以海量数据计算揭示人类疾病发生机制及相关分子标志物

高通量芯片和深度测序技术为在全基因组水平上绘制高分辨率的基因组变异、RNA转录、转录因子结合及组蛋白修饰图谱等研究提供了前所未有的机遇.这些技术彻底改变了以往有关转录组学、调控网络以及表观遗传调控的研究方法,产生了海量的多水平组学数据,并开启了高效数据整合研究的先河.然而,如何有效地整合这些数据仍然是一个巨大的挑战.本文总结了高通量组学数据的产生对相关领域研究的主要影响及其与人类疾病的关系,并介绍了多种用于数据整合分析的生物信息学方法.最后,以炎症疾病为例进行说明.     

系统生物学研究中不同组学数据的整合

高通量实验方法的发展导致大量基因组、转录组、代谢组等组学数据的出现,组学数据的整合为全面了解生物学系统提供了条件.但是,由于当前实验技术手段的限制,高通量组学数据大多存在系统偏差,数据类型和可靠程度也各不相同,这给组学数据的整合带来了困难.本文以转录组、蛋白质组和代谢组为重点,综述了近年来组学数据整合方面的研究进展,包括新的数据整合方法和分析平台.虽然现存的数据统计和网络分析的方法有助于发现不同组学数据之间的关联,但是生物学意义上的深层次的数据整合还有待于生物、数学、计算机等各种领域的全面发展.     

医学研究生开展转录组学课程必要性的探讨

转录组学是生命科学领域的一门交叉型、发展快速的前沿性学科。随着高通量测序技术的迅猛发展,在收集、整合及数据挖掘的基础上全面系统的研究转录组成为可能。目前,利用转录组学的理论及技术研究疾病的转录组信息,系统全面阐明其基因表达调控规律,构建其基因调控网络,已经成为医学研究领域的热点。通过在医学研究生中开展转录组学这门课程,使研究生掌握其中的科研思维和方法,帮助研究生更清晰地认识疾病发生发展的分子机制,并通过学习这门课程提高研究生的科研能力和水平。     

鸟枪脂组学研究进展及其应用

继基因组学之后,针对各种代谢物的组学研究蓬勃兴起,鸟枪脂组学(shotgun lipidom ics)作为脂类研究的重要新兴手段,在创立和初期发展的过程中便已经展示出惊人的潜力,随着相关技术的进一步完善和发展,必将成为系统生物学的组成部分,在生物医学的研究和应用中发挥难以替代的重要作用。鸟枪脂组学利用质谱技术对全部或单一脂类及其相关分子进行系统分析,研究其改变对生物体所产生的作用并探讨其作用机制。传统脂类分析中的瓶颈问题在以电喷射离子质谱为基础的脂组学方法出现后获得了突破,使脂类分析进入高通量、高精度和高效能的时代。脂类在生物体内分布广泛、种类众多,并且与人类疾病密切相关。将脂组学分析方法运用于疾病相关的特异脂类标志物的发现并揭示其在疾病发生发展等复杂过程中的作用,可能为疾病的诊断治疗提供新的思路和策略。     

功能代谢组学技术在微生物研究中的应用

功能代谢组学是以代谢组学技术发现关键代谢物为基础,结合体内体外实验和分子生物学等技术手段,研究差异代谢物及相关蛋白、酶和基因的功能,从而揭示生物体内在的分子调控机制。功能代谢组学技术具有精准识别关键调控代谢物及其相关基因或酶的特性,近年来在微生物相关疾病的防控和工业化生产等方面受到了广泛的关注。本文介绍了功能代谢组学技术的分析流程、相关研究方法与平台及其在微生物研究方面的应用,其中重点阐述了真核、原核以及病毒微生物的代谢特性、调控靶点及相关防控策略等。最后,提出功能代谢组学研究在未来面临的问题与挑战,为后续功能代谢组学的研究与发展提供新的思路。     

功能代谢组学技术在微生物研究中的应用

功能代谢组学是以代谢组学技术发现关键代谢物为基础,结合体内体外实验和分子生物学等技术手段,研究差异代谢物及相关蛋白、酶和基因的功能,从而揭示生物体内在的分子调控机制。功能代谢组学技术具有精准识别关键调控代谢物及其相关基因或酶的特性,近年来在微生物相关疾病的防控和工业化生产等方面受到了广泛的关注。本文介绍了功能代谢组学技术的分析流程、相关研究方法与平台及其在微生物研究方面的应用,其中重点阐述了真核、原核以及病毒微生物的代谢特性、调控靶点及相关防控策略等。最后,提出功能代谢组学研究在未来面临的问题与挑战,为后续功能代谢组学的研究与发展提供新的思路。     

