1. NIH Common Fund Library of Integrated Network- based Cellular Signatures LINCS Applicant Information Webinar for RFA-RM13-013 September 6, 2013 3:00 – 4:30 PM EDT

2. LINCS: Applicant Information Webinar RFA-RM-13-013: Perturbation-Induced Data and Signature Generation Centers (U54) Today’s Webinar: LINCS program goals and background Overview of new FOA Questions

3. Perturbations Human cell types Phenotypic assays LINCS aims to inform a network-based understanding of biological systems in health and disease that can facilitate drug and biomarker development. LINCS is:  Developing a library of molecular and cellular signatures that describe how different cell types respond to a variety of perturbations.  Addressing challenges in high-throughput data generation, data integration, annotation, and analysis.  Actively exploring collaborations with new biomedical research communities. http://lincsproject.org LINCS: Library of Integrated Network-based Cellular Signatures RNAi small molecules gene expression protein level metabolites

4. LINCS Program (2014 – 2020) LINCS goals – informing a network-based understanding of cellular functions and response – expand the scope and richness of cellular responses to be measured. – support the addition of a broader and more informative range of human cell types, perturbations, and measurements. LINCS Program Structure – 3-5 Data and Signature Generating Centers (RFA-RM13-013) to be funded in FY14 – One Data Coordination and Integration Center (TBA) to be funded in FY15 – 6 year program with Mid-Course Review (~July 2017)

5. RFA-RM13-013 LINCS Data and Signature Generating Centers Will fund 3-5 DSGC awards Direct costs are limited to a maximum of $1.7M in each year for up to 6 years Part of a collaborative LINCS program DSGC structure: 1.Data Generation (40% effort) 2.Data Analysis and Signature Identification (40% effort) 3.Community Interactions Outreach 4.Administrative (20% effort)

6. DSGC: Data Generation Data and Signature production at scale, within first year of award (tens of thousands of data points per year) Proposal should be aware of existing perturbation response data in the public domain Cell Types: human cells (cell lines, primary tissue, iPS cells and their differentiated derivatives) Perturbagens: – Pilot: small molecules, growth factors, and genetic (knockdown or up- regulation by gene overexpression) – These will continue but applicants may propose other perturbations Assays: – Should be medium to high throughput – Provide measures of wide interest to biomedical researchers – Should be flexible and amenable to multiple cell types – Should be replicable with high level of QC/QA under SOPs

7. DSGC: Data Generation Include a strategy for most informative data and signature generation Maximize usefulness of new data in relation to existing resources Laboratory Technology Development. – Some technology development in first 3 years ($200K DC per year) – Should focus primarily on the further development or refinement of the assays proposed in this application – Minor refinements of new technologies that are sufficiently advanced (but not being proposed to be used within the DSGC) are also permissible – Establish suitable milestones and identify the approaches that would enable improvements in the technology

8. DSGC: Data Analysis and Signature Generation Each DSGC should devote roughly equal effort to data generation and data analysis Goal: to build meaningful, generalizable signatures with sufficient predictive power and/or broad applicability from the LINCS assays Computational research that would lead to improved signatures is an important scientific goal LINCS aims to create a single user interface via the separate DCIC for all of the LINCS resources for all biomedical researchers, including computational biologists

9. DSGC: Data Analysis and Signature Generation LINCS will have a distributed data resource and infrastructure to support queries Each DSGC will build an appropriate database and an underlying infrastructure to support queries and other analytical requirements on their datasets Support a robust pipeline to handle large-scale data generation and cleanup and to generate and validate signatures Each DSGC will develop novel approaches to visualize, browse, and query the data and signatures for all data and signature types the DSGC proposes

10. DSGC: Data Analysis and Signature Generation Unified access to DSGC resources will be developed by a future DCIC, to be solicited in a separate FOA. An essential feature of accessibility to all of LINCS data and resources is adequate annotation and documentation. Metadata annotation for both data and software resources is crucial. Should aim to facilitate innovative methods to support data provenance.

11. LINCS: Community Interaction and Outreach Access to Bench Scientists in collaboration with the DCIC and other DSGCs. Access to Computational Scientists to support development, validation, and implementation of new analytical, visualization, and integration approaches. Creative solutions to providing access the large data sets and resource are encouraged.

12. LINCS: Community Interaction and Outreach To facilitate the incorporation of related data types from external sources into LINCS, each DSGC will: Take the scientific lead in identifying relevant external data (and signatures, as appropriate) from the larger community; Identify high quality, relevant non-LINCS data sets that might be incorporated into LINCS; Work with the DCIC and the outside community to facilitate identification and inclusion of such resources into LINCS.

13. LINCS: Community Interaction and Outreach Community Interaction Projects: each DSGC should support scientific collaborations with external researchers (10% of the entire DSGC budget) Support specific and challenging research collaborations in conjunction with researchers in specific communities [identify some communities that will be receptive, and a few where it might be a challenge] The resulting data and signatures will be made publicly available to the research community.

14. LINCS: program administration Cooperative agreement, with substantial collaboration between LINCS grantees and involvement of program staff. – DSGCs are expected to work on common projects, on cell lines and/or perturbations shared across multiple DSGCs – Support and adopt common methods for annotation of resources and ways to make such resources available – Participate in LINCS working groups, committees, and meetings.

15. LINCS: Midcourse Review Will be in 2017, via an external group of scientists convened by NIH. – Assess the productivity of the center to date, – its collaborations within the LINCS consortium; – the use and adoption of its data by the greater research community. – DSGC opportunity: propose refinements to their proposed activities (based on tech dev) for the remainder of the project period. – The NIH will determine funding levels for each center past 2017 based, in part, on the results of the mid-course review.

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17. NIH Common Fund Supports cross-cutting programs that are expected to have exceptionally high impact. Develops bold, innovative, and often risky approaches to address problems that may seem intractable or to seize new opportunities that offer the potential for rapid progress. NIH LINCS Program Co-Chairs: – Alan Michelson, PhD (NHLBI) – Mark Guyer, PhD (NHGRI) NIH LINCS Coordinators – Ajay Pillai, PhD (NHGRI) – Jennie Larkin, PhD (NHLBI)

18. LINCS Pilot Phase (2010 – 2013) Pilot goals: – Develop a limited yet coherent data, and signature resource that could be used by the general research community. – Identify key issues in data annotation, integration, and analysis. Pilot activities: – Two data and signature generating U54 awards – Development of new high-throughput assays to detect perturbation-induced cellular responses – Novel computational methods for integrative data analysis – Active collaborations and working groups http://lincsproject.org