GNPS: Global Natural Products Social Molecular Networking Delivering data-enabled, community-driven research

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GNPS: Global Natural Products Social Molecular Networking Delivering data-enabled, community-driven research Mingxun Wang 1,2,4, Jeremy Carver 1,4, Julie Wertz 1,4, Laurence Bernstein 1,4, Seungjin

1 GNPS: Global Natural Products Social Molecular Networking Delivering data-enabled, community-driven research Mingxun Wang 1,2,4, Jeremy Carver 1,4, Julie Wertz 1,4, Laurence Bernstein 1,4, Seungjin Na 1,2,4, Nuno Bandeira Center for Computational Mass Spectrometry 2 Computer Science and Engineering 3 Skaggs School of Pharmacy and Pharmaceutical Sciences 4 University of California, San Diego Center for Computational Mass Spectrometry

Proteins run life How much is in the genes?

Elegans) Rice/Corn: ~32,000 45,000 Each gene may generate several

splicing Sequence variation EMILG EFILG Post-Translational

turnover Protein Structure and interactions Endogenous and Immune

2 Proteins run life How much is in the genes? Human: ~20,000-22,000 Mouse: ~20,000-22,000 Worm: ~19,000 (C.Elegans) Rice/Corn: ~32,000 45,000 Each gene may generate several proteins the functional workhorses of the cell Combinatorial splicing Sequence variation EMILG EFILG Post-Translational Modifications Hundreds of types and sites Quantification and turnover Protein Structure and interactions Endogenous and Immune peptides Microbiome: x more genes Center for Computational Mass Spectrometry

TRADITIONAL WORKFLOW Design sample MS Raw data De novo

`fingerprints for biomolecules (10k-250k spectra, up to 100 GB

presence and abundance http://media.gizmodo.co.

3 TRADITIONAL WORKFLOW Design sample MS Raw data De novo interpretation Reference lookup Mass spectrometry generates `fingerprints for biomolecules (10k-250k spectra, up to 100 GB per sample) Identified peptide spectrum determines protein presence and abundance

4 PARADIGM SHIFT NETWORKED RESEARCH Design sample MS Raw data What are the similar datasets? De novo interpretation What are the most important or common unidentified spectra? Which spectra were identified before? Who analyzed similar data? Reference lookup How good is this identification?

5 BIG SOLUTIONS? Big Data Big Algorithms [271.1] F (SK) S G T E C R A S M S E C D P A E H C T G Q S MassIVE Mass Spectrometry Interactive Virtual Environment Thousands of datasets, hundreds of terabytes b-ions in each spectrum Mass difference between b-ions Oxidized Methionine Designed to build on rather than just tolerate big data Big Compute Big Community Proteomics Scalable, Accessible and Flexible environment ProteoSAFe 50+ data analysis workflows scalable to thousands of cores Empower and enable community-wide sharing of knowledge

6 KNOWING THE PROTEOME To know the proteome we should be able to: Present the evidence for any identified peptide, protein or posttranslational modification Basic: tandem MS evidence for claimed observations Provenance: experimental conditions of data acquisition Structure the knowledge in a reusable format Spectral Libraries Curated collections of identified reference spectra Personal knowledge is not community knowledge unless it is reusable in some way Simplest form of reutilization is database that can be consulted useful but low throughput Searchable spectral libraries enable high-throughput automated reutilization PNNL microbial reference largest dataset to date (12 TB) includes 100+ searchable libraries How much do we know?

