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MotifML A Novel Ontology-based XML Model for DataExchange of Regulatory DNA Motif Profiles Eric Neumann, Beyond Genomics Tian Niu, Harvard University Ken Baclawski, Northeastern University DNA Motifs ========== = ============ human GCTTGAATTAGACAGGATTAAAGGC bovine GCTTGAATTAAATAGGATTAAAGGC mouse GCTTGAATTAGACAGGATTAAAGGC | -70 === = ===== === = = ===== == ======= TTACTGGAGCTGGAAGCCTTGCCCC -AACTCAGGAGTTTAGCCCCA TTATCAGGGCTGGGAGCTACACCCC -AACTCCTGAGTTTAGCCCCA TTAGCAGAGCTGGAAGCCTCACATC TAACTCCCACATTGAGCCCCA | -45 Motifs Functional Significance? | -20 Alignment Profile | +1 Motif Finding Tools AlignACE GIBBS Consensus Propsector The Need for motifML Information resides at multiple sources Data follow multiple Structures Multiple Interfaces Integrated XML view MotifML BioProspector Gibbs AlignACE Consensus Motif Function Gene expression regulation that is dependent on activated transcriptional factors Key element of Gene Networks: Complex analysis of microarrays Transcriptional Factors + Cis-Elements Associated with a Gene Regulated Gene Expression motifML Goals to allow the full specification of all experimental information known about motifs to provide an extensible framework for this annotation and provide a common vehicle for exchanging the motif information to provide a single document interface to integrate all project information, complete with protocols for network data retrieval. motifML Design formal and concise- ontology based motifML documents easy to create clarity more important than brevity use both XML schema and XML DTD motifML Semantics Annotation » The collection of features for a given set of sequence(s) that have built in semantics Features » Characteristics supported by analytic evidence Analyses » Computational » Experimental motifML Semantics Property Annotation Semantically Definable & Searchable Pragmatic Ontolog y Objects Analyse s Features Motifs Intentional Extraction Results motifML Sequence Item <seq id=“demo_seq” name=“Human HAL Gene Exon 18”> <dbxref> <database>GenBank</database> <unique_id>14588658 </unique_id> </dbxref> <feature> <motif type=“cis-regulatory” name=“CBE” id=“dm312”/> <description> CRX Binding Element </description> <position start=“21” end=“32” /> <evidence> <reference paper=“Davies, J Mol Biol. 1993 296:1205-14”/> </evidence> </feature> <residues type=“dna”> ATAATGTCCAAGATCTTCTGGAGAGTGTATCCCATGCTGTGGAGCACTCTGTGGAAGCCACGG GTCCTTTAGACAGCTCATCCTATGAGGAGCACTTCTTAACTGGCACTGGTCTCTTGCAGTTTCT GAGAACAAGGCTCTGTGCCATCCCTCGTCTGTTGACTCCCTCTCCACCAGCGCAGCCACGGA GGACCACGTCTCCATGGGAGGATGGGCAGCAAGGAAAGCCCTCAGGGTCATCGAGCATGTGG AGCAAGGTAATGCTGATGAGTTCGGGGTGGCGGGCCTGCCTGATAGACCACTGTGCCTGTGG TTCTCAAGTGGGATCTCCCACCAGCAACATCAGCATC ACCTGGAAAC Computational Analysis <!ELEMENT computational_analysis (date?, program, version?, parameter*, database?, result_set+)> <!ATTLIST computational_analysis seq IDREF #REQUIRED> <!ELEMENT program (#PCDATA)> <!ELEMENT result_set (score?, output*, result*)> <!ELEMENT result (score, type, subtype?, seq_relationship+, output*)> <!ATTLIST result id ID #IMPLIED> <!ELEMENT seq_relationship (location, alignment?)> <!ATTLIST seq_relationship seq IDREF #REQUIRED type (query | subject | peer ) #REQUIRED> <!ELEMENT <!ELEMENT <!ELEMENT <!ELEMENT <!ELEMENT <!ELEMENT <!ELEMENT <!ELEMENT alignment type value parameter output database version score (#PCDATA)> (#PCDATA)> (#PCDATA)> (type, value)> (type, value)> (name, date?, version?)