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Ha T, Swanson D, Larouche M, Glenn R, Weeden D, Zhang P, Hamre K, Langston M, Phillips C, Song M, Ouyang Z, Chesler E, Duvvurru S, Yordanova R, Cui Y, Campbell K, Ricker G, Phillips C, Homayouni R, Goldowitz D. CbGRiTS: cerebellar gene regulation in time and space. Dev Biol 2014; 397:18-30. [PMID: 25446528 DOI: 10.1016/j.ydbio.2014.09.032] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2014] [Revised: 08/23/2014] [Accepted: 09/27/2014] [Indexed: 01/09/2023]
Abstract
The mammalian CNS is one of the most complex biological systems to understand at the molecular level. The temporal information from time series transcriptome analysis can serve as a potent source of associative information between developmental processes and regulatory genes. Here, we introduce a new transcriptome database called, Cerebellar Gene Regulation in Time and Space (CbGRiTS). This dataset is populated with transcriptome data across embryonic and postnatal development from two standard mouse strains, C57BL/6J and DBA/2J, several recombinant inbred lines and cerebellar mutant strains. Users can evaluate expression profiles across cerebellar development in a deep time series with graphical interfaces for data exploration and link-out to anatomical expression databases. We present three analytical approaches that take advantage of specific aspects of the time series for transcriptome analysis. We demonstrate the use of CbGRiTS dataset as a community resource to explore patterns of gene expression and develop hypotheses concerning gene regulatory networks in brain development.
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Affiliation(s)
- Thomas Ha
- Centre for Molecular Medicine and Therapeutics, Child and Family Research Institute, Department of Medical Genetics, University of British Columbia, 950 West 28th Avenue, Vancouver, BC, Canada V5Z 4H4
| | - Douglas Swanson
- Centre for Molecular Medicine and Therapeutics, Child and Family Research Institute, Department of Medical Genetics, University of British Columbia, 950 West 28th Avenue, Vancouver, BC, Canada V5Z 4H4
| | - Matt Larouche
- Centre for Molecular Medicine and Therapeutics, Child and Family Research Institute, Department of Medical Genetics, University of British Columbia, 950 West 28th Avenue, Vancouver, BC, Canada V5Z 4H4
| | - Randy Glenn
- Centre for Molecular Medicine and Therapeutics, Child and Family Research Institute, Department of Medical Genetics, University of British Columbia, 950 West 28th Avenue, Vancouver, BC, Canada V5Z 4H4
| | - Dave Weeden
- Centre for Molecular Medicine and Therapeutics, Child and Family Research Institute, Department of Medical Genetics, University of British Columbia, 950 West 28th Avenue, Vancouver, BC, Canada V5Z 4H4
| | - Peter Zhang
- Centre for Molecular Medicine and Therapeutics, Child and Family Research Institute, Department of Medical Genetics, University of British Columbia, 950 West 28th Avenue, Vancouver, BC, Canada V5Z 4H4
| | - Kristin Hamre
- Department of Anatomy and Neurobiology, University of Tennessee Health Science Center, Memphis, TN, USA
| | - Michael Langston
- Department of Electrical Engineering and Computer Science, University of Tennessee, Knoxville, TN, USA
| | - Charles Phillips
- Department of Electrical Engineering and Computer Science, University of Tennessee, Knoxville, TN, USA
| | - Mingzhou Song
- Department of Computer Science, New Mexico State University, Las Cruces, NM, USA
| | - Zhengyu Ouyang
- Department of Computer Science, New Mexico State University, Las Cruces, NM, USA
| | | | | | | | - Yan Cui
- Department of Molecular Science, University of Tennessee Health Science Center, Memphis, TN, USA
| | - Kate Campbell
- Centre for Molecular Medicine and Therapeutics, Child and Family Research Institute, Department of Medical Genetics, University of British Columbia, 950 West 28th Avenue, Vancouver, BC, Canada V5Z 4H4
| | - Greg Ricker
- Department of Biology, Bowdoin College, Brunswick, ME, USA
| | - Carey Phillips
- Department of Biology, Bowdoin College, Brunswick, ME, USA
| | - Ramin Homayouni
- Bioinformatics Program, Department of Biology, University of Memphis, Memphis, TN, USA
| | - Dan Goldowitz
- Centre for Molecular Medicine and Therapeutics, Child and Family Research Institute, Department of Medical Genetics, University of British Columbia, 950 West 28th Avenue, Vancouver, BC, Canada V5Z 4H4.
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Chin SL, Marcus IM, Klevecz RR, Li CM. Dynamics of oscillatory phenotypes in Saccharomyces cerevisiae reveal a network of genome-wide transcriptional oscillators. FEBS J 2012; 279:1119-30. [PMID: 22289124 DOI: 10.1111/j.1742-4658.2012.08508.x] [Citation(s) in RCA: 23] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
Abstract
Genetic and environmental factors are well-studied influences on phenotype; however, time is a variable that is rarely considered when studying changes in cellular phenotype. Time-resolved microarray data revealed genome-wide transcriptional oscillation in a yeast continuous culture system with ∼ 2 and ∼ 4 h periods. We mapped the global patterns of transcriptional oscillations into a 3D map to represent different cellular phenotypes of redox cycles. This map shows the dynamic nature of gene expression in that transcripts are ordered and coupled to each other through time and concentration space. Although cells differed in oscillation periods, transcripts involved in certain processes were conserved in a deterministic way. When oscillation period lengthened, the peak to trough ratio of transcripts increased and the fraction of cells in the unbudded (G0/G1) phase of the cell division cycle increased. Decreasing the glucose level in the culture medium was one way to increase the redox cycle, possibly from changes in metabolic flux. The period may be responding to lower glucose levels by increasing the fraction of cells in G1 and reducing S-phase gating so that cells can spend more time in catabolic processes. Our results support that gene transcripts are coordinated with metabolic functions and the cell division cycle.
