1. Yike Guo/Jiancheng Lin InforSense Ltd. 15 September 2015 Bioinformatics workflow integration

2. Life Science Challenges  Information resides on different:  Granularity levels (individual records vs. massive repositories)  Abstraction levels (models ranging from entire systems to compound patterns)  Domain levels (clinical, sequence, instrument…)  Researchers  Grouped in Virtual Organizations (VOs)  Working on the Grid  Need to communicate across physical and scientific/cultural barriers  Tools  Legacy, well-established in the process  Novel, essential to innovation  In need of a consistent infrastructure to connect the two groups

3. Discovery Informatics in Post-Genome Era ATGCAAGTCCCT AAGATTGCATAA GCTCGCTCAGTT polymorphism patient records epidemiology linkage maps cytogenetic maps physical maps sequences alignments expression patterns physiology receptors signals pathways secondary structure tertiary structure

4. Integrative Analytics Workflow Environment Data Applications Components Inbuilt Analytics Oracle Data Preprocess Files DB Workflow Warehouse Informatician Deployed Web App for End Users Portal Oracle DM Matlab R KXEN WEKA S-Plus SAS Integrative Analytics Workflow Environment 3 rd Party & Custom Apps MDL Spotfire Daylight Healthcare Web Services BioTeam iNquiry Data Analysis Group

5. InforSense Workflow Life Cycle  Constructing a ubiquitous workflow : by scientists  Integrate your information resources/software applications cross- domain  Support innovation and capture the best practice of your scientific research  Warehousing workflows: for scientists  Manage discovery processes in your organisation  Construct an enterprise process knowledge bank  Deployment workflow: to scientists  Turn your workflows into reusable applications  Turn every scientist into a solution builder

6. Workflow Creation, Integration, and Deployment Data Sources Select:1 Data Mining / Statistics Connect data and components in GUI Connect: 2 Workflow describes complex data processing and analysis “In database” processing & analytics Execute: 3 Define parameters of workflow to expose Deploy:4 Publish as: portlet, web application, SOAP service, command line app Data Processing / Transformation 3 rd Party applications (e.g.Haploview) Interactive data visualization / reporting “Cluster / Grid” execution

7. Biology to Chemistry  Novel sequences are compared to known protein structures  The resulting set of ligands on these matching structures is used to search small molecule databases for similar compounds  Compounds are then analyzed using KDE tools such as PCA and clustering to provide a diverse, representative subset for further assays

8. Navigating KEGG pathways  Gene names from EMBL are used to query KEGG via their Webservice API for appropriate pathways  Further Webservice API calls allow navigation of the data to find:  Pathway compounds  Other genes in the pathways  Visualization of query genes on their pathways

9. cDNA sequence annotation and alignment  A novel cDNA is annotated using EMBOSS tools, and a BLAST similarity search perfomed against human proteins  Annotations used to aid identification of predicted proteins derived from the cDNA

10. Ortholog analysis using BLAST  Sequence libraries from 2 organisms are cross-compared using BLAST to determine the best bi-directional matches of sufficient quality

11. Clustering of Affymetrix data with R  Native Affymetrix CEL files are loaded using R/Bioconductor  Differentially expressed genes calculated using KDE statistical nodes  The resulting list of genes is then clustered using HCLUST in R

12. Microarray analysis using text mining  Microarray data normalized in KDE  Upregulated genes annotated from Pubmed to obtain a set of related scientific papers  Text mining used to mine the paper collection and extract information most relevant to the researcher

13. Genetic data Mouse ID Cage ID Environmental conditions Management records Normal Diet Fat Fed Physiological Data prior change In Diet Weight Blood analysis Urine analysis Physiological Data after change In Diet. One time point in end-point experiment Several time points in longitudinal study Weight Blood analysis Physiological parameters Metabonomics Urine analysis Physiological parameter Metabonomics Tissue sampling Liver,Fat, Muscle, Kidney Metabonomics Proteomics (general, glyco-, phospho- proteomics) Transcriptomics Culling conditions Endpoint Culling or death 6 to 10 animals Sampling conditions Sample Storage conditions Ref of Biological assays used across the study Data Formats Affymetrix XLS files Chromatograms Filemaker Pro Metabonomics NMR spectra Raw Data Normalised Data Processed Data Similar data will be recorded regarding experiments performed with cells lines cDNA arrays ATF, GAL files Time  BAIR project Biological Atlas of Insulin Resistance

14. Collaborative Visualisation

15. Literature mining and compound analysis

16. Grid Computing

17. BAIR Portal

18. Integrative support   Information:   Data models to support individual domains (sequences, NMR profiles…) and methods to map them into generic analysis (tables, text)   Annotation databases integrated through Web Service APIs   Researchers   Sharing of work and knowledge through reusable workflow components   Aim for minimum technical overhead when linking new resources   Tools   Focus on integration methods rather than one-off tool linkage   Researchers able to link to standard tools without the need for an IT specialist   Databases accessed through aggregators (SRS, BioMart…)