1. Predicting interactions between genes based on genome sequence comparisons The “genomic context” component of STRING Bioinformatics seminar series 15-11-2005 Berend Snel

2. TodayToday Announcement: the seminar of Jakob de Vlieg on 22 November is canceled. Please consult the website of the seminar series (www.cmbi.ru.nl/edu/seminars) for the new date.Announcement: the seminar of Jakob de Vlieg on 22 November is canceled. Please consult the website of the seminar series (www.cmbi.ru.nl/edu/seminars) for the new date.www.cmbi.ru.nl/edu/seminars Seminar (today); please ask questions !!!Seminar (today); please ask questions !!! Handing out article and questions : “Identification of a bacterial regulatory system for ribonucleotide reductases by phylogenetic profiling.” Read the article and hand in the answers to the questions by Monday November 28th.Handing out article and questions : “Identification of a bacterial regulatory system for ribonucleotide reductases by phylogenetic profiling.” Read the article and hand in the answers to the questions by Monday November 28th. Announcement: the seminar of Jakob de Vlieg on 22 November is canceled. Please consult the website of the seminar series (www.cmbi.ru.nl/edu/seminars) for the new date.Announcement: the seminar of Jakob de Vlieg on 22 November is canceled. Please consult the website of the seminar series (www.cmbi.ru.nl/edu/seminars) for the new date.www.cmbi.ru.nl/edu/seminars Seminar (today); please ask questions !!!Seminar (today); please ask questions !!! Handing out article and questions : “Identification of a bacterial regulatory system for ribonucleotide reductases by phylogenetic profiling.” Read the article and hand in the answers to the questions by Monday November 28th.Handing out article and questions : “Identification of a bacterial regulatory system for ribonucleotide reductases by phylogenetic profiling.” Read the article and hand in the answers to the questions by Monday November 28th.

3. ContentsContents Predicting functional interactions between proteins; what & whyPredicting functional interactions between proteins; what & why Genomic context methodsGenomic context methods –General –Gene fusion –Gene order –Presence / absence of genes across genomes Integration and benchmarking of predictionsIntegration and benchmarking of predictions Biochemistry by other means BolABiochemistry by other means BolA In addition to genomic context: functional genomics dataIn addition to genomic context: functional genomics data Predicting functional interactions between proteins; what & whyPredicting functional interactions between proteins; what & why Genomic context methodsGenomic context methods –General –Gene fusion –Gene order –Presence / absence of genes across genomes Integration and benchmarking of predictionsIntegration and benchmarking of predictions Biochemistry by other means BolABiochemistry by other means BolA In addition to genomic context: functional genomics dataIn addition to genomic context: functional genomics data

4. Complete genomes, now what? Post-genomic era = we have the parts list (complete genomes)Post-genomic era = we have the parts list (complete genomes) to understand the cell we need to know the functions of the genesto understand the cell we need to know the functions of the genes Post-genomic era = we have the parts list (complete genomes)Post-genomic era = we have the parts list (complete genomes) to understand the cell we need to know the functions of the genesto understand the cell we need to know the functions of the genes

