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Methods in Molecular Biology (Clifton,... 2016High-throughput platforms such as microarray, mass spectrometry, and next-generation sequencing are producing an increasing volume of omics data that needs large data...
High-throughput platforms such as microarray, mass spectrometry, and next-generation sequencing are producing an increasing volume of omics data that needs large data storage and computing power. Cloud computing offers massive scalable computing and storage, data sharing, on-demand anytime and anywhere access to resources and applications, and thus, it may represent the key technology for facing those issues. In fact, in the recent years it has been adopted for the deployment of different bioinformatics solutions and services both in academia and in the industry. Although this, cloud computing presents several issues regarding the security and privacy of data, that are particularly important when analyzing patients data, such as in personalized medicine. This chapter reviews main academic and industrial cloud-based bioinformatics solutions; with a special focus on microarray data analysis solutions and underlines main issues and problems related to the use of such platforms for the storage and analysis of patients data.
Topics: Cloud Computing; Computational Biology; Humans; Microarray Analysis
PubMed: 25863787
DOI: 10.1007/7651_2015_236 -
Methods in Molecular Biology (Clifton,... 2008
Review
Topics: Algorithms; Animals; Biotin; Embryonic Development; Gene Expression Profiling; Gene Expression Regulation, Developmental; Microarray Analysis; Nucleic Acid Hybridization; Oligonucleotide Array Sequence Analysis; Polymerase Chain Reaction; RNA; Transcription, Genetic
PubMed: 19030827
DOI: 10.1007/978-1-60327-483-8_42 -
Methods in Molecular Biology (Clifton,... 2009Although all of the DNA in an eukaryotic cell replicates during the S-phase of cell cycle, there is a significant difference in the actual time in S-phase when a given...
Although all of the DNA in an eukaryotic cell replicates during the S-phase of cell cycle, there is a significant difference in the actual time in S-phase when a given chromosomal segment replicates. Methods are described here for generation of high-resolution temporal maps of DNA replication in synchronized human cells. This method does not require amplification of DNA before microarray hybridization and so avoids errors introduced during PCR. A major advantage of using this procedure is that it facilitates finer dissection of replication time in S-phase. Also, it helps delineate chromosomal regions that undergo biallelic or asynchronous replication, which otherwise are difficult to detect at a genome-wide scale by existing methods. The continuous TR50 (time of completion of 50% replication) maps of replication across chromosomal segments identify regions that undergo acute transitions in replication timing. These transition zones can play a significant role in identifying insulators that separate chromosomal domains with different chromatin modifications.
Topics: Algorithms; DNA Replication Timing; HeLa Cells; Humans; Microarray Analysis; S Phase; Time Factors
PubMed: 19488880
DOI: 10.1007/978-1-60327-192-9_14 -
Computational Biology and Chemistry Oct 2005Accurately and reliably identifying the actual number of clusters present with a dataset of gene expression profiles, when no additional information on cluster structure...
Accurately and reliably identifying the actual number of clusters present with a dataset of gene expression profiles, when no additional information on cluster structure is available, is a problem addressed by few algorithms. GeneMCL transforms microarray analysis data into a graph consisting of nodes connected by edges, where the nodes represent genes, and the edges represent the similarity in expression of those genes, as given by a proximity measurement. This measurement is taken to be the Pearson correlation coefficient combined with a local non-linear rescaling step. The resulting graph is input to the Markov Cluster (MCL) algorithm, which is an elegant, deterministic, non-specific and scalable method, which models stochastic flow through the graph. The algorithm is inherently affected by any cluster structure present, and rapidly decomposes a graph into cohesive clusters. The potential of the GeneMCL algorithm is demonstrated with a 5,730 gene subset (IGS) of the Van't Veer breast cancer database, for which the clusterings are shown to reflect underlying biological mechanisms.
Topics: Algorithms; Breast Neoplasms; Cluster Analysis; Computational Biology; Databases, Genetic; Female; Gene Expression Profiling; Humans; Markov Chains; Microarray Analysis; Multigene Family
PubMed: 16172020
DOI: 10.1016/j.compbiolchem.2005.07.002 -
Annual Review of Chemical and... 2011The field of microfluidics has exploded in the past decade, particularly in the area of chemical and biochemical analysis systems. Borrowing technology from the... (Review)
Review
The field of microfluidics has exploded in the past decade, particularly in the area of chemical and biochemical analysis systems. Borrowing technology from the solid-state electronics industry and the production of microprocessor chips, researchers working with glass, silicon, and polymer substrates have fabricated macroscale laboratory components in miniaturized formats. These devices pump nanoliter volumes of liquid through micrometer-scale channels and perform complex chemical reactions and separations. The detection of reaction products is typically done fluorescently with off-chip optical components, and the analysis time from start to finish can be significantly shorter than that of conventional techniques. In this review we describe these microfluidic analysis systems, from the original continuous flow systems relying on electroosmotic pumping for liquid motion to the large diversity of microarray chips currently in use to the newer droplet-based devices and segmented flow systems. Although not currently widespread, microfluidic systems have the potential to become ubiquitous.
