Classification des séquences régulatrices sur base de motifs

Transcription

1 LIFL - 3 mars 2003 lassification des séquences régulatrices sur base de motifs Jacques van Helden Jacques.van.Helden@ulb.ac.be

2 Regulatory Sequence Analysis Regulatory regions and regulatory elements Jacques van Helden

3 The non-coding genome Organism Year Size Genes genes/mb coding non-coding repetitive Mb % % % % Transcribed Mycoplasma genitalium Haemophilus influenzae Escherichia coli Saccharomyces cerevisiae Arabidiopsis thaliana aenorhabditis elegans Drosophila melanogaster Homo sapiens

4 Transcriptional activation Transcriptional activator activation domain RNA polymerase DNA-binding domain enhancer initiation

5 Transcription factor-dna interfaces A B D

6 Regulatory sites : string description Binding sites for the yeast Pho4p transcription factor (Source : Oshima et al. Gene 179, 1996; ) Gene Site Name Sequence Affinity PHO5 UASp2 ---ataaagtgggatag- high PHO84 Site D ---TTTAGAGTGGGGGGA-- high PHO81 UAS ----TTATGGAGTGGAATAA-- high PHO8 Proximal GTGATGTGAGTGGGA--- high PHO5 UASp3 --TAATTTGGATGTGGATT-- low PHO84 Site -----AGTAGTGGAATAT-- low PHO84 Site A -----TTTATAGTGAATTTTT low group 1 consensus gagtgggac----- high-low. PHO5 UASp1 --TAAATTAGAGTTTTG---- medium PHO84 Site E ----AATAGAGTTTTTAATTA medium PHO84 Site B -----TTAGAGTTGGTGTG-- low PHO8 Distal ---TTAGAGTTAATAT--- low group 2 consensus cgagttt med-low Degenerate consensus GAGTKKk

7 IUPA ambiguous nucleotide code. A A Adenine ytosine G G Guanine T T Thymine R A or G purine Y or T pyrimidine W A or T Weak hydrogen bonding S G or Strong hydrogen bonding M A or amino group at common position K G or T Keto group at common position H A, or T not G B G, or T not A V G, A, not T D G, A or T not N G, A, or T any

8 Regulatory sites : matrix description Alignment matrix Pos Base A G T V A G T K B Binding site for the yeast Pho4p transcription factor (Source : Transfac matrix F$PHO4_01)

9 Position-weight matrix Prior Pos A G T Sum W i, j = ln f ' i, p i j A alphabet size (=4) p i prior residue probability for residue i f i,j relative frequency of residue i at position j k pseudo weight (arbitrary, 1 in this case) corrected frequency of residue i at position j f' i,j f ' i, j = n i, j A i=1 n + i, j p k i + k

10 Scanning a sequence with a profile matrix Ex: sequence...gtgagtgg.. Position-Spe cific Scoring Matrix A G T Sca nning 1 S UM G T G A G T G G T G A G T G G T G A G T G G

11 Differences between species organism yeast coli higher organisms location upstream upstream upstream overlap. Initiation downstream within introns distance range -800 to -1 bp -400 to +50 bp over 100s of Kb position effect often irrelevant often essential often irrelevant strand insensitive sensitive or symmetric insensitive most common ~5-8 conserved bp spaced pair of 3nt ~5-8 conserved bp core repeated sites occasional rare frequent composite elements frequent

12 Matrix search : matching positions

13 Regulatory Sequence Analysis Regulation of phosphate and methionine metabolism in the yeast Saccharomyces cerevisiae Jacques van Helden

14 Phosphate utilization in yeast Up-regulates Pho2p Pho4p PHO4 expression codes for Pho4p PHO2 expression is translocates tranferred up-regulates up-regulates up-regulates up-regulates up-regulates up-regulates up-regulates PHO3 PHO5, 11,12 PHO84, 86,87,88,89 PHO81 Pho4p-Phosphate PHO8 (nucleus) expression codes for expression codes for expression codes for expression codes for expression codes for acid phosphatase alkaline phosphatase catalyzes orthophosphoric monoester Pi H2O alcohol is secretion secreted Pi transporter transports facilitates is tranferred transport acid phosphatase catalyzes orthophosphoric monoester Pi alcohol H2O Pho80p catalyzes Pho85p inhibits Pho81p inhibits Pho85-Pho80 complex (cytoplasm) extracellular space