5. A bacterial genome gene 408..1748gene /gene="dnaA" /locus_tag="BCE33L0001" /old_locus_tag="BCZK0001" CDS 408..1748CDS /gene="dnaA" /locus_tag="BCE33L0001“ /old_locus_tag="BCZK0001" /inference="non-experimental evidence, no additional details recorded“ /codon_start=1 /transl_table=1111 /product="chromosomal replication initiator protein“ /protein_id="AAU20227.1"AAU20227.1 /db_xref="GI:51978677“ /translation="MENISDLWNSALKELEKKVSKPSYETWLKSTTAHNLKKDVLTIT APNEFARDWLESHYSELISETLYDLTGAKLAIRFIIPQSQAEEEIDLPPAKPNAAQDD SNHLPQSMLNPKYTFDTFVIGSGNRFAHAASLAVAEAPAKAYNPLFIYGGVGLGKTHL MHAIGHYVIEHNPNAKVVYLSSEKFTNEFINSIRDNKAVDFRNKYRNVDVLLIDDIQF LAGKEQTQEEFFHTFNALHEESKQIVISSDRPPKEIPTLEDRLRSRFEWGLITDITPP DLETRIAILRKKAKAEGLDIPNEVMLYIANQIDSNIRELEGALIRVVAYSSLINKDIN ADLAAEALKDIIPNSKPKIISIYDIQKAVGDVYQVKLEDFKAKKRTKSVAFPRQIAMY LSRELTDSSLPKIGEEFGGRDHTTVIHAHEKISKLLKTDTQLQKQVEEINDILK" gene 1927..3066 /gene="dnaN" /locus_tag="BCE33L0002" /old_locus_tag="BCZK0002" CDS 1927..3066 /gene="dnaN" /locus_tag="BCE33L0002" /old_locus_tag="BCZK0002" /EC_number="2.7.7.7" /inference="non-experimental evidence, no additional details recorded" /codon_start=1 /transl_table=11 /product="DNA polymerase III, beta subunit" /protein_id="AAU20226.1" /db_xref="GI:51978676" /translation="MRFTIQKDYLVRSVQDVMKAVSSRTTIPILTGIKVVATEEGVTL TGSDADISIESFIPVEEDGKEIVEVKQSGSIVLQAKYFSEIVKKLPKETVEISVENHL MTKITSGKSEFNLNGLDSAEYPLLPQIEEHHVFKIPTDLLKHMIRQTVFAVSTSETRP ILTGVNWKVYNSELTCIATDSHRLALRKAKIEGIADEFQANVVIPGKSLNELSKILDE SEEMVDIVITEYQVLFRTKHLLFFSRLLEGNYPDTTRLIPAESKTDIFVNTKEFLQAI DRASLLARDGRNNVVKLSTLEQAMLEISSNSPEIGKVVEEVQCEKVDGEELKISFSAK YMMDALKALDSTEIKISFTGAMRPFLIRTVNDESIIQLILPVRTY" geneCDS2.7.7.711AAU20226.1

6. For most genes in any genome we need function prediction - - E. Coli, the most intensively studied organism: only 1924 genes (~43%) have been (partially) experimentally characterized. - - E. Coli, the most intensively studied organism: only 1924 genes (~43%) have been (partially) experimentally characterized.

7. What is function ? Various levels of description: Sequence similarity/homology has the largest relevance for “Molecular Function”. This aspect of protein function is best conserved. Molecular function can often be predicted from similarities between protein sequences (BLAST), or structures. What is function ? Various levels of description: Sequence similarity/homology has the largest relevance for “Molecular Function”. This aspect of protein function is best conserved. Molecular function can often be predicted from similarities between protein sequences (BLAST), or structures. Predicting protein function

8. Homology: BLAST and / or SMART/PFAM/CDD gi|22209068|gi|22209068|Mayven [Homo sapiens]11591159 gi|21410410|gi|21410410|Klhl2 protein [Mus musculus]11451145... i|55725960|i|55725960|hypothetical protein [Pongo pygmaeus] 887887 gi|6644176|gi|6644176|Klhl3 [Homo sapiens] 885885 gi|19354513|gi|19354513|Klhl3 protein [Mus musculus] 765765 gi|12644384|gi|12644384| Ring canal kelch protein [Drosophila melanogaster] 676676

