Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Statistical Software for Data Analysis and Clinical Trials01:12

Statistical Software for Data Analysis and Clinical Trials

1.4K
Statistical software is pivotal in data analysis and clinical trials by providing tools to analyze data, draw conclusions, and make predictions. These software packages range from simple data management applications to complex analytical platforms, supporting various statistical tests, models, and simulation techniques. Their significance lies in their ability to handle vast amounts of data with precision and efficiency, enabling researchers to validate hypotheses, identify trends, and make...
1.4K
GIS Software, Hardware, and Sources of GIS Data01:23

GIS Software, Hardware, and Sources of GIS Data

764
A Geographic Information System (GIS) combines specialized software and hardware to effectively manage, analyze, and present spatial and related data. GIS software includes critical functionalities such as a user interface for easy navigation, database management tools for handling spatial and attribute data, and data retrieval features for efficient access. Analytical tools transform raw data into insights, while display functions produce maps and reports in various formats for effective...
764
Analysis of Population Pharmacokinetic Data01:12

Analysis of Population Pharmacokinetic Data

694
Analysis of population pharmacokinetic data involves studying the behavior of drugs within diverse populations to understand their pharmacokinetic parameters. Traditional pharmacokinetic methods typically involve collecting samples from a few individuals and estimating these parameters. While these methods are commonly used, they have limitations in capturing the variability in drug response among individuals or heterogeneous populations. Population pharmacokinetics is employed to address these...
694
Overview of Microsoft Excel as a Data Analysis Tool01:13

Overview of Microsoft Excel as a Data Analysis Tool

1.5K
Microsoft Excel is a cornerstone tool for data analysis and statistical operations, offering a wide array of functionalities to manage, analyze, and visualize data efficiently. Recognized for its versatility, Excel facilitates the performance of basic to complex statistical operations, serving as an indispensable asset for analysts, researchers, and students alike. Excel's significance in data analysis emanates from its spreadsheet environment, where data can be organized in rows and...
1.5K
Performing a Simple Data Analysis using MS-Excel Function01:17

Performing a Simple Data Analysis using MS-Excel Function

928
Microsoft Excel offers a suite of functions and tools ideal for statistical analysis, making it accessible to students and researchers. This article outlines fundamental Excel functions pivotal for data analysis.
SUM: This function calculates the total sum of a range of values. It's the foundation for aggregating data, essential for determining overall trends and totals in datasets.
AVERAGE: It computes the mean value of a given set of numbers, providing a quick insight into the central...
928
Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis00:59

Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis

327
Noncompartmental analyses offer an alternative method for describing drug pharmacokinetics without relying on a specific compartmental model. In this approach, the drug's pharmacokinetics are assumed to be linear, with the terminal phase log-linear. This assumption allows for simplified analysis and interpretation of the drug's behavior in the body.
One important characteristic of noncompartmental analyses is that drug exposure increases proportionally with increasing doses. This...
327

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Field-induced phase transitions in ferro-antiferromagnetic diblock copolymers.

The Journal of chemical physics·2026
Same author

Supercoiling DNA with a free end.

Soft matter·2026
Same author

Nonequilibrium polymer models for chromatin.

Current opinion in genetics & development·2026
Same author

Cohesin forms fountains at active enhancers in C. elegans.

Nature communications·2025
Same author

Bridging-Induced Phase Separation and Loop Extrusion Drive Noise in Chromatin Transcription.

Physical review letters·2025
Same author

Coordinated control of genome-nuclear lamina interactions by topoisomerase 2B and lamin B receptor.

Nucleic acids research·2025
Same journal

conMItion: an R package adjusting confounding factors for associations in multi-omics.

Bioinformatics (Oxford, England)·2026
Same journal

SpaMFG: a Spatial Multi-omics Integration Method based on Feature Grouping.

Bioinformatics (Oxford, England)·2026
Same journal

CSCN: Inference of Cell-Specific Causal Networks Using Single-Cell RNA-Seq Data.