> (#PCDATA)> (#PCDATA)> HSP and HSE Heat shock and other environmental and pathophysiologic stresses stimulate synthesis of heat shock proteins (Hsps). These proteins enable the cell to survive and recover from stressful conditions by as yet incompletely understood mechanisms. A conserved 14 base pair regulatory sequence, referred to as the heat shock element (HSE), is found in multiple imperfect copies upstream of the TATA box of all heat shock genes. Genes with an HSE at the upstream region may be coregulated Dataset (Vertebrates)* > gid 3004462, start=1, end=1027 > gid 7861931, start=1, end=666 > gid 7108904, start=1, end=1519 > gid 7739662, start=1, end=800 > gid 64795, start=1, end=487 > gid 64791, start=1, end=614 > gid 64789, start=1, end=1128 > gid 64786, start=1, end=374 > gid 32480, start=1, end=483 > gid 32484, start=1, end=711 > gid 7669470, start=1, end=424 > gid 5729878, start=1, end=313 > gid 5031770, start=1, end=760 > gid 1816451, start=1, end=2179 > gid 184422, start=1, end=2634 > gid 184416, start=1, end=488 > gid 188491, start=1, end=959 > gid 4691417, start=1, end=2631 > gid 188489, start=1, end=485 > gid 188487, start=1, end=489 > gid 184416, start=1, end=488 > gid 211940, start=1, end=391 > gid 63508, start=1, end=1421 > gid 63512, start=1, end=2300 > gid 409185, start=1, end=1231 > gid 163160, start=1, end=491 > gid 414974, start=1, end=426 *Data are from GenBank AlignACE program uses a Gibbs sampling strategy which is similar to that described by Neuwald et al., 1995 An iterative masking procedure is used to allow multiple distinct motifs to be found within a single data set Reference: Hughes et al., J Mol Biol. 2000 296:1205-14 AlignACE Results ... Motif 1 GGGGAGGGGGTGGGGGGGC GGCGGGCGGGCGGCGGGGG GGACAGCGGCGGCTGGCTG GGGGTGCGGGGGCAGGCGC CCGCGGGGGCGGGCGGGGC ... ** * ***** ** *** * MAP Score: 794.004 Motif 2 GGGGAGGGGGTGGGGGGGCGGGG GTGCGGGGGCAGGCGCGGAGAGC GCGGAGCGGGAGGGGGCGTGGCC GGGGTGCGGGAGGGCGGGCGGGC GGGCAGTGGGCGGCTGGCAGCTG ... 23 23 11 23 13 788 867 107 1417 2034 0 1 0 1 1 23 23 13 23 14 784 1420 1932 1448 1452 0 1 1 1 1 Gibbs Motif Sampler Program Uses Stochastic Iterative Sampling The Bernoulli motif sampler assumes that each sequence can contain zero or more ungapped motif elements of each motif type Reference: » Lawrence et al., Science 1993;262(5131):208-14; » Neuwald et al., Protein Sci. 1995 Aug;4(8):1618-32. Gibbs Results ... 4, 4, 4, 5, 7, 9, ... 1 2 3 1 1 1 284 425 643 239 401 26 agtgc ggtat atgga atgga agtgt ggagt AGAGTCTGGAGAGC AGATGTCGGAGAGT AGCCTCGGGAAACT AGCCTCGGGAAACT GGGTGCTGGAGGCT GGCGGTGGGAAGGG ************** ... cgaat cgttt tcggg tcggg gacgg tgttg 271 412 656 252 388 13 0.87 0.79 0.86 0.86 0.99 0.99 R R F F R R gid gid gid gid gid gid 7739662, start=1, end=800 7739662, start=1, end=800 7739662, start=1, end=800 64795, start=1, end=487 64789, start=1, end=1128 32480, start=1, end=483 Consensus Program Uses entropy-based scoring functions References: » Stormo and Hartzell, PNAS 1989;86:1183-1187 » Hertz et al., 1990, CABIOS, 6:81-92 Consensus Results MATRIX 1 ... 1|23 : 2|9 : 3|10 : ... MATRIX 2 ... 1|23 : 2|9 : 3|10 : ... MATRIX 3 1|23 : 2|9 : 3|10 : ... MATRIX 4 1|21 : 2|9 : 3|10 : ... 1/593 2/8 3/889 TGCAAGATTTTTAA TGGAGGCTTCCAGA TGGAGGCTTCCAGA 1/593 2/8 3/889 TGCAAGATTTTTAA TGGAGGCTTCCAGA TGGAGGCTTCCAGA 1/593 2/8 3/889 TGCAAGATTTTTAA TGGAGGCTTCCAGA TGGAGGCTTCCAGA 1/38 2/8 3/889 GGGAAAGCTCGAGA TGGAGGCTTCCAGA TGGAGGCTTCCAGA BioProspector Program a program that examines the upstream region of genes in the same gene expression pattern group to search for regulatory sequence motifs. uses zero to third-order Markov background models allows for the searching of gapped motifs and motifs with palindromic patterns Reference: Liu et al., Pac Symp Biocomput. 2001:127-38 BioProspector Results ... Motif #1: ... Seq #1 seg Seq #2 seg Seq #3 seg ... Motif #2: ... Seq #1 seg Seq #2 seg Seq #3 seg ... Motif #3: ... Seq #1 seg Seq #2 seg Seq #3 seg ... 1 1 1 r998 f91 r638 TCATCCAATCAGAG TCAACCGAACAGAA TCGACCAATCAAAA 1 1 1 f38 r648 r620 GGGAAAGCTCGAGA TGGAAGCCTCCAGT TGGAAGCCTCCAGT 1 1 1 r997 f90 r637 CTCATCCAATCAGA CTCAACCGAACAGA TTCGACCAATCAAA Conceptions and Interactions of the Underlying Statistical Algorithms Used by the Motif Searching Programs Gibbs AlignACE Gibbs Sampler; Iterative Updating Strategy BioProspector Two Block Motif Model Information Content CONSENSUS T I seq f b log 2 ( f b / pb ) A Motif Data Representation Common data representation for motif information. Uses XML Schema to specify format. Both human and machine readable. Supports “knowledge mining”. Statements can be asserted about a motif