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

Genomics02:02

Genomics

41.2K
Genomics is the science of genomes: it is the study of all the genetic material of an organism. In humans, the genome consists of information carried in 23 pairs of chromosomes in the nucleus, as well as mitochondrial DNA. In genomics, both coding and non-coding DNA is sequenced and analyzed. Genomics allows a better understanding of all living things, their evolution, and their diversity. It has a myriad of uses: for example, to build phylogenetic trees, to improve productivity and...
41.2K
Biostatistics: Overview01:20

Biostatistics: Overview

986
Biostatistics plays a crucial role in understanding and analyzing data in healthcare and biology. Biostatisticians conduct experiments, gather evidence, and draw meaningful conclusions using statistical methods and techniques. Different variables form the foundation of biostatistical analysis, allowing researchers to understand and interpret data effectively. These variables are classified into different types, each serving a specific purpose in statistical analysis.
Discrete variables are...
986
Distributions to Estimate Population Parameter01:26

Distributions to Estimate Population Parameter

5.3K
The accurate values of population parameters such as population proportion, population mean, and population standard deviation (or variance) are usually unknown. These are fixed values that can only be estimated from the data collected from the samples. The estimates of each of these parameters are sample proportion, the sample mean, and sample standard deviation (or variance). To obtain the values of these sample statistics, data are required that have particular distribution and central...
5.3K
Multiple Allele Traits01:49

Multiple Allele Traits

38.5K
The Concept of Multiple Allelism
38.5K
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

1.1K
Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
1.1K
Binomial Probability Distribution01:15

Binomial Probability Distribution

16.2K
A binomial distribution is a probability distribution for a procedure with a fixed number of trials, where each trial can have only two outcomes.
The outcomes of a binomial experiment fit a binomial probability distribution. A statistical experiment can be classified as a binomial experiment if the following conditions are met:
There are a fixed number of trials. Think of trials as repetitions of an experiment. The letter n denotes the number of trials.
There are only two possible outcomes,...
16.2K

You might also read

Related Articles

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

Sort by
Same author

Convergence and divergence of DNA methylation and gene expression patterns in neopolyploid Arabidopsis kamchatica.

Nature communications·2026
Same author

Hybrid untargeted short-read and targeted long-read RNA sequencing facilitates genotype-phenotype associations at single-cell resolution.

Genome biology·2026
Same author

Resolving Sialylated N-Glycans and Immune Cell Landscapes Using a Unified Same-Section IMC-MSI Workflow.

bioRxiv : the preprint server for biology·2026
Same author

MIMIC: a flexible pipeline to register and summarize IMC-MSI experiments.

Communications biology·2026
Same author

Patches: A Representation Learning Framework for Decoding Shared and Condition-Specific Transcriptional Programs in Wound Healing.

bioRxiv : the preprint server for biology·2026
Same author

Sample-specific haplotype-resolved protein isoform characterization via long-read RNA-seq-based proteogenomics.

bioRxiv : the preprint server for biology·2026
Same journal

Sentiment Analysis of Acceptance TVET Online Courses on the Skill Academy App from Google Play: Leveraging Text Mining with Comparison Machine Learning Model.

F1000Research·2026
Same journal

Emotional intelligence: An important skill to learn now more than ever.

F1000Research·2026
Same journal

East Mediterranean Lineage of <i>Brucella melitensis</i> in Human Isolates and Milk Samples in Oman Using MLVA-14.

F1000Research·2026
Same journal

Application of K-Means Clustering for Job Applicant Analysis in Construction Firms Using R.

F1000Research·2026
Same journal

The influence of self-esteem and emotional intelligence on addiction to social networks in Peruvian university students.

F1000Research·2026
Same journal

A Bibliometric Analysis of Music's Role in Promoting Well-Being in Health Science Research.

F1000Research·2026
See all related articles

Related Experiment Video

Updated: Mar 8, 2026

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress
05:22

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Published on: July 29, 2022

4.1K

DRIMSeq: a Dirichlet-multinomial framework for multivariate count outcomes in genomics.

Malgorzata Nowicka1, Mark D Robinson1

  • 1Institute for Molecular Life Sciences, University of Zurich, Zurich, 8057, Switzerland; SIB Swiss Institute of Bioinformatics, University of Zurich, Zurich, 8057, Switzerland.

F1000Research
|January 24, 2017
PubMed
Summary
This summary is machine-generated.

This study introduces a Dirichlet-multinomial model for analyzing RNA sequencing data to detect changes in gene isoform usage and identify splicing quantitative trait loci (sQTLs). The method improves statistical performance, especially with limited replicates, by sharing information.

Keywords:
DRIMSeqRNA-seqgenomicssingle nucleotide polymorphismsplicingstatistical framework

More Related Videos

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
05:53

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

10.8K
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

34.9K

Related Experiment Videos

Last Updated: Mar 8, 2026

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress
05:22

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Published on: July 29, 2022

4.1K
Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
05:53

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

10.8K
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

34.9K

Area of Science:

  • Genomics
  • Statistical Genetics
  • Bioinformatics

Background:

  • Multivariate responses are common in genomics, such as alternative splicing leading to multiple gene isoforms.
  • Differences in isoform ratios can impact phenotypes and prognosis, and single nucleotide polymorphisms (SNPs) affecting splicing (sQTLs) are crucial for understanding genetic variation's impact on gene expression.
  • RNA sequencing (RNA-seq) enables detailed analysis of alternative splicing, with recent advances in transcript quantification.

Purpose of the Study:

  • To develop a statistical framework for discovering changes in isoform usage between conditions.
  • To identify SNPs that influence the relative expression of transcripts.
  • To provide a robust method for analyzing multivariate genomics data, particularly RNA-seq.

Main Methods:

  • A statistical framework based on the Dirichlet-multinomial distribution is proposed.
  • The model jointly models isoform expression, accounting for their correlated nature and differential gene expression without losing overall gene abundance information.
  • Information sharing is employed to achieve robust parameter estimates, especially with limited replicates.

Main Results:

  • The proposed Dirichlet-multinomial model effectively discovers changes in isoform usage and identifies sQTLs.
  • The method demonstrates improved performance over existing approaches based on standard statistical metrics.
  • The framework provides robust estimates even with a limited number of biological replicates.

Conclusions:

  • The Dirichlet-multinomial model offers a powerful approach for analyzing multivariate genomics data, including RNA-seq.
  • The DRIMSeq R package implements this framework, making it accessible for researchers.
  • This method enhances the understanding of genetic variation's role in gene expression and splicing.