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Sebastian S, Roy S, Kalita J. A generic parallel framework for inferring large-scale gene regulatory networks from expression profiles: application to Alzheimer's disease network. Brief Bioinform 2023; 24:6868522. [PMID: 36534961 DOI: 10.1093/bib/bbac482] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2022] [Revised: 09/14/2022] [Accepted: 10/11/2022] [Indexed: 12/23/2022] Open
Abstract
The inference of large-scale gene regulatory networks is essential for understanding comprehensive interactions among genes. Most existing methods are limited to reconstructing networks with a few hundred nodes. Therefore, parallel computing paradigms must be leveraged to construct large networks. We propose a generic parallel framework that enables any existing method, without re-engineering, to infer large networks in parallel, guaranteeing quality output. The framework is tested on 15 inference methods (not limited to) employing in silico benchmarks and real-world large expression matrices, followed by qualitative and speedup assessment. The framework does not compromise the quality of the base serial inference method. We rank the candidate methods and use the top-performing method to infer an Alzheimer's Disease (AD) affected network from large expression profiles of a triple transgenic mouse model consisting of 45,101 genes. The resultant network is further explored to obtain hub genes that emerge functionally related to the disease. We partition the network into 41 modules and conduct pathway enrichment analysis, revealing that a good number of participating genes are collectively responsible for several brain disorders, including AD. Finally, we extract the interactions of a few known AD genes and observe that they are periphery genes connected to the network's hub genes. Availability: The R implementation of the framework is downloadable from https://github.com/Netralab/GenericParallelFramework.
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Affiliation(s)
- Softya Sebastian
- Network Reconstruction and Analysis (NetRA) Lab, Department of Computer Applications, Sikkim University, 6th Mile, Gangtok, 737102, Sikkim, India
| | - Swarup Roy
- Network Reconstruction and Analysis (NetRA) Lab, Department of Computer Applications, Sikkim University, 6th Mile, Gangtok, 737102, Sikkim, India
| | - Jugal Kalita
- Department of Computer Science, University of Colorado at Colorado Springs, CO, 80918 USA
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2
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Rowland BE, Henriquez MA, Nilsen KT, Subramaniam R, Walkowiak S. Unraveling Plant-Pathogen Interactions in Cereals Using RNA-seq. Methods Mol Biol 2023; 2659:103-118. [PMID: 37249889 DOI: 10.1007/978-1-0716-3159-1_9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/31/2023]
Abstract
Over the past two decades, there have been significant advancements in the realm of transcriptomics, or the study of genes and their expression. Modern RNA sequencing technologies and high-performance computing are creating a "big data" revolution that provides new opportunities to explore the interactions between cereals and pathogens that affect grain yield and food safety. These data are being used to annotate genes and gene variants, as well as identify differentially expressed genes and create global gene co-expression networks. Moreover, these data can unravel the complex interactions between pathogen and host and identify genes and pathways involved in these interactions. This information can then be used for disease mitigation and the development of crops with superior resistance.
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Affiliation(s)
- Bronwyn E Rowland
- Department of Biomedical and Molecular Sciences, Queen's University, Kingston, ON, Canada
| | - Maria Antonia Henriquez
- Morden Research and Development Centre, Agriculture and Agri-Food Canada, Morden, MB, Canada
| | - Kirby T Nilsen
- Brandon Research and Development Centre, Agriculture and Agri-Food Canada, Brandon, MB, Canada.
| | - Rajagopal Subramaniam
- Ottawa Research and Development Centre, Agriculture and Agri-Food Canada, Ottawa, ON, Canada.
| | - Sean Walkowiak
- Grain Research Laboratory, Canadian Grain Commission, Winnipeg, MB, Canada.
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3
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Bystrykh L. Python for gene expression. F1000Res 2022; 10:870. [PMID: 35646329 PMCID: PMC9130758 DOI: 10.12688/f1000research.53842.2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 06/21/2022] [Indexed: 11/20/2022] Open
Abstract
Genome biology shows substantial progress in its analytical and computational part in the last decades. Differential gene expression is one of many computationally intense areas; it is largely developed under R programming language. Here we explain possible reasons for such dominance of R in gene expression data. Next, we discuss the prospects for Python to become competitive in this area of research in coming years. We indicate that Python can be used already in a field of a single cell differential gene expression. We pinpoint still missing parts in Python and possibilities for improvement.
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Affiliation(s)
- Leonid Bystrykh
- ERIBA, University Medical Center Groningen, University of Groningen, Groningen, 9713 AV, The Netherlands
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4
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Abstract
Transcriptomes are known to organize themselves into gene co-expression clusters or modules where groups of genes display distinct patterns of coordinated or synchronous expression across independent biological samples. The functional significance of these co-expression clusters is suggested by the fact that highly coexpressed groups of genes tend to be enriched in genes involved in common functions and biological processes. While gene co-expression is widely assumed to reflect close regulatory proximity, the validity of this assumption remains unclear. Here we use a simple synthetic gene regulatory network (GRN) model and contrast the resulting co-expression structure produced by these networks with their known regulatory architecture and with the co-expression structure measured in available human expression data. Using randomization tests, we found that the levels of co-expression observed in simulated expression data were, just as with empirical data, significantly higher than expected by chance. When examining the source of correlated expression, we found that individual regulators, both in simulated and experimental data, fail, on average, to display correlated expression with their immediate targets. However, highly correlated gene pairs tend to share at least one common regulator, while most gene pairs sharing common regulators do not necessarily display correlated expression. Our results demonstrate that widespread co-expression naturally emerges in regulatory networks, and that it is a reliable and direct indicator of active co-regulation in a given cellular context.
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5
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Prioritizing disease biomarkers using functional module based network analysis: A multilayer consensus driven scheme. Comput Biol Med 2020; 126:104023. [DOI: 10.1016/j.compbiomed.2020.104023] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2020] [Revised: 09/24/2020] [Accepted: 09/26/2020] [Indexed: 12/19/2022]
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6
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Hoel E, Levin M. Emergence of informative higher scales in biological systems: a computational toolkit for optimal prediction and control. Commun Integr Biol 2020; 13:108-118. [PMID: 33014263 PMCID: PMC7518458 DOI: 10.1080/19420889.2020.1802914] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2020] [Revised: 07/22/2020] [Accepted: 07/26/2020] [Indexed: 02/07/2023] Open
Abstract
The biological sciences span many spatial and temporal scales in attempts to understand the function and evolution of complex systems-level processes, such as embryogenesis. It is generally assumed that the most effective description of these processes is in terms of molecular interactions. However, recent developments in information theory and causal analysis now allow for the quantitative resolution of this question. In some cases, macro-scale models can minimize noise and increase the amount of information an experimenter or modeler has about "what does what." This result has numerous implications for evolution, pattern regulation, and biomedical strategies. Here, we provide an introduction to these quantitative techniques, and use them to show how informative macro-scales are common across biology. Our goal is to give biologists the tools to identify the maximally-informative scale at which to model, experiment on, predict, control, and understand complex biological systems.
