GRANITE: A Tool to Generate Gene Relational Networks Jahangheer Shaik, Ph.D. Department of Pathology and Immunology, Washington University School of Medicine

Outline What is the need of Network analysis tool? What kind of data is available? Currently available tools Why GRANITE? GRANITE Architecture Web-based Interface Analytical Service Future Directions

Need for Network Analysis Diseases such as cancers are quite complex and involve genes and gene products operating at multiple levels of genetic program Advent of profiling technologies such as microarrays, ChIP-ChIP and ChIP-seq, genome wide analysis studies are now possible Gene Relational Networks (GRNs) offer a consolidated picture of functional relationships among genes

Available Data Pathway Databases Protein Interaction Databases Transcription factor-target interaction databases Gene Ontology PubMed Microarray Data

Available Tools TF-Target – PathBLAST, promoter analysis pipeline (PAP), Promoter Analysis and Interaction Network Generation Tool (PAINT), GeneVis Microarray Data – COEXPRESSdb, Simulated Annealing to Realize GEnetic networks (SARGE) GO Tree Machine (GOTM) Literature – GeneIndexer, PubGene Protein Interaction Data – Prediction Of INTeractome database (POINT), Agile Protein Interaction Data analyzer (APID), Protein Interaction Network Inference Tool (PIN-IT) Commercial – Ingenuity pathway analysis – GeneGO

Why GRANITE? GRANITE makes use of information from Pathway, Protein interaction, TranscriptionFactor-target, Gene Ontology, Literature and Microarray databases to draw rich gene relational networks GRANITE provides several advanced options for users to customize networks For advanced users, GRANITE also provides programmatic access to the tool AND ITS FREE!

Functional Overview Functional overview of GRANITE- Input Page accepts identifiers in several different formats such as Entrez Gene ID,Gene Symbol, Ensembl Gene ID, Unigene Cluster ID, Ensembl Transcript ID, Ensembl Peptide ID, Refseq ID and UniprotKb Primary Accession. The input identifiers are internally mapped to Entrez Gene ID to perform network analysis. Network Options page provides two different options- i) Regular Network and ii) Minimal Network. Regular Network connects all the input elements using all the databases whereas Minimal Network minimally connects the elements (see Functional Overview). Network Drawing page renders the interactions on a java applet using a Cytoscape plugin. The interaction information may be saved as an image or delimited text. 'Advanced Options' provide multiple options for user to customize networks- The user can pick databases using 'Choose Databases', set up appropriate thresholds using 'Choose Thresholds', create sub-network using 'Choose Subnetwork', select relevant pathways using 'Choose Pathways' or select appropriate GO terms using 'Choose GO Terms'. 'Other Options' provide the related information such as diseases associated with elements in the network using 'Get Phenotypes', other elements that might be interacting with elements in the network using 'Expand Network' or publications associated with input elements using 'Bibliography' features.

Granite Workflow GRANITE Work Flow showing order in which different databases from GRAND are queried. 'Regular Network' option provides the user with interactions corresponding to input elements from all the databases. When 'Minimal Network' option is chosen, GRANITE first interrogates pathway databases and protein interaction databases to establish connections among the genes. Other databases are queried in order shown until all elements are connected. Once all the elements are connected, GRANITE does not query rest of the databases. When 'Regular Network' option is chosen, GRANITE displays all interactions from all the databases and therefore, this order is not significant.

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Analytical Service caBIG is equipped with caGrid infrastructure that leverages Globus tool kit as a grid middleware component to provide grid environment. A service may be defined as a wrapper around an analysis algorithm or a database which is exposed on the grid meeting caGrid standards. A common way to generate these services is to employ caGrid provided open source interactive development environment (IDE) tool called introduce The core logic of GRANITE is implemented in the service skeleton generated by introduce and analysis methods are exposed through the Grid to provide programmatic access to the tool.

Case Study1: Chemokine network Case Study 1- Chemokine Network a) Minimal network generated by using chemokines and their receptors as input to GRANITE. Specific interactions (highlighted by box) correspond to receptors that bind specifically to ligands whereas unshaded region shows chemokine receptors that bind to multiple ligands. GRANITE was successful in identifying chemokine receptors that bind specifically and non-specifically. b) Regular network generated by using only chemokine receptors as input. c) Network of receptors and ligands generated using 'Expand Network' feature of GRANITE. This demonstrates that GRANITE was able to identify chemokines that bind to the receptors even without the chemokines being presented as input.

Breast Cancer Network Case Study 2- Breast cancer network of 60 known breast cancer genes created using GRANITE. KRAS and PIK3CA in Sub-network 1 have somatic mutations in breast cancers. The transcription factor TP53 in sub-network2 is a tumor protein coding gene, which is a key regulator of transcription factors in sub-networks 3 and 5 respectively. CYP1A1, CYP19A1, CYP1B1 and ESR1 in sub-network 4 are associated with estrogen receptor positive breast cancers and BRCA1 is associated with breast ovarian cancer in females. The interactions between HFE and TFRC in sub-network 4 are associated with high grade neoplastic cancers including breast cancer. AR in sub-network 5 is associated with breast cancers in male. Genetic polymorphisms of NAT1 and NAT2 in sub-network 6 modulate breast cancers in the presence of GSTM1 and GSTT1 genotypes.

Human Viral Gene Network Case Study 3- Human Viral Gene Network of 43 genes from Obayashi et. al : a) Correlation network by Obayashi et. al. showing 5 sub- networks obtained by using COEXPRESSdb b) Correlation network using GRANITE showing the same 5 sub-networks c) Reference generated by finding interactions from publicly available databases and selecting 331 interactions that are confirmed by at least 2 databases. Venn Diagrams Comparing: i) Interactions from Obayashi et. al. Vs reference- 23 (7%) out of 67 interactions from Obayashi et. al. are confirmed by Ground truth, ii) Correlation Interactions from GRANITE Vs. reference- 69 (21%) out of 158 interactions from GRANITE are confirmed by reference iii) Interactions from Obayashi et. al. vs GRANITE correlation network- 19 (12%) interactions are common to both the networks. New reference is generated by selecting 644 interactions from public databases confirmed by at least one database. Iv) New reference vs. Obayashi et. al's correlation network-38(6%) interactions are confirmed by new ground truth. viii) New reference vs. GRANITE correlation network-91(41%) interactions are confirmed by new ground truth.

Work under Progress- NLP based Mining PubMed is a service of the U.S. National Library of Medicine that includes over 19 million citations from MEDLINEU.S. National Library of Medicine MedLine adds more than abstracts each month! Why PubMed? 80% of biological knowledge is only in research papers

Things to do GRANITE currently does not include micro RNA (miRNA) regulatory information. Information related to miRNA regulation is available from multiple databases (Griffiths 2006; Papadopoulos, Reczko et al. 2009; Wang 2009) and will be included in GRANITE in future GRANITE currently shows correlation information by using human chips. The correlation information extracted using mouse microarray chips will also be included in GRANITE