Towards a consensus on datasets and evaluation metrics for developing B-cell epitope prediction tools

A B-cell epitope is the three-dimensional structure within an antigen that can be bound to the variable region of an antibody. The prediction of B-cell epitopes is highly desirable for various immunological applications, but has presented a set of unique challenges to the bioinformatics and immunology communities. Improving the accuracy of B-cell epitope prediction methods depends on a community consensus on the data and metrics utilized to develop and evaluate such tools. A workshop, sponsored by the National Institute of Allergy and Infectious Disease (NIAID), was recently held in Washington, DC to discuss the current state of the B-cell epitope prediction field. Many of the currently available tools were surveyed and a set of recommendations was devised to facilitate improvements in the currently existing tools and to expedite future tool development. An underlying theme of the recommendations put forth by the panel is increased collaboration among research groups. By developing common datasets, standardized data formats, and the means with which to consolidate information, we hope to greatly enhance the development of B-cell epitope prediction tools.     

Computational methods and challenges in analyzing intratumoral microbiome data

The human microbiome is intimately related to cancer biology and plays a vital role in the efficacy of cancer treatments, including immunotherapy. Extraordinary evidence has revealed that several microbes influence tumor development through interaction with the host immune system, that is, immuno–oncology–microbiome (IOM). This review focuses on the intratumoral microbiome in IOM and describes the available data and computational methods for discovering biological insights of microbial profiling from host bulk, single-cell, and spatial sequencing data. Critical challenges in data analysis and integration are discussed. Specifically, the microorganisms associated with cancer and cancer treatment in the context of IOM are collected and integrated from the literature. Lastly, we provide our perspectives for future directions in IOM research.     

Immunome Research

Immunology research has been transformed in the post-genomics era, with high throughput molecular biology and information technologies taking an increasingly central role. This has led to the development of a new area of science termed "Immunomics", that encompasses genomic, high throughput and bioinformatic approaches to immunology. In recognition of the increasing importance of this field, Immunome Research is a new Open Access, online journal, that will publish cutting edge research across the field of Immunomics. Immunome Research will publish a wide range of article types including specialty immunology databases, immunology database tools, immunome epitope research, epitope analysis tools, high-throughput technologies (gene sequencing, microarrays, proteomics), white papers, mathematical and theoretical models, and prediction tools. Immunome Research is the official journal of the International Immunomics Society (IIMMS).     

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.     

Critical role of bioinformatics in translating huge amounts of next-generation sequencing data into personalized medicine

Realizing personalized medicine requires integrating diverse data types with bioinformatics. The most vital data are genomic information for individuals that are from advanced next-generation sequencing (NGS) technologies at present. The technologies continue to advance in terms of both decreasing cost and sequencing speed with concomitant increase in the amount and complexity of the data. The prodigious data together with the requisite computational pipelines for data analysis and interpretation are stressors to IT infrastructure and the scientists conducting the work alike. Bioinformatics is increasingly becoming the rate-limiting step with numerous challenges to be overcome for translating NGS data for personalized medicine. We review some key bioinformatics tasks, issues, and challenges in contexts of IT requirements, data quality, analysis tools and pipelines, and validation of biomarkers.     

全基因组测序在病原菌分型与溯源中的应用研究进展

细菌分子分型已成为监测细菌感染性疾病的暴发流行与明确病原菌传播途径的重要工具.随着全基因组测序技术的日益兴起,公共数据库中已产生大量的细菌基因组数据,迫切需要研究人员充分认识和理解该技术,并掌握多种生物信息学工具挖掘并解读测序数据.本文系统概述了全基因组测序技术与生物信息学工具在病原菌分型与溯源中的应用,并对全基因组测...     

Glycoproteogenomics: Setting the Course for Next-generation Cancer Neoantigen Discovery for Cancer Vaccines

Molecular-assisted precision oncology gained tremendous ground with high-throughput next-generation sequencing(NGS), supported by robust bioinformatics. The quest for genomicsbased cancer medicine set the foundations for improved patient stratification, while unveiling a wide array of neoantigens for immunotherapy. Upfront pre-clinical and clinical studies have successfully used tumor-specific peptides in vaccines with minimal off-target effects. However, the low mutational burden presented by many lesions challenges the generalization of these solutions, requiring the diversification of neoantigen sources. Oncoproteogenomics utilizing customized databases for protein annotation by mass spectrometry(MS) is a powerful tool toward this end. Expanding the concept toward exploring proteoforms originated from post-translational modifications(PTMs)will be decisive to improve molecular subtyping and provide potentially targetable functional nodes with increased cancer specificity. Walking through the path of systems biology, we highlight that alterations in protein glycosylation at the cell surface not only have functional impact on cancer progression and dissemination but also originate unique molecular fingerprints for targeted therapeutics. Moreover, we discuss the outstanding challenges required to accommodate glycoproteomics in oncoproteogenomics platforms. We envisage that such rationale may flag a rather neglected research field, generating novel paradigms for precision oncology and immunotherapy.     

