Unlocking biological insights from differentially expressed genes: Concepts, methods, and future perspectives.

Background: Identifying differentially expressed genes (DEGs) is a core task of transcriptome analysis, as DEGs can reveal the molecular mechanisms underlying biological processes. However, interpreting the biological significance of large DEG lists is challenging. Currently, gene ontology, pathway enrichment and protein-protein interaction analysis are common strategies employed by biologists.

MSPJ: Discovering potential biomarkers in small gene expression datasets ensemble learning.

In transcriptomics, differentially expressed genes (DEGs) provide fine-grained phenotypic resolution for comparisons between groups and insights into molecular mechanisms underlying the pathogenesis of complex diseases or phenotypes. The robust detection of DEGs from large datasets is well-established. However, owing to various limitations (e.

The complete mitogenome of (Hymenoptera: Halictidae) and phylogenetic analysis.

The complete mitogenome of (Hymenoptera: Halictidae) was sequenced and analyzed. The whole mitogenome is 17,352 bp (AT%=84.1%) and encodes 37 typical eukaryotic mitochondrial genes, including 13 protein-coding genes (PCGs), 22 , two , and an AT-rich region.

Systematic Identification of Housekeeping Genes Possibly Used as References in by Large-Scale Data Integration.

For accurate gene expression quantification, normalization of gene expression data against reliable reference genes is required. It is known that the expression levels of commonly used reference genes vary considerably under different experimental conditions, and therefore, their use for data normalization is limited. In this study, an unbiased identification of reference genes in was performed based on 145 microarray datasets (2296 gene array samples) covering different developmental stages, different tissues, drug treatments, lifestyle, and various stresses.

Consistent Biomarkers and Related Pathogenesis Underlying Asthma Revealed by Systems Biology Approach.

Asthma is a common chronic airway disease worldwide. Due to its clinical and genetic heterogeneity, the cellular and molecular processes in asthma are highly complex and relatively unknown. To discover novel biomarkers and the molecular mechanisms underlying asthma, several studies have been conducted by focusing on gene expression patterns in epithelium through microarray analysis.