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Affiliation(s)
- Erik Hoel
- Allen Discovery Center, Tufts University, Medford, MA, USA
| | - Michael Levin
- Allen Discovery Center, Tufts University, Medford, MA, USA
- Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA, USA
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7
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Yang J, Li Y, Liu Q, Li L, Feng A, Wang T, Zheng S, Xu A, Lyu J. Brief introduction of medical database and data mining technology in big data era. J Evid Based Med 2020; 13:57-69. [PMID: 32086994 PMCID: PMC7065247 DOI: 10.1111/jebm.12373] [Citation(s) in RCA: 250] [Impact Index Per Article: 62.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/27/2019] [Accepted: 01/23/2020] [Indexed: 01/14/2023]
Abstract
Data mining technology can search for potentially valuable knowledge from a large amount of data, mainly divided into data preparation and data mining, and expression and analysis of results. It is a mature information processing technology and applies database technology. Database technology is a software science that researches manages, and applies databases. The data in the database are processed and analyzed by studying the underlying theory and implementation methods of the structure, storage, design, management, and application of the database. We have introduced several databases and data mining techniques to help a wide range of clinical researchers better understand and apply database technology.
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Affiliation(s)
- Jin Yang
- Department of Clinical ResearchThe First Affiliated Hospital of Jinan UniversityGuangzhouGuangdongChina
- School of Public HealthXi'an Jiaotong University Health Science CenterXi'anShaanxiChina
| | - Yuanjie Li
- Department of Human AnatomyHistology and Embryology, School of Basic Medical Sciences, Xi'an Jiaotong University Health Science CenterXi'anShaanxiChina
| | - Qingqing Liu
- Department of Clinical ResearchThe First Affiliated Hospital of Jinan UniversityGuangzhouGuangdongChina
- School of Public HealthXi'an Jiaotong University Health Science CenterXi'anShaanxiChina
| | - Li Li
- Department of Clinical ResearchThe First Affiliated Hospital of Jinan UniversityGuangzhouGuangdongChina
| | - Aozi Feng
- Department of Clinical ResearchThe First Affiliated Hospital of Jinan UniversityGuangzhouGuangdongChina
| | - Tianyi Wang
- School of Public HealthShaanxi University of Chinese MedicineXianyangShaanxiChina
- Xianyang Central HospitalXianyangShaanxiChina
| | - Shuai Zheng
- School of Public HealthShaanxi University of Chinese MedicineXianyangShaanxiChina
| | - Anding Xu
- Department of NeurologyThe First Affiliated Hospital of Jinan UniversityGuangzhouGuangdongChina
| | - Jun Lyu
- Department of Clinical ResearchThe First Affiliated Hospital of Jinan UniversityGuangzhouGuangdongChina
- School of Public HealthXi'an Jiaotong University Health Science CenterXi'anShaanxiChina
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8
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Wan X, Wang Z, Han QL, Wu M. A Recursive Approach to Quantized H ∞ State Estimation for Genetic Regulatory Networks Under Stochastic Communication Protocols. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2019; 30:2840-2852. [PMID: 30668504 DOI: 10.1109/tnnls.2018.2885723] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
This paper deals with the finite-horizon quantized H∞ state estimation problem for a class of discrete time-varying genetic regulatory networks with quantization effects under stochastic communication protocols (SCPs). To better reflect the data-driven flavor of today's biological research, the network measurements (typically gigabytes in size by high-throughput sequencing technologies) are transmitted to a remote state estimator via two independent communication networks of limited bandwidths. To lighten the communication loads and avoid undesired data collisions, the measurement outputs are quantized and then transmitted under two SCPs introduced to schedule the large-scale data transmissions. The purpose of this paper is to design a time-varying state estimator such that the error dynamics of the state estimation satisfies a prescribed H∞ performance requirement over a finite horizon in the presence of nonlinearities, quantization effects, and SCPs. By utilizing the completing-the-square technique, sufficient conditions are derived to ensure the H∞ estimation performance and the parameters of the state estimator are designed by solving coupled backward recursive Riccati difference equations. A numerical example is given to illustrate the effectiveness of the design scheme of the proposed state estimator.
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9
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Manners HN, Roy S, Kalita JK. Intrinsic-overlapping co-expression module detection with application to Alzheimer's Disease. Comput Biol Chem 2018; 77:373-389. [PMID: 30466046 DOI: 10.1016/j.compbiolchem.2018.10.014] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2018] [Revised: 10/28/2018] [Accepted: 10/29/2018] [Indexed: 11/18/2022]
Abstract
Genes interact with each other and may cause perturbation in the molecular pathways leading to complex diseases. Often, instead of any single gene, a subset of genes interact, forming a network, to share common biological functions. Such a subnetwork is called a functional module or motif. Identifying such modules and central key genes in them, that may be responsible for a disease, may help design patient-specific drugs. In this study, we consider the neurodegenerative Alzheimer's Disease (AD) and identify potentially responsible genes from functional motif analysis. We start from the hypothesis that central genes in genetic modules are more relevant to a disease that is under investigation and identify hub genes from the modules as potential marker genes. Motifs or modules are often non-exclusive or overlapping in nature. Moreover, they sometimes show intrinsic or hierarchical distributions with overlapping functional roles. To the best of our knowledge, no prior work handles both the situations in an integrated way. We propose a non-exclusive clustering approach, CluViaN (Clustering Via Network) that can detect intrinsic as well as overlapping modules from gene co-expression networks constructed using microarray expression profiles. We compare our method with existing methods to evaluate the quality of modules extracted. CluViaN reports the presence of intrinsic and overlapping motifs in different species not reported by any other research. We further apply our method to extract significant AD specific modules using CluViaN and rank them based the number of genes from a module involved in the disease pathways. Finally, top central genes are identified by topological analysis of the modules. We use two different AD phenotype data for experimentation. We observe that central genes, namely PSEN1, APP, NDUFB2, NDUFA1, UQCR10, PPP3R1 and a few more, play significant roles in the AD. Interestingly, our experiments also find a hub gene, PML, which has recently been reported to play a role in plasticity, circadian rhythms and the response to proteins which can cause neurodegenerative disorders. MUC4, another hub gene that we find experimentally is yet to be investigated for its potential role in AD. A software implementation of CluViaN in Java is available for download at https://sites.google.com/site/swarupnehu/publications/resources/CluViaN Software.rar.
