Enabling Large Scale Sequencing Studies through Science as a Service


Published on


“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.

Published in: Technology
1 Like
  • Be the first to comment

No Downloads
Total views
On SlideShare
From Embeds
Number of Embeds
Embeds 0
No embeds

No notes for slide
  • 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
  • Enabling Large Scale Sequencing Studies through Science as a Service

    1. 1. Enabling Large Scale Sequencing Studies through Science as a Service (ScaaS)<br />Justin H. Johnson<br />Director of Bioinformatics<br />EdgeBio<br />Washington DC, USA<br />
    2. 2. Agenda<br />Who We Are<br />NGS at 30K<br />Challenges and Enabling Through ScaaS<br />Transcriptome Projects<br />Exome Projects<br />Ion Torrent Data<br />
    3. 3.
    4. 4. Life Tech Service Provider<br />
    5. 5. Contract Research Division<br />Five SOLiD4 sequencing platforms<br />One Life Techologies 5500XL<br />Two Ion Torrent PGMs<br />Automation thru Caliper Sciclone& BiomekFX<br />Life Technologies Preferred Service Provider<br />Agilent Certified Service Provider<br />Commercial partnerships with companies such as CLCBio, DNANexusand Genologics<br />MD/PhD & Masters Level Scientists and Bioinformaticians<br />IT Infrastructure of >100 CPUs and >100TB storage<br />
    6. 6. Edge BioServ<br />Scientific Advisory Board<br />Elaine Mardis, Ph.D.<br />Co-Director, Genome Sequencing Center<br />Washington University School of Medicine<br />Sam Levy, Ph.D.<br />Director of Genome SciencesScripps Translational Science Institute<br />Scripps Genomic Medicine<br />Michael Zody, M.S.<br />Chief Technologist<br />Broad Institute<br />Ken Dewar, Ph.D.<br />Assistant Professor<br />McGill University and Genome Quebec<br />Steven Salzberg, Ph.D.<br />Director, Center for Bioinformatics and Computational Biology<br />University of Maryland<br />Gabor Marth, Ph.D.<br />Professor of Bioinformatics<br />Boston College<br />Elliott Margulies, Ph.D.<br />Investigator<br />Genome Informatics Section<br />National Human Genome Research Institute<br />National Institutes of Health<br />
    7. 7.
    8. 8. Machines and Vendors<br />GnuBio<br />
    9. 9. Obligatory NGS Exponential Growth Slide<br />Nature Biotechnology Volume 26 Number10 October2008<br />
    10. 10. Ultra High Throughput + Lower Cost = Broader Applications<br />
    11. 11.
    12. 12.
    13. 13. Experimental Design Considerations<br /><ul><li>Sequencing Platform in Use
    14. 14. Choice of Library Construction
    15. 15. Depth of coverage
    16. 16. Re$ources
    17. 17. Number of Replicates
    18. 18. Number of Samples and Control
    19. 19. Etc…</li></li></ul><li>
    20. 20. Flexibility with Standards and Scale<br />Then (CE) – The Norm<br />10 Machines, 30 – 360 Days, 1 Project<br />Now (Illumina/SOLiD/454) – Scale<br />1 machine, 14 Days, 30 Projects<br />Now (Ion Torrent) - Flexibility<br />1 machine, 1 Day, 1 Project.<br />Future (CLCBio, Nexus, Open Source)<br />Standardization of analysis<br />
    21. 21. Partial List of Mappers<br /> * 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.<br />Courtesy of SeqAnswers.com<br />
    22. 22. Evolving Sequencing & Analysis Methods to Enable Genomic Research<br />
    23. 23. Real World Examples - Scale<br />1500+ Sample Epigenetic Study<br />Challenges<br /><ul><li>Sample Prep (MethyMiner)
    24. 24. Tracking (LIMS)
    25. 25. QC (Automation and Standardization)
    26. 26. Delivery (Automation and Standardization)</li></ul>Solution<br /><ul><li>Mix of Commercial and Open Tools
    27. 27. CLC Bio and Genologics
    28. 28. Custom Algorithms
    29. 29. HPC and Storage
    30. 30. Onsite 100 TB NAS
    31. 31. S3 for Backup and Delivery</li></li></ul><li>Real World Examples – Standards<br />Rapid sequenced the genome of the Escherichia coli strain from European outbreak<br />“…[University of Münster & Life Tech] ]received the samples on Monday, began sequencing that evening, and began analyzing the data on Wednesday…”<br />“…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…<br />
    32. 32.
