Bioinformatics Tools for ncRNAs

A special issue of Computation (ISSN 2079-3197). This special issue belongs to the section "Computational Biology".

Deadline for manuscript submissions: closed (31 December 2021) | Viewed by 3794

Special Issue Editor

Special Issue Information

Dear Colleagues,

Non-coding RNAs (ncRNAs) are transcripts that do not encode for proteins. Traditionally, ribosomal RNAs (rRNAs) and transfer RNAs (tRNAs) have been studied extensively. In recent years, the advancement of next generation sequencing (NGS) has accelerated the discovery of other types of ncRNAs, including microRNAs (micRNAs), circular RNAs (circRNAs), and long non-coding RNAs (lncRNAs). Moreover, as opposed to mRNAs for polypeptides (i.e., proteins), many ncRNAs are functional as in the case of transcriptional regulation by lncRNAs, post-transcriptional control by miRNAs, and protein synthesis by tRNAs. Interestingly, the recent emergence of epitranscriptomics (biochemical modifications of RNAs) has expanded our understanding about RNAs in general as well as their modifications, including those of ncRNAs. Needless to say, all of these discoveries have been aided by the development of computational methods, especially in the field of bioinformatics. To further broaden our understanding of ncRNAs from the perspective of computational works, we invite contributors to this edition to submit manuscripts about ncRNAs, including their detection, annotations, bioinformatics tools, and databases.

Prof. Dr. Shizuka Uchida
Guest Editor

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Keywords

  • Bioinformatics
  • circRNAs
  • Database
  • microRNAs
  • ncRNAs
  • lncRNAs

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Published Papers (1 paper)

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Research

20 pages, 1281 KiB  
Article
Weighted Consensus Segmentations
by Halima Saker, Rainer Machné, Jörg Fallmann, Douglas B. Murray, Ahmad M. Shahin and Peter F. Stadler
Computation 2021, 9(2), 17; https://doi.org/10.3390/computation9020017 - 5 Feb 2021
Cited by 1 | Viewed by 2801
Abstract
The problem of segmenting linearly ordered data is frequently encountered in time-series analysis, computational biology, and natural language processing. Segmentations obtained independently from replicate data sets or from the same data with different methods or parameter settings pose the problem of computing an [...] Read more.
The problem of segmenting linearly ordered data is frequently encountered in time-series analysis, computational biology, and natural language processing. Segmentations obtained independently from replicate data sets or from the same data with different methods or parameter settings pose the problem of computing an aggregate or consensus segmentation. This Segmentation Aggregation problem amounts to finding a segmentation that minimizes the sum of distances to the input segmentations. It is again a segmentation problem and can be solved by dynamic programming. The aim of this contribution is (1) to gain a better mathematical understanding of the Segmentation Aggregation problem and its solutions and (2) to demonstrate that consensus segmentations have useful applications. Extending previously known results we show that for a large class of distance functions only breakpoints present in at least one input segmentation appear in the consensus segmentation. Furthermore, we derive a bound on the size of consensus segments. As show-case applications, we investigate a yeast transcriptome and show that consensus segments provide a robust means of identifying transcriptomic units. This approach is particularly suited for dense transcriptomes with polycistronic transcripts, operons, or a lack of separation between transcripts. As a second application, we demonstrate that consensus segmentations can be used to robustly identify growth regimes from sets of replicate growth curves. Full article
(This article belongs to the Special Issue Bioinformatics Tools for ncRNAs)
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