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A general-purpose signal processing algorithm for biological profiles using only first-order derivative information.

Yuanjie Liu1,2, Jianhan Lin3,4

  • 1College of Information and Electrical Engineering, China Agricultural University, Haidian, Beijing, 100083, People's Republic of China. yjliu@cau.edu.cn.

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Summary

This study introduces the derivative passing accumulation (DPA) method for bioinformatics signal processing. DPA efficiently corrects baselines and extracts signal peaks simultaneously using first-order derivatives.

Keywords:
Baseline correctionFirst derivativePassing accumulationSignal extraction

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Signal Processing

Background:

  • Automatic signal-feature extraction is vital in bioinformatics profile processing.
  • Baseline drift and noise complicate signal position and peak area determination.
  • Existing methods often struggle with simultaneous baseline correction and signal extraction.

Purpose of the Study:

  • To present an efficient algorithm for simultaneous baseline correction and signal extraction.
  • To address challenges in processing 1-dimensional detector outputs in biological instruments.
  • To introduce the derivative passing accumulation (DPA) method.

Main Methods:

  • Developed a novel signal feature extraction procedure.
  • Utilized first-order derivatives obtained through simple differences.
  • Accumulated negative and positive parts of the derivative vector to create a signal descriptor.
  • Employed thresholding on the descriptor for signal-background separation and peak localization.

Main Results:

  • Successfully separated signals from background fluctuations using the descriptor.
  • Simultaneously located signal peaks by analyzing descriptor intervals.
  • Achieved effective baseline correction and signal peak extraction.
  • The derivative passing accumulation (DPA) method demonstrated efficacy.

Conclusions:

  • Introduced a new method for joint baseline computation and signal peak identification.
  • The DPA method proves powerful for practical signal processing applications.
  • Testing on authentic and synthesized data validates the method's effectiveness.