This document summarizes a presentation on next-generation genomics and integrative analysis. It discusses the types of genomic data available from techniques like genome sequencing, RNA sequencing, ChIP-seq, and epigenomics. It explains that integrative analysis can help annotate functional features, infer variant function, and understand gene regulation. Approaches to integration include data reduction, unsupervised clustering, and supervised Bayesian networks. Large-scale datasets can be accessed through browsers, add-ons, and standalone tools to generate novel hypotheses. Future work includes more integrated community resources with search capabilities.

1. Korea Center for Disease Control & Prevention
Next-generation genomics:
an integrative approach
Chang Bum Hong
Division of Structural and functional Genomics, Center for Genome Sciences, NIH
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2. twitter

3. APPLICATIONS OF NEXT-GENERATION SEQUENCING
2011
• Genome structural variation discovery and genotyping
• RNA sequencing: advances, challenges and opportunities
• Charting histon modifications and the functional organization of mammalian genomes
2010
• Evaluating genome-scale approaches to eukaryotic DNA replication
• Advances in understanding cancer genomes through second-generation sequencing
• Genome-wide allele-specific analysis: insights into regulatory variation
• Next-generation genomics: an integrative approach
• Uncovering the roles of rare variants in common disease through whole-genome sequencing
• Principles and challenges of genome-wide DNA methylation analysis
• Prokaryotic transcriptomics: a new view on regulation, physiology and pathogenicity
• Sequencing technologies - the next generation
• RNA processing and its regulation: global insights into biological networks
2009
• The complex eukaryotic transcriptome: unexpected pervasive transcription and novel small RNAs
• ChIP-seq: advantages and challenges of a maturing technology
• Insights from genomic profiling of transcription factors
• RNA-Seq: a revolutionary tool for transcriptomics

5. Genome-scale data
GWAS, ChIP-seq and RNA-seq
Phenotype
Disease
Proteomics
Translated into
proteins Protein
Chromatin immuniprecipitation sequencing
Sequencing of bisulfite-treated DNA
Transcriptomics
DNA being
transcribed into RNA
RNA
Transcriptome sequencing Epigenome
Small RNA sequencing
DNA Genomics
Complete genome resequencing
Targeted genomic resequencing
de novo sequencing

6. Next-generation sequencing
• We define this as the use of established sequencing
platforms, including the
• Illumia/Solexa Genome Analyzer HiSeq 2000 MiSeq
• Roche/454 Genome Sequencer
• Applied Biosystems SOLiD Genome Sequencer
FLX System
GS Junior
• Helicos and Pacific Biosciences
5500xl SOLid System Ion Personal
Genome Machine
HeliScope Single Molecule
Jay Flatley Greg Lucier PACBIO RS
Sequncer

7. Jim Watson Craig Venter
Jay Flatley Greg Lucier Stephen Quake
?
John West
Illumina CEO Life Technogoies CEO Founder of Helicos Former Illumina CEO

8. 94 x Illumina GA2, 10 x 454, 8 x SOLiD3/4, 1 x Heliscope, 1 x Polonator, 1 x PacBio
Broad Institute
BGI
1 x 454, 27 x SOLiD3/4, 128 x Illumina HiSeq
Next Generation Genomics: World Map of High-throughput Sequencers
http://pathogenomics.bham.ac.uk/hts/
GMI at Seoul National University College of Medicine 10 x Illumina GA2
Macrogen 10 x Illumina GA2, 1 x 454, 2 x SOLiD3/4
NICEM Illumina GA2, 454
Gachon University of Medicine and Science Illumina GA2, 2 x SOLiD 3/4
KRIBB 1x Illumina GA2

9. Sequencing technologies
• Next-next....-generation: how many ‘next’s are there?
• First Generation: automated version of Sanger sequencing(DNA-sequencing method
invented by Fred Sanger in the 1970s)
• Second Generation
• Roche/454 sequencing machine from 454 Life Science(2005)
• 450 bases per read / $0.02 per 1000 bases / 2 days per Gb
• Solexa from Illumina(2006)
• 75 bases per read / $0.01 per 1000 bases / 0.5 days per Gb
• SOLiD from Applied BioSystem(2006)
• 50 bases per read / $0.001 per 1000 bases / 0.5 days per Gb
• Next-Next-Gen - Third Generation?
• Hiseq2000 from Illumina - 0.04 days per Gb
• Helicos Heliscope
• Pacific Biosciences SMART

10. Sequencing technologies
Feature generation
Shendure & Ji, 2008
Michael L. Metzker, 2010

11. Sequencing technologies
Sequencing by synthesis
Michael L. Metzker, 2010

12. NGS typical procedure
• Sequencing
• How deep?
• Single, Paired read or both
• Alignment
• References, assemble or both
• Experimental specific analysis
• A ‘one-size-fits-all’ program dose not exist

