GeneAnswers Integrated Interpretation of Genes. Dr. Gang (Gilbert) Feng Northwestern University Biomedical Informatics Center

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1 GeneAnswers Integrated Interpretation of Genes Dr. Gang (Gilbert) Feng Northwestern University Biomedical Informatics Center

2 Introduction! High throughput methods, like microarray, have been employed for more than one decade in biological and medical research.! Primary analysis of such experiments identify a group of genes based on certain statistical criteria.! Identified genes are not enough for many piloting projects.! Some commercial and open source translational tools have been developed Commercial software: Friendly presentation, Web based program,license fee, Not support for customized database, such as Ingenuity Open source resource: Poor visualization, Onsite or web based program, Free, Partially support for customized database, such as David s website, Category package in Bioconductor.

3 Goal! An integrated tool for users to get as more as possible translational features based on given genes. Well known annotation libraries. Fully support for customized annotation libraries.! Open source program for everyone to free access.! Support both graphics visualization and text output.! Homologous analysis is also available (animal model for human disease).

4 ! Hypergeometric Test Behind Statistics How statistically significant to pick n balls containing k white balls, without replacement, from N balls containing m white balls. white balls black balls total picked k n-k n not picked m-k N+k-n-m N-n total m N-m N Pr(K = k) = f(k;n,m,n) = " m% " $ ' N ( m % $ ' # k &# n ( k & " N % $ ' # n &

5 Workflow --- Input! A group of interested genes. Optional biological features! Optional characteristic experimental data. Microarray expression profile, etc! Optional well known or customized annotation libraries. Gene ontology KEGG pathway Disease ontology Entrez keywords search Customized annotation libraries

6 Disease Ontology! History Initially developed as part of the NUgene project starting in 2003 at Northwestern.! Missions Create a defined, logically structured vocabulary representing the domain of human disease. Linked to well-established, well-adopted terminologies. Such as SNOMED, ICD-9 and ICD-10, MeSH, and UMLS! Teams Northwestern University Biomedical Informatics Center. Institute for Genome Sciences at University of Maryland, Baltimore.! Wiki

7 Workflow --- Implementation! R R is a free software environment for statistical computing and graphics. It compiles and runs on a wide variety of UNIX platforms, Windows and MacOS Bioconductor Bioconductor is an open source and open development software project to provide tools for the analysis and comprehension of genomic data More than 300 packages, and 4 millions + download in the latest two years.

8 Workflow --- Output! Pie chart and Barplot! Network Concept-gene Concept-relationship! Concept-tabulation! Text Table

9 GeneAnswers Package

10 Straightforward Presentation of Genes! Pie chart and Barplot Simple Less informative " Invisible detailed relationship

11 Concept-Gene Network!!! Reveal detailed relationship between interested concepts and genes. Show statistical significance of concepts. Present optional characteristic features.

12 Ontological Structure of Interested Terms

13 ! Integration of gene interaction information give more clues for biologists to find potential functions and mechanisms. Literature supported gene interaction information are publicly released by National Center for Biotechnology Information (NCBI). Gene Interaction

14 Concept Tabulation! Optional heatmap (tabulated) data can be integrated in concept analysis Users can easily identify the potential relationship between functions and experimental data. Customized annotation libraries can combine concepts from difference resources.

15 Homologous Concept-gene Analysis! Disease Ontology is human-centric.! GeneAnswers provides a solution for animal model data analysis by homologous gene mapping between human and other species.

16 ! Gene interaction network includes interacting genes not shown in the given gene list. Gene Interaction Network

17 Summary! GeneAnswers provides guides for potentially translational interpretation of given genes.! Besides integrating well known annotation libraries, customized annotation libraries support more flexible interpretation.! Complicated networks can be easily understood by varies of graphics visualization for the relationship between given genes and potentially interested concepts.! Homologous gene mapping make disease ontology analysis possible for animal model data.! Open source R based Bioconductor package. GeneAnswers could be simply integrated in a typical pipeline for system biological or medical research.

18 Acknowledgement! NUBIC: Pan Du, Simon Lin, Warren Kibbe, Jared Flatow.! NU Biologic Therapies of Cancer Team: Nancy L Krett, Michael Tessel, Steven Rosen.! Bioconductor community internal and external package reviewers! BMC Research Notes reviewers.! Disease Ontology PIs Warren Kibbe, NU, and Lynn Schriml, UMB! NIH Grant: 1R01RR (Disease Ontology)

19 Reference 1. Feng, G., Du, P., Krett, N.L., Tessel, M., Rosen, S., Kibbe, W.A. and Lin, S.M. (2010) A collection of bioconductor methods to visualize gene-list annotations, BMC Research Notes, 3, Du, P., Feng, G., Flatow, J., Song, J., Holko, M., Kibbe, W.A. and Lin, S.M. (2009) From disease ontology to disease-ontology lite: statistical methods to adapt a general-purpose ontology for the test of gene-ontology associations. Bioinformatics, 25, i Osborne, J.D., Flatow, J., Holko, M., Lin, S.M., Kibbe, W.A., Zhu, L.J., Danila, M.I., Feng, G. and Chisholm, R.L. (2009) Annotating the human genome with Disease Ontology. BMC Genomics, 10 Suppl 1, S6. 4. GeneRIFcompendiate : Quantitative gene annotation using GeneRIF associations and ontology terms (Submitted) 5. Exploring Genes Using Functional Disease Ontology Annotations (Submitted) 6.

20 Thanks!!!

21 Multiple group Concept-gene Analysis