Data Management for Quantitative Biology - Lecture 1, Apr 16, 2015

Dr. Sven Nahnsen, Quantitative Biology Center (QBiC)

Data Management for Quantitative Biology

Lecture 1: Introduction and overview

Overview

• Administrative stuff (credits, requirements)

• Motivation/quick review of relevant contents

(Bioinformatics I and II)

•  Introduction to this lecture series

• Semester overview

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Course requirements

To pass this course you must:

•  regularly and actively participate in the weekly problem sessions,

•  pass the final exam, assignments and projects

•  You have to work on assignments alone

•  You will work in small groups for the problem-orientated research

project

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Course credits and grading •  Credits -  MSc Bioinfo: 4 LP, module “Wahlpflichtbereich Bioinformatik” -  MSc Info: 4 LP, area “Wahlpflichtbereich Informatik” • Grade

-  30% assignments -  20% project -  50% finals

•  Finals: oral exam (30 minutes) covering the contents of the whole lecture, the assignments and the project

•  Finals will be scheduled at the end of the semester (Thu, 30/07/2015)

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Recommended literature • We will point to relevant papers during the course of the literature •  Important overview papers:

§  Hastings et al., 2005, Quantitative Bioscience for the 21st century. BioScience. Vol 55 No. 6

§  Cohen JE (2004) Mathematics Is Biology's Next Microscope, Only Better; Biology Is Mathematics' Next Physics, Only Better. PLoS Biol 2(12): e439.

•  Books §  Free E-Book: Data management in Bioinformatics (

http://en.wikibooks.org/wiki/Data_Management_in_Bioinformatics) §  Lacroix, Z.; Critchlow, T. (eds): Bioinformatics: Managing Scientific Data. Morgan

Kaufmann Publishers, San Francisco 2003 §  Michale E. Wall, Quantitative Biology: From Molecular to Cellular Systems. 2012.

Chapman & Hall §  Pierre Bonnet. Enterprise Data Governance: Reference and Master Data

Management Semantic Modeling. 2013. Wiley

• Web resources §  http://www.ariadne.ac.uk: Ariadne, Web Magazine for Information Professionals §  http://www.dama.org: THE GLOBAL DATA MANAGEMENT COMMUNITY §  H.D. Ehrich: http://www.ifis.cs.tu-bs.de/node/2855

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Recommended Software •  These software tools/framework and webservers will be used

during the problem sessions

http://www.cisd.ethz.ch/software/openBIS https://usegalaxy.org

https://vaadin.com/home https://www.knime.org/

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Contact and organization •  Questions concerning the lecture/assignments

§  [email protected] •  Website

§  abi.inf.uni-tuebingen.de/Teaching/ws-2013-14/CPM

•  Christopher Mohr (Sand 14, C322) , Andreas Friedrich (Sand 14, C 304)

•  Dr. Sven Nahnsen (Quantitative Biology Center, Auf der Morgenstelle 10, C2P43, please send e-mail first)

•  Course material will be available on the website (see above), through social media channels and (if wished) as a hard copy during the lecture

facebook.com/qbic.tuebingen twitter.com/qbic_tue

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Who am I • Most of me and on our work can be found here: www.qbic.uni-tuebingen.de

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Contents of this lecture

Date Lecturer Lecture 8-‐10 AM Thursday 16 April 15 Nahnsen Introduc8on and overview Thursday 23 April 15 Nahnsen Biological Data Management

Thursday 30 April 15 Czemmel Data sources ("Next-‐genera8on"

technologies)

Dr. Stefan Czemmel

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Date Lecturer Lecture 8-‐10 AM

Thursday 7 May 15 Codrea Database systems (mySQL, noSQL, etc.)

