1. Improving the Sensitivity of Peptide Identification by Meta-Search, Grid-Computing, and Machine-Learning Nathan Edwards Georgetown University Medical Center

2. 2 Searching under the street-light… Tandem mass spectrometry doesn’t discriminate against novel peptides......but protein sequence databases do! Searching traditional protein sequence databases biases the results in favor of well-understood and/or computationally predicted proteins and protein isoforms!

3. 3 Lost peptide identifications Missing from the sequence database Search engine strengths, weaknesses, quirks Poor score or statistical significance Thorough search takes too long

4. 4 Lost peptide identifications Missing from the sequence database Build exhaustive peptide sequence databases Build evidence for unannotated proteins and protein isoforms Search engine strengths, weaknesses, quirks Use multiple search engines and combine results Poor score or statistical significance Use search-engine consensus to boost confidence Use machine-learning to distinguish true from false Thorough search takes too long Harness the power of heterogeneous computational grids

5. 5 Unannotated Splice Isoform Human Jurkat leukemia cell-line Lipid-raft extraction protocol, targeting T cells von Haller, et al. MCP 2003. Peptide Atlas raftflow, raftapr, raftaug LIME1 gene: LCK interacting transmembrane adaptor 1 LCK gene: Leukocyte-specific protein tyrosine kinase Proto-oncogene Chromosomal aberration involving LCK in leukemias. Multiple significant peptide identifications

6. 6 Unannotated Splice Isoform

7. 7

8. 8 Splice Isoform Anomaly Human erythroleukemia K562 cell-line Depth of coverage study Resing et al. Anal. Chem. 2004. Peptide Atlas A8_IP SALT1A2 gene: Sulfotransferase family, cytosolic, 1A 2 ESTs, 1 mRNA mRNA from lung, small cell-cancinoma sample Single (significant) peptide identification Five agreeing search engines PepArML FDR < 1%. All source engines have non-significant E-values

9. 9 Splice Isoform Anomaly

10. 10 Splice Isoform Anomaly

11. 11 Peptide Sequence Databases All amino-acid seqs of at most 30 amino-acids from: IPI and all IPI constituent protein sequences IPI, HInvDB, VEGA, UniProt, EMBL, RefSeq, GenBank SwissProt variants, conflicts, splices, and annotated signal peptide truncations. Genbank and RefSeq mRNA sequence 3 frame translation GenBank EST and HTC sequences 6 frame translation and found in at least 2 sequences Grouped by Gene/UniGene cluster and compressed.

12. 12 Formatted as a FASTA sequence database Easy integration with search engines. One entry per gene/cluster. Automated rebuild every few months. Peptide Sequence Databases OrganismSize (AA)Size (Entries) Human248Mb74,976 Mouse171Mb55,887 Rat 76Mb42,372 Zebra-fish 94Mb40,490

13. 13 Peptide evidence, in context Statistically significant identified peptides can be misleading… Isobaric amino-acid/PTM substitutions Unsubstantiated peptide termini Few b-ions or y-ions suggest “random” mass match Single amino-acids on upstream or downstream exons Peptides in 5’ UTR with no upstream Met Need tools to quickly check the corroborating (genomic, transcript, SNP) evidence

14. 14 PeptideMapper Web Service Counts: by gene and evidence EST, mRNA, Protein Sequences: accessions by gene UniProt variants nucleotide sequence & link to BLAT alignment Genomic Loci: one-click projection onto the UCSC genome browser peptides with cSNPs too!

15. 15 PeptideMapper Web Service I’m Feeling Lucky

16. 16 PeptideMapper Web Service I’m Feeling Lucky

17. 17 Combining search engine results – harder than it looks! Consensus boosts confidence, but... How to assess statistical significance? Gain specificity, but lose sensitivity! Incorrect identifications are correlated too! How to handle weak identifications? Consensus vs disagreement vs abstention Threshold at some significance? We apply unsupervised machine-learning.... Lots of related work unified in a single framework.

