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A DNA microarray is a collection of microscopic DNA spots attached to a solid surface. Scientists use DNA microarrays to measure the expression levels of large numbers of genes simultaneously or to genotype multiple regions of a genome. Each DNA spot contains picomoles of a specific DNA sequence, known as probes. These can be a short section of a gene or other DNA element that are used to hybridize a cDNA or cRNA sample under high-stringency conditions. Probe-target hybridization is usually detected and quantified by detection of fluorophore-, silver-, or chemiluminescence-labeled targets to determine relative abundance of nucleic acid sequences in the target. The original nucleic acid arrays were macro arrays approximately 9 cm × 12 cm and the first computerized image based analysis was published in 1981. It was invented by Patrick O. Brown. An example of its application is in SNPs arrays for polymorphisms in cardiovascular diseases, cancer, pathogens and GWAS analysis. It is also used for the identification of structural variations and the measurement of gene expression.
In computational biology, gene prediction or gene finding refers to the process of identifying the regions of genomic DNA that encode genes. This includes protein-coding genes as well as RNA genes, but may also include prediction of other functional elements such as regulatory regions. Gene finding is one of the first and most important steps in understanding the genome of a species once it has been sequenced.
High-throughput screening (HTS) is a method for scientific discovery especially used in drug discovery and relevant to the fields of biology, materials science and chemistry. Using robotics, data processing/control software, liquid handling devices, and sensitive detectors, high-throughput screening allows a researcher to quickly conduct millions of chemical, genetic, or pharmacological tests. Through this process one can quickly recognize active compounds, antibodies, or genes that modulate a particular biomolecular pathway. The results of these experiments provide starting points for drug design and for understanding the noninteraction or role of a particular location.
Computational genomics refers to the use of computational and statistical analysis to decipher biology from genome sequences and related data, including both DNA and RNA sequence as well as other "post-genomic" data. These, in combination with computational and statistical approaches to understanding the function of the genes and statistical association analysis, this field is also often referred to as Computational and Statistical Genetics/genomics. As such, computational genomics may be regarded as a subset of bioinformatics and computational biology, but with a focus on using whole genomes to understand the principles of how the DNA of a species controls its biology at the molecular level and beyond. With the current abundance of massive biological datasets, computational studies have become one of the most important means to biological discovery.
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Whole genome sequencing (WGS), also known as full genome sequencing, complete genome sequencing, or entire genome sequencing, is the process of determining the entirety, or nearly the entirety, of the DNA sequence of an organism's genome at a single time. This entails sequencing all of an organism's chromosomal DNA as well as DNA contained in the mitochondria and, for plants, in the chloroplast.
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FAIRE-Seq is a method in molecular biology used for determining the sequences of DNA regions in the genome associated with regulatory activity. The technique was developed in the laboratory of Jason D. Lieb at the University of North Carolina, Chapel Hill. In contrast to DNase-Seq, the FAIRE-Seq protocol doesn't require the permeabilization of cells or isolation of nuclei, and can analyse any cell type. In a study of seven diverse human cell types, DNase-seq and FAIRE-seq produced strong cross-validation, with each cell type having 1-2% of the human genome as open chromatin.
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Mark Joseph Daly is Director of the Finnish Institute for Molecular Medicine (FIMM) at the University of Helsinki, a Professor of Genetics at Harvard Medical School, Chief of the Analytic and Translational Genetic Unit at Massachusetts General Hospital, and a member of the Broad Institute of MIT and Harvard. In the early days of the Human Genome Project, Daly helped develop the genetic model by which linkage disequilibrium could be used to map the haplotype structure of the human genome. In addition, he developed statistical methods to find associations between genes and disorders such as Crohn's disease, inflammatory bowel disease, autism and schizophrenia.
In molecular biology, genome architecture mapping (GAM) is a cryosectioning method to map colocalized DNA regions in a ligation independent manner. It overcomes some limitations of Chromosome conformation capture (3C), as these methods have a reliance on digestion and ligation to capture interacting DNA segments. GAM is the first genome-wide method for capturing three-dimensional proximities between any number of genomic loci without ligation.
Robert E. Kass is the Maurice Falk Professor of Statistics and Computational Neuroscience in the Department of Statistics and Data Science, the Machine Learning Department, and the Neuroscience Institute at Carnegie Mellon University.
Martin Kulldorff is a Swedish biostatistician. He has been a professor of medicine at Harvard Medical School since 2003, though on leave as of 2023. He is a member of the US Food and Drug Administration's Drug Safety and Risk Management Advisory Committee and a former member of the Vaccine Safety Subgroup of the Advisory Committee on Immunization Practices at the US Centers for Disease Control and Prevention.
SaTScan is a software tool that employs scan statistics for the spatial and temporal analysis of clusters of events. The software is trademarked by Martin Kulldorff, and was designed originally for public health and epidemiology to identify clusters of cases in both space and time and to perform statistical analysis to determine if these clusters are significantly different from what would be expected by chance The software provides a user-friendly interface and a range of statistical methods, making it accessible to researchers and practitioners. While not a full Geographic Information System, the outputs from SaTScan can be integrated with software such as ArcGIS or QGIS to visualize and analyze spatial data, and to map the distribution of various phenomena.