9. “Beyond” homology and molecular function Homology based function prediction works very well, yet: a large fraction of genes are poorly described (no homologs, uncharacterized homologs; this holds for ~60% of the human genes)a large fraction of genes are poorly described (no homologs, uncharacterized homologs; this holds for ~60% of the human genes) There are other aspects of function: functional associations, e.g. the target of a protein kinase or a transcriptional regulator, I.e. to understand the cell we need to know the interactions of the genesThere are other aspects of function: functional associations, e.g. the target of a protein kinase or a transcriptional regulator, I.e. to understand the cell we need to know the interactions of the genes Thus: predicting associations Homology based function prediction works very well, yet: a large fraction of genes are poorly described (no homologs, uncharacterized homologs; this holds for ~60% of the human genes)a large fraction of genes are poorly described (no homologs, uncharacterized homologs; this holds for ~60% of the human genes) There are other aspects of function: functional associations, e.g. the target of a protein kinase or a transcriptional regulator, I.e. to understand the cell we need to know the interactions of the genesThere are other aspects of function: functional associations, e.g. the target of a protein kinase or a transcriptional regulator, I.e. to understand the cell we need to know the interactions of the genes Thus: predicting associations

10. Transcription regulation Transcription regulation P P Signalling pathways Protein complexes Metabolic pathways There are many types of functional associations (AKA functional interactions, interactions, functional links, functional relations) in molecular biology Cellular process

11. Types of functional associations metabolic pathways: filling gaps

12. Types of functional associations Transcription regulation P P Signalling pathways

13. Types of functional associations Cellular process (“DNA repair”, “Apoptosis”) Cellular process (“DNA repair”, “Apoptosis”) Protein complexes

14. So how can knowledge of the functional associations help? If we did not know anything about the function of the protein we can now say in which process it is involvedIf we did not know anything about the function of the protein we can now say in which process it is involved If we already knew something about the function, we might now know much more about the function (I.e. if we knew it was a hydrolase we might now know in which metabolic pathway it is active)If we already knew something about the function, we might now know much more about the function (I.e. if we knew it was a hydrolase we might now know in which metabolic pathway it is active) If the gene was already well characterized, we might understand its role better (I.e. new targets for a kinase)If the gene was already well characterized, we might understand its role better (I.e. new targets for a kinase) If we did not know anything about the function of the protein we can now say in which process it is involvedIf we did not know anything about the function of the protein we can now say in which process it is involved If we already knew something about the function, we might now know much more about the function (I.e. if we knew it was a hydrolase we might now know in which metabolic pathway it is active)If we already knew something about the function, we might now know much more about the function (I.e. if we knew it was a hydrolase we might now know in which metabolic pathway it is active) If the gene was already well characterized, we might understand its role better (I.e. new targets for a kinase)If the gene was already well characterized, we might understand its role better (I.e. new targets for a kinase)

15. ContentsContents Predicting functional interactions between proteinsPredicting functional interactions between proteins Genomic context methodsGenomic context methods –General (how do we predict functional interactions) –Gene fusion –Gene order –Presence / absence of genes across genomes Integration and benchmarking of predictionsIntegration and benchmarking of predictions Biochemistry by other means BolABiochemistry by other means BolA In addition to genomic context: functional genomics dataIn addition to genomic context: functional genomics data Predicting functional interactions between proteinsPredicting functional interactions between proteins Genomic context methodsGenomic context methods –General (how do we predict functional interactions) –Gene fusion –Gene order –Presence / absence of genes across genomes Integration and benchmarking of predictionsIntegration and benchmarking of predictions Biochemistry by other means BolABiochemistry by other means BolA In addition to genomic context: functional genomics dataIn addition to genomic context: functional genomics data

16. How can we now predict / detect functional associations? Functional genomics / high throughput experimentsFunctional genomics / high throughput experiments GENOMIC CONTEXTGENOMIC CONTEXT Functional genomics / high throughput experimentsFunctional genomics / high throughput experiments GENOMIC CONTEXTGENOMIC CONTEXT

17. functionally associated proteins leave evolutionary traces of their relation in genomes