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Affiliation(s)
- Hazel Nicolette Manners
- Department of Information Technology, North Eastern Hill University, Shillong, Meghalaya, India.
| | - Swarup Roy
- Department of Computer Applications, Sikkim University, Gangtok, Sikkim, India; Department of Information Technology, North Eastern Hill University, Shillong, Meghalaya, India.
| | - Jugal K Kalita
- Department of Computer Science, University of Colorado, Colorado Springs, USA.
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10
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Gene Coexpression Network Comparison via Persistent Homology. Int J Genomics 2018; 2018:7329576. [PMID: 30327773 PMCID: PMC6169238 DOI: 10.1155/2018/7329576] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2018] [Revised: 07/21/2018] [Accepted: 07/26/2018] [Indexed: 11/17/2022] Open
Abstract
Persistent homology, a topological data analysis (TDA) method, is applied to microarray data sets. Although there are a few papers referring to TDA methods in microarray analysis, the usage of persistent homology in the comparison of several weighted gene coexpression networks (WGCN) was not employed before to the very best of our knowledge. We calculate the persistent homology of weighted networks constructed from 38 Arabidopsis microarray data sets to test the relevance and the success of this approach in distinguishing the stress factors. We quantify multiscale topological features of each network using persistent homology and apply a hierarchical clustering algorithm to the distance matrix whose entries are pairwise bottleneck distance between the networks. The immunoresponses to different stress factors are distinguishable by our method. The networks of similar immunoresponses are found to be close with respect to bottleneck distance indicating the similar topological features of WGCNs. This computationally efficient technique analyzing networks provides a quick test for advanced studies.
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11
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Zhao Y, Zheng D, Cvekl A. A comprehensive spatial-temporal transcriptomic analysis of differentiating nascent mouse lens epithelial and fiber cells. Exp Eye Res 2018; 175:56-72. [PMID: 29883638 PMCID: PMC6167154 DOI: 10.1016/j.exer.2018.06.004] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2018] [Revised: 06/01/2018] [Accepted: 06/03/2018] [Indexed: 02/07/2023]
Abstract
Elucidation of both the molecular composition and organization of the ocular lens is a prerequisite to understand its development, function, pathology, regenerative capacity, as well as to model lens development and disease using in vitro differentiation of pluripotent stem cells. Lens is comprised of the anterior lens epithelium and posterior lens fibers, which form the bulk of the lens. Lens fibers differentiate from lens epithelial cells through cell cycle exit-coupled differentiation that includes cellular elongation, accumulation of crystallins, cytoskeleton and membrane remodeling, and degradation of organelles within the central region of the lens. Here, we profiled spatiotemporal expression dynamics of both mRNAs and non-coding RNAs from microdissected mouse nascent lens epithelium and lens fibers at four developmental time points (embryonic [E] day 14.5, E16.5, E18.5, and P0.5) by RNA-seq. During this critical time window, multiple complex biosynthetic and catabolic processes generate the molecular and structural foundation for lens transparency. Throughout this developmental window, 3544 and 3518 genes show consistently and significantly greater expression in the nascent lens epithelium and fibers, respectively. Comprehensive data analysis confirmed major roles of FGF-MAPK, Wnt/β-catenin, PI3K/AKT, TGF-β, and BMP signaling pathways and revealed significant novel contributions of mTOR, EIF2, EIF4, and p70S6K signaling in lens formation. Unbiased motif analysis within promoter regions of these genes with consistent expression changes between epithelium and fiber cells revealed an enrichment for both established (e.g. E2Fs, Etv5, Hsf4, c-Maf, MafG, MafK, N-Myc, and Pax6) transcription factors and a number of novel regulators of lens formation, such as Arntl2, Dmrta2, Stat5a, Stat5b, and Tulp3. In conclusion, the present RNA-seq data serves as a comprehensive reference resource for deciphering molecular principles of normal mammalian lens differentiation, mapping a full spectrum of signaling pathways and DNA-binding transcription factors operating in both lens compartments, and predicting novel pathways required to establish lens transparency.
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Affiliation(s)
- Yilin Zhao
- Department of Ophthalmology and Visual Sciences, Albert Einstein College of Medicine, Bronx, NY 10461, USA; Department of Genetics, Albert Einstein College of Medicine, Bronx, NY 10461, USA
| | - Deyou Zheng
- Department of Genetics, Albert Einstein College of Medicine, Bronx, NY 10461, USA; Department of Neurology, Albert Einstein College of Medicine, Bronx, NY 10461, USA; Department of Neuroscience, Albert Einstein College of Medicine, Bronx, NY 10461, USA; Department of Neurosurgery, Shanghai East Hospital, Tongji University School of Medicine, Shanghai 200120, China.
| | - Ales Cvekl
- Department of Ophthalmology and Visual Sciences, Albert Einstein College of Medicine, Bronx, NY 10461, USA; Department of Genetics, Albert Einstein College of Medicine, Bronx, NY 10461, USA.
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12
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Kanhaiya K, Rogojin V, Kazemi K, Czeizler E, Petre I. NetControl4BioMed: a pipeline for biomedical data acquisition and analysis of network controllability. BMC Bioinformatics 2018; 19:185. [PMID: 30066633 PMCID: PMC6069765 DOI: 10.1186/s12859-018-2177-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022] Open
Abstract
BACKGROUND Network controllability focuses on discovering combinations of external interventions that can drive a biological system to a desired configuration. In practice, this approach translates into finding a combined multi-drug therapy in order to induce a desired response from a cell; this can lead to developments of novel therapeutic approaches for systemic diseases like cancer. RESULT We develop a novel bioinformatics data analysis pipeline called NetControl4BioMed based on the concept of target structural control of linear networks. Our pipeline generates novel molecular interaction networks by combining pathway data from various public databases starting from the user's query. The pipeline then identifies a set of nodes that is enough to control a given, user-defined set of disease-specific essential proteins in the network, i.e., it is able to induce a change in their configuration from any initial state to any final state. We provide both the source code of the pipeline as well as an online web-service based on this pipeline http://combio.abo.fi/nc/net_control/remote_call.php . CONCLUSION The pipeline can be used by researchers for controlling and better understanding of molecular interaction networks through combinatorial multi-drug therapies, for more efficient therapeutic approaches and personalised medicine.
