-DNA interaction

Mutation discovery

Copy number variation

Data Analysis Flow

SOLiD machine:

Raw data

Central ServerBasic processing

decoding, filter and mapping

Local MachineDownstream analysis

Short Read Mapping

• DNA-Resequencing BLAST-like approach• RNA-Seq

Read length and pairing

• Short reads are problematic, because short sequences do not map uniquely to the genome.

• Solution #1: Get longer reads.• Solution #2: Get paired reads.

ACTTAAGGCTGACTAGC TCGTACCGATATGCTG

Post-alignment Analysis

• DNA-SEQ• SNP calling• RNA-SEQ• Quantifying gene expression level

ConceptsThe reference genome: hg19 (GRC37)Main assembly: Chr1-22, X, and Y3,095,677,412 bp

Target Region: exonome

Ensembl: 85.3 Million (2.94%)RefSeq: 67.7Million (2.34%)ccds: 31,266,049 (1.08%) consisting of 185,446 nr exons

Target Coverage

SOLiD

color coding

Maridis Annu. Rev. Genome. Human Genet. 2008

SNP calling

Array-based High-throughput Dataset

Limitations of hybridization-based approach

• Reliance existing knowledge about genome sequence

• Background noise and a limited dynamic detecting range

• Cross-experiment comparison is difficult• Requiring complicated normalization methods

Wang et al. Nature Reviews Genetics 2009

Quantifying gene expression using RNA-Seq data

RPKM: Reads Per Kb exon length and Millions of mapped readings

Large Dynamic Range

Mortazavi et al. Nature Methods 2008

High reproducibility

Mortazavi et al. Nature Methods 2008

High Accuracy

Wang et al. Nature 2008

Advantages of RNA-Seq

• Not limited to the existing genomic sequence• Very low (if any) background signal• Large dynamic detecting range• Highly reproducibility• Highly accurate• Less sample • Low cost per base

Wang et al. Nature Reviews Genetics 2009

Huge amount of data!

• For a typical RNA-Seq SOLiD run, ~ 2T image file ~ 120G text file for downstream analysis ~ 75 M short reads per sample

Efficient methods for data storage and management

Considerable sequencing error

High-quality image analysis for base calling

Genome alignment and assembly: time consuming and memory demanding

• To perform genome mapping for SOLiD data

32-opteron HP DL785 with 128GB of ram 12~14 hours per sample

High-performance parallel computing

Bioinformatics Challenges

• Efficient methods to store, retrieve and process huge amount of data

• To reduce errors in image analysis and base calling

• Fast and accurate for genome alignment and assembly

• New algorithms in downstream analyses

Experimental ChallengesLibrary fragmentation

Strand specific

Wang et al. Nature Reviews Genetics 2009

Question& Answer

Han LiangE-mail: hliang1@mdanderson.orgTel: 713-745-9815