15 Pho4p binding sites gene start end sequence PHO GATAAGTGGGATA PHO GATAAGTGGGA PHO TGGATAAGTGGGATAGA PHO TGGGAGTGAGGAT PHO ATATTAAGGTGGGGTAA PHO TTATGGAGTGGAATAA PHO TTTAGAGTGGGGGG PHO TAGTTAGTGGAGTG PHO aaaagtgtAGTGataaaaat PHO TTAAAAAGTGGTATTA PHO TTAGAGTTGGTGTG PHO AATTAGAGTTTTGATA PHO5 (?) (?)..AAATTAGAGTTTG PHO TAAATTAGAGTTTTGATAGA

16 Pho4p binding specificity - matrix descriptions D E Pho 4p A G T Pho 4p.c ac g tg A G T Pho 4p.c ac g tt A G T

17 Methionine Biosynthesis in S.cerevisiae Aspartate biosynthesis ATP ADP L-Aspartate Aspartate kinase HOM3 L-aspartyl-4-P NADPH NADP+; Pi L-aspartic semialdehyde Aspartate semialdehyde deshydrogenase HOM2 Threonine biosynthesis NADPH NADP+ AcetlyoA oa L-Homoserine O-acetyl-homoserine Homoserine deshydrogenase Homoserine O-acetyltransferase HOM6 MET2 Met31p met32p MET31 MET32 Sulfur assimilation Sulfide O-acetylhomoserine (thiol)-lyase MET17 ysteine biosynthesis Homocysteine bf1p/met4p/met28p complex MET28 BF1 MET4 5-methyltetrahydropteroyltri-L-glutamate 5-tetrahydropteroyltri-L-glutamate L-Methionine Methionine synthase (vit B12-independent) MET6 Gcn4p GN4 S-adenosyl-methionine H 2 0; ATP synthetase I Pi, PPi S-adenosyl-methionine synthetase II S-Adenosyl-L-Methionine SAM1 SAM2 Met30p MET30

18 Met4p binding sites gene start end sequence MET GAAAAGTAGTGTAATTT MET AAAAGGTAGTGAAGA MET TAATTTAGTGATAAT MET ATATTTAGTGGTAGT EM ATTTATAGTGGTATT EM TTTGTAGTGATATTT MET AAAGTGAGTTAT MET TAGAAGAGTGAAAA MET GTATTTTAGTGATGG MET TAATAATAGTGATATTT MET AAATGGAGTGAAGTGT MET TTGAGGTAATGATGA MET GAATAGTGAATT MET AATATTTAGTGATTA SAM TTAAGTGATATAA SAM TTTAATGTGATTAT A G T

19 Met31p binding sites gene start end sequence MET TAAAAAATGTGGAATGG MET TGAAAAAATTGTGGATGA MET TATGAAAATGTGTAAATA MET GTGAAAATGTGGTAGTA SAM GTTGAAAATGTGGGTTTT SAM AAGGAAAATGTGGTGGG MET ATAAGAAATGTGGGTTAT MUP GGAAAAAATGTGGGTG MET GGAAAAAAAATGTGAAAATG MET ATAATAAATGTGAAGGA MET AAAAGAAGTTTTAAA MET TAAAAAGTTTTGGGG MET TTTGTGAGTTTTATTG MET GGGAAGAAGTTTGGGG MET TATGAATGTTTAGTG A G T