18. We can thus detect evolutionary traces of a functional association by comparing genomes

19. Use the genome sequences Themselves (through comparative genome analysis) for interaction prediction: genomic context methods Use the genome sequences Themselves (through comparative genome analysis) for interaction prediction: genomic context methods Genomic context is an tool to predict functional associations between genes Genomic context methods have been shown to be reliable indicators for functional interactionGenomic context methods have been shown to be reliable indicators for functional interaction Genomic context is also known as in silico interaction prediction, or genomic associations Genomic context is also known as in silico interaction prediction, or genomic associations Genomic context methods have been shown to be reliable indicators for functional interactionGenomic context methods have been shown to be reliable indicators for functional interaction Genomic context is also known as in silico interaction prediction, or genomic associations Genomic context is also known as in silico interaction prediction, or genomic associations

20. http://string.embl.de

21. Three different genomic context methods in STRING Gene fusion, Rosetta stone methodGene fusion, Rosetta stone method Conserved gene order between divergent genomesConserved gene order between divergent genomes Co-occurrence of genes across genomes, phylogenetic profilesCo-occurrence of genes across genomes, phylogenetic profiles Gene fusion, Rosetta stone methodGene fusion, Rosetta stone method Conserved gene order between divergent genomesConserved gene order between divergent genomes Co-occurrence of genes across genomes, phylogenetic profilesCo-occurrence of genes across genomes, phylogenetic profiles

22. ContentsContents Predicting functional interactions between proteinsPredicting functional interactions between proteins Genomic context methodsGenomic context methods –General –Gene fusion –Gene order –Presence / absence of genes across genomes Integration and benchmarking of predictionsIntegration and benchmarking of predictions Biochemistry by other means BolABiochemistry by other means BolA In addition to genomic context: functional genomics dataIn addition to genomic context: functional genomics data Predicting functional interactions between proteinsPredicting functional interactions between proteins Genomic context methodsGenomic context methods –General –Gene fusion –Gene order –Presence / absence of genes across genomes Integration and benchmarking of predictionsIntegration and benchmarking of predictions Biochemistry by other means BolABiochemistry by other means BolA In addition to genomic context: functional genomics dataIn addition to genomic context: functional genomics data

23. Gene fusion i.e. the orthologs of two genes in another organism are fused into one polypeptidei.e. the orthologs of two genes in another organism are fused into one polypeptide A very reliable indicator for functional interaction; partly because it is an relatively infrequent evolutionary event: 3470 distinct fusions when surveying 179 genomesA very reliable indicator for functional interaction; partly because it is an relatively infrequent evolutionary event: 3470 distinct fusions when surveying 179 genomes i.e. the orthologs of two genes in another organism are fused into one polypeptidei.e. the orthologs of two genes in another organism are fused into one polypeptide A very reliable indicator for functional interaction; partly because it is an relatively infrequent evolutionary event: 3470 distinct fusions when surveying 179 genomesA very reliable indicator for functional interaction; partly because it is an relatively infrequent evolutionary event: 3470 distinct fusions when surveying 179 genomes FusionFusion

24. Gene fusion: an example

25. ContentsContents Predicting functional interactions between proteinsPredicting functional interactions between proteins Genomic context methodsGenomic context methods –General –Fusion –Gene order –Presence / absence of genes across genomes Integration and benchmarking of predictionsIntegration and benchmarking of predictions Biochemistry by other means BolABiochemistry by other means BolA In addition to genomic context: functional genomics dataIn addition to genomic context: functional genomics data Predicting functional interactions between proteinsPredicting functional interactions between proteins Genomic context methodsGenomic context methods –General –Fusion –Gene order –Presence / absence of genes across genomes Integration and benchmarking of predictionsIntegration and benchmarking of predictions Biochemistry by other means BolABiochemistry by other means BolA In addition to genomic context: functional genomics dataIn addition to genomic context: functional genomics data

26. Gene order evolves rapidly But …

27. Differential retention of divergent / convergent gene pairs suggests that conservation implies a functional association “Operons”

28. Comparison to pathways conservation implies a functional association

29. Conserved gene order i.e. genes that are present over ‘sufficiently large’ evolutionary distances in the same gene clusteri.e. genes that are present over ‘sufficiently large’ evolutionary distances in the same gene cluster Contributes by far the most predictionsContributes by far the most predictions i.e. genes that are present over ‘sufficiently large’ evolutionary distances in the same gene clusteri.e. genes that are present over ‘sufficiently large’ evolutionary distances in the same gene cluster Contributes by far the most predictionsContributes by far the most predictions

30. Conserved gene order NB1 predicting operons is not trivial; in fact conserved gene order or functional association is a major clue NB2 using ‘only’ operons without requiring conservation results in much less reliable function prediction

31. Conserved gene order: an example from Conserved gene order: an example from metabolism of propionyl-CoA “query” “target”

32. Biochemical assays confirm the function of members of COG0346 as a DL- methylmalonyl-CoA racemase

33. ContentsContents Predicting functional interactions between proteinsPredicting functional interactions between proteins Genomic context methodsGenomic context methods –General –Gene fusion –Gene order –Presence / absence of genes across genomes Integration and benchmarking of predictionsIntegration and benchmarking of predictions Biochemistry by other means BolABiochemistry by other means BolA In addition to genomic context: functional genomics dataIn addition to genomic context: functional genomics data Predicting functional interactions between proteinsPredicting functional interactions between proteins Genomic context methodsGenomic context methods –General –Gene fusion –Gene order –Presence / absence of genes across genomes Integration and benchmarking of predictionsIntegration and benchmarking of predictions Biochemistry by other means BolABiochemistry by other means BolA In addition to genomic context: functional genomics dataIn addition to genomic context: functional genomics data

34. Presence / absence of genes Gene content  co-evolution. (The easy case, few genomes. ) Genomes share genes for phenotypes they have in common Differences between gene Content reflect differences in Phenotypic potentialities Differences between gene Content reflect differences in Phenotypic potentialities

35. Presence / absence of genes L. innocua (non-pathogen) L. monocytogenes (pathogen)

36. Presence / absence of genes L. innocua (non-pathogenic) L. monocytogenes (pathogenic) Genes involved in pathogenecity

37. Generalization: phylogenetic profiles / co-occurence Gene 1: Gene 2: Gene 3:.... Gene 1: Gene 2: Gene 3:.... species 1 species 2 species 3 species 4 species 5........... species 1 species 2 species 3 species 4 species 5........... Gene 1: 1 0 1 1 0 1 Gene 2: 1 1 0 0 1 0 Gene 3: 0 1 0 0 1 0.... Gene 1: 1 0 1 1 0 1 Gene 2: 1 1 0 0 1 0 Gene 3: 0 1 0 0 1 0.... species 1 species 2 species 3 species 4 species 5........... species 1 species 2 species 3 species 4 species 5...........

38. Co-occurrence of genes across genomes i.e. two genes have the same presence/ absence pattern over multiple genomes: they have ‘co- evolved’ i.e. two genes have the same presence/ absence pattern over multiple genomes: they have ‘co- evolved’ AKA phylogenetic profilesAKA phylogenetic profiles

39. Predicting function of a disease gene protein with unknown function, frataxin, using co-occurrence of genes across genomes Friedreich’s ataxiaFriedreich’s ataxia No (homolog with) known functionNo (homolog with) known function Friedreich’s ataxiaFriedreich’s ataxia No (homolog with) known functionNo (homolog with) known function