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Affiliation(s)
- Krishna Kanhaiya
- Computational Biomodeling Laboratory, Turku Centre for Computer Science, and Department of Computer Science, Å bo Akademi University, Domkyrkotorget 3, Turku, 20500 Finland
| | - Vladimir Rogojin
- Computational Biomodeling Laboratory, Turku Centre for Computer Science, and Department of Computer Science, Å bo Akademi University, Domkyrkotorget 3, Turku, 20500 Finland
| | - Keivan Kazemi
- Computational Biomodeling Laboratory, Turku Centre for Computer Science, and Department of Computer Science, Å bo Akademi University, Domkyrkotorget 3, Turku, 20500 Finland
| | - Eugen Czeizler
- Computational Biomodeling Laboratory, Turku Centre for Computer Science, and Department of Computer Science, Å bo Akademi University, Domkyrkotorget 3, Turku, 20500 Finland
- National Institute for Research and Development for Biological Sciences, Splaiul Independentei 296, Bucharest, 060031 Romania
| | - Ion Petre
- Computational Biomodeling Laboratory, Turku Centre for Computer Science, and Department of Computer Science, Å bo Akademi University, Domkyrkotorget 3, Turku, 20500 Finland
- National Institute for Research and Development for Biological Sciences, Splaiul Independentei 296, Bucharest, 060031 Romania
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13
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Zamanighomi M, Zamanian M, Kimber M, Wang Z. Gene Regulatory Network Inference from Perturbed Time-Series Expression Data via Ordered Dynamical Expansion of Non-Steady State Actors. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2018; 15:1093-1106. [PMID: 26701893 DOI: 10.1109/tcbb.2015.2509992] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
The reconstruction of gene regulatory networks from gene expression data has been the subject of intense research activity. A variety of models and methods have been developed to address different aspects of this important problem. However, these techniques are narrowly focused on particular biological and experimental platforms, and require experimental data that are typically unavailable and difficult to ascertain. The more recent availability of higher-throughput sequencing platforms, combined with more precise modes of genetic perturbation, presents an opportunity to formulate more robust and comprehensive approaches to gene network inference. Here, we propose a step-wise framework for identifying gene-gene regulatory interactions that expand from a known point of genetic or chemical perturbation using time series gene expression data. This novel approach sequentially identifies non-steady state genes post-perturbation and incorporates them into a growing series of low-complexity optimization problems. The governing ordinary differential equations of this model are rooted in the biophysics of stochastic molecular events that underlie gene regulation, delineating roles for both protein and RNA-mediated gene regulation. We show the successful application of our core algorithms for network inference using simulated and real datasets.
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14
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Wan X, Wang Z, Wu M, Liu X, Liu X, Wang Z, Wu M, Wan X. State Estimation for Discrete Time-Delayed Genetic Regulatory Networks With Stochastic Noises Under the Round-Robin Protocols. IEEE Trans Nanobioscience 2018; 17:145-154. [PMID: 29870338 DOI: 10.1109/tnb.2018.2797124] [Citation(s) in RCA: 78] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
This paper investigates the problem of state estimation for discrete time-delayed genetic regulatory networks with stochastic process noises and bounded exogenous disturbances under the Round-Robin (RR) protocols. The network measurement outputs obtained by two groups of sensors are transmitted to two remote sub-estimators via two independent communication channels, respectively. To lighten the communication loads of the networks and reduce the occurrence rate of data collisions, two RR protocols are utilized to orchestrate the transmission orders of sensor nodes in two groups, respectively. The error dynamics of the state estimation is governed by a switched system with periodic switching parameters. By constructing a transmission-order-dependent Lyapunov-like functional and utilizing the up-to-date discrete Wirtinger-based inequality together with the reciprocally convex approach, sufficient conditions are established to guarantee the exponentially ultimate boundedness of the estimation error dynamics in mean square with a prescribed upper bound on the decay rate. An asymptotic upper bound of the outputs of the estimation errors in mean square is derived and the estimator parameters are then obtained by minimizing such an upper bound subject to linear matrix inequality constraints. The repressilator model is utilized to illustrate the effectiveness of the designed estimator.
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15
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Guo WL, Huang DS. An efficient method to transcription factor binding sites imputation via simultaneous completion of multiple matrices with positional consistency. MOLECULAR BIOSYSTEMS 2018; 13:1827-1837. [PMID: 28718849 DOI: 10.1039/c7mb00155j] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Transcription factors (TFs) are DNA-binding proteins that have a central role in regulating gene expression. Identification of DNA-binding sites of TFs is a key task in understanding transcriptional regulation, cellular processes and disease. Chromatin immunoprecipitation followed by high-throughput sequencing (ChIP-seq) enables genome-wide identification of in vivo TF binding sites. However, it is still difficult to map every TF in every cell line owing to cost and biological material availability, which poses an enormous obstacle for integrated analysis of gene regulation. To address this problem, we propose a novel computational approach, TFBSImpute, for predicting additional TF binding profiles by leveraging information from available ChIP-seq TF binding data. TFBSImpute fuses the dataset to a 3-mode tensor and imputes missing TF binding signals via simultaneous completion of multiple TF binding matrices with positional consistency. We show that signals predicted by our method achieve overall similarity with experimental data and that TFBSImpute significantly outperforms baseline approaches, by assessing the performance of imputation methods against observed ChIP-seq TF binding profiles. Besides, motif analysis shows that TFBSImpute preforms better in capturing binding motifs enriched in observed data compared with baselines, indicating that the higher performance of TFBSImpute is not simply due to averaging related samples. We anticipate that our approach will constitute a useful complement to experimental mapping of TF binding, which is beneficial for further study of regulation mechanisms and disease.
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Affiliation(s)
- Wei-Li Guo
- Institute of Machine Learning and Systems Biology, School of Electronics and Information Engineering, Tongji University, Shanghai, 201804, China.
