Importance of Statistics in creating high dimensional data

Save this PDF as:
 WORD  PNG  TXT  JPG

Size: px
Start display at page:

Download "Importance of Statistics in creating high dimensional data"

Transcription

1 Importance of Statistics in creating high dimensional data Hemant K. Tiwari, PhD Section on Statistical Genetics Department of Biostatistics University of Alabama at Birmingham

2 History of Genomic Data Blood Groups (ABO, Rh, etc.) Linkage data (RFLPs etc.) Microarray Gene Expression data Whole Genome SNPs (100K to 5M) Next Generation Sequencing

3 High Dimensional Data Sets (Big Data) SNPs Next Generation Sequencing Gene Expression Gene Expression dbgap GenBank/ Amazon Cloud GEO

4 GEO and MIAME (Minimum Information About a Microarray Experiment) The raw data for each hybridization (e.g., CEL or GPR files) The final processed (normalized) data for the set of hybridizations in the experiment (study) (e.g., the gene expression data matrix used to draw the conclusions from the study) The essential sample annotation including experimental factors and their values (e.g., compound and dose in a dose response experiment) The experimental design including sample data relationships (e.g., which raw data file relates to which sample, which hybridizations are technical, which are biological replicates) Sufficient annotation of the array (e.g., gene identifiers, genomic coordinates, probe oligonucleotide sequences or reference commercial array catalog number) The essential laboratory and data processing protocols (e.g., what normalization method has been used to obtain the final processed data)

5 Figure 1 : The phenomenal growth of sequence data in GenBank. (Obtained from GenBank release notes: ftp://ftp.ncbi.nih.gov/genbank/gbrel.txt and list size obtained from NAR archives: Nature Education

6 1000 Genomes Project The 1000 Genomes Project data are freely available through the 1000 Genomes website, at and from each of the two institutions that work together as the project DCC: the NCBI at ftp://ftptrace.ncbi.nlm.nih.gov/1000genomes, and EBI, with DCC support from the Wellcome Trust, at ftp://ftp.1000genomes.ebi.ac.uk. Cloud access to the 1000 Genomes Project data through AWS is at

7 Is this GOOD or Hmm..? GOOD: WOW, I have more data now, which means more Power to detect, more biological entities (SNPs and Genes) I can discover and curate. HMMM?: Now, I have to change my software blueprint and design. How to store and QUICKLY analyze this Big Data Biologist / Geneticist Informatician / Statistician / Software Architect

8 From big data emerges clarity. Published: MARCH 6, 2012

9

10 Big Data Challenges Data & Task Parallelism Data Aggregation Semantic Web Give me all the RAW data (Computing Savvy Consumers) Give me TOOLS and FRAMEWORKS to get to what I want. ( Non-Computing Savvy Consumers)

11 PROBLEM (High-Dim Data Crunching Challenge) SOLUTION Parallel (Task and Data) Approach Reference Genome (Hg) Genetic Data for Samples Data Decomposition / Parallelism + One Chromsome + One Sample on Single Compute Node UAB HPC Lab)? Data

12 FASTQ Read Alignment BAM SAM SOLUTION Parallel (Task and Data) Approach + One Chromsome + One Sample on Single Compute Node Pseudo Code Instruction Flow Diagram SAM to BAM BAM Sort and Index BAM BAM & BAI Target Region BAM BAM Sort and Index BAM BAM & BAI Quality Control on Aligned BAMs 2 BAM 4 BAM Report Read Coverage Stats Report Read Coverage Stats B A M S T A G E R Q. C

13 Map Reduce on Big Data

14 Cloud Computing and the DNA Data Race Michael C. Schatz1, Ben Langmead2, and Steven L. Salzberg1 1Center for Bioinformatics and Computational Biology, University of Maryland 2Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health Nat Biotechnol July ; 28(7): doi: /nbt Common element across all these algorithms is Big Data

15 Toolkit of BIG DATA solutions Technology is the Solutions Architect to BD Challenges Processing and Hadoop: The Elephant In The Room Storage and NoSQL

16

17 Issues in data Creation Different QC protocols are used at different stages of the data creation, for example in GWAS, or EXOME sequencing protocols. Need for Uniform protocols similar to MIAME.

18 Future: Data Integration SNPs CNVs Gene Expression Methylation EXOME Disease or Trait? Proteome Env? NGS

New solutions for Big Data Analysis and Visualization

New solutions for Big Data Analysis and Visualization New solutions for Big Data Analysis and Visualization From HPC to cloud-based solutions Barcelona, February 2013 Nacho Medina imedina@cipf.es http://bioinfo.cipf.es/imedina Head of the Computational Biology

More information

European Genome-phenome Archive database of human data consented for use in biomedical research at the European Bioinformatics Institute

European Genome-phenome Archive database of human data consented for use in biomedical research at the European Bioinformatics Institute European Genome-phenome Archive database of human data consented for use in biomedical research at the European Bioinformatics Institute Justin Paschall Team Leader Genetic Variation / EGA ! European Genome-phenome

More information

Cloud-Based Big Data Analytics in Bioinformatics

Cloud-Based Big Data Analytics in Bioinformatics Cloud-Based Big Data Analytics in Bioinformatics Presented By Cephas Mawere Harare Institute of Technology, Zimbabwe 1 Introduction 2 Big Data Analytics Big Data are a collection of data sets so large