Topics: High-Throughput Screening Assays; Indicators and Reagents; Microarray Analysis; Microfluidic Analytical Techniques; Microfluidics
PubMed: 22432622
DOI: 10.1146/annurev-chembioeng-061010-114215 -
Ugeskrift For Laeger May 2006Microarrays might be used for future diagnostic and prognostic purposes. High-density oligonucleotide arrays are promising in this respect. The microarray data consist...
Microarrays might be used for future diagnostic and prognostic purposes. High-density oligonucleotide arrays are promising in this respect. The microarray data consist of intensity files, which are transformed into expression matrices by the application of several mathematical modifications. However, pitfalls regarding data analysis seem to be a critical factor for the impact of this new technology. This article focuses on the data analysis, from raw data file to marker gene lists used to retrieve knowledge about underlying biological processes.
Topics: Gene Expression Profiling; Humans; Microarray Analysis; Neoplasms; Oligonucleotide Array Sequence Analysis; Prognosis
PubMed: 16768955
DOI: No ID Found -
Omics : a Journal of Integrative Biology Aug 2010Glycomics is the study of comprehensive structural elucidation and characterization of all glycoforms found in nature and their dynamic spatiotemporal changes that are... (Review)
Review
Glycomics is the study of comprehensive structural elucidation and characterization of all glycoforms found in nature and their dynamic spatiotemporal changes that are associated with biological processes. Glycocalyx of mammalian cells actively participate in cell-cell, cell-matrix, and cell-pathogen interactions, which impact embryogenesis, growth and development, homeostasis, infection and immunity, signaling, malignancy, and metabolic disorders. Relative to genomics and proteomics, glycomics is just growing out of infancy with great potential in biomedicine for biomarker discovery, diagnosis, and treatment. However, the immense diversity and complexity of glycan structures and their multiple modes of interactions with proteins pose great challenges for development of analytical tools for delineating structure function relationships and understanding glyco-code. Several tools are being developed for glycan profiling based on chromatography, mass spectrometry, glycan microarrays, and glyco-informatics. Lectins, which have long been used in glyco-immunology, printed on a microarray provide a versatile platform for rapid high throughput analysis of glycoforms of biological samples. Herein, we summarize technological advances in lectin microarrays and critically review their impact on glycomics analysis. Challenges remain in terms of expansion to include nonplant derived lectins, standardization for routine clinical use, development of recombinant lectins, and exploration of plant kingdom for discovery of novel lectins.
Topics: Animals; Biomarkers; Glycomics; Humans; Lectins; Mass Spectrometry; Microarray Analysis
PubMed: 20726799
DOI: 10.1089/omi.2009.0150 -
Methods in Molecular Biology (Clifton,... 2009
Topics: Microarray Analysis; Point-of-Care Systems; Reagent Kits, Diagnostic
PubMed: 19216130
DOI: 10.1007/978-1-59745-372-1 -
Archives of Disease in Childhood Feb 2015
Topics: Child; Chromosome Disorders; Developmental Disabilities; Humans; Microarray Analysis; Oligonucleotide Array Sequence Analysis
PubMed: 25260514
DOI: 10.1136/archdischild-2014-307189 -
Methods in Molecular Biology (Clifton,... 2012Scientists employing microarray profiling technology to compare sample sets generate data for a large number of endpoints. Assuming the experimental design minimized...
Scientists employing microarray profiling technology to compare sample sets generate data for a large number of endpoints. Assuming the experimental design minimized sources of bias, and the analytical technology was reliable, precise, and accurate, how does the experimentalist determine which endpoints are meaningfully different between the groups? Comparison of two population means for individual analysis measurements is the most common statistical problem associated with microarray data analysis. This chapter focuses on the hands-on procedures using SAS software to describe how to choose statistical methods to find the statistically significantly different endpoints between two groups of data generated from reverse phase protein microarrays. The four methods outlined are: (a) two-sample t-test, (b) Wilcoxon rank sum test, (c) one-sample t-test, and (d) Wilcoxon signed rank test. Two sample t-test is used for two independently normally distributed groups. One-sample t-test is used for a normally distributed difference of paired observations. Wilcoxon rank sum test is considered a nonparametric version of the two-sample t-test, and Wilcoxon signed rank test is considered a nonparametric version of the one-sample t-test.
Topics: Biometry; Microarray Analysis; Protein Array Analysis; Statistics, Nonparametric
PubMed: 22081355
DOI: 10.1007/978-1-60327-216-2_21