DNA microarrays: a bridge between genome sequence information and biological understanding

2002 ◽  
Vol 10 (3) ◽  
pp. 389-408 ◽  
Author(s):  
KEITH HARSHMAN ◽  
CARLOS MARTÍNEZ-A
Keyword(s):  
Large Scale ◽  
Dna Microarrays ◽  
Expression Patterns ◽  
Dna Polymorphisms ◽  
Model Organisms ◽  
Sequence Information ◽  
Single Experiment ◽  
Cellular Processes ◽  
Large Scale Analysis ◽  
Sequence Variations

The development, refinement and increasingly widespread use of DNA microarrays have been important responses to the explosion of sequence information produced by genome science. The high sample densities possible with DNA microarrays, coupled with the complete or nearly complete genome sequences available for humans and model organisms, provide a powerful analytical method to measure both qualitative and quantitative variations in RNA and DNA. Principal among the applications of microarrays is the large-scale analysis of RNA expression, often referred to as expression profiling. The power of this application lies in its ability to determine the expression patterns of tens of thousands of genes in a single experiment. Additionally, the ability to detect DNA polymorphisms makes microarrays useful in studies designed to correlate DNA sequence variations with variations in phenotype. The unprecedented scale on which microarrays allow both experimentation and generation of results should make possible a more complete and comprehensive understanding of cells and cellular processes.

Seed genomics: germinating opportunities

2002 ◽  
Vol 12 (3) ◽  
pp. 145-153 ◽  
Author(s):  
A. (Lonneke) ◽  
H.M. van der Geest
Keyword(s):  
High Throughput ◽  
Large Scale ◽  
Plant Biology ◽  
Sequence Information ◽  
Molecular Tools ◽  
Seed Biology ◽  
Physiological Processes ◽  
Large Scale Analysis ◽  
Quantum Leap ◽  
Mutant Phenotypes

With the sequencing of theArabidopsis thalianagenome, the field of plant biology has made a quantum leap. The sequence information available to the community has greatly facilitated the identification of genes responsible for mutant phenotypes and the large-scale analysis of gene expression inArabidopsis. High-throughput laboratory tools for DNA sequencing (genomics), mutant analysis (functional genomics), mRNA quantification (transcriptomics) and protein analysis (proteomics) are being used to scrutinize this model plant. For seed physiologists, the challenge lies in translating this information into physiological processes in seeds from other plant species. Combining more traditional seed biology with the new high-throughput molecular tools should yield breakthroughs in seed science.

scholarly journals Shedding new light on opsin evolution

2011 ◽  
Vol 279 (1726) ◽  
pp. 3-14 ◽  
Author(s):  
Megan L. Porter ◽  
Joseph R. Blasic ◽  
Michael J. Bok ◽  
Evan G. Cameron ◽  
Thomas Pringle ◽  
...  
Keyword(s):  
Evolutionary History ◽  
Large Scale ◽  
Current Model ◽  
Expression Patterns ◽  
Model Organisms ◽  
Biological Functions ◽  
Site Specific ◽  
History Of ◽  
Functional Features

Opsin proteins are essential molecules in mediating the ability of animals to detect and use light for diverse biological functions. Therefore, understanding the evolutionary history of opsins is key to understanding the evolution of light detection and photoreception in animals. As genomic data have appeared and rapidly expanded in quantity, it has become possible to analyse opsins that functionally and histologically are less well characterized, and thus to examine opsin evolution strictly from a genetic perspective. We have incorporated these new data into a large-scale, genome-based analysis of opsin evolution. We use an extensive phylogeny of currently known opsin sequence diversity as a foundation for examining the evolutionary distributions of key functional features within the opsin clade. This new analysis illustrates the lability of opsin protein-expression patterns, site-specific functionality (i.e. counterion position) and G-protein binding interactions. Further, it demonstrates the limitations of current model organisms, and highlights the need for further characterization of many of the opsin sequence groups with unknown function.

scholarly journals Applications of community detection algorithms to large biological datasets

2019 ◽  
Author(s):  
Itamar Kanter ◽  
Gur Yaari ◽  
Tomer Kalisky
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Community Detection ◽  
Large Scale ◽  
Heuristic Algorithms ◽  
Relevant Information ◽  
Biological Data ◽  
Sequence Information ◽  
Single Experiment ◽  
Or Genes