40. A. a e o l i c u s S y n e c h o c y s t i s B. s u b t i l i s M. g e n i t a l i u m M. t u b e r c u l o s i s D. r a d i o d u r a n s R. p r o w a z e k i i C. c r e s c e n t u s M. l o t i N. m e n i n g i t i d i s X. f a s t i d i o s a P. a e r u g i n o s a B u c h n e r a V. c h o l e r a e H. i n f l u e n z a e P. m u l t o c i d a E. coli A. p e r n i x M. j a n n a s c h i i A. t h a l i a n a S. c e r e v i s i a e s C. j e j u n i C. a l b i c a n s S. p o m b e H. s a p i e n s C. e l e g a n H. pylori D.melan. cyaY Yfh1 hscB Jac1 hscA ssq1 Nfu1 iscA Isa1-2 fdx Yah1 Arh1 RnaM IscR Hyp iscS Nfs1 iscU Isu1-2 Atm1 Atm1 Frataxin has co-evolved with hscA and hscB indicating that it plays a role in iron-sulfur cluster assembly

41. Iron-Sulfur (2Fe-2S) cluster in the Rieske protein

42. Prediction: Confirmation:

43. The opposite of co-occurrence: anti-correlation / complementary patterns: predicting analogous enzymes ABAB Genes with complementary phylogenetic profiles tend to have a similar biochemical function.

44. Complementary patterns in thiamin biosynthesis predict analogous enzymes Morett E, Korbel JO, Rajan E, Saab-Rincon G, Olvera L, Olvera M, Schmidt S, Snel B, Bork P.Morett E, Korbel JO, Rajan E, Saab-Rincon G, Olvera L, Olvera M, Schmidt S, Snel B, Bork P. Nature Biotech 2003 Morett E, Korbel JO, Rajan E, Saab-Rincon G, Olvera L, Olvera M, Schmidt S, Snel B, Bork P.

45. Prediction of analogous enzymes is confirmed

46. ContentsContents Predicting functional interactions between proteinsPredicting functional interactions between proteins Genomic context methodsGenomic context methods –General –Gene fusion –Gene order –Presence / absence of genes across genomes Integration and benchmarking of predictionsIntegration and benchmarking of predictions Biochemistry by other means BolABiochemistry by other means BolA In addition to genomic context: functional genomics dataIn addition to genomic context: functional genomics data Predicting functional interactions between proteinsPredicting functional interactions between proteins Genomic context methodsGenomic context methods –General –Gene fusion –Gene order –Presence / absence of genes across genomes Integration and benchmarking of predictionsIntegration and benchmarking of predictions Biochemistry by other means BolABiochemistry by other means BolA In addition to genomic context: functional genomics dataIn addition to genomic context: functional genomics data

47. Benchmark and integration: KEGG maps

48. Integrating genomic context scores into one single score Compare each individual method against an independent benchmark (KEGG), and find “equivalency” Compare each individual method against an independent benchmark (KEGG), and find “equivalency” Multiply the chances that two proteins are not interacting and subtract from 1; naive bayesian i.e. assuming independence Multiply the chances that two proteins are not interacting and subtract from 1; naive bayesian i.e. assuming independence

49. BenchmarkBenchmark 0.50.60.70.80.91.0 Accuracy (fraction of confirmed predictions, i.e. same KEGG map) 10 100 1000 10000 100000 Fusion (norm.) Fusion (abs.) Gene Order (norm.) Gene Order (abs.) Cooccurrence Integrated Coverage (number of predicted links between orthologous groups)

50. Accuracy Coverage purified complexes TAP yeast two-hybrid two methods three methods Purified Complexes HMS-PCI combined evidence mRNA co-expression genomic context synthetic lethality fraction of reference set covered by data fraction of data confirmed by reference set filtered data raw data parameter choices Performance of genomic context compared to high-throughput interaction data

51. ContentsContents Predicting functional interactions between proteinsPredicting functional interactions between proteins Genomic context methodsGenomic context methods –General –Gene fusion –Gene order –Presence / absence of genes across genomes Integration and benchmarking of predictionsIntegration and benchmarking of predictions Biochemistry by other means BolABiochemistry by other means BolA In addition to genomic context: functional genomics dataIn addition to genomic context: functional genomics data Predicting functional interactions between proteinsPredicting functional interactions between proteins Genomic context methodsGenomic context methods –General –Gene fusion –Gene order –Presence / absence of genes across genomes Integration and benchmarking of predictionsIntegration and benchmarking of predictions Biochemistry by other means BolABiochemistry by other means BolA In addition to genomic context: functional genomics dataIn addition to genomic context: functional genomics data

