Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Genomics

Genomics

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...

Synthetic Biology

Synthetic Biology

Synthetic biology is an interdisciplinary science that involves using principles from disciplines such as engineering, molecular biology, cell biology, and systems biology. It involves remodeling existing organisms from nature or constructing completely new synthetic organisms for applications such as protein or enzyme production, bioremediation, value-added macromolecule production, and the addition of desirable traits to crops, to name a few.
Golden rice
Golden rice is a genetically modified...

Epistasis Analysis

Epistasis Analysis

Although Mendel chose seven unrelated traits in peas to study gene segregation, most traits involve multiple gene interactions that create a spectrum of phenotypes. When the interaction of various genes or alleles at different locations influences a phenotype, this is called epistasis. Epistasis often involves one gene masking or interfering with the expression of another (antagonistic epistasis). Epistasis often occurs when different genes are part of the same biochemical pathway. The...

Genome-wide Association Studies-GWAS

Genome-wide Association Studies-GWAS

Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
GWAS does not require the identification of the target gene involved in...

You might also read

Related Articles

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

Sort by

Same author

HuBIE: The human blood immunome encyclopedia of TCRs and BCRs in bloodstream infections and cancer.

Frontiers in immunology·2026

Same author

Real-world use of lecanemab: patient pathway findings from a US multicenter study.

Alzheimer's & dementia (Amsterdam, Netherlands)·2026

Same author

Amyloid Imaging and APOE Genotype Disclosure and Short-Term Psychological Distress.

JAMA network open·2026

Same author

Contrasting effects of SARS-CoV-2 vaccination vs. infection on antibody and TCR repertoires.

PloS one·2026

Same author

Real-world strategies for pulsed field ablation using a variable-loop circular catheter: a compendium of best practices from early adopters.

Journal of interventional cardiac electrophysiology : an international journal of arrhythmias and pacing·2026

Same author

HuBIE: The Human Blood Immunome Encyclopedia Of TCRs and BCRs in Bloodstream Infections and Cancer.

bioRxiv : the preprint server for biology·2025

Same journal

Cross-Domain Transfer Learning from Peptides to Metabolites Using a Multi-Property Fine-Tuned LLM.

Bioinformatics (Oxford, England)·2026

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

See all related articles

Search research articles

Related Experiment Video

Updated: May 13, 2026

A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq

A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq

Published on: May 28, 2021

FunFrame: functional gene ecological analysis pipeline.

David Weisman¹, Michie Yasuda, Jennifer L Bowen

¹Department of Biology, University of Massachusetts Boston, Boston, MA 02125, USA.

Bioinformatics (Oxford, England)

|March 21, 2013

Summary

This summary is machine-generated.

FunFrame is a new R-based pipeline for analyzing functional gene pyrosequences. It reduces errors and spurious diversity in microbial community analysis, improving functional gene data accuracy.

More Related Videos

A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information

A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information

Published on: July 1, 2020

Comprehensive Workflow for the Genome-wide Identification and Expression Meta-analysis of the ATL E3 Ubiquitin Ligase Gene Family in Grapevine

Comprehensive Workflow for the Genome-wide Identification and Expression Meta-analysis of the ATL E3 Ubiquitin Ligase Gene Family in Grapevine

Published on: December 22, 2017

Related Experiment Videos

Last Updated: May 13, 2026

A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq

A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq

Published on: May 28, 2021

A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information

A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information

Published on: July 1, 2020

Comprehensive Workflow for the Genome-wide Identification and Expression Meta-analysis of the ATL E3 Ubiquitin Ligase Gene Family in Grapevine

Comprehensive Workflow for the Genome-wide Identification and Expression Meta-analysis of the ATL E3 Ubiquitin Ligase Gene Family in Grapevine

Published on: December 22, 2017

Area of Science:

Microbial Ecology
Bioinformatics
Genomics

Background:

Pyrosequencing of 16S rDNA is standard for microbial community analysis, but software for functional protein-coding gene analysis lags.
Homopolymer errors in pyrosequencing inflate microbial diversity estimates, necessitating de-noising methods.
Existing tools for functional gene analysis are disparate and not unified.

Purpose of the Study:

To introduce FunFrame, an R-based pipeline for de-noising and analyzing functional gene pyrosequences.
To provide a unified set of tools for examining specific protein-coding genes in microbial communities.
To reduce bias in microbial diversity estimation caused by pyrosequencing errors.

Main Methods:

Developed an R-based data-analysis pipeline named FunFrame.
Implemented recently described algorithms for de-noising functional gene pyrosequences.
Performed ecological analysis on de-noised sequence data.
Evaluated FunFrame on functional genes from four PCR-amplified clones.

Main Results:

FunFrame produced 1–9 Operational Taxonomic Units per clone with error rates of 0–0.18%.
The pipeline successfully reduced spurious diversity in the sequence data.
FunFrame retained more sequences compared to a common de-noising method that discards frameshift errors.

Conclusions:

FunFrame offers a unified approach for analyzing functional gene pyrosequences, addressing limitations in current software availability.
The pipeline effectively de-noises data, reducing bias and improving the accuracy of microbial diversity estimation.
FunFrame enhances the analysis of functional differences among microbial communities by providing reliable sequence data.