More information

Building Bioinformatics Capacity in Africa. Nicky Mulder CBIO Group, UCT

Building Bioinformatics Capacity in Africa. Nicky Mulder CBIO Group, UCT Building Bioinformatics Capacity in Africa Nicky Mulder CBIO Group, UCT Outline What is bioinformatics? Why do we need IT infrastructure? What e-infrastructure does it require? How we are developing this

More information

Comparing Methods for Identifying Transcription Factor Target Genes

Comparing Methods for Identifying Transcription Factor Target Genes Comparing Methods for Identifying Transcription Factor Target Genes Alena van Bömmel (R 3.3.73) Matthew Huska (R 3.3.18) Max Planck Institute for Molecular Genetics Folie 1 Transcriptional Regulation TF

More information

AGILENT S BIOINFORMATICS ANALYSIS SOFTWARE

AGILENT S BIOINFORMATICS ANALYSIS SOFTWARE ACCELERATING PROGRESS IS IN OUR GENES AGILENT S BIOINFORMATICS ANALYSIS SOFTWARE GENESPRING GENE EXPRESSION (GX) MASS PROFILER PROFESSIONAL (MPP) PATHWAY ARCHITECT (PA) See Deeper. Reach Further. BIOINFORMATICS

More information

Gene expression analysis. Ulf Leser and Karin Zimmermann

Gene expression analysis. Ulf Leser and Karin Zimmermann Gene expression analysis Ulf Leser and Karin Zimmermann Ulf Leser: Bioinformatics, Wintersemester 2010/2011 1 Last lecture What are microarrays? - Biomolecular devices measuring the transcriptome of a

More information

School of Nursing. Presented by Yvette Conley, PhD

School of Nursing. Presented by Yvette Conley, PhD Presented by Yvette Conley, PhD What we will cover during this webcast: Briefly discuss the approaches introduced in the paper: Genome Sequencing Genome Wide Association Studies Epigenomics Gene Expression

More information

Module 1. Sequence Formats and Retrieval. Charles Steward

Module 1. Sequence Formats and Retrieval. Charles Steward The Open Door Workshop Module 1 Sequence Formats and Retrieval Charles Steward 1 Aims Acquaint you with different file formats and associated annotations. Introduce different nucleotide and protein databases.

More information

Lecture 11 Data storage and LIMS solutions. Stéphane LE CROM lecrom@biologie.ens.fr

Lecture 11 Data storage and LIMS solutions. Stéphane LE CROM lecrom@biologie.ens.fr Lecture 11 Data storage and LIMS solutions Stéphane LE CROM lecrom@biologie.ens.fr Various steps of a DNA microarray experiment Experimental steps Data analysis Experimental design set up Chips on catalog

More information

NCBI resources III: GEO and ftp site. Yanbin Yin Spring 2013

NCBI resources III: GEO and ftp site. Yanbin Yin Spring 2013 NCBI resources III: GEO and ftp site Yanbin Yin Spring 2013 1 Homework assignment 2 Search colon cancer at GEO and find a data Series and perform a GEO2R analysis Write a report (in word or ppt) to include

More information

Preparing the scenario for the use of patient s genome sequences in clinic. Joaquín Dopazo

Preparing the scenario for the use of patient s genome sequences in clinic. Joaquín Dopazo Preparing the scenario for the use of patient s genome sequences in clinic Joaquín Dopazo Computational Medicine Institute, Centro de Investigación Príncipe Felipe (CIPF), Functional Genomics Node, (INB),

More information

Delivering the power of the world s most successful genomics platform

Delivering the power of the world s most successful genomics platform Delivering the power of the world s most successful genomics platform NextCODE Health is bringing the full power of the world s largest and most successful genomics platform to everyday clinical care NextCODE

More information

Row Quantile Normalisation of Microarrays

Row Quantile Normalisation of Microarrays Row Quantile Normalisation of Microarrays W. B. Langdon Departments of Mathematical Sciences and Biological Sciences University of Essex, CO4 3SQ Technical Report CES-484 ISSN: 1744-8050 23 June 2008 Abstract

More information

HADOOP IN THE LIFE SCIENCES:

HADOOP IN THE LIFE SCIENCES: White Paper HADOOP IN THE LIFE SCIENCES: An Introduction Abstract This introductory white paper reviews the Apache Hadoop TM technology, its components MapReduce and Hadoop Distributed File System (HDFS)

More information

High Performance Compu2ng Facility

High Performance Compu2ng Facility High Performance Compu2ng Facility Center for Health Informa2cs and Bioinforma2cs Accelera2ng Scien2fic Discovery and Innova2on in Biomedical Research at NYULMC through Advanced Compu2ng Efstra'os Efstathiadis,

More information

A Primer of Genome Science THIRD

A Primer of Genome Science THIRD A Primer of Genome Science THIRD EDITION GREG GIBSON-SPENCER V. MUSE North Carolina State University Sinauer Associates, Inc. Publishers Sunderland, Massachusetts USA Contents Preface xi 1 Genome Projects:

More information

Factors for success in big data science

Factors for success in big data science Factors for success in big data science Damjan Vukcevic Data Science Murdoch Childrens Research Institute 16 October 2014 Big Data Reading Group (Department of Mathematics & Statistics, University of Melbourne)

More information

Hadoop s Rise in Life Sciences

Hadoop s Rise in Life Sciences Exploring EMC Isilon scale-out storage solutions Hadoop s Rise in Life Sciences By John Russell, Contributing Editor, Bio IT World Produced by Cambridge Healthtech Media Group By now the Big Data challenge

More information

An Introduction to Genomics and SAS Scientific Discovery Solutions

An Introduction to Genomics and SAS Scientific Discovery Solutions An Introduction to Genomics and SAS Scientific Discovery Solutions Dr Karen M Miller Product Manager Bioinformatics SAS EMEA 16.06.03 Copyright 2003, SAS Institute Inc. All rights reserved. 1 Overview!

More information

Hadoopizer : a cloud environment for bioinformatics data analysis

Hadoopizer : a cloud environment for bioinformatics data analysis Hadoopizer : a cloud environment for bioinformatics data analysis Anthony Bretaudeau (1), Olivier Sallou (2), Olivier Collin (3) (1) anthony.bretaudeau@irisa.fr, INRIA/Irisa, Campus de Beaulieu, 35042,

More information

GC3 Use cases for the Cloud

GC3 Use cases for the Cloud GC3: Grid Computing Competence Center GC3 Use cases for the Cloud Some real world examples suited for cloud systems Antonio Messina Trieste, 24.10.2013 Who am I System Architect

More information

UCLA Team Sequences Cell Line, Puts Open Source Software Framework into Production

UCLA Team Sequences Cell Line, Puts Open Source Software Framework into Production Page 1 of 6 UCLA Team Sequences Cell Line, Puts Open Source Software Framework into Production February 05, 2010 Newsletter: BioInform BioInform - February 5, 2010 By Vivien Marx Scientists at the department

More information

GeneSifter: Next Generation Data Management and Analysis for Next Generation Sequencing

GeneSifter: Next Generation Data Management and Analysis for Next Generation Sequencing for Next Generation Sequencing Dale Baskin, N. Eric Olson, Laura Lucas, Todd Smith 1 Abstract Next generation sequencing technology is rapidly changing the way laboratories and researchers approach the

More information

SeqPig: simple and scalable scripting for large sequencing data sets in Hadoop

SeqPig: simple and scalable scripting for large sequencing data sets in Hadoop SeqPig: simple and scalable scripting for large sequencing data sets in Hadoop André Schumacher, Luca Pireddu, Matti Niemenmaa, Aleksi Kallio, Eija Korpelainen, Gianluigi Zanetti and Keijo Heljanko Abstract

More information

Next generation sequencing (NGS)

Next generation sequencing (NGS) Next generation sequencing (NGS) Vijayachitra Modhukur BIIT modhukur@ut.ee 1 Bioinformatics course 11/13/12 Sequencing 2 Bioinformatics course 11/13/12 Microarrays vs NGS Sequences do not need to be known

More information

BIOINF 525 Winter 2016 Foundations of Bioinformatics and Systems Biology http://tinyurl.com/bioinf525-w16

BIOINF 525 Winter 2016 Foundations of Bioinformatics and Systems Biology http://tinyurl.com/bioinf525-w16 Course Director: Dr. Barry Grant (DCM&B, bjgrant@med.umich.edu) Description: This is a three module course covering (1) Foundations of Bioinformatics, (2) Statistics in Bioinformatics, and (3) Systems

More information

Workshop on Establishing a Central Resource of Data from Genome Sequencing Projects

Workshop on Establishing a Central Resource of Data from Genome Sequencing Projects Report on the Workshop on Establishing a Central Resource of Data from Genome Sequencing Projects Background and Goals of the Workshop June 5 6, 2012 The use of genome sequencing in human research is growing

More information

Leading Genomics. Diagnostic. Discove. Collab. harma. Shanghai Cambridge, MA Reykjavik

Leading Genomics. Diagnostic. Discove. Collab. harma. Shanghai Cambridge, MA Reykjavik Leading Genomics Diagnostic harma Discove Collab Shanghai Cambridge, MA Reykjavik Global leadership for using the genome to create better medicine WuXi NextCODE provides a uniquely proven and integrated

More information

Software and Methods for the Analysis of Affymetrix GeneChip Data. Rafael A Irizarry Department of Biostatistics Johns Hopkins University

Software and Methods for the Analysis of Affymetrix GeneChip Data. Rafael A Irizarry Department of Biostatistics Johns Hopkins University Software and Methods for the Analysis of Affymetrix GeneChip Data Rafael A Irizarry Department of Biostatistics Johns Hopkins University Outline Overview Bioconductor Project Examples 1: Gene Annotation

More information

Putting Genomes in the Cloud with WOS TM. ddn.com. DDN Whitepaper. Making data sharing faster, easier and more scalable

Putting Genomes in the Cloud with WOS TM. ddn.com. DDN Whitepaper. Making data sharing faster, easier and more scalable DDN Whitepaper Putting Genomes in the Cloud with WOS TM Making data sharing faster, easier and more scalable Table of Contents Cloud Computing 3 Build vs. Rent 4 Why WOS Fits the Cloud 4 Storing Sequences