52. Data-mining proteins for protein function prediction: BolA

53. An interaction of BolA with a mono-thiol glutaredoxin ? (STRING) BolA

54. BolA and Grx occur as neighbors in a number of genomes Bola Grx

55. BolA and Grx have an (almost) identical phylogenetic distribution

56. BolA and Grx have been shown to interact in Y2H in S.cerevisiae and D.melanogaster, and in Flag tag in S.cerevisiae BolA phylogeny

57. BolA does have (predicted) interactions with cell-division / cell-wall proteins. Those appear secondary to the link with GrX  STRING has obtained a higher resolution in function prediction than phenotypic analyses Cell division / Cell wall oxidative) stress (oxidative) stress

58. BolA is homologous to the peroxide reductase OsmC, suggesting a similar function

59. OsmC uses thiol groups of two, evolutionary conserved cysteines to reduce substrates Problem: The BolA family does not have conserved cysteines. …It would have to obtain its reducing equivalents from elsewhere… BolA family alignment

60. BolA is (homologous to) a reductaseBolA interacts with GrX ? GrX provides BolA with reducing equivalents !? (or “scaffolding?”) Prediction of interaction partner and molecular function complement each other

61. Genomic context: biochemistry by other means Despite the high performance of genomic context methods, as a tool for function prediction it is not a button press method It is more like biochemistry by other means. Often quite a lot of manual input and expert knowledge from the researcher is needed to distill associations into a concrete function prediction Small-scale bioinformatics?

62. ContentsContents Predicting functional interactions between proteinsPredicting functional interactions between proteins Genomic context methodsGenomic context methods –General –Fusion –Gene order –Co-occurrence across genomes Integration and benchmarking of predictionsIntegration and benchmarking of predictions Interaction networksInteraction networks In addition to genomic context: functional genomics dataIn addition to genomic context: functional genomics data Predicting functional interactions between proteinsPredicting functional interactions between proteins Genomic context methodsGenomic context methods –General –Fusion –Gene order –Co-occurrence across genomes Integration and benchmarking of predictionsIntegration and benchmarking of predictions Interaction networksInteraction networks In addition to genomic context: functional genomics dataIn addition to genomic context: functional genomics data

63. STRING currently in addition includes: Functional association data from large scale / high- throughput biochemical experiments (functional genomics data) Functional association data from large scale / high- throughput biochemical experiments (functional genomics data) protein complex purification protein complex purification yeast-2-hybrid yeast-2-hybrid ChIP-on-chip ChIP-on-chip micro-array gene expression micro-array gene expression “known” functional relations, so called “legacy data”, as present in PubMed abstracts and databases like MIPS or KEGG. “known” functional relations, so called “legacy data”, as present in PubMed abstracts and databases like MIPS or KEGG. STRING currently in addition includes: Functional association data from large scale / high- throughput biochemical experiments (functional genomics data) Functional association data from large scale / high- throughput biochemical experiments (functional genomics data) protein complex purification protein complex purification yeast-2-hybrid yeast-2-hybrid ChIP-on-chip ChIP-on-chip micro-array gene expression micro-array gene expression “known” functional relations, so called “legacy data”, as present in PubMed abstracts and databases like MIPS or KEGG. “known” functional relations, so called “legacy data”, as present in PubMed abstracts and databases like MIPS or KEGG.

65. Handing out article and questions : “Identification of a bacterial regulatory system for ribonucleotide reductases by phylogenetic profiling.” Read the article and hand in the answers to the questions by Monday November 28th.Handing out article and questions : “Identification of a bacterial regulatory system for ribonucleotide reductases by phylogenetic profiling.” Read the article and hand in the answers to the questions by Monday November 28th.