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Related Concept Videos

Proteomics01:33

Proteomics

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A proteome is the entire set of proteins that a cell type produces. We can study proteomes using the knowledge of genomes because genes code for mRNAs, and the mRNAs encode proteins. Although mRNA analysis is a step in the right direction, not all mRNAs are translated into proteins.
Proteomics is the study of proteomes' function. It involves the large-scale systematic study of the proteome to denote the protein complement expressed by a genome. Scientist Mark Wilkins coined the term...
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Integration by Parts: Indefinite Integrals01:26

Integration by Parts: Indefinite Integrals

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Integration by parts is a fundamental technique in calculus for evaluating integrals involving the product of two functions. It is particularly useful when direct integration is not feasible. The method is based on the product rule for differentiation, which states that the derivative of a product equals the derivative of the first function times the second, plus the first function times the derivative of the second. By integrating this identity and rearranging terms, the integration by parts...
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Integration by Parts: Definite Integrals01:23

Integration by Parts: Definite Integrals

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Definite integrals involving the product of two functions over a fixed interval can be evaluated using integration by parts. This method rewrites the integral as the difference of a product evaluated at the endpoints and a remaining definite integral that is often simpler to compute.A representative example is the definite integral of the inverse tangent function. Since there is no direct integration formula for arctan ⁡x, the integrand is rewritten as a product of arctan⁡ x and the...
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How Data are Classified: Categorical Data01:11

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A variable, usually notated by capital letters such as X and Y, is a characteristic or measurement that can be determined for each member of a population. Data are the actual values of variables. They may be numbers, or they may be words. Datum is a single value.
Data are classified based on whether they are measurable or not. Categorical data cannot be measured; instead, it can be divided into categories. For example, if Y denotes a person's party affiliation, some examples of Y include...
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How Data are Classified: Numerical Data00:59

How Data are Classified: Numerical Data

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Data that are countable or measurable in specific units are called numerical or quantitative data. Quantitative data are always numbers. Quantitative data are the result of counting or measuring the attributes of a population. Amount of money, pulse rate, weight, number of people living in a town, and number of students who opt for statistics are examples of quantitative data.
Quantitative data may be either discrete or continuous. All quantitative data that take on only specific numerical...
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The Integrated Rate Law: The Dependence of Concentration on Time02:39

The Integrated Rate Law: The Dependence of Concentration on Time

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While the differential rate law relates the rate and concentrations of reactants, a second form of rate law called the integrated rate law relates concentrations of reactants and time. Integrated rate laws can be used to determine the amount of reactant or product present after a period of time or to estimate the time required for a reaction to proceed to a certain extent. For example, an integrated rate law helps determine the length of time a radioactive material must be stored for its...
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Updated: Feb 5, 2026

Navigating the Mass Spectrometry-Based Proteomic Data Using Free Computational Tools
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Navigating the Mass Spectrometry-Based Proteomic Data Using Free Computational Tools

Published on: August 19, 2025

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PDV: an integrative proteomics data viewer.

Kai Li1,2, Marc Vaudel3,4, Bing Zhang5,6

  • 1BGI-Shenzhen, Shenzhen, China.

Bioinformatics (Oxford, England)
|September 1, 2018
PubMed
Summary
This summary is machine-generated.

PDV is a new proteomics data viewer for visualizing diverse datasets, aiding quality control and peptide identification validation. This lightweight tool offers fast exploration of large-scale proteomics data via GUI and command line.

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Area of Science:

  • Proteomics
  • Bioinformatics
  • Computational Biology

Background:

  • Data visualization is crucial for proteomics, supporting MS/MS data quality control and peptide identification.
  • Diverse proteomics data formats and large dataset sizes pose challenges for effective visualization.

Purpose of the Study:

  • To present PDV, an integrative proteomics data viewer.
  • To enable intuitive and fast exploration of various proteomics datasets.

Main Methods:

  • PDV integrates and visualizes multiple proteomics data types.
  • Supports database search results, de novo sequencing, proteogenomics, mzML/mzXML, and public repository data.
  • Offers both graphical user interface (GUI) and command-line modes.

Main Results:

  • PDV provides a unified platform for visualizing diverse proteomics data.
  • Enables efficient analysis on standard desktop computers.
  • Facilitates quality control and validation of peptide identification.

Conclusions:

  • PDV is a versatile and accessible tool for proteomics data exploration.
  • Enhances the analysis workflow for large-scale proteomics studies.
  • Promotes efficient data interpretation and validation in proteomics research.