Advances in Bioinformatics

Ebola Virus Disease (EVD) is a life-threatening haemorrhagic fever in humans. Even though there are many reports on EVD, the protein precursor functions and virulent factors of ebolaviruses remain poorly understood. Comparative analyses of Ebolavirus genomes will help in the identification of these important features. This prompted us to develop the Ebolavirus Database (EDB) and we have provided links to various tools that will aid researchers to locate important regions in both the genomes and proteomes of Ebolavirus. The genomic analyses of ebolaviruses will provide important clues for locating the essential and core functional genes. The aim of EDB is to act as an integrated resource for ebolaviruses and we strongly believe that the database will be a useful tool for clinicians, microbiologists, health care workers, and bioscience researchers.

Rayapadi G. Swetha

Sudha Ramaiah

Anand Anbarasu

Kanagaraj Sekar

<i>Ebolavirus</i> Database: Gene and Protein Information Resource for Ebolaviruses

埃博拉病毒数据库：埃博拉病毒基因与蛋白质信息资源

Epitope-based design of vaccines, immunotherapeutics, and immunodiagnostics is complicated by structural changes that radically alter immunological outcomes. This is obscured by expressing redundancy among linear-epitope data as fractional sequence-alignment identity, which fails to account for potentially drastic loss of binding affinity due to single-residue substitutions even where these might be considered conservative in the context of classical sequence analysis. From the perspective of immune function based on molecular recognition of epitopes, functional redundancy of epitope data (FRED) thus may be defined in a biologically more meaningful way based on residue-level physicochemical similarity in the context of antigenic cross-reaction, with functional similarity between epitopes expressed as the Shannon information entropy for differential epitope binding. Such similarity may be estimated in terms of structural differences between an immunogen epitope and an antigen epitope with reference to an idealized binding site of high complementarity to the immunogen epitope, by analogy between protein folding and ligand-receptor binding; but this underestimates potential for cross-reactivity, suggesting that epitope-binding site complementarity is typically suboptimal as regards immunologic specificity. The apparently suboptimal complementarity may reflect a tradeoff to attain optimal immune function that favors generation of immune-system components each having potential for cross-reactivity with a variety of epitopes.

Salvador Eugenio C. Caoili

Expressing Redundancy among Linear-Epitope Sequence Data Based on Residue-Level Physicochemical Similarity in the Context of Antigenic Cross-Reaction

基于抗原交叉反应背景下残基水平理化相似性的线性表位序列数据冗余表达

Biomarkers able to characterise and predict multifactorial diseases are still one of the most important targets for all the “omics” investigations. In this context, Matrix-Assisted Laser Desorption/Ionisation-Mass Spectrometry Imaging (MALDI-MSI) has gained considerable attention in recent years, but it also led to a huge amount of complex data to be elaborated and interpreted. For this reason, computational and machine learning procedures for biomarker discovery are important tools to consider, both to reduce data dimension and to provide predictive markers for specific diseases. For instance, the availability of protein and genetic markers to support thyroid lesion diagnoses would impact deeply on society due to the high presence of undetermined reports (THY3) that are generally treated as malignant patients. In this paper we show how an accurate classification of thyroid bioptic specimens can be obtained through the application of a state-of-the-art machine learning approach (i.e., Support Vector Machines ) on MALDI-MSI data, together with a particular wrapper feature selection algorithm (i.e., recursive feature elimination ). The model is able to provide an accurate discriminatory capability using only 20 out of 144 features, resulting in an increase of the model performances, reliability, and computational efficiency. Finally, tissue areas rather than average proteomic profiles are classified, highlighting potential discriminating areas of clinical interest.

Manuel Galli

Italo Zoppis

Gabriele De Sio

Clizia Chinello

Fabio Pagni

Fulvio Magni

Giancarlo Mauri

A Support Vector Machine Classification of Thyroid Bioptic Specimens Using MALDI-MSI Data

使用MALDI-MSI数据的支持向量机甲状腺活检样本分类

The increasing diagnostic use of gene sequencing has led to an expanding dataset of novel variants that lie within consensus splice junctions. The challenge for diagnostic laboratories is the evaluation of these variants in order to determine if they affect splicing or are merely benign. A common evaluation strategy is to use in silico analysis, and it is here that a number of programmes are available online; however, currently, there are no consensus guidelines on the selection of programmes or protocols to interpret the prediction results. Using a collection of 222 pathogenic mutations and 50 benign polymorphisms, we evaluated the sensitivity and specificity of four in silico programmes in predicting the effect of each variant on splicing. The programmes comprised Human Splice Finder (HSF), Max Entropy Scan (MES), NNSplice, and ASSP. The MES and ASSP programmes gave the highest performance based on Receiver Operator Curve analysis, with an optimal cut-off of score reduction of 10%. The study also showed that the sensitivity of prediction is affected by the level of conservation of individual positions, with in silico predictions for variants at positions -4 and +7 within consensus splice sites being largely uninformative.

