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Alignment of time-resolved data from high throughput experiments.

Nada Abidi1, Raimo Franke2, Peter Findeisen3

  • 11 Biostatistics, Helmholtz Centre for Infection Research, Inhoffenstr. 7, D-38124 Braunschweig, Germany.

Journal of Bioinformatics and Computational Biology
|September 16, 2016
PubMed
Summary
This summary is machine-generated.

This study introduces a modified Dynamic Time Warping (DTW) algorithm for aligning time-resolved biological data. The new method effectively handles irregular time points and gaps in high-throughput experiments, improving data analysis.

Keywords:
DTWTime-resolved dataseries alignment

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Area of Science:

  • Biotechnology
  • Bioinformatics
  • Computational Biology

Background:

  • Cellular processes require time-course measurements for dynamic understanding.
  • High-throughput technologies generate time-resolved data with limited, irregular time points and potential missing values.
  • Replicate experiments often lack perfect synchronization, necessitating data alignment.

Purpose of the Study:

  • To develop a robust method for aligning time-resolved data from high-throughput biological experiments.
  • To address challenges posed by non-equidistant time points, irregular intervals, and missing data in biological time series.
  • To adapt the Dynamic Time Warping (DTW) algorithm for non-equidistant, time-resolved sequences.

Main Methods:

  • Modification of the Dynamic Time Warping (DTW) algorithm.
  • Incorporation of monotone cubic interpolation splines for alignment.
  • Development of the R package 'tra' (Time-Resolved data Alignment).

Main Results:

  • The modified DTW algorithm successfully aligns time-resolved sequences with non-equidistant time points and gaps.
  • The approach provides nonlinear alignment, accommodating biological data characteristics.
  • Evaluation with artificial and real data demonstrates the method's efficacy.

Conclusions:

  • The proposed DTW modification is effective for aligning time-resolved data in high-throughput biological experiments.
  • This method overcomes limitations of classical time series analysis for biological dynamics.
  • The freely available 'tra' R package facilitates the application of this alignment technique.