20 Training and validation set There is a subset of genes which can be assigned to predefined classes, on the basis of prior information (e.g. known regulation) These classes will be used as criterion variable. Note : the sample class column might contain some errors. Phosphate-responding genes Methionine-responding genes ontrol genes # ORF Gene name Family # ORF Gene name Family # ORF Gene name Family 1 YBR093 PHO5 PHO 14 YBR213W MET8 MET 33 YAL038W D19 TL 2 YDR481 PHO8 PHO 15 YDR253 MET32 MET 34 YBL005W PDR3 TL 3 YAR071W PHO11 PHO 16 YDR502 SAM2 MET 35 YBL005W-A YBL005W-A TL 4 YHR215W PHO12 PHO 17 YER091 MET6 MET 36 YBL005W-B YBL005W-B TL 5 YOL001W PHO80 PHO 18 YFR030W MET10 MET 37 YBL030 PET9 TL 6 YGR233 PHO81 PHO 19 YHL036W MUP3 MET 38 YBR006W UGA5 TL 7 YML123 PHO84 PHO 20 YIL046W MET30 MET 39 YBR018 GAL7 TL 8 YPL031 PHO85 PHO 21 YIR017 MET28 MET 40 YBR020W GAL1 TL 9 YJL117W PHO86 PHO 22 YJR010W MET3 MET 41 YBR115 LYS2 TL 10 YR037 PHO87 PHO 23 YJR137 EM17 MET 42 YBR184W YBR184W TL 11 YBR106W PHO88 PHO 24 YKL001 MET14 MET 43 YL018W LEU2 TL 12 YBR296 PHO89 PHO 25 YKR069W MET1 MET 44 YDL131W LYS21 TL 13 YHR136 SPL2 PHO 26 YLR180W SAM1 MET 45 YDL182W LYS20 TL 27 YLR303W MET17 MET 46 YDL205 HEM3 TL 28 YLR396 VPS33 MET 47 YDL210W UGA4 TL 29 YNL241 ZWF1 MET 48 YDR011W SNQ2 TL 30 YNL277W MET2 MET 49 YDR044W HEM13 TL 31 YOL064 MET22 MET 50 YDR234W LYS4 TL 32 YPL038W MET31 MET 51 YDR285W ZIP1 TL YPR065W ROX1 TL 113 YPR138 MEP3 TL 114 YPR145W ASN1 TL

21 Regulatory Sequence Analysis lassifying genes on the basis of upstream motifs Jacques van Helden

22 Questions Is it possible to predict the regulation of a gene on the basis of its upstream sequence? A challenging case : given the similarity of Met4p and Pho4p binding sites, can we distinguish their respective target genes? Genome-scale classification : having at hand the complete yeast genome, can we classify genes according to their predicted regulatory responses?

23 Discrimination between MET and PHO genes On the basis of upstream motifs, can we predict the regulation of a gene? Pho4p binds AGTGgg and AGTTtt (consensus AGTkkk) Met4p binds tagtga Met31p binds AAAATGTGG lues ombinatorial aspect : several MET genes are regulated by both Met4p and Met31p Multiple motifs : many PHO genes have several Pho4p sites Approach Build position weight matrices reflecting the specificity of each factor For each upstream region, find the 3 top scores obtained with the different matrices Define a training set with known PHO, MET and control genes Apply discriminant analysis

24 Data types Patterns can be represented either as strings or as positions-specific scoring matrices (PSSM). We collected two data sets, representing the putative regulatory signals in upstream sequences : top scores obtained with position-specific scoring matrices counts of occurrences for selected hexanucleotides and dyads These patterns were matched against each one of the 6300 yeast upstream sequences.

25 Data - matrix scores matrix scores: top scores obtained with PSSM for Met4p, Met31p, Pho4p, Pho4p.cacgtg, and Pho4p.cacgtt; 5 matrices, 3 top scores per matrix. P ho 4p Met4p Met31p P ho4p.ca cgtg.1 P ho4p.ca cgtg.2 P ho4p.ca cgtg.3 P ho4p.ca cgtk.1 P ho4p.ca cgtk.2 P ho4p.ca cgtk.3 ;orf ge ne YAL001 TF YAL002W V P S YAL003W EFB YAL004W YAL004W YAL005 S S A YAL007 ERP YAL008W FUN YAL009W S P O YAL010 M DM YAL011W YAL011W YAL012W YS YAL013W DEP YP R203W YP R203W P ho4p.ca cgtt.1 P ho4p.ca cgtt.2 P ho4p.ca cgtt.3 M e t4p.1 M e t4p.2 M e t4p.3 M e t31p.1 M e t31p.2 M e t31p.3