Bioinformatics (Oxford, England)·2026
Same journal

Sparse CCA-Based Mediation Analysis with High-Dimensional Exposures and Mediators.

Bioinformatics (Oxford, England)·2026
Same journal

Enhancing Cross-Context Generalization in Drug Perturbation Prediction with a Multimodal Conditional Diffusion Framework.

Bioinformatics (Oxford, England)·2026
Same journal

Primer Design through Submodular Function Estimation.

Bioinformatics (Oxford, England)·2026
See all related articles

Related Experiment Video

Updated: Jan 23, 2026

High-Throughput Analysis of Optical Mapping Data Using ElectroMap
07:36

High-Throughput Analysis of Optical Mapping Data Using ElectroMap

Published on: June 4, 2019

10.0K

capC-MAP: software for analysis of Capture-C data.

Adam Buckle1, Nick Gilbert1, Davide Marenduzzo2

  • 1Medical Research Council Human Genetics Unit, Medical Research Council Institute of Genetics & Molecular Medicine, University of Edinburgh, Western General Hospital, Edinburgh EH4 2XU, UK.

Bioinformatics (Oxford, England)
|June 8, 2019
PubMed
Summary
This summary is machine-generated.

A new software package, capC-MAP, simplifies the analysis of Capture-C data, a method for studying chromosome 3D organization. This tool enhances understanding of genome-wide chromatin interactions and gene regulation.

More Related Videos

Quantifying X-Ray Fluorescence Data Using MAPS
14:58

Quantifying X-Ray Fluorescence Data Using MAPS

Published on: February 17, 2018

11.3K
Software for Analysis of Heart Rate and Blood Pressure Time-series Data from the Valsalva Maneuver
14:28

Software for Analysis of Heart Rate and Blood Pressure Time-series Data from the Valsalva Maneuver

Published on: June 27, 2025

995

Related Experiment Videos

Last Updated: Jan 23, 2026

High-Throughput Analysis of Optical Mapping Data Using ElectroMap
07:36

High-Throughput Analysis of Optical Mapping Data Using ElectroMap

Published on: June 4, 2019

10.0K
Quantifying X-Ray Fluorescence Data Using MAPS
14:58

Quantifying X-Ray Fluorescence Data Using MAPS

Published on: February 17, 2018

11.3K
Software for Analysis of Heart Rate and Blood Pressure Time-series Data from the Valsalva Maneuver
14:28

Software for Analysis of Heart Rate and Blood Pressure Time-series Data from the Valsalva Maneuver

Published on: June 27, 2025

995

Area of Science:

  • Genomics
  • Molecular Biology
  • Bioinformatics

Background:

  • Chromosome conformation capture (Capture-C) methods investigate the 3D organization of chromosomes within the cell nucleus.
  • Capture-C provides high-resolution, genome-wide chromatin interaction data, crucial for understanding cis-regulation and gene function.
  • Current analysis of Capture-C data is complex and lacks dedicated, user-friendly software.

Purpose of the Study:

  • To present capC-MAP, a novel software package designed for the automated analysis of Capture-C data.
  • To provide an accessible and efficient tool for researchers studying genome architecture and gene regulation.

Main Methods:

  • capC-MAP is a suite of programs developed in C++ and Python.
  • The software is designed for both ease of use and flexibility, allowing individual program execution or complete analysis via a single command line.
  • It is available as open-source software and via the conda package manager for broad accessibility.

Main Results:

  • capC-MAP automates the complex analysis of Capture-C data.
  • The software facilitates a more streamlined and efficient approach to studying genome-wide chromatin interactions.
  • Implementation details and availability ensure broad usability across standard Unix-style systems.

Conclusions:

  • capC-MAP addresses the need for dedicated, user-friendly software for Capture-C data analysis.
  • The package enhances the utility of Capture-C as a tool for investigating chromosome 3D organization, cis-regulation, and gene function.
  • Its open-source nature and flexible implementation promote wider adoption and advancement in the field.