GenomeQuest -- The Bioinformatics Bottleneck

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The Pistoia Alliance Conference in April 2011 included a series of 10-minute "lightning talks" from vendors about what they think pharma will look like in 2020. This presentation was delivered by Richard Resnick of GenomeQuest (and yes, this 41 slide talk was over in just 8 minutes!)

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% Time Spent on NGS Lifecycle* * source: IlluminaConnect Presentation

% Cost Breakdown* * source: IlluminaConnect Presentation

Why not? p-values diagnostics translational medicine 1000 genomes clinical personalized medicine inheritance patterns pathology my genome ms cure population study biological significance genomic medicine cancer genomics pharmacogenomics HIPPA compliance

“ I want to outsource the elements of our research pipeline that are pre-competitive .” - VP Research, unnamed Top 10 pharma “ We’ve decided to build our own internal pipeline for managing sequence data.” - Director of Bioinformatics, same company

“ We need to be innovative straight across the company .” - CEO, Unnamed Top 5 pharma “ We don’t have a genomics strategy .” - Director of Bioinformatics, same company “ Collaborations to rescue drugs using genomics are a key strategy for us this year.” - VP Translational Medicine, same company

“ We want to keep our intellectual property safe and secure.” - Chief Counsel, Top 10 pharma “ Over 50% of our company compares proprietary sequences directly against the open, unsecured NCBI website.” - Director of Bioinformatics, same company

Have you ever compared two different mapping algorithms ?

Have you written a program to simulate reads and error rates ?

Have you ever written a parser to work with a BAM/SAM, BLAST, GFF, or FASTA file?

Have you ever installed a genome browser onto a UNIX machine?

Do people come to your office and ask you to write PERL scripts to transform format A into format B ?

Have you compared the Broad’s SNP-calling pipeline to BGI’s ?

Do you scan the literature looking for new alignment algorithms ?

Ever built up a data center to back up NGS data ?

Ever provisioned any hardware on Amazon’s EC2 ?

You’re doing the same thing as all of your competitors.

“ I’ve been waiting 4 months for my bioinformatics team to do my NGS analysis.”

“ I can’t easily share and collaborate with these large data volumes.”

“ When I finally get my data back I can’t interact with it .”

“ NGS could be used today in drug rescue but we’re working on a corporate IT strategy that will take 12 months to shake out.

“ My bioinfo support team is getting better at handling one exome, but I’m sequencing 1000 exomes this month.

“ I guess my only hope is to learn UNIX ?”

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Overview of GSK Machine Learning and Artificial Intelligence activities, by Kim Branson, SVP and Head of AI at GSK Pharma, November 3rd, 2021. AI methods are becoming widely used due to the exponential nature of data generation. AI is used to collect the data, process it, derive causal relations. AI is being used to aid design the next experiment in an efficient manner. (RL, Bandits ..). The exponential nature of data improves AI in a virtuous cycle. Target discovery: integration of Functional Genomic, Genetic and other data and other sources for target discovery. Companion Software: for each asset we we will generate software for stratification, and individual response prediction Fundamental AI Research: Fundamental research into causal machine learning, automated machine learning, and multi modal data combination. We are developing a feedback loop for each AI system we build. We have best in industry full automated discovery biology robotics. We ask the model what data it needs. We only know what to do with 15% of the genetic variants we obtain from genetic association studies. How do we unlock all the value of our investments in genetic data? We build AI for Variant to Gene Prediction: It transforms a complex genetic locus, To a ranked list of candidate genes with confidence bounds, That are tested experimentally through Functional Genomics. Variant to Gene AI: A multi AI system for solving the variant to gene problem. Teaching our AI what we know about the world- Internal and external data, GSK AI team developed a custom NLP model for biomedical data, Knowledge Graph of all data. Data becomes a critical factor for AI success. Private Data Sources, Generate data allow us determine the Value of other public / private sources. Models trained on private and public Data are unique. Common Public data sources. Moving Beyond medical records for cohort definition. Image Derived Phenotype (IDP) discovery & generation using AI/ML. Computational companion diagnostics and learning from clinical trials. Focusing on Computational Pathology- Applying the advances in AI for image analysis. Tissues are collected as part of the biopsy for pathology. Digital versions of these H&E slides as a tool for diagnosis/prognosis by human pathologist. What else can we do with this image data? Genetic differences are not human discernible. Currently determined by sequencing the tumor. Should we be constrained by human ability? AI can determine HRD genetic status from image.

