1. Department of Bioinformatics and Computational Biology
Introduction to Next-Generation Sequencing Data and Related Bioinformatic Analysis
Han Liang, Ph.D.
Department of Bioinformatics and Computational Biology
Rice University

2. Outline History NGS Platforms Applications Bioinformatics Analysis
Challenges

3. Central Dogma

4. Sanger sequencing DNA is fragmented Cloned to a plasmid vector
Cyclic sequencing reaction
Separation by electrophoresis
Readout with fluorescent tags

5. Sanger vs NGS
‘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 .

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

7. Quickly reduced Cost

8. Three Leading Sequencing Platforms
Roche 454
Illumina Solexa
Applied Biosystems SOLiD

9. The general experimental procedure
Wang et al. Nature Reviews Genetics 2009

10. 454 bead microreactor
Maridis Annu. Rev. Genome. Human Genet. 2008

12. Illumina (Solexa) Bridge amplification
Maridis Annu. Rev. Genome. Human Genet. 2008

13. SOLiD color coding
Maridis Annu. Rev. Genome. Human Genet. 2008

14. Comparison of existing methods

15. Real Data – nucleotide space
Solexa
@SRR :8:1:325:773 length=33
AAAGAACATTAAAGCTATATTATAAGCAAAGAT
+SRR :8:1:325:773 length=33
@SRR :8:1:409:432 length=33
AAGTTATGAAATTGTAATTCCAATATCGTAAGC
+SRR :8:1:409:432 length=33
IIIIIIIIIIIIIIIIIIIIIIIIIIIIIII07
@SRR :8:1:488:490 length=33
AATTTCTTACCATATTAGACAAGGCACTATCTT
+SRR :8:1:488:490 length=33
IIIIIIIIIIIIIIIIIIIIIIIIIIIIIII&I

16. Real Data – color space SOLiD Data >1_24_47_F3

17. 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)

18. 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

19. Ancient Genomes Resurrected
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

21. Elucidating DNA-protein interactions through chromoatin immunoprecipitation sequencing
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

22. Discovering noncoding RNAs
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)

23. Metagenomics 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

24. Defining variability in many human genomes
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

25. Epigenomic variation
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.

26. :Integrating Omics
Mutation discovery
Protein-DNA interaction
Copy number variation
mRNA expression
microRNA expression
Alternative Splicing
Kahvejian et al. 2008

27. decoding, filter and mapping
Data Analysis Flow
SOLiD machine:
Raw data
Central Server
Basic processing
decoding, filter and mapping
Local Machine
Downstream analysis

28. Short Read Mapping
DNA-Resequencing
BLAST-like approach
RNA-Seq

31. Read length and pairing
ACTTAAGGCTGACTAGC
TCGTACCGATATGCTG
Short reads are problematic, because short sequences do not map uniquely to the genome.
Solution #1: Get longer reads.
Solution #2: Get paired reads.

32. Post-alignment Analysis
DNA-SEQ
SNP calling
RNA-SEQ
Quantifying gene expression level

33. Concepts The reference genome: Target Region: exonome
hg19 (GRC37)
Main assembly: Chr1-22, X, and Y
3,095,677,412 bp
Target Region: exonome
Ensembl: 85.3 Million (2.94%)
RefSeq: Million (2.34%)
ccds: 31,266,049 (1.08%) consisting of 185,446 nr exons

34. Target Coverage

36. SOLiD color coding
Maridis Annu. Rev. Genome. Human Genet. 2008

37. SNP calling

39. Array-based High-throughput Dataset

40. 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

41. Quantifying gene expression using RNA-Seq data
RPKM: Reads Per Kb exon length and Millions of mapped readings

42. Large Dynamic Range
Mortazavi et al. Nature Methods 2008

43. High reproducibility
Mortazavi et al. Nature Methods 2008

44. High Accuracy
Wang et al. Nature 2008

45. 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

46. 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

47. Considerable sequencing error
High-quality image analysis for base calling

48. 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

49. 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

50. Experimental Challenges
Library fragmentation
Strand specific
Wang et al. Nature Reviews Genetics 2009

51. Question& Answer Han Liang E-mail: hliang1@mdanderson.org
Tel: