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丁香实验推荐阅读
Feulgen-DNA Cytophotometry for Estimating C Values

The genome size of an organism, commonly known as its C value, is defined as the content of DNA (measured by weight or numbers of basepairs) in a single copy of the sequence of DNA found within the cells of an organism (i.e., the amount of DNA in a haploid chromosome set, where 1n=1C) (1). A single sperm of Drosophila melan ...

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Polytene Chromosomes From Ovarian Nurse Cells of Drosophila melanogaster otu Mutants

Polytene chromosomes from the salivary gland cells of Drosophila melanogaster larvae have been the most useful model for studying the functional morphology of interphase eukaryotic chromosomes since their discovery almost 70 yr ago (1). The question remains as to whether the polytene ...

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In-Depth Query of Large Genomes Using Tiling Arrays

Identification of the transcribed regions in the newly sequenced genomes is one of the major challenges of postgenomic biology. Among different alternatives for empirical transcriptome mapping, whole-genome tiling array experiment emerged as the most comprehensive and unbi ...

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Integrated High-Resolution Genome-Wide Analysis of Gene Dosage and Gene Expression in Human Brain Tumors

A hallmark genomic feature of human brain tumors is the presence of multiple complex structural and numerical chromosomal aberrations that result in altered gene dosages. These genetic alterations lead to widespread, genome-wide gene expression changes. Both gene expression as we ...

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Analysis of Comparative Genomic Hybridization Data on cDNA Microarrays

We present a detailed method to analyze DNA copy number data generated on cDNA microarrays. A web interface is made available for those steps in the workflow that are not typically used in gene expression analysis so that these steps can be carried out online. The end result of the analysis is a list of p-valu ...

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Interpreting Microarray Results With Gene Ontology and MeSH

Methods are described to take a list of genes generated from a microarray experiment and interpret these results using various tools and ontologies. A workflow is described that details how to convert gene identifiers with SOURCE and MatchMiner and then use these converted gene lists to sear ...

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Progression-Associated Genes in Astrocytoma Identified by Novel Microarray Gene Expression Data Reanalysis

Astrocytoma is graded as pilocytic (WHO grade I), diffuse (WHO grade II), anaplastic (WHO grade III), and glioblastoma multiforme (WHO grade IV). The progression from low- to high-grade astrocytoma is associated with distinct molecular changes that vary with patient age, yet the prognosis of ...

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Predicting Survival in Follicular Lymphoma Using Tissue Microarrays

A tissue microarray (TMA) containing diagnostic biopsies was used to develop predictors of outcome in a group of 105 patients having advanced-stage follicular lymphoma (FL). The patients were staged and uniformly treated, and the usable cases had been randomly divided into a subgroup of 50 p ...

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Incorporation of Gene Ontology Annotations to Enhance Microarray Data Analysis

Typical microarray or GeneChip™ experiments now provide genome-wide measurements on gene expression across many conditions. Analysis often focuses on only a few of the genes, looking for those that are “differentially expressed” between conditions or groups of conditions. Howeve ...

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Genomic Signal Processing: From Matrix Algebra to Genetic Networks

DNA microarrays make it possible, for the first time, to record the complete genomic signals that guide the progression of cellular processes. Future discovery in biology and medicine will come from the mathematical modeling of these data, which hold the key to fundamental understanding of l ...

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Microarray Data Analysis: An Overview of Design, Methodology, and Analysis

Microarray analysis results in the gathering of massive amounts of information concerning gene expression profiles of different cells and experimental conditions. Analyzing these data can often be a quagmire, with endless discussion as to what the appropriate statistical analy ...

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Online Analysis of Microarray Data Using Artificial Neural Networks

Herein we have set forth a detailed method to analyze microarray data using artificial neural networks (ANN) for the purpose of classification, diagnosis, or prognosis. All aspects of this analysis can be carried out online via a website. The reader is guided through each step of the analysis incl ...

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Signal Processing and the Design of Microarray Time-Series Experiments

Recent findings of a genome-wide oscillation involving the transcriptome of the budding yeast Saccharomyces cerevisiae suggest that the most promising path to an understanding of the cell as a dynamic system will proceed from carefully designed time-series sampling followed by the d ...

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Predictive Models of Gene Regulation: Application of Regression Methods to Microarray Data

Eukaryotic transcription is a complex process. A myriad of biochemical signals cause activators and repressors to bind specific cis-elements on the promoter DNA, which help to recruit the basal transcription machinery that ultimately initiates transcription. In this chapter, we d ...

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Statistical Framework for Gene Expression Data Analysis

DNA (mRNA) microarray, a highly promising technique with a variety of applications, can yield a wealth of data about each sample, well beyond the reach of every individual’s comprehension. A need exists for statistical approaches that reliably eliminate insufficient and uninformative ...

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Comparing Microarray Studies

We present a practical guide to some of the issues involved in comparing or integrating different microarray studies. We discuss the influence that various factors have on the agreement between studies, such as different technologies and platforms, statistical analysis criteria, pr ...

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Gene Expression Profiles and Prognostic Markers for Primary Breast Cancer

Genome-wide measures of gene expression have been used to classify breast tumors into clinically relevant subtypes, as well as provide a better means of risk assessment on an individual basis for lymph node-negative (LNN) breast cancer patients. We have applied Affymetrix GeneChips of 22, ...

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Gene Silencing by a DNA Vector-Based RNAi Technology

Double-stranded RNA (dsRNA) can suppress gene expression by inducing mRNA degradation of the homologous gene, known as RNA interference (RNAi). First, an RNase III-like dsRNA-specific endonuclease, Dicer, cleaves long dsRNA into 21- to 23-nucleotide (nt) small, interfering RNAs (siR ...

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Plasmid-Based RNA Interference: Construction of Small-Hairpin RNA Expression Vectors

Recent work demonstrates that RNA interference (RNAi) helps coordinate the flow of information from transcription to protein expression, complicating tremendously our former understanding of how protein expression is regulated (1,2). Inhibitory RNAs can be expressed natur ...

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Peptide-Based Strategy for siRNA Delivery into Mammalian Cells

The potential to control and alter gene expression constitutes an essential strategy in both fundamental and pharmaceutical research. The recent discovery of the RNA interference pathway in a wide variety of eukaryotic organisms has provided a novel means of characterizing gene fun ...

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