26 Top scores - Met4p and Pho4p matrices M et4p P HO M ET TL Pho4p

27 Top scores - Met31p and Met4p M et31p P HO M ET TL M et4p

28 Met4p - first top score vs second top score M et4p - second score M et4p - first score

29 Met31p - first top score vs second top score M et31p - second score M et31p - first score

30 Principal omponent Analysis - matrix scores 5 position-weight matrices Met4p Met31p Pho4p Pho4p.cacgtg Pho4p.cacgtt 3 top scores per matrix Each gene is characterized by 15 scores The 15-dimensional score space can be projected onto a 2- dimensional space The two first components (P1 and P2) are the directions with the highest variance in the 15- dimentional space Previously characterized PHO and MET genes are labelled The TL genes are genes with known regulation, and supposedly not regulated by MET or PHO P PA P 1 all TL M ET PHO

31 Data - pattern counts word counts: number of occurrences of 44 hexanucleotides involved in the regulation of the MET, PHO and NIT genes. Some of these words are very well conserved in the core of the binding site (e.g. AGTG, AGTT,...) whereas some other represent partial conservation of the flanking nucleotides (e.g. AGTGg, AGTTt,...). ge ne cttn{0}a tc.g a tn{0}a ag ca gn{2}cgg.ccgn{2}ctg ga tn{1}a ga.tctn{1}a tc ctta tc.ga taag ccgcgc.gcg cgg cg gca c.gtgccg a ta a ga.tctta t a ca tct.a ga tgt ctga ta.ta tca g a ga taa.ttatct ca cn{0}gtg.ca cn{0}gtg ca cn{1}tga.tca n{1}g tg ca cn{2}ga c.gtcn{2}gtg cca n{0}ca g.ctg n{0}tgg a cgn{0}tga.tcan{0}cgt aacn{1}gtg.cacn{1}gtt gca n{6}ggc.gccn{6}tgc gccn{0}a ca.tgtn{0}g gc gggn{4}cac.gtgn{4}ccc ca gn{7}atc.gatn{7}ctg ca cgtg.ca cgtg a cgtga.tca cgt cca ca g.ctgtgg cgtga c.gtca cg gcca ca.tgtggc a tca cg.cgtga t cgccac.gtggcg ccggag.ctccgg TF VPS EFB YAL004W SSA ERP FUN SPO M DM YP R204W

32 ombining multiple patterns for classification 64 genes NIT, PHO and MET families 44 patterns obtained from oligoanalysis and dyad-analysis occurrence counts for all patterns in each upstream region 64x44 multivariate table Projection of the 44 dimensions onto a 2D space (Principal omponent Analysis) PA is suboptimal: the axes with highest variance are not mandatorily the most discriminant PHO NIT MET TL

