Äîêóìåíò âçÿò èç êýøà ïîèñêîâîé ìàøèíû. Àäðåñ îðèãèíàëüíîãî äîêóìåíòà : http://www.mccme.ru/albio/slides/turenne.pdf Äàòà èçìåíåíèÿ: Thu Oct 9 23:39:02 2008 Äàòà èíäåêñèðîâàíèÿ: Tue Oct 2 11:22:36 2012 Êîäèðîâêà: Ïîèñêîâûå ñëîâà: annular solar eclipse

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A Data-Mining Approach To Time-Series Microarray Alignment for Crossing Large-Scale Biomolecular and Literature Information
3rd Workshop on Algorithms in bioinformatics October 7-9, 2008, Laboratoire J.-V. Poncelet, Moscow

Nicolas Turenne
INRA ­ Jouy-en-Josas centre
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Issue
Part 1 Project Part 2 Microarray alignment

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The Cattle Model

· INRA => french institute of life sciences and food sciences · 4000 research scientists, 20 centres, 400 laboratories · Cattle => Bovine model of interest ­ Perspective for pharmacopea ­ Species to experiment understand life phenomenon as cancer, celullar engineering · Few data about this species · Not enough in Litterature · Home microarray about proliferation , on-going published

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The Cattle Model : elongation

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The Cattle Model : day0-day23
No elongation in human and mouse No elongation without proliferation Process known in human and mouse And without Embryo development Process known in mouse Process not very well known because embryo at this stages develops freely in uterus (no placenta)

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Heterogeneous Sources Approach

· Issue : understand which genes of Cattle are related to proliferation and development at embryo stage · Hypothesis : Inference of knowledge from Standard Model species : human, mouse 1- Public-Domain microarrays exist in GEO server about Human and Mouse · our goal : data-oriented (time-series) developmental biology 2- Database
· Genome of Cattle is known 30000 genes, GeneBank Id can be accessible · Knowledge Exploration Software, available: Metacore, Ingenuity, David

3- Available Prolific Literature about Human and Mouse (>12 millions documents)
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What does we find in Literature ?
· Rough query on Medline server (http://www.ncbi.nlm.nih.gov/pubmed/) · bovine and (embryo or placenta) -> 14000 documents · human and (embryo or placenta) -> 185000 documents · mouse and (embryo or placenta) -> 57000 documents

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More concretly in Literature, two corpus
· 77333 documents 06 Aug 2007 #req1 OR #req2 OR #req3 OR #req4 #req4 #req3 #req2 #req1 · h h h h u u u u man man man man AND AND AND AND embryo Field: embryo Field: placenta AND placenta AND Title/Abstract, Limits: Humans MeSH Terms , Limits: Humans cancer Field: Title/Abstract, Limits: Humans cancer Field: MeSH Terms , Limits: Humans

34529 documents #req1 #req2

06 Aug 2007

#req1 OR #req2 mouse AND embryo Field: Mesh Terms, Limits: Animals mouse AND embryo Field: Title/Abstract, Limits: Animals

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Named Entities Extraction Tools
· Since 1998 more than 50 tools of named entities tools has been developped
· Gene name extraction · Network reconstruction

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LingPipe [Carpenter, 2004]
­ sentence segmentation
CorpusH -> 515500 sentences CorpusM -> 276100 sentences

PMID - 15556029 DP - 2004 Dec TI - Sporulation of Bacillus subtilis. AB - Differentiation of vegetative Bacillus subtilis into heat resistant spores is initiated by the activation of the key transcription regulator Spo0A through the phosphorelay. Subsequent events depend on the cell compartment-specific action of a series of RNA polymerase sigma factors. Analysis of genes in the Spo0A regulon has helped delineate the mechanisms of axial chromatin formation and asymmetric division. There have been considerable advances in our understanding of critical controls that act to regulate the phosphorelay and to activate the sigma factors. AD - Department of Microbiology and Immunology, Temple University School of Medicine. 3400N. Broad St., Philadelphia, Pennsylvania 19140, USA. FAU - Piggot, Patrick J AU - Piggot PJ FAU - Hilbert, David W AU - Hilbert DW SO - Curr Opin Microbiol 2004 Dec;7(6):579-86.

Sporulation of Bacillus subtilis. Differentiation of vegetative Bacillus subtilis into heat resistant spores is initiated by the activation of the key transcription regulator Spo0A through the phosphorelay. Subsequent events depend on the cell compartment-specific action of a series of RNA polymerase sigma factors. Analysis of genes in the Spo0A regulon has helped delineate the mechanisms of axial chromatin formation and asymmetric division. There have been considerable advances in our understanding of critical controls that act to regulate the phosphorelay and to activate the sigma factors.