More information

Just the Facts: A Basic Introduction to the Science Underlying NCBI Resources

Just the Facts: A Basic Introduction to the Science Underlying NCBI Resources 1 of 8 11/7/2004 11:00 AM National Center for Biotechnology Information About NCBI NCBI at a Glance A Science Primer Human Genome Resources Model Organisms Guide Outreach and Education Databases and Tools

More information

Guideline for the submission of DNA sequences and associated annotations within the framework of Directive 2001/18/EC and Regulation (EC) No 1829/2003

Guideline for the submission of DNA sequences and associated annotations within the framework of Directive 2001/18/EC and Regulation (EC) No 1829/2003 Guideline for the submission of DNA sequences and associated annotations within the framework of Directive 2001/18/EC and Regulation (EC) No 1829/2003 European Reference Laboratory for Genetically Modified

More information

Service courses for graduate students in degree programs other than the MS or PhD programs in Biostatistics.

Service courses for graduate students in degree programs other than the MS or PhD programs in Biostatistics. Course Catalog In order to be assured that all prerequisites are met, students must acquire a permission number from the education coordinator prior to enrolling in any Biostatistics course. Courses are

More information

Automated and Scalable Data Management System for Genome Sequencing Data

Automated and Scalable Data Management System for Genome Sequencing Data Automated and Scalable Data Management System for Genome Sequencing Data Michael Mueller NIHR Imperial BRC Informatics Facility Faculty of Medicine Hammersmith Hospital Campus Continuously falling costs

More information

Practical Solutions for Big Data Analytics

Practical Solutions for Big Data Analytics Practical Solutions for Big Data Analytics Ravi Madduri Computation Institute (madduri@anl.gov) Paul Dave (pdave@uchicago.edu) Dinanath Sulakhe (sulakhe@uchicago.edu) Alex Rodriguez (arodri7@uchicago.edu)

More information

Molecular Genetics: Challenges for Statistical Practice. J.K. Lindsey

Molecular Genetics: Challenges for Statistical Practice. J.K. Lindsey Molecular Genetics: Challenges for Statistical Practice J.K. Lindsey 1. What is a Microarray? 2. Design Questions 3. Modelling Questions 4. Longitudinal Data 5. Conclusions 1. What is a microarray? A microarray

More information

Introduction to NGS data analysis

Introduction to NGS data analysis Introduction to NGS data analysis Jeroen F. J. Laros Leiden Genome Technology Center Department of Human Genetics Center for Human and Clinical Genetics Sequencing Illumina platforms Characteristics: High

More information

Applying data integration into reconstruction of gene networks from micro

Applying data integration into reconstruction of gene networks from micro Applying data integration into reconstruction of gene networks from microarray data PhD Thesis Proposal Dipartimento di Informatica e Scienze dell Informazione Università degli Studi di Genova December

More information

Data search and visualization tools at the Comparative Evolutionary Genomics of Cotton Web resource

Data search and visualization tools at the Comparative Evolutionary Genomics of Cotton Web resource Data search and visualization tools at the Comparative Evolutionary Genomics of Cotton Web resource Alan R. Gingle Andrew H. Paterson Joshua A. Udall Jonathan F. Wendel 1 CEGC project goals set the context

More information

Sequencing and microarrays for genome analysis: complementary rather than competing?

Sequencing and microarrays for genome analysis: complementary rather than competing? Sequencing and microarrays for genome analysis: complementary rather than competing? Simon Hughes, Richard Capper, Sandra Lam and Nicole Sparkes Introduction The human genome is comprised of more than

More information

NIH Genomic Data Sharing (GDS) Policy Guidance Memo #2 1

NIH Genomic Data Sharing (GDS) Policy Guidance Memo #2 1 MEMORANDUM TO: Principal Investigators and Research Staff DATE: 2/22/15 FROM: Anne Klibanski, MD, Partners Chief Academic Officer (CAO) Paul Anderson, MD, PhD, BWH CAO Harry Orf, PhD, MGH Sr. Vice President-Research

More information

Assuring the Quality of Next-Generation Sequencing in Clinical Laboratory Practice. Supplementary Guidelines

Assuring the Quality of Next-Generation Sequencing in Clinical Laboratory Practice. Supplementary Guidelines Assuring the Quality of Next-Generation Sequencing in Clinical Laboratory Practice Next-generation Sequencing: Standardization of Clinical Testing (Nex-StoCT) Workgroup Principles and Guidelines Supplementary

More information

Next Generation Sequencing: Adjusting to Big Data. Daniel Nicorici, Dr.Tech. Statistikot Suomen Lääketeollisuudessa 29.10.2013

Next Generation Sequencing: Adjusting to Big Data. Daniel Nicorici, Dr.Tech. Statistikot Suomen Lääketeollisuudessa 29.10.2013 Next Generation Sequencing: Adjusting to Big Data Daniel Nicorici, Dr.Tech. Statistikot Suomen Lääketeollisuudessa 29.10.2013 Outline Human Genome Project Next-Generation Sequencing Personalized Medicine

