The 22nd International Conference on Bioinformatics (InCoB 2023): Translational Bioinformatics Transforming Life

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closed (15 August 2024)

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Topic Information

Dear Colleagues,

We are launching a topic, entitled “The 22nd International Conference on Bioinformatics (InCoB 2023): Translational Bioinformatics Transforming Life”, that seeks to publish original research, reviews and combined research–review papers. In recent years, we have witnessed a series of breakthroughs in our understanding of biology aided by high-throughput integrative approaches such as CRISPR, single-cell sequencing and metabolomics. These powerful methods are used to generate high-resolution transcriptomes, identify complex and rare cell populations, discover regulatory relationships between genes and assess cellular heterogeneity. The wealth of data generated by these technologies raises many challenges and has caused an exponential growth in the demand for tools for storing and managing data and software for the analysis, visualization, modelling and prediction of large data sets. The InCoB 2023 edition is committed to providing an opportunity for the scientific community to showcase their research and discuss this important topic through keynote talks, presentations, plenary sessions, poster sessions, workshops, software demos and panel discussions, among others.

This topic will comprise selected research works from the proceedings of the 22nd International Conference on Bioinformatics (InCoB2023; https://incob.apbionet.org/incob23/) 12–15 November 2023, the flagship annual conference of the Asia & Pacific Bioinformatics Network (APBioNET; https://www.apbionet.org), hosted by the Queensland University of Technology and the Translational Research Institute (TRI), Brisbane, Australia.

This issue highlights the current state of the art in bioinformatics applications and future prospects for improving informatics applications through integrated multi-omics approaches. We encourage submissions of manuscripts focusing on, but not limited to, current research, novel concepts, technologies and approaches in basic and advanced aspects of bioinformatics- and genomics-related research.

Publishing Areas Cancers:
Biomedical informatics;
Functional genomics;
New tools for computational biology;
Multi-level omics;
Novel methods to identify novel drug targets;
Molecular signatures of cancer;
Machine learning;
Artificial intelligence;
Systems biology;
Oncology.

Publishing Areas Genes:
Metagenomics;
Meta-transcriptomics;
Functional genomics;
Computational algorithms;
Genomic databases;
Candidate gene identification;
Big data analytics;
AI applications;
Systems medicine.

Publishing Areas IJMS:
Machine learning: applications in biology;
Multi-omics: co-expression network analysis;
Medical genetics;
Epigenetics;
Cell biology;
Drug design and discovery;
Genomics;
Computational biology;
Pan-genomics;
Gene expression analysis;
Disease gene identification.

Publishing Areas Biology:
Big data in biology: analytics, machine learning methods and datasets;
Database management;
Drug design and discovery;
Genome and proteome manipulation;
Genomics;
Metagenomics;
Molecular evolution and phylogeny;
Next-generation sequencing;
Systems biology.

Publishing Areas BioMedInformatics:
Structural bioinformatics;
Scalable data storage;
Proteomics;
Synthetic biology;
Translational bioinformatics;
Workflow and knowledge management;
Immunoinformatics;
Medical and health informatics;
High-throughput omics and imaging platforms;
Protein interactions and diseases;
Protein folding and conformational diseases;
Bioimaging;
Bioinformatics applications;
Bioinformatics models, methods and algorithms;
Biological sequence analysis;
Clinical bioinformatics;
Data mining and biomedical knowledge discovery.

Dr. Jyotsna Batra
Dr. Srilakshmi Srinivasan
Prof. Dr. Shoba Ranganathan
Dr. Asif M. Khan
Dr. Harpreet Singh
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Biology biology	3.6	5.7	2012	16.1 Days	CHF 2700
BioMedInformatics biomedinformatics	-	1.7	2021	21.3 Days	CHF 1000
Cancers cancers	4.5	8.0	2009	16.3 Days	CHF 2900
Genes genes	2.8	5.2	2010	16.3 Days	CHF 2600
International Journal of Molecular Sciences ijms	4.9	8.1	2000	18.1 Days	CHF 2900

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Published Papers (2 papers)

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All Biology BioMedInformatics Cancers Genes IJMS

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Open AccessArticle

RNA m6a Methylation Regulator Expression in Castration-Resistant Prostate Cancer Progression and Its Genetic Associations

by Chamikara Liyanage, Achala Fernando, Audrey Chamberlain, Afshin Moradi and Jyotsna Batra

Cancers 2024, 16(7), 1303; https://doi.org/10.3390/cancers16071303 - 27 Mar 2024

Cited by 2 | Viewed by 1857

Abstract

N6-methyladenosine (m6A) methylation, a prevalent epitranscriptomic modification, plays a crucial role in regulating mRNA expression, stability, and translation in mammals. M6A regulators have gained attention for their potential implications in tumorigenesis and clinical applications, such as cancer diagnosis and therapeutics. The existing literature [...] Read more.

