Bioinformatics: The Impact of Accurate Quantification on by Yu Liu

By Yu Liu

The ebook introduces bioinformatic and statistical technique and exhibits methods to bias correction and blunder estimation. It additionally provides quantitative tools for genome and proteome analysis.

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Extra resources for Bioinformatics: The Impact of Accurate Quantification on Proteomic and Genetic Analysis and Research

Example text

Edinburgh, Oliver and Boyd Ltd; 1951. Auer P, Doerge RW. Statistical design and analysis of RNA sequencing data. Genetics. 2010;185:405–416. 114983. Leakey ADB, Xu F, Gillespie KM, McGrath JM, Ainsworth EA, Ort DR. Genomic basis for stimulated respiration by plants growing under elevated carbon dioxide. PNAS. 2009;106:3597–3602. 0810955106. Conklin PL, Barth C. Ascorbic acid, a familiar small molecule intertwined in the response of plants to ozone, pathogens, and the onset of senescence. Plant Cell Environ.

Finally, we highlight several aspects of RNA-Seq analysis that have the potential to lead to misleading conclusions and discuss options to minimize these pitfalls. 1 GENERATING HIGH QUALITY READS IS DEPENDENT ON INITIAL RNA QUALITY Prior to library construction and sequencing-by-synthesis, the quality of the isolated RNA was assessed by gel electrophoresis to ensure purity (Additional file 1). Three replicate samples were isolated from soybean leaves that had been grown in either chronic O3 (150 parts per billion) or ambient O3 for six weeks.

The expression profile of each significant functional category was different from the expression profile of all other categories. When the lists of marginally significant genes were analyzed subsequently, most of these categories were found not to be significantly different, indicating that the eliminated genes did not respond in a manner similar to the optimized list of genes. However, statistical significance was achieved for several categories. Despite having an expression profile consistent with 8 Bioinformatics: The Impact of Accurate Quantification the remaining genes included in the optimized list, 320 RNA, 70 stress, 36 hormone metabolism, 19 DNA, and 10 mitochondrial electron transportrelated genes were eliminated based on a non-significant determination by one of the two statistical tools.

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