Bioinformatics: The Impact of Accurate Quantification on by Yu Liu

By Yu Liu

The publication introduces bioinformatic and statistical technique and indicates techniques to bias correction and mistake estimation. It additionally offers quantitative equipment for genome and proteome analysis.

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S1 ⊕ s2 = true iff Align(s1, s2) >τ. The exact value of the threshold τ varies depending on the context and in 26 Bioinformatics: The Impact of Accurate Quantification all cases is user-configurable, so we leave it as an implicit parameter and omit it from the notation. The depth-of-coverage for a particular position in the reference is defined as the number of reads that cover that position. We will define μDOC as the mean depth-of-coverage across the entire reference genome, and μDOC[r] as the mean depth-of-coverage across the positions in the reference that are covered by read r.

1223. Smyth GK. In: Bioinformatics and Computational Biology Solutions using R and Bioconductor. Gentleman R, Carey V, Dudoit S, Irizarry R, Huber W, editor. Springer, New York; 2005. Limma: linear models for microarray data; pp. 397–420. Ainsworth EA, Yendrek CR, Sitch S, Collins WJ, Emberson LD. The effects of tropospheric ozone on net primary production and implications for climate change. Annu Rev Plant Biol. 2012;63:637–661. 1146/annurev-arplant-042110-103829. Thimm O, Blaesing O, Gibon Y, Nagel A, Meyer S, Krüger P, Selbig J, Müller LA, Rhee SY, Stitt M.

1186/1756-0500-5-506. 2 Bioinformatics: The Impact of Accurate Quantification Essentially, RNA-Seq consists of five distinct phases, 1) RNA isolation, 2) library preparation, 3) sequencing-by-synthesis, 4) mapping of raw reads to a reference transcriptome or genome and 5) determining significance for differential gene expression (for review see [1]). In an effort to familiarize the bench scientist with the post-sequencing analysis of RNA-Seq data (phase 5), we have developed an analysis strategy based on currently available bioinformatics tools.

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