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Enabling Large Scale Sequencing Studies through Science as a Service


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Now …


“Now” generation sequencing has drastically changed the traditional costs and infrastructure within the sequencing community. There are several technologies, platforms and algorithms that show promise, but it is not always intuitive where to start. This uncertainty is compounded by the fact that commonly used analysis tools are difficult to build, maintain, and run effectively. Sample acquisition and preparation is quickly becoming a bottleneck as projects move from small sample sizes to hundreds or even thousands of samples. We will present case studies highlighting information, methods, challenges and opportunities in leveraging large scale high throughput sequencing and bioinformatics. Specifically we will highlight a recent genome-wide study of methylation patterns in 1575 individuals with Schizophrenia. We will also discuss several cancer transcriptome and exome sequencing projects as well as a human pathogen transcriptome characterization project consisting of multiple organisms and almost a billion reads.

The Future

The Ion Torrent PGM machine is a very promising, rapid throughput, ultra scalable sequencer that could play an integral part in future human health studies. Applications such as microbial whole genome sequencing, metagenomic characterization of environmental and microbiome sample, and targeted resequencing projects stand to benefit from this technology over time. To date we have completed more than 25 runs on a single PGM and will comment on the setup as well as sequence data and analysis.

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  • Evolving Sequencing Methods to Enable Genomic Research
  • Every house is built with a sturdy foundation.
  • Evolving Sequencing Methods to Enable Genomic Research
  • Because of this…
  • We have this…
  • Which allows this…As a CRO – we especially see how this is happening with those that may not have had access to these applications before due to access or finances.
  • But with constantly expanding applications come…
  • How does one stay technically relevant in a dramatically changing landscape?
  • With sequencing becoming ubiquitous – not as simple as just sequence then science…Many questions to answer and expertise to be gained to make each project successful. We spend upwards of 25% of our time in this phase.
  • We now have the issues of scale, compressed timelines, and standardization of sample prep and informatics.
  • To illustrate the informatics challenge of standardization…Each can be run in hundreds of combinations to produce answers. All different.
  • But when challenges are addressed, there can be immense power in discovery and eventually diagnostics. I will quickly mention 2 current projects that highlight and address some of the challenges, then jump into Transcriptome, Exome, and Ion Torrent sequencing.
  • I can share a bit more of the finding later on..
  • Key Points:New approach enabled by NGS , but it’s based on mature methodsIn highlighting the two benefits, say “which is called” DGE/RNA-Seq respectively to initially define the two terms. The next two slides clarify these definitions.Old Slide below:A (somewhat) new approach to RNA profiling using Sequencing rather than HybridizationVariations on the theme have been used since mid-90sEST Sequencing, SAGE, LongSAGE, MPSSHowever, limitations and cost of sequencing technology, as well as lack of a finished, well-annotated genome reference, had prevented broad use vs. microarrays Digital Gene Expression using Next-Gen SequencingA transformative technology Improved sensitivity, dynamic range, and linearity over microarraysRemoves background and biases seen with microarraysCan provide a comprehensive view of splicing and transcription If desired, not required.Now dozens of published papers validating the approachAggressively competing vendors making it better/faster/cheaper
  • We’re no longer in the early stages of technology adoption, and the biology is becoming more important. More accurate biology requires more refined samples, and that leads to issues in NGS which generally has voracious material requirements. Amplification is generally the solution, but this leads to additional problems in analysis.
  • Total number of genes expressed as a function of the union of the two methods was 17,258. The picture was the same for Stimulated cells except that androgen stimulation very slightly reduced the complexity of the transcriptome with a total of 17,128 genes expressed in the union of the two methods. Different results doesn’t necessarily mean one more accurate…just different. This analysis was performed with a single RNA-Seq pipeline. We subsequently discovered that different pipelines also give different results.
  • You can conclude that the very significant changes in biology associated with androgen stimulation are more closely correlated than using the different methods of sample preparation. The data also suggest that different analytic tools may have a greater impact than the biology as well.
  • It used to be sequencing chewed up the costs for projects, now inverse.
  • The next slide shows the difference between ribosomal depletion and poly(A)+ selection in the distribution of genes. Integrating the informatics pipelines with sample preparation methods and researcher’s needs is critical. There are amplification methods that don’t have the particular bias of the method used in these studies.
  • Again choice and goal of project is paramount when choosing and designing a capture
  • Start to lose your return on investment.
