数据库

Chromatin immunoprecipitation followed by genome tiling array hybridization (ChIP-chip) is a powerful approach to map transcription factor binding sites (TFBSs). Similar to other high-throughput genomic technologies, ChIP-chip often produces noisy data. Distinguishi ...

It is well accepted that a set of genes must act in concert to drive various cellular processes. However, under different biological phenotypes, not all the members of a gene set will participate in a biological process. Hence, it is useful to construct a discriminative classifier by focusing on the c ...

Genome-wide association studies (GWAS) have shown notable success in identifying susceptibility genetic variants of common and complex diseases. To date, the analytical methods of published GWAS have largely been limited to single single nucleotide polymorphism (SNP) or SNP–SNP ...

DNA motifs are short sequences varying from 6 to 25 bp and can be highly variable and degenerated. One major approach for predicting transcription factor (TF) binding is using position weight matrix (PWM) to represent information content of regulatory sites; however, when used as the sole means ...

Protein–DNA interactions play key roles in determining gene-expression programs during cellular development and differentiation. Chromatin immunoprecipitation (ChIP) is the most widely used assay for probing such interactions. With recent advances in sequencing tech ...

Epigenetic modifications are critical to gene regulations and genome functions. Among different epigenetic modifications, it is of great interest to study the differential histone modification sites (DHMSs), which contribute to the epigenetic dynamics and the gene regulatio ...

Chromatin immunoprecipitation followed by sequencing (ChIP-seq) is a high-throughput antibody-based method to study genome-wide protein–DNA binding interactions. ChIP-seq technology allows scientist to obtain more accurate data providing genome-wide coverage with ...

RNA-Seq is arising as a powerful method for transcriptome analyses that will eventually make microarrays obsolete for gene expression analyses. Improvements in high-throughput sequencing and efficient sample barcoding are now enabling tens of samples to be run in a cost-effective m ...

Next generation sequencing is a common and versatile tool for biological and medical research. We describe the basic steps for analyzing next generation sequencing data, including quality checking and mapping to a reference genome. We also explain the further data analysis for three com ...

Owing to the quick development of high-throughput techniques and the generation of various “omics” datasets, it creates a prospect of performing the study of genome-wide genetic regulatory networks. Here, we present a sophisticated modelling framework together with the correspon ...

Regulatory networks inferred from microarray data sets provide an estimated blueprint of the functional interactions taking place under the assayed experimental conditions. In each of these experiments, the gene expression pathway exerts a finely tuned control simultaneous ...

Dynamic Bayesian networks (DBNs) have received increasing attention from the computational biology community as models of gene regulatory networks. However, conventional DBNs are based on the homogeneous Markov assumption and cannot deal with inhomogeneity and nonstation ...

Gene regulation networks are composed of transcription factors, their interactions, and targets. It is of great interest to reconstruct and study these regulatory networks from genomics data. Ordinary differential equations (ODEs) are popular tools to model the dynamic system of ge ...

Functional comparison across microarray platforms is used to assess the comparability or similarity of the biological relevance associated with the gene expression data generated by multiple microarray platforms. Comparisons at the functional level are very important cons ...

There is a high prevalence of alternatively spliced genes (isoforms) in the human genome. Studies toward understanding aberrantly spliced genes and their association with diseases have lead researchers to profile the expression of alternatively spliced products. High-throug ...

Use of microarray data to generate expression profiles of genes associated with disease can aid in identification of markers of disease and potential therapeutic targets. Pathway analysis methods further extend expression profiling by creating inferred networks that provide an ...

Clustering is a popular data exploration technique widely used in microarray data analysis. In this chapter, we review ideas and algorithms of bicluster and its applications in time series microarray analysis. We introduce first the concept and importance of biclustering and its diffe ...

Classification approaches have been developed, adopted, and applied to distinguish disease classes at the molecular level using microarray data. Recently, a novel class of hierarchical probabilistic models based on a kernel-imbedding technique has become one of the best classif ...

Single nucleotide polymorphism (SNP) arrays are powerful tools to delineate genomic aberrations in cancer genomes. However, the analysis of these SNP array data of cancer samples is complicated by three phenomena: (a) aneuploidy: due to massive aberrations, the total DNA content of a canc ...

Microarrays were one of the first technologies of the genomic revolution to gain widespread adoption, rapidly expanding from a cottage industry to the source of thousands of experimental results. They were one of the first assays for which data repositories and metadata were standardized ...