Studying biological networks, such as protein-protein interactions, is key to understanding complex biological activities. Various types of large-scale biological datasets have been collected and analyzed with high-throughput technologies, including DNA microarray, next-generation sequencing, and the two-hybrid screening system, for this purpose. In this review, we focus on network-based approaches that help in understanding biological systems and identifying biological functions. Accordingly, this paper covers two major topics in network biology: reconstruction of gene regulatory networks and network-based applications, including protein function prediction, disease gene prioritization, and network-based genome-wide association study.
Biology ; Dataset ; Gene Regulatory Networks ; Genome-Wide Association Study ; Mass Screening ; Oligonucleotide Array Sequence Analysis

No Abstract Available.
DNA* ; Oligonucleotide Array Sequence Analysis*

DNA chips are becoming increasingly popular as a convenient way to perform vast amounts of experiments related to genes on a single chip. And the importance of analyzing the data that is provided by such DNA chips is becoming significant. A very important analysis on DNA chip data would be clustering genes to identify gene groups which have similar properties such as cancer. Clustering data for DNA chips usually deal with a large search space and has a very fuzzy characteristic. The Particle Swarm Optimization algorithm which was recently proposed is a very good candidate to solve such problems. In this paper, we propose a clustering mechanism that is based on the Particle Swarm Optimization algorithm. Our experiments show that the PSO-based clustering algorithm developed is efficient in terms of execution time for clustering DNA chip data, and thus be used to extract valuable information such as cancer related genes from DNA chip data with high cluster accuracy and in a timely manner.
Cluster Analysis ; DNA ; Oligonucleotide Array Sequence Analysis

To understand how differentially methylated genes (DMGs) might affect the pathogenesis of Kashin-Beck disease (KBD). Genome-wide methylation profiling of whole blood from 12 matched KBD and controls pairs was performed using a high-resolution Infinium 450 K methylation array. In total, 97 CpG sites were differentially methylated in KBD compared to the normal controls; of these sites, 36 sites were significantly hypermethylated (covering 22 genes) and 61 sites were significantly hypomethylated (covering 34 genes). Of these genes, 14 significant pathways were identified, the most significant P value pathway was type I diabetes mellitus pathway and pathways associated with autoimmune diseases and inflammatory diseases were included in this study. Subsequently, 4 CpG sites in HLA-DRB1 were validated using bisulfite sequencing polymerase chain reaction (BSP) in articular cartilage, and the results showed significant differences in the methylation status between KBD and controls, consistent with the results of the high-resolution array. These results suggested that differences in genome-wide DNA methylation exist between KBD and the controls, and the biological pathways support the autoimmune disease and inflammatory disease hypothesis of KBD.
Adult ; Case-Control Studies ; Cluster Analysis ; CpG Islands ; DNA Methylation ; Female ; Genetic Variation ; Genome-Wide Association Study ; Humans ; Kashin-Beck Disease ; genetics ; Middle Aged ; Oligonucleotide Array Sequence Analysis

Toxicogenomics has recently emerged in the field of toxicology and the DNA microarray technique has become common strategy for predictive toxicology which studies molecular mechanism caused by exposure of chemical or environmental stress. Although microarray experiment offers extensive genomic information to the researchers, yet high dimensional characteristic of the data often makes it hard to extract meaningful result. Therefore we developed toxicant enrichment analysis similar to the common enrichment approach. We also developed web-based system graPT to enable considerable prediction of toxic endpoints of experimental chemical.
Oligonucleotide Array Sequence Analysis ; Toxicogenetics ; Toxicology*

No abstract available.
DNA* ; Genome* ; Genomics* ; Oligonucleotide Array Sequence Analysis*

DNA chips are used for experiments on genes and provide useful information that could be further analyzed. Using the data extracted from the DNA chips to find useful patterns or information has become a very important issue. In this paper, we explain the application developed for classifying DNA chip data using a classification method based on the Particle Swarm Optimization (PSO) algorithm. Considering that DNA chip data is extremely large and has a fuzzy characteristic, an algorithm that imitates the ecosystem such as the PSO algorithm is suitable to be used for analyzing such data. The application enables researchers to customize the PSO algorithm parameters and see detail results of the classification rules.
DNA ; Ecosystem ; Oligonucleotide Array Sequence Analysis

OBJECTIVE: To assess the clinical application of single nucleotide polymorphism microarray (SNP array) in patients with intellectual disability/developmental delay(ID/DD). METHODS: SNP array was performed to detect genome-wide DNA copy number variants (CNVs) for 145 patients with ID/DD in Women's Hospital, Zhejiang University School of Medicine from January 2013 to June 2018. The CNVs were analyzed by CHAS software and related databases. RESULTS: Among 145 patients, pathogenic chromosomal abnormalities were detected in 32 cases, including 26 cases of pathogenic CNVs and 6 cases of likely pathogenic CNVs. Meanwhile, 18 cases of uncertain clinical significance and 14 cases of likely benign were identified, no significant abnormalities were found in 81 cases (including benign). CONCLUSIONS SNP array is effective for detecting chromosomal abnormalities in patients with ID/DD with high efficiency and resolution.
Chromosome Aberrations ; DNA Copy Number Variations ; Genome-Wide Association Study ; Humans ; Intellectual Disability ; diagnosis ; genetics ; Oligonucleotide Array Sequence Analysis ; standards ; Polymorphism, Single Nucleotide

With the rapid development of molecular biotechnology, transcriptomics has been widely used in the study of gene expression. In recent years, the techniques for microbial transcriptomics research have also been rapidly developing. At the gene level, the way for obtaining sequence information has been developed from complementary validation of RNA fragment through DNA microarray to direct sequencing of full-length RNA. Spatially, the traditional population transcriptomics technique has been developed into spatial, single cell and epigenetic transcriptomics studies. With the application of transcriptomics techniques in the field of microbial research, the corresponding defects were gradually revealed and constantly improved. In this paper, the traditional and new transcriptomics techniques in the field of microbial research are summarized to provide reference for microbial transcriptomics research.
Transcriptome ; Oligonucleotide Array Sequence Analysis ; Sequence Analysis ; RNA ; Biotechnology

The analysis of DNA microarry data is one of the most important things for functional genomics research. The matrix representation of microarray data and its successive 'optimal' incisional hyperplanes is a useful platform for developing optimization algorithms to determine the optimal partitioning of pairwise proximity matrix representing completely connected and weighted graph. We developed Deterministic Annealing (DA) approach to determine the successive optimal binary partitioning. DA algorithm demonstrated good performance with the ability to find the 'globally optimal' binary partitions. In addition, the objects that have not been clustered at small non-zero temperature, are considered to be very sensitive to even small randomness, and can be used to estimate the reliability of the clustering.
Cluster Analysis ; DNA* ; Gene Expression* ; Genomics ; Oligonucleotide Array Sequence Analysis*