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16
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Olfson E, Ross DA. Genes Orchestrating Brain Function. Biol Psychiatry 2017; 82:e17-e19. [PMID: 28693738 PMCID: PMC5712901 DOI: 10.1016/j.biopsych.2017.06.003] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/22/2017] [Revised: 05/31/2017] [Accepted: 06/01/2017] [Indexed: 10/19/2022]
Affiliation(s)
- Emily Olfson
- Department of Psychiatry, Yale School of Medicine, New Haven, Connecticut; Child Study Center, Yale School of Medicine, New Haven, Connecticut.
| | - David A Ross
- Department of Psychiatry, Yale School of Medicine, New Haven, Connecticut
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17
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Sarig O, Sprecher E. The Molecular Revolution in Cutaneous Biology: Era of Next-Generation Sequencing. J Invest Dermatol 2017; 137:e79-e82. [PMID: 28411851 DOI: 10.1016/j.jid.2016.02.818] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2015] [Revised: 12/22/2015] [Accepted: 02/01/2016] [Indexed: 11/20/2022]
Abstract
Like any true conceptual revolution, next-generation sequencing (NGS) has not only radically changed research and clinical practice, it has also modified scientific culture. With the possibility to investigate DNA contents of any organism and in any context, including in somatic disorders or in tissues carrying complex microbial populations, it initially seemed as if the genetic underpinning of any biological phenomenon could now be deciphered in an almost streamlined fashion. However, over the past recent years, we have once again come to understand that there is no such a thing as great opportunities without great challenges. The steadily expanding use of NGS and related applications is now facing biologists and physicians with novel technological obstacles, analytical hurdles and increasingly pressing ethical questions.
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Affiliation(s)
- Ofer Sarig
- Department of Dermatology, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
| | - Eli Sprecher
- Department of Dermatology, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel; Department of Human Molecular Genetics & Biochemistry, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel.
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Banf M, Rhee SY. Computational inference of gene regulatory networks: Approaches, limitations and opportunities. BIOCHIMICA ET BIOPHYSICA ACTA-GENE REGULATORY MECHANISMS 2016; 1860:41-52. [PMID: 27641093 DOI: 10.1016/j.bbagrm.2016.09.003] [Citation(s) in RCA: 40] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/13/2016] [Revised: 09/08/2016] [Accepted: 09/08/2016] [Indexed: 10/21/2022]
Abstract
Gene regulatory networks lie at the core of cell function control. In E. coli and S. cerevisiae, the study of gene regulatory networks has led to the discovery of regulatory mechanisms responsible for the control of cell growth, differentiation and responses to environmental stimuli. In plants, computational rendering of gene regulatory networks is gaining momentum, thanks to the recent availability of high-quality genomes and transcriptomes and development of computational network inference approaches. Here, we review current techniques, challenges and trends in gene regulatory network inference and highlight challenges and opportunities for plant science. We provide plant-specific application examples to guide researchers in selecting methodologies that suit their particular research questions. Given the interdisciplinary nature of gene regulatory network inference, we tried to cater to both biologists and computer scientists to help them engage in a dialogue about concepts and caveats in network inference. Specifically, we discuss problems and opportunities in heterogeneous data integration for eukaryotic organisms and common caveats to be considered during network model evaluation. This article is part of a Special Issue entitled: Plant Gene Regulatory Mechanisms and Networks, edited by Dr. Erich Grotewold and Dr. Nathan Springer.
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Affiliation(s)
- Michael Banf
- Department of Plant Biology, Carnegie Institution for Science, 260 Panama Street, Stanford 93405, United States.
| | - Seung Y Rhee
- Department of Plant Biology, Carnegie Institution for Science, 260 Panama Street, Stanford 93405, United States.
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Kashyap H, Ahmed HA, Hoque N, Roy S, Bhattacharyya DK. Big data analytics in bioinformatics: architectures, techniques, tools and issues. ACTA ACUST UNITED AC 2016. [DOI: 10.1007/s13721-016-0135-4] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
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Moyer E, Hagenauer M, Lesko M, Francis F, Rodriguez O, Nagarajan V, Huser V, Busby B. MetaNetVar: Pipeline for applying network analysis tools for genomic variants analysis. F1000Res 2016; 5:674. [PMID: 27158457 PMCID: PMC4857755 DOI: 10.12688/f1000research.8288.1] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 04/12/2016] [Indexed: 01/02/2023] Open
Abstract
Network analysis can make variant analysis better. There are existing tools like HotNet2 and dmGWAS that can provide various analytical methods. We developed a prototype of a pipeline called MetaNetVar that allows execution of multiple tools. The code is published at
https://github.com/NCBI-Hackathons/Network_SNPs. A working prototype is published as an Amazon Machine Image - ami-4510312f .
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Affiliation(s)
- Eric Moyer
- National Center for Biotechnology Information, Bethesda, USA
| | - Megan Hagenauer
- Molecular, Behavioral Neuroscience Institute, University of Michigan, Ann Arbor, USA
| | - Matthew Lesko
- National Center for Biotechnology Information, National Library of Medicine, Bethesda, USA
| | - Felix Francis
- Bioinformatics and Systems Biology program, University of Delaware, Newark, USA
| | - Oscar Rodriguez
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, USA
| | - Vijayaraj Nagarajan
- Bioinformatics and Computational Biosciences Branch, National Institute of Allergy and Infectious Diseases, National Institute of Mental Health, Bethesda, USA
| | - Vojtech Huser
- Lister Hill National Center for Biomedical Communications, National Library of Medicine, National Institute of Mental Health, Bethesda, USA
| | - Ben Busby
- National Center for Biotechnology Information, National Library of Medicine, Bethesda, USA
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Thijs S, Sillen W, Rineau F, Weyens N, Vangronsveld J. Towards an Enhanced Understanding of Plant-Microbiome Interactions to Improve Phytoremediation: Engineering the Metaorganism. Front Microbiol 2016; 7:341. [PMID: 27014254 PMCID: PMC4792885 DOI: 10.3389/fmicb.2016.00341] [Citation(s) in RCA: 98] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2015] [Accepted: 03/03/2016] [Indexed: 11/23/2022] Open
Abstract
Phytoremediation is a promising technology to clean-up contaminated soils based on the synergistic actions of plants and microorganisms. However, to become a widely accepted, and predictable remediation alternative, a deeper understanding of the plant-microbe interactions is needed. A number of studies link the success of phytoremediation to the plant-associated microbiome functioning, though whether the microbiome can exist in alternative, functional states for soil remediation, is incompletely understood. Moreover, current approaches that target the plant host, and environment separately to improve phytoremediation, potentially overlook microbial functions and properties that are part of the multiscale complexity of the plant-environment wherein biodegradation takes place. In contrast, in situ studies of phytoremediation research at the metaorganism level (host and microbiome together) are lacking. Here, we discuss a competition-driven model, based on recent evidence from the metagenomics level, and hypotheses generated by microbial community ecology, to explain the establishment of a catabolic rhizosphere microbiome in a contaminated soil. There is evidence to ground that if the host provides the right level and mix of resources (exudates) over which the microbes can compete, then a competitive catabolic and plant-growth promoting (PGP) microbiome can be selected for as long as it provides a competitive superiority in the niche. The competition-driven model indicates four strategies to interfere with the microbiome. Specifically, the rhizosphere microbiome community can be shifted using treatments that alter the host, resources, environment, and that take advantage of prioritization in inoculation. Our model and suggestions, considering the metaorganism in its natural context, would allow to gain further knowledge on the plant-microbial functions, and facilitate translation to more effective, and predictable phytotechnologies.
