The AMADEUS Motif Discovery Platform

1. TheAMADEUSMotif Discovery Platform C. Linhart, Y. Halperin, R. Shamir Tel-Aviv University Genome Research 2008 ApoSys workshop May ‘08

2. TF TF 5’ 3’ Gene BS BS Promoter Analysis: Exteremely brief intro • Transcription is regulated primarily by transcription factors (TFs) – proteins that bind to DNA subsequences, called binding sites (BSs) • TFBSs are located mainly (not always!) in the gene’s promoter– the DNA sequence upstream the gene’s transcription start site (TSS) • TFs can promote or repress transcription TSS

3. Promoter Analysis (cont.)TFBS models • The BSs of a particular TF share a common pattern, or motif, which is often modeled using: • Consensus string TASDAC (S={C,G} D={A,G,T}) • Position weight matrix (PWM / PSSM) > Threshold = 0.01: TACACC (0.06) TAGAGC (0.06) TACAAT (0.015) …

4. Motifdiscovery Promoter Analysis (cont.): Typical pipeline Promotersequences Co-regulated gene set Cluster I Gene expressionmicroarrays Clustering Cluster II Cluster III Location analysis(ChIP-chip, …) Functional group(e.g., GO term)

5. Promoter Analysis (cont.): Goals Reverse-engineer the transcriptional regulatory network = find the TFs (and their BSs) that regulate the studied biological process Input: A set of co-expressed genes Output:“Interesting” motif(s): • Known motifs: PRIMA, ROVER, … • Novel motifs: MEME, AlignACE, … • A group of co-occurring motifs = cis-regulatory module (CRM): MITRA, CREME, … AMADEUS

6. Promoter Analysis: Status of motif discovery tools • Extant tools perform reasonably well for: • Finding known/novel motifs in organisms with short, simple promoters, e.g., yeast • Identifying some of the known motifs in complex species, e.g., TFs whose BSs are usually close to the TSS • … but often fail in other cases! • Each tool is custom-built for a specific target score, often parametric (i.e., assumes a BG model) or uses a small part of the genome as BG reference; Majority of tools can efficiently handle only dozens of genes • Comparison of tools: [Tompa et al. ’05]

7. AMADEUS A Motif Algorithm for Detecting Enrichment in mUltiple Species • Research platform: • Extensible: add new algs, scores, motif models • Flexible: control params, algs, scores of execution • Experimental tool: • Sensitive: find subtle signals • Efficient: analyze many long sequences • Informative: show lots of info on motifs • User-friendly: nice GUI

8. Main features: I/O • Input: • Type: target set / expression data • Multiple species / target-sets • Sequence region (promoter, 1st intron, 3’ UTR, …) • Output: • Non-redundant set of motifs • Rich info per output motif: • Graphical motif logo • Multiple scores & combined p-value • Similarity to known TFBS models • List of target genes • BS localization graph • Targets mean expression graph

9. Main features: alg. • Algorithm: Multiple refinement phases: • Each phase receives best candidates of previous phase,and refines them (e.g., uses a more complex motif model) • First phases are simple and fast (e.g., try all k-mers); Last phases are more complex (e.g., optimize PWM using EM)

10. Main features: scores • Motif scores: • User selects scores to use, a subset of: • Target-set: Over/under-representation: • Hypergeometric • GC-content+length binned binomial • Expression: • Enrichment of ranked expression (multiple conditions) (Not yet in the public version) • Global/spatial: • Localization • Strand-bias • Chromosomal preference • Scores are combined into a single p-value • Doesn’t assume specific models for distribution of BSs and/or expression values

11. Main features: misc. • GUI: • Control all parameters • Save/load parameters from file • Save textual+graphical output to file • TFBS viewer • Other: • Ignore redundant sequences (with identical subsequence) • Applicable to multiple genome-scale promoter sequences • Bootstrapping: Empirical p-value estimation using random target sets / shuffled data • Execution modes: GUI , batch • Interoperability: Java application

12. Case study:G2 & G2/M phases of human cell cycle [Whitfield et al. ’02] CHR(not in TRANSFAC) NF-Y Module: CHR and NF-Y motifs co-occur (Module was reported in [Linhart et al., ’05], [Tabach et al. ’05])

13. Benchmark I:Yeast TF target sets [Harbison et al. ’04] Source: ChIP-chip [Harbison et al., ’04] Data: target-sets of 83 TFs with known BS motifs Average set size: 58 genes (=35 Kbps) Success rates: (for top 2 motifs of lengths 8 & 10)

14. Performance on metazoan datasets • Results on 42 target-sets: • Collected from 29 publications • Based on high-throughput expr’s • Species: human, mouse, fly, worm • Sets: 26 TFs, 8 microRNAs • All have known motifs

15. Global Analysis I:Localized human+mouse motifs • Input: • All human & mouse promoters (2 x ~20,000) • Region: -500…100 (w.r.t. TSS) • Total sequence length: ~26 Mbps • [No target-set / expression data] • Score: localization • Results: • Recovered known TFs: Sp1, NF-Y, GABP, TATA, Nrf-1, ATF/CREB, Myc, RFX1 • Recovered the splice donor site • Identified several novel motifs

16. Global Analysis II:Chromosomal preference • Input: • All fly promoters (~14,000) • Region: -1000…200 (w.r.t. TSS) • Total sequence length: ~11 Mbps • [No target-set / expression data] • Score: chromosomal preference • Results: • DNA Replication Element Factor (DREF) on X chromosome

17. Global Analysis II:Chromosomal preference (cont.) • Input: • All worm promoters (~18,000) • Region: -500…100 (w.r.t. TSS) • Total sequence length: 6.6 Mbps • [No target-set / expression data] • Score: chromosomal preference • Results: • Novel motif on chrom IV

18. Summary • Developed Amadeus motif discovery platform: • Easy to use • Feature-rich, informative • Sensitive & efficient • Constructed a large, real-life, heterogeneousbenchmark for testing motif finding tools • Demonstrated various applications of motif discovery • http://acgt.cs.tau.ac.il/amadeus

19. Acknowledgements Tel-Aviv University Chaim Linhart Yonit Halperin Ron Shamir The Hebrew University of Jerusalem Gidi Weber