More information

Data Analysis & Management of High-throughput Sequencing Data. Quoclinh Nguyen Research Informatics Genomics Core / Medical Research Institute

Data Analysis & Management of High-throughput Sequencing Data. Quoclinh Nguyen Research Informatics Genomics Core / Medical Research Institute Data Analysis & Management of High-throughput Sequencing Data Quoclinh Nguyen Research Informatics Genomics Core / Medical Research Institute Current Issues Current Issues The QSEQ file Number files per

More information

Information and Data Sharing Policy* Genomics:GTL Program

Information and Data Sharing Policy* Genomics:GTL Program Appendix 1 Information and Data Sharing Policy* Genomics:GTL Program Office of Biological and Environmental Research Office of Science Department of Energy Appendix 1 Final Date: April 4, 2008 Introduction

More information

GENETICS COURSES AT SCHOOLS OF PUBLIC HEALTH WEB SEARCH - 38 SCHOOLS SACGHS - 11/20/07

GENETICS COURSES AT SCHOOLS OF PUBLIC HEALTH WEB SEARCH - 38 SCHOOLS SACGHS - 11/20/07 Number of Biostatistically, Epidemiologically, or Biologically based Courses Number of Health Management/Law based classes School Courses Centers Other Boston U BS858 Statistical 3 2 BS860 Statistical

More information

Embargoed until 14:30 CEST European time, 13:30 BST UK, 8:30 Eastern US summer time Contacts:

Embargoed until 14:30 CEST European time, 13:30 BST UK, 8:30 Eastern US summer time Contacts: Embargoed until 14:30 CEST European time, 13:30 BST UK, 8:30 Eastern US summer time Contacts: Louisa Wood or Katrina Pavelin, EMBL EBI louisa@ebi.ac.uk katrina@ebi.ac.uk +44 (0)1223 494665 Sonia Furtado,

More information

Sharing Data from Large-scale Biological Research Projects: A System of Tripartite Responsibility

Sharing Data from Large-scale Biological Research Projects: A System of Tripartite Responsibility Sharing Data from Large-scale Biological Research Projects: A System of Tripartite Responsibility Report of a meeting organized by the Wellcome Trust and held on 14 15 January 2003 at Fort Lauderdale,

More information

Next Generation Sequencing: Technology, Mapping, and Analysis

Next Generation Sequencing: Technology, Mapping, and Analysis Next Generation Sequencing: Technology, Mapping, and Analysis Gary Benson Computer Science, Biology, Bioinformatics Boston University gbenson@bu.edu http://tandem.bu.edu/ The Human Genome Project took

More information

RETRIEVING SEQUENCE INFORMATION. Nucleotide sequence databases. Database search. Sequence alignment and comparison

RETRIEVING SEQUENCE INFORMATION. Nucleotide sequence databases. Database search. Sequence alignment and comparison RETRIEVING SEQUENCE INFORMATION Nucleotide sequence databases Database search Sequence alignment and comparison Biological sequence databases Originally just a storage place for sequences. Currently the

More information

NGS and complex genetics

NGS and complex genetics NGS and complex genetics Robert Kraaij Genetic Laboratory Department of Internal Medicine r.kraaij@erasmusmc.nl Gene Hunting Rotterdam Study and GWAS Next Generation Sequencing Gene Hunting Mendelian gene

More information

CCR Biology - Chapter 9 Practice Test - Summer 2012

CCR Biology - Chapter 9 Practice Test - Summer 2012 Name: Class: Date: CCR Biology - Chapter 9 Practice Test - Summer 2012 Multiple Choice Identify the choice that best completes the statement or answers the question. 1. Genetic engineering is possible

More information

Big Data Analytics and Healthcare

Big Data Analytics and Healthcare Big Data Analytics and Healthcare Anup Kumar, Professor and Director of MINDS Lab Computer Engineering and Computer Science Department University of Louisville Road Map Introduction Data Sources Structured

More information

A Tutorial in Genetic Sequence Classification Tools and Techniques

A Tutorial in Genetic Sequence Classification Tools and Techniques A Tutorial in Genetic Sequence Classification Tools and Techniques Jake Drew Data Mining CSE 8331 Southern Methodist University jakemdrew@gmail.com www.jakemdrew.com Sequence Characters IUPAC nucleotide

More information

Databases and platforms for data analysis from NGS of MTB

Databases and platforms for data analysis from NGS of MTB Databases and platforms for data analysis from NGS of MTB Derrick Crook MMM Consortium MMM Consortium Linking Clinical record systems and NHS databases Translating next generation sequencing for patient

More information

Personalized Medicine and IT

Personalized Medicine and IT Personalized Medicine and IT Data-driven Medicine in the Age of Genomics www.intel.com/healthcare/bigdata Ketan Paranjape General Manager, Life Sciences Intel Corp. @Portlandketan 1 The Central Dogma of

More information

ENABLING DATA TRANSFER MANAGEMENT AND SHARING IN THE ERA OF GENOMIC MEDICINE. October 2013

ENABLING DATA TRANSFER MANAGEMENT AND SHARING IN THE ERA OF GENOMIC MEDICINE. October 2013 ENABLING DATA TRANSFER MANAGEMENT AND SHARING IN THE ERA OF GENOMIC MEDICINE October 2013 Introduction As sequencing technologies continue to evolve and genomic data makes its way into clinical use and

More information

Euro-BioImaging European Research Infrastructure for Imaging Technologies in Biological and Biomedical Sciences

Euro-BioImaging European Research Infrastructure for Imaging Technologies in Biological and Biomedical Sciences Euro-BioImaging European Research Infrastructure for Imaging Technologies in Biological and Biomedical Sciences WP11 Data Storage and Analysis Task 11.1 Coordination Deliverable 11.2 Community Needs of

More information

How Can Institutions Foster OMICS Research While Protecting Patients?