33 Regulatory Sequence Analysis Unsupervised classification (clustering) Jacques van Helden

34 hclust (*, "complete") NIT_DAL5 P HO _P HO 87 NIT_HA1 NIT_YG R125W NIT_DAL1 NIT_DAL4 NIT_M EP3 NIT_DAL3 NIT_G AT1 NIT_G AP 1 P HO _P HO 81 M ET_M ET22 P HO _P HO 89 M ET_M UP 3 M ET_M ET10 M ET_EM 17 RAND_rand_4 RAND_rand_21 RAND_rand_24 RAND_rand_5 RAND_rand_17 RAND_rand_16 RAND_rand_6 RAND_rand_19 M ET_M ET31 RAND_rand_26 RAND_rand_13 RAND_rand_3 RAND_rand_9 NIT_BAT2 RAND_rand_1 RAND_rand_22 RAND_rand_10 RAND_rand_11 M ET_M ET8 RAND_rand_15 P HO _P HO 88 RAND_rand_7 NIT_LAP 4 P HO _P HO 85 NIT_AP G 14 RAND_rand_14 NIT_UG A1 RAND_rand_2 P HO _P HO 80 RAND_rand_25 RAND_rand_29 NIT_P EP4 RAND_rand_30 RAND_rand_8 NIT_UG A4 RAND_rand_28 NIT_DAL2 NIT_PS1 NIT_YBR043 NIT_DUR3 NIT_AG P 1 NIT_P RB1 NIT_DAL7 NIT_G ZF3 NIT_G DH3 NIT_AN1 NIT_DAL80 M ET_VPS33 RAND_rand_23 M ET_M ET14 M ET_M ET16 NIT_DG 1 RAND_rand_12 RAND_rand_18 RAND_rand_20 M ET_S AM 1 RAND_rand_27 M ET_S AM 2 M ET_M ET32 M ET_M ET1 M ET_M ET17 PHO _SP L2 P HO _P HO 86 NIT_P UT4 M ET_M ET28 M ET_ZW F1 P HO _P HO 11 P HO _P HO 12 P HO _P HO 5 P HO _P HO 8 M ET_M ET3 M ET_M ET30 M ET_M ET6 M ET_M ET2 NIT_M EP1 NIT_M EP2 NIT_YHR029 P HO _P HO 84 Height euclidian.dist - complete Genes from different classes are intermingled The four main clusters do not correspond to the prior functional classes lustering method: UPGMA (complete) Distance metric: Euclidian Predicted Sequence clustering on the basis of pattern counts R AN D MET N IT PH O SU M R AN D MET N IT PH O SU M Errors % orrect % K nown lustering - Euclidian distance

35 Poisson-based similarity metrics proba Poisson ommon occurrences Distinct occurrences d= occ

36 lustering - Poisson-based distance metrics Sequence clustering on the basis of pattern counts Similarity metric based on probability of patterns found in common in two sequences Three of the 4 main clusters correspond to the regulatory mechanism (PHO, MET, NIT) Most errors consist in false negative Known RAND MET NIT PHO SUM RAND MET NIT PHO SUM Errors % orrect % po isson.sim ilarity.product.d - w ard RAND NIT PHO MET RAND_ra nd_20 P HO _P HO 88 RAND_ra nd_7 NIT_UG A4 NIT_AN1 RAND_ra nd_29 M ET_M ET8 RAND_ra nd_15 NIT_DAL7 RAND_ra nd_21 RAND_ra nd_24 NIT_BAT2 RAND_ra nd_1 P HO _P HO 89 RAND_ra nd_10 RAND_ra nd_11 NIT_PEP 4 RAND_ra nd_30 RAND_ra nd_17 RAND_ra nd_3 RAND_ra nd_9 RAND_ra nd_6 RAND_ra nd_19 P HO _P HO 80 RAND_ra nd_5 NIT_G DH3 NIT_AP G14 RAND_ra nd_18 RAND_ra nd_27 M ET_M ET31 RAND_ra nd_26 P HO _P HO 85 NIT_LAP 4 RAND_ra nd_14 NIT_UG A1 RAND_ra nd_2 RAND_ra nd_16 RAND_ra nd_13 RAND_ra nd_25 NIT_DAL1 NIT_DAL4 NIT_DUR3 NIT_YHR029 NIT_DG 1 RAND_ra nd_12 NIT_YBR043 NIT_DAL2 M ET_V P S 33 RAND_ra nd_23 NIT_P UT4 NIT_DAL5 NIT_M EP3 NIT_HA1 NIT_DAL3 NIT_M EP1 NIT_G AP 1 NIT_G AT1 NIT_M EP2 NIT_P RB1 NIT_P S1 NIT_AG P 1 NIT_G ZF3 NIT_DAL80 NIT_YG R125W RAND_ra nd_28 RAND_ra nd_8 RAND_ra nd_22 P HO _P HO 11 P HO _P HO 12 P HO _P HO 8 P HO _P HO 81 P HO _P HO 5 P HO _P HO 87 P HO _P HO 86 P HO _S P L2 P HO _P HO 84 M ET_M ET6 M ET_M ET30 M ET_S AM 2 M ET_M ET28 M ET_M UP 3 M ET_M ET3 M ET_ZW F1 M ET_S AM 1 M ET_M ET17 M ET_M ET32 M ET_M ET1 M ET_EM 17 M ET_M ET16 M ET_M ET14 M ET_M ET22 RAND_ra nd_4 M ET_M ET10 M ET_M ET2 Height Predicted hclust (*, "ward")

