Inferring Noncoding RNA Families and Classes by Means of Genome-Scale Structure-Based Clustering

By Sebastian Will, Kristin Reiche, Ivo L. Hofacker, Peter F. Stadler, Rolf Backofen. PLoS Comput. Biol., 2007

534 citations recorded by the source corpus.

The RFAM database defines families of ncRNAs by means of sequence similarities that are sufficient to establish homology. In some cases, such as microRNAs and box H/ACA snoRNAs, functional commonalities define classes of RNAs that are characterized by structural similarities, and typically consist of multiple RNA families. Recent advances in high-throughput transcriptomics and comparative genomics have produced very large sets of putative noncoding RNAs and regulatory RNA signals. For many of them, evidence for stabilizing selection acting on their secondary structures has been derived, and at least approximate models of their structures have been computed. The overwhelming majority of these hypothetical RNAs cannot be assigned to established families or classes. We present here a structure-based clustering approach that is capable of extracting putative RNA classes from genome-wide surveys for structured RNAs. The LocARNA (local alignment of RNA) tool implements a novel variant of the Sankoff algorithm that is sufficiently fast to deal with several thousand candidate sequences. The method is also robust against false positive predictions, i.e., a contamination of the input data with unstructured or nonconserved sequences. We have successfully tested the LocARNA-based clustering approach on the sequences of the RFAM-seed alignments. Furthermore, we have applied it to a previously published set of 3,332 predicted structured elements in the Ciona intestinalis genome (Missal K, Rose D, Stadler PF (2005) Noncoding RNAs in Ciona intestinalis. Bioinformatics 21 (Supplement 2): i77–i78). In addition to recovering, e.g., tRNAs as a structure-based class, the method identifies several RNA families, including microRNA and snoRNA candidates, and suggests several novel classes of ncRNAs for which to date no representative has been experimentally characterized.

Subfields: Cancer Research, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/69c265feb49e2f928c7d00b61217a7dbe387355c

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Citations in the corpus

Recent developments in the MAFFT multiple sequence alignment program (2008, background): “Many research groups are now working on the RNA alignment issue [77–88], and the accuracy and speed of ncRNA aligners have rapidly improved in the last several months, as shown in Table 2.”
Rfam 14: expanded coverage of metagenomic, viral and microRNA families (2020, methodology): “These alignments are refined by local RNA secondary structure information using LocARNA (35).”
ViennaRNA Package 2.0 (2011, methodology): “An improved and much more efficient implementation is provided by the locarna package [59] developed in cooperation with Rolf Backofen and Sebastian Will and distributed separately.”
PicXAA-R: Efficient structural alignment of multiple RNA sequences using a greedy approach (2011, methodology): “Murlet [12], RAF [13], PARTS [14], STRAL [15], LocARNA [16], CentroidAlign [17], and PMcomp [18] exploit probabilistic approaches by implementing base-pairing probabilities in a restricted Sankoff-style framework or employing the Needleman-Wunsch algorithm with structural scores.”
A fast structural multiple alignment method for long RNA sequences (2008, methodology): “We compared the performance of MXSCARNA with that of Murlet [21], ProbCons [32], MAFFT [38], ClustalW [7], StrAl [18], MARNA [17], RNASampler [19], RNAlara [20], FoldalignM [15], Locarna [16], PMmulti [12], and Stemloc [13] on the three datasets described earlier.”
LocARNA-P: accurate boundary prediction and improved detection of structural RNAs. (2012, methodology): “In contrast, PMcomp (Hofacker and Stadler 2004) and LocARNA (Will et al. 2007) evaluate a more lightweight energy model during the alignment.”