Next-Generation Sequencing: Challenges and Opportunities Ion Mandoiu Computer Science and Engineering Department University of Connecticut

Outline Background on high-throughput sequencing Identification of tumor-specific epitopes Estimation of gene and isoform expression levels Viral quasispecies reconstruction Future work

Advances in High-Throughput Sequencing (HTS) Roche/454 FLX Titanium million reads/run 400bp avg. length Illumina HiSeq 2000 Up to 6 billion PE reads/run bp read length SOLiD billion PE reads/run 35-50bp read length

Illumina Workflow – Library Preparation Genomic DNA mRNA

Illumina Workflow – Cluster Generation

Illumina Workflow – Sequencing by Synthesis

Cost of Whole Genome Sequencing C.Venter J. Watson NA18507

HTS is a transformative technology Numerous applications besides de novo genome sequencing: – RNA-Seq – Non-coding RNAs – ChIP-Seq – Epigenetics – Structural variation – Metagenomics – Paleogenomics – … HTS applications

Outline Background on high-throughput sequencing Identification of tumor-specific epitopes Estimation of gene and isoform expression levels Viral quasispecies reconstruction Future work

Genomics-Guided Cancer Immunotherapy CTCAATTGATGAAATTGTTCTGAAACT GCAGAGATAGCTAAAGGATACCGGGTT CCGGTATCCTTTAGCTATCTCTGCCTC CTGACACCATCTGTGTGGGCTACCATG … AGGCAAGCTCATGGCCAAATCATGAGA Tumor mRNA Sequencing SYFPEITHI ISETDLSLL CALRRNESL … Tumor Specific Epitopes Peptide Synthesis Immune System Stimulation Mouse Image Source: Tumor Remission

Bioinformatics Pipeline Tumor mRNA reads CCDS Mapping Genome Mapping Read Merging CCDS mapped reads Genome mapped reads SNVs Detection Mapped reads Epitope Prediction Tumor specific epitopes Haplotyping Tumor- specific SNVs Close SNV Haplotypes Primers Design Primers for Sanger Sequencing

Bioinformatics Pipeline Tumor mRNA reads CCDS Mapping Genome Mapping Read Merging CCDS mapped reads Genome mapped reads SNVs Detection Mapped reads Epitope Prediction Tumor specific epitopes Haplotyping Tumor- specific SNVs Close SNV Haplotypes Primers Design Primers for Sanger Sequencing

Mapping mRNA Reads

Read Merging GenomeCCDSAgree?Hard MergeSoft Merge Unique YesKeep Unique NoThrow UniqueMultipleNoThrowKeep UniqueNot MappedNoKeep MultipleUniqueNoThrowKeep Multiple NoThrow MultipleNot MappedNoThrow Not mappedUniqueNoKeep Not mappedMultipleNoThrow Not mappedNot MappedYesThrow

SNV Detection and Genotyping AACGCGGCCAGCCGGCTTCTGTCGGCCAGCAGCCAGGAATCTGGAAACAATGGCTACAGCGTGC AACGCGGCCAGCCGGCTTCTGTCGGCCAGCCGGCAG CGCGGCCAGCCGGCTTCTGTCGGCCAGCAGCCCGGA GCGGCCAGCCGGCTTCTGTCGGCCAGCCGGCAGGGA GCCAGCCGGCTTCTGTCGGCCAGCAGCCAGGAATCT GCCGGCTTCTGTCGGCCAGCAGCCAGGAATCTGGAA CTTCTGTCGGCCAGCCGGCAGGAATCTGGAAACAAT CGGCCAGCAGCCAGGAATCTGGAAACAATGGCTACA CCAGCAGCCAGGAATCTGGAAACAATGGCTACAGCG CAAGCAGCCAGGAATCTGGAAACAATGGCTACAGCG GCAGCCAGGAATCTGGAAACAATGGCTACAGCGTGC Reference Locus i RiRi r(i) : Base call of read r at locus i ε r(i) : Probability of error reading base call r(i) G i : Genotype at locus i

SNV Detection and Genotyping Use Bayes rule to calculate posterior probabilities and pick the genotype with the largest one

SNV Detection and Genotyping Calculate conditional probabilities by multiplying contributions of individual reads