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Affiliation(s)
- Sofie Thijs
- Department of Biology, Centre for Environmental Sciences, Hasselt UniversityDiepenbeek, Belgium
| | | | | | | | - Jaco Vangronsveld
- Department of Biology, Centre for Environmental Sciences, Hasselt UniversityDiepenbeek, Belgium
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23
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O'Leary NA, Wright MW, Brister JR, Ciufo S, Haddad D, McVeigh R, Rajput B, Robbertse B, Smith-White B, Ako-Adjei D, Astashyn A, Badretdin A, Bao Y, Blinkova O, Brover V, Chetvernin V, Choi J, Cox E, Ermolaeva O, Farrell CM, Goldfarb T, Gupta T, Haft D, Hatcher E, Hlavina W, Joardar VS, Kodali VK, Li W, Maglott D, Masterson P, McGarvey KM, Murphy MR, O'Neill K, Pujar S, Rangwala SH, Rausch D, Riddick LD, Schoch C, Shkeda A, Storz SS, Sun H, Thibaud-Nissen F, Tolstoy I, Tully RE, Vatsan AR, Wallin C, Webb D, Wu W, Landrum MJ, Kimchi A, Tatusova T, DiCuccio M, Kitts P, Murphy TD, Pruitt KD. Reference sequence (RefSeq) database at NCBI: current status, taxonomic expansion, and functional annotation. Nucleic Acids Res 2015; 44:D733-45. [PMID: 26553804 PMCID: PMC4702849 DOI: 10.1093/nar/gkv1189] [Citation(s) in RCA: 3447] [Impact Index Per Article: 383.0] [Reference Citation Analysis] [Abstract] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2015] [Accepted: 10/24/2015] [Indexed: 12/12/2022] Open
Abstract
The RefSeq project at the National Center for Biotechnology Information (NCBI) maintains and curates a publicly available database of annotated genomic, transcript, and protein sequence records (http://www.ncbi.nlm.nih.gov/refseq/). The RefSeq project leverages the data submitted to the International Nucleotide Sequence Database Collaboration (INSDC) against a combination of computation, manual curation, and collaboration to produce a standard set of stable, non-redundant reference sequences. The RefSeq project augments these reference sequences with current knowledge including publications, functional features and informative nomenclature. The database currently represents sequences from more than 55,000 organisms (>4800 viruses, >40,000 prokaryotes and >10,000 eukaryotes; RefSeq release 71), ranging from a single record to complete genomes. This paper summarizes the current status of the viral, prokaryotic, and eukaryotic branches of the RefSeq project, reports on improvements to data access and details efforts to further expand the taxonomic representation of the collection. We also highlight diverse functional curation initiatives that support multiple uses of RefSeq data including taxonomic validation, genome annotation, comparative genomics, and clinical testing. We summarize our approach to utilizing available RNA-Seq and other data types in our manual curation process for vertebrate, plant, and other species, and describe a new direction for prokaryotic genomes and protein name management.
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Affiliation(s)
- Nuala A O'Leary
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Building 38A, 8600 Rockville Pike, Bethesda, MD 20894, USA
| | - Mathew W Wright
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Building 38A, 8600 Rockville Pike, Bethesda, MD 20894, USA
| | - J Rodney Brister
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Building 38A, 8600 Rockville Pike, Bethesda, MD 20894, USA
| | - Stacy Ciufo
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Building 38A, 8600 Rockville Pike, Bethesda, MD 20894, USA
| | - Diana Haddad
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Building 38A, 8600 Rockville Pike, Bethesda, MD 20894, USA
| | - Rich McVeigh
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Building 38A, 8600 Rockville Pike, Bethesda, MD 20894, USA
| | - Bhanu Rajput
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Building 38A, 8600 Rockville Pike, Bethesda, MD 20894, USA
| | - Barbara Robbertse
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Building 38A, 8600 Rockville Pike, Bethesda, MD 20894, USA
| | - Brian Smith-White
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Building 38A, 8600 Rockville Pike, Bethesda, MD 20894, USA
| | - Danso Ako-Adjei
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Building 38A, 8600 Rockville Pike, Bethesda, MD 20894, USA
| | - Alexander Astashyn
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Building 38A, 8600 Rockville Pike, Bethesda, MD 20894, USA
| | - Azat Badretdin
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Building 38A, 8600 Rockville Pike, Bethesda, MD 20894, USA
| | - Yiming Bao
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Building 38A, 8600 Rockville Pike, Bethesda, MD 20894, USA
| | - Olga Blinkova
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Building 38A, 8600 Rockville Pike, Bethesda, MD 20894, USA
| | - Vyacheslav Brover
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Building 38A, 8600 Rockville Pike, Bethesda, MD 20894, USA
| | - Vyacheslav Chetvernin
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Building 38A, 8600 Rockville Pike, Bethesda, MD 20894, USA
| | - Jinna Choi
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Building 38A, 8600 Rockville Pike, Bethesda, MD 20894, USA
| | - Eric Cox
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Building 38A, 8600 Rockville Pike, Bethesda, MD 20894, USA
| | - Olga Ermolaeva
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Building 38A, 8600 Rockville Pike, Bethesda, MD 20894, USA
| | - Catherine M Farrell
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Building 38A, 8600 Rockville Pike, Bethesda, MD 20894, USA
| | - Tamara Goldfarb
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Building 38A, 8600 Rockville Pike, Bethesda, MD 20894, USA
| | - Tripti Gupta
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Building 38A, 8600 Rockville Pike, Bethesda, MD 20894, USA
| | - Daniel Haft
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Building 38A, 8600 Rockville Pike, Bethesda, MD 20894, USA
| | - Eneida Hatcher
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Building 38A, 8600 Rockville Pike, Bethesda, MD 20894, USA
| | - Wratko Hlavina
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Building 38A, 8600 Rockville Pike, Bethesda, MD 20894, USA
| | - Vinita S Joardar
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Building 38A, 8600 Rockville Pike, Bethesda, MD 20894, USA
| | - Vamsi K Kodali
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Building 38A, 8600 Rockville Pike, Bethesda, MD 20894, USA
| | - Wenjun Li
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Building 38A, 8600 Rockville Pike, Bethesda, MD 20894, USA
| | - Donna Maglott
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Building 38A, 8600 Rockville Pike, Bethesda, MD 20894, USA
| | - Patrick Masterson
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Building 38A, 8600 Rockville Pike, Bethesda, MD 20894, USA
| | - Kelly M McGarvey
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Building 38A, 8600 Rockville Pike, Bethesda, MD 20894, USA
| | - Michael R Murphy
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Building 38A, 8600 Rockville Pike, Bethesda, MD 20894, USA
| | - Kathleen O'Neill