ABSTRACTRecent advances in data acquiring technologies in biology have led to major challenges in mining relevant information from large datasets. For example, single-cell RNA sequencing technologies are producing expression and sequence information from tens of thousands of cells in every single experiment. A common task in analyzing biological data is to cluster samples or features (e.g. genes) into groups sharing common characteristics. This is an NP-hard problem for which numerous heuristic algorithms have been developed. However, in many cases, the clusters created by these algorithms do not reflect biological reality. To overcome this, a Networks Based Clustering (NBC) approach was recently proposed, by which the samples or genes in the dataset are first mapped to a network and then community detection (CD) algorithms are used to identify clusters of nodes.Here, we created an open and flexible python-based toolkit for NBC that enables easy and accessible network construction and community detection. We then tested the applicability of NBC for identifying clusters of cells or genes from previously published large-scale single-cell and bulk RNA-seq datasets.We show that NBC can be used to accurately and efficiently analyze large-scale datasets of RNA sequencing experiments.

scholarly journals DNA Array-Based Transcriptional Analysis of Asporogenous, Nonsolventogenic Clostridium acetobutylicum Strains SKO1 and M5

2003 ◽  
Vol 185 (15) ◽  
pp. 4539-4547 ◽  
Author(s):  
Christopher A. Tomas ◽  
Keith V. Alsaker ◽  
Hendrik P. J. Bonarius ◽  
Wouter T. Hendriksen ◽  
He Yang ◽  
...  
Keyword(s):  
Electron Transport ◽  
Clostridium Acetobutylicum ◽  
Large Scale ◽  
Dna Microarrays ◽  
Expression Patterns ◽  
Central Carbon Metabolism ◽  
Transcriptional Analysis ◽  
Dna Arrays ◽  
Major Cluster ◽  
Solvent Formation

ABSTRACT The large-scale transcriptional program of two Clostridium acetobutylicum strains (SKO1 and M5) relative to that of the parent strain (wild type [WT]) was examined by using DNA microarrays. Glass DNA arrays containing a selected set of 1,019 genes (including all 178 pSOL1 genes) covering more than 25% of the whole genome were designed, constructed, and validated for data reliability. Strain SKO1, with an inactivated spo0A gene, displays an asporogenous, filamentous, and largely deficient solventogenic phenotype. SKO1 displays downregulation of all solvent formation genes, sigF, and carbohydrate metabolism genes (similar to genes expressed as part of the stationary-phase response in Bacillus subtilis) but also several electron transport genes. A major cluster of genes upregulated in SKO1 includes abrB, the genes from the major chemotaxis and motility operons, and glycosylation genes. Strain M5 displays an asporogenous and nonsolventogenic phenotype due to loss of the megaplasmid pSOL1, which contains all genes necessary for solvent formation. Therefore, M5 displays downregulation of all pSOL1 genes expressed in the WT. Notable among other genes expressed more highly in WT than in M5 were sigF, several two-component histidine kinases, spo0A, cheA, cheC, many stress response genes, fts family genes, DNA topoisomerase genes, and central-carbon metabolism genes. Genes expressed more highly in M5 include electron transport genes (but different from those downregulated in SKO1) and several motility and chemotaxis genes. Most of these expression patterns were consistent with phenotypic characteristics. Several of these expression patterns are new or different from what is known in B. subtilis and can be used to test a number of functional-genomic hypotheses.

scholarly journals Regulatory non-coding small RNAs are diverse and abundant in an extremophilic microbial community

2019 ◽  
Author(s):  
Diego R. Gelsinger ◽  
Gherman Uritskiy ◽  
Rahul Reddy ◽  
Adam Munn ◽  
Katie Farney ◽  
...  
Keyword(s):  
Small Rnas ◽  
Large Scale ◽  
Field Experiments ◽  
Target Prediction ◽  
Model Organisms ◽  
Natural Environments ◽  
Cellular Processes ◽  
Non Coding Rna ◽  
Domains Of Life ◽  
Gene Regulatory