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Genes names extraction
[Settles, 2005] Training annotated corpus Conditional random fields Models Uses regular expression formalism No explicit syntactic and semantic rules

abner

60611 nouns phrases (CorpusM) 82903 nouns phrases (CorpusH)

genia

[Tsuruoka et al, 2005] Training annotated corpus Part-of-speech tagging with cyclic dependency network Maximum Entropy Classifier No explicit syntactic and semantic rules [Carpenter, 2004] Training annotated corpus Bayesian Generative Model and Maximum Likelihood Viterbi decoder No explicit syntactic and semantic rules [Mika et al, 2004] Training corpus Syntactic-Rules and Support Vector Machine classifiers Use of biology name dictionaries No explicit semantic rules.

37607 nouns phrases (CorpusM) 48909 nouns phrases (CorpusH)

lingpipe

80308 nouns phrases (CorpusM) 93673 nouns phrases (CorpusH)

nlprot

42427 nouns phrases (CorpusM) 48086 nouns phrases (CorpusH)

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Expert Extraction Software : Metacore, Ingenuity, David
http://www.ingenuity.com/ Ingenuity Systems, Inc. (California, USA) · · · · 1.7 millions « biological findings » Own ontology (knowledge base)

Ingenuity

IPA - ingenuity pathway analysis software ( liccnce = 6000 /year; 25000 users )

Since 1997 Knowledge base (ontology) build upon criteria : · 300 reviews (full papers) · manual extraction (1000 documentalists) · 5 years · update each 3-month , 80000 new findings · optimized rules for manual scan (less people required)
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Link with Gene Ontology (GO) Available Synonyms and homonyms names (« ingenuity facets ») Grabbed information from NCBI, Swissprott and Kegg 12 branches in the global ontology (only 3 in GO)

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Crossing Information Sources
Ingenuity / Information Extraction Tools Database Literature

Why ? · expert extraction interpretation-dependent · multipe-interpretation in documents · merging results from automatic extraction and expert extraction can be more riched if hypotheseoriented

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Crossing Information Sources
Ingenuity / Information Extraction Tools Database Literature
Connective + tissue (B) Cellular + development (C) A B C proliferation + development (D)

Gene Lists extracted from Ingenuity about development
Tissue + development (A) From Ingenuity From GO

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532

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CorpusM
abner + genia + lingpipe + nlprot

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CorpusH
abner + genia + lingpipe + nlprot

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Crossing Information Sources http://migale.jouy.inra.fr/time/

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What about knowledge from microarrays
· Knowledge are related to large sets of genes at a same time ­ High-throuhgput data management and analysis · We can identify groups ­ acting in a same way , ­ or associations between a gene and others in a same context (biological hypothesis)

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Data
GSM23324 GSM23325 GSM26511 GSM23326 GSM23327 GSM23328 GSM23330 -0.12095261 -0.159064695 -0.112117298 -0.442279081 0.044055627 -0.138586163 -0.030866648 1 -0.134408201 -0.160850872 -0.043401834 -0.381694889 -0.124970576 -0.249941744 0.046745013 1 -1.550597412 -0.675447603 -0.146603474 -2.525728644 -0.566395475 -1.945910149 -0.211309094 1 -0.064720191 0.066624028 -0.152385454 -0.234877715 -0.041641026 -0.162003333 0.064983488 1 -0.063476064 0.041528459 0.030614636 -0.186829974 -0.155733209 -0.066511481 -0.038183787 1 -0.379489622 -0.341170757 -0.538660423 -3.496507561 -0.149345289 -0.972986076 -0.035755649 1 -0.027779564 -0.024667232 -0.110130824 -0.304353607 -0.037582711 -0.234010656 -0.12351371 1 -0.236664298 -0.030277259 0.086709399 -0.394753453 -0.115896291 -0.139846692 0.056384719 1 C

ID_REF 3069 2173 1105 4449 1520 560 1706 3334

NAME 3069 2173 1105 4449 1520 560 1706 3334

Measure
Log (base 2) of the ratio of the mean of Channel 2 (635 nm) to Channel 1 (532 nm)

Value : between -10 (very inhibited) and +10 (very activated)
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Datasets of interest

· GSE 1414 only kinetics about bovine and dealing with same biological problem : elongation and implantation in bovine embryo (2,000 unique genes ) (Ushizawa et al, Reprod Biol Endocrinol, 2004) on-going INRA-home made microarray · GSE 9046 time-course experiment with embryoid bodies of CGR8 mouse embryonic stem cells (12,000 unique genes ) (Mitiku and Baker, Dev Cell. 2007) INRA-home made microarray about a kinetics of development in mouse, based totipotent embryo stem cell (degrelle et al, dev biol, 2005)

· GSE 3553 interesting for human cell differentiation in trophoblast in human under effect of BMP4 (25,000 unique genes ) (Xu et al, Nat Biotechnol. 2002)

