Computational Approaches for Functional Prediction and Characterisation of Long Noncoding RNAs
Although a considerable portion of eukaryotic genomes is transcribed as long noncoding RNAs (lncRNAs), the vast majority are functionally uncharacterised. The rapidly expanding catalogue of mechanistically investigated lncRNAs has provided evidence for distinct functional subclasses, which are now r...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Signal, Bethany [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2016transfer abstract |
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| Schlagwörter: |
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| Umfang: |
18 |
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| Übergeordnetes Werk: |
Enthalten in: Degrading chlorinated aliphatics by reductive dechlorination of groundwater samples from the Santa Susana Field Laboratory - Dutta, Nalok ELSEVIER, 2022, Amsterdam [u.a.] |
|---|---|
| Übergeordnetes Werk: |
volume:32 ; year:2016 ; number:10 ; pages:620-637 ; extent:18 |
| Links: |
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| DOI / URN: |
10.1016/j.tig.2016.08.004 |
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| 520 | |a Although a considerable portion of eukaryotic genomes is transcribed as long noncoding RNAs (lncRNAs), the vast majority are functionally uncharacterised. The rapidly expanding catalogue of mechanistically investigated lncRNAs has provided evidence for distinct functional subclasses, which are now ripe for exploitation as a general model to predict functions for uncharacterised lncRNAs. By utilising publicly-available genome-wide datasets and computational methods, we present several developed and emerging in silico approaches to characterise and predict the functions of lncRNAs. We propose that the application of these techniques provides valuable functional and mechanistic insight into lncRNAs, and is a crucial step for informing subsequent functional studies. | ||
| 520 | |a Although a considerable portion of eukaryotic genomes is transcribed as long noncoding RNAs (lncRNAs), the vast majority are functionally uncharacterised. The rapidly expanding catalogue of mechanistically investigated lncRNAs has provided evidence for distinct functional subclasses, which are now ripe for exploitation as a general model to predict functions for uncharacterised lncRNAs. By utilising publicly-available genome-wide datasets and computational methods, we present several developed and emerging in silico approaches to characterise and predict the functions of lncRNAs. We propose that the application of these techniques provides valuable functional and mechanistic insight into lncRNAs, and is a crucial step for informing subsequent functional studies. | ||
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Elektronische Aufsätze |
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Signal, Bethany |
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10.1016/j.tig.2016.08.004 |
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570 333.7 |
| title_sort |
computational approaches for functional prediction and characterisation of long noncoding rnas |
| title_auth |
Computational Approaches for Functional Prediction and Characterisation of Long Noncoding RNAs |
| abstract |
Although a considerable portion of eukaryotic genomes is transcribed as long noncoding RNAs (lncRNAs), the vast majority are functionally uncharacterised. The rapidly expanding catalogue of mechanistically investigated lncRNAs has provided evidence for distinct functional subclasses, which are now ripe for exploitation as a general model to predict functions for uncharacterised lncRNAs. By utilising publicly-available genome-wide datasets and computational methods, we present several developed and emerging in silico approaches to characterise and predict the functions of lncRNAs. We propose that the application of these techniques provides valuable functional and mechanistic insight into lncRNAs, and is a crucial step for informing subsequent functional studies. |
| abstractGer |
Although a considerable portion of eukaryotic genomes is transcribed as long noncoding RNAs (lncRNAs), the vast majority are functionally uncharacterised. The rapidly expanding catalogue of mechanistically investigated lncRNAs has provided evidence for distinct functional subclasses, which are now ripe for exploitation as a general model to predict functions for uncharacterised lncRNAs. By utilising publicly-available genome-wide datasets and computational methods, we present several developed and emerging in silico approaches to characterise and predict the functions of lncRNAs. We propose that the application of these techniques provides valuable functional and mechanistic insight into lncRNAs, and is a crucial step for informing subsequent functional studies. |
| abstract_unstemmed |
Although a considerable portion of eukaryotic genomes is transcribed as long noncoding RNAs (lncRNAs), the vast majority are functionally uncharacterised. The rapidly expanding catalogue of mechanistically investigated lncRNAs has provided evidence for distinct functional subclasses, which are now ripe for exploitation as a general model to predict functions for uncharacterised lncRNAs. By utilising publicly-available genome-wide datasets and computational methods, we present several developed and emerging in silico approaches to characterise and predict the functions of lncRNAs. We propose that the application of these techniques provides valuable functional and mechanistic insight into lncRNAs, and is a crucial step for informing subsequent functional studies. |
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10 |
| title_short |
Computational Approaches for Functional Prediction and Characterisation of Long Noncoding RNAs |
| url |
https://doi.org/10.1016/j.tig.2016.08.004 |
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| author2 |
Gloss, Brian S. Dinger, Marcel E. |
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Gloss, Brian S. Dinger, Marcel E. |
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| doi_str |
10.1016/j.tig.2016.08.004 |
| up_date |
2024-07-06T22:02:18.138Z |
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