Protein-protein interaction networks: from interactions to networks

The goal of interaction proteomics that studies the protein-protein interactions of all expressed proteins is to understand biological processes that are strictly regulated by these interactions. The availability of entire genome sequences of many organisms and high-throughput analysis tools has led scientists to study the entire proteome (Pandey and Mann, 2000). There are various high-throughput methods for detecting protein interactions such as yeast two-hybrid approach and mass spectrometry to produce vast amounts of data that can be utilized to decipher protein functions in complicated biological networks. In this review, we discuss recent developments in analytical methods for large-scale protein interactions and the future direction of interaction proteomics.     

工程菌种自动化高通量编辑与筛选研究进展

合成生物学(synthetic biology)与经典生物学研究的革命性区别之一是合成生物学能将生物实验的对象、方法、技术和流程高度标准化和模块化,创建出自动化与高通量的合成生物铸造模式。该模式通过复杂生物过程与自动化设施的结合,颠覆过往劳动密集型的研究范式,获得更高的技术迭代能力,极大促进了合成生物学的发展和产业化应用。值此天津工业生物技术研究所创立10周年之际,本文回顾了研究所在工业菌种自动化高通量编辑与筛选领域的系列重要工作进展,对基因克隆(gene cloning)、基因组编辑(genome editing)、编辑序列设计(editing sequence design)等生物技术的自动化实现,以及流式细胞、液滴微流控、全基因组规模扰动测序等高通量筛选技术进行了分析讨论,并展望了本领域未来的发展方向。望借此为创建具有自主知识产权的优秀菌种及其产业应用提供智能化、自动化和全链条覆盖的整体支撑能力。     

Mouse monoclonal antibodies in biological research: strategies for high-throughput production

Mouse monoclonal antibodies have become key components in basic research as well as in the clinical laboratory. Being invaluable tools in many biological assays, they continue to be the primary choice in the research field, although the conventional technology used for hybridoma generation and screening is a still lengthy, time-consuming and low-throughput process. With the advent of genetic immunisation and the application of automation and microarray to the traditional biological assays, the monoclonal antibody field has been revolutionised. Here, we will briefly review the most relevant strategies which have made the manufacture of murine monoclonal antibodies a faster and high-throughput technology.     

Pathway analysis for genome-wide genetic variation data: Analytic principles,latest developments,and new opportunities

Pathway analysis, also known as gene-set enrichment analysis, is a multilocus analytic strategy that integrates a priori, biological knowledge into the statistical analysis of high-throughput genetics data. Originally developed for the studies of gene expression data, it has become a powerful analytic procedure for indepth mining of genome-wide genetic variation data. Astonishing discoveries were made in the past years,uncovering genes and biological mechanisms underlying common and complex disorders. However, as massive amounts of diverse functional genomics data accrue, there is a pressing need for newer generations of pathway analysis methods that can utilize multiple layers of high-throughput genomics data. In this review, we provide an intellectual foundation of this powerful analytic strategy, as well as an update of the state-of-the-art in recent method developments. The goal of this review is threefold:(1) introduce the motivation and basic steps of pathway analysis for genome-wide genetic variation data;(2) review the merits and the shortcomings of classic and newly emerging integrative pathway analysis tools; and(3)discuss remaining challenges and future directions for further method developments.     

Identification of aberrant pathways and network activities from high-throughput data

Many complex diseases such as cancer are associated with changes in biological pathways and molecular networks rather than being caused by single gene alterations. A major challenge in the diagnosis and treatment of such diseases is to identify characteristic aberrancies in the biological pathways and molecular network activities and elucidate their relationship to the disease. This review presents recent progress in using high-throughput biological assays to decipher aberrant pathways and network activities. In particular, this review provides specific examples in which high-throughput data have been applied to identify relationships between diseases and aberrant pathways and network activities. The achievements in this field have been remarkable, but many challenges have yet to be addressed.     

CloudNMF: A MapReduce Implementation of Nonnegative Matrix Factorization for Large-scale Biological Datasets

In the past decades,advances in high-throughput technologies have led to the generation of huge amounts of biological data that require analysis and interpretation.Recently,nonnegative matrix factorization(NMF) has been introduced as an efficient way to reduce the complexity of data as well as to interpret them,and has been applied to various fields of biological research.In this paper,we present CloudNMF,a distributed open-source implementation of NMF on a MapReduce framework.Experimental evaluation demonstrated that CloudNMF is scalable and can be used to deal with huge amounts of data,which may enable various kinds of a high-throughput biological data analysis in the cloud.CloudNMF is freely accessible at http://admis.fudan.edu.cn/projects/CloudNMF.html.     

