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

Animal Mitochondrial Genetics02:59

Animal Mitochondrial Genetics

9.3K
Among all the organelles in an animal cell, only mitochondria have their own independent genomes. Animal mitochondrial DNA is a double-stranded, closed-circular molecule with around 20,000 base pairs. Mitochondrial DNA is unique in that one of its two strands, the heavy, or H, -strand is guanine rich, whereas the complementary strand is cytosine rich and called the light, or L, -strand. Compared to nuclear DNA, mitochondrial DNA has a very low percentage of non-coding regions and is marked by...
9.3K
Genetics of Speciation02:16

Genetics of Speciation

21.5K
Speciation is the evolutionary process resulting in the formation of new, distinct species—groups of reproductively isolated populations.
21.5K
What is Population Genetics?01:25

What is Population Genetics?

64.8K
A population is composed of members of the same species that simultaneously live and interact in the same area. When individuals in a population breed, they pass down their genes to their offspring. Many of these genes are polymorphic, meaning that they occur in multiple variants. Such variations of a gene are referred to as alleles. The collective set of all the alleles within a population is known as the gene pool.
64.8K
What is Genetic Engineering?00:49

What is Genetic Engineering?

80.2K
Overview
80.2K
How Data are Classified: Categorical Data01:11

How Data are Classified: Categorical Data

44.7K
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...
44.7K
How Data are Classified: Numerical Data00:59

How Data are Classified: Numerical Data

38.1K
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...
38.1K

You might also read

Related Articles

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

Sort by
Same author

Pawsitive impact: How pet contact ameliorates adult inflammatory stress responses in individuals raised in an urban environment.

Brain, behavior, and immunity·2025
Same author

Efficacy of integrated social cognitive remediation vs neurocognitive remediation in schizophrenia: Results from the multicenter randomized controlled ISST (Integrated Social Cognition And Social Skills Therapy) study.

Schizophrenia research·2025
Same author

Sensori- and psychomotor abnormalities, psychopathological symptoms and functionality in schizophrenia-spectrum disorders: a network analytic approach.

Schizophrenia (Heidelberg, Germany)·2025
Same author

Anhedonia relates to reduced striatal reward anticipation in depression but not in schizophrenia or bipolar disorder: A transdiagnostic study.

Cognitive, affective & behavioral neuroscience·2025
Same author

Real-time mechanism-based interventions for daily alcohol challenges: Protocol for ecological momentary assessment and intervention.

Digital health·2025
Same author

Loneliness is associated with different structural brain changes in schizophrenia spectrum disorders and major depression.

Schizophrenia research·2025
Same journal

Biomedical Concept Recognition with Error-aware Negative-enhanced Ranking Framework.

Bioinformatics (Oxford, England)·2026
Same journal

TEDLH: Domain HMMs for sensitive detection of remote homologues.

Bioinformatics (Oxford, England)·2026
Same journal

PLNFGL: Joint Estimation of Multi-Condition Gene Networks from Single-cell RNA-seq Data.

Bioinformatics (Oxford, England)·2026
Same journal

MCFST: Spatial domain identification method based on multi-view graph convolutional network and graph fusion network.

Bioinformatics (Oxford, England)·2026
Same journal

SpaBiT: Enhancing Spatial Transcriptomics Resolution via Bidirectional Attention Transformers.

Bioinformatics (Oxford, England)·2026
Same journal

EDEL: Enhancing Dense Retrievers for Curation of Biomedical Knowledge Bases.

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

Related Experiment Video

Updated: Feb 5, 2026

A Bioinformatics Pipeline to Accurately and Efficiently Analyze the MicroRNA Transcriptomes in Plants
06:34

A Bioinformatics Pipeline to Accurately and Efficiently Analyze the MicroRNA Transcriptomes in Plants

Published on: January 21, 2020

8.9K

Gimpute: an efficient genetic data imputation pipeline.

Junfang Chen1, Dietmar Lippold1, Josef Frank2

  • 1Department of Psychiatry and Psychotherapy, Heidelberg University, Mannheim, Germany.

Bioinformatics (Oxford, England)
|September 22, 2018
PubMed
Summary
This summary is machine-generated.

Gimpute is an automated pipeline for genome-wide association studies (GWAS) that streamlines genotype imputation. This R package simplifies data processing, quality control, and imputation for enhanced genetic research.

More Related Videos

Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease
09:34

Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease

Published on: April 4, 2018

34.8K
Author Spotlight: Advancing Erythropoiesis Research - A Simplified Pipeline for Assessing Hematopoietic Stem Cell Function in Myelodysplastic Syndromes
08:53

Author Spotlight: Advancing Erythropoiesis Research - A Simplified Pipeline for Assessing Hematopoietic Stem Cell Function in Myelodysplastic Syndromes

Published on: January 10, 2025

1.0K

Related Experiment Videos

Last Updated: Feb 5, 2026

A Bioinformatics Pipeline to Accurately and Efficiently Analyze the MicroRNA Transcriptomes in Plants
06:34

A Bioinformatics Pipeline to Accurately and Efficiently Analyze the MicroRNA Transcriptomes in Plants

Published on: January 21, 2020

8.9K
Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease
09:34

Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease

Published on: April 4, 2018

34.8K
Author Spotlight: Advancing Erythropoiesis Research - A Simplified Pipeline for Assessing Hematopoietic Stem Cell Function in Myelodysplastic Syndromes
08:53

Author Spotlight: Advancing Erythropoiesis Research - A Simplified Pipeline for Assessing Hematopoietic Stem Cell Function in Myelodysplastic Syndromes

Published on: January 10, 2025

1.0K

Area of Science:

  • Genetics
  • Bioinformatics

Background:

  • Genotype imputation is crucial for genome-wide association studies (GWAS) to analyze untyped variants and ensure cross-study comparability.
  • A gap exists in automated pipelines for the complete processing workflow before and after genotype imputation.

Purpose of the Study:

  • To develop Gimpute, an automated pipeline for processing and imputing genome-wide association data.
  • To provide a comprehensive, user-friendly solution for genotype imputation in genetic research.

Main Methods:

  • Developed Gimpute as an open-source R package.
  • Integrated essential steps: genotype liftOver, quality control, population outlier detection, haplotype pre-phasing, imputation, and post-imputation processing.
  • Ensured compatibility and extension capabilities with existing pipelines.

Main Results:

  • Gimpute offers an automated workflow for genotype imputation in GWAS.
  • The pipeline incorporates quality control, outlier detection, and pre/post-imputation steps.
  • It is built using widely available, free tools.

Conclusions:

  • Gimpute provides an automated and comprehensive solution for genotype imputation in GWAS.
  • The R package facilitates data management and comparability across genetic studies.
  • It is freely available, promoting wider adoption in the research community.