  • Wrap up with Ion Torret…
  • Nature Preceeding
  • Transcript

    • 1. Enabling Large Scale Sequencing Studies through Science as a Service (ScaaS)
      Justin H. Johnson
      Director of Bioinformatics
      Washington DC, USA
    • 2. Agenda
      Who We Are
      NGS at 30K
      Challenges and Enabling Through ScaaS
      Transcriptome Projects
      Exome Projects
      Ion Torrent Data
    • 3.
    • 4. Life Tech Service Provider
    • 5. Contract Research Division
      Five SOLiD4 sequencing platforms
      One Life Techologies 5500XL
      Two Ion Torrent PGMs
      Automation thru Caliper Sciclone& BiomekFX
      Life Technologies Preferred Service Provider
      Agilent Certified Service Provider
      Commercial partnerships with companies such as CLCBio, DNANexusand Genologics
      MD/PhD & Masters Level Scientists and Bioinformaticians
      IT Infrastructure of >100 CPUs and >100TB storage
    • 6. Edge BioServ
      Scientific Advisory Board
      Elaine Mardis, Ph.D.
      Co-Director, Genome Sequencing Center
      Washington University School of Medicine
      Sam Levy, Ph.D.
      Director of Genome SciencesScripps Translational Science Institute
      Scripps Genomic Medicine
      Michael Zody, M.S.
      Chief Technologist
      Broad Institute
      Ken Dewar, Ph.D.
      Assistant Professor
      McGill University and Genome Quebec
      Steven Salzberg, Ph.D.
      Director, Center for Bioinformatics and Computational Biology
      University of Maryland
      Gabor Marth, Ph.D.
      Professor of Bioinformatics
      Boston College
      Elliott Margulies, Ph.D.
      Genome Informatics Section
      National Human Genome Research Institute
      National Institutes of Health
    • 7.
    • 8. Machines and Vendors
    • 9. Obligatory NGS Exponential Growth Slide
      Nature Biotechnology Volume 26 Number10 October2008
    • 10. Ultra High Throughput + Lower Cost = Broader Applications
    • 11.
    • 12.
    • 13. Experimental Design Considerations
      • Sequencing Platform in Use
      • 14. Choice of Library Construction
      • 15. Depth of coverage
      • 16. Re$ources
      • 17. Number of Replicates
      • 18. Number of Samples and Control
      • 19. Etc…
    • 20. Flexibility with Standards and Scale
      Then (CE) – The Norm
      10 Machines, 30 – 360 Days, 1 Project
      Now (Illumina/SOLiD/454) – Scale
      1 machine, 14 Days, 30 Projects
      Now (Ion Torrent) - Flexibility
      1 machine, 1 Day, 1 Project.
      Future (CLCBio, Nexus, Open Source)
      Standardization of analysis
    • 21. Partial List of Mappers
      * BFAST - Blat-like Fast Accurate Search Tool. Written by Nils Homer, Stanley F. Nelson and Barry Merriman at UCLA.* Bowtie - Ultrafast, memory-efficient short read aligner. It aligns short DNA sequences (reads) to the human genome at a rate of 25 million reads per hour on a typical workstation with 2 gigabytes of memory. Uses a Burrows-Wheeler-Transformed (BWT) index. Link to discussion thread here. Written by Ben Langmead and Cole Trapnell. Linux, Windows, and Mac OS X.* BWA - Heng Lee's BWT Alignment program - a progression from Maq. BWA is a fast light-weighted tool that aligns short sequences to a sequence database, such as the human reference genome. By default, BWA finds an alignment within edit distance 2 to the query sequence. C++ source.* ELAND - Efficient Large-Scale Alignment of Nucleotide Databases. Whole genome alignments to a reference genome. Written by Illumina author Anthony J. Cox for the Solexa 1G machine.* Exonerate - Various forms of pairwise alignment (including Smith-Waterman-Gotoh) of DNA/protein against a reference. Authors are Guy St C Slater and Ewan Birney from EMBL. C for POSIX.* GenomeMapper - GenomeMapper is a short read mapping tool designed for accurate read alignments. It quickly aligns millions of reads either with ungapped or gapped alignments. A tool created by the 1001 Genomes project. Source for POSIX.* GMAP - GMAP (Genomic Mapping and Alignment Program) for mRNA and EST Sequences. Developed by Thomas Wu and Colin Watanabe at Genentec. C/Perl for Unix.* gnumap - The Genomic Next-generation Universal MAPper (gnumap) is a program designed to accurately map sequence data obtained from next-generation sequencing machines (specifically that of Solexa/Illumina) back to a genome of any size. It seeks to align reads from nonunique repeats using statistics. From authors at Brigham Young University. C source/Unix.