多组学整合网络: 一把精准解码玉米功能基因组的钥匙

高通量组学技术的快速发展使生命科学进入大数据时代。科学家们从基因组、转录组、蛋白质组和代谢组等多组学数据中剥茧抽丝, 逐步揭示生物体内复杂而巧妙的调控网络。近日, 华中农业大学李林课题组联合杨芳课题组和严建兵课题组构建了玉米(Zea mays)首个多组学整合网络。该网络包括3万个玉米基因在三维基因组水平、转录水平、翻译水平和蛋白质互作水平的调控关系, 由280万个网络连接组成, 构成1 412个调控模块。利用该整合网络, 研究团队预测并证实了5个调控玉米分蘖、侧生器官发育和籽粒皱缩的新基因。进一步结合机器学习方法, 他们预测出2 651个影响玉米开花期的候选基因, 鉴定到8条可能参与玉米开花期的调控通路, 并利用基因编辑技术和EMS突变体证实了20个候选基因的生物学功能。此外, 通过对整合调控网络的进化分析, 他们发现玉米两套亚基因组在转录组、翻译组和蛋白互作组水平上存在渐进式的功能分化。这套集合多组学数据构建的整合网络图谱是玉米功能基因组学研究的重大进展, 为玉米重要性状新基因克隆、分子调控通路解析和玉米基因组进化分析提供了新工具, 是解锁玉米功能基因组学的一把新钥匙。     

肠道微生物组与宿主的表观遗传修饰

高通量测序技术已经增加了人们对肠道微生物组和表观遗传学修饰的理解,将肠道微生物组和宿主表观遗传学修饰紧密联系起来,阐明了很多疾病的发生过程如免疫、代谢、心血管疾病甚至是癌症。肠道微生物组与宿主具有相互作用,与人体密不可分,相辅相成。肠道微生物组的生态失调可能诱导疾病的发生并能调控宿主表观遗传学修饰。宿主表观遗传学调控和肠道微生物组(或其代谢产物)变化的相互关系在很多疾病中都有报道。因此,肠道微生物组可作为某些疾病的诊断标记,健康肠道微生物组的移植会逆转这种微生态失调,可作为一种有效的治疗策略。本文主要探讨了肠道微生物组直接调控宿主表观修饰和通过小分子生物活性物质和其他酶辅因子间接影响表观修饰,以及基于肠道微生物组调控宿主表观修饰的诊断和治疗应用等。     

黑曲霉组学研究进展

黑曲霉作为重要的工业发酵菌株,被广泛用于多种有机酸和工业用酶的生产。随着组学技术的日益发展和成熟,黑曲霉的基因组、转录组、蛋白质组、代谢组等组学数据不断增长,宣告着黑曲霉生物过程研究大数据时代的到来。从单一组学的数据分析、多组学的比较到以基因组代谢网络模型为中心的多组学整合研究,人们对黑曲霉高效生产机制的理解不断深入和系统,这为通过遗传改造和过程调控对菌株的生产性能进行理性的全局优化提供了可能。本文回顾和总结了近年来黑曲霉的组学研究进展,并提出黑曲霉组学研究未来的发展方向。     

基于新一代高通量技术的人类疾病组学研究策略

近年来,包括第二代测序技术和蛋白质谱技术等在内的新一代高通量技术越来越多的应用于解决生物学问题尤其是人类疾病的研究。这种以数据为导向,大规模、工业化的研究模式,使得从基因组水平、转录组水平、蛋白质组水平等角度对疾病展开全方位、多层次的研究成为可能。文章综述了新一代高通量技术在DNA、RNA、表观遗传、宏基因组和蛋白质组水平的人类疾病研究进展以及在转化医学领域的应用。在基因组水平上,外显子组测序是近年来持续的研究热点,随着测序成本的不断降低,全基因组重测序也越来越凸显了其在全基因组范围内检测大型结构变异的优势,并使得个人基因组引领的个体化医疗逐渐成为可能。在转录组水平,如小RNA测序技术可用来检测已知小RNA和预测新的小RNA,这些小RNA不仅可以作为疾病诊断和预后的分子标志物,在疾病治疗方面也具有无限潜力。在蛋白质组水平,如目标蛋白质组学可以有目标地测定可能与疾病相关的特定蛋白质或多肽,能够很好地应用于疾病的临床分期分型。文章进一步阐述了跨组学研究在疾病研究领域中的应用和发展趋势,借助生物信息学分析方法进行多组学整合研究,能更加系统地阐释疾病的发生及发展机理,为疾病的诊断治疗提供强有力的工具。     