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Building 38A, 8600 Rockville Pike, Bethesda, MD 20894, USA
| | - Shashikant Pujar
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Building 38A, 8600 Rockville Pike, Bethesda, MD 20894, USA
| | - Sanjida H Rangwala
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Building 38A, 8600 Rockville Pike, Bethesda, MD 20894, USA
| | - Daniel Rausch
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Building 38A, 8600 Rockville Pike, Bethesda, MD 20894, USA
| | - Lillian D Riddick
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Building 38A, 8600 Rockville Pike, Bethesda, MD 20894, USA
| | - Conrad Schoch
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Building 38A, 8600 Rockville Pike, Bethesda, MD 20894, USA
| | - Andrei Shkeda
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Building 38A, 8600 Rockville Pike, Bethesda, MD 20894, USA
| | - Susan S Storz
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Building 38A, 8600 Rockville Pike, Bethesda, MD 20894, USA
| | - Hanzhen Sun
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Building 38A, 8600 Rockville Pike, Bethesda, MD 20894, USA
| | - Francoise Thibaud-Nissen
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Building 38A, 8600 Rockville Pike, Bethesda, MD 20894, USA
| | - Igor Tolstoy
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Building 38A, 8600 Rockville Pike, Bethesda, MD 20894, USA
| | - Raymond E Tully
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Building 38A, 8600 Rockville Pike, Bethesda, MD 20894, USA
| | - Anjana R Vatsan
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Building 38A, 8600 Rockville Pike, Bethesda, MD 20894, USA
| | - Craig Wallin
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Building 38A, 8600 Rockville Pike, Bethesda, MD 20894, USA
| | - David Webb
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Building 38A, 8600 Rockville Pike, Bethesda, MD 20894, USA
| | - Wendy Wu
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Building 38A, 8600 Rockville Pike, Bethesda, MD 20894, USA
| | - Melissa J Landrum
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Building 38A, 8600 Rockville Pike, Bethesda, MD 20894, USA
| | - Avi Kimchi
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Building 38A, 8600 Rockville Pike, Bethesda, MD 20894, USA
| | - Tatiana Tatusova
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Building 38A, 8600 Rockville Pike, Bethesda, MD 20894, USA
| | - Michael DiCuccio
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Building 38A, 8600 Rockville Pike, Bethesda, MD 20894, USA
| | - Paul Kitts
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Building 38A, 8600 Rockville Pike, Bethesda, MD 20894, USA
| | - Terence D Murphy
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Building 38A, 8600 Rockville Pike, Bethesda, MD 20894, USA
| | - Kim D Pruitt
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Building 38A, 8600 Rockville Pike, Bethesda, MD 20894, USA
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Abstract
Candida species are the most prevalent human fungal pathogens, with Candida albicans being the most clinically relevant species. Candida albicans resides as a commensal of the human gastrointestinal tract but is a frequent cause of opportunistic mucosal and systemic infections. Investigation of C. albicans virulence has traditionally relied on candidate gene approaches, but recent advances in functional genomics have now facilitated global, unbiased studies of gene function. Such studies include comparative genomics (both between and within Candida species), analysis of total RNA expression, and regulation and delineation of protein-DNA interactions. Additionally, large collections of mutant strains have begun to aid systematic screening of clinically relevant phenotypes. Here, we will highlight the development of functional genomics in C. albicans and discuss the use of these approaches to addressing both commensalism and pathogenesis in this species.
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25
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Raoof AA, Aerssens J. Patient-centered drug discovery as the means to improved R&D productivity. Drug Discov Today 2015; 20:1044-8. [DOI: 10.1016/j.drudis.2015.04.002] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2015] [Revised: 03/23/2015] [Accepted: 04/14/2015] [Indexed: 01/06/2023]
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Sukumar N, Krein MP, Prabhu G, Bhattacharya S, Sen S. Network measures for chemical library design. Drug Dev Res 2015; 75:402-11. [PMID: 25195584 DOI: 10.1002/ddr.21218] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Abstract
In this overview, we examine recent developments in network approaches to drug design. A brief overview of networks is followed by a discussion of how chemical similarity networks and their properties address challenges in drug design. Multiple methods used to assess or enhance chemical diversity for early-stage drug discovery are discussed, as well as methods that can be used for drug repositioning and ligand polypharmacology.
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Affiliation(s)
- Nagamani Sukumar
- Department of Chemistry, Shiv Nadar University, Dadri, Gautam Budh Nagar, U.P., 201314, India; Center for Informatics, Shiv Nadar University, Dadri, Gautam Budh Nagar, U.P., 201314, India
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27
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Tsuda K, Somssich IE. Transcriptional networks in plant immunity. THE NEW PHYTOLOGIST 2015; 206:932-947. [PMID: 25623163 DOI: 10.1111/nph.13286] [Citation(s) in RCA: 287] [Impact Index Per Article: 31.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/18/2014] [Accepted: 12/09/2014] [Indexed: 05/18/2023]
Abstract
Next to numerous abiotic stresses, plants are constantly exposed to a variety of pathogens within their environment. Thus, their ability to survive and prosper during the course of evolution was strongly dependent on adapting efficient strategies to perceive and to respond to such potential threats. It is therefore not surprising that modern plants have a highly sophisticated immune repertoire consisting of diverse signal perception and intracellular signaling pathways. This signaling network is intricate and deeply interconnected, probably reflecting the diverse lifestyles and infection strategies used by the multitude of invading phytopathogens. Moreover it allows signal communication between developmental and defense programs thereby ensuring that plant growth and fitness are not significantly retarded. How plants integrate and prioritize the incoming signals and how this information is transduced to enable appropriate immune responses is currently a major research area. An important finding has been that pathogen-triggered cellular responses involve massive transcriptional reprogramming within the host. Additional key observations emerging from such studies are that transcription factors (TFs) are often sites of signal convergence and that signal-regulated TFs act in concert with other context-specific TFs and transcriptional co-regulators to establish sensory transcription regulatory networks required for plant immunity.
