Special Issue "Selected Papers from the 19th International Conference on Bioinformatics (InCoB 2020)"

A special issue of Genes (ISSN 2073-4425). This special issue belongs to the section "Technologies and Resources for Genetics".

Deadline for manuscript submissions: closed (30 September 2020) | Viewed by 3074

Special Issue Editors

Prof. Dr. Alok Sharma
E-Mail Website
Guest Editor
1. School of Engineering and Physics, University of the South Pacific, Suva, Fiji
2. Institute for Integrated and Intelligent Systems, Griffith University, Nathan, Qld 4111, Australia
3. RIKEN Center for Integrative Medical Sciences, Yokohama 230-0045, Japan
Interests: proteomics; gene selection; drug analysis using multi-omics data; artificial intelligence (AI) and data mining; bioinformatics
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Kenta Nakai
E-Mail Website
Guest Editor
The Institute of Medical Sciences, the University of Tokyo, Tokyo 108-8639, Japan
Interests: sequence analysis in molecular biology; bioinformatics in genome analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The 19th International Conference on Bioinformatics (InCoB 2020) will be held virtually from Nov 25–29, 2020 across Asia-Pacific and beyond. The event webpage is: https://incob.apbionet.org/incob20/.

InCoB has been held annually since 2002. Originally organized through coordination between the Asia Pacific Bioinformatics Network (APBioNet) and the Thailand National Center for Genetic Engineering and Biotechnology (BIOTEC) in 2002, the meeting has since been the flagship conference of the APBioNet. InCoB 2020 has taken on the theme of “Bioinformatics and the translation of data-driven discoveries”.

The deadline for submissions is a tentative one and will follow InCoB 2020 dates preferably (12 August—26 September, 2020). Here we will send each manuscript for peer-review as soon as we receive it, and have it published as soon as it is accepted after peer-review. A broad area of topics will be covered in the current edition: https://incob.apbionet.org/incob20/call-for-papers/.

Prof. Alok Sharma
Prof. Kenta Nakai
Guest Editors

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. Genes 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 2400 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.

For this Special Issue, all submissions will be entitled a 15% discount on the above mentioned APC

Keywords

  • bioinformatics models, methods and algorithms
  • biological sequence analysis
  • clinical bioinformatics
  • data mining
  • database management
  • drug design
  • protein folding
  • genetics of cell differentiation and reprogramming
  • high-throughput omics
  • immunoinformatics
  • metagenomics
  • NGS
  • molecular evolution and phylogeny
  • population genetics
  • structural bioinformatics
  • synthetic biology
  • system biology
  • translational bioinformatics

Published Papers (3 papers)