such as a role in gene regulation. Example of a motif Blk1 A G C 1 0.00 0.21 0.21 0.59 2 0.00 0.44 0.50 0.06 3 0.70 0.29 0.00 0.00 4 0.32 0.62 0.00 0.06 5 0.03 0.00 0.97 0.00 6 0.00 0.00 1.00 0.00 7 0.85 0.09 0.03 0.03 8 0.88 0.12 0.00 0.00 9 0.03 0.00 0.03 0.94 10 0.03 0.09 0.88 0.00 11 0.70 0.12 0.18 0.00 ... T <motif id="GXY1"> <block> <base type="G">0.21</base> <base type="C">0.21</base> <base type="T">0.59</base> </block> <block> <base type="G">0.44</base> <base type="C">0.50</base> <base type="T">0.06</base> </block> <block> <base type="A">0.70</base> <base type="G">0.29</base> </block> ... </motif> XML Schema Extends the XML document type language: » Data format restrictions. » Data value (min and max) restrictions. » Element occurrence (min and max) restrictions. No sophisticated restrictions: » Probability distribution. XML Schema for MotifML <xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema"> <xsd:element name="motif" type="MotifType"/> <!-- A motif consists of a sequence of blocks. --> <xsd:complexType name="MotifType"> <xsd:sequence> <xsd:element name="block" minOccurs="0" maxOccurs="unbounded" type="BlockType"/> </xsd:sequence> </xsd:complexType> <!-- A block specifies a probability for each DNA base type. --> <xsd:complexType name="BlockType"> <xsd:sequence> <xsd:element name="base" minOccurs="1" maxOccurs="4"> ... Statements about motifs <?xml version="1.0"?> <RDF xmlns="http://www.w3.org/1999/02/22-rdf-syntax-ns#” xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#” xmlns:mml="http://www.beyondgenomics.com/2001/07/motifml#" xmlns:bp="http://www.beyondgenomics.com/2001/07/biopathway#"/> <Description about="http://www.beyondgenomics.com/motifdb/gxy1"> <bp:upregulate rdf:resource="http://www.beyondgenomics.com/motifdb/awy5"/> <bp:upregulate rdf:resource="http://www.beyondgenomics.com/motifdb/ftg6"/> <bp:downregulate rdf:resource="http://www.beyondgenomics.com/motifdb/bgt3"/> </Description> </RDF> The Need for Bio-Ontologies How do biologists learn the element structure of a document describing the heterogeneous sequence alignment output? How do biologists share the structure and meta-data on motif profiles efficiently and unambiguously? A multiple sequence alignment linked with TRANSFAC/TRANSPATH ========== = ============ === = ===== === = = ===== == ======= human GCTTGAATTAGACAGGATTAAAGGC TTACTGGAGCTGGAAGCCTTGCCCC -AACTCAGGAGTTTAGCCCCA bovine GCTTGAATTAAATAGGATTAAAGGC TTATCAGGGCTGGGAGCTACACCCC -AACTCCTGAGTTTAGCCCCA mouse GCTTGAATTAGACAGGATTAAAGGC TTAGCAGAGCTGGAAGCCTCACATC TAACTCCCACATTGAGCCCCA PCE-I -CBE-AP-4 8888 cETS cETS | | | | -70 -45 -20 +1 Alignment Profile Shown here is the alignment from -70 to +1. The numbering shown corresponds to the mouse sequence. Identical bases are shown by the = above each nucleotide. Consensus sequence matches conserved among all three species are: the Ret-1/PCE-I element at -65 to -60, the CRX-binding element (CBE) at -55 to -50, an AP-4 consensus core sequence at -37 to -34, a cETS consensus core at -35 to -31 and another at positions -57 to -54, and an S8 homeodomain is shown by "8888" at -64 to -61. Only the core bases are marked. The criteria for searching the TRANSFAC Database by MatInspector were a match to the core sequence of at least 80% and to the entire consensus sequence of at least 85%. The Genbank entries for human, bovine, and mouse are X53044, M32733, and M32734, respectively. (Boatright, Mol Vis 1997; 3:15) Transcriptional Factors Ontology Composite Element Upstream to Site Part of •Disease Gene Within •Tissue •Stage contains Kind of Context produces Transcript •Env.Cond. •Induced Found in Observation Transcriptional Motif Elements Binds to Transcriptional Factors MotifML Applications Develop a data exchange format for DNA motif data Handling output from motif analyses Annotation and data mining of micro-array data Important in modeling transcriptional regulatory networks in eukaryotes Future Directions Annotation System – Lincoln Stein, Open-Bio Exchange with Other XML Dialects DAML development Distributed