18. 18 PepArML – Peptide identification Arbiter by Machine-Learning

19. 19 Peptide Atlas A8_IP LTQ Dataset

20. 20 Peptide Atlas Halobacterium Dataset

21. 21 Running many search engines Search engine configuration can be difficult: Correct spectral format Search parameter files and command-line Pre-processed sequence databases. Tracking spectrum identifiers Extracting peptide identifications, especially modifications and protein identifiers

22. 22 Peptide Identification Meta-Search Parameters Instrument Precursor Tolerance Fragment Tolerance Max. Charge Sequence Database Target and # of Decoys Modification Fixed/Variable Amino-Acids Position Delta Proteolytic Agent Motif Peptide Candidates Termini Specificity Precursor Tolerance Missed cleavages Charge State Handling # 13 C Peaks Search Engines Mascot, X!Tandem, K-Score, OMSSA, MyriMatch

23. 23 Peptide Identification Meta-Search Simple unified search interface for: Mascot, X!Tandem, K-Score, OMSSA, MyriMatch Automatic decoy searches Automatic spectrum file "chunking" Automatic scheduling Serial, Multi- Processor, Cluster, Grid

24. 24 PepArML Meta-Search Engine NSF TeraGrid 1000+ CPUs UMIACS 250+ CPUs Edwards Lab Scheduler & 48+ CPUs Secure communication Heterogeneous compute resources Single, simple search request Scales easily to 250+ simultaneous searches X!Tandem, KScore, OMSSA, MyriMatch, Mascot (1 core). X!Tandem, KScore, OMSSA, MyriMatch. X!Tandem, KScore, OMSSA.

25. 25 PepArML Meta-Search Engine NSF TeraGrid 1000+ CPUs UMIACS 250+ CPUs Edwards Lab Scheduler & 48+ CPUs Secure communication Heterogeneous compute resources Single, simple search request Scales easily to 250+ simultaneous searches X!Tandem, KScore, OMSSA, MyriMatch, Mascot (1 core). X!Tandem, KScore, OMSSA, MyriMatch. X!Tandem, KScore, OMSSA.

26. 26 PepArML Meta-Search Engine NSF TeraGrid 1000+ CPUs UMIACS 250+ CPUs Edwards Lab Scheduler & 48+ CPUs Secure communication Heterogeneous compute resources Simple search request

27. 27 PepArML Meta-Search Engine NSF TeraGrid 1000+ CPUs UMIACS 250+ CPUs Edwards Lab Scheduler & 48+ CPUs Secure communication Heterogeneous compute resources Simple search request

28. 28 Peptide Atlas A8_IP LTQ Dataset Tryptic search of Human ESTs using PepSeqDB 107084 spectra (145 files) searched ~ 26 times: Target + 2 decoys, 5 engines, 1+ vs 2+/3+ charge 8685 search jobs 25.7 days of CPU time. 5211 TeraGrid TKO jobs < 2 hours Using 143 different machines Total elapsed time < 26 hours Bottleneck: Mascot license (1 core, 4 CPUs)

29. 29 PepArML Meta-Search Engine Access to high-performance computing resources for the proteomics community NSF TeraGrid Community Portal University/Institute HPC clusters Individual lab compute resources Contribute cycles to the community and get access to others’ cycles in return. Centralized scheduler Compute capacity can still be exclusive, or prioritized. Compute client plays well with HPC grid schedulers.

30. 30 Conclusions Improve sensitivity of peptide identification, using Exhaustive peptide sequence databases Machine-learning for combining Meta-search tools to maximize consensus Grid-computing for thorough search Tools & cycles available to the community... http://edwardslab.bmcb.georgetown.edu

31. 31 Acknowledgements Dr. Catherine Fenselau University of Maryland Biochemistry Dr. Rado Goldman Georgetown University Medical Center Dr. Chau-Wen Tseng & Dr. Xue Wu University of Maryland Computer Science Funding: NIH/NCI