$$MC^2$$ : An Integrated Toolbox for Change, Causality and Motif Discovery

Mohammad, Yasser; Nishida, Toyoaki

doi:10.1007/978-3-319-42007-3_12

Cited by 2 publications

(1 citation statement)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…So far, this had been done by meticulous visual inspection, which is bounded by the complexity of the data and the inherent biases of our perception. Relying on our time series representation, these explorations could be done using de-novo motif discovery algorithms, in which a sequence dataset is searched for statistically overrepresented segments PLOS COMPUTATIONAL BIOLOGY in a fast, systematic, and unbiased manner [53,54]. Such modular decomposition approaches proved to be transformative in dealing with large volumes of data from sequencing and structural studies of DNA, RNA, and proteins [55][56][57].…”

Section: Plos Computational Biologymentioning

confidence: 99%

Template-based mapping of dynamic motifs in tissue morphogenesis

2020

View full text Add to dashboard Cite

Tissue morphogenesis relies on repeated use of dynamic behaviors at the levels of intracellular structures, individual cells, and cell groups. Rapidly accumulating live imaging datasets make it increasingly important to formalize and automate the task of mapping recurrent dynamic behaviors (motifs), as it is done in speech recognition and other data mining applications. Here, we present a "template-based search" approach for accurate mapping of sub-to multi-cellular morphogenetic motifs using a time series data mining framework. We formulated the task of motif mapping as a subsequence matching problem and solved it using dynamic time warping, while relying on high throughput graph-theoretic algorithms for efficient exploration of the search space. This formulation allows our algorithm to accurately identify the complete duration of each instance and automatically label different stages throughout its progress, such as cell cycle phases during cell division. To illustrate our approach, we mapped cell intercalations during germband extension in the early Drosophila embryo. Our framework enabled statistical analysis of intercalary cell behaviors in wild-type and mutant embryos, comparison of temporal dynamics in contracting and growing junctions in different genotypes, and the identification of a novel mode of iterative cell intercalation. Our formulation of tissue morphogenesis using time series opens new avenues for systematic decomposition of tissue morphogenesis.

show abstract