Advances in technologies for 3D genomics research

The spatial structure of the orderly organized chromatin in the nucleus has important roles in maintaining normal cell function and in regulation of gene expression, and the high-throughput Hi-C and Ch IA-PET methods have been widely used in various biological studies for determining potential spatial genome structures and their functions. However, there are still great difficulties and challenges in three-dimensional(3D) genomics research. More efficient, economical, and unbiased approaches to studying 3D genomics need to be developed for more widespread and easier applications. Here, we review the most recent studies on new 3D genomics research technologies, such as improvements of the traditional Hi-C and Ch IA-PET methods, new approaches based on non-proximal-ligation strategies, and imaging-based methods improved in recent years. Especially, we review the CRISPR-based methods for functional validations in 3D genomics, which could be the forthcoming directions. We hope this review can show some insights into the potential improvements for future 3D genomics.     

Multiomics metabolic and epigenetics regulatory network in cancer: A systems biology perspective

Genetic, epigenetic, and metabolic alterations are all hallmarks of cancer. However, the epigenome and metabolome are both highly complex and dynamic biological networks in vivo. The interplay between the epigenome and metabolome contributes to a biological system that is responsive to the tumor microenvironment and possesses a wealth of unknown biomarkers and targets of cancer therapy. From this perspective, we first review the state of high-throughput biological data acquisition(i.e. multiomics data)and analysis(i.e. computational tools) and then propose a conceptual in silico metabolic and epigenetic regulatory network(MER-Net) that is based on these current high-throughput methods. The conceptual MER-Net is aimed at linking metabolomic and epigenomic networks through observation of biological processes, omics data acquisition, analysis of network information, and integration with validated database knowledge. Thus, MER-Net could be used to reveal new potential biomarkers and therapeutic targets using deep learning models to integrate and analyze large multiomics networks. We propose that MER-Net can serve as a tool to guide integrated metabolomics and epigenomics research or can be modified to answer other complex biological and clinical questions using multiomics data.     

植物根际微生物群落构建的研究进展

植物根际是指植物根系与土壤的交界面,是根系自身生命活动和代谢对土壤影响最直接、最强烈的区域,其物理、化学和生物性质不同于土体土壤。在这个区域里,与植物发生相互作用的大量微生物,被称为根际微生物。根际微生物在植物的生长发育和植物病虫害的生物防治等方面都具有十分重要的意义。本文总结了根际微生物群落构建的研究现状,介绍了根际微生物的经典和最新的研究方法,包括根箱法、同位素技术以及高通量测序、菌群定量分析、高通量分离培养等方法在根际微生物研究中的应用,讨论了植物根系分泌物(碳水化物、氨基酸、黄酮类、酚类、激素及其信号物质)和土壤物理化学性质对根际微生物群落的影响,概述了根际微生物-植物的互作机制,以及根际微生物群落对植物的促生作用、提高植物抗逆性和抑制作用,并对根际微生物群落研究中存在的问题和未来发展方向进行了展望。     

Quantitative analysis of nucleic acids--the last few years of progress

DNA and RNA quantifications are widely used in biological and biomedical research. In the last ten years, many technologies have been developed to enable automated and high-throughput analyses. In this review, we first give a brief overview of how DNA and RNA quantifications are carried out. Then, five technologies (microarrays, SAGE, differential display, real time PCR and real competitive PCR) are introduced, with an emphasis on how these technologies can be applied and what their limitations are. The technologies are also evaluated in terms of a few key aspects of nucleic acids quantification such as accuracy, sensitivity, specificity, cost and throughput.     

Visualization of the interactome: What are we looking at?

Network visualization of the interactome has been become routine in systems biology research. Not only does it serve as an illustration on the cellular organization of protein-protein interactions, it also serves as a biological context for gaining insights from high-throughput data. However, the challenges to produce an effective visualization have been great owing to the fact that the scale, biological context and dynamics of any given interactome are too large and complex to be captured by a single visualization. Visualization design therefore requires a pragmatic trade-off between capturing biological concept and being comprehensible. In this review, we focus on the biological interpretation of different network visualizations. We will draw on examples predominantly from our experiences but elaborate them in the context of the broader field. A rich variety of networks will be introduced including interactomes and the complexome in 2D, interactomes in 2.5D and 3D and dynamic networks.     