Thursday 14 May 15 Ascension Day (Himmelfahrt)

Thursday 21 May 15 Czemmel LIMS and E-‐Lab books Thursday 28, May 15 Kenar Experimental Design

Dr. Marius Codrea Erhan Kenar

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Date Lecturer Lecture 8-‐10 AM

Thursday 4 June 15 Corpus Chris8 Day (Fronleichnam)

Thursday 11 June 15 Nahnsen Data analysis workflows (I) Thursday 18 June 15 Nahnsen Data analysis workflows (II) Thursday 25 June 15 Nahnsen Standardiza8on Thursday 2 July 15 Nahnsen Big Data

Thursday 9 July 15 Nahnsen Integrated data management (OpenBIS, OpenBEB)

Thursday 16 July 15 Nahnsen Applica8ons Thursday 23 July 15 Nahnsen Exam prepara8on

Thursday 30 July 15 Nahnsen, Mohr, Friedrich EXAMS

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What is your background? Ad hoc collection from the audience, Apr. 16, 2015 •  Computer Science •  Bioinformatics(immonoinformatics; User Front-end;integration ,

visualization) •  Biology •  Drug design •  Agricultural biology (plant breeding) •  Bioinformatics (Tx, NGS) • Geoecology •  (ecology) •  Biochemistry; Molecular Biology •  Structural Biology •  Electronic business

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Let us brainstorm Ad hoc collection from the audience, Apr. 16, 2015 • What is data management?

-  Rapid access to data -  Selective access to data; database queries -  Combine data; manipulate data efficiently -  Big data storage/analysis -  Curating quality -  Data visualization -  Make data interpretable

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Let us brainstorm • What is data management?

http://zonese7en.com/wp-content/uploads/2014/04/Data-Management.jpg, accessed Apr 10, 2015, 11 AM

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Data Management •  The official definition provided by DAMA (Data management

association) International, the professional organization for those in the data management profession, is: "Data Resource Management is the development and execution of architectures, policies, practices and procedures that properly manage the full data lifecycle needs of an enterprise.”

•  Further, the DAMA – Data management Body of Knowledge ((DAMA-DMBOK)) states:” Data management is the development, execution and supervision of plans, policies, programs and practices that control, protect, deliver and enhance the value of data and information assets ”

Wikipedia: http://en.wikipedia.org/wiki/Data_management accessed Mar 30, 2015, 10 PM 15

10 Data Management functions According to the DAMA Data Management Body of Knowledge (DMBOK)

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Data governance •  Strategy

• Organization and roles

•  Policies and standards

•  Projects and services

•  Issues

•  Valuation

Source: DAMA DMBOK Guide, p. 10

“Planning, supervision and control over data management and use”

http://meship.com

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Data quality management •  Data cleansing

•  Data integrity

•  Data enrichment

•  Data quality

•  Data quality assurance


“defining, monitoring and improving data quality”

http://www.arcplan.com/

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Data architecture management •  Data architecture

•  Data analysis

•  Data design (modeling)


atasourceconsulting.com

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Data development •  Analysis

•  Data modeling

•  Database design

•  Implementation


dataone.org

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“Data development is the process of building a data set for a specific purpose. The process includes identifying what data are required and how feasible it is to obtain the data. Data development includes developing or adopting data standards in consultation with stakeholders to ensure uniform data collection and reporting, and obtaining authoritative approval for the data set.”, A guide to data development, Australian Institute of Health and Welfare Canberra, 2007

Database management •  Data maintenance

•  Data administration

•  Database management system

Source: DAMA DMBOK Guide, p. 10 21

Data Security Management •  Standards

•  Classification

•  Administration

•  Authentication/Authorization

•  Auditing


http://www.techieapps.com/wp-content/uploads/2013/07/2-1024x768.jpg

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Reference and Master Data management •  External/internal codes

•  Customer Data

•  Product Data

•  Dimension management (why do different dimensions (entities) relate to each other)

•  Taxonomy/Ontology


Master Reference

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Reference data management

Master data

Data warehousing and business intelligence management

•  Architecture

•  Implementation

•  Training and Support

• Monitoring and Tuning


Raw data

Metadata

…

Summary data

Data warehouse

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Data warehousing and business intelligence management

Raw data

Metadata

…

Summary data

Data warehouse

Input

Report Business intelligence

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Document, record and content management •  Acquisition and storage

•  Backup and Recovery

•  Content Management

•  Retrieval

•  Retention


Metadata management Metadata is data about data

•  Architecture

•  Integration

•  Control

•  Delivery


DAMA – DMBOK •  A broad collection of all discipline and subtopics that are

summarized under the umbrella of data management •  These concern many business-related issues, but many concepts

are very well applicable to the field of bioscience • We will come back to various aspects of the DAMA DMBOK during

the course

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Data management needs in science and research •  Survey at the University of Oregon, USA (Brian Westra. "Data Services for the Sciences: A