37 Sequence clustering on the basis of pattern counts The choice of the distance metric and clustering method is crucial lassical distance metric give very bad results Poisson-based metric bring a sensible improvement Weaknesses Dependency between variables are not (yet) taken into account Unsupervised approach. We could obtain better results by taking advantage of our prior knowledge of the functional classes.# Reference van Helden, J. (2003). Metrics for comparing regulatory sequences on the basis of pattern counts. Bioinformatics accepted.

38 Regulatory Sequence Analysis Supervised classification (discriminant analysis) Jacques van Helden

39 Discriminant analysis Training set alibration Discriminant function Objects of known class Testing set Prediction Predicted class omparison onfusion table (for evaluation) Objects of unknown class Prediction Predicted class

40 Approach Extract upstream sequences for each one of the 6000 yeast genes Use position-weight matrices to predict putative regulatory elements Use genes with known PHO or MET regulation, plus a control group (TL) as training set Build a classification rule based on predicted regulatory elements Evaluate the accuracy of the classification rule Select the best classification method and parameters Apply the classification to each one of the 6000 yeast genes

41 Difficulties The training sets are very small 13 PHO genes 19 MET genes 82 control genes (supposed to respond neither to phosphate nor to methionine) The training set may contain errors Over-fitting The number of variables (15 matrix scores, 44 pattern counts) is higher than the number of elements in some classes of the training set Size of the prediction set After training and evaluation, the discriminant function will b used to classify each one of the 6300 yeast genes. Even a small error rate (e.g. 1%) would lead to an important number of false predictions (60 false positives).

42 Evaluation of the discriminant function - confusion table The results of the evaluation are summarized in a confusion table, which contains the count of the predicted versus known class. The confusion table can be used to calculate the accuracy of the predictions. The same object should not be used for training and testing. Ideally, one would split the data set in two parts : training and evaluation sets. The training set is so small that splitting it further would lead to a sensible loss in information. In such cases, a Leave-one-out evaluation is performed. Internal validation (biased) Leave-one-out validation Known Known PHO MET TL Sum PHO MET TL Sum PHO PHO Predicted MET MET TL TL Sum Sum Error rate 0.07 Error rate 0.14 Hit rate 0.93 Hit rate 0.86 Predicted

43 Linear versus quadratic classification rule Under assumption of multivariate normality There is one covariance matrix per group g. When all covariance matrices are assumed to be identical, the classification rule can be simplified to obtain a linear function -> Linear Discriminant Analysis (LDA) When the groups have not the same covariance matrix, Quadratic Discriminant Analysis (QDA) is more appropriate.

44 Variable selection When there are too many variables, the classification is less accurate. In particular, the number of variables must be much smaller than the number of elements in the training groups. In our case, we have 15 variables, but the PHO group contains only 13 genes. We select the best subset of variables via a stepwise procedure

45 Stepwise linear discriminant analysis E rror rate Predicted n it_m et_pho_rand - Stepwise PDA - Error rates LD A LD A, randomized data n um b er of variables Known TL MET NIT PHO SUM TL MET NIT PHO SUM Errors % orrect % Genes : NIT, PHO, MET + random selection (TL) 44 variables (pattern counts) Optimum: 7 variables Best variables cttatc.gataag acgtga.tcacgt acgtgg.ccacgt cggcac.gtgccg ccgcgc.gcgcgg aacgtg.cacgtt ctgcac.gtgcag

46 Estimation of the expected error rate E rror rate E rror rates - stepwise LDA - m atrix scores LD A pe rmuted Even a random classification would assign some objects to the correct group. The random rate of correct assignation depends on The relative size of the groups The structure of the data The number of variables The expected error rate can be estimated with a permutation test n um b er of variables The method is applied to the real data set, but the training labels are randomly assigned.

