Summary of Data Dissemination Working Group

22Jan2016

Overview

•  Review of Data Dissemination Working Group – Strategy for data dissemination

•  Testing of model and submission process – Systems Biology Centers test submissions – Current work converting IRD/ViPR to SysBio v2.0

DDWG Background and objective

•  DDWG started fall 2014 •  Tasked with developing a data dissemination

strategy for all five systems biology centers. •  Key issues: – What types of data should be disseminated? – Where should the data go? – How should the metadata be represented?

Projected data types for dissemination Original listorder

ExperimentType Analyte Methodology FluDyNeMo Flu-OMICS MaHPIC Omics-4TB OMICS-LHV

# of SysBioCenters

Currentlysupported? Data archives

Disseminationpriority

1 OMICS Type mRNA (transcriptome) microarray No No No Yes Yes 2 Y GEO & BRC 1

2 OMICS Type miRNA microarray No No No Yes Yes 2 Y GEO & BRC 1

3 OMICS Type mRNA (transcriptome) RNA-seq Yes Yes Yes Yes Yes 5 N 1

4 OMICS Type miRNA RNA-seq Yes No Yes 2 N 1

5 OMICS Typemicrobial RNA(metatranscriptome) RNA-seq Yes No No No 1 N 3

6 OMICS Type influenza metagenome RNA-seq Yes No No No 1 N 3

7 OMICS Type bacterial 16S profiling targeted sequencing Yes No No No 1 N 3

8 OMICS Type mRNA (transcriptome)Microfluidic multiplexqRT-PCR No Yes Yes No 2 N 2

9 OMICS Type protein-DNA interactions ChIP-seq No Yes No Yes Yes 3 N 2

10 OMICS Type open chromatin Faire-SEQ No No No No Yes 1 N 2

11 OMICS Type DNA methylation No Yes No No Yes 2 N 2

12 OMICS Type protein (proteome) mass spectrometry No Yes Yes Yes Yes 4 Y Peptide Atlas & BRC 1

13 OMICS Typephosphoproteins(phosphoproteome) mass spectrometry No Yes Yes Yes Yes 4 N 1

14 OMICS Type metabolites (metabolome) mass spectrometry No Yes Yes Yes Yes 4 Y Metabolites & BRC 1

15 OMICS Type lipids (lipidome) mass spectrometry No Yes Yes Yes Yes 4 Y BRC 1

16 OMICS Type protein-protein interactions yeast two hybrid No No No No No 0 N 4

17 OMICS Type protein-protein interactions co-immunoprecipitation No Yes No No Yes 2 N 2

18 Phenotypic Weight Yes Yes Yes No Yes 4 N 1

19 Phenotypic Body Temperature No No Yes No No 1 N 3

20 Phenotypic Virus Titers plaque assay Yes Yes No No Yes 3 N 2

21 Phenotypic Virus genomic RNA levels qPCR No No No No Yes 1 N 3

22 Phenotypic Virus mRNA levels qPCR No No No No Yes 1 N 3

23 Phenotypic Hematology (??)CBC (manual &automated) No No Yes No No 1 N 3

24 Phenotypic Lung Function (??) No No No? No No 0 N 4

25 Phenotypic Clinical Score Direct Observation Yes No No ? No Yes 2 N 2

26 Phenotypic tissue architecture histology with H&E stain Yes Yes? Yes ? Yes Yes 5 N 1

27 Phenotypic protein tissue expression immunohistochemistry Yes No Yes ? No Yes 3 N 2

28 Phenotypic serum antibody ELISA Yes No No Yes No 2 N 2

29 Phenotypic cellular cytotoxicity Cell Titer Go (Promega) No No No No Yes 1 N 3

30 Phenotypic cytokine protein levels cytokine bead arrays Yes No Yes Yes Yes 4 N 1

31 Phenotypic cytokine protein levels ELISA Yes No Yes? No Yes 3 N 2

32 Phenotypic cytokine protein levels Bioplex assay Yes No No No Yes 2 N 2

33 Phenotypic cytokine protein secretion ELISPOT Yes No No No 1 N 3

34 Phenotypic parasitemiathin and thick smearslides No No Yes No No 1 N 3

35 Phenotypic

(MPSS) MacaquePhysiological ScoringSystem [numeric value 0-16] No No Yes No No 1 N 3

36 Phenotypic serum chemical levels iSTAT chem profile No No Yes No No 1 N 3

Leveraging public archives to store raw and processed data

•  Primary “omics” type data and unstructured metadata to public archives – GEO / SRA / Array Express – PeptideAtlas / Metabolites / massIVE

•  Derived “omics” data and structured metadata to BRCs

•  Phenotypic data –  If no archive exists, BRC will accept data

•  where possible, SysBio metadata standards should be used

Derived data from SBCs to respective Bioinformatics Resource Centers (BRCs)

Flu-Omics

Derived data in the form of biosets

– Biosets are interesting interpreted results from an experiment

– Biosets can be directly provided by the SBCs to BRCs or BRCs may choose to generate from processed data

– Bioset example – genes/proteins that are differentially expressed in a: •  comparison of human mock infected and influenza infected

cells after 7 HPI •  comparison of influenza infected wild-type mice and CXCR3

KO mice after 2 days of infection •  comparison of H5N1 infected wild-type mice to H1N1

infected wild-type mice •  comparison of H5N1 at 5 MOI to H5N1 at 1 MOI in human

cells

Metadata representation

•  Enhancements of SysBio v1.0 in SysBio v2.0 – Added experimental time line using a “Reference

Time Zero (T0)” to support multiple treatment, multiple sampling and complex study designs

– Added “Analysis Workflows” and “Data Processing Events” to capture data transformation and relationships between data

– Added “Disease” and “Disease Course Stage” objects to explicitly capture disease manifestation (previously associated with viral agent)

Data model and submission process testing

Getting started

•  One-on-one calls between System Centers and BRCs identified use cases for initial test of metadata standard and submission process

•  Testing results and potential issues to be presented later by individual centers

•  Converting IRD/ViPR previous contract data from SysBio v1.0 to SysBio v2.0 underway

IRD/ViPR update

•  Have begun implementing data model based on SysBio v2.0 at IRD/ViPR

•  Converting data from previous SBC contracts •  Preparing loading and validation submission

infrastructure •  Updates to UI pending

IRD/ViPR data model

Study/Experiment Assay Data Analysis

Conclusion

•  SysBio v2.0 adopted in summer 2015 –  Testing of new data types may require revisions

•  Submissions to begin in 2016 •  Areas still under consideration

•  Controlled vocabulary •  Data formatting •  Data archive selection

– Unified approach?

•  Stable & unique entity identifiers (post-translational modifications, metabolites, etc.)

Acknowledgement

EupathDB Brian Brunk Omar Harb Jessica Kissinger

MaHPIC Jessica Kissinger Mary Galinkski Suman Pakala Mustafa Veysi Nural Regina C Joice

Omics-LHV Michelle Craft Kelly Stratton Katrina Waters Amie Eisfeld Miron Livny Allison Thompson

Data Dissemination Working Group

NIAID Vivian Dugan Alison Yao Megan Hoffmann Eric Choi

ViPR/IRD Richard Scheuermann Brian Aevermann

Flu-Omics Sumit Chandra Lars Pache Crystal Herndon Andre Gatarano

Flu-DyNeMo Elodie Ghedin Lauren Lashua Alan Twaddle Abhishek Pratap

Omics-4TB Serdar Turkasian Micheleen Harris

PATRIC Rebecca Will Tom Brettin Rebecca Wattam Maulik Shukla