Noncoding Rna Annotation
Noncoding Rna Annotation - Related Videos
07:35
Computational Analysis Tutorial for Chimeric Small Noncoding RNA: Target RNA Sequencing Libraries
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Cited by 2 •
2023
Here, we present a protocol demonstrating the installation and use of a bioinformatics pipeline to analyze chimeric RNA sequencing data used in the study of in vivo RNA:RNA interactions.
06:44
Isolation of Small Noncoding RNAs from Human Serum
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Cited by 13 •
2014
This protocol describes a method for extracting small RNAs from human serum. We have used this method to isolate microRNAs from cancer serum for use in DNA arrays and also singleplex quantitative PCR. The protocol utilizes phenol and guanidinium thiocyanate reagents with modifications to yield high quality RNA.
03:36
Genome Annotation and Assembly
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2021
The genome refers to all of the genetic material in an organism. It can range from a few million base pairs in microbial cells to several billion base pairs in many eukaryotic organisms. Genome assembly refers to the process of taking the DNA sequencing data and putting it all back together in a correct order to create a close representation of the original genome. This is followed by the identification of functional elements on the newly assembled genome, a process called genome annotation.
08:09
Annotation of Plant Gene Function via Combined Genomics, Metabolomics and Informatics
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Cited by 8 •
2012
Combination of genomics, co-expression gene analysis and the identification of target compounds via metabolism give gene functional annotation.
05:07
Rup (RNA-seq Usability Assessment Pipeline) - Quality Control for Bulk RNA-seq Experiments in Eukaryotes
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2025
This protocol allows initial quality control for RNA-seq experiments for wet-lab biologists with limited bioinformatics experience.
FAQs
Reliable annotation comes from combining evidence rather than relying on sequence alone. Sequence analysis can be paired with transcriptome data, genomic-location mapping, evolutionary conservation, structural evidence, and expression evidence. Agreement among these sources strengthens confidence that a transcript is a genuine functional genomic element and helps assign features or probable functions. Differences between evidence types can also identify cases needing experimental validation.
The distinction depends on integrating several observations. Sequence analysis examines transcript characteristics, while transcriptome data shows where transcripts are detected. Genomic-location mapping places them within genome organization, and conservation, structural, or expression evidence adds independent support. Considering these dimensions together helps annotation systems separate noncoding transcripts from protein-coding transcripts and attach features without relying on one signal alone.
Conservation, structure, and expression answer different annotation questions. Evolutionary conservation can highlight transcripts or regions retained across species, structural evidence can indicate RNA organization, and expression evidence shows activity across cells, tissues, or conditions. Used together, these clues help researchers assign probable functions and prioritize candidates for experimental validation rather than treating any single observation as conclusive.
A practical workflow starts by identifying relevant RNA transcripts and building a catalog. Researchers then map each transcript to its genomic location, compare sequence and transcriptome evidence, and add conservation, structural, or expression information where available. The final annotation records features or probable functions, while uncertain entries can be directed toward experimental validation and later catalog refinement.
Catalogs created through this process support several biological applications. They help researchers study regulatory RNAs and genome organization, examine developmental processes, and investigate disease-associated changes. Because the same annotation framework can be applied across cells, tissues, species, and biological conditions, it also enables comparisons of noncoding RNA repertoires and highlights where RNA patterns differ.
In biology, accurate catalogs do more than list transcripts. They can improve genome assemblies, guide experimental validation, and provide a reference for interpreting functional elements. This makes annotation useful when researchers connect RNA repertoires with genome organization, development, or disease-associated changes. Its value depends on integrating evidence and distinguishing supported features from probable functions.