Search Results for: classify of lncrna
It was traditionally thought that the transcriptome would be mostly comprised of mRNAs, however advances in high-throughput RNA sequencing technologies have revealed the complexity of our genome. Non-coding RNA is now known to make up the majority of transcribed RNAs and in addition to those that carry out well-known housekeeping functions (e.g. tRNA, rRNA etc), many different types of regulatory RNAs have been and continue to be discovered. Many of these non-coding RNAs are thought to have a wide range of functions in cellular and developmental processes.
Long noncoding RNAs (lncRNAs) are a large and diverse class of transcribed RNA molecules with a length of more than 200 nucleotides that do not encode proteins. Their expression is developmentally regulated and lncRNAs can be tissue- and cell-type specific. A significant proportion of lncRNAs are located exclusively in the nucleus. They are comprised of many types of transcripts that can structurally resemble mRNAs, and are sometimes transcribed as whole or partial antisense transcripts to coding genes. LncRNAs are thought to carry out important regulatory functions, adding yet another layer of complexity to our understanding of genomic regulation.
Functions of lncRNA
LncRNAs may exert their functions…
Incoming search terms:
- difference between coding rna (crna) & non-coding rna (ncrna)
- antisense ncRNA download
PredcircRNA: computational classification of circular RNA from other long non-coding RNA using hybrid features
Recently circular RNA (circularRNA) has been discovered as an increasingly important type of long non-coding RNA (lncRNA), playing an important role in gene regulation, such as functioning as miRNA sponges. So it is very promising to identify circularRNA transcripts from de novo assembled transcripts obtained by high-throughput sequencing, such as RNA-seq data.
In this study, researchers from the University of Copenhagen present a machine learning approach, named as PredcircRNA, focused on distinguishing circularRNA from other lncRNAs using multiple kernel learning. Firstly they extracted different sources of discriminative features, including graph features, conservation information and sequence compositions, ALU and tandem repeats, SNP densities and open reading frames (ORFs) from transcripts. Secondly, to better integrate features from different sources, they proposed a computational approach based on a multiple kernel learning framework to fuse those heterogeneous features. Their preliminary 5-fold cross-validation result showed that our proposed method can classify circularRNA from other types of lncRNAs with an accuracy of 0.778, sensitivity of 0.781, specificity of 0.770, precision of 0.784 and MCC of 0.554 in our constructed gold-standard dataset, respectively. Their feature importance analysis based on Random Forest illustrated some discriminative features, such as conservation features and a GTAG sequence motif.
Availability – PredcircRNA tool is available for download at: https://github.com/xypan1232/PredcircRNA
- Pan X, Xiong K. (2015) PredcircRNA: computational classification of circular RNA from other long non-coding RNA using hybrid features. Mol Biosyst [Epub ahead of print]. [abstract]
Large noncoding RNAs are emerging as an important component in cellular regulation. Considerable evidence indicates that these transcripts act directly as functional RNAs rather than through an encoded protein product. However, a recent study of ribosome occupancy reported that many large intergenic ncRNAs (lincRNAs) are bound by ribosomes, raising the possibility that they are translated into proteins.
Here, researchers from the Broad Institute of MIT and Harvard show that classical noncoding RNAs and 5′ UTRs show the same ribosome occupancy as lincRNAs, demonstrating that ribosome occupancy alone is not sufficient to classify transcripts as coding or noncoding. Instead, they define a metric based on the known property of translation whereby translating ribosomes are released upon encountering a bona fide stop codon. They show that this metric accurately discriminates between protein-coding transcripts and all classes of known noncoding transcripts, including lincRNAs. Taken together, these results argue that the large majority of lincRNAs do not function through encoded proteins.
- Guttman M, Russell P, Ingolia NT, Weissman JS, Lander ES. (2013) Ribosome Profiling Provides Evidence that Large Noncoding RNAs Do Not Encode Proteins. Cell 154(1), 240-51. [abstract]
Incoming search terms:
- encode lincRNA database
Long non-coding RNAs (lncRNAs) have been found to perform various functions in a wide variety of important biological processes. To make easier interpretation of lncRNA functionality and conduct deep mining on these transcribed sequences, it is convenient to classify lncRNAs into different groups. Here, researchers from the CAS Key Laboratory of Genome Sciences and Information, China summarize classification methods of lncRNAs according to their four major features, namely, genomic location and context, effect exerted on DNA sequences, mechanism of functioning and their targeting mechanism. In combination with the presently available function annotations, they explore potential relationships between different classification categories, and generalize and compare biological features of different lncRNAs within each category. Finally, they present our view on potential further studies. The researchers believe that the classifications of lncRNAs as indicated above are of fundamental importance for lncRNA studies, helpful for further investigation of specific lncRNAs, for formulation of new hypothesis based on different features of lncRNA and for exploration of the underlying lncRNA functional mechanisms.
Ma L, Bajic VB, Zhang Z. (2013) On the classification of long non-coding RNAs. RNA Biol 10(6). [article]
Incoming search terms:
- On the classification of long non-coding RNAs