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

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

by Yu Liu (Editor)

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Overview

Bioinformatics: The Impact of Accurate Quantification on Proteomic and Genetic Analysis and Research by Yu Liu

This title includes a number of Open Access chapters.

The book introduces bioinformatic and statistical methodology and shows approaches to bias correction and error estimation. It also presents quantitative methods for genome and proteome analysis.

Product Details

ISBN-13: 9781771880190
Publisher: Apple Academic Press
Publication date: 03/13/2014
Pages: 412
Product dimensions: 6.30(w) x 9.10(h) x 1.20(d)

About the Author

Dr. Yu Liu is a bioinformatician with special interest in next-gen sequencing and its applications. His specialties are molecular biology, DNA sequence analysis, next-gen sequencing application on gene expression analysis and comparative genomics, and microarray gene expression analysis. He is the director of the Bioinformatics Resource Center at the University of Wisconsin-Madison. He has a master's degree in computer science from the University of Wisconsin-Madison, a master's degree in developmental biology from the Chinese Academy of Science, and PhD in molecular biology from The Ohio State University.

Table of Contents

Introduction

Part I: RNA-Seq

The Bench Scientist's Guide to Statistical Analysis of RNA-Seq Data; Craig R. Yendrek, Elizabeth A. Ainsworth, and Jyothi Thimmapuram

Assembly of Non-Unique Insertion Content Using Next-Generation Sequencing; Nathaniel Parrish, Farhad Hormozdiari, and Eleazar Eskin

RSEM: Accurate Transcript Quantification from RNA-Seq Data With or Without a Reference Genome; Bo Li and Colin N. Dewey

Part II: Microarray

A Regression System for Estimation of Errors Introduced by Confocal Imaging into Gene Expression Data In Situ; Ekaterina Myasnikova, Svetlana Surkova, Grigory Stein, Andrei Pisarev, and Maria Samsonova

SPACE: An Algorithm to Predict and Quantify Alternatively Spliced Isoforms Using Microarrays; Miguel A. Anton, Dorleta Gorostiaga, Elizabeth Guruceaga, Victor Segura, Pedro Carmona-Saez, Alberto Pascual-Montano, Ruben Pio, Luis M. Montuenga, and Angel Rubio

Link-Based Quantitative Methods to Identify Differentially Coexpressed Genes and Gene Pairs; Hui Yu, Bao-Hong Liu, Zhi-Qiang Ye, Chun Li, Yi-Xue Li, and Yuan-Yuan Li

Dimension Reduction with Gene Expression Data Using Targeted Variable Importance Measurement; Hui Wang and Mark J. van der Laan

Part III: GWAS

Genome-Wide Association Study of Stevens-Johnson Syndrome and Toxic Epidermal Necrolysis in Europe; Emmanuelle Génin, Martin Schumacher, Jean-Claude Roujeau, Luigi Naldi,Yvonne Liss, Rémi Kazma, Peggy Sekula, Alain Hovnanian, and Maja Mockenhaupt

Genotyping Common and Rare Variation Using Overlapping Pool Sequencing; Dan He, Noah Zaitlen, Bogdan Pasaniuc, Eleazar Eskin, and Eran Halperin

Learning Genetic Epistasis Using Bayesian Network Scoring Criteria; Xia Jiang, Richard E. Neapolitan, M. Michael Barmada, and Shyam Visweswaran

Combined Analysis of Three Genome-Wide Association Studies on vWF and FVIII Plasma Levels; Guillemette Antoni, Tiphaine Oudot-Mellakh, Apostolos Dimitromanolakis, Marine Germain, William Cohen, Philip Wells, Mark Lathrop, France Gagnon, Pierre-Emmanuel Morange, and David-Alexandre Tregouet

Part IV: Proteomics

Statistical Methods for Quantitative Mass Spectrometry Proteomic Experiments with Labeling; Ann L. Oberg and Douglas W. Mahoney

MRCQuant: An Accurate LC-MS Relative Isotopic Quantification Algorithm on TOF Instruments; William E. Haskins, Konstantinos Petritis, and Jianqiu Zhang

Index

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