Needs Assessment". July 2010, Ariadne Issue 64 http://www.ariadne.ac.uk/issue64/westra/)

•  Different scientific discipline

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Data management in science and research

Brian Westra. "Data Services for the Sciences: A Needs Assessment". July 2010, Ariadne Issue 64 http://www.ariadne.ac.uk/issue64/westra/, accessed Apr. 10, 2015, 11 AM

1 2 3 4 5 6 7 8 9 10 11

1 Data storage and backup 7 Finding and accessing related data from others

2 Making scientific data findable by others 8 Connecting data storage to data analysis

3 Connecting data acquisition to data storage 9 Liniking this data to publications or other asset

4 Allowing or controlling access to scientific data by others 10 Ensuring data is secure and trustworthy

5 Documenting and tracking updates 11 Others

6 Data analysis and manipulation

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Let us brainstorm • What is Quantitative Biology? Ad hoc collection from the audience, Apr. 16, 2015

-  Not only yes/no, but put amounts to entities -  Huge amount of data -  Qunatitative methods to study biology -  System-wide analysis; specific pathways -  Make results human readable and accesible

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Quantitative Biology •  The term quantitative biology has been coined by Hastings et al.,

2005. •  High-throughput methods have led to a paradigm shift in

biomedical research •  Traditionally, the focus was on one-molecule-at-a-time for most

bio(medical) research projects •  Now, data on whole genomes, exomes, epigenomes,

transcriptomes, proteomes and metabolomes can be generated at low cost.

•  The term quantitative biology is used to describe this paradigm shift. Improvements in this area have been driven mainly by two technological developments:

Hastings et al., 2005, Quantitative Bioscience for the 21st century. BioScience. Vol 55 No. 6 33

Technological innovations •  State-of-the-art mass spectrometers coupled to high-

performance liquid chromatography through soft ionization techniques (HPLC-ESI-MS) have quickly changed the way we do proteomics, metabolomics, and lipidomics.

•  Next-generation sequencing has similarly changed the way we look at genomes, epigenomes, transcriptomes, and metagenomes. Due to advances in chemistry and imaging, sequencing reactions have been parallelized on a very large scale. The comprehensiveness of the data produced by high-throughput methods makes them particularly interesting as general-purpose analytical and diagnostic techniques.

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Technological innovations •  Imaging technologies can now produce high-resolution pictures

of fine-grained cellular details at a very high speed

•  Finally methods from bioinformatics and computational biology have matured to rapidly analyze the huge raw data sets that are generated by these high throughput technologies

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Contents from Bioinformatics 2 (high-throughput technologies

• Most of the high throughput technologies have been introduced during the Bioinformatics II lecture

•  There are specialized lectures on “Transcriptomics” and on “Computational Proteomics and Metabolomics”

• We will give a short Recap on the Bioinformatics II contents that are relevant for this lecture

• More advanced topics on data generation methods will be introduced in lecture 3 by Dr. Stefan Czemmel (focus on next generation sequencing)

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Origin of the “Central Dogma of Molecular Biology” (Francis Crick, 1956)

The central dogma of molecular biology •  First articulation by Francis Crick in 1956 •  Published in Nature in 1970

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The central dogma – classical view •  In general, the classic view reflects how biology is (biological data

are) organized • Genomics, however, enabled a more complex view

Cox Systems Biology Lab | Research, University of Toronto, Canada 38

Reminder (Bioinformatics 2)

Oltvai-Barabasi, Science, 2002 39

Recap Bioinformatics II: Systems biology •  Quantitative data on various levels of biological complexity build

fundaments of systems biology •  Mathematical modeling has been based on gene expression •  Recent important technological improvements allow the analysis of protein

and metabolite profiles to a great depth •  Important layers for understanding biology •  New experimental techniques offer tremendous challenges for

computational analysis

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Recap Bioinformatics II: Aims of Systems Biology •  Describe large-scale organization • Quantitative modeling •  Describe cell as system of networks

-  Fundamental research: time-resolved quantitative

understanding of living systems -  Medicine: enable personalized medicine (e.g., improve

treatment strategies for cancer patients) -  Biotechnology: improve production, degradation, construction

of synthetic organisms, etc.

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Exp. Methods – Transcriptomics •  Extract and amplify RNA •  Hybridization on microarray •  Identify and quantify by fluorescence signal •  Sequences can be mapped back to genome

Lindsay, Nature Rev. Drug Discovery, 2003, 2, 803 42

Microarray Data Analysis •  Key problems in microarray

data analysis are -  Data normalization -  Clustering -  Dimension reduction -  Diagnostics/classification -  Network inference -  Visualization of results

Janko Dietzsch , Nils Gehlenborg and Kay Nieselt. Mayday-a microarray data analysis workbench. Bioinformatics 2006 22(8):1010-1012 43

Genome sequencing

February 15, 2001 February 16, 2001

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Genome sequencing •  2001: initial publication •  2003: 2nd draft “Human Genome” •  > 13 years of work and > 3*109 $ •  2010: 8 days 1*104 $ •  Today: approximately 5.5 days and < 1*104 $ •  Future: within 3 years Biotech company (Pacific Biosciences)

expects similar amount of data in < 15 min for < 1*103 $

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Status genomics/transcriptomics •  Dramatic drop in cost for genome sequencing •  Number of sequenced genomes grows continuously • Genome is a very static snapshot of living system •  Biological adaption is rather slow; long-term information storage •  Proteins and their reaction products, metabolites are much closer

to reality • Genome and transcriptome databases are essential bases for

proteomics and metabolomics research

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Genomics vs. Proteomics

Genomics Proteomics Genomes rather static

~ 20 k genes

established technology

(capillary sequencer)

Proteomes are dynamic

(age, tissue, breakfast, …)

up to 1000 k proteins

emerging technologies

(MS, HPLC/MS, protein chips)

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Proteomics

http://www.iamashcash.com/wp-content/uploads/2011/03/caterpillar-to-butterfly1.jpg, accessed: 14/10/2013 6 PM

Genome remains the same

Proteome changes

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Main fields of proteomics

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Applications of proteomics

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Shotgun Proteomics

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Next generation sequencing 1st generation 2nd generation

Illumina / Solexa Genetic Analyzer 2000 Mb / run (96h)

Roche / 454 Genome Sequencer FLX 400 Mb / run (8h)

Applied Biosystems SOLiD 3000 Mb / run (120h)

300 : 1 (2008)

Applied Biosystems 3730xl 0,08 Mb / run 1 Mb / 24 h

>3000 : 1 (2010)

1st generation 2nd generation

Slide: Prof. Peter Bauer

In 2008

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„3rd“ generation sequencing

Drmanac Science (2010) 5961: 78-81.

CompleteGenomics DNB sequencing 18x 210Gb / run

>37.000 : 1 (2010)

3rd generation sequencing Slide: Prof. Peter Bauer

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High resolution imaging “Imaging in biology may refer to >15 different technologies” Prominent and data-intense examples include: • Optical (bioluminescence and fluorescence imaging)

• Magnetic resonance imaging

•  X-ray computed tomography

•  Positron emission tomography

http://en.wikipedia.org/wiki/Biological_imaging, accessed Apr. 13, 4 PM 54

Imaging workflow

Eliceiri et al., Nature Methods 9, 697–710 (2012) 55

Database systems •  Relational databases

-  Example MySQL

•  NoSQL databases

-  Example MongoDB

•  How to query databases

•  Entity relationship models

•  Repositories (e.g. Pride, PeptideAtlas)

-  Annotations

-  Sequences

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Many database concepts

http://dataconomy.com/wp-content/uploads/2014/07/fig2large.jpg 57

Databases/Repositories in Genomics/Proteomics

http://www.ebi.ac.uk/ena

http://www.peptideatlas.org

http://www.ebi.ac.uk/pride/archive/

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Large-scale study data – 1000 Genomes •  Sample lists and sequencing progress •  Variant Calls •  Alignments •  Raw sequence files

http://www.1000genomes.org/data 59

Large-scale study data – The cancer genome atlas (TCGA)

•  TCGA aims to help to diagnose, treat and prevent cancer •  explore the entire spectrum of genomic changes involved in more

than 20 types of human cancer. •  Approx. 2 PB of genomic raw data

http://cancergenome.nih.gov 60

Laboratory information management systems/ Electronic Lab Books •  How to track all information that is generated in the laboratory

•  Automated annotation of all experimental parameters is essential for reproducible science

•  Currently, most experiments are protocolled manually in lab textbooks

•  Data security (intellectual property versus open data)

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Experimental design •  Biological experiments are very complex •  Statistical significance requires a high number of biological

replicates • Often many different conditions and time points need to be

considered • One study can involve many different experiments (multi omics

studies involve different omics layers, e.g. genomics + transcriptomics + proteomics)

•  All experiments come with different meta data requirements •  For various reasons the experimental design is not always

balanced (e.g. 5 samples in group A and and only 3 samples are available for group B)

Friedrich, A., et al. Biomed Research International, April 2015 – in press. Nahnsen, S., Drug Target, May 2015 – in press. 62

Experimental design

Friedrich, A., et al. Biomed Research International, April 2015 – in press. Nahnsen, S., Drug Target, May 2015 – in press. 63

Data analysis workflows •  Chain different (heterogeneous) tools •  Parameter handling •  Execution in high performance computing environment made easy

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Standardization in bioinformatics • Many world-wide bioinformatics initiatives need to rely on open

standards •  Development of standards has to be a community effort •  Standardized data formats are important to guarantee

-  Sustainability -  Independence of instrument vendors -  Independence of analysis software -  Exchangeability of raw data

•  Standard formats increase the amount of data by a factor of x (x =

2-4) • Many people refrain from using open standards

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http://en.wikipedia.org/wiki/Big_Data, accessed Apr 24, 2014

Big data is the term for a collection of data sets so large and complex that it becomes difficult to process using on-hand database management tools or traditional data processing applications. The challenges include capture, curation, storage, search, sharing, transfer, analysis and visualization. The trend to larger data sets is due to the additional information derivable from analysis of a single large set of related data, as compared to separate smaller sets with the same total amount of data, allowing correlations to be found ……

Big data

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Big data examples •  European Council for Nuclear Research (CERN) Geneva,

Swizerland 25 Petabyte/Jahr at LHC (Large Hadron collider) (~6.2 Mio. DVDs)

CERN LHD data

Big data Beispiele

ep.ph.bham.ac.uk, 2014 67

Big data examples • Google verarbeitet 9.1 Exabyte/year (300 Mio. DVDs)

GOOGLE data

Mayer-Schönberger, 2013, ititch.com, 2014 68

Biology and Big data? •  Klassisch: Beobachtung der Natur

und deren Phänomene

DNA RNA Proteine

Träger der Erbinformation

Expression von bestimmten Genen

Üben nötige Funktion in der Zelle aus

1956 formuliert Francis Crick das zentrale Dogma der Molekularbiologie:

•  1950er JahreDurchbruch in der Molekularbiologie

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Big data

Vivien Marx, Biology: The big challenges of big data, Nature. 2013, doi:10.1038/498255a 70

Integrated data management in biology/biomedicine

71 http://media.americanlaboratory.com/m/20/Article/35231-fig1.jpg

QBiC infrastructure

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NGS Lab

Lab Storage

Data movers

•  Automatically moves large to huge file-based data to a remote (central) storage

•  Uses rsync routine; easy configuration using config file •  Data mover athentification: public/private key ssh authentification • Moves data to openbis dropboxes (individual boxes and users for

each of the five member labs)

Data Mover

DataMover: •  Developed at ETH Zurich as part of OpenBIS •  http://www.cisd.ethz.ch/software/Data_Mover

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openBIS (meta) data store

• Open, distributed system for managing biological information

•  Captures different experiment types (OMICS, imaging, screening,...)

•  Tracking, annotating and sharing of experiments, samples and datasets for distributed research

•  Different servers for meta data and bulk raw data

•  Underlying PostgreSQL database •  ETL routines for extraction of meta data and

linking

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Data organization •  http://www.cisd.ethz.ch/software/openBIS

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Data organization •  http://www.cisd.ethz.ch/software/openBIS

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Applications •  Personalized medicine: Individualized vaccination in cancer •  Large-scale clinical studies: example Hepatocellular carcinoma

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Contact: Quantitative Biology Center (QBiC) Auf der Morgenstelle 10 72076 Tübingen · Germany [email protected]

Thanks for listening – See you next week

Data Management for Quantitative Biology - Lecture 1, Apr 16, 2015

Education

Transcript of Data Management for Quantitative Biology - Lecture 1, Apr 16, 2015