Details zur Publikation |
| Kategorie | Textpublikation |
| Referenztyp | Buchkapitel |
| DOI | 10.1007/978-1-61779-027-0_14 |
| Titel (primär) | Bioinformatics for RNomics |
| Titel (sekundär) | Bioinformatics for Omics data |
| Autor | Reiche, K.; Schutt, K.; Boll, K.; Horn, F.; Hackermüller, J.
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| Herausgeber | Mayer, B. |
| Quelle | Methods in Molecular Biology |
| Erscheinungsjahr | 2011 |
| Department | PROTEOM |
| Band/Volume | 719 |
| Seite von | 299 |
| Seite bis | 330 |
| Sprache | englisch |
| Keywords | RNomics; Non-coding RNA; ncRNA; Transcriptome; Bioinformatics; Regulatory RNA |
| Abstract | Rapid improvements in high-throughput experimental technologies make it nowadays possible to study the expression, as well as changes in expression, of whole transcriptomes under different environmental conditions in a detailed view. We describe current approaches to identify genome-wide functional RNA transcripts (experimentally as well as computationally), and focus on computational methods that may be utilized to disclose their function. While genome databases offer a wealth of information about known and putative functions for protein-coding genes, functional information for novel non-coding RNA genes is almost nonexistent. This is mainly explained by the lack of established software tools to efficiently reveal the function and evolutionary origin of non-coding RNA genes. Here, we describe in detail computational approaches one may follow to annotate and classify an RNA transcript. |
| Reiche, K., Schutt, K., Boll, K., Horn, F., Hackermüller, J. (2011): Bioinformatics for RNomics In: Mayer, B. (ed.) Bioinformatics for Omics data Methods in Molecular Biology 719 Springer Nature, p. 299 - 330 10.1007/978-1-61779-027-0_14 |
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