遗传学实验技术

Data quality is intrinsically influenced by design, technical, and analytical parameters. Quality parameters have not yet been well defined for gene expression analysis by microarrays, though ad interim, following recommended good experimental practice guidelines should e ...

Inferring gene regulatory networks from microarray data has become a popular activity in recent years, resulting in an ever-increasing volume of publications. There are many pitfalls in network analysis that remain either unnoticed or scantily understood. A critical discussion of s ...

One of the main issues in statistical analysis of gene expression data is testing levels of differentially expressed genes. There are different approaches to address this issue. If in one case, given two probe sets of gene expression levels, we test whether they are differentially expressed or n ...

This review chapter presents a statistical point of view to microarray experiments with the purpose of understanding the apparent contradictions that often appear in relation to their results. We give a brief introduction of molecular biology for nonspecialists. We describe microa ...

This chapter briefly reviews how laboratories generate microarray data. This information may give data analysts a better appreciation of the technical sources of variability in the data and the importance of minimizing such variability by normalization methods or exclusion of abe ...

The process of screening for differentially expressed genes using microarray samples can usually be reduced to a large set of statistical hypothesis tests. In this situation, statistical issues arise which are not encountered in a single hypothesis test, related to the need to identify the ...

Normalizations of gene expression data are commonly used in practice. They are used for removing systematic variation which affects the measure of gene expression levels. But one can object to the using of normalized data for testing hypotheses. By using normalized data, tests can break nomi ...

In this chapter, we discuss a method of selecting differentially expressed genes based on a newly discovered structure termed as the δ-sequence. Together with the nonparametric empirical Bayes methodology, it leads to dramatic gains in terms of the mean numbers of true and false discoverie ...

Modern high-throughput technologies allow us to simultaneously measure the expressions of a huge number of candidate predictors, some of which are likely to be associated with survival. One difficult task is to search among an enormous number of potential predictors and to correctly ide ...

There are two distinct but related clustering problems with microarray data. One problem concerns the clustering of the tissue samples (gene signatures) on the basis of the genes; the other concerns the clustering of the genes on the basis of the tissues (gene profiles). The clusters of tissues so o ...

In the analysis of high-throughput data, a very common goal is the detection of genes or of differential expression between two groups or classes. A recent finding from the scientific literature in prostate cancer demonstrates that by searching for a different pattern of differential expr ...

Multivariate microarray gene expression data are commonly collected to study the genomic responses under ordered conditions such as over increasing/decreasing dose levels or over time during biological processes, where the expression levels of a give gene are expected to be depend ...

The distinction between orthologs and paralogs, genes that started diverging by speciation versus duplication, is relevant in a wide range of contexts, most notably phylogenetic tree inference and protein function annotation. In this chapter, we provide an overview of the methods used ...

A major current molecular evolution challenge is to link comparative genomic patterns to species’ biology and ecology. Breeding systems are pivotal because they affect many population genetic processes, and thus genome evolution. We review theoretical predictions and empirical ...

Most genomes are populated by thousands of sequences that originated from mobile elements. On the one hand, these sequences present a real challenge in the process of genome analysis and annotation. On the other hand, there are very interesting biological subjects involved in many cellular p ...

Identification of intragenomic conservation of gene compositions in multiple chromosomal segments led to evidence of whole genome (WGDs) duplications. The process by which WGDs have been maintained and decayed provides us with clues for understanding how the genome evolves. In this ...

Recent research is starting to shed light on the factors that influence the population and evolutionary dynamics of transposable elements (TEs) and TE life cycles. Genomes differ sharply in the number of TE copies, in the level of TE activity, in the diversity of TE families and types, and in the propor ...

The purpose of this chapter is to provide a comprehensive review of the field of genome rearrangement, i.e., comparative genomics, based on the representation of genomes as ordered sequences of signed genes. We specifically focus on the “hard part” of genome rearrangement, how to handle duplic ...

Various genetic events during the process of natural evolution shape the landscape of the genomes. In this chapter, we explore an approach to investigating multiple genomes in order to unravel their complex relationships that go beyond their placement on a phylogeny. To this end, we treat genes ...

Flower scent is a composite character determined by a complex mixture of low-molecular-weight volatile molecules. Despite the importance of floral fragrance, our knowledge on factors regulating these pathways remains sketchy. Virus-induced gene silencing (VIGS) and virus-ai ...