Protein-protein networks analysis of differentially expressed genes unveils the key phenomenon of biological process with respect to reproduction in endangered catfish, C. magur (2023)

FAQs

How do you analyze DEGs? ›

In molecular biology, STRING (Search Tool for the Retrieval of Interacting Genes/Proteins) is a biological database and web resource of known and predicted protein–protein interactions.

Identification of Differentially Expressed Genes (DEGs) Relevant to Prognosis of Ovarian Cancer by Use of Integrated Bioinformatics Analysis and Validation by Immunohistochemistry Assay. Med Sci Monit.

Protein-protein interaction networks (PPIN) are mathematical representations of the physical contacts between proteins in the cell. These contacts: are specific. occur between defined binding regions in the proteins. have a particular biological meaning (i.e., they serve a specific function)

Differential gene expression (DGE) analysis is an RNA-seq technology where the gene expression of cells is evaluated, quantified, and compared. DGE analysis determines which genes are actively being expressed, how often those genes are being expressed, and which genes are silenced.

Differential gene expression (DGE) analysis