Cloud Computing and Innovations for Optimizing Life Sciences Research
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This document discusses how cloud computing and big data analytics can optimize life sciences research. It outlines some key benefits of cloud computing like scalability, flexibility, and pay-as-you-go models. The document also discusses challenges in healthcare big data like volume, velocity, variety and veracity of data. It provides examples of data analysis pipelines and tools for tasks like quality control, variant calling, clustering and enrichment analysis that can help researchers. Finally, it argues that cloud computing has the potential to address challenges and transform biology and healthcare research.
Cloud Computing and Innovations for Optimizing Life Sciences Research
- 1. Cloud Computing and Innovations for Optimizing Life Sciences Research Krittika Sasmal @ InterpretOmics 19th February 2014
- 2. 2 Acknowledgement Organizers of this event Scientists and Researchers who work for others and make their research findings OPEN Entrepreneurs who translate this knowledge GNU, Open Software, NIH and other funding bodies that keep Biology & Medical information OPEN 19.02.14
- 3. 19.02.14 3 4 Grand Social Challenges Food Security Health Security Energy Security Environmental Security Common Thread is Biology
- 4. 19.02.14 4 The 21st Century Biology – The Quantitative Biology Ref: A New Biology for the 21st Century, The National Academies Will create a discovery engine able to tackle extremely complex biological and societal problems
- 5. 19.02.14 5 Decoding the Book of Life – milestone for Quantitative Biology A Milestone for Humanity – the Human genome Human Genome Completed, June 26th, 2000
- 6. 19.02.14 6 Journey is - From Reduction to Integration − There are many diseases that were researched and understood through the process of reduction − However as the understanding of diseases mature, and the need for proactive medicine increases, researchers find that many diseases including cancer are due to somatic mutations that cannot be understood in the reduced space − Understanding of such disease and discover a drug for these diseases will need a reverse operation - integration and systems biology Ref: Hiroaki Kitano, et al. Systems Biology: A Brief Overview, Science 295, 1662 (March 2002);
- 7. 19.02.14 8 Translational Medicine – Genomics + Clinical + Non-clinical Data to Discovery of Novel therapeutics Data Information Knowledge Literature/ Molecular Data Clinical/Bedside Data Medical Knowledge Target Data Preprocessed Data Transformed Data Patterns iOmics Disease/Drug Data
- 8. 9 Next Generation Sequence Data • FASTQ (Illumina) • Sff (454) • CCS (PacBio) • ... • Microarray Single End Sequences Insert Size Library Size Sequence SequencePaired End or Mate-paired DNA/RNA/miRNA OverlappedOverlapped reads Random Order & Orientation Long reads Short reads Fixed length reads Variable length reads cDNA/mRNA Hundreds to Billions Bases Circular Consensus reads Billions to Hundreds Bases 19.02.14
- 9. Data domains and Challenges Source: Clevergene Biocorp 1119.02.14
- 10. 12 Exploratory Data Analysis Exploratory Data Analysis (EDA) is an approach/philosophy for data analysis that employs a variety of techniques (mostly graphical) to 1. Maximize insight into a data set 2. Uncover underlying structure 3. Extract important variables 4. Detect outliers and anomalies 5. Test underlying assumptions 6. Develop parsimonious models 7. Determine optimal factor settings 19.02.14
- 11. Each of these is addressed through algorithms to solve a computational problem, typically based upon optimizing some mathematical criterion 1319.02.14
- 12. 15 Trends in Genomic Medicine J. J. McCarthy et al., Sci Transl Med 2013;5:189sr4 19.02.14
- 13. 16 Data Driven Healthcare Personalized Health Care Translational Medicine Health Care Today Digital Imaging Episodic Treatment Electronic Health Records Artificial Expert Systems Clinical Genomics Genetic Predisposition Testing Molecular Medicine CA Diagnosis Pre-symptomatic Treatment Lifetime Treatment Evolutionary Practices RevolutionaryTechnology Automated Systems Non-specific (Treat Symptoms) Information Correlation Organized (Error Reduction) Personalized (Disease Prevention) Data and Systems Integration DistributedHigh-Throughput Analytics 19.02.14
- 14. 17 P6 Medicine – Preventive, Predictive, Participatory, Personalized, Precision, and Pervasive 19.02.14
- 16. 19 Population Data Registry Registry Claims Data Clinical Trial Drug reaction Literature Genomic Data Survivability Public Health Epidemiology Population Data 19.02.14
- 17. 20 Biomedical DataClinical Repositories Online Mendelian Inheritance in Man Medical Subject Heading Genome/Gene Annotations University of Washington Digital Anatomist UWDA NCBI TaxonomiesHuman Metabolome RxNorm Drug ICD10 Logical Observation Identifiers Names and Code UMLS (Unified Medical Language System) 19.02.14
- 18. 7V's in Healthcare Big-data Vexing. Proper algorithm needs to be designed to ensure data processing time is linear. Genomic data are generally NP-Hard and proper parallel algorithms need to be designed to access data in a near real-time manner. Volume. Physical volume of data that needs to be online. This includes structured and unstructured data. Storage is available, however, the challenge is to determine relevance within large data volumes and how to use analytics to create value from relevant data. Velocity. Data must be retrieved in a timely manner. In healthcare, many data sources are outside the enterprise. Reacting quickly enough to deal with data velocity is critical for most healthcare applications. 2119.02.14
