Primetime for KNIME:

Transcription

1 Primetime for KNIME: Towards an Integrated Analysis and Visualization Environment for RNAi Screening Data F. Oliver Gathmann, Ph. D. Director IT, Cenix BioScience Presentation for: KNIME User Group Meeting 2011 Zürich, March 3rd 2011

2 Overview Explain RNAi Screening IT infrastructure for HT-HCS (High-Throughput, High-Content Screening) at Cenix: past, present, and future

3 Explain RNAi Screening How RNAi works sirna RISC Unwinding of sirna Target mrna Target mrna recognition Degradation of mrna First Take Home Message: RNAi allows you to investigate the function of genes by knocking them down selectively

4 Explain RNAi Screening The Drug Discovery Pipeline Target Discovery (in vitro) Direct Direct LoF LoF Screens Screens Modifier Modifier Screens Screens Target Validation (in vitro) Phenotypic Phenotypic Profiling Profiling Target Discovery Phenotypic Titration Target Target Lead Lead Validation Validation Identification Optimization in vivo in vitro ADME/ Tox Clinical Phase I Clinical Phase II Clinical Phase III Registration Second Take Home Message: Early In The Drug Discovery Pipeline means highthroughput and lots of data

5 Explain RNAi Screening Information Layers Metabolic Pathway Gene network, disease conditions Gene Sequence, species, pathway annotations, transcripts Silencing Reagent Structure, targeted Gene(s), stock and order information Experiment Meta data (sample and control positions), production data Phenotype Cell images, morphology data Hit Phenotype annotations, knock down, reproducibility, significance Last Take Home Message: High-Content means complex data structures

6 IT Infrastructure for HT-HCS Cenix LDAP Database Scientist Workstations LIMS Tube Handler Pipetting Robot Farm File Automated Microscope

7 Terminology: Workflows Process-centric Workflows vs. Data-centric Workflows Process-centric: mapping a work process in the physical world; focused on data acquisition Data-centric: mapping an algorithm; focused on data processing Not always clear-cut, but still useful distinction

8 Primordial Process Workflows: Design

9 Primordial Process Workflows: Implementation

10 Data Analysis Workflows: Excel In the beginning, there was Excel. + Advantages: Ubiquitous and easy to use Full flexibility for the end user (in theory, anyways) Disadvantages: Hard to debug Nightmarish version control Slow and cumbersome

11 Data Analysis Workflows: Excel Load phenotype data files; run analysis; generate graphs Engines Submit image processing job Job Store image data Run image Image Processing processing job; store phenotype data Excel Img. Analysis Client Data Analysis Store experiment data; track experiment; wait for image data LIMS qpcr Design experiment Excel Plate reader LIMS Client Autoscope Data Acquisition Post image data File Storage Database Submit experiment Experiment Design

12 Data Analysis Workflows: Web Tools Next: Web tools with tabular data as input and output. + Advantages: Encapsulation of complex functionality Centralized administration Executed on server Disadvantages: Low flexibility Frugal web interface

13 Data Analysis Workflows: Web Tools Load result data files; generate graphs Run analysis Download result data files Engines Web Tools Spotfire Browser Upload phenotype and design data files Img. Analysis Job Client Image Processing Data Analysis LIMS qpcr Excel Plate reader LIMS Client Autoscope Data Acquisition File Storage Database Experiment Design

14 Data Analysis Workflows: KNIME! KNIME: A giant leap forward Flexible and easy to use and yet robust, scalable, performant and extensible! Current KNIME infrastructure: Centrally administered Windows and Mac installations, configured to point to a user-specific workspace on the file server Workflow curation policy: Versioned reference workflows for each project, owned by power users Experiment meta data provided through database nodes, raw data through files Complex statistics implemented with (remote) R scripting nodes

15 Data Analysis Workflows: KNIME! Load result data files; generate graphs Engines Spotfire KNIME Job Run workflow on Img. Analysis phenotype data and Client experiment design Image Processing Data Analysis LIMS qpcr Excel Plate reader LIMS Client Autoscope Data Acquisition File Storage Database Experiment Design

16 Primetime: Requirements Streamlining the Screening Pipeline Analysis has become the bottleneck: Potential for % increase in overall throughput Even Higher Content: More parameters using advanced analysis methods Single object rather than population data Integrate gene annotations and pathway data Enable customers to explore and (re-)analyze delivered data sets Selecting/weighing parameters Tight integration with Spotfire, including raw data

17 Primetime: IRIS Integrated computational environment for high throughput RNA Interference Screening Engines Post phenotype data; run workflow on phenotype data and experiment design; post result data Submit image analysis job; wait for phenotype data Spotfire Post phenotype data KNIME Job Store image data; KNIME launch image processing workflow Image Processing Retrieve result data; run Spotfire Data Analysis LIMS qpcr Excel Plate reader LIMS Client Autoscope Data Acquisition File Storage Database Experiment Design

18 Primetime: Beyond IRIS Use KNIME for process-centric workflows as well This would require Standard interface to the LIMS server to drive the business logic (REST) Easily configurable User Interfaces to parameterize processing steps (something like RGG?)

19 Primetime: Beyond IRIS KNIME solutions : Hide complexity of workflows by exposing only a few knobs to the end user Features: Again, a User Interface generator to make it easy for non-it power users to create new solutions Ideally, a way to publish the solution to a server and run it remotely

20 Conclusions KNIME has quickly become an integral part of the HT-HCS screening pipeline at Cenix Current work on the data analysis infrastructure around KNIME is focused on tight integration with the LIMS server, with Definiens for image processing, and with Spotfire for data visualization Further down the road, we plan to use KNIME for all workflows at Cenix and to build pre-packaged solutions

21 Thank you! Any questions?