By Daniel P. Berrar, Werner Dubitzky, Martin Granzow
The booklet addresses the requirement of scientists and researchers to achieve a simple realizing of microarray research methodologies and instruments. it's meant for college kids, lecturers, researchers, and study managers who are looking to comprehend the cutting-edge and of the awarded methodologies and the parts during which gaps in our wisdom call for additional examine and improvement. The ebook is designed for use through the practising expert tasked with the layout and research of microarray experiments or as a textual content for a senior undergraduate- or graduate point path in analytical genetics, biology, bioinformatics, computational biology, information and information mining, or utilized machine technology.
Read or Download A Practical Approach to Microarray Data Analysis PDF
Similar bioinformatics books
This publication describes a strong and potent software for the molecular biologist researcher. Readers will detect how DNA chromatography can be utilized of their personal paintings. meant as an creation for the molecular biologist reader, this also will function a realistic advisor for the skilled consumer. The transparent and concise writing sort is usually beautiful to the chemist who desires to research extra approximately molecular biology.
This publication describes equipment for the research of high-throughput genome series info, the id of noncoding RNA from series info, the great research of gene expression through microarrays, and metabolomic research, all of that are supported by means of scripts to assist their computational use.
Observe tips on how to streamline advanced bioinformatics functions with parallel computingThis booklet permits readers to deal with extra advanced bioinformatics functions and bigger and richer facts units. because the editor truly indicates, utilizing strong parallel computing instruments may end up in major breakthroughs in interpreting genomes, figuring out genetic illness, designing personalized drug cures, and figuring out evolution.
This choice of easily reproducible concepts for repairing mammalian DNA comprises fourteen completely new chapters and plenty of largely revised chapters. The tools offered disguise cytogenetic research, measuring the mobile reaction to ionizing radiation, detecting single-strand (nicks) and double-strand DNA breaks, detecting the presence of "adducted" bases in DNA, and getting ready mismatch fix (MMR) plasmid substrates.
- The Molecules of Life: DNA, RNA, and proteins (Genetics and Evolution)
- Medical Image Analysis
- Computational Biology and Genome Informatics
- Pattern Recognition in Bioinformatics: 9th IAPR International Conference, PRIB 2014, Stockholm, Sweden, August 21-23, 2014. Proceedings
- Bioinformatics: converting data to knowledge: a workshop summary
- Bioinformatics of Genome Regulation and Structure
Additional info for A Practical Approach to Microarray Data Analysis
2009 Sep 9;10:86 Baranzini SE, Galwey NW, Wang J, Khankhanian P, Lindberg R, Pelletier D, Wu W, Uitdehaag BMJ, Kappos L, GeneMSA Consortium, Polman CH, Matthews PM, Hauser SL, Gibson RA, Oksenberg JR, Barnes MR (2009) Pathway and network-based analysis of genome-wide association studies in multiple sclerosis. Hum Mol Genet 18:2078–2090. 1093/hmg/ddp120 Greene CS, Penrod NM, Kiralis J, Moore JH (2009) Spatially uniform relieff (SURF) for computationally-efficient filtering of gene-gene interactions.
16. 17. 18. 19. 20. 21. 22. 23. 24. 9 × 1011 pair-wise interaction tests. Hum Hered 69:268–284. 1159/000295896 Evans DM, Marchini J, Morris AP, Cardon LR (2006) Two-stage two-locus models in genome-wide association. PLoS Genet 2:e157. 0020157 Ueki M, Cordell HJ (2012) Improved statistics for genome-wide interaction analysis. PLoS Genet 8:e1002625. 1002625 Herold C, Steffens M, Brockschmidt FF, Baur MP, Becker T (2009) INTERSNP: genomewide interaction analysis guided by a priori information. Bioinform Oxf Engl 25:3275– 3281.
Validate” rather than “replicate” was used here because linked and proximate SNPs were included in the validation in addition to the original SNPs, as follows. Assume there are n1 and n2 SNPs, 40 Li Ma et al. respectively, surrounding SNPs A and B and the number of statistical tests to be conducted is 1, n1 + n2, and n2 in the three validation stages, respectively. To reduce the number of tests and the cost of multiple-testing correction on power, the validation process proceeds sequentially and stops at any stage where significant results were found after multiple-testing correction.
A Practical Approach to Microarray Data Analysis by Daniel P. Berrar, Werner Dubitzky, Martin Granzow