scholarly journals A time warping approach to multiple sequence alignment

2017 ◽  
Vol 16 (2) ◽  
Author(s):  
Ana Arribas-Gil ◽  
Catherine Matias
Keyword(s):  
Sequence Alignment ◽  
Functional Data Analysis ◽  
Multiple Sequence Alignment ◽  
Functional Data ◽  
Dynamic Time Warping ◽  
Proof Of Concept ◽  
Time Warping ◽  
Synthetic Experiment ◽  
Multiple Sequence ◽  
Dynamic Time

AbstractWe propose an approach for multiple sequence alignment (MSA) derived from the dynamic time warping viewpoint and recent techniques of curve synchronization developed in the context of functional data analysis. Starting from pairwise alignments of all the sequences (viewed as paths in a certain space), we construct a median path that represents the MSA we are looking for. We establish a proof of concept that our method could be an interesting ingredient to include into refined MSA techniques. We present a simple synthetic experiment as well as the study of a benchmark dataset, together with comparisons with 2 widely used MSA softwares.

scholarly journals EventDTW: An Improved Dynamic Time Warping Algorithm for Aligning Biomedical Signals of Nonuniform Sampling Frequencies

Sensors ◽  
2020 ◽  
Vol 20 (9) ◽  
pp. 2700 ◽  
Author(s):  
Yihang Jiang ◽  
Yuankai Qi ◽  
Will Ke Wang ◽  
Brinnae Bent ◽  
Robert Avram ◽  
...  
Keyword(s):  
Pattern Matching ◽  
Sequence Alignment ◽  
Dynamic Time Warping ◽  
Alignment Quality ◽  
Time Warping ◽  
Biomedical Signals ◽  
Signal Alignment ◽  
Dynamic Time ◽  
Dynamic Sampling ◽  
Uneven Sampling

The dynamic time warping (DTW) algorithm is widely used in pattern matching and sequence alignment tasks, including speech recognition and time series clustering. However, DTW algorithms perform poorly when aligning sequences of uneven sampling frequencies. This makes it difficult to apply DTW to practical problems, such as aligning signals that are recorded simultaneously by sensors with different, uneven, and dynamic sampling frequencies. As multi-modal sensing technologies become increasingly popular, it is necessary to develop methods for high quality alignment of such signals. Here we propose a DTW algorithm called EventDTW which uses information propagated from defined events as basis for path matching and hence sequence alignment. We have developed two metrics, the error rate (ER) and the singularity score (SS), to define and evaluate alignment quality and to enable comparison of performance across DTW algorithms. We demonstrate the utility of these metrics on 84 publicly-available signals in addition to our own multi-modal biomedical signals. EventDTW outperformed existing DTW algorithms for optimal alignment of signals with different sampling frequencies in 37% of artificial signal alignment tasks and 76% of real-world signal alignment tasks.

scholarly journals Knowledge-based expert systems and a proof-of-concept case study for multiple sequence alignment construction and analysis

2008 ◽  
Vol 10 (1) ◽  
pp. 11-23 ◽  
Author(s):  
M. R. Aniba ◽  
S. Siguenza ◽  
A. Friedrich ◽  
F. Plewniak ◽  
O. Poch ◽  
...  
Keyword(s):  
Expert Systems ◽  
Sequence Alignment ◽  
Multiple Sequence Alignment ◽  
Proof Of Concept ◽  
Multiple Sequence ◽  
Knowledge Based
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