7 SPECTRAL LIBRARIES COVERAGE Spectral library: curated collection of identified reference spectra Current coverage of 19,874 Human genes 12,773 with 1+ unique peptides (2,250 with shared peptides only) 4,851 with no peptides NIST SWATH Atlas % Peptides 14.9% 12.0% # Peptides 207K 152K Average protein sequence coverage is ~15% Low coverage of active/functional sites

8 BIG DATA TO THE RESCUE? 70 terabytes of Human mass spectrometry data available in 976 public ProteomeXchange datasets (~1,000 papers) containing 88,049 LCMS runs How limited is the knowledge shared through ProteomeXchange? MassIVE Mass Spectrometry Interactive Virtual Environment 86 % - partial submissions, no identifications 6 % - data format lost provenance metadata (e.g., only peptide identifications) 8 % - have results files with provenance information ono statistical controls 6.7M IDs oreport FDR > 1% - 4.5M IDs oreport FDR 1% - 1.7M IDs + 14M IDs (CPTAC colorectal) Final tally: ~4.7% of all data minimally ready to be reused (<1% without CPTAC colorectal)

MASSIVE REANALYSIS Community knowledge requires reproducible, well-characterized results MS-GF+ standard database search Search workflow, source code and exact search parameters available at

9 MASSIVE REANALYSIS Community knowledge requires reproducible, well-characterized results MS-GF+ standard database search Search workflow, source code and exact search parameters available at Everyone can reproduce the searches and test the search protocol Reanalyzed 14 TB of Human data with ~200M MS/MS spectra 95 million new statistically controlled identifications (~8-50x more) 4.2 million modified versions of 3.1 million unique peptide sequences Search results are attached to each dataset Interactive visualization, available for download in open formats

transcriptomics sequences (Enosi) 6.8M total IDs, 19,728 proteogenomic events Blind modification search (MODa) 7.8M IDs, 2.

10 BROADER LOOK: CPTAC COLON CANCER 125 colorectal samples: 95 TCGA cases, 30 controls Imported CPTAC results 6.9M IDs MS-GF+ database search 8.9M IDs, 70k variants (169k total) Spectral library search (MSPLIT) 10M IDs, including 387K mixture spectra Proteogenomics searches of TCGA transcriptomics sequences (Enosi) 6.8M total IDs, 19,728 proteogenomic events Blind modification search (MODa) 7.8M IDs, 2.8M IDs for 221k variants (306k total) 203K new variants (unique modified peptides) Enosi: K.K(V A)LGAFSDGLAHLDNLK.G MODa: K.K(V,-28)LGAFSDGLAHLDNLK.G

TRANSLATING GENOMICS ALGORITHMS Sequence Alignment and

b-ions in each spectrum Mass difference between b-ions

1 KQGGTLDD LEE QAREL 2 KQGGTLDD LEE QARE 3 KQGGTLDD LEE

11 TRANSLATING GENOMICS ALGORITHMS Sequence Alignment and Assembly Spectrum Alignment and Assembly Global alignment Local alignment [271.1] F (SK) S G T E C R A S M S E C D P A E H C T G Q S b-ions in each spectrum Mass difference between b-ions Oxidized Methionine Spectral Networks MassIVE KQGGTLDD LEE QAREL 2 KQGGTLDD LEE QARE 3 KQGGTLDD LEE QAR 4 KQGGTLDD LEE QA 5 KQGGTLDD LEE -18 QAR 6 KQGGTLDD LEE -18 Q 7 QGGTLDD LEE QAR 8 QGGTLDD -53 LEE QAR 8 6

12 SPECTRAL NETWORKS ANALYSIS New information from algorithms that were not available at time of publication K(V,-28)LGAFSDGLAHLDNLK VLGAFSDGLAHLDNLK (V,28)LGAFSDGLAHLDNLK Ac-VLGAFSDGLAHLDNLK

SPECTRAL NETWORKS OF CATARACTOUS LENS Tandem MS acquisition sensitivity is still very low for deep PTM analysis we typically don t see most peptides and most modifications

modifications over time Also technically useful Sample complexity is low enough to allow for deep acquisition of low abundance peptide variants Lens dataset is probably