Data Filtering

Accuracy per RPKM bins

Bioinformatics Pipeline Tumor mRNA reads CCDS Mapping Genome Mapping Read Merging CCDS mapped reads Genome mapped reads SNVs Detection Mapped reads Epitope Prediction Tumor specific epitopes Haplotyping Tumor- specific SNVs Close SNV Haplotypes Primers Design Primers for Sanger Sequencing

Haplotyping Human somatic cells are diploid, containing two sets of nearly identical chromosomes, one set derived from each parent. ACGTTACATTGCCACTCAATC--TGGA ACGTCACATTG-CACTCGATCGCTGGA Heterozygous variants

Haplotyping LocusEventAlleles 1SNVC,T 2DeletionC,- 3SNVA,G 4Insertion-,GC LocusEventAlleles Hap 1Alleles Hap 2 1SNVTC 2DeletionC- 3SNVAG 4Insertion-GC

RefHap Algorithm Reduce the problem to Max-Cut. Solve Max-Cut Build haplotypes according with the cut Locus12345 f1f f2f f3f f4f h h

Bioinformatics Pipeline Tumor mRNA reads CCDS Mapping Genome Mapping Read Merging CCDS mapped reads Genome mapped reads SNVs Detection Mapped reads Epitope Prediction Tumor specific epitopes Haplotyping Tumor- specific SNVs Close SNV Haplotypes Primers Design Primers for Sanger Sequencing

Immunology Background J.W. Yedell, E Reits and J Neefjes. Making sense of mass destruction: quantitating MHC class I antigen presentation. Nature Reviews Immunology, 3: , 2003

Epitope Prediction C. Lundegaard et al. MHC Class I Epitope Binding Prediction Trained on Small Data Sets. In Lecture Notes in Computer Science, 3239: , 2004

Results on Tumor Data

Experimental Validation Mutations reported by [Noguchi et al 94] found by the pipeline Confirmed with Sanger sequencing 18 out of 20 mutations for MethA and 26 out of 28 mutations for CMS5 Immunogenic potential under experimental validation in the Srivastava lab at UCHC

Outline Background on high-throughput sequencing Identification of tumor-specific epitopes Estimation of gene and isoform expression levels Viral quasispecies reconstruction Future work

RNA-Seq ABCDE Make cDNA & shatter into fragments Sequence fragment ends Map reads Gene Expression (GE) ABC AC DE Isoform Discovery (ID) Isoform Expression (IE)

Alternative Splicing [Griffith and Marra 07]

Challenges to Accurate Estimation of Gene Expression Levels Read ambiguity (multireads) What is the gene length? ABCDE

Previous approaches to GE Ignore multireads [Mortazavi et al. 08] – Fractionally allocate multireads based on unique read estimates [Pasaniuc et al. 10] – EM algorithm for solving ambiguities Gene length: sum of lengths of exons that appear in at least one isoform  Underestimates expression levels for genes with 2 or more isoforms [Trapnell et al. 10]

Read Ambiguity in IE ABCDE AC

Previous approaches to IE [Jiang&Wong 09] – Poisson model + importance sampling, single reads [Richard et al. 10] EM Algorithm based on Poisson model, single reads in exons [Li et al. 10] – EM Algorithm, single reads [Feng et al. 10] – Convex quadratic program, pairs used only for ID [Trapnell et al. 10] – Extends Jiang’s model to paired reads – Fragment length distribution

Our contribution Unified probabilistic model and Expectation- Maximization Algorithm for IE considering – Single and/or paired reads – Fragment length distribution – Strand information – Base quality scores

Read-Isoform Compatibility

Fragment length distribution Paired reads ABC AC ABC ACAC ABC i j F a (i) F a (j)

Fragment length distribution Single reads ABC AC ABC AC ABC AC i j F a (i) F a (j)

IsoEM algorithm E-step M-step

Error Fraction Curves - Isoforms 30M single reads of length 25 (simulated)

Error Fraction Curves - Genes 30M single reads of length 25 (simulated)

Validation on MAQC Samples

Outline Background on high-throughput sequencing Identification of tumor-specific epitopes Estimation of gene and isoform expression levels Viral quasispecies reconstruction Future work

Viral Quasispecies RNA viruses (HIV, HCV) Many replication mistakes Quasispecies Quasispecies (qsps) = co-existing closely related variants Variants differ in virulence ability to escape the immune system resistance to antiviral therapies tissue tropism How do qsps contribute to viral persistence and evolution?