* MAQ - Mapping and Assembly with Qualities (renamed from MAPASS2). Particularly designed for Illumina with preliminary functions to handle ABI SOLiD data. Written by Heng Li from the Sanger Centre. Features extensive supporting tools for DIP/SNP detection, etc. C++ source* MOSAIK - MOSAIK produces gapped alignments using the Smith-Waterman algorithm. Features a number of support tools. Support for Roche FLX, Illumina, SOLiD, and Helicos. Written by Michael Strömberg at Boston College. Win/Linux/MacOSX* MrFAST and MrsFAST - mrFAST & mrsFAST are designed to map short reads generated with the Illumina platform to reference genome assemblies; in a fast and memory-efficient manner. Robust to INDELs and MrsFAST has a bisulphite mode. Authors are from the University of Washington. C as source.* MUMmer - MUMmer is a modular system for the rapid whole genome alignment of finished or draft sequence. Released as a package providing an efficient suffix tree library, seed-and-extend alignment, SNP detection, repeat detection, and visualization tools. Version 3.0 was developed by Stefan Kurtz, Adam Phillippy, Arthur L Delcher, Michael Smoot, Martin Shumway, Corina Antonescu and Steven L Salzberg - most of whom are at The Institute for Genomic Research in Maryland, USA. POSIX OS required.* Novocraft - Tools for reference alignment of paired-end and single-end Illumina reads. Uses a Needleman-Wunsch algorithm. Can support Bis-Seq. Commercial. Available free for evaluation, educational use and for use on open not-for-profit projects. Requires Linux or Mac OS X.* PASS - It supports Illumina, SOLiD and Roche-FLX data formats and allows the user to modulate very finely the sensitivity of the alignments. Spaced seed intial filter, then NW dynamic algorithm to a SW(like) local alignment. Authors are from CRIBI in Italy. Win/Linux.* RMAP - Assembles 20 - 64 bp Illumina reads to a FASTA reference genome. By Andrew D. Smith and Zhenyu Xuan at CSHL. (published in BMC Bioinformatics). POSIX OS required.* SeqMap - Supports up to 5 or more bp mismatches/INDELs. Highly tunable. Written by Hui Jiang from the Wong lab at Stanford. Builds available for most OS's.* SHRiMP - Assembles to a reference sequence. Developed with Applied Biosystem's colourspace genomic representation in mind. Authors are Michael Brudno and Stephen Rumble at the University of Toronto. POSIX.* Slider- An application for the Illumina Sequence Analyzer output that uses the probability files instead of the sequence files as an input for alignment to a reference sequence or a set of reference sequences. Authors are from BCGSC. Paper is here.* SOAP - SOAP (Short Oligonucleotide Alignment Program). A program for efficient gapped and ungapped alignment of short oligonucleotides onto reference sequences. The updated version uses a BWT. Can call SNPs and INDELs. Author is Ruiqiang Li at the Beijing Genomics Institute. C++, POSIX.* SSAHA - SSAHA (Sequence Search and Alignment by Hashing Algorithm) is a tool for rapidly finding near exact matches in DNA or protein databases using a hash table. Developed at the Sanger Centre by Zemin Ning, Anthony Cox and James Mullikin. C++ for Linux/Alpha.* SOCS - Aligns SOLiD data. SOCS is built on an iterative variation of the Rabin-Karp string search algorithm, which uses hashing to reduce the set of possible matches, drastically increasing search speed. Authors are Ondov B, Varadarajan A, Passalacqua KD and Bergman NH.* SWIFT - The SWIFT suit is a software collection for fast index-based sequence comparison. It contains: SWIFT — fast local alignment search, guaranteeing to find epsilon-matches between two sequences. SWIFT BALSAM — a very fast program to find semiglobal non-gapped alignments based on k-mer seeds. Authors are Kim Rasmussen (SWIFT) and Wolfgang Gerlach (SWIFT BALSAM)* SXOligoSearch - SXOligoSearch is a commercial platform offered by the Malaysian based Synamatix. Will align Illumina reads against a range of Refseq RNA or NCBI genome builds for a number of organisms. Web Portal. OS independent.* Vmatch - A versatile software tool for efficiently solving large scale sequence matching tasks. Vmatch subsumes the software tool REPuter, but is much more general, with a very flexible user interface, and improved space and time requirements. Essentially a large string matching toolbox. POSIX.* Zoom - ZOOM (Zillions Of Oligos Mapped) is designed to map millions of short reads, emerged by next-generation sequencing technology, back to the reference genomes, and carry out post-analysis. ZOOM is developed to be highly accurate, flexible, and user-friendly with speed being a critical priority. Commercial. Supports Illumina and SOLiD data.
