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 3/25/2014 @ 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 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 .

Platforms 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=33 AAAGAACATTAAAGCTATATTATAAGCAAAGAT +SRR002051.1 :8:1:325:773 length=33 IIIIIIIIIIIIIIIIIIIIIIIII'II@I$)- @SRR002051.2 :8:1:409:432 length=33 AAGTTATGAAATTGTAATTCCAATATCGTAAGC +SRR002051.2 :8:1:409:432 length=33 IIIIIIIIIIIIIIIIIIIIIIIIIIIIIII07 @SRR002051.3 :8:1:488:490 length=33 AATTTCTTACCATATTAGACAAGGCACTATCTT +SRR002051.3 :8:1:488:490 length=33 IIIIIIIIIIIIIIIIIIIIIIIIIIIIIII&I

Real Data – color space SOLiD Data >1_24_47_F3

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

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

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

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)

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

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

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.

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

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

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

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.

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

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

Question& Answer Han Liang E-mail: hliang1@mdanderson.org Tel: 713-745-9815