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

RNA-seq03:21

RNA-seq

12.4K
RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases. 
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...
12.4K
RACE - Rapid Amplification of cDNA Ends02:35

RACE - Rapid Amplification of cDNA Ends

7.5K
Rapid Amplification of cDNA Ends, or RACE, is one of the most effective methods to obtain a full-length cDNA from an mRNA sequence between a known internal region to the unknown sequence at the 5’ or 3’ end. The unknown region is cloned in the cDNA by a gene-specific primer that binds the known end, and a hybrid primer that attaches a predefined anchor sequence to the unknown end of the cDNA. The sequence in between is amplified by PCR with an anchor primer and a gene-specific...
7.5K
Next-generation Sequencing03:00

Next-generation Sequencing

100.4K
The first human genome sequencing project cost $2.7 billion and was declared complete in 2003, after 15 years of international cooperation and collaboration between several research teams and funding agencies. Today, with the advent of next-generation sequencing technologies, the cost and time of sequencing a human genome have dropped over 100 fold.
Next-Generation Sequencing Methods
Although all next-generation methods use different technologies, they all share a set of standard features....
100.4K

You might also read

Related Articles

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

Sort by
Same author

Multi-modal circulating cell-free DNA profiling to predict response to docetaxel in metastatic castration-resistant prostate cancer.

NPJ precision oncology·2026
Same author

Clinical validation of a high-performance somatic exome sequencing assay: from target-enrichment strategy to variant calling.

NPJ genomic medicine·2026
Same author

Daratumumab in high-risk MGUS and low-risk smoldering myeloma: results of the Phase II D-PRISM study.

Nature communications·2026
Same author

A linguistics-based algorithm for RBP motif and context discovery.

bioRxiv : the preprint server for biology·2026
Same author

Transposable element-gene chimera cartography, origination and role in enhancing transcriptome plasticity.

Nature structural & molecular biology·2026
Same author

A novel NLP-based method and algorithm to discover RNA-binding protein (RBP) motifs, contexts, binding preferences, and interactions.

RNA (New York, N.Y.)·2026
Same journal

Analysis of strength degradation of coal and rock masses and stability of mined areas under long term immersion environment.

PloS one·2026
Same journal

Biogenic Silver-Selenium nanocomposite with anticancer activity and potent efficacy against vancomycin-resistant Staphylococcus aureus.

PloS one·2026
Same journal

Preparation and physicochemical characterization of a biodegradable chitosan/carboxymethyl cellulose hydrogel synthesized in NaOH/urea medium.

PloS one·2026
Same journal

Action-guilt, survivor-guilt, and depression in combat-related PTSD.

PloS one·2026
Same journal

Explainable machine learning for predicting activities of daily living at discharge in stroke patients: A retrospective study using SHAP interpretability.

PloS one·2026
Same journal

Deep learning based two-way feature depiction model for brain tumor detection.

PloS one·2026
See all related articles

Related Experiment Video

Updated: Mar 13, 2026

Improving Small RNA-seq: Less Bias and Better Detection of 2'-O-Methyl RNAs
08:49

Improving Small RNA-seq: Less Bias and Better Detection of 2'-O-Methyl RNAs

Published on: September 16, 2019

8.2K

DNApi: A De Novo Adapter Prediction Algorithm for Small RNA Sequencing Data.

Junko Tsuji1, Zhiping Weng1

  • 1Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, Massachusetts, United States of America.

Plos One
|October 14, 2016
PubMed
Summary
This summary is machine-generated.

DNApi is a new Python tool that accurately predicts missing 3´ adapter sequences in small RNA sequencing data. It also identifies processed libraries, ensuring reliable downstream analysis for meta-analysis studies.

More Related Videos

Author Spotlight: A Computational Pipeline for Analyzing Chimeric Noncoding RNA-Target RNA Interactions in High-Throughput Sequencing Data
07:35

Author Spotlight: A Computational Pipeline for Analyzing Chimeric Noncoding RNA-Target RNA Interactions in High-Throughput Sequencing Data

Published on: December 1, 2023

1.2K
Author Spotlight: AQRNA-seq Role in Mapping Small RNAs and Unraveling Protein Translation Mechanisms
05:12

Author Spotlight: AQRNA-seq Role in Mapping Small RNAs and Unraveling Protein Translation Mechanisms

Published on: February 2, 2024

1.5K

Related Experiment Videos

Last Updated: Mar 13, 2026

Improving Small RNA-seq: Less Bias and Better Detection of 2'-O-Methyl RNAs
08:49

Improving Small RNA-seq: Less Bias and Better Detection of 2'-O-Methyl RNAs

Published on: September 16, 2019

8.2K
Author Spotlight: A Computational Pipeline for Analyzing Chimeric Noncoding RNA-Target RNA Interactions in High-Throughput Sequencing Data
07:35

Author Spotlight: A Computational Pipeline for Analyzing Chimeric Noncoding RNA-Target RNA Interactions in High-Throughput Sequencing Data

Published on: December 1, 2023

1.2K
Author Spotlight: AQRNA-seq Role in Mapping Small RNAs and Unraveling Protein Translation Mechanisms
05:12

Author Spotlight: AQRNA-seq Role in Mapping Small RNAs and Unraveling Protein Translation Mechanisms

Published on: February 2, 2024

1.5K

Area of Science:

  • Bioinformatics
  • Genomics
  • Computational Biology

Background:

  • Small RNA sequencing generates vast datasets crucial for meta-analysis.
  • Accurate adapter sequence removal is vital for small RNA sequencing analysis.
  • Adapter sequence information is often missing or erroneous in metadata.

Purpose of the Study:

  • To develop a tool for de novo prediction of 3´ adapter sequences in small RNA sequencing data.
  • To provide cleansed small RNA sequences for downstream analysis.
  • To classify processed small RNA libraries ready for mapping.

Main Methods:

  • Developed DNApi, a lightweight Python software package.
  • Tested DNApi on 539 publicly available small RNA libraries with known adapter sequences.
  • Evaluated DNApi's accuracy, runtime, and memory usage.

Main Results:

  • DNApi achieved 98.5% accuracy in predicting 3´ adapter sequences.
  • The tool demonstrated fast runtime (~2.85 seconds/library) and efficient memory usage (~43 MB/library).
  • DNApi perfectly identified 192 processed libraries as 'ready-to-map'.

Conclusions:

  • DNApi is a highly accurate and efficient tool for small RNA sequencing data preprocessing.
  • The software facilitates reliable meta-analysis by ensuring data quality.
  • Curated datasets for evaluation are provided for integration into other studies.