- 19. Variety. Data today comes in all types of formats. Structured, numeric, unstructured data like CT scan, MRI, Ultrasound, X-Rays etc. in different forms. Also, most healthcare data is categorical, with or without any order. Managing, merging and governing different varieties of data is a challenge. Variability. Healthcare data are highly inconsistent with periodic peaks. Cancer for example has four different types of variability viz., Intratumoral, Intermetastatic, Intrametastatic, and Interpatient. Discovery of independent variables and the causal attributes are critical. Veracity. Quality, relevance, repeatability, quantification, meaningfulness, predictive value, reduction of error Value. The final result and its quantification from ROI or reduction of readmission or reducing the morbidity is the key that will finally be measured. 2219.02.14
- 20. 23 Healthcare Analytics & Decision Support System Analytics of 7Vs 19.02.14
- 21. 24 Big Data in Life Sciences 19.02.14
- 22. 25 Question is, NOT whether we can do it! But, HOW QUICKLY and HOW ACCURATELY we can do it; where, Speed, Repeatability, Reliability, Predictability, and Precision matter! 19.02.14
- 23. 26 The Cloud You don't buy a COW when you need Milk! Likewise, a Biologist, a Breeder, or a Clinician need not Worry about Data Analysis, Algorithms, Supercomputer, Pipelines, or even the Analytics and the Biomedical Informatics! 19.02.14
- 24. 27 Cloud Computing Defined Cloud computing is an emerging computing paradigm where data and applications reside in the cyberspace, it allows users to access their data and information through any web-connected device be it fixed or mobile. Source: John B. Horrigan, Use of Cloud Computing Applications & Services, Data memo, PEW Internet & American Life project, September 2008 19.02.14
- 25. 28 Cloud Computing User – I (Amir) 19.02.14
- 26. 29 Cloud Computing User – II (Fakir) 19.02.14
- 27. 30 Characteristics of Cloud Computing Virtual – Physical location and underlying infrastructure details are transparent to users Scalable – Able to break complex workloads into pieces to be served across an incrementally expandable infrastructure Efficient – Services Oriented Architecture for dynamic provisioning of shared compute resources Flexible – Can serve a variety of workload types – both consumer and commercial 19.02.14
- 28. 31 Benefits of Cloud Unlimited Resource Unlimited Computing power Unlimited storage (Filestore & online memory) Users can use resources without owning anything – converting Capex to Opex Helping Green computing by lending out idle resources through Cycle Scavenging Pay as you go 19.02.14
- 29. 32 Cloud Computing Stack Facilities Hardware Facilities Integration & Middleware Data Metadata Content Application API Presentation Modality Presentation Platform Infrastructure as a Service Platform as a Service Software as a Service Connectivity & delivery API Facilities Hardware Facilities Connectivity & delivery API Integration & Middleware Q O E & Q O S S E C U R I T Y User/ Customer/ Device M I D D L E W A R E Original Cloud ProviderCloud VendorCloud User Next Gen Network Next Generation Network / Intranet 19.02.14
- 30. 33 Divide and Conquer: MapReduce Output files Split 1 Split 2 Split 3 Split 4 Worker Worker Worker k1:v1 k3:v2 k1:v3 k2:v4 k2:v5 k4:v6 k1:v1,v3 k2:v4,v5 k3:v2 Worker Worker Worker Output 1 Output 2 k4:v6 Output 3 Master Input Files Map Intermediate files Reduce Sort/Group 19.02.14
- 31. 34 Open Source MapReduce Hadoop − Implemented in Java; enabled on Amazon Twister − Light weight new arrival in town 19.02.14
- 32. 35 Security in the Cloud Security in the cloud needs to answer few specific questions like: 1. How much trust do you have on virtualized environment or the hypervisors in the cloud as against your own physical hardware? 2. How much trust do you have on cloud vendor versus your own infrastructure? 3. How do you address regulatory and compliance requirement in an environment when your application might be running on an infrastructure in a foreign country? 19.02.14
- 33. 36 New generation software in bioinformatics needs to be: Fast/Very fast software, with a low memory consumption Be able to handle and analyze TB of data Store data efficiently to query Distribute computation, not data Focused, and useful in analysis 19.02.14
- 34. 37 www.iomics.in The Omics Lab in the Internet 19.02.14
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- 37. Analysis Pipelines in iOMICS QA/QC of raw reads SNP/InDel/CNV analysis miRNA discovery Exome sequencing ChIP sequencing ... (New additions) Meta-analysis Visualization of results Omics data Systems Biology 4019.02.14
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- 42. 46 SNP & CNV Analysis 19.02.14
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- 47. 51 Cloud computing Holds the Potential to Address the Challenges and Transform Biology and Heathcare Krittika Sasmal Email: “krittika” dot “sasmal” (at) “interpretomics” dot “co” 19.02.14