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What about knowledge from microarrays
Issue · Time-series microarrays with several timepoints (3 to 10) · Two different species (for instance bovine / human or bovine / mouse)
[Husmeier, 2001] Challenge · state of the art : clustering is largely used but only work for same conditions , in our case , microarrays are different-conditions made · state of the art : time warping is used for timecomparison scales (curve alignment) but in our case time scales are different from one species to another and a same ortholog gene can occur at different time-point because of genome evolution over time. [Aach, 2001] · · · ·· · · ·

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What about knowledge from microarrays
T1 G1 G2 G3 T2 T3 T4 T5 T6

Goal ­ Patterns Identification ­ Data format is matrix-like ­ 2 ta b le s

G4 G5 G6 T'1 G7 G8 G3 G9 G10 G11 T'2 T'3 T'4

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A combinatorics issue
T1 T2 T3 T4 T5 T6

G2

G2 G3 G3 G5 G5

G5

G5

T'1

T'2

T'3

T'4

G8

The issue of Alignment

G3 G10

G3 G10

· How to place G8 before G2 or during G2 ? · We can not fit T1 and T'1, T2 and T'2 ... · Even infer that T4 = T'2 is not jusiified by the fact it is the same gene G3

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A combinatorics issue
1 Bn = e
G2 G5 G2 G5 G8 G10 G2 G5 G2 G2 G5 G8 G10 G3

Dobinski formula
T1 T2 T3 T4 T5 T6

k= 0

k k!
G8 G10 G5 G10 G10 G3

n

Number of partitions of size n

G2 G5

G2 G3 G5 G3 G5 G5

G3 G5 G3 G5 G8 G8 G10 G5 G3 G10

G3

G10

G3 G2G5 G5 G2

G3 G10 G3

G10 G3 G5

T'1

T'2

T'3

T'4

G8 G3 G10 G3 G10

Very small set of constraints about strict order (<), such as G2 before G3 G3 before and after G10 G8 before G3 ....etc

G3

And many many many others ...

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A solution in a two-step clustering

· Step 1: make clusters of similar genes into a unique time-series ­ relative expression profile · Step 2 : make a clustering between 2-sets of clusters through common points ­ consensus clustering over two sets of clusters

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Step 1
T1 T2 T3 T4 T5 T6

· make clusters of similar genes expression profile · using a classical euclidian-distance metrics and dendrogram computation · See TreeView (1998) http://rana.lbl.gov/EisenSoftware.htm

Cut-off
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Step 2

· make consensus clustering between two sets of clusters · Works if some objects belongs to both sets of clusters · Result is a set of MegaClusters overlapping microarrays (idea of alignment)
Dictionary of Genes [G1-G6] from microarray Bio1, [G1;G7-G12] from microarray Bio2
( G1 , G2 , G3 , G4 , G5 , G6 , G7 , G8 , G9 , G10, G11, G12 )

partition Bio1 partition Bio2 result

( C1 , C1 , C1 , C2 , C2 , C2 , C3 , C4 , C5 , C6 , C7 , C8 ) ( C16, C10, C11, C12, C13, C14, C15, C15, C15, C16, C16, C16 ) ( C1 , C1 , C1 , C2 , C2 , C2 , C3 , C3 , C3 , C1 , C1 , C1 )

Because G1 belongs to C1 and C16, C1 and C16 are merged
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Consensus clustering approach

Definition
Merging of several clustering into a unique clustering Three · · · kinds of clusterings: axiomatic (we suppose we can formalize property of the resulting partition constructive (some rules are given to achieve the merging) optimization (a criteria to minimize is defined)

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Consensus clustering approach

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optimization approach for consensus

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Consensus clustering approach

· CLUE library · R-project · function cl_consensus(method="DWH") · Fuzzy clustering · E. Dimitriadou, A. Weingessel and K. Hornik (2002). A combination scheme for fuzzy clustering. International Journal of Pattern Recognition and Artificial Intelligence, 16, 901­912

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Consensus clustering approach

· CLUE library · heuristic-based · locally single-pass through the ensemble of clusterings · starting with

Result is a fuzzy membership but it is possible to get a hard clustering

C1(1, 1, 2, 2) Memberships: [,1] [1,] 0.0 [2,] 0.0 [3,] 0.5 [4,] 1.0

C2(3,3,3,4) [,2] 1.0 1.0 0.5 0.0

Hard clustering (1 1 2 2)

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Temporal profile

Time Correlation Matrix
· Use notion of precedence and simultaneity, using the symbol B for before, A for after and D for during · about expression · for a given gene · comparison between time neigbourghood

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Temporal profile
T1(Bio1) T2(Bio1)T3(bio1)T4(Bio1) AD ABD ABD BD T1(Bio2) T2(Bio2) T3(Bio2) B A D

Cluster 1 2

Target 4 4

Cluster p

Target 4

T1(Bio1) T2(Bio1)T3(bio1)T4(Bio1) AD ABD ABD BD

T1(Bio2) T2(Bio2) T3(Bio2) B A D

For a given Gene, for instance G4, We take its MegaCluster (c1, c2) obtained from consensus clustering For each timepoint and for each cluster, for instance T3 (microarray 1) and cluster 1 we test if expression is high during (D), before (T2)or after (at T4). It is ok for before and during so the value for T3-C1 is BD.