13 SPECTRAL NETWORKS OF CATARACTOUS LENS Tandem MS acquisition sensitivity is still very low for deep PTM analysis we typically don t see most peptides and most modifications for lysate experiments Cataractous Lens data Lens samples from 9 different patients Spans stages of development, age and disease Proteins do not turn over, accumulate modifications over time Also technically useful Sample complexity is low enough to allow for deep acquisition of low abundance peptide variants Lens dataset is probably the most analyzed for discovery of modifications (original publication in 2005) Searle et al, J Proteome Res. 4(2):546-54,

14 RAVEN ANALYSIS OF CATARACTOUS LENS Improved spectrum identification MS-GF MODa Spectral Networks MS-GF+ MODa Discovery of proteome diversity ~100 different variations, defined as (mass,site) pairs Every variation is supported by at least one spectral pair with corroborating fragmentation patterns VQDDFVEIHGK Identificatio ns 15,771 10,462 6,355 Gain +51% +148% VQ(D,14)DFVEIHGK z=1 RaVen VQ(D,14)DFVEIHGK z=3 Identifications also in multiple charge states

15 REVEALING PROTEOME DIVERSITY Proteome diversity is determined by variation within protein regions Spectral networks significantly improve the detection of variants Highly-variant regions with 70+ variants 78 regions in 14 proteins with 10+ variants 34 regions in 10 proteins with 20+ variants RaVen MS-GF MODa Alpha-crystallin A aa variants RYRLPSNVDQSALSCSLSADGMLTFCGPK RYRLPSNVDQSALSCSLSADGMLTFCGPK YRLPSNVDQSAL YRLPSNVDQSALS YRLPSNVDQSALSC YRLPSNVDQSALSCS YRLPSNVDQSALSCSL YRLPSNVDQSALSCSLS YRLPSNVDQSALSCSLSA YRLPSNVDQSALSCSLSAD YRLPSNVDQSALSCSLSADGML YRLPSNVDQSALSCSLSADGMLTF YRLPSNVDQSALSCSLSADGMLTF YRLPSNVDQSALSCSLSADGMLTF YRLPSNVDQSALSCSLSADGMLTFCGPK YRLPSNVDQSALSCSLSADGMLTFCGPK YRLPSNVDQSALSCSLSADGMLTFCGPK YRLPSNVDQSALSCSLSADGMLTFCGPK xyrlpsnvdqsalscslsadgmltfcgpk YRLPSNVDQSALSCSLSADGMLTFCGPK xyrlpsnvdqsalscslsadgmltfcgpk YRLPSNVDQSALSCSLSADGMLTFCGPK YRLPSNVDQSALSCSLSADGMLTFCGPK LPSNVDQSALSC LPSNVDQSALSC LPSNVDQSALSCSL LPSNVDQSALSCSLS LPSNVDQSALSCSLSA LPSNVDQSALSCSLSADGMLTF LPSNVDQSALSCSLSADGMLTF LPSNVDQSALSCSLSADGMLTF LPSNVDQSALSCSLSADGMLTF LPSNVDQSALSCSLSADGMLTFCGP LPSNVDQSALSCSLSADGMLTFCGP LPSNVDQSALSCSLSADGMLTFCGP LPSNVDQSALSCSLSADGMLTFCGP LPSNVDQSALSCSLSADGMLTFCGPK LPSNVDQSALSCSLSADGMLTFCGPK LPSNVDQSALSCSLSADGMLTFCGPK LPSNVDQSALSCSLSADGMLTFCGPK LPSNVDQSALSCSLSADGMLTFCGPK LPSNVDQSALSCSLSADGMLTFCGPK LPSNVDQSALSCSLSADGMLTFCGPK LPSNVDQSALSCSLSADGMLTFCGPK LPSNVDQSALSCSLSADGMLTFCGPK LPSNVDQSALSCSLSADGMLTFCGPK LPSNVDQSALSCSLSADGMLTFCGPK LPSNVDQSALSCSLSADGMLTFCGPK LPSNVDQSALSCSLSADGMLTFCGPK LPSNVDQSALSCSLSADGMLTFCGPK LPSNVDQSALSCSLSADGMLTFCGPK xlpsnvdqsalscslsadgmltfcgpk LPSNVDQSALSCSLSADGMLTFCGPK LPSNVDQSALSCSLSADGMLTFCGPKI SNVDQSALSCSLSADGMLTFCGPK SNVDQSALSCSLSADGMLTFCGPK SNVDQSALSCSLSADGMLTFCGPK NVDQSALSCSLSADGMLTFCGPK NVDQSALSCSLSADGMLTFCGPK VDQSALSCSLSADGMLTFCGPK QSALSCSLSADGMLTFCGPK QSALSCSLSADGMLTFCGPK QSALSCSLSADGMLTFCGPK QSALSCSLSADGMLTFCGPK SALSCSLSADGMLTFCGPK SCSLSADGMLTFCGPK SLSADGMLTFCGP SLSADGMLTFCGPK SLSADGMLTFCGPK SLSADGMLTFCGPK SLSADGMLTFCGPK xslsadgmltfcgpk SLSADGMLTFCGPK LSADGMLTFCGPK SADGMLTFCGPK SADGMLTFCGPK ADGMLTFCGPK GMLTFCGPK