47 lassification with the principal components Variable selection represents a loss of information. P transformation can be applied before discriminant analysis. This allows to "concentrate" more information in a few variables.

48 Stepwise discriminant analysis - forward variable selection PHO and MET signals, matrix scores P HO and MET upstream motifs - Stepwise PDA - Error rates E rro r rate LD A LD A with P LD A, permuted labels LD A with P, permuted labels Q D A Q D A with P Q D A, permuted labels Q D A with P, permuted labels n um b er of variables

49 Optimal conditions Pre-processed data (Principal component analysis) Linear Discriminant Analysis 4 components selected (components 1, 2, 3, and 6). External validation with the leave-one-out method Predicted onfusion table K now n PH O MET TL SU M PH O ME T TL SU M E rrors % orrect %

50 omparison of predicted and prior class Letters indicate the predicted class olors indicate the prior class P P P M P P M P M M M P M P P M M M M M M M M P M M m atrix_scores ross-validation (LOO) LD1 LD2

51 "Misclassified units" M isclassifications lassified as TL PHO88 PHO86 SA M1 V PS33 ZWF1 PHO80 MET22 PHO85 MET M isclassifications lassified as MET GA L

52 hoice of the prior probabilities The classes may have different proportions between the sample and the population For example, we could decide, on the basis of our biological knowledge, that it is likely to have 1% rather than 11% of yeast gene responding to phosphate. Popula tion la ss Sa m ple Priors from sa m ple Arbitra ry priors PHO % 11% 1% M ET % 17% 1% TL % 72% 98% TOTAL

53 Genome-scale prediction The discriminant function is used to assign each yeast gene to a regulation class (PHO, MET or control). 97 genes predicted as methionine-regulated 103 genes predicted as phosphate-responding The confidence of each prediction can be assessed with the posterior probability. ORF Gene prediction post.tl post.met post.pho Description YAL001 TF3 TL TFIII (transcription initiation factor) subunit, 138 kd YAL002W VPS8 PHO vacuolar sorting protein, 134 kd YAL003W EFB1 TL translation elongation factor eef1beta YAL004W YAL004w TL strong similarity to A.klebsiana glutamate dehydrogenase YAL005 SSA1 TL heat shock protein of HSP70 family, cytosolic YAL007 ERP2 TL p24 protein involved in membrane trafficking YAL008W FUN14 TL hypothetical protein YAL009W SPO7 TL meiotic protein YAL010 MDM10 TL involved in mitochondrial morphology and inheritance YAL011W FUN36 TL weak similarity to Mus musculus p53-associated cellular protein YAL012W YS3 MET cystathionine gamma-lyase YAL013W DEP1 TL regulator of phospholipid metabolism