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• FINISH IVF • NATURAL FAMILY PLANNING • Preimplantation Genetic Diagnosis (PGD) • Surrogate motherhood • “snowflake babies” • Artificial Insemination (AI) Preimplantation Genetic Diagnosis (PGD) ZYGOTE M O RU LA COMPACTION BLASTOMERES MALE & FEMALE PRONUCLEI Surrogate motherhood https://en.wikipedia.org/wiki/2014_Thai_surrogacy_controversy INTRINSIC BIOETHICAL EVIL/WRONG: NATURAL RIGHT TO BE GESTATED BY BIOLOGICAL MOTHER “snowflake babies” = ivf embryo transfer http://www.vatican.va/roman_curia/congregations/cfaith/documents/rc_con_cfaith_doc_20081208_dignitas-personae_en.html Artificial Insemination (AI) NATURAL FAMILY PLANNING (NFP) 1.OVULATION SYMPTOMS 2.BIOETHICAL EVALUATION NATURAL FAMILY PLANNING (NFP) 1.OVULATION SYMPTOMS a) 3 PRIMARY b) 7 SECONDARY PRIMARY OVULATION SYMPTOMS: 1) BASAL BODY TEMPERATURE (BBT) 2) CERVIX ACTIVITY 3) CERVICAL MUCUS SECONDARY OVULATION SYMPTOMS: 1) MITTELSCHMERZ 2) SPOTTING 3) SWOLLEN VAGINA AND/OR VULVA 4) INCREASED LIBIDO 5) BREAST TENDERNESS 6) GENERAL BLOATING 7) FERNING SOME MAJOR PROTOCOLS AND METHODS: • CREIGHTON MODEL (NaPro Technology) • COUPLE TO COUPLE (CCL) • SYMPTO-THERMAL METHOD • BILLINGS METHOD • FAMILY OF THE AMERICAS (BASED ON BILLINGS) ACTIVITY OF THE CERVIX AND CERIVCAL OS DURING MENSTRUAL CYCLE INFERTILEFERTILE 1 DAY BEFORE OVULATION: OS OPEN, CERVIX HIGH, SOFT AND CENTRAL, EGGWHITE FLUID INFERTILE PHASE: OS CLOSED, CERVIX FIRM, ANGLED SLIGHTLY, TACKY FLUID Examples of cervical mucus during various days of the menstrual cycle. Transparent and elastic is fertile. Opaque and tacky is infertile. WHAT ABOUT THE HUSBAND? • DISCIPLINE, RESPECT, COMMUNICATION, SACRIFICIAL LOVE • OPENNESS TO THE PRESENCE OF GOD IN THEIR DAILY LIFE 2. BIOETHICAL EVALUATION OF NFP: a) AS A MEANS b) AS AN END / GOAL / OBJECTIVE a) AS A MEANS: • NO SEPARATION ÷ UNITIVE / PROCREATIVE DIMENSIONS • RESPECTFUL OF HUMAN NATURE • MARRITAL INTIMACY = UNION OF BODY AND SOUL b) AS AN END: HUMANAE VITAE 16b: “If therefore there are well-grounded reasons for spacing births, arising from the physical or psychological condition of husband or wife, or from external circumstances… then take advantage of the natural cycles immanent in the reproductive system…” b) AS AN END: THEREFORE, TO BE AVOIDED IS A CONTRACEPTIVE MENTALITY, WHEREBY PREGNANCY / CHILDREN ARE SEEN AS AN EVIL, TO BE AVOIDED BY ANY MEANS. INSTEAD, A FUNDAMENTAL OPENNESS TO LIFE, COLLABORATING WITH GOD’S PLAN TO BE CO-CREATORS OF A UNIQUE HUMAN LIFE. Slide Number 1Slide Number 2Slide Number 3Slide Number 4Slide Number 5Slide Number 6Slide Number 7Slide Number 8Slide Number 9Slide Number 10Slide Number 11Slide Number 12Slide Number 13Slide Number 14Slide Number 15Slide Number 16Slide Number 17Slide Number 18Slide Number 19 See discussions, stats, and author profiles for this publication at: https://www.researchgate.net/publication/220672617 .

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GenomeQuest -- The Bioinformatics Bottleneck

1. The Bioinformatics Bottleneck

2. % Time Spent on NGS Lifecycle* * source: IlluminaConnect Presentation

3. % Cost Breakdown* * source: IlluminaConnect Presentation

5. Why not? p-values diagnostics translational medicine 1000 genomes clinical personalized medicine inheritance patterns pathology my genome ms cure population study biological significance genomic medicine cancer genomics pharmacogenomics HIPPA compliance

6. “ I want to outsource the elements of our research pipeline that are pre-competitive .” - VP Research, unnamed Top 10 pharma “ We’ve decided to build our own internal pipeline for managing sequence data.” - Director of Bioinformatics, same company

7. “ We need to be innovative straight across the company .” - CEO, Unnamed Top 5 pharma “ We don’t have a genomics strategy .” - Director of Bioinformatics, same company “ Collaborations to rescue drugs using genomics are a key strategy for us this year.” - VP Translational Medicine, same company

8. “ We want to keep our intellectual property safe and secure.” - Chief Counsel, Top 10 pharma “ Over 50% of our company compares proprietary sequences directly against the open, unsecured NCBI website.” - Director of Bioinformatics, same company

9. Questions for the Bioinformaticist

10. Have you ever compared two different mapping algorithms ?

11. Have you written a program to simulate reads and error rates ?

12. Have you ever written a parser to work with a BAM/SAM, BLAST, GFF, or FASTA file?

13. Have you ever installed a genome browser onto a UNIX machine?

14. Do people come to your office and ask you to write PERL scripts to transform format A into format B ?

15. Have you compared the Broad’s SNP-calling pipeline to BGI’s ?

16. Do you scan the literature looking for new alignment algorithms ?

17. Ever built up a data center to back up NGS data ?

18. Ever provisioned any hardware on Amazon’s EC2 ?

19. You’re doing the same thing as all of your competitors.

20.