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Unison: An Integrated Platform for Computational Biology Discovery
Unison is an integrated platform for computational biology discovery freely available at http://unison-db.org/. It contains over 13 million protein sequences, precomputed predictions for over 200 million sequences across 23 feature types, auxiliary data like GO terms and structures, and tools for complex queries and mining of sequences with specified features. Some examples of mining include finding sequences with immunoglobulin domains, transmembrane domains, and ITIM domains, or locating SNPs and domains on protein structures. Unison facilitates diverse computational biology tasks through its comprehensive integrated data and ability to easily perform complex queries and analysis.
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Unison: An Integrated Platform for Computational Biology Discovery
- 1. Unison: An IntegratedPlatform for Computational Biology Discovery Freely accessible and available at http://unison-db.org/ . Reece Hart, Kiran Mukhyala Genentech, Inc. Pacific Symposium on Biocomputing 2009
- 2. assert(Sequence Analysis !=Sequence Mining) feature types/models HMM, TM, signal, etc. sequences Sequence Analysis i.e., show predictions for a given sequence Typically involves minutes to hours of computing per sequence. Typically entails days to months of computing results. i.e., show sequences that contain specified features. Feature-Based Mining Prediction results non-redundant superset of all sequences method-specific data such as score, e- value, p-value, kinase probability, etc. parameters execution arguments/options for every prediction type and result
- 3. Unison in aNutshell Domain, Structures Structure & Homology & Ligands Predictions Protein Sequences and Annotations Genomes, Auxiliary Gene Mapping & Annotations Structure, GO, RIF, SCOP, Probes etc. Sequences and Annotations Auxiliary Data Precomputed predictions UniProt, IPI, Ensembl, RefSeq, PDB HomoloGene, Gene Domains, homology, structure, TMs, STRING, PHANTOM, HUGE, ROUGE, Ontology, taxonomy, localization, signals, disorder, etc. MGC, Derwent, pataa, nr, etc. PDB, HUGO, SCOP, >200M predictions, 23 types, >13M seqs, >17k species, 69 origins etc. ~6 CPU-years
- 4. Unison has manyapplications. Unison Web Tools Other In-House Tools Ad Hoc Mining Mining and analysis projects Domain, Structures Structure & Homology & Ligands Predictions Protein Sequences and Annotations Genomes, Auxiliary Gene Mapping & Annotations Structure, GO, RIF, SCOP, Probes etc. Sequences and Annotations Auxiliary Data Precomputed predictions UniProt, IPI, Ensembl, RefSeq, PDB HomoloGene, Gene Domains, homology, structure, TMs, STRING, PHANTOM, HUGE, ROUGE, Ontology, taxonomy, localization, signals, disorder, etc. MGC, Derwent, pataa, nr, etc. PDB, HUGO, SCOP, >200M predictions, 23 types, >13M seqs, >17k species, 69 origins etc. ~6 CPU-years
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- 6. Unison is aplatform for diverse tools. Matt Brauer Guy Cavet Josh Kaminker Scott Lohr Kathryn Woods Jean Yuan Peng Yue
- 7. Unison facilitates complexmining. Mining for TNF ligands Mining for E3 Ligases Mining for 4H Cytokines Mining for ITxM Mining for deubiquitinases Analyzing SNP impact on binding interfaces Jason Hackney Nandini Krishnamurthy Li Li Yun Li Jinfeng Liu Shiu-ming Loh Kiran Mukhyala
- 8. Mining for ITIMsthe old way. Ig TM ITIM ➢ Collect sequences. ➢ Prune redundant sequences. (How?!) ➢ For each unique sequence, predict ● Immunoglobulin domains. ● Transmembrane domains. ● ITIM domains. ➢ Write a program that filters predictions. ➢ Summarize hits with external data. ➢ Do it again when source data are updated.
