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Related Concept Videos

RNA-seq03:21

RNA-seq

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 microarray-based...
Ribosome Profiling02:24

Ribosome Profiling

Ribosome profiling or ribo-sequencing is a deep sequencing technique that produces a snapshot of active translation in a cell. It selectively sequences the mRNAs protected by ribosomes to get an insight into a cell’s translation landscape at any given point in time.
Applications of ribosome profiling
Ribosome profiling has many applications, including in vivo monitoring of translation inside a particular organ or tissue type and quantifying new protein synthesis levels.
The technique helps...
RNA Splicing01:32

RNA Splicing

Splicing is the process by which eukaryotic RNA is edited before its translation into protein. The RNA strand transcribed from eukaryotic DNA is called the primary transcript. The primary transcripts that become mRNAs are called precursor messenger RNAs (pre-mRNAs). Eukaryotic pre-mRNA contains alternating sequences of exons and introns. Exons are nucleotide sequences that code for proteins, whereas introns are the non-coding regions. In RNA splicing, introns are removed and exons are bonded...
RNA Interference01:23

RNA Interference

RNA interference (RNAi) is a process in which a small non-coding RNA molecule blocks the post-transcriptional expression of a gene by binding to its messenger RNA (mRNA) and preventing the protein from being translated.
This process occurs naturally in cells, often through the activity of genomically-encoded microRNAs. Researchers can take advantage of this mechanism by introducing synthetic RNAs to deactivate specific genes for research or therapeutic purposes. For example, RNAi could be used...
RNA Editing02:23

RNA Editing

RNA editing is a post-transcriptional modification where a precursor mRNA (pre-mRNA) nucleotide sequence is changed by base insertion, deletion, or modification. The extent of RNA editing varies from a few hundred bases, in mitochondrial DNA of trypanosomes, to a just single base, in nuclear genes of mammals. Even a single base change in the pre-mRNA can convert a codon for one amino acid into the codon for another amino acid or a stop codon. This type of re-coding can significantly affect the...
RNA Structure01:23

RNA Structure

Overview
The basic structure of RNA consists of a five-carbon sugar and one of four nitrogenous bases. Although most RNA is single-stranded, it can form complex secondary and tertiary structures. Such structures play essential roles in the regulation of transcription and translation.
Different Types of RNA Have the Same Basic Structure
There are three main types of ribonucleic acid (RNA): messenger RNA (mRNA), transfer RNA (tRNA), and ribosomal RNA (rRNA). All three RNA types consist of a...

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Related Experiment Video

Updated: Jun 30, 2026

Rup (RNA-seq Usability Assessment Pipeline) - Quality Control for Bulk RNA-seq Experiments in Eukaryotes
05:07

Rup (RNA-seq Usability Assessment Pipeline) - Quality Control for Bulk RNA-seq Experiments in Eukaryotes

Published on: November 7, 2025

The recount3 Python package for programmatic access to uniformly processed RNA-seq data.

Alexander Alsalihi, Robert M Flight, Hunter N B Moseley

    Biorxiv : the Preprint Server for Biology
    |June 29, 2026
    PubMed
    Summary
    This summary is machine-generated.

    The recount3 Python package offers easy access to large RNA-seq datasets for Python users. This tool simplifies data discovery and download, enabling advanced analysis in machine learning and bioinformatics.

    More Related Videos

    AQRNA-seq for Quantifying Small RNAs
    05:12

    AQRNA-seq for Quantifying Small RNAs

    Published on: February 2, 2024

    Related Experiment Videos

    Last Updated: Jun 30, 2026

    Rup (RNA-seq Usability Assessment Pipeline) - Quality Control for Bulk RNA-seq Experiments in Eukaryotes
    05:07

    Rup (RNA-seq Usability Assessment Pipeline) - Quality Control for Bulk RNA-seq Experiments in Eukaryotes

    Published on: November 7, 2025

    AQRNA-seq for Quantifying Small RNAs
    05:12

    AQRNA-seq for Quantifying Small RNAs

    Published on: February 2, 2024

    Area of Science:

    • Bioinformatics
    • Computational Biology
    • Genomics

    Background:

    • The recount3 resource provides extensive uniformly processed RNA-seq data.
    • Existing access methods primarily cater to the R/Bioconductor ecosystem.
    • There is a growing need for efficient Python-based tools for transcriptomic data analysis.

    Purpose of the Study:

    • To introduce the recount3 Python package for accessing and utilizing recount3 data.
    • To provide Python users with native, efficient tools for RNA-seq data discovery and download.
    • To bridge the gap between large-scale transcriptomic datasets and Python-based machine learning pipelines.

    Main Methods:

    • Development of a Python package with an application programming interface (API) and command-line interface (CLI).
    • Implementation of uniform resource locator (URL) resolution and persistent on-disk caching.
    • Automatic parsing of downloaded data into analysis-ready formats (Pandas DataFrames, BiocPy RangedSummarizedExperiment objects).

    Main Results:

    • The recount3 Python package facilitates discovery, download, and materialization of recount3 resources.
    • The software handles URL resolution, caching, and data parsing efficiently.
    • Analysis-ready data structures are automatically generated for Python users.

    Conclusions:

    • The recount3 Python package significantly lowers the barrier for large-scale RNA-seq data utilization in Python.
    • It enables seamless integration of public transcriptomic data into Python-based computational workflows.
    • This facilitates the application of modern machine learning techniques to vast RNA-seq datasets.