16 ALPHA-CRYSTALLIN A New discoveries in decade-old data Cataract-specific modification (methylation) or polymorphism (D E, bovine homolog) on Alpha-crystallin A VQDDFVEIHGK VQ(D,14)DFVEIHGK Only present in cataracts group

NATURAL PRODUCTS DNA makes RNA makes PROTEIN makes PEPTIDE transcription translation non-ribosomal peptide synthesis Natural products account for most antibiotics in clinical use and for 75% of

), immunosuppressants (cyclosporin), antiviral agents (luzopeptin A), antitumor agents (bleomycin), Mass spectrometry analysis of natural products differs from proteomics and ribosomal peptides in

17 NATURAL PRODUCTS DNA makes RNA makes PROTEIN makes PEPTIDE transcription translation non-ribosomal peptide synthesis Natural products account for most antibiotics in clinical use and for 75% of antibacterials introduced in : antibiotics (penicillin, vancomicine, etc.), immunosuppressants (cyclosporin), antiviral agents (luzopeptin A), antitumor agents (bleomycin), Mass spectrometry analysis of natural products differs from proteomics and ribosomal peptides in many respects: non-linear structures, e.g., cyclic and branch-cyclic peptides they often contain non-standard amino acids increasing the number of possible building blocks from 20 to 100+ they are often heavily modified with complex PTMs they often have a non-standard backbone Each of these complications (let alone all of them) invalidates traditional algorithms for MS-based peptide sequencing and identification.

18 subtilis ACN 0_1FA MS2_ # RT: AV: 5 NL: 1.50E2 F: ITMS + p NSI d Full ms @cid35.00 [ ] m/z subtilis ACN 0_1FA MS2_ # RT: AV: 2 NL: 4.78E2 F: ITMS + p NSI d Full ms @cid35.00 [ ] m/z Relative Abundance Relative Abundance subtilis ACN 0_1FA MS2_ /24/ :40:50 PM. MOLECULAR SPECTRAL NETWORKS Watrous et al., PNAS 2012

19 gnps.ucsd.edu First MassIVE Knowledge Base, open March 2014 Wang M et al, Nature Biotech, 2016 ProteoSAFe Co-analyze private+public data MassIVE Mass Spectrometry Interactive Virtual Environment Share data Crowdsourced curated libraries Explore unknown molecules

20 GNPS DATA SHARING Since March datasets 167,058 files 3.6 TB of data 100s of species

IDENTIFICATION: SPECTRAL LIBRARY SEARCH New public or private datasets GNPS Spectral Libraries ProteoSAFe Dereplication spectral library search Exact match: compound identification using