      Courtesy of
    • 22. Evolving Sequencing & Analysis Methods to Enable Genomic Research
    • 23. Real World Examples - Scale
      1500+ Sample Epigenetic Study
      • Sample Prep (MethyMiner)
      • 24. Tracking (LIMS)
      • 25. QC (Automation and Standardization)
      • 26. Delivery (Automation and Standardization)
      • Mix of Commercial and Open Tools
      • 27. CLC Bio and Genologics
      • 28. Custom Algorithms
      • 29. HPC and Storage
      • 30. Onsite 100 TB NAS
      • 31. S3 for Backup and Delivery
    • Real World Examples – Standards
      Rapid sequenced the genome of the Escherichia coli strain from European outbreak
      “…[University of Münster & Life Tech] ]received the samples on Monday, began sequencing that evening, and began analyzing the data on Wednesday…”
      “…Justin Johnson, director of bioinformatics at EdgeBio, assembled and analyzed the raw reads made publicly available by BGI using CLC Bio's software…Johnson said his analysis took just a couple of hours…
    • 32.
    • 33. tiRNA
      genomic DNA
      Mammalian transcriptional
      Mammalian Transcriptome Complexity
      spliced intron
      polyadenylation signal
      transcription start site
      protein coding regions
      translation start site
      non-coding regions
      Courtesy of Life Technologies
    • 34. RNA-Seq
      • New Approach to RNA Profiling enabled by Next-Gen Sequencing
      • 35. Yet based on well-established methodologies
      • 36. Substantial Benefits over Hybridization-Based Methods
      • 37. Better quantitative gene expression performance (DGE)
      • 38. In addition, can allow a comprehensive view of transcription (Whole Transcriptome)
      • 39. Transcriptome projects overview
      • 40. Identification of imprinted genes contributing to specific brain regions by whole transcriptome sequencing
      • 41. 24 sample cohort for basic human expression and variant analysis in diseased patients.
      • 42. 32 Sample cohort looking at novel splice junctions, gene fusions, and differential expression of colon cancer samples over a time series
      • 43. Collaboration with Scripps Translational on Colon Cancer Transciptomes
    • 44. Sample Sourcing for Transcriptome Projects
      Blood: Large quantities of sample available, but with limited utility in transcriptome analysis
      Tissue: Needle biopsy most common, but sample quantity very low
      Surgical section: Larger quantities available, but limited utility; need laser capture microdissection to provide useful results, sample quantity very low
      FFPE Slides: Very useful in clinical research but amount of sample and quality low.
    • 45. Unamplified vs Amplified
      Prostate Cancer Cell Line (Vcap) from CPDR
      Well characterized
      Differential Expression upon the addition of androgens.
      Compared transcriptome from a single pool of RNA
      Unamplified, ribosomally depleted (Ribominus™)
      Amplified, no ribosomal depletion required
      Two Pipelines for analysis
    • 46. Amplification Gives Different Results
      Gene Expression in Unstimulated Cells
    • 47. Spearman’s Correlation from 2 Pipelines
    • 48.
    • 49. RNA-Seq Analysis Between Pipelines is Either Concordant
      Amplified, Stimulated, Pipe A
      Amplified, Stimulated, Pipe B
    • 50. Or not…
      Unamplified, Stimulated, Pipe A
      Unamplified, Stimulated, Pipe B
    • 51. Even if you remove all SNORA and SNORD
      Unamplified, Stimulated, Pipe A
      Unamplified, Stimulated, Pipe B
    • 52. NM refseq
      NR refseq
      Histones (circles)
      rRNA dots
      PolyA Selection vs Ribosomal Depletion
      Courtesy of Life Technologies
    • 53.
    • 54. Not what you want to hear…
      • Lots of manual work to run multiple pipelines
      • 55. Join discordance
      • 56. Scripting
      • 57. Visualization
      • 58. Filtering techniques based on YOUR data.