    33. 33. tiRNA<br />ATG<br />AAA<br />AAA<br />ATG<br />ATG<br />ATG<br />AAA<br />AAA<br />ATG<br />ATG<br />AAA<br />ATG<br />AAA<br />genomic DNA<br />Mammalian transcriptional<br />complexity<br />Mammalian Transcriptome Complexity<br />TSS<br />TSS<br />TSS<br />pA<br />pA<br />pA<br />ATG<br />ATG<br />TSS<br />pA<br />PASR<br />TASR<br />miRNA<br />AAA<br />spliced intron<br />microRNAs<br />TSS<br />pA<br />polyadenylation signal<br />transcription start site<br />protein coding regions<br />AAA<br />translation start site<br />polyadenylation<br />non-coding regions<br />ATG<br />Courtesy of Life Technologies<br />
    34. 34. RNA-Seq<br /><ul><li>New Approach to RNA Profiling enabled by Next-Gen Sequencing
    35. 35. Yet based on well-established methodologies
    36. 36. Substantial Benefits over Hybridization-Based Methods
    37. 37. Better quantitative gene expression performance (DGE)
    38. 38. In addition, can allow a comprehensive view of transcription (Whole Transcriptome)
    39. 39. Transcriptome projects overview
    40. 40. Identification of imprinted genes contributing to specific brain regions by whole transcriptome sequencing
    41. 41. 24 sample cohort for basic human expression and variant analysis in diseased patients.
    42. 42. 32 Sample cohort looking at novel splice junctions, gene fusions, and differential expression of colon cancer samples over a time series
    43. 43. Collaboration with Scripps Translational on Colon Cancer Transciptomes</li></li></ul><li>
    44. 44. Sample Sourcing for Transcriptome Projects<br />Blood: Large quantities of sample available, but with limited utility in transcriptome analysis<br />Tissue: Needle biopsy most common, but sample quantity very low<br />Surgical section: Larger quantities available, but limited utility; need laser capture microdissection to provide useful results, sample quantity very low<br />FFPE Slides: Very useful in clinical research but amount of sample and quality low.<br />
    45. 45. Unamplified vs Amplified<br />Prostate Cancer Cell Line (Vcap) from CPDR<br />Well characterized<br />Differential Expression upon the addition of androgens.<br />Compared transcriptome from a single pool of RNA<br />Unamplified, ribosomally depleted (Ribominus™)<br />Amplified, no ribosomal depletion required<br />Two Pipelines for analysis<br />
    46. 46. Amplification Gives Different Results<br />Gene Expression in Unstimulated Cells<br />Unamp<br />Amplified<br />14,075<br />2112<br />1071<br />
    47. 47. Spearman’s Correlation from 2 Pipelines<br />
    48. 48.
    49. 49. RNA-Seq Analysis Between Pipelines is Either Concordant<br />Amplified, Stimulated, Pipe A<br />Amplified, Stimulated, Pipe B<br />
    50. 50. Or not…<br />Unamplified, Stimulated, Pipe A<br />Unamplified, Stimulated, Pipe B<br />
    51. 51. Even if you remove all SNORA and SNORD<br />Unamplified, Stimulated, Pipe A<br />Unamplified, Stimulated, Pipe B<br />
    52. 52. NM refseq<br />NR refseq<br />Histones (circles)<br />SNORD/SNORA<br />rRNA dots<br />PolyA Selection vs Ribosomal Depletion<br />Courtesy of Life Technologies<br />
    53. 53.
    54. 54. Not what you want to hear…<br /><ul><li>Lots of manual work to run multiple pipelines
    55. 55. Join discordance
    56. 56. Scripting
    57. 57. Visualization
    58. 58. Filtering techniques based on YOUR data.</li></li></ul><li>
    59. 59. Exome and Targeted Resequencing <br /><ul><li>Capturing and interrogating a portion of the genome in many samples post GWAS
    60. 60. Fine map a region
    61. 61. Capturing and interrogating the exome
    62. 62. Catalogue variants for downstream filtering and identification of causative mutation(s)
    63. 63. Exome and Targeted Resequencing projects overview
    64. 64. Identification of the genetic basis of colorectal cancer through exome sequencing
    65. 65. 600+ sample cohort to identify the genetic basis of a novel syndrome
    66. 66. Exome sequencing of Tumor/Normal Leukemia patients to identify novel mutations present in tumor samples
    67. 67. Exome sequencing of a large cohort (80+) to identify novel mutations linked to phenotypic changes</li></li></ul><li>