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Comparison of temporal profile

· Jaccard index similarity · A given a gene G and its Time matrix correlation TMC(G) · We look for all genes have similar their TMC to G one. · for each gene in both microarray (dictionary of gene) · Compute J( TMC(G), TMC(g) ) · Export all genes if J > 0.99

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Algorithm ­ AlibR (R Script )

1. 2. 3. 4. 5. 6. 7. 8. 9.

Read 2 Datasets (D) and input a Given Gene (G) Compute mean expression values for clusters Create Gene Dictionary Create Partition of Gene Dictionary with Clusters for D Apply consensus Create a Mapping MegaCluster <-> clusters (MGC) Generate the Temporal Matrix (TM) for all clusters Compute a submatrix of TM for G (TMG) using MGC For each gene g 1. compute submatrix (TMg) using MGC and 2. compute Jaccard value J 10. Export Temporally Similar Gene List with J < 0.99
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Complexity

· Tests has been done on 30% of microarrays (~9000 genes) · Time-computation 20-lines microarray 0.42 s 600-lines microarray 18.25 s 2000-lines microarray 60.50 s 15000-lines microarray 18000 s 0.5 100 900 7000 Mb Mb Mb Mb

· DHW consensus method complexity · O( n x k ) in memory · O( n x k3 ) in time · Optimisation solver O( n2 ) in memory (Hungarian algorithm)
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Similar genes...

ta rge t ge ne s

s imila rity thre s hold
Tb=0.7 ;T=0.9 Tb=0.7 ;T=0.1 Tb=0.7 ;T=0.9 Tb=0.7 ;T=0.1

Bovine (B) & Huma n (H) a rra ys m egaclu ster (# clu ste r) 16 11 16 10 B&H genes 14 14 12 76 B genes H genes 18 0 18 0 10 81 0 574

Bovine (B) & M urine (M ) a rra ys m egaclu ster (# clu ste r) 12 15 15 5 B&M genes 25 12 2 08 6 B genes M genes 43 37 20 0 298 16 2265 0

alg5 eif2s3

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Similar genes... case of ALG5
Microarray Bovine
gene: gene: gene: gene: gene: gene: gene: gene: gene: gene: gene: gene: gene: gene: gene: gene: gene: gene: bp107457 bpl11933 bpl10819 af069434 y16359 bp111692 bp110718 loc536818 cfdp2 bp110964 loc509824 bp112639 u01924 bp109437 loc531522 sepx1 aa112300 v00125

Microarray bovine/human : similarity threshold0.1/0.7

Microarray Bovine & Human
gene: gene: gene: gene: gene: gene: gene: gene: gene: gene: gene: gene: gene: gene: vsig4 cask hdac1 mmp14 vegfa syt1 actr2 akap9 furin alg5 mmp1 foxred1 npepps sdf4

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Similar genes... case of ALG5

Crossing with IPA (ingenuity)

genes

networks

score

Alg5 bov hum Connective tissue disorders, genetic disorders, cancer Alg5 bov mus Cancer, cell to cell signalling and interaction, cellular assembly and organisation
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Similar genes... case of ALG5

Crossing with IPA (ingenuity) Microarray bovine/human Network 1

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Similar genes... case of ALG5

Crossing with IPA (ingenuity) Microarray bovine/human Network 2

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Similar genes... case of ALG5

Crossing with IPA (ingenuity) Microarray bovine/human Network 1 & 2

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Genes with similar time matrix correlation

· role of relationships (interaction) · not only based on genomic data · transcriptomics approach · role of expression over time · not only facts about inhibition / activation · comparison of relative expression · comparative transcriptomics

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conclusion
· Approach with a double-step clustering using time-dependent molecular high-throughput expression data · Make a temporal profile over two datasets by consensus clustering even if a gene does not belong to one of them · Fast and easy to understand · Need to make deeper benchmark with Ingenuity Usage for validation · Need re-programming for time/memory optimization ( R + C-language)
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Co-operations...
Dr Isabelle Hue (INRA, BDR Unit) (Reproductive and Developmental Biology)

INRA has recently signed a cooperation agreement with the Russian Foundation for Basic Research (RFBR/RFFI) call for project proposals on 1st septembre 2008

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MERCI

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