54 Phosphate predictions ORF NAM E know n pre dicte d post.tl post.m ET post.pho De scription YBR093 PHO5 PHO PHO re pre ssible acid phosphatase precursor YEL017W YEL017w NA PHO hypothetica l protein YGR233 PHO81 PHO PHO cyclin-dependent kinase inhibitor YBR296 PHO89 PHO PHO Na +-coupled phosphate transport protein, high affinity YAR071W PHO11 PHO PHO secre te d acid phosphatase YHR215W PHO12 PHO PHO secre te d acid phosphatase YDR303 RS3 NA PHO sim ilarity to transcriptional regulator proteins YJR058 APS2 NA PHO AP-2 complex subunit, sigma2 subunit, 17 KD YKR048 NAP1 NA PHO nucle osom e assembly protein I YR037 PHO87 PHO PHO m e m be r of the phosphate permease family YJR059W PTK2 NA PHO involve d in polyamine uptake YML123 PHO84 PHO PHO high-affinity inorganic phosphate/h+ symporter YHR136 SPL2 PHO PHO suppressor of plc1-delta YKR050W TRK2 NA PHO m oderate-affinity potassium transport protein YHR137W ARO9 NA PHO a rom a tic amino acid aminotransferase II YAR070 YAR070c NA PHO hypothetica l protein YJR060W BF1 NA PHO kinetochore protein YDR281 PHM6 NA PHO hypothetica l protein, has a role in phosphate metabolism YJR070 YJR070c NA PHO sim ilarity to.elegans hypothetica l protein 14A4.1 YNL116W YNL116w NA PHO w e ak similarity to RING zinc finger protein from Gallus ga llus YKR047W YKR047w NA PHO que stionable ORF YEL017-A PMP2 NA PHO H+-ATPa se subunit, plasma membrane YPL088W YPL088w NA PHO sim ilarity to aryl-alcohol dehydrogenases YPL089 RLM1 NA PHO tra nscription factor of the MADS box family YDR310 SUM1 NA PHO suppressor of SIR mutations YDR311W TFB1 NA PHO TFIIH subunit (transcription initiation factor), 75 kd YMR253 YMR253c NA PHO strong similarity to YPL264c YMR255W GFD1 NA PHO prote in of the nuclear pore complex YKR045 YKR045c NA PHO hypothetica l protein YMR256 OX7 NA PHO cytochrome-c oxidase, subunit VII

55 Methionine predictions ORF NAME known predicted post.tl post.met post.pho Description YGR155W YS4 NA MET cystathionine beta-synthase YGR154 YGR154c NA MET strong similarity to hypothetical proteins YKR076w and YMR251w YKL001 MET14 MET MET ATP adenosine-5prime-phosphosulfate 3primephosphotransferase YDR502 SAM2 MET MET S-adenosylmethionine synthetase 2 YLR303W MET17 MET MET O-acetylhomoserine sulfhydrylase YDR253 MET32 MET MET transcriptional regulator of sulfur amino acid metabolism YJR010W MET3 MET MET sulfate adenylyltransferase YIL074 SER33 NA MET phosphoglycerate dehydrogenase YDR254W HL4 NA MET chromosome segregation protein YNL277W MET2 MET MET homoserine O-acetyltransferase YDL073W YDL073w NA MET weak similarity to yprinus carpio calcium channel protein YDL074 BRE1 NA MET weak similarity to spindle pole body protein NUF1 YOR367W SP1 NA MET similarity to mammalian smooth muscle protein SM22 and chicken calponin alpha YOR001W RRP6 NA MET similarity to human nucleolar 100K polymyositis-scleroderma protein YLR149 YLR149c NA MET weak similarity to hypothetical protein SP4G3.03 S. pombe YDL058W USO1 NA MET intracellular protein transport protein YBR213W MET8 MET MET siroheme synthase YLL060 GTT2 NA MET glutathione S-transferase YIL047 SYG1 NA MET member of the major facilitator superfamily YIL046W MET30 MET MET involved in regulation of sulfur assimilation genes and cell cycle progression YNL283 WS2 NA MET glucoamylase III (alpha-1,4-glucan-glucosidase) YFL032W YFL032w NA MET questionable ORF YHL038 BP2 NA MET apo-cytochrome b pre-mrna processing protein 2 YHL036W MUP3 MET MET very low affinity methionine permease YLR150W STM1 NA MET specific affinity for guanine-rich quadruplex nucleic acids YFL031W HA1 NA MET transcription factor YFL033 RIM15 NA MET protein kinase involved in expression of meiotic genes YFL018 LPD1 NA MET dihydrolipoamide dehydrogenase precursor YGR204W ADE3 NA MET tetrahydrofolate synthase (trifunctional enzyme),cytoplasmic YHR001W-A QR10 NA MET ubiquinol--cytochrome-c reductase 8.5 kda subunit