How Can Institutions Foster OMICS Research While Protecting Patients? IOM Workshop on the Review of Omics-Based Tests for Predicting Patient Outcomes in Clinical Trials How Can Institutions Foster OMICS Research While Protecting Patients? E. Albert Reece, MD, PhD, MBA Vice

More information

Integrated Rule-based Data Management System for Genome Sequencing Data

Integrated Rule-based Data Management System for Genome Sequencing Data Integrated Rule-based Data Management System for Genome Sequencing Data A Research Data Management (RDM) Green Shoots Pilots Project Report by Michael Mueller, Simon Burbidge, Steven Lawlor and Jorge Ferrer

More information

How-To: SNP and INDEL detection

How-To: SNP and INDEL detection How-To: SNP and INDEL detection April 23, 2014 Lumenogix NGS SNP and INDEL detection Mutation Analysis Identifying known, and discovering novel genomic mutations, has been one of the most popular applications

More information

Analysis of ChIP-seq data in Galaxy

Analysis of ChIP-seq data in Galaxy Analysis of ChIP-seq data in Galaxy November, 2012 Local copy: https://galaxy.wi.mit.edu/ Joint project between BaRC and IT Main site: http://main.g2.bx.psu.edu/ 1 Font Conventions Bold and blue refers

More information

PREDA S4-classes. Francesco Ferrari October 13, 2015

PREDA S4-classes. Francesco Ferrari October 13, 2015 PREDA S4-classes Francesco Ferrari October 13, 2015 Abstract This document provides a description of custom S4 classes used to manage data structures for PREDA: an R package for Position RElated Data Analysis.

More information

Challenges associated with analysis and storage of NGS data

Challenges associated with analysis and storage of NGS data Challenges associated with analysis and storage of NGS data Gabriella Rustici Research and training coordinator Functional Genomics Group gabry@ebi.ac.uk Next-generation sequencing Next-generation sequencing

More information

Analyze Human Genome Using Big Data

Analyze Human Genome Using Big Data Analyze Human Genome Using Big Data Poonm Kumari 1, Shiv Kumar 2 1 Mewar University, Chittorgargh, Department of Computer Science of Engineering, NH-79, Gangrar-312901, India 2 Co-Guide, Mewar University,

More information

Core Bioinformatics. Degree Type Year Semester. 4313473 Bioinformàtica/Bioinformatics OB 0 1

Core Bioinformatics. Degree Type Year Semester. 4313473 Bioinformàtica/Bioinformatics OB 0 1 Core Bioinformatics 2014/2015 Code: 42397 ECTS Credits: 12 Degree Type Year Semester 4313473 Bioinformàtica/Bioinformatics OB 0 1 Contact Name: Sònia Casillas Viladerrams Email: Sonia.Casillas@uab.cat

More information

HPC pipeline and cloud-based solutions for Next Generation Sequencing data analysis

HPC pipeline and cloud-based solutions for Next Generation Sequencing data analysis HPC pipeline and cloud-based solutions for Next Generation Sequencing data analysis HPC4NGS 2012, Valencia Ignacio Medina imedina@cipf.es Scientific Computing Unit Bioinformatics and Genomics Department

More information

Shouguo Gao Ph. D Department of Physics and Comprehensive Diabetes Center

Shouguo Gao Ph. D Department of Physics and Comprehensive Diabetes Center Computational Challenges in Storage, Analysis and Interpretation of Next-Generation Sequencing Data Shouguo Gao Ph. D Department of Physics and Comprehensive Diabetes Center Next Generation Sequencing

More information

G E N OM I C S S E RV I C ES

G E N OM I C S S E RV I C ES GENOMICS SERVICES THE NEW YORK GENOME CENTER NYGC is an independent non-profit implementing advanced genomic research to improve diagnosis and treatment of serious diseases. capabilities. N E X T- G E

More information

Acceleration for Personalized Medicine Big Data Applications

Acceleration for Personalized Medicine Big Data Applications Acceleration for Personalized Medicine Big Data Applications Zaid Al-Ars Computer Engineering (CE) Lab Delft Data Science Delft University of Technology 1" Introduction Definition & relevance Personalized

More information

The Future of the Electronic Health Record. Gerry Higgins, Ph.D., Johns Hopkins

The Future of the Electronic Health Record. Gerry Higgins, Ph.D., Johns Hopkins The Future of the Electronic Health Record Gerry Higgins, Ph.D., Johns Hopkins Topics to be covered Near Term Opportunities: Commercial, Usability, Unification of different applications. OMICS : The patient

