Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
4.0
3.8
Journals
Bioengineering
Special Issues
New Bioinformatics Tools
share announcement

New Bioinformatics Tools

A special issue of Bioengineering (ISSN 2306-5354).

Deadline for manuscript submissions: closed (30 June 2021) | Viewed by 15546

Special Issue Editor

Dr. Florencio Pazos

E-Mail Website
Guest Editor
Spanish National Research Council, Spanish National Center for Biotechnology Madrid, Madrid, Spain

Special Issue Information

Modern biology is characterized by the generation of massive datasets that require a computational treatment in order to distill useful information from them. Although computers are essential today in all human activities, their importance in biological research is greater due to the irreversible data-oriented trend in this field. Consequently, from being mere helping hands to the experimental work, computational tools are now indispensable for biological research and form an intrinsic part of most workflows. Daily work in molecular biology presently depends on a large ecosystem of computational tools. This ecosystem evolves with time and adapts to different requirements imposed by the peculiarities of biological data and methods popular at a given moment.

Bioengineering welcomes submissions for its Special Issue on “New Bioinformatics Tools”. This Special Issue aims to cover the main current trends in the development and application of computational tools and methods to biological problems.

Dr. Florencio Pazos
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. Bioengineering 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 2700 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.

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue polices can be found here.

Published Papers (3 papers)

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

Research

18 pages, 1462 KiB  
Article
WinBEST-KIT: Biochemical Reaction Simulator for Analyzing Multi-Layered Metabolic Pathways
by Tatsuya Sekiguchi, Hiroyuki Hamada and Masahiro Okamoto
Bioengineering 2021, 8(8), 114; https://doi.org/10.3390/bioengineering8080114 - 11 Aug 2021
Cited by 1 | Viewed by 3326
Abstract
We previously developed the biochemical reaction simulator WinBEST-KIT. In recent years, research interest has shifted from analysis of individual biochemical reactions to analysis of metabolic pathways as systems. These large-scale and complicated metabolic pathways can be considered as characteristic multi-layered structures, which, for [...] Read more.
We previously developed the biochemical reaction simulator WinBEST-KIT. In recent years, research interest has shifted from analysis of individual biochemical reactions to analysis of metabolic pathways as systems. These large-scale and complicated metabolic pathways can be considered as characteristic multi-layered structures, which, for convenience, are separated from whole biological systems according to their specific roles. These pathways include reactants having the same name but with unique stoichiometric coefficients arranged across many different places and connected between arbitrary layers. Accordingly, in this study, we have developed a new version of WinBEST-KIT that allows users (1) to utilize shortcut symbols that can be arranged with multiple reactants having the same name but with unique stoichiometric coefficients, thereby providing a layout that is similar to metabolic pathways depicted in biochemical textbooks; (2) to create layers that divide large-scale and complicated metabolic pathways according to their specific roles; (3) to connect the layers by using shortcut symbols; and (4) to analyze the interactions between these layers. These new and existing features allow users to create and analyze such multi-layered metabolic pathways efficiently. Furthermore, WinBEST-KIT supports SBML, making it possible for users to utilize these new and existing features to create and publish SBML models. Full article
(This article belongs to the Special Issue New Bioinformatics Tools)
►▼ Show Figures

Figure 1

7 pages, 1380 KiB  
Communication
SeqFu: A Suite of Utilities for the Robust and Reproducible Manipulation of Sequence Files
by Andrea Telatin, Piero Fariselli and Giovanni Birolo
Bioengineering 2021, 8(5), 59; https://doi.org/10.3390/bioengineering8050059 - 7 May 2021
Cited by 26 | Viewed by 5907
Abstract
Sequence files formats (FASTA and FASTQ) are commonly used in bioinformatics, molecular biology and biochemistry. With the advent of next-generation sequencing (NGS) technologies, the number of FASTQ datasets produced and analyzed has grown exponentially, urging the development of dedicated software to handle, parse, [...] Read more.
Sequence files formats (FASTA and FASTQ) are commonly used in bioinformatics, molecular biology and biochemistry. With the advent of next-generation sequencing (NGS) technologies, the number of FASTQ datasets produced and analyzed has grown exponentially, urging the development of dedicated software to handle, parse, and manipulate such files efficiently. Several bioinformatics packages are available to filter and manipulate FASTA and FASTQ files, yet some essential tasks remain poorly supported, leaving gaps that any workflow analysis of NGS datasets must fill with custom scripts. This can introduce harmful variability and performance bottlenecks in pivotal steps. Here we present a suite of tools, called SeqFu (Sequence Fastx utilities), that provides a broad range of commands to perform both common and specialist operations with ease and is designed to be easily implemented in high-performance analytical pipelines. SeqFu includes high-performance implementation of algorithms to interleave and deinterleave FASTQ files, merge Illumina lanes, and perform various quality controls (identification of degenerate primers, analysis of length statistics, extraction of portions of the datasets). SeqFu dereplicates sequences from multiple files keeping track of their provenance. SeqFu is developed in Nim for high-performance processing, is freely available, and can be installed with the popular package manager Miniconda. Full article
(This article belongs to the Special Issue New Bioinformatics Tools)
►▼ Show Figures

Figure 1

12 pages, 1904 KiB  
Article
P3CMQA: Single-Model Quality Assessment Using 3DCNN with Profile-Based Features
by Yuma Takei and Takashi Ishida
Bioengineering 2021, 8(3), 40; https://doi.org/10.3390/bioengineering8030040 - 19 Mar 2021
Cited by 6 | Viewed by 3794
Abstract
Model quality assessment (MQA), which selects near-native structures from structure models, is an important process in protein tertiary structure prediction. The three-dimensional convolution neural network (3DCNN) was applied to the task, but the performance was comparable to existing methods because it used only [...] Read more.
Model quality assessment (MQA), which selects near-native structures from structure models, is an important process in protein tertiary structure prediction. The three-dimensional convolution neural network (3DCNN) was applied to the task, but the performance was comparable to existing methods because it used only atom-type features as the input. Thus, we added sequence profile-based features, which are also used in other methods, to improve the performance. We developed a single-model MQA method for protein structures based on 3DCNN using sequence profile-based features, namely, P3CMQA. Performance evaluation using a CASP13 dataset showed that profile-based features improved the assessment performance, and the proposed method was better than currently available single-model MQA methods, including the previous 3DCNN-based method. We also implemented a web-interface of the method to make it more user-friendly. Full article
(This article belongs to the Special Issue New Bioinformatics Tools)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-3
Back to TopTop