21 IDENTIFICATION: SPECTRAL LIBRARY SEARCH New public or private datasets GNPS Spectral Libraries ProteoSAFe Dereplication spectral library search Exact match: compound identification using spectral matching Analog match: find putative related molecules using approximate spectral matching (spectral alignment) One-step comparison against all accumulated knowledge of known molecules

22 GNPS SPECTRAL LIBRARIES Open spectral libraries Antibiotics and natural products FDA approved drugs 10,454 spectra for 6,810 compounds Over 500 library revisions by curators Previously in antibiotics Hardly any spectra publicly available No natural products spectral libraries

23 CREATING AND CURATING SPECTRAL LIBRARIES I know what this is! Quality levels: Gold: approved users, synthetic compounds Silver: published compounds Bronze: everything else

24 HOW DO GNPS LIBRARIES COMPARE?

25 HOW DO GNPS LIBRARIES COMPARE?

26 HOW DO GNPS LIBRARIES COMPARE?

27 . FINDING NEW ANALOGS

28 DATASET UPDATES AND COMMENTS Datasets are almost never complete at the time of initial submission Dataset MSV0001 Here is some new data Add/Request Missing Metadata Dataset MSV0002 Dataset MSV0003 This data seems great, but could you add this

29 DATASET SUBSCRIPTIONS Living data: promote and track evolution of public datasets Subscribe to this interesting dataset What s new with my data? Dataset MSV0001 As owner, you have metadata requests Dataset MSV0002 Dataset MSV0003 What is a subscription? Digest of comments Digest of new annotations in dataset All datasets are periodically re-searched against the whole knowledge base

30 SUBSCRIPTION YOUR DATA CALLS HOME

comments Datasets continuously reanalyzed + New

31 Datasets immediately available for reanalysis LIVE DATASET VIEW Subscriptions available for new IDs, comments Datasets continuously reanalyzed + New results sent to subscribers Public comments, metadata, etc

32 ENABLING LIVING DATA We have shown that living data is a workable concept Continuous reanalysis upgrades data to knowledge Automated reanalysis with new or updated algorithms Community-contributed reanalyses Community-wide knowledge aggregated in crowdsourced spectral libraries Most identifications come from community reference spectra people contribute what they see Hundreds of knowledge revisions improve reference metadata New discoveries in published data Already over 20-fold increases since publication New algorithms being contributed by 3 rd party computational groups Buy-in from the community Thousands of users from over 100 countries, most joined before publication Data deposited at acquisition, not publication Subscriptions keep scientists engaged in the life cycle of published data

33 FUTURE OUTLOOK What can the community do to help? Scientists Data sharing will increase your productivity avoid rediscovery, reuse knowledge Reutilization and reanalysis increases the impact of data that otherwise is lost Journals Editors: Publication guidelines should require proper deposition of supporting raw data Just raw data is not enough complete submissions require linking claims to raw data Reviewers: confirm key claims and findings by checking supporting data at repository Data citations the best datasets will support many publications whose citations may need to be indirectly accounted through the data (e.g., accession numbers only) Academic institutions and funding agencies Understand that `data dumps are useless Published results are only as strong as the supporting data Link claims and conclusions from manuscript to the raw data

34 ACKNOWLEDGEMENTS Mingxun Wang Jeremy Carver Julie Wertz Laurence Bernstein Seungjin Na GNPS, MassIVE, ProteoSAFe Spectral networks, large-scale workflows

Sangtae Kim. Dissertation: Generating Functions of Tandem Mass Spectra and Their Applications to Peptide Identifications Advisor: Pavel Pevzner

Sangtae Kim. Dissertation: Generating Functions of Tandem Mass Spectra and Their Applications to Peptide Identifications Advisor: Pavel Pevzner Sangtae Kim and Engineering 9500 Gilman Drive, La Jolla, CA sak008@ucsd.edu Tel: (858) 525-5284 http://cseweb.ucsd.edu/~sak008 Education Ph.D. in Computer Science Sep. 2006 May 2012 Dissertation: Generating