    • 59. Exome and Targeted Resequencing
      • Capturing and interrogating a portion of the genome in many samples post GWAS
      • 60. Fine map a region
      • 61. Capturing and interrogating the exome
      • 62. Catalogue variants for downstream filtering and identification of causative mutation(s)
      • 63. Exome and Targeted Resequencing projects overview
      • 64. Identification of the genetic basis of colorectal cancer through exome sequencing
      • 65. 600+ sample cohort to identify the genetic basis of a novel syndrome
      • 66. Exome sequencing of Tumor/Normal Leukemia patients to identify novel mutations present in tumor samples
      • 67. Exome sequencing of a large cohort (80+) to identify novel mutations linked to phenotypic changes
    • 68. Targeted Capture Technologies
      NimblegenSeqCap EZ
      Agilent SureSelect
      NimblegenSeqCap EZ
      Agilent SureSelect
      Raindance Technologies
      1 MB
      2 MB
      3 MB
      4 MB
      5 MB
      Genomic Region Captured
    • 69.
    • 70. Ultimately Comes to Variation
      Project Design
      Algorithms a Solved Problem?
      Single open source pipelines
      Single commercial pipelines
      Proprietary internal algorithms.
      A mixture?
    • 71. Ultimately Comes to Variation
      Project Design
      Algorithms Solved Problem?
      Single open source pipelines
      Single commercial pipelines
      Proprietary internal algorithms.
      A mixture?
    • 72. EdgeBioExome Coverage Statistics
    • 73. EdgeBio Exon Coverage StatisticsHow well is the exome covered?*
      * Data from Fragment Runs – Since moving to PE, seeing 15% improvement
    • 74. Venter Genome - Algorithms
      PLOS genetics 2008 vol 4 issue 8 e10000160
      ~21K SNP in exons (29MB Targeted)
      36,206 expected SNPs for 50MB Kit
    • 75. 3 Tools and Associated SNP Counts
      Software A
      Software B
      Software C
    • 76. Software B v. Software A
      Union: 54,075
      Intersection: 21,250
      Not to Scale
    • 77. Software B v. Software C
      Union: 47,322
      Intersection 23,456
    • 78. Software A v. Software C
      Union: 55,702
      Intersection: 30,773
    • 79. A
      Union: 60,452
      Intersection: 19,642
      Voting Scheme (2/3): 36,195
    • 80.
    • 81. Again not what you want to hear…
      • Lots of manual/semi-automated work to run multiple pipelines
      • 82. Join discordance
      • 83. Scripting
      • 84. Visualization
      • 85. Better algorithms for variant calling
      • 86. Cancer specific
      • 87. Standardization of algorithms for variant calling
      • 88. It all begins with mapping
    • Exome Analysis – Cancer Specific
      Dana Farber Cancer Institute
      Multi-Pipeline Variant Calling and LOH
      Loss of heterozygosity detection in tumor vsgermline exome: candidate LOH genes selected with the following algorithm
      • Non-synonymous heterozygous SNP in germline gene
      • 89. Non-synonymous homozygous SNP in tumor or additional Non-synonymous heterozygous SNP on the other allele
    • 90. Ion Torrent PGM
      Longer, Accurate Reads in 2.5 Hours
      Microbial & Viral Resequencing
      Microbial & Viral De novo Applications
      Eukaryotic Amplicon Sequencing
      16S Surveys
    • 91. Ion Torrent PGM
    • 92. Ion Torrent PGM
    • 93. Ion Torrent PGM
    • 94. Real World Examples – Speed
      Rapid sequenced the genome of the Escherichia coli strain from European outbreak
      “…[University of Münster & Life Tech] ]received the samples on Monday, began sequencing that evening, and began analyzing the data on Wednesday…”
      “…Justin Johnson, director of bioinformatics at EdgeBio, assembled and analyzed the raw reads made publicly available by BGI using CLC Bio's software…Johnson said his analysis took just a couple of hours…
    • 95. Acknowledgements
      CPDR (Center for Prostate Disease Research) Collaboration
      Shyh-Han Tan, Ph.D.
      DNA Farber Cancer Institute Collaboration
      Andrew Lane M.D.,Ph.D.; David Weinstock M.D.; Oliver Weigert M.D.,Ph.D
      Scripps Translational Health
      Samuel Levy
      Sequencing Team led by Joy Adigun
      EdgeBio Research IFX led by John Seed, Ph.D. and Quang Nguyen MD, Ph.D.
    • 96. QuestionsTwitter: