Leveraging transformers-based language models in proteome bioinformatics

In recent years, the rapid growth of biological data has increased interest in using bioinformatics to analyze and interpret this data. Proteomics, which studies the structure, function, and interactions of proteins, is a crucial area of bioinformatics. Using natural language processing (NLP) techniques in proteomics is an emerging field that combines machine learning and text mining to analyze biological data. Recently, transformer-based NLP models have gained significant attention for their ability to process variable-length input sequences in parallel, using self-attention mechanisms to capture long-range dependencies. In this review paper, we discuss the recent advancements in transformer-based NLP models in proteome bioinformatics and examine their advantages, limitations, and potential applications to improve the accuracy and efficiency of various tasks. Additionally, we highlight the challenges and future directions of using these models in proteome bioinformatics research. Overall, this review provides valuable insights into the potential of transformer-based NLP models to revolutionize proteome bioinformatics.     

生物信息技术发展态势分析

随着计算机科学和互联网技术的进步,生物信息学得到了长足的发展。综述了国际生物信息技术的发展态势,分析了生物信息技术研发和应用的热点和重点,并且在概述国内生物信息发展状况的基础上,提出发展我国生物信息技术的建议。     

Bioinformatics

Bioinformatics is an interdisciplinary field that blends computer science and biostatistics with biological and biomedical sciences such as biochemistry, cell biology, developmental biology, genetics, genomics, and physiology. An important goal of bioinformatics is to facilitate the management, analysis, and interpretation of data from biological experiments and observational studies. The goal of this review is to introduce some of the important concepts in bioinformatics that must be considered when planning and executing a modern biological research study. We review database resources as well as data mining software tools.     

Revealing biological information using data structuring and automated learning

The intermediary steps between a biological hypothesis, concretized in the input data, and meaningful results, validated using biological experiments, commonly employ bioinformatics tools. Starting with storage of the data and ending with a statistical analysis of the significance of the results, every step in a bioinformatics analysis has been intensively studied and the resulting methods and models patented. This review summarizes the bioinformatics patents that have been developed mainly for the study of genes, and points out the universal applicability of bioinformatics methods to other related studies such as RNA interference. More specifically, we overview the steps undertaken in the majority of bioinformatics analyses, highlighting, for each, various approaches that have been developed to reveal details from different perspectives. First we consider data warehousing, the first task that has to be performed efficiently, optimizing the structure of the database, in order to facilitate both the subsequent steps and the retrieval of information. Next, we review data mining, which occupies the central part of most bioinformatics analyses, presenting patents concerning differential expression, unsupervised and supervised learning. Last, we discuss how networks of interactions of genes or other players in the cell may be created, which help draw biological conclusions and have been described in several patents.     

Penalized feature selection and classification in bioinformatics

In bioinformatics studies, supervised classification with high-dimensional input variables is frequently encountered. Examples routinely arise in genomic, epigenetic and proteomic studies. Feature selection can be employed along with classifier construction to avoid over-fitting, to generate more reliable classifier and to provide more insights into the underlying causal relationships. In this article, we provide a review of several recently developed penalized feature selection and classification techniques--which belong to the family of embedded feature selection methods--for bioinformatics studies with high-dimensional input. Classification objective functions, penalty functions and computational algorithms are discussed. Our goal is to make interested researchers aware of these feature selection and classification methods that are applicable to high-dimensional bioinformatics data.     

生物信息学在发现新基因方面的应用

自生物信息学作为一门交叉学科诞生以来,其在计算机、农业和生命科学等各方面发挥了重要的作用,在后基因组时代,更是成为发现新基因的重要手段。对生物信息学的概况做了回顾与展望,并简述了生物信息学近年在发现新基因方面所取得的成果。     

Artificial intelligence techniques for bioinformatics

This review provides an overview of the ways in which techniques from artificial intelligence (AI) can be usefully employed in bioinformatics, both for modelling biological data and for making new discoveries. The paper covers three techniques: symbolic machine learning approaches (nearest neighbour and identification tree techniques), artificial neural networks and genetic algorithms. Each technique is introduced and supported with examples taken from the bioinformatics literature. These examples include folding prediction, viral protease cleavage prediction, classification, multiple sequence alignment and microarray gene expression analysis.     

Performance measures in evaluating machine learning based bioinformatics predictors for classifications

Background: Many existing bioinformatics predictors are based on machine learning technology. When applying these predictors in practical studies, their predictive performances should be well understood. Different performance measures are applied in various studies as well as different evaluation methods. Even for the same performance measure, different terms, nomenclatures or notations may appear in different context. Results: We carried out a review on the most commonly used performance measures and the evaluation methods for bioinformatics predictors. Conclusions: It is important in bioinformatics to correctly understand and interpret the performance, as it is the key to rigorously compare performances of different predictors and to choose the right predictor.     

保护性病毒抗原的筛选策略及其在新型疫苗研发中的应用

随着疫苗研发技术的发展,新型疫苗在传染病的预防中得到了广泛应用。由于新型疫苗安全性良好,因此其在烈性病疫苗的应用中有着得天独厚的优势,然而研制新型疫苗的前提是筛选出保护性抗原。随着各种组学研究的发展,针对真核生物的多种生物信息学方法代表着最前沿的技术手段。相对于真核细胞,病毒具有更为简单的结构,对应着相对简单的研究方法,未来的保护性抗原筛选策略,需要结合生物信息学和传统分子生物学方法的优势。本文分别从宿主和病毒入手,论述了病毒保护性抗原的筛选策略,列举了一系列基于真核细胞开发的可能用于保护性抗原筛选的生物信息学方法,并总结了应用保护性抗原进行新型疫苗设计的案例,以便加深对病毒保护性抗原筛选策略的认知,为新型疫苗的研发提供借鉴。     

血清多肽组谱图的生物信息学

血清多肽组谱图（简称血肽图,serum peptidome profiling）是指通过质谱分析技术获得的血清中多肽组的精确质量数的谱图,是临床蛋白质组学研究领域的一个分支,在生物标志物的发现、疾病早期诊断和个性化治疗等领域有着广阔的应用前景。而且在这些应用中,生物信息学分析是其中一个重要环节。为了给有关的生物医学工作者提供较好的支持,文章就与血肽图相关的生物信息学方法进行综述,内容涉及基线删除、标准化、峰检测、峰比对和模型建立等方面。     

Early bioinformatics: the birth of a discipline--a personal view

MOTIVATION: The field of bioinformatics has experienced an explosive growth in the last decade, yet this 'new' field has a long history. Some historical perspectives have been previously provided by the founders of this field. Here, we take the opportunity to review the early stages and follow developments of this discipline from a personal perspective. RESULTS: We review the early days of algorithmic questions and answers in biology, the theoretical foundations of bioinformatics, the development of algorithms and database resources and finally provide a realistic picture of what the field looked like from a resources and finally provide a realistic picture of what the field looked like from a practitioner's viewpoint 10 years ago, with a perspective for future developments.     

An overview of human protein databases and their application to functional proteomics in health and disease

Functional proteomics can be defined as a strategy to couple proteomic information with biochemical and physiological analyses with the aim of understanding better the functions of proteins in normal and diseased organs. In recent years, a variety of publicly available bioinformatics databases have been developed to support protein-related information management and biological knowledge discovery. In addition to being used to annotate the proteome, these resources also offer the opportunity to develop global approaches to the study of the functional role of proteins both in health and disease. Here, we present a comprehensive review of the major human protein bioinformatics databases. We conclude this review by discussing a few examples that illustrate the importance of these databases in functional proteomics research.     

基因转录调控相关数据库集成系统及其应用

通过互联网访问的有关基因转录调控的数据库集成系统及其应用 ,包括调控区 (3’和 5’调控区、内显子和外显子调控区等 )、调控单元 (启动子 ,增强子 ,沉默子等 )和转录因子结合位点相关数据库及其数据库系统的性质、组成和功能。也介绍了这些数据库和系统的查询和搜索方法以及相关开发的程序工具。这些生物信息学资源对于从事生物信息学、分子生物学、遗传工程、基因功能、生物技术、代谢工程、药物设计、病理学和药理学研究的机构及人员在教学研究方面具一定的参考价值和帮助。     

中国在翻译后修饰的生物信息学研究领域的进展与前瞻

翻译后修饰在调控蛋白质构象变化、活性以及功能方面具有重要作用,并参与了几乎所有细胞通路和过程。蛋白质翻译后修饰的鉴定是阐明细胞内分子机理的基础。相对于劳动密集的、耗费时间的实验工作,利用各种生物信息学方法开展翻译后修饰预测,能够提供准确、简便和快速的研究方案,并产生有价值的信息为进一步实验研究提供参考。文章主要综述了中国生物信息学者在翻译后修饰生物信息学领域所取得的研究进展,包括修饰底物与位点预测的计算方法学设计与完善、在线或本地化工具的设计与维护、修饰相关数据库及数据资源的构建及基于修饰蛋白质组学数据的生物信息学分析。通过比较国内外的同类研究,发现优势和不足,并对未来的研究作出前瞻。     

A library of efficient bioinformatics algorithms

In this paper we review some of the existing projects available in the bioinformatics field for facilitating the development of programs, but for which minimising the running time is not of primary importance. We point out the advantages of open source libraries for such tasks and we discuss some of the open source licenses available. Finally, we present the project ALiBio, which is aimed at facilitating the development of efficient programs in bioinformatics.     

Bayesian methods in bioinformatics and computational systems biology

Bayesian methods are valuable, inter alia, whenever there is a need to extract information from data that are uncertain or subject to any kind of error or noise (including measurement error and experimental error, as well as noise or random variation intrinsic to the process of interest). Bayesian methods offer a number of advantages over more conventional statistical techniques that make them particularly appropriate for complex data. It is therefore no surprise that Bayesian methods are becoming more widely used in the fields of genetics, genomics, bioinformatics and computational systems biology, where making sense of complex noisy data is the norm. This review provides an introduction to the growing literature in this area, with particular emphasis on recent developments in Bayesian bioinformatics relevant to computational systems biology.     

Web Resources for Metagenomics Studies

The development of next-generation sequencing(NGS) platforms spawned an enormous volume of data. This explosion in data has unearthed new scalability challenges for existing bioinformatics tools. The analysis of metagenomic sequences using bioinformatics pipelines is complicated by the substantial complexity of these data. In this article, we review several commonly-used online tools for metagenomics data analysis with respect to their quality and detail of analysis using simulated metagenomics data. There are at least a dozen such software tools presently available in the public domain. Among them, MGRAST, IMG/M, and METAVIR are the most well-known tools according to the number of citations by peer-reviewed scientific media up to mid-2015. Here, we describe 12 online tools with respect to their web link, annotation pipelines, clustering methods, online user support, and availability of data storage. We have also done the rating for each tool to screen more potential and preferential tools and evaluated five best tools using synthetic metagenome. The article comprehensively deals with the contemporary problems and the prospects of metagenomics from a bioinformatics viewpoint.