Rongying Tang

Debra O. Prosser

Donald R. Love

Evaluation of Bioinformatic Programmes for the Analysis of Variants within Splice Site Consensus Regions

评估用于分析剪接位点共识区域变异的生物信息学程序

Modeling of stem cells not only describes but also predicts how a stem cell's environment can control its fate. The first stem cell populations discovered were hematopoietic stem cells (HSCs). In this paper, we present a deterministic model of bone marrow (that hosts HSCs) that is consistent with several of the qualitative biological observations. This model incorporates stem cell death (apoptosis) after a certain number of cell divisions and also demonstrates that a single HSC can potentially populate the entire bone marrow. It also demonstrates that there is a production of sufficient number of differentiated cells (RBCs, WBCs, etc.). We prove that our model of bone marrow is biologically consistent and it overcomes the biological feasibility limitations of previously reported models. The major contribution of our model is the flexibility it allows in choosing model parameters which permits several different simulations to be carried out in silico without affecting the homeostatic properties of the model. We have also performed agent-based simulation of the model of bone marrow system proposed in this paper. We have also included parameter details and the results obtained from the simulation. The program of the agent-based simulation of the proposed model is made available on a publicly accessible website.

Manish Kurhekar

Umesh Deshpande

Agent-Based Deterministic Modeling of the Bone Marrow Homeostasis

基于代理的骨髓稳态确定性建模

Bioinformatics has emerged as a vital component of almost all the fields of biological sciences. Its ability to quickly generate, analyze, and interpret large amounts of data has enabled researchers to integrate it into drug discovery and development. The traditional process of drug discovery had its limitations including increased time consumption and cost, low success rates, inaccurate drug target selection, regulatory and ethical concerns, and lack of personalization. To overcome these challenges, bioinformatics has gained much interest in different stages of drug discovery. In this chapter, we summarize the role of bioinformatics in the high-throughput drug screening process involving both ligand-based and structure-based screening strategies. The most prominent advantage it offers is the ability to handle large amounts of data within seconds to minutes. This chapter also provides a brief account of various bioinformatics tools and databases which have a prominent role in the drug screening process. Although bioinformatics has been proven beneficial, it still has some limitations in terms of the complexity of data that is handled. With the advent of artificial intelligence and machine learning, it is expected that it would definitely strengthen the biomedical field.

Tanya Waseem

Mustafeez Mujtaba Babar

Gholamreza Abdi

Jayakumar Rajadas

Use of Bioinformatics in High-Throughput Drug Screening

生物信息学在高通量药物筛选中的应用

In today's healthcare industry, we prefer methods that are highly effective and minimize risks. Precision medicine is a new field that utilizes algorithms in bioinformatics to provide precise treatment for individuals. Bioinformatics tools not only assist doctors find the most appropriate therapeutic solutions for each patient but also aid in uncovering vast amounts of life science and clinical data for healthcare development. Scientists have found that single nucleotide polymorphism is the primary agent of genetic modification and a potential tool for genetic mapping. Bioinformatics tools combine genetic information with phenotypes and drug responses to help doctors choose the most appropriate treatment for a patient. In addition, algorithms in bioinformatics also help doctors reduce their workload, both in the field of diagnosis and the field of treatment. Besides, the development of bioinformatics also helps scientists create comparative models of biomolecular sequences or molecular mechanisms and generate medical examination records of patients. By optimizing the treatment method based on the potential of bioinformatics, we can minimize the cost of drugs, medical equipment, and the patient's treatment time.

Mai-Anh Nguyen

Chia-Ching Wu

Dinh-Toi Chu

Bioinformatics in Precision Medicine and Healthcare

精准医学与医疗保健中的生物信息学

Bioinformatics plays a crucial role in advancing genome editing techniques by utilizing computational tools and algorithms to analyze biological data. This chapter provides an overview of the role of bioinformatics in genome editing, focusing on key areas where it is applied. These areas include computational analysis of target sequences, prediction, and evaluation of off-target effects, designing and optimizing CRISPR systems, functional annotation of genomic variants, comparative genomics, homology analysis, and integration of multi-omics data. By leveraging bioinformatics, researchers can identify target sites for gene editing, predict and minimize off-target effects, enhance the efficiency of CRISPR systems, understand the functional consequences of genetic alterations, explore evolutionary relationships, and gain comprehensive insights into biological systems. Moreover, comparative genomics and homology analysis are discussed as vital approaches that leverage bioinformatics to understand evolutionary relationships and identify conserved elements across species. Integration of multi-omics data, such as genomics, transcriptomics, and proteomics, is highlighted as a powerful strategy for gaining comprehensive insights into biological systems. While challenges exist, such as accurate off-target prediction and data management, future directions involving machine learning and user-friendly tools hold promise. Bioinformatics continues to revolutionize genome editing, advancing precision medicine, agriculture, and biological research.