High-throughput RNAi screening to dissect cellular pathways: A how-to guide

RNA interference (RNAi) has become a powerful tool to dissect cellular pathways and characterize gene functions. The availability of genome-wide RNAi libraries for various model organisms and mammalian cells has enabled high-throughput RNAi screenings. These RNAi screens successfully identified key components that had previously been missed in classical forward genetic screening approaches and allowed the assessment of combined loss-of-function phenotypes. Crucially, the quality of RNAi screening results depends on quantitative assays and the choice of the right biological context. In this review, we provide an overview on the design and application of high-throughput RNAi screens as well as data analysis and candidate validation strategies.     

The representation of heart development in the gene ontology

An understanding of heart development is critical in any systems biology approach to cardiovascular disease. The interpretation of data generated from high-throughput technologies (such as microarray and proteomics) is also essential to this approach. However, characterizing the role of genes in the processes underlying heart development and cardiovascular disease involves the non-trivial task of data analysis and integration of previous knowledge. The Gene Ontology (GO) Consortium provides structured controlled biological vocabularies that are used to summarize previous functional knowledge for gene products across all species. One aspect of GO describes biological processes, such as development and signaling.In order to support high-throughput cardiovascular research, we have initiated an effort to fully describe heart development in GO; expanding the number of GO terms describing heart development from 12 to over 280. This new ontology describes heart morphogenesis, the differentiation of specific cardiac cell types, and the involvement of signaling pathways in heart development. This work also aligns GO with the current views of the heart development research community and its representation in the literature. This extension of GO allows gene product annotators to comprehensively capture the genetic program leading to the developmental progression of the heart. This will enable users to integrate heart development data across species, resulting in the comprehensive retrieval of information about this subject.The revised GO structure, combined with gene product annotations, should improve the interpretation of data from high-throughput methods in a variety of cardiovascular research areas, including heart development, congenital cardiac disease, and cardiac stem cell research. Additionally, we invite the heart development community to contribute to the expansion of this important dataset for the benefit of future research in this area.     

哺乳动物细胞小干扰RNA库的建立和应用

双链小干扰RNA（siRNA）在多种类型细胞中介导特异性的基因沉默,这一现象的发现为深入研究单个基因的功能提供了重要的方法学基础,从而得到了广泛的应用.最近的文献报道了全基因组siRNA库的建立,为高通量基因功能分析和研究提供了新的方法,成为新的研究热点.小干扰RNA库可以用来筛选和研究介导细胞复杂表型和生物学过程的关键基因,通过建立一系列具有目的表型的细胞系,有可能对特定细胞信号调节通路进行更为全面的解析.本文综述了目前在siRNA建库方法方面的进展,并探讨了建立小干扰RNA库中的关键问题.     

长链非编码RNA数据库资源

长链非编码RNA（long non coding RNA, lncRNA)在多个水平参与调节机体的各项基础生物进程,其功能紊乱常伴随疾病的发生。鉴定lncRNA的生物学功能已成为近年来的研究热点。然而,目前从各种真核生物高通量测序中鉴定的几十万个lncRNA中,只有极少数的功能已被实验验证,这对于该领域的深入研究是个巨大的挑战。因此,许多科研机构都建立了lncRNA数据库,并且持续周期性更新,这为研究者共享、注释和分析lncRNA功能提供了十分有效的工具。本文从lncRNA原始资源整合、筛选、鉴定及功能分析和lncRNA与人类疾病等4个方面介绍各lncRNA数据库资源的最新特征和应用范围。这为研究者在选择不同数据库资源进行lncRNA鉴定和分析时提供参考。     

Comprehensive literature review and statistical considerations for microarray meta-analysis

With the rapid advances of various high-throughput technologies, generation of '-omics' data is commonplace in almost every biomedical field. Effective data management and analytical approaches are essential to fully decipher the biological knowledge contained in the tremendous amount of experimental data. Meta-analysis, a set of statistical tools for combining multiple studies of a related hypothesis, has become popular in genomic research. Here, we perform a systematic search from PubMed and manual collection to obtain 620 genomic meta-analysis papers, of which 333 microarray meta-analysis papers are summarized as the basis of this paper and the other 249 GWAS meta-analysis papers are discussed in the next companion paper. The review in the present paper focuses on various biological purposes of microarray meta-analysis, databases and software and related statistical procedures. Statistical considerations of such an analysis are further scrutinized and illustrated by a case study. Finally, several open questions are listed and discussed.