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Selected Papers from the 20th International Conference on Bioinformatics (InCoB...
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Selected Papers from the 20th International Conference on Bioinformatics (InCoB 2021)

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 (15 November 2021) | Viewed by 9713

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 20th International Conference on Bioinformatics (InCoB 2021) will be held on 6–8 November, in Kunming, Yunnan, China, hosted by Kunming University of Science and Technology (KMUST), Kunming, China. The event webpage is: http://www.incob2021.cn/.

The International Conference on Bioinformatics (InCoB) is a scientific conference on bioinformatics, primarily aimed at scientists in the Asia Pacific region. It has been held annually since 2002. Since COVID-19 is still ongoing, one of the main topics of InCoB 2021 will be understanding the relationship between biodiversity and human health with a computational approach.

Prof. Dr. Alok Sharma
Prof. Dr. 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 2600 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

  • biostatistics
  • epigenomics
  • metagenomics
  • systems biology
  • structural biology
  • population genetics
  • molecular evolution
  • single cell omics
  • comparative genomics
  • motif search/discovery
  • sequencing technologies
  • physical and genetic maps
  • data mining and visualization
  • software tools and applications
  • databases and data integration
  • drug discovery and repositioning
  • computational genetic epidemiology

Published Papers (3 papers)

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Research

16 pages, 2338 KiB  
Article
Identifying Cancer Subtypes Using a Residual Graph Convolution Model on a Sample Similarity Network
by Wei Dai, Wenhao Yue, Wei Peng, Xiaodong Fu, Li Liu and Lijun Liu
Genes 2022, 13(1), 65; https://0-doi-org.brum.beds.ac.uk/10.3390/genes13010065 - 27 Dec 2021
Cited by 8 | Viewed by 2998
Abstract
Cancer subtype classification helps us to understand the pathogenesis of cancer and develop new cancer drugs, treatment from which patients would benefit most. Most previous studies detect cancer subtypes by extracting features from individual samples, ignoring their associations with others. We believe that [...] Read more.
Cancer subtype classification helps us to understand the pathogenesis of cancer and develop new cancer drugs, treatment from which patients would benefit most. Most previous studies detect cancer subtypes by extracting features from individual samples, ignoring their associations with others. We believe that the interactions of cancer samples can help identify cancer subtypes. This work proposes a cancer subtype classification method based on a residual graph convolutional network and a sample similarity network. First, we constructed a sample similarity network regarding cancer gene co-expression patterns. Then, the gene expression profiles of cancer samples as initial features and the sample similarity network were passed into a two-layer graph convolutional network (GCN) model. We introduced the initial features to the GCN model to avoid over-smoothing during the training process. Finally, the classification of cancer subtypes was obtained through a softmax activation function. Our model was applied to breast invasive carcinoma (BRCA), glioblastoma multiforme (GBM) and lung cancer (LUNG) datasets. The accuracy values of our model reached 82.58%, 85.13% and 79.18% for BRCA, GBM and LUNG, respectively, which outperformed the existing methods. The survival analysis of our results proves the significant clinical features of the cancer subtypes identified by our model. Moreover, we can leverage our model to detect the essential genes enriched in gene ontology (GO) terms and the biological pathways related to a cancer subtype. Full article
(This article belongs to the Special Issue Selected Papers from the 20th International Conference on Bioinformatics (InCoB 2021))
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15 pages, 2100 KiB  
Article
R-CRISPR: A Deep Learning Network to Predict Off-Target Activities with Mismatch, Insertion and Deletion in CRISPR-Cas9 System
by Rui Niu, Jiajie Peng, Zhipeng Zhang and Xuequn Shang
Genes 2021, 12(12), 1878; https://0-doi-org.brum.beds.ac.uk/10.3390/genes12121878 - 25 Nov 2021
Cited by 10 | Viewed by 3271
Abstract
The Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)—associated protein 9 (Cas9) system is a groundbreaking gene-editing tool, which has been widely adopted in biomedical research. However, the guide RNAs in CRISPR-Cas9 system may induce unwanted off-target activities and further affect the practical application [...] Read more.
The Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)—associated protein 9 (Cas9) system is a groundbreaking gene-editing tool, which has been widely adopted in biomedical research. However, the guide RNAs in CRISPR-Cas9 system may induce unwanted off-target activities and further affect the practical application of the technique. Most existing in silico prediction methods that focused on off-target activities possess limited predictive precision and remain to be improved. Hence, it is necessary to propose a new in silico prediction method to address this problem. In this work, a deep learning framework named R-CRISPR is presented, which devises an encoding scheme to encode gRNA-target sequences into binary matrices, a convolutional neural network as feature extractor, and a recurrent neural network to predict off-target activities with mismatch, insertion, or deletion. It is demonstrated that R-CRISPR surpasses six mainstream prediction methods with a significant improvement on mismatch-only datasets verified by GUIDE-seq. Compared with the state-of-art prediction methods, R-CRISPR also achieves competitive performance on datasets with mismatch, insertion, and deletion. Furthermore, experiments show that data concatenate could influence the quality of training data, and investigate the optimal combination of datasets. Full article
(This article belongs to the Special Issue Selected Papers from the 20th International Conference on Bioinformatics (InCoB 2021))
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9 pages, 1714 KiB  
Article
DBtRend: A Web-Server of tRNA Expression Profiles from Small RNA Sequencing Data in Humans
by Jin-Ok Lee, Minho Lee and Yeun-Jun Chung
Genes 2021, 12(10), 1576; https://0-doi-org.brum.beds.ac.uk/10.3390/genes12101576 - 03 Oct 2021
Cited by 2 | Viewed by 2401
Abstract
Transfer RNA (tRNA), a key component of the translation machinery, plays critical roles in stress conditions and various diseases. While knowledge regarding the importance of tRNA function is increasing, its biological roles are still not well understood. There is currently no comprehensive database [...] Read more.
Transfer RNA (tRNA), a key component of the translation machinery, plays critical roles in stress conditions and various diseases. While knowledge regarding the importance of tRNA function is increasing, its biological roles are still not well understood. There is currently no comprehensive database or web server providing the expression landscape of tRNAs across a variety of human tissues and diseases. Here, we constructed a user-friendly and interactive database, DBtRend, which provides a profile of mature tRNA expression across various biological conditions by reanalyzing the small RNA or microRNA sequencing data from the Cancer Genome Atlas (TCGA) and NCBI’s Gene Expression Omnibus (GEO) in humans. Users can explore not only the expression values of mature individual tRNAs in the human genome, but also those of isodecoders and isoacceptors based on our specific pipelines. DBtRend provides the expressed patterns of tRNAs, the differentially expressed tRNAs in different biological conditions, and the information of samples or patients, tissue types, and molecular subtype of cancers. The database is expected to help researchers interested in functional discoveries of tRNAs. Full article
(This article belongs to the Special Issue Selected Papers from the 20th International Conference on Bioinformatics (InCoB 2021))
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