- 9. Mining for ITIMsthe Unison way. Ig TM ITIM SELECT IG.pseq_id, IG.start as ig_start,IG.stop as ig_stop,IG.score,IG.eval, TM.start as tm_start,TM.stop as tm_stop, ITIM.start as itim_start,ITIM.stop as itim_stop FROM pahmm_current_pfam_v IG JOIN pftmhmm_tms_v TM ON IG.pseq_id=TM.pseq_id AND IG.stop<TM.start JOIN pfregexp_v ITIM ON TM.pseq_id=ITIM.pseq_id AND TM.stop<ITIM.start WHERE IG.name='ig' AND IG.eval<1e-2 AND ITIM.acc='MOD_TYR_ITIM'; Ig Ig TM Tm ITIM ITIM pseq_id start stop score eval start stop start stop best_annotation 234 262 316 30 7.40E-06 440 462 518 523 UniProtKB/Swiss-Prot:SIGL5_HUMAN (RecName: Fu 254 158 213 36 1.90E-07 284 306 386 391 UniProtKB/Swiss-Prot:VSIG4_HUMAN (RecName: F 544 157 215 24 6.60E-04 348 370 431 436 UniProtKB/Swiss-Prot:SIGL9_HUMAN (RecName: Fu 797 254 312 40 7.60E-09 1099 1121 1361 1366 UniProtKB/Swiss-Prot:DCC_HUMAN (RecName: Ful 1113 42 102 30 1.20E-05 243 265 300 305 UniProtKB/Swiss-Prot:KI2L2_HUMAN (RecName: Fu 1114 42 102 30 6.50E-06 243 265 330 335 UniProtKB/Swiss-Prot:KI2L1_HUMAN (RecName: Fu 1115 42 102 31 4.20E-06 243 265 301 306 UniProtKB/Swiss-Prot:KI2L3_HUMAN (RecName: Fu 1116 42 97 30 1.10E-05 339 361 396 401 UniProtKB/TrEMBL:Q95368_HUMAN (SubName: Fu 1134 340 388 26 1.40E-04 603 625 688 693 UniProtKB/Swiss-Prot:PECA1_HUMAN (RecName: F
- 10. “Are you sureabout this Stan? It seems odd that a pointy head and a long beak is what makes them fly.” J. Workman, Science 245:1399 (1989)
- 11. Kiran Mukhyala Fernando Bazan,Matt Brauer, Jason Hackney, Pete Haverty, Ken Jung, Josh Kaminker, Nandini Krishnamurthy, Li Li, Yun Li, Shiuh-ming Loh, Jinfeng Liu, Peng Yue, Jianjun Zhang, Yan Zhang http://unison-db.org/ Open access web site, downloads, documentation, references unison-db.org:5432 PostgreSQL & odbc/jdbc/sdbc access
- 12. Unison Contents patents HUGO Geneseq:AAP60074 TNFSF9 1991-10-29 SUNTORY TNFSF10 TNFSF11 homologs NP_000585.2 NP_036807.1 | RAT EP205038-A; New tumour... NP_000585.2 NP_038721.1 | MOUSE NP_000585.2 XP_858423.1 | CANFA GO SNPs Function P84L transcription A94T initiation elongation aliases TNFA_HUMAN Entrez Q1XHZ6 IPI00001671.1 sequences protein features gene_id >Unison:98 INCY:1109711.FL1p symbol MSTESMIRDVE...FGIIAL CCDS4702.1 locus >Unison:23782 gi:25952111 VRSSSRTPSD...FGIIAL 1 | 23 | | SS 108 | 143 | 1.8e-06 | EGF 162 | 184 | | TM taxonomy alignments 133 | 138 | | ITIM 9606 Homo sapiens 10090 Mus musculus TNFA 1tnfA 10028 Rattus rattus TNFA 1tnfB aa-to-resid loci ... TNFA 5tswF MSTESMIR DVEFGIIA 1 233 6+:31651498-31653288 TESMIRDV IIAMDAC structures 1tnf SCOP genomes 1a8m all alpha Hs35 Hs36 probes 2tun 4tsv all beta Ig HGU133P 5tsw TNF-like RAT WHG alpha+beta
- 13. Ex1: Mine forsequences w/conserved features. patents HUGO Geneseq:AAP60074 TNFSF9 1991-10-29 SUNTORY TNFSF10 TNFSF11 homologs NP_000585.2 NP_036807.1 | RAT EP205038-A; New tumour... NP_000585.2 NP_038721.1 | MOUSE NP_000585.2 XP_858423.1 | CANFA GO SNPs Function P84L transcription A94T initiation elongation aliases TNFA_HUMAN Entrez Q1XHZ6 IPI00001671.1 sequences protein features gene_id >Unison:98 INCY:1109711.FL1p symbol MSTESMIRDVE...FGIIAL CCDS4702.1 locus >Unison:23782 gi:25952111 VRSSSRTPSD...FGIIAL 1 | 23 | | SS 108 | 143 | 1.8e-06 | EGF 162 | 184 | | TM taxonomy alignments 133 | 138 | | ITIM 9606 Homo sapiens 10090 Mus musculus TNFA 1tnfA 10028 Rattus rattus TNFA 1tnfB aa-to-resid loci ... TNFA 5tswF MSTESMIR DVEFGIIA 1 233 6+:31651498-31653288 TESMIRDV IIAMDAC structures 1tnf SCOP genomes 1a8m all alpha Hs35 Hs36 probes 2tun 4tsv all beta Ig HGU133P 5tsw TNF-like RAT WHG alpha+beta
- 14. Ex2: Locate SNPsand Domains on Structure patents HUGO Geneseq:AAP60074 TNFSF9 1991-10-29 SUNTORY TNFSF10 TNFSF11 homologs NP_000585.2 NP_036807.1 | RAT EP205038-A; New tumour... NP_000585.2 NP_038721.1 | MOUSE NP_000585.2 XP_858423.1 | CANFA GO SNPs Function P84L transcription A94T initiation elongation aliases TNFA_HUMAN Entrez Q1XHZ6 IPI00001671.1 sequences protein features gene_id >Unison:98 INCY:1109711.FL1p symbol MSTESMIRDVE...FGIIAL CCDS4702.1 locus >Unison:23782 gi:25952111 VRSSSRTPSD...FGIIAL 1 | 23 | | SS 108 | 143 | 1.8e-06 | EGF 162 | 184 | | TM taxonomy alignments 133 | 138 | | ITIM 9606 Homo sapiens 10090 Mus musculus TNFA 1tnfA 10028 Rattus rattus TNFA 1tnfB aa-to-resid loci ... TNFA 5tswF MSTESMIR DVEFGIIA 1 233 6+:31651498-31653288 TESMIRDV IIAMDAC structures 1tnf SCOP genomes 1a8m all alpha Hs35 Hs36 probes 2tun 4tsv all beta Ig HGU133P 5tsw TNF-like RAT WHG alpha+beta
- 15. Unison can alsohelp you... ➢ Answer more sophisticated questions. ● Require orthologs or a specified exon structure. ➢ Annotate hits. ● Annotate with locus, probes, HUGO gene name, structures, PubMed refs, external links. ● Group splice forms by locus. ➢ Explore alternatives. ● How do parameters influence results? ● Try other prediction algorithms. ➢ Stay current. ● When new data are available, just rerun the query. ➢ Move on. ● The same data are available to other projects and other people.