More information

Web-based Gene Expression Handling with the Genetic Data Warehouse

Web-based Gene Expression Handling with the Genetic Data Warehouse Web-based Gene Expression Handling with the Genetic Data Warehouse Jörg Lange, Toralf Kirsten Microarray-Workshop, June 2006 Outline Requirements for Gene Expression Analyses Intensity values MIAME Genetic

More information

OpenCB a next generation big data analytics and visualisation platform for the Omics revolution

OpenCB a next generation big data analytics and visualisation platform for the Omics revolution OpenCB a next generation big data analytics and visualisation platform for the Omics revolution Development at the University of Cambridge - Closing the Omics / Moore s law gap with Dell & Intel Ignacio

More information

Open Access to Manuscripts, Open Science, and Big Data

Open Access to Manuscripts, Open Science, and Big Data Open Access to Manuscripts, Open Science, and Big Data Progress, and the Elsevier Perspective in 2013 Presented by: Dan Morgan Title: Senior Manager Access Relations, Global Academic Relations Company

More information

Managing and Conducting Biomedical Research on the Cloud Prasad Patil

Managing and Conducting Biomedical Research on the Cloud Prasad Patil Managing and Conducting Biomedical Research on the Cloud Prasad Patil Laboratory for Personalized Medicine Center for Biomedical Informatics Harvard Medical School SaaS & PaaS gmail google docs app engine

More information

Appendix 2 Molecular Biology Core Curriculum. Websites and Other Resources

Appendix 2 Molecular Biology Core Curriculum. Websites and Other Resources Appendix 2 Molecular Biology Core Curriculum Websites and Other Resources Chapter 1 - The Molecular Basis of Cancer 1. Inside Cancer http://www.insidecancer.org/ From the Dolan DNA Learning Center Cold

More information

PaRFR : Parallel Random Forest Regression on Hadoop for Multivariate Quantitative Trait Loci Mapping. Version 1.0, Oct 2012

PaRFR : Parallel Random Forest Regression on Hadoop for Multivariate Quantitative Trait Loci Mapping. Version 1.0, Oct 2012 PaRFR : Parallel Random Forest Regression on Hadoop for Multivariate Quantitative Trait Loci Mapping Version 1.0, Oct 2012 This document describes PaRFR, a Java package that implements a parallel random

More information

Removing Sequential Bottlenecks in Analysis of Next-Generation Sequencing Data

Removing Sequential Bottlenecks in Analysis of Next-Generation Sequencing Data Removing Sequential Bottlenecks in Analysis of Next-Generation Sequencing Data Yi Wang, Gagan Agrawal, Gulcin Ozer and Kun Huang The Ohio State University HiCOMB 2014 May 19 th, Phoenix, Arizona 1 Outline

More information

Routine processing of large scale human whole genome sequencing data.

Routine processing of large scale human whole genome sequencing data. Routine processing of large scale human whole genome sequencing data. Ies Nijman, UMCU, CPCT, Hartwig Medical Foundation Compute Resources for Life Science Research 16-12-2015 Center for Personalized Cancer

More information

REGULATIONS FOR THE DEGREE OF BACHELOR OF SCIENCE IN BIOINFORMATICS (BSc[BioInf])

REGULATIONS FOR THE DEGREE OF BACHELOR OF SCIENCE IN BIOINFORMATICS (BSc[BioInf]) 820 REGULATIONS FOR THE DEGREE OF BACHELOR OF SCIENCE IN BIOINFORMATICS (BSc[BioInf]) (See also General Regulations) BMS1 Admission to the Degree To be eligible for admission to the degree of Bachelor

More information

Richmond, VA. Richmond, VA. 2 Department of Microbiology and Immunology, Virginia Commonwealth University,

Richmond, VA. Richmond, VA. 2 Department of Microbiology and Immunology, Virginia Commonwealth University, Massive Multi-Omics Microbiome Database (M 3 DB): A Scalable Data Warehouse and Analytics Platform for Microbiome Datasets Shaun W. Norris 1 (norrissw@vcu.edu) Steven P. Bradley 2 (bradleysp@vcu.edu) Hardik

More information

Microarray Analysis Using R/Bioconductor

Microarray Analysis Using R/Bioconductor Microarray Analysis Using R/Bioconductor Reddy Gali, Ph.D. rgali@hms.harvard.edu h"p://catalyst.harvard.edu Agenda Introduction to microarrays Workflow of a gene expression microarray experiment Publishing

More information

SAP HANA Enabling Genome Analysis

SAP HANA Enabling Genome Analysis SAP HANA Enabling Genome Analysis Joanna L. Kelley, PhD Postdoctoral Scholar, Stanford University Enakshi Singh, MSc HANA Product Management, SAP Labs LLC Outline Use cases Genomics review Challenges in

More information

Deep Sequencing Data Analysis

Deep Sequencing Data Analysis Deep Sequencing Data Analysis Ross Whetten Professor Forestry & Environmental Resources Background Who am I, and why am I teaching this topic? I am not an expert in bioinformatics I started as a biologist

More information

Focusing on results not data comprehensive data analysis for targeted next generation sequencing

Focusing on results not data comprehensive data analysis for targeted next generation sequencing Focusing on results not data comprehensive data analysis for targeted next generation sequencing Daniel Swan, Jolyon Holdstock, Angela Matchan, Richard Stark, John Shovelton, Duarte Mohla and Simon Hughes

More information

Introduction of KISTI and NISN Resource and Services Bioinformatics applications Conclusion

Introduction of KISTI and NISN Resource and Services Bioinformatics applications Conclusion Introduction of KISTI and NISN Resource and Services Bioinformatics applications Conclusion President National Nano-Technology Policy Center National Institute of Supercomputing and Networking Div. of

More information

Scalable Cloud Computing Solutions for Next Generation Sequencing Data

Scalable Cloud Computing Solutions for Next Generation Sequencing Data Scalable Cloud Computing Solutions for Next Generation Sequencing Data Matti Niemenmaa 1, Aleksi Kallio 2, André Schumacher 1, Petri Klemelä 2, Eija Korpelainen 2, and Keijo Heljanko 1 1 Department of

More information

Bioinformatics: course introduction

Bioinformatics: course introduction Bioinformatics: course introduction Filip Železný Czech Technical University in Prague Faculty of Electrical Engineering Department of Cybernetics Intelligent Data Analysis lab http://ida.felk.cvut.cz

More information

A Multi-locus Genetic Risk Score for Abdominal Aortic Aneurysm

A Multi-locus Genetic Risk Score for Abdominal Aortic Aneurysm A Multi-locus Genetic Risk Score for Abdominal Aortic Aneurysm Zi Ye, 1 MD, Erin Austin, 1,2 PhD, Daniel J Schaid, 2 PhD, Iftikhar J. Kullo, 1 MD Affiliations: 1 Division of Cardiovascular Diseases and

More information

Course plan Academic Year Qualification MSc on Bioinformatics for Health Sciences

Course plan Academic Year Qualification MSc on Bioinformatics for Health Sciences Course plan 2015-2016 Academic Year Qualification MSc on Bioinformatics for Health Sciences 1. Description of the subject Subject name: Information Extraction from Omics technologies Code: 31032 Total

More information

Eoulsan Analyse du séquençage à haut débit dans le cloud et sur la grille

Eoulsan Analyse du séquençage à haut débit dans le cloud et sur la grille Eoulsan Analyse du séquençage à haut débit dans le cloud et sur la grille Journées SUCCES Stéphane Le Crom (UPMC IBENS) stephane.le_crom@upmc.fr Paris November 2013 The Sanger DNA sequencing method Sequencing

More information

Minimum information about a microarray experiment (MIAME) toward standards for microarray data

Minimum information about a microarray experiment (MIAME) toward standards for microarray data Minimum information about a microarray experiment (MIAME) toward standards for microarray data Alvis Brazma 1, Pascal Hingamp 2, John Quackenbush 3, Gavin Sherlock 4, Paul Spellman 5, Chris Stoeckert 6,

More information

Towards Integrating the Detection of Genetic Variants into an In-Memory Database

Towards Integrating the Detection of Genetic Variants into an In-Memory Database Towards Integrating the Detection of Genetic Variants into an 2nd International Workshop on Big Data in Bioinformatics and Healthcare Oct 27, 2014 Motivation Genome Data Analysis Process DNA Sample Base

More information

BIOL 3200 Spring 2015 DNA Subway and RNA-Seq Data Analysis

BIOL 3200 Spring 2015 DNA Subway and RNA-Seq Data Analysis BIOL 3200 Spring 2015 DNA Subway and RNA-Seq Data Analysis By the end of this lab students should be able to: Describe the uses for each line of the DNA subway program (Red/Yellow/Blue/Green) Describe

More information

Genome Viewing. Module 2. Using Genome Browsers to View Annotation of the Human Genome

Genome Viewing. Module 2. Using Genome Browsers to View Annotation of the Human Genome Module 2 Genome Viewing Using Genome Browsers to View Annotation of the Human Genome Bert Overduin, Ph.D. PANDA Coordination & Outreach EMBL - European Bioinformatics Institute Wellcome Trust Genome Campus

More information

Large-scale Research Data Management and Analysis Using Globus Services. Ravi Madduri Argonne National Lab University of Chicago @madduri

Large-scale Research Data Management and Analysis Using Globus Services. Ravi Madduri Argonne National Lab University of Chicago @madduri Large-scale Research Data Management and Analysis Using Globus Services Ravi Madduri Argonne National Lab University of Chicago @madduri Outline Who we are Challenges in Big Data Management and Analysis

More information

Biological Sequence Data Formats

Biological Sequence Data Formats Biological Sequence Data Formats Here we present three standard formats in which biological sequence data (DNA, RNA and protein) can be stored and presented. Raw Sequence: Data without description. FASTA

More information

Alison Yao, Ph.D. July 2014

Alison Yao, Ph.D. July 2014 * Alison Yao, Ph.D. Program Officer, Office of Genomics and Advanced Technologies Division of Microbiology and Infectious Diseases National Institute of Allergy and Infectious Diseases National Institutes

More information

The EcoCyc Curation Process

The EcoCyc Curation Process The EcoCyc Curation Process Ingrid M. Keseler SRI International 1 HOW OFTEN IS THE GOLDEN GATE BRIDGE PAINTED? Many misconceptions exist about how often the Bridge is painted. Some say once every seven

More information