Amit Joshi

Ajay Kumar

Vikas Kaushik

Prashant Kumar

Sushma Dubey

Role of Bioinformatics in Genome Editing

生物信息学在基因编辑中的作用

Structural bioinformatics is a captivating discipline that delves into the intricate realm related to proteins, RNA, and DNA, the macromolecules of life. Its primary focus lies in comprehending and foreseeing the enigmatic three-dimensional (3D) architecture of these fundamental entities. By employing cutting-edge computational techniques and advanced algorithms, structural bioinformatics unravels the complex interplay between structure and function, shedding light on the inner workings of life's molecular machinery. Bioinformatics is an interdisciplinary field that combines experimental and computational approaches to investigate various aspects of macromolecular 3D structure. By utilizing experimentally determined structures and computational models, bioinformatics aims to explore diverse inquiries related to macromolecules. These inquiries encompass understanding the distinctions and similarities between macro and micro structures, understanding the rules of molecular interaction, evolution, and folding, and revealing the complexity of structure-function correlations. Structural bioinformatics, a specialized domain within the realm of computational structural biology, encompasses the study and analysis of biological structures. The term "structural" in this context aligns with its definition in the field of structural biology. The field of structural bioinformatics is dedicated to addressing biological challenges and unveiling novel insights through the development of innovative methodologies for the analysis of data pertaining to biological macromolecules.

Juveriya Israr

Shabroz Alam

Sahabjada Siddiqui

Sankalp Misra

Indrajeet Singh

Advances in Structural Bioinformatics

结构生物信息学进展

Next-generation sequencing (NGS) technologies have revolutionized the field of genomics by enabling high-throughput, cost-effective, and rapid DNA sequencing on an unprecedented scale. This introduction offers a synopsis of NGS and its profound implications across diverse areas of biological research and medical diagnostics. The fundamental principles underlying NGS, including library preparation, sequencing-by-synthesis, and data generation, are outlined. The different NGS platforms, such as Illumina, Ion Torrent, and Oxford Nanopore, as well as their respective strengths and limitations, are discussed. Recent advancements in sequencing technologies, such as single-cell sequencing, long-read sequencing, and spatial transcriptomics, are explored, expanding the capabilities of NGS and facilitating comprehensive genomic investigations. Subsequently, the applications of NGS in genomics, transcriptomics, epigenomics, metagenomics, and personalized medicine are examined. The accelerated discovery of genetic variants, gene expression patterns, DNA methylation profiles, and microbial communities through NGS is emphasized. Moreover, the role of NGS in uncovering disease mechanisms, identifying therapeutic targets, and enabling precision medicine approaches is discussed. Furthermore, the computational challenges associated with NGS data analysis, including read alignment, variant calling, and data interpretation, are addressed. The pivotal role of bioinformatics and data analysis pipelines in transforming raw sequencing data into biologically meaningful insights is highlighted. Additionally, the integration of NGS data with other omics datasets and the emerging field of multi-omics integration, providing a holistic view of biological systems, are briefly touched upon. Additionally, the impact of NGS on clinical diagnostics, encompassing the detection of genetic disorders, cancer genomics, infectious disease surveillance, and pharmacogenomics, is elucidated. The potential of NGS-based liquid biopsies and non-invasive prenatal testing in revolutionizing clinical practice is underscored. Lastly, the challenges and considerations associated with NGS, such as data storage, privacy concerns, ethical considerations, and the importance of standardization and quality control measures, are addressed. The significance of interdisciplinary collaborations among scientists, clinicians, and bioinformaticians in harnessing the full potential of NGS and driving innovation in genomic research and healthcare is emphasized. In conclusion, this comprehensive introduction to next-generation sequencing provides an overview of the technology, its applications, and its impact across various fields. By empowering researchers and clinicians with unprecedented genomic information, NGS has the potential to revolutionize our understanding of biological systems, unravel disease complexities, and facilitate personalized approaches to healthcare.

ISSN	1687-8027, 1687-8035
h-index	26

涉及主题	生物化学遗传学生物化学计算机科学基因计算生物学数学医学人工智能物理数据挖掘生物信息学机器学习统计程序设计语言内科学量子力学
出版信息	出版商: Hindawi Limited，出版周期: ，期刊类型: journal，开源期刊: 否
基本数据	创刊年份: 2010，原创研究文献占比: 0.00%，自引率:N.A.， Gold OA占比: 0.00%

Advances in Bioinformatics

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Ebolavirus Database: Gene and Protein Information Resource for Ebolaviruses