References
- ↑ Naus, J. I. (1982). "Approximations for Distributions of Scan Statistics". Journal of the American Statistical Association . 77 (377): 177–183. doi:10.1080/01621459.1982.10477783. JSTOR 2287786.
- ↑ Naus, Joseph Irwin (1964). Clustering of random points in line and plane (Ph. D.). Retrieved 6 January 2014.
- ↑ Wallenstein, S. (2009). "Joseph Naus: Father of the Scan Statistic". Scan Statistics. pp. 1–25. doi:10.1007/978-0-8176-4749-0_1. ISBN 978-0-8176-4748-3.
- ↑ Glaz, J.; Naus, J.; Wallenstein, S. (2001). "Introduction". Scan Statistics. Springer Series in Statistics. pp. 3–9. doi:10.1007/978-1-4757-3460-7_1. ISBN 978-1-4419-3167-2.
- ↑ Kulldorff, Martin (1997). "A spatial scan statistic" (PDF). Communications in Statistics – Theory and Methods. 26 (6): 1481–1496. doi:10.1080/03610929708831995.
- ↑ "Most Cited Articles". Communications in Statistics – Theory and Methods. Retrieved 11 October 2015.
- ↑ Walther, Guenther; Perry, Andrew (November 2022). "Calibrating the scan statistic: Finite sample performance versus asymptotics". Journal of the Royal Statistical Society, Series B (Statistical Methodology). 84 (5): 1608–1639. doi:10.1111/rssb.12549. ISSN 1369-7412. S2CID 221713232.
- ↑ Li, Zilin; Li, Xihao; Zhou, Hufeng; Gaynor, Sheila M.; Margaret, Sunitha Selvaraj; Arapoglou, Theodore; Qiuck, Corbin; Liu, Yaowu; Chen, Han; Sun, Ryan; Dey, Rounak; Arnett, Donna K.; Auer, Paul L.; Bielak, Lawrence F.; Bis, Joshua C.; Blackwell, Thomas W.; Blangero, John; Boerwinkle, Eric; Bowden, Donald W.; Brody, Jennifer A.; Cade, Brian E.; Conomos, Matthew P.; Correa, Adolfo; Cupples, L. Adrienne; Curran, Joanne E.; de Vries, Paul S.; Duggirala, Ravindranath; Franceschini, Nora; Freedman, Barry I.; Goring, Harald H.H.; Guo, Xiuqing; Kalyani, Rita R.; Kooperberg, Charles; Kral, Brian G.; Lange, Leslie A.; Lin, Bridget; Manichaikul, Ani; Martin, Lisa W.; Mathias, Rasika A.; Meigs, James B.; Mitchell, Braxton D.; Mitchell, Braxton D.; Montasser, May E.; Morrison, Alanna C.; Naseri, Take; O’Connell, Jeffrey R.; Palmer, Nicholette D.; Reupena, Muagututi’a Sefuiva; Rice, Kenneth M.; Rich, Stephen S.; Smith, Jennifer A.; Taylor, Kent D.; Taub, Margaret A.; Vasan, Ramachandran S.; Weeks, Daniel E.; Wilson, James G.; Yanek, Lisa R.; Zhao, Wei; NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium; TOPMed Lipids Working Group; Rotter, Jerome I.; Willer, Cristen; Natarajan, Pradeep; Peloso, Gina M.; Lin, Xihong (2022). "A framework for detecting noncoding rare-variant associations of large-scale whole-genome sequencing studies". Nature Methods. 19 (12): 1599–1611. doi:10.1038/s41592-022-01640-x. PMC 10008172 . PMID 36303018. S2CID 243873361.
- ↑ Li, Zilin; Liu, Yaowu; Lin, Xihong (2022). "Simultaneous Detection of Signal Regions Using Quadratic Scan Statistics With Applications to Whole Genome Association Studies". Journal of the American Statistical Association. 117 (538): 823–834. doi:10.1080/01621459.2020.1822849. PMC 9285665 . PMID 35845434.
- ↑ Li, Zilin; Li, Xihao; Liu, Yaowu; Shen, Jincheng; Chen, Han; Zhou, Hufeng; Morrison, Alanna C.; Boerwinkle, Eric; Lin, Xihong (2019). "Dynamic Scan Procedure for Detecting Rare-Variant Association Regions in Whole-Genome Sequencing Studies". American Journal of Human Genetics. 104 (5): 802–814. doi:10.1016/j.ajhg.2019.03.002. PMC 6507043 . PMID 30982610.
- ↑ He, Zihuai; Xu, Bin; Buxbaum, Joseph; Ionita-Laza, Iuliana (2019). "A genome-wide scan statistic framework for whole-genome sequence data analysis". Nature Communications. 10 (1): 3018. doi:10.1038/s41467-019-11023-0. PMC 6616627 . PMID 31289270.