    68. 68. Targeted Capture Technologies<br />NimblegenSeqCap EZ<br />Agilent SureSelect<br />NimblegenSeqCap EZ<br />FebitHybSelect<br />Agilent SureSelect<br />LR-PCR<br />Raindance Technologies<br />Fluidigm<br />20Kb<br />1 MB<br />2 MB<br />3 MB<br />4 MB<br />5 MB<br />30-50MB<br />Exome<br />Genomic Region Captured<br />
    69. 69.
    70. 70. Ultimately Comes to Variation<br />Coverage<br />Project Design<br />Cohorts<br />Cancer<br />Algorithms a Solved Problem?<br />Single open source pipelines<br />Single commercial pipelines<br />Proprietary internal algorithms.<br />A mixture?<br />
    71. 71. Ultimately Comes to Variation<br />Coverage<br />Project Design<br />Cohorts<br />Cancer<br />Algorithms Solved Problem?<br />Single open source pipelines<br />Single commercial pipelines<br />Proprietary internal algorithms.<br />A mixture?<br />
    72. 72. EdgeBioExome Coverage Statistics<br />
    73. 73. EdgeBio Exon Coverage StatisticsHow well is the exome covered?*<br />* Data from Fragment Runs – Since moving to PE, seeing 15% improvement<br />
    74. 74. Venter Genome - Algorithms<br />PLOS genetics 2008 vol 4 issue 8 e10000160<br />~21K SNP in exons (29MB Targeted)<br />36,206 expected SNPs for 50MB Kit<br />
    75. 75. 3 Tools and Associated SNP Counts<br />Software A<br />45,551<br />Software B<br />29,814<br />Software C<br />40,964<br />
    76. 76. Software B v. Software A<br />A<br />45,511<br />B<br />29,814<br />21,250<br />24,261<br />8,564<br />Union: 54,075<br />Intersection: 21,250<br />Not to Scale<br />
    77. 77. Software B v. Software C<br />C<br />40,964<br />B<br />29,814<br />23,456<br />17,508<br />6,358<br />Union: 47,322<br />Intersection 23,456<br />
    78. 78. Software A v. Software C<br />C<br />40,964<br />A<br />45,511<br />30,773<br />10,191<br />14,738<br />Union: 55,702<br />Intersection: 30,773<br />
    79. 79. A<br />45,511<br />B<br />29,814<br />13,130<br />4,750<br />1,608<br />19,642<br />3,814<br />11,131<br />6,377<br />Union: 60,452<br />Intersection: 19,642<br />Voting Scheme (2/3): 36,195<br />C<br />40,964<br />
    80. 80.
    81. 81. Again not what you want to hear…<br /><ul><li>Lots of manual/semi-automated work to run multiple pipelines
    82. 82. Join discordance
    83. 83. Scripting
    84. 84. Visualization
    85. 85. Better algorithms for variant calling
    86. 86. Cancer specific
    87. 87. Standardization of algorithms for variant calling
    88. 88. It all begins with mapping</li></li></ul><li>Exome Analysis – Cancer Specific<br />Dana Farber Cancer Institute<br />Multi-Pipeline Variant Calling and LOH<br />Loss of heterozygosity detection in tumor vsgermline exome: candidate LOH genes selected with the following algorithm<br /><ul><li>Non-synonymous heterozygous SNP in germline gene
    89. 89. Non-synonymous homozygous SNP in tumor or additional Non-synonymous heterozygous SNP on the other allele</li></li></ul><li>
    90. 90. Ion Torrent PGM<br />Longer, Accurate Reads in 2.5 Hours<br />Microbial & Viral Resequencing<br />Microbial & Viral De novo Applications<br />Eukaryotic Amplicon Sequencing<br />Metagenomics<br />WGS<br />16S Surveys<br />
    91. 91. Ion Torrent PGM<br />
    92. 92. Ion Torrent PGM<br />
    93. 93. Ion Torrent PGM<br />
    94. 94. Real World Examples – Speed<br />Rapid sequenced the genome of the Escherichia coli strain from European outbreak<br />“…[University of Münster & Life Tech] ]received the samples on Monday, began sequencing that evening, and began analyzing the data on Wednesday…”<br />“…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…<br />
    95. 95. Acknowledgements<br />CPDR (Center for Prostate Disease Research) Collaboration<br />Shyh-Han Tan, Ph.D.<br />DNA Farber Cancer Institute Collaboration<br />Andrew Lane M.D.,Ph.D.; David Weinstock M.D.; Oliver Weigert M.D.,Ph.D<br />Scripps Translational Health<br />Samuel Levy<br />Sequencing Team led by Joy Adigun <br />EdgeBio Research IFX led by John Seed, Ph.D. and Quang Nguyen MD, Ph.D.<br />
    96. 96. QuestionsTwitter: @Bioinfojjohnson@edgebio.com<br />