N6-methyladenosine (m6A) methylation, a prevalent epitranscriptomic modification, plays a crucial role in regulating mRNA expression, stability, and translation in mammals. M6A regulators have gained attention for their potential implications in tumorigenesis and clinical applications, such as cancer diagnosis and therapeutics. The existing literature predominantly addresses m6A regulators in the context of primary prostate cancer (PCa). However, a notable gap in the knowledge emerges regarding the dynamic expression patterns of these regulators as PCa progresses towards the castration-resistant stage (CRPC). Employing sequential window acquisition of all theoretical mass spectra (SWATH-MS) and RNAseq analysis, we comprehensively profiled the expression of 27 m6A regulators in hormone/androgen-dependent and -independent PCa cell lines, revealing distinct clustering between tumor and adjacent normal prostate tissues. High-grade PCa tumors demonstrated the upregulation of METTL3, RBM15B, and HNRNAPA2B1 and the downregulation of ZC3H13, NUDT21, and FTO. Notably, we identified six m6A regulators associated with PCa survival. Additionally, association analysis of the PCa-associated risk loci in the cancer genome atlas program (TCGA) data unveiled genetic variations near the WTAP, HNRNPA2B1, and FTO genes as significant expression quantitative trait loci. In summary, our study unraveled abnormalities in m6A regulator expression in PCa progression, elucidating their association with PCa risk loci. Considering the heterogeneity within the PCa phenotypes and treatment responses, our findings suggest that prognostic stratification based on m6A regulator expression could enhance PCa diagnosis and prognosis. Full article

(This article belongs to the Topic The 22nd International Conference on Bioinformatics (InCoB 2023): Translational Bioinformatics Transforming Life)

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Open AccessArticle

iBio-GATS—A Semi-Automated Workflow for Structural Modelling of Insect Odorant Receptors

by Vaanathi Chidambara Thanu, Amara Jabeen and Shoba Ranganathan

Int. J. Mol. Sci. 2024, 25(5), 3055; https://doi.org/10.3390/ijms25053055 - 6 Mar 2024

Cited by 1 | Viewed by 1284

Abstract

Insects utilize seven transmembrane (7TM) odorant receptor (iOR) proteins, with an inverted topology compared to G-protein coupled receptors (GPCRs), to detect chemical cues in the environment. For pest biocontrol, chemical attractants are used to trap insect pests. However, with the influx of invasive [...] Read more.

Insects utilize seven transmembrane (7TM) odorant receptor (iOR) proteins, with an inverted topology compared to G-protein coupled receptors (GPCRs), to detect chemical cues in the environment. For pest biocontrol, chemical attractants are used to trap insect pests. However, with the influx of invasive insect pests, novel odorants are urgently needed, specifically designed to match 3D iOR structures. Experimental structural determination of these membrane receptors remains challenging and only four experimental iOR structures from two evolutionarily distant organisms have been solved. Template-based modelling (TBM) is a complementary approach, to generate model structures, selecting templates based on sequence identity. As the iOR family is highly divergent, a different template selection approach than sequence identity is needed. Bio-GATS template selection for GPCRs, based on hydrophobicity correspondence, has been morphed into iBio-GATS, for template selection from available experimental iOR structures. This easy-to-use semi-automated workflow has been extended to generate high-quality models from any iOR sequence from the selected template, using Python and shell scripting. This workflow was successfully validated on Apocrypta bakeri Orco and Machilis hrabei OR5 structures. iBio-GATS models generated for the fruit fly iOR, OR59b and Orco, yielded functional ligand binding results concordant with experimental mutagenesis findings, compared to AlphaFold2 models. Full article

(This article belongs to the Topic The 22nd International Conference on Bioinformatics (InCoB 2023): Translational Bioinformatics Transforming Life)

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