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Research

Article
Feature Selection for Topological Proximity Prediction of Single-Cell Transcriptomic Profiles in Drosophila Embryo Using Genetic Algorithm
Genes 2021, 12(1), 28; https://doi.org/10.3390/genes12010028 - 28 Dec 2020
Cited by 2 | Viewed by 1125
Abstract
Single-cell transcriptomics data, when combined with in situ hybridization patterns of specific genes, can help in recovering the spatial information lost during cell isolation. Dialogue for Reverse Engineering Assessments and Methods (DREAM) consortium conducted a crowd-sourced competition known as DREAM Single Cell Transcriptomics [...] Read more.
Single-cell transcriptomics data, when combined with in situ hybridization patterns of specific genes, can help in recovering the spatial information lost during cell isolation. Dialogue for Reverse Engineering Assessments and Methods (DREAM) consortium conducted a crowd-sourced competition known as DREAM Single Cell Transcriptomics Challenge (SCTC) to predict the masked locations of single cells from a set of 60, 40 and 20 genes out of 84 in situ gene patterns known in Drosophila embryo. We applied a genetic algorithm (GA) to predict the most important genes that carry positional and proximity information of the single-cell origins, in combination with the base distance mapping algorithm DistMap. Resulting gene selection was found to perform well and was ranked among top 10 in two of the three sub-challenges. However, the details of the method did not make it to the main challenge publication, due to an intricate aggregation ranking. In this work, we discuss the detailed implementation of GA and its post-challenge parameterization, with a view to identify potential areas where GA-based approaches of gene-set selection for topological association prediction may be improved, to be more effective. We believe this work provides additional insights into the feature-selection strategies and their relevance to single-cell similarity prediction and will form a strong addendum to the recently published work from the consortium. Full article
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Article
RAM-PGK: Prediction of Lysine Phosphoglycerylation Based on Residue Adjacency Matrix
Genes 2020, 11(12), 1524; https://doi.org/10.3390/genes11121524 - 20 Dec 2020
Cited by 1 | Viewed by 881
Abstract
Background: Post-translational modification (PTM) is a biological process that is associated with the modification of proteome, which results in the alteration of normal cell biology and pathogenesis. There have been numerous PTM reports in recent years, out of which, lysine phosphoglycerylation has emerged [...] Read more.
Background: Post-translational modification (PTM) is a biological process that is associated with the modification of proteome, which results in the alteration of normal cell biology and pathogenesis. There have been numerous PTM reports in recent years, out of which, lysine phosphoglycerylation has emerged as one of the recent developments. The traditional methods of identifying phosphoglycerylated residues, which are experimental procedures such as mass spectrometry, have shown to be time-consuming and cost-inefficient, despite the abundance of proteins being sequenced in this post-genomic era. Due to these drawbacks, computational techniques are being sought to establish an effective identification system of phosphoglycerylated lysine residues. The development of a predictor for phosphoglycerylation prediction is not a first, but it is necessary as the latest predictor falls short in adequately detecting phosphoglycerylated and non-phosphoglycerylated lysine residues. Results: In this work, we introduce a new predictor named RAM-PGK, which uses sequence-based information relating to amino acid residues to predict phosphoglycerylated and non-phosphoglycerylated sites. A benchmark dataset was employed for this purpose, which contained experimentally identified phosphoglycerylated and non-phosphoglycerylated lysine residues. From the dataset, we extracted the residue adjacency matrix pertaining to each lysine residue in the protein sequences and converted them into feature vectors, which is used to build the phosphoglycerylation predictor. Conclusion: RAM-PGK, which is based on sequential features and support vector machine classifiers, has shown a noteworthy improvement in terms of performance in comparison to some of the recent prediction methods. The performance metrics of the RAM-PGK predictor are: 0.5741 sensitivity, 0.6436 specificity, 0.0531 precision, 0.6414 accuracy, and 0.0824 Mathews correlation coefficient. Full article
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Article
PupStruct: Prediction of Pupylated Lysine Residues Using Structural Properties of Amino Acids
Genes 2020, 11(12), 1431; https://doi.org/10.3390/genes11121431 - 28 Nov 2020
Cited by 2 | Viewed by 660
Abstract
Post-translational modification (PTM) is a critical biological reaction which adds to the diversification of the proteome. With numerous known modifications being studied, pupylation has gained focus in the scientific community due to its significant role in regulating biological processes. The traditional experimental practice [...] Read more.
Post-translational modification (PTM) is a critical biological reaction which adds to the diversification of the proteome. With numerous known modifications being studied, pupylation has gained focus in the scientific community due to its significant role in regulating biological processes. The traditional experimental practice to detect pupylation sites proved to be expensive and requires a lot of time and resources. Thus, there have been many computational predictors developed to challenge this issue. However, performance is still limited. In this study, we propose another computational method, named PupStruct, which uses the structural information of amino acids with a radial basis kernel function Support Vector Machine (SVM) to predict pupylated lysine residues. We compared PupStruct with three state-of-the-art predictors from the literature where PupStruct has validated a significant improvement in performance over them with statistical metrics such as sensitivity (0.9234), specificity (0.9359), accuracy (0.9296), precision (0.9349), and Mathew’s correlation coefficient (0.8616) on a benchmark dataset. Full article
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