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Related Concept Videos

Confidence Coefficient01:24

Confidence Coefficient

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The confidence coefficient is also known as the confidence level or degree of confidence. It is the percent expression for the probability, 1-α, that the confidence interval contains the true population parameter assuming that the confidence interval is obtained after sufficient unbiased sampling; for example, if the CL = 90%, then in 90 out of 100 samples the interval estimate will enclose the true population parameter. Here α is the area under the curve, distributed equally under...
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DNA Packaging00:58

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Overview
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Chromatin Packaging01:32

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Each human somatic cell contains 6 billion base pairs of DNA. Each base pair is 0.34 nm long, meaning each diploid cell contains a staggering 2 meters of DNA. This long DNA strand is packed inside a nucleus measuring only 10-20 microns in diameter with the help of specialized DNA-binding proteins called histones. Together they form a compact DNA-protein complex called chromatin. The chromatin is further compacted into higher-order structures. The highest level of compaction is achieved during...
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Each human somatic cell contains 6 billion base-pairs of DNA. Each base-pair is 0.34 nm long, which means that each diploid cell contains a staggering 2 meters of DNA. How is such a long DNA strand packed inside a nucleus measuring only 10 - 20 microns in diameter? 
The chromatin
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An unbiased point estimate is often insufficient to predict a population estimate, such as population mean or population proportion. In this scenario, a confidence interval is used. A confidence interval is an estimate similar to a  sample proportion. However, unlike the point estimate which is a single value, the confidence interval  contains a range of values. These values have lower and upper limits, known as confidence limits, and can be designated as L1 and L2, respectively.
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LipiDex: An Integrated Software Package for High-Confidence Lipid Identification.

Paul D Hutchins1, Jason D Russell2, Joshua J Coon3

  • 1Department of Chemistry, University of Wisconsin-Madison, Madison, WI 53706, USA; Genome Center of Wisconsin, University of Wisconsin-Madison, Madison, WI 53706, USA.

Cell Systems
|April 30, 2018
PubMed
Summary

LipiDex is a new software suite that automates lipid identification for discovery lipidomics using liquid chromatography-tandem mass spectrometry (LC-MS/MS). This tool significantly reduces processing time and enhances the accuracy of lipid analysis in complex datasets.

Keywords:
data analysislipid fragmentationlipidomicsmass spectrometryopen-source softwarespectral library

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Area of Science:

  • Biochemistry
  • Analytical Chemistry
  • Bioinformatics

Background:

  • Proteomics software is advanced, but discovery lipidomics lacks mature informatics tools.
  • Liquid chromatography-tandem mass spectrometry (LC-MS/MS) is a key technique in lipidomics.
  • Manual processing of lipidomics data is time-consuming and labor-intensive.

Purpose of the Study:

  • To introduce LipiDex, a freely available software suite for automating lipid identification in LC-MS/MS data.
  • To reduce hands-on processing time for lipidomics datasets.
  • To improve the accuracy and confidence of lipid identification and quantification.

Main Methods:

  • LipiDex utilizes in silico fragmentation templates and optimized MS/MS spectral matching.
  • It employs unique algorithms for spectral and chromatographic peak purity analysis.
  • The software incorporates routines for removing ionization artifacts to reduce data redundancy.

Main Results:

  • LipiDex unifies and automates all stages of lipid identification.
  • It reduces processing time from hours to minutes for large datasets.
  • The software accurately identifies and quantifies hundreds of lipid species and unknown compounds, including isobaric lipids.

Conclusions:

  • LipiDex provides a robust solution for lipid identification in discovery lipidomics.
  • It integrates seamlessly with existing LC-MS/MS software packages.
  • The tool enhances the structural resolution and reliability of lipidomics data analysis.