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TIME WARPING OF EVOLUTIONARY DISTANT TEMPORAL GENE EXPRESSION DATA BASED ON NOISE SUPPRESSIONYury Goltsev and Dmitri Papatsenko

*Department of Molecular and Cell biology, University of California, Berkeley, USA

* BMC Bioinformatics Oct. 2009

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OVERVIEW

Introduction Dynamic Time Wraping

Kruskal's algorithm Orthologous Genes

Alignments of cell cycles Simulation data Real data

Conclusion Reference

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INTRODUCTION

Dynamic Time Wraping(DTW)

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INTRODUCTION

Dynamic Time Wraping(DTW)

*http://www.evanlin.com/document/DataMiniing/Discovering similar time series patterns with fuzzy clustering and DTW methonds.ppt

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DYNAMIC TIME WRAPING

Kruskal's algorithm

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INTRODUCTION

Orthologous Genes Homology

Budding yeast Fission yeast

Saccharomyces cerevisiae Schizosaccharomyces pombe

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INTRODUCTION

Cell cycles

* Rustici G, et al., Nat Genet. 2004, 36:809-817

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ALIGNMENTS OF CELL CYCLES

Periodic patterns (A,B: simulation data)

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ALIGNMENTS OF CELL CYCLES

Periodic patterns (real data) A – Euclidean distance matrix B – Alignment path

No alignment

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ALIGNMENTS OF CELL CYCLES

Periodic patterns (real data) A – Pearson distance matrix B – Alignment path

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ALIGNMENTS OF CELL CYCLES

Use Pearson distance rather than Euclidean distance

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ALIGNMENTS OF CELL CYCLES

Remove noise from the data(use Gaussian filter) A – Pearson distance matrix B – Alignment path

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ALIGNMENTS OF CELL CYCLES

matching cell cycle markers to good valleys

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ALIGNMENTS OF CELL CYCLES

matching cell cycle markers to good valleys G1 phase is longer in

S.cerevisiae G2 is longer in S.pombe.

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CONCLUSION

Desynchronization of gene expression in evolution

Microarray data and low-level data processing

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REFERENCE

http://dblab.cs.nccu.edu.tw/presentation/DynamicTimeWarping.ppt

http://www.evanlin.com/document/DataMiniing/Discovering similar time series patterns with fuzzy clustering and DTW methonds.ppt

http://Fmccmb.belozersky.msu.ru/2009/Presentations/20090721MCCMB-Papatsenko.ppt