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Affiliation(s)
- Kenichi Tsuda
- Department of Plant Microbe Interactions, Max Planck Institute for Plant Breeding Research, Carl-von-Linne Weg 10, Cologne, 50829, Germany
| | - Imre E Somssich
- Department of Plant Microbe Interactions, Max Planck Institute for Plant Breeding Research, Carl-von-Linne Weg 10, Cologne, 50829, Germany
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28
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Sikkink KL, Reynolds RM, Cresko WA, Phillips PC. Environmentally induced changes in correlated responses to selection reveal variable pleiotropy across a complex genetic network. Evolution 2015; 69:1128-42. [PMID: 25809411 PMCID: PMC5523853 DOI: 10.1111/evo.12651] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2014] [Accepted: 03/06/2015] [Indexed: 12/28/2022]
Abstract
Selection in novel environments can lead to a coordinated evolutionary response across a suite of characters. Environmental conditions can also potentially induce changes in the genetic architecture of complex traits, which in turn could alter the pattern of the multivariate response to selection. We describe a factorial selection experiment using the nematode Caenorhabditis remanei in which two different stress-related phenotypes (heat and oxidative stress resistance) were selected under three different environmental conditions. The pattern of covariation in the evolutionary response between phenotypes or across environments differed depending on the environment in which selection occurred, including asymmetrical responses to selection in some cases. These results indicate that variation in pleiotropy across the stress response network is highly sensitive to the external environment. Our findings highlight the complexity of the interaction between genes and environment that influences the ability of organisms to acclimate to novel environments. They also make clear the need to identify the underlying genetic basis of genetic correlations in order understand how patterns of pleiotropy are distributed across complex genetic networks.
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Affiliation(s)
- Kristin L Sikkink
- Institute of Ecology and Evolution, University of Oregon, Eugene, Oregon, 97403
- Department of Ecology, Evolution and Behavior, University of Minnesota, Minneapolis, Minnesota, 55108
| | - Rose M Reynolds
- Institute of Ecology and Evolution, University of Oregon, Eugene, Oregon, 97403
- Department of Biology, William Jewell College, Liberty, Missouri, 64068
| | - William A Cresko
- Institute of Ecology and Evolution, University of Oregon, Eugene, Oregon, 97403.
| | - Patrick C Phillips
- Institute of Ecology and Evolution, University of Oregon, Eugene, Oregon, 97403.
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29
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Zhang S, Tian D, Tran NH, Choi KP, Zhang L. Profiling the transcription factor regulatory networks of human cell types. Nucleic Acids Res 2014; 42:12380-7. [PMID: 25300490 PMCID: PMC4227771 DOI: 10.1093/nar/gku923] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
Neph et al. (2012) (Circuitry and dynamics of human transcription factor regulatory networks. Cell, 150: 1274-1286) reported the transcription factor (TF) regulatory networks of 41 human cell types using the DNaseI footprinting technique. This provides a valuable resource for uncovering regulation principles in different human cells. In this paper, the architectures of the 41 regulatory networks and the distributions of housekeeping and specific regulatory interactions are investigated. The TF regulatory networks of different human cell types demonstrate similar global three-layer (top, core and bottom) hierarchical architectures, which are greatly different from the yeast TF regulatory network. However, they have distinguishable local organizations, as suggested by the fact that wiring patterns of only a few TFs are enough to distinguish cell identities. The TF regulatory network of human embryonic stem cells (hESCs) is dense and enriched with interactions that are unseen in the networks of other cell types. The examination of specific regulatory interactions suggests that specific interactions play important roles in hESCs.
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Affiliation(s)
- Shihua Zhang
- National Center for Mathematics and Interdisciplinary Sciences, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China
| | - Dechao Tian
- Department of Statistics and Applied Probability, National University of Singapore, Singapore 117546, Singapore
| | - Ngoc Hieu Tran
- Division of Mathematical Sciences, Nanyang Technological University, Singapore 637371, Singapore
| | - Kwok Pui Choi
- Department of Statistics and Applied Probability, National University of Singapore, Singapore 117546, Singapore Department of Mathematics, National University of Singapore, Singapore 119076, Singapore
| | - Louxin Zhang
- Department of Mathematics, National University of Singapore, Singapore 119076, Singapore National University of Singapore Graduate School for Integrative Sciences and Engineering, Singapore 117456, Singapore
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30
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Rossetto M, Henry RJ. Escape from the laboratory: new horizons for plant genetics. TRENDS IN PLANT SCIENCE 2014; 19:554-555. [PMID: 25008042 DOI: 10.1016/j.tplants.2014.06.011] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/23/2014] [Revised: 06/23/2014] [Accepted: 06/30/2014] [Indexed: 06/03/2023]
Abstract
Next generation sequencing (NGS) is changing the way biologists work, as large amounts of genetic data can be easily outsourced commercially. Consequently, crucial research efforts in plant genetics can now be found outside the traditional laboratory setting, allowing for novel and more challenging scientific questions to be answered by virtual collaborative networks.
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Affiliation(s)
- Maurizio Rossetto
- National Herbarium of NSW, The Royal Botanic Gardens & Domain Trust, Mrs Macquaries Road, Sydney 2000, NSW Australia.
| | - Robert J Henry
- Queensland Alliance for Agriculture and Food Innovation, University of Queensland, Brisbane 4072, QLD Australia
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31
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Bolouri H. Network dynamics in the tumor microenvironment. Semin Cancer Biol 2014; 30:52-9. [PMID: 24582766 DOI: 10.1016/j.semcancer.2014.02.007] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2014] [Revised: 02/14/2014] [Accepted: 02/18/2014] [Indexed: 02/07/2023]
Abstract
The evolutionary path from tumor initiation to metastasis can only be fully understood by considering cancer cells as part of a multi-species ecosystem within the tumor microenvironment. This paper reviews and suggests two important recent trends. Firstly, I review arguments that interactions among diverse cells in the tumor microenvironment create a distinct cellular environment that can confer growth advantages, resist interventions, and allow tumors to remain dormant for long periods. Second, I review and highlight a trend toward data-rich, molecularly detailed, computational models of the tumor microenvironment. I argue that data-driven molecularly detailed tumor microenvironment models can now be built using data from multiple emerging high-throughput technologies, and that such models can pinpoint mechanisms of dysregulation and suggest specific drug targets and follow up experiments.
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Affiliation(s)
- Hamid Bolouri
- Division of Human Biology, Fred Hutchinson Cancer Research Center, USA.
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