Prioritizing and selecting likely novel miRNAs from NGS data.

Prioritizing and selecting likely novel miRNAs from NGS data. Backes, C., Meder, B., Hart, M., Ludwig, N., Leidinger, P., Vogel, B., Galata, V., Roth, P., Menegatti, J., Grässer, F., Ruprecht, K., Kahraman, M., Grossmann, T., Haas, J., Meese, E., & Keller, A. Nucleic acids research, 44:e53, April, 2016.
doi abstract bibtex

Small non-coding RNAs play a key role in many physiological and pathological processes. Since 2004, miRNA sequences have been catalogued in miRBase, which is currently in its 21st version. We investigated sequence and structural features of miRNAs annotated in the miRBase and compared them between different versions of this reference database. We have identified that the two most recent releases (v20 and v21) are influenced by next-generation sequencing based miRNA predictions and show significant deviation from miRNAs discovered prior to the high-throughput profiling period. From the analysis of miRBase, we derived a set of key characteristics to predict new miRNAs and applied the implemented algorithm to evaluate novel blood-borne miRNA candidates. We carried out 705 individual whole miRNA sequencings of blood cells and collected a total of 9.7 billion reads. Using miRDeep2 we initially predicted 1452 potentially novel miRNAs. After excluding false positives, 518 candidates remained. These novel candidates were ranked according to their distance to the features in the early miRBase versions allowing for an easier selection of a subset of putative miRNAs for validation. Selected candidates were successfully validated by qRT-PCR and northern blotting. In addition, we implemented a web-server for ranking potential miRNA candidates, which is available at:www.ccb.uni-saarland.de/novomirank.

@Article{Backes2016a,
  author          = {Backes, Christina and Meder, Benjamin and Hart, Martin and Ludwig, Nicole and Leidinger, Petra and Vogel, Britta and Galata, Valentina and Roth, Patrick and Menegatti, Jennifer and Grässer, Friedrich and Ruprecht, Klemens and Kahraman, Mustafa and Grossmann, Thomas and Haas, Jan and Meese, Eckart and Keller, Andreas},
  title           = {Prioritizing and selecting likely novel miRNAs from NGS data.},
  journal         = {Nucleic acids research},
  year            = {2016},
  volume          = {44},
  pages           = {e53},
  month           = apr,
  issn            = {1362-4962},
  abstract        = {Small non-coding RNAs play a key role in many physiological and pathological processes. Since 2004, miRNA sequences have been catalogued in miRBase, which is currently in its 21st version. We investigated sequence and structural features of miRNAs annotated in the miRBase and compared them between different versions of this reference database. We have identified that the two most recent releases (v20 and v21) are influenced by next-generation sequencing based miRNA predictions and show significant deviation from miRNAs discovered prior to the high-throughput profiling period. From the analysis of miRBase, we derived a set of key characteristics to predict new miRNAs and applied the implemented algorithm to evaluate novel blood-borne miRNA candidates. We carried out 705 individual whole miRNA sequencings of blood cells and collected a total of 9.7 billion reads. Using miRDeep2 we initially predicted 1452 potentially novel miRNAs. After excluding false positives, 518 candidates remained. These novel candidates were ranked according to their distance to the features in the early miRBase versions allowing for an easier selection of a subset of putative miRNAs for validation. Selected candidates were successfully validated by qRT-PCR and northern blotting. In addition, we implemented a web-server for ranking potential miRNA candidates, which is available at:www.ccb.uni-saarland.de/novomirank. },
  chemicals       = {MicroRNAs},
  citation-subset = {IM},
  completed       = {2016-08-29},
  country         = {England},
  doi             = {10.1093/nar/gkv1335},
  issn-linking    = {0305-1048},
  issue           = {6},
  keywords        = {Algorithms; Base Sequence; Blood Cells, chemistry, metabolism; Blotting, Northern; Computational Biology, methods; Gene Expression Profiling; High-Throughput Nucleotide Sequencing; Humans; MicroRNAs, blood, genetics; Molecular Sequence Data; Real-Time Polymerase Chain Reaction; Sequence Analysis, RNA, statistics & numerical data; Software; Transcriptome},
  nlm-id          = {0411011},
  owner           = {NLM},
  pii             = {gkv1335},
  pmc             = {PMC4824081},
  pmid            = {26635395},
  pubmodel        = {Print-Electronic},
  pubstatus       = {ppublish},
  revised         = {2016-04-09},
}

Downloads: 0