454 Pyrosequencing Pyrosequencing Pyrosequencing =Sequencing by Synthesis. GS FLX Titanium GS FLX Titanium : reads  Fragments (reads): bp  Sequence of the reads single  System software assembles reads into a single genome  We need a software that assembles reads into multiple genomes!

Quasispecies Spectrum Reconstruction (QSR) Problem Given pyrosequencing reads from a quasispecies population of unknown size and distribution Reconstructspectrum Reconstruct the quasispecies spectrum sequences frequencies

ViSpA Viral Spectrum Assembler

454 Sequencing Errors Error rate ~0.1%. Fixed number of incorporated bases vs. light intensity value. Incorrect resolution of homopolymers => over-calls (insertions) 65-75% of errors under-calls (deletions) 20-30% of errors

Preprocessing of Aligned Reads D 1.Deletions in reads: D all N. Replace deletion, confirmed by a single read, with either allele value that is present in all other reads or N. I 2.Insertions into reference: I Remove insertions, confirmed by a single read. 3.Imputation of missing values N

Read Graph: Vertices Subread with n mismatches Subread = completely contained in some read with ≤ n mismatches. Superread Superread = not a subread => the vertex in the read graph. ACTGGTCCCTCCTGAGTGT GGTCCCTCCT TGGTCACTCGTGAG ACCTCATCGAAGCGGCGTCCT

Read Graph: Edges Edge b/w two vertices exists if there is an overlap between superreads with m mismatches. they agree on their overlap with ≤ m mismatches. Auxiliary vertices: source and sink

Read Graph: Edge Cost The most probable source-sink path through each vertex Cost: uncertainty that two superreads are from the same qsps. OverhangΔ Overhang Δ is the shift in start positions of two overlapping superreads. Δ

Contig Assembling Max Bandwidth Path Max Bandwidth Path through vertex path minimizing maximum edge cost for the path and each subpath Consensus of path’s superreads N Each position: >70%-majority or N Weighted consensus obtained on all reads Remove duplicates Duplicated sequences = statistical evidence rl read r of length l sL qsps s of length L k k is #mismatches, t/L t/L is a mutation rate

Expectation Maximization Bipartite graph:  Q q f q  Q q is a candidate with frequency f q  R r o r  R r is a read with observed frequency o r h q,r r q j  Weight h q,r = probability that read r is produced by qsps q with j mismatches E step:M step:

HCV Qsps (P. Balfe) reads from 5.2Kb-long region of HCV-1a genomes intravenous drug user being infected for less than 3 months => mutation rate is in [1.75%, 8%] reads average length=292bp Indels: ~77% of reads Insertions length: 1 (86%), 3 (9.8%) Deletions length: 1 (98%) N N: ~7% of reads

HCV Data Statistics

NJ Tree for 12 Most Frequent Qsps (No Insertions) The top sequence: 26.9% (no mismatches) and 50.4% (≤1 mismatch) of the reads. In sum: 35.6% (no mismatches ) and 64.5% (≤1 mismatch) of the reads. Reconstructed sequence with highest frequency 99% identical to one of the ORFs obtained by cloning the quasispecies.

Conclusions & Future Work Freely available implementations of these methods available at Ongoing work – Monitoring immune responses by TCR sequencing – Isoform discovery – Computational deconvolution of heterogeneous samples – Reconstruction & frequency estimation of virus quasispecies from Ion Torrent reads

Acknowledgments Immunogenomics Jorge Duitama (KU Leuven) Pramod K. Srivastava, Adam Adler, Brent Graveley, Duan Fei (UCHC) Matt Alessandri and Kelly Gonzalez (Ambry Genetics) IsoEM Marius Nicolae (Uconn) Alex Zelikovsky, Serghei Mangul (GSU) ViSpA Alex Zelikovsky, Irina Astrovskaya, Bassam Tork, Serghei Mangul, (GSU), and Kelly Westbrooks (Life Technologies) Peter Balfe (Birmingham University, UK) Funding NSF awards IIS , IIS , and DBI UCONN Research Foundation UCIG grant