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Related Concept Videos

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.
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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.
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Method validation is a crucial process in analytical chemistry designed to confirm that a given method consistently produces reliable and high-quality results. This process is essential when a method is applied to different sample matrices or when procedural modifications are made, ensuring that the results meet acceptable standards across various applications.
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Related Experiment Video

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ChIP-Atlas: a data-mining suite powered by full integration of public ChIP-seq data.

Shinya Oki1, Tazro Ohta2, Go Shioi3

  • 1Department of Developmental Biology, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan soki@dev.med.kyushu-u.ac.jp meno@dev.med.kyushu-u.ac.jp.

EMBO Reports
|November 11, 2018
PubMed
Summary

ChIP-Atlas integrates over 70,000 public chromatin immunoprecipitation sequencing (ChIP-seq) and DNase-seq datasets. This platform enables comprehensive data mining for gene regulatory networks and epigenetic insights.

Keywords:
ChIP‐seqDNase‐seqdata miningenhancertranscription factor

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Area of Science:

  • Genomics
  • Epigenetics
  • Bioinformatics

Background:

  • Chromatin immunoprecipitation sequencing (ChIP-seq) and DNase-seq are crucial for understanding gene regulation.
  • Vast amounts of public ChIP-seq and DNase-seq data exist across multiple model organisms.
  • Integrating and mining this data presents a significant challenge for researchers.

Purpose of the Study:

  • To develop a comprehensive data-mining platform for public ChIP-seq and DNase-seq data.
  • To provide a centralized resource for analyzing epigenetic modifications and transcriptional regulator binding.
  • To facilitate the study of gene regulatory networks and epigenetic mechanisms.

Main Methods:

  • Integrated over 70,000 public ChIP-seq and DNase-seq datasets from six model organisms (human, mouse, rat, fruit fly, nematode, budding yeast).
  • Developed ChIP-Atlas, a data-mining platform accessible at http://chip-atlas.org.
  • Utilized data from NCBI Sequence Read Archive (SRA), including GEO, ArrayExpress, DDBJ, ENCODE, Roadmap Epigenomics, and literature.

Main Results:

  • ChIP-Atlas visualizes alignment and peak-call results for all integrated public ChIP-seq and DNase-seq data.
  • The platform integrates peak-call data to visualize histone modifications and transcriptional regulator (TR) binding sites at genomic loci.
  • Enables analysis of TR-gene and TR-TR interactions, and enrichment of protein binding for specified genomic coordinates or gene names.

Conclusions:

  • ChIP-Atlas offers superior data volume and functionality for mining thousands of ChIP-seq experiments.
  • The platform provides valuable insights into gene regulatory networks and epigenetic mechanisms.
  • ChIP-Atlas serves as a powerful resource for researchers in genomics and epigenetics.