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lncRNA - Long Non-coding RNAs

lncRNA - Long Non-coding RNAs

In humans, more than 80% of the genome gets transcribed. However, only around 2% of the genome codes for proteins. The remaining part produces non-coding RNAs which includes ribosomal RNAs, transfer RNAs, telomerase RNAs, and regulatory RNAs, among other types. A large number of regulatory non-coding RNAs have been classified into two groups depending upon their length – small non-coding RNAs, such as microRNA, which are less than 200 nucleotides in length, and long non-coding RNA...

lncRNA - Long Non-coding RNAs

lncRNA - Long Non-coding RNAs

siRNA - Small Interfering RNAs

siRNA - Small Interfering RNAs

Small interfering RNAs, or siRNAs, are short regulatory RNA molecules that can silence genes post-transcriptionally, as well as the transcriptional level in some cases. siRNAs are important for protecting cells against viral infections and silencing transposable genetic elements.
In the cytoplasm, siRNA is processed from a double-stranded RNA, which comes from either endogenous DNA transcription or exogenous sources like a virus. This double-stranded RNA is then cleaved by the...

piRNA - Piwi-interacting RNAs

piRNA - Piwi-interacting RNAs

PIWI-interacting RNAs, or piRNAs, are the most abundant short non-coding RNAs. More than 20,000 genes have been found in humans that code for piRNAs while only 2000 genes have been found for miRNAs. piRNAs can act at the transcriptional and post-transcriptional levels and have a vital role in silencing transposable elements present in germ cells. They are also involved in epigenetic silencing and activation. Previously, they were thought to function only in germ cells but new evidence suggests...

Functional Classification of Joints

Functional Classification of Joints

Functional Classification of Joints
The functional classification of joints is determined by the amount of mobility between the adjacent bones. Joints are functionally classified as a synarthrosis or immobile joint, an amphiarthrosis or slightly moveable joint, or as a diarthrosis, a freely moveable joint. Fibrous and cartilaginous joints can be functionally classified as either synarthroses or amphiarthroses, whereas all synovial joints are classified as diarthroses.
Synarthrosis
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Genome Annotation and Assembly

Genome Annotation and Assembly

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.

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Related Experiment Video

Updated: Feb 16, 2026

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Published on: December 7, 2021

Ontological function annotation of long non-coding RNAs through hierarchical multi-label classification.

Jingpu Zhang^1,2, Zuping Zhang¹, Zixiang Wang³

¹School of Information Science and Engineering, Central South University, Changsha 410083, China.

Bioinformatics (Oxford, England)

|January 3, 2018

Summary

NeuraNetL2GO, a novel computational method, predicts functions for long non-coding RNAs (lncRNAs) using neural networks. This approach offers superior performance in annotating lncRNA functions compared to existing methods.

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Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Long non-coding RNAs (lncRNAs) represent a vast class of functional non-coding RNAs, with many identified but largely uncharacterized.
Computational methods are crucial for inferring potential functions of lncRNAs and understanding their biological roles.

Purpose of the Study:

To introduce NeuraNetL2GO, a computational approach for predicting ontological functions of lncRNAs.
To leverage a hierarchical multi-label classification strategy with multiple neural networks for lncRNA function prediction.

Main Methods:

NeuraNetL2GO employs a hierarchical multi-label classification strategy using multiple neural networks.
Neural networks are trained incrementally, level by level, to predict Gene Ontology (GO) terms.
Topological features from the lncRNA similarity network serve as input for the neural networks.

Main Results:

NeuraNetL2GO demonstrates superior performance in predicting lncRNA functions.
The method achieves the best performance in terms of maximum F-measure and coverage on the lncRNA2GO-55 dataset.
It outperforms other state-of-the-art methods in lncRNA function annotation.

Conclusions:

NeuraNetL2GO provides an effective computational tool for annotating lncRNA functions.
The hierarchical multi-label classification approach enhances the accuracy and coverage of functional predictions.
This method advances our understanding of the functional landscape of lncRNAs.