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A z score (or standardized value) is measured in units of the standard deviation. It indicates how many standard deviations the value x is above (to the right of) or below (to the left of) the mean, μ. Values of x that are larger than the mean have positive z scores, and values of x that are smaller than the mean have negative z scores. If x equals the mean, then x has a zero z score. It is important to note that the mean of the z scores is zero, and the standard deviation is one.
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A z score (or standardized value) is measured in units of the standard deviation. It tells you how many standard deviations the value x is above (to the right of) or below (to the left of) the mean, μ. Values of x that are larger than the mean have positive z scores, and values of x that are smaller than the mean have negative z scores. If x equals the mean, then x has a zero z score. It is important to note that the mean of the z scores is zero, and the standard deviation is one.
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Transmission-line series resistance and shunt conductance cause three primary effects: attenuation, distortion, and power losses.
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The z score is one of the three measures of relative standing. It describes the location of a value in a dataset relative to the mean. z scores are obtained after the standardization of the values in a dataset. The z score for the mean is 0.
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In electrical engineering, a lossless transmission line is characterized by a purely imaginary propagation constant and a resistive characteristic impedance. The ABCD parameters, which describe the relationship between the input and output voltages and currents, indicate an equivalent π circuit with an imaginary series impedance and a shunt admittance. This results in a transmission line that, when the product of the phase constant (beta) and the length of the line is less than pi, exhibits...
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LCQS: an efficient lossless compression tool of quality scores with random access functionality.

Jiabing Fu1,2, Bixin Ke1,2, Shoubin Dong3,4

  • 1School of Computer Science & Engineering, South China University of Technology, Wushan Road, Guangzhou, 510006, China.

BMC Bioinformatics
|March 19, 2020
PubMed
Summary

A new lossless compression tool, LCQS, efficiently compresses sequencing data quality scores, offering improved speed, file size reduction, and robustness for genomic research.

Keywords:
EfficientLossless compressionParallelizationQuality scoreRandom accessRobustZPAQ

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Area of Science:

  • Genomics
  • Bioinformatics
  • Data Compression

Background:

  • Advanced sequencing technologies generate vast genomic data, necessitating efficient compression.
  • Quality score compression in FASTQ files is a significant challenge.
  • Existing methods struggle with robustness, speed, and practicality.

Purpose of the Study:

  • To develop an efficient lossless compressor for sequencing data quality scores.
  • To address limitations of existing compressors regarding robustness, speed, and file size.

Main Methods:

  • Proposed LCQS, a lossless compression tool for quality scores.
  • Utilized partitioning, indexing, packing, and parallelizing processing steps.
  • Focused on maximizing hardware resource utilization.

Main Results:

  • LCQS outperforms state-of-the-art compressors in most criteria.
  • Demonstrated strong robustness across various datasets.
  • Achieved up to 29.1x faster compression, 28.78% file size reduction, and 2.1x faster random access decompression.

Conclusions:

  • LCQS is a highly efficient and advanced lossless quality score compressor.
  • Offers superior compression ratio, speed, and fast random access decompression.
  • Handles diverse quality score types effectively.