CHOmics: A web-based tool for multi-omics data analysis and interactive visualization in CHO cell lines

Chinese hamster ovary (CHO) cell lines are widely used in industry for biological drug production. During cell culture development, considerable effort is invested to understand the factors that greatly impact cell growth, specific productivity and product qualities of the biotherapeutics. While high-throughput omics approaches have been increasingly utilized to reveal cellular mechanisms associated with cell line phenotypes and guide process optimization, comprehensive omics data analysis and management have been a challenge. Here we developed CHOmics, a web-based tool for integrative analysis of CHO cell line omics data that provides an interactive visualization of omics analysis outputs and efficient data management. CHOmics has a built-in comprehensive pipeline for RNA sequencing data processing and multi-layer statistical modules to explore relevant genes or pathways. Moreover, advanced functionalities were provided to enable users to customize their analysis and visualize the output systematically and interactively. The tool was also designed with the flexibility to accommodate other types of omics data and thereby enabling multi-omics comparison and visualization at both gene and pathway levels. Collectively, CHOmics is an integrative platform for data analysis, visualization and management with expectations to promote the broader use of omics in CHO cell research.     

多组学技术及其在生命科学研究中应用概述

随着新一代测序技术、高分辨质谱技术、多组学整合分析方法及数据库的发展,组学技术正从传统的单一组学向多组学技术发展。以多组学驱动的系统生物学研究将带来生命科学研究的新范式。本文简要概述了基因组学、表观基因组学、转录组学,蛋白质组学及代谢组学的进展,重点介绍多组学技术平台的组成和功能,多组学技术的应用现状及在合成生物学及生物医学等领域的应用前景。     

Interpretation of network-based integration from multi-omics longitudinal data

Multi-omics integration is key to fully understand complex biological processes in an holistic manner. Furthermore, multi-omics combined with new longitudinal experimental design can unreveal dynamic relationships between omics layers and identify key players or interactions in system development or complex phenotypes. However, integration methods have to address various experimental designs and do not guarantee interpretable biological results. The new challenge of multi-omics integration is to solve interpretation and unlock the hidden knowledge within the multi-omics data. In this paper, we go beyond integration and propose a generic approach to face the interpretation problem. From multi-omics longitudinal data, this approach builds and explores hybrid multi-omics networks composed of both inferred and known relationships within and between omics layers. With smart node labelling and propagation analysis, this approach predicts regulation mechanisms and multi-omics functional modules. We applied the method on 3 case studies with various multi-omics designs and identified new multi-layer interactions involved in key biological functions that could not be revealed with single omics analysis. Moreover, we highlighted interplay in the kinetics that could help identify novel biological mechanisms. This method is available as an R package netOmics to readily suit any application.     

Integration of omics sciences to advance biology and medicine

In the past two decades, our ability to study cellular and molecular systems has been transformed through the development of omics sciences. While unlimited potential lies within massive omics datasets, the success of omics sciences to further our understanding of human disease and/or translating these findings to clinical utility remains elusive due to a number of factors. A significant limiting factor is the integration of different omics datasets (i.e., integromics) for extraction of biological and clinical insights. To this end, the National Cancer Institute (NCI) and the National Heart, Lung and Blood Institute (NHLBI) organized a joint workshop in June 2012 with the focus on integration issues related to multi-omics technologies that needed to be resolved in order to realize the full utility of integrating omics datasets by providing a glimpse into the disease as an integrated “system”. The overarching goals were to (1) identify challenges and roadblocks in omics integration, and (2) facilitate the full maturation of ‘integromics’ in biology and medicine. Participants reached a consensus on the most significant barriers for integrating omics sciences and provided recommendations on viable approaches to overcome each of these barriers within the areas of technology, bioinformatics and clinical medicine.     