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Affiliation(s)
- Shwe L Chin
- Dynamic Systems Group, Division of Biology, City of Hope Beckman Research Institute, Duarte, CA, USA
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Saei AA, Omidi Y. A glance at DNA microarray technology and applications. BIOIMPACTS : BI 2011; 1:75-86. [PMID: 23678411 DOI: 10.5681/bi.2011.011] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Received: 06/27/2011] [Revised: 07/13/2011] [Accepted: 07/20/2011] [Indexed: 01/06/2023]
Abstract
INTRODUCTION Because of huge impacts of "OMICS" technologies in life sciences, many researchers aim to implement such high throughput approach to address cellular and/or molecular functions in response to any influential intervention in genomics, proteomics, or metabolomics levels. However, in many cases, use of such technologies often encounters some cybernetic difficulties in terms of knowledge extraction from a bunch of data using related softwares. In fact, there is little guidance upon data mining for novices. The main goal of this article is to provide a brief review on different steps of microarray data handling and mining for novices and at last to introduce different PC and/or web-based softwares that can be used in preprocessing and/or data mining of microarray data. METHODS To pursue such aim, recently published papers and microarray softwares were reviewed. RESULTS It was found that defining the true place of the genes in cell networks is the main phase in our understanding of programming and functioning of living cells. This can be obtained with global/selected gene expression profiling. CONCLUSION Studying the regulation patterns of genes in groups, using clustering and classification methods helps us understand different pathways in the cell, their functions, regulations and the way one component in the system affects the other one. These networks can act as starting points for data mining and hypothesis generation, helping us reverse engineer.
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Affiliation(s)
- Amir Ata Saei
- Research Center for Pharmaceutical Nanotechnology, Faculty of Pharmacy, Tabriz University of Medical Sciences, Tabriz, Iran
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Abstract
Over the past 20 years, Omics technologies emerged as the consensual denomination of holistic molecular profiling. These techniques enable parallel measurements of biological -omes, or "all constituents considered collectively", and utilize the latest advancements in transcriptomics, proteomics, metabolomics, imaging, and bioinformatics. The technological accomplishments in increasing the sensitivity and throughput of the analytical devices, the standardization of the protocols and the widespread availability of reagents made the capturing of static molecular portraits of biological systems a routine task. The next generation of time course molecular profiling already allows for extensive molecular snapshots to be taken along the trajectory of time evolution of the investigated biological systems. Such datasets provide the basis for application of the inverse scientific approach. It consists in the inference of scientific hypotheses and theories about the structure and dynamics of the investigated biological system without any a priori knowledge, solely relying on data analysis to unveil the underlying patterns. However, most temporal Omics data still contain a limited number of time points, taken over arbitrary time intervals, through measurements on biological processes shifted in time. The analysis of the resulting short and noisy time series data sets is a challenge. Traditional statistical methods for the study of static Omics datasets are of limited relevance and new methods are required. This chapter discusses such algorithms which enable the application of the inverse analysis approach to short Omics time series.
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Rosen GL, Sokhansanj BA, Polikar R, Bruns MA, Russell J, Garbarine E, Essinger S, Yok N. Signal processing for metagenomics: extracting information from the soup. Curr Genomics 2009; 10:493-510. [PMID: 20436876 PMCID: PMC2808676 DOI: 10.2174/138920209789208255] [Citation(s) in RCA: 12] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2008] [Revised: 03/31/2009] [Accepted: 04/25/2009] [Indexed: 11/08/2022] Open
Abstract
Traditionally, studies in microbial genomics have focused on single-genomes from cultured species, thereby limiting their focus to the small percentage of species that can be cultured outside their natural environment. Fortunately, recent advances in high-throughput sequencing and computational analyses have ushered in the new field of metagenomics, which aims to decode the genomes of microbes from natural communities without the need for cultivation. Although metagenomic studies have shed a great deal of insight into bacterial diversity and coding capacity, several computational challenges remain due to the massive size and complexity of metagenomic sequence data. Current tools and techniques are reviewed in this paper which address challenges in 1) genomic fragment annotation, 2) phylogenetic reconstruction, 3) functional classification of samples, and 4) interpreting complementary metaproteomics and metametabolomics data. Also surveyed are important applications of metagenomic studies, including microbial forensics and the roles of microbial communities in shaping human health and soil ecology.
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Affiliation(s)
- Gail L. Rosen
- Electrical and Computer Engineering Department, Drexel University, Philadelphia, PA, USA
| | - Bahrad A. Sokhansanj
- School of Biomedical Engineering, Science, and Health Systems, Drexel University, Philadelphia, PA, USA
| | - Robi Polikar
- Electrical and Computer Engineering Department, Rowan University, Glassboro, NJ, USA
| | - Mary Ann Bruns
- Soil Science/Microbial Ecology, Pennsylvania State University, University Park, PA, USA
| | - Jacob Russell
- Biology Department, Drexel University, Philadelphia, PA, USA
| | - Elaine Garbarine
- Electrical and Computer Engineering Department, Drexel University, Philadelphia, PA, USA
| | - Steve Essinger
- Electrical and Computer Engineering Department, Drexel University, Philadelphia, PA, USA
| | - Non Yok
- Electrical and Computer Engineering Department, Drexel University, Philadelphia, PA, USA
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