卵巢癌侵袭转移机制的生物信息学研究进展

卵巢癌因其侵袭转移特性,致死率极高,居所有妇科恶性肿瘤之首。近年来随着高通量测序技术及生物信息学方法的快速发展,越来越多调控卵巢癌侵袭转移机制的相关生物大分子被发现。本文对卵巢癌侵袭转移机制的研究背景及现状进行了综述,归纳总结了侵袭转移机制相关调控因素,并对蛋白质组学和单细胞组学的生物信息学分析工具及数据库进行了汇总和介绍,以期为卵巢肿瘤细胞侵袭转移机制的深入研究提供理论依据和科研线索。     

Proteome informatics for cancer research: from molecules to clinic

Proteomics offers the most direct approach to understand disease and its molecular biomarkers. Biomarkers denote the biological states of tissues, cells, or body fluids that are useful for disease detection and classification. Clinical proteomics is used for early disease detection, molecular diagnosis of disease, identification and formulation of therapies, and disease monitoring and prognostics. Bioinformatics tools are essential for converting raw proteomics data into knowledge and subsequently into useful applications. These tools are used for the collection, processing, analysis, and interpretation of the vast amounts of proteomics data. Management, analysis, and interpretation of large quantities of raw and processed data require a combination of various informatics technologies such as databases, sequence comparison, predictive models, and statistical tools. We have demonstrated the utility of bioinformatics in clinical proteomics through the analysis of the cancer antigen survivin and its suitability as a target for cancer immunotherapy.     

Workflow management systems for gene sequence analysis and evolutionary studies – A Review

Post ‘omic’ era has resulted in the development of many primary, secondary and derived databases. Many analytical and visualization bioinformatics tools have been developed to manage and analyze the data available through large sequencing projects. Availability of heterogeneous databases and tools make it difficult for researchers to access information from varied sources and run different bioinformatics tools to get desired analysis done. Building integrated bioinformatics platforms is one of the most challenging tasks that bioinformatics community is facing. Integration of various databases, tools and algorithm is a challenging problem to deal with. This article describes the bioinformatics analysis workflow management systems that are developed in the area of gene sequence analysis and phylogeny. This article will be useful for biotechnologists, molecular biologists, computer scientists and statisticians engaged in computational biology and bioinformatics research.     

Bioinformatics tools and databases for analysis of next-generation sequence data

Genome sequencing has been revolutionized by next-generation technologies, which can rapidly produce vast quantities of data at relatively low cost. With data production now no longer being limited, there is a huge challenge to analyse the data flood and interpret biological meaning. Bioinformatics scientists have risen to the challenge and a large number of software tools and databases have been produced and these continue to evolve with this rapidly advancing field. Here, we outline some of the tools and databases commonly used for the analysis of next-generation sequence data with comment on their utility.     

Genome research in the cloud

High-throughput genome research has long been associated with bioinformatics, as it assists genome sequencing and annotation projects. Along with databases, to store, properly manage, and retrieve biological data, a large number of computational tools have been developed to decode biological information from this data. However, with the advent of next-generation sequencing (NGS) technology the sequence data starts generating at a pace never before seen. Consequently researchers are facing a threat as they are experiencing a potential shortage of storage space and tools to analyze the data. Moreover, the voluminous data increases traffic in the network by uploading and downloading large data sets, and thus consume much of the network's available bandwidth. All of these obstacles have led to the solution in the form of cloud computing.     

GranatumX: A Community-engaging,Modularized, and Flexible Webtool for Single-cell Data Analysis

We present GranatumX, a next-generation software environment for single-cell RNA sequencing (scRNA-seq) data analysis. GranatumX is inspired by the interactive webtool Granatum. GranatumX enables biologists to access the latest scRNA-seq bioinformatics methods in a web-based graphical environment. It also offers software developers the opportunity to rapidly promote their own tools with others in customizable pipelines. The architecture of GranatumX allows for easy inclusion of plugin modules, named Gboxes, which wrap around bioinformatics tools written in various programming languages and on various platforms. GranatumX can be run on the cloud or private servers and generate reproducible results. It is a community-engaging, flexible, and evolving software ecosystem for scRNA-seq analysis, connecting developers with bench scientists. GranatumX is freely accessible at http://garmiregroup.org/granatumx/app.     

Genome data mining of lactic acid bacteria: the impact of bioinformatics

Lactic acid bacteria (LAB) have been widely used in food fermentations and, more recently, as probiotics in health-promoting food products. Genome sequencing and functional genomics studies of a variety of LAB are now rapidly providing insights into their diversity and evolution and revealing the molecular basis for important traits such as flavor formation, sugar metabolism, stress response, adaptation and interactions. Bioinformatics plays a key role in handling, integrating and analyzing the flood of 'omics' data being generated. Reconstruction of metabolic potential using bioinformatics tools and databases, followed by targeted experimental verification and exploration of the metabolic and regulatory network properties, are the present challenges that should lead to improved exploitation of these versatile food bacteria.     

Harnessing the antigenic fingerprint of each individual cancer for immunotherapy of human cancer: genomics shows a new way and its challenges

The idea that individual tumors are antigenically unique has been around since the very dawn of our recognition of adaptive immune response to tumors. That idea has inspired a small number of attempts at individualized immunotherapy of human cancers. Such previous attempts for solid tumors have been hobbled by an inability to define the individually unique antigenic repertoire of tumors because of technological difficulties. The new availability of rapid and cheap high throughput DNA sequencing promises to overcome that hurdle. Using this new ability, coupled with bio-informatic tools, it is now possible to define the immunogenic repertoire of any tumor to a high degree of granularity within a practical time frame and an acceptable cost. The development of these ideas, and a small number of such studies that underscore this promise, is discussed. This new way—of characterizing the tumor immunome through characterization of the tumor genome—has distinct challenges, including selection of the appropriate peptides, choosing methods of immunizations that can incorporate tens of epitopes, and addressing issues of antigenic heterogeneity of tumors. However, tools for meeting these challenges exist and are emergent.     

B细胞表位的计算机预测

B细胞表位预测对于多种免疫学研究是必不可少的重要工具.本文概括了计算机预测B细胞表位的现状,汇总了多种表位预测工具及其原理和应用,介绍了一些用于建立与评价预测工具的数据库和数据集,对各类预测工具和数据库的特点和网址进行整理列表.另外,还分析了B细胞表位预测领域存在的问题,并对其未来发展提出了建议.