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Continuous Wavelet Dynamic Time Warping for unbalanced global mapping of two signals

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#==========
# Abstract:
#==========

Title:
  cwDTW: Continuous Wavelet Dynamic Time Warping for unbalanced global mapping of two signals

Author:
  Renmin Han,
  Yu Li,
  Xin Gao,
  Sheng Wang

Contact email:
  xin.gao@kaust.edu.sa
  realbigws@gmail.com

Reference:
   https://academic.oup.com/bioinformatics/article-abstract/34/17/i722/5093233



#=========
# Install:
#=========

./install.sh


#=======
# Usage:
#=======

#---- type ----#
./cwDTW -h


#---- usage ---#
----------- cwDTW ----------
version v0.03 (OCT 22 2017)
-------------------------------------------------------------
required:
[-i SHORT SIGNAL][-p LONG SIGNAL][-o OUTPUT]
optional:
([-r RADIUS])([-l LEVEL])([-s SCALE])
-------------------------------------------------------------
**** required: ******
SHORT SIGNALE: (reference) sequence signal from ATCG...;
LONG SIGNAL:   (nanopore) raw electrical current signal;
OUTPUT:   signal mapping result; if not specified, then no output be generated;
**** key parameters: ******
RADIUS:   warp search radius (default 50);
LEVEL:    sampling level in continous wavelet (default 3);
SCALE:    base scale in continous wavelet (default sqrt(2));




#===============
# Screen output:
#===============

                  1                                    2                       3         4     5
-------------------------------------------------------------------------------------------------------
002a08b9-0248-4929-9759-2d5c0942076d 002a08b9-0248-4929-9759-2d5c0942076d 22089.546144 127006 0.173925

---------
[Legend]:
---------

the 1st column shows the name of the first input signal,
the 2nd column shows the name of the second input signal,

the 3rd column shows the overall distance after Dynamic Time Warping (DTW),
the 4th colmnn shows the length of the warping path (or, length of alignment),

the 5th column shows the normalized distance of a warping path.




#===============
# Output format:
#===============

         1          2                3               4             5               6                            7
------------------------------------------------------------------------------------------------------------------------
         1          1 |             316             550 |        -1.16628         1.04565          diff:        2.21193
         2          2 |             347             433 |       -0.526507       -0.560209          diff:       0.033702
         3          3 |             319             411 |        -1.10436       -0.862165          diff:       0.242199
         3          4 |             319             409 |        -1.10436       -0.889616          diff:       0.214749
         3          5 |             319             408 |        -1.10436       -0.903341          diff:       0.201023
         3          6 |             319             389 |        -1.10436        -1.16412          diff:      0.0597573
         3          7 |             319             389 |        -1.10436        -1.16412          diff:      0.0597573
         3          8 |             319             377 |        -1.10436        -1.32883          diff:       0.224461
         3          9 |             319             391 |        -1.10436        -1.13667          diff:      0.0323067

---------
[Legend]:
---------

the 1st column shows the mapping of the first position (e.g., on expected signal) starting from 1,
the 2nd column shows the mapping of the second position (e.g., on raw signal) starting from 1,

the 3rd column displays the original value of the first input signal (e.g., on expected signal),
the 4th column displays the original value of the second input signal (e.g., on raw signal),

the 5th column indicates the Z-normalized value of the first input signal (e.g., on expected signal),
the 6th column indicates the Z-normalized value of the second input signal (e.g., on raw signal),

the 7th column illustrates the absolute difference between the two Z-normalized values.



#=========
# Example:
#=========

#-> 1. generate genome signal from DNA sequence via pore model.
./seq2sig -i example/002a08b9-0248-4929-9759-2d5c0942076d.fasta -o 002a08b9-0248-4929-9759-2d5c0942076d.seqsig -s 1 -z 0

#-> 2. generate coarsening signal via peak-pick on CWT spectrum.
./sig2peak -i example/002a08b9-0248-4929-9759-2d5c0942076d.seqsig -o 002a08b9-0248-4929-9759-2d5c0942076d.peak_seqsig -s 2.828 -z 1
./sig2peak -i example/002a08b9-0248-4929-9759-2d5c0942076d.rawsig -o 002a08b9-0248-4929-9759-2d5c0942076d.peak_rawsig -s 22.715 -z 1  #-> 22.715=2.828*126965/15807:wq

#-> 3. perform mapping between nanopore signal and genome signal.
./cwDTW -i example/002a08b9-0248-4929-9759-2d5c0942076d.seqsig -p example/002a08b9-0248-4929-9759-2d5c0942076d.rawsig -o align_out


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