Min-redundancy and max-relevance multi-view feature selection for predicting ovarian cancer survival using multi-omics data

Background

Large-scale collaborative precision medicine initiatives (e.g., The Cancer Genome Atlas (TCGA)) are yielding rich multi-omics data. Integrative analyses of the resulting multi-omics data, such as somatic mutation, copy number alteration (CNA), DNA methylation, miRNA, gene expression, and protein expression, offer tantalizing possibilities for realizing the promise and potential of precision medicine in cancer prevention, diagnosis, and treatment by substantially improving our understanding of underlying mechanisms as well as the discovery of novel biomarkers for different types of cancers. However, such analyses present a number of challenges, including heterogeneity, and high-dimensionality of omics data.

Methods

We propose a novel framework for multi-omics data integration using multi-view feature selection. We introduce a novel multi-view feature selection algorithm, MRMR-mv, an adaptation of the well-known Min-Redundancy and Maximum-Relevance (MRMR) single-view feature selection algorithm to the multi-view setting.

Results

We report results of experiments using an ovarian cancer multi-omics dataset derived from the TCGA database on the task of predicting ovarian cancer survival. Our results suggest that multi-view models outperform both view-specific models (i.e., models trained and tested using a single type of omics data) and models based on two baseline data fusion methods.

Conclusions

Our results demonstrate the potential of multi-view feature selection in integrative analyses and predictive modeling from multi-omics data.

     

Applications and Challenges of Machine Learning Methods in Alzheimer's Disease Multi-Source Data Analysis

BackgroundRecent development in neuroimaging and genetic testing technologies have made it possible to measure pathological features associated with Alzheimer''s disease (AD) in vivo. Mining potential molecular markers of AD from high-dimensional, multi-modal neuroimaging and omics data will provide a new basis for early diagnosis and intervention in AD. In order to discover the real pathogenic mutation and even understand the pathogenic mechanism of AD, lots of machine learning methods have been designed and successfully applied to the analysis and processing of large-scale AD biomedical data.ObjectiveTo introduce and summarize the applications and challenges of machine learning methods in Alzheimer''s disease multi-source data analysis.MethodsThe literature selected in the review is obtained from Google Scholar, PubMed, and Web of Science. The keywords of literature retrieval include Alzheimer''s disease, bioinformatics, image genetics, genome-wide association research, molecular interaction network, multi-omics data integration, and so on.ConclusionThis study comprehensively introduces machine learning-based processing techniques for AD neuroimaging data and then shows the progress of computational analysis methods in omics data, such as the genome, proteome, and so on. Subsequently, machine learning methods for AD imaging analysis are also summarized. Finally, we elaborate on the current emerging technology of multi-modal neuroimaging, multi-omics data joint analysis, and present some outstanding issues and future research directions.     

Estimating gene expression from DNA methylation and copy number variation: A deep learning regression model for multi-omics integration

Gene expression analysis plays a significant role for providing molecular insights in cancer. Various genetic and epigenetic factors (being dealt under multi-omics) affect gene expression giving rise to cancer phenotypes. A recent growth in understanding of multi-omics seems to provide a resource for integration in interdisciplinary biology since they altogether can draw the comprehensive picture of an organism's developmental and disease biology in cancers. Such large scale multi-omics data can be obtained from public consortium like The Cancer Genome Atlas (TCGA) and several other platforms. Integrating these multi-omics data from varied platforms is still challenging due to high noise and sensitivity of the platforms used. Currently, a robust integrative predictive model to estimate gene expression from these genetic and epigenetic data is lacking. In this study, we have developed a deep learning-based predictive model using Deep Denoising Auto-encoder (DDAE) and Multi-layer Perceptron (MLP) that can quantitatively capture how genetic and epigenetic alterations correlate with directionality of gene expression for liver hepatocellular carcinoma (LIHC). The DDAE used in the study has been trained to extract significant features from the input omics data to estimate the gene expression. These features have then been used for back-propagation learning by the multilayer perceptron for the task of regression and classification. We have benchmarked the proposed model against state-of-the-art regression models. Finally, the deep learning-based integration model has been evaluated for its disease classification capability, where an accuracy of 95.1% has been obtained.