Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
3.3
2.2
Journals
Computation
Special Issues
Bioinformatics Tools for ncRNAs
share announcement

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 3289

Special Issue Editor

Prof. Dr. Shizuka Uchida

E-Mail Website
Guest Editor
Center for RNA Medicine, Department of Clinical Medicine, Aalborg University, Frederikskaj 10B, 2. (Building C), DK-2450 Copenhagen SV, Denmark
Interests: bioinformatics; cardiovascular; circRNA; database; epitranscriptomics; lncRNA; miRNA; non-coding RNA
Special Issues, Collections and Topics in MDPI journals

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

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Computation is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

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

Published Papers (1 paper)

Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

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://0-doi-org.brum.beds.ac.uk/10.3390/computation9020017 - 05 Feb 2021
Viewed by 2415
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)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-1
Back to TopTop