ABSTRACTRegulatory small RNAs (sRNAs) represent a major class of regulatory molecules that play large-scale and essential roles in many cellular processes across all domains of life. Microbial sRNAs have been primarily investigated in a few model organisms and little is known about the dynamics of sRNA synthesis in natural environments, and the roles of these short transcripts at the community level. Analyzing the metatranscriptome of a model extremophilic community inhabiting halite nodules (salt rocks) from the Atacama Desert with SnapT – a new sRNA annotation pipeline – we discovered hundreds of intergenic (itsRNAs) and antisense (asRNAs) sRNAs. The halite sRNAs were taxonomically diverse with the majority expressed by members of the Halobacteria. We found asRNAs with expression levels negatively correlated with that of their putative overlapping target, suggesting a potential gene regulatory mechanism. A number of itsRNAs were conserved and significantly differentially expressed (FDR<5%) between 2 sampling time points allowing for stable secondary structure modeling and target prediction. This work demonstrates that metatranscriptomic field experiments link environmental variation with changes in RNA pools and have the potential to provide new insights into environmental sensing and responses in natural microbial communities through non-coding RNA mediated gene regulation.

scholarly journals ALEdb 1.0: A Database of Mutations from Adaptive Laboratory Evolution Experimentation

2018 ◽  
Author(s):  
Patrick V. Phaneuf ◽  
Dennis Gosting ◽  
Bernhard O. Palsson ◽  
Adam M. Feist
Keyword(s):  
Experimental Evolution ◽  
Large Scale ◽  
Experimental Approach ◽  
Genetic Alterations ◽  
Model Organisms ◽  
Multiple Model ◽  
Adaptive Laboratory Evolution ◽  
Functional Interpretation ◽  
Laboratory Evolution ◽  
Large Scale Analysis

ABSTRACTFull genomic sequences are readily available, but their functional interpretation remains a fundamental challenge. Adaptive Laboratory Evolution (ALE) has emerged as an experimental approach to discover causal mutations that confer desired phenotypic functions. Thus, ALE not only represents a controllable experimental approach to systematically discover genotype-phenotype relationships, but it also allows for the revelation of the series of genetic alterations required to acquire the new phenotype. Numerous ALE studies have appeared in the literature providing a strong impetus for developing structured databases to warehouse experimental evolution information and make it retrievable for large-scale analysis. Here, the first step towards establishing this capability is presented: ALEdb (http://aledb.org). This initial release contains over 11,000 mutations that have been discovered in ALE experiments. ALEdb is the first of its kind; (1) it is a web-based platform that comprehensively reports on ALE acquired mutations and their conditions, (2) it reports key mutations using previously established trends, (3) it enables a search-driven workflow to enhance user mutation functional analysis, (4) it allows exporting of mutation query results for custom analysis, (5) it has a bibliome that describes the underlying published literature, and (6) contains experimental evolution mutations from multiple model organisms. Thus, ALEdb is an informative platform which will become increasingly revealing as the number of reported ALE experiments and identified mutations continue to expand.

Gene Discovery Using the Serial Analysis of Gene Expression Technique: Implications for Cancer Research

2001 ◽  
Vol 19 (11) ◽  
pp. 2948-2958 ◽  
Author(s):  
Kornelia Polyak ◽  
Gregory J. Riggins
Keyword(s):  
Gene Expression ◽  
Tumor Suppressor ◽  
Dna Microarrays ◽  
Expression Patterns ◽  
Suppressor Gene ◽  
Sequence Information ◽  
Regulatory Pathways ◽  
Quantitative Manner ◽  
Cancer Phenotype ◽  
Definition Of

ABSTRACT: Cancer is a genetic disease. As such, our understanding of the pathobiology of tumors derives from analyses of the genes whose mutations are responsible for those tumors. The cancer phenotype, however, likely reflects the changes in the expression patterns of hundreds or even thousands of genes that occur as a consequence of the primary mutation of an oncogene or a tumor suppressor gene. Recently developed functional genomic approaches, such as DNA microarrays and serial analysis of gene expression (SAGE), have enabled researchers to determine the expression level of every gene in a given cell population, which represents that cell population’s entire transcriptome. The most attractive feature of SAGE is its ability to evaluate the expression pattern of thousands of genes in a quantitative manner without prior sequence information. This feature has been exploited in three extremely powerful applications of the technology: the definition of transcriptomes, the analysis of differences between the gene expression patterns of cancer cells and their normal counterparts, and the identification of downstream targets of oncogenes and tumor suppressor genes. Comprehensive analyses of gene expression not only will further understanding of growth regulatory pathways and the processes of tumorigenesis but also may identify new diagnostic and prognostic markers as well as potential targets for therapeutic intervention.

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