13. Applications
• Sequence assembly
• Whole Genome Assembly (Reference, De novo)
• Transcriptome Assembly
• Short Sequence Alignment
• Single read
• Paired read
• Genomic Variation Detection
• Detection of Single Nucleotide Polymorphism (SNP)
• Detection of Alternative Splicing Event
• Detection of major/minor transcript isoforms

14. Applications
Shendure & Ji, 2008

15. Bioinformatics tools
Shendure & Ji, 2008

16. File Format
• Sequence Reads
• fastq
• fasta
• Alignment
• Sequence Alignment Map (SAM)
• BAM (Binary Alignment Map)
• Variation
• VCF (Variation Call Format)

17. Data: Sequence Reads

18. Data: Sequence Reads
A challenge call for a new compression algorithm
Compression of genomic sequences in FASTQ format

19. Data: Sequence Reads
Sebastian Deorowicz et.al, 2011
Compress type Compress time Size
gzip 14s 28M
bzip2 9.75s 23M
dsrc 1.36s 21M

20. Example of Applications
• ChIP-Seq
• allows you to assay the amount of binding and location
of a protein to DNA, such as a transcription factor
bound to the start site of a gene, or a histones of a
certain type
• RNA-Seq
• Mapping transcription start sites
• Characterization of alternative splicing patterns
• Gene fusion detection
• Estimation of the abundance of the transcripts from
their depth of coverage in the mapping

21. ChIP-Seq
Chromatin immunoprecipitation (ChIP)
Kharchenko et al, 2008
Barski A & Zhao K, 2009 Shirely et al, 2009

22. ChIP-Seq
Shirely et al, 2009

23. ChIP-Seq Software packages
Shirely et al, 2009

24. RNA-Seq
RNA-Seq (De novo RNA-Seq(Transcriptome
transcriptome assembly) resequencing)
Zhong Wang, 2009

25. RNA-Seq
RNA-Seq mapping of short reads over exon-exon
RNA-Seq mapping of short reads in exon-exon junctions junctions, depending on where each end maps to, it could
be defined a Transor a Cis event.
from wikipedia.org

26. RNA-Seq Software packages
Shirely et al, 2009

27. DNA encodes heritable traits
• Genes in DNA being transcribed into RNA
• might be spliced
• transported to an appropriate cellular compartment
• translated into proteins
• Regulated at many levels
• DNA methylation
• chromatin modification
• binding of transcription factors to the DNA
• binding of splicing factors to the RNA and RNA transport

28. NGG(Next-generation genomics)
an integrative approach
• What types of genomic data sets are available?
• Why perform integrative genomic analysis?
• Approaches to an integrative analysis
• Using large-scale data sets for integrative analysis
• Future perspectives

29. What types of genomic data sets
are available?
• Sequence variation data
• SNP genotyping arrays
• resequencing
• Transcriptomic data
• RNA-Seq
• identify transcripts arising from gene fusion events
• detect novel classes of non-coding RNAs
• Epigenomic data
• Bisulphite tratment
• Chromatin immunoprecipitation
• Interactome data
• RNA-protein interaction
• protein -protein interaction networks
• define genetic and signaling pathways

30. Why perform integrative genomic
analysis?
• Annotating functional features of the genome
• Inferring the function of genetic variants
• Understanding mechanisms of gene regulation
Figure 1 | Annotating the genome through detecting transcription-factor binding sites and histone-modification states.
Figure 2 | Identification of regulatory SNPs

31. Approaches to an integrative
analysis
• Data complexity reduction
• summarize each experiment as a collection of genomic regions with
strong enrichment of signal
• especially important to inspect at least some of the results by eye
• Unsupervised integration
• 목적은 어떤 올바른 답을 찾는 것이 아니라 데이터 집합 내에서
구조를 발견
• Clustering: partitioning a large data set into easily digestible,
conceptual pieces
• Supervised integration
• 예제 입출력을 사용해 예측하는 방법을 학습하는 기법
• Bayesian network

32. Approaches to an integrative
analysis
Promoter
an intromic H3K4me1 peak predicts an enhancer elements
Transcribed

33. UCSC browser with EnCODE data

34. Using large-scale data sets for
integrative analysis
• For the bench scientist
• open-source web browser, such as FireFox
• add-ons: gatekeepers

35. Using large-scale data sets for
integrative analysis
• For the bench scientist
• stand-alone analytical system: CisGenome
• genome browser: UCSC browser, Anno-J
Galaxy
Figure 4 | Flow chart for data analysis
Workflow for ChIP-seq analysis
UCSC browser
Online or stand-alone tools

36. Using large-scale data sets for
integrative analysis
• Bioinformatics hurdles
• normalized data

37. Future perspectives
• Data integration itself is not an end
• designed to generate novel hypotheses and help to test
them
• Community-wide effort, akin to Wikipedia
• Searchable with Google-like capabilities

38. Future perspectives

39. Future perspectives

40. 사트남 알랙
토비 세가란 생명과학 커뮤니티를 위한 버티컬 검색 엔진을 개발
Genstruct에서 약제 발현원리 이해를 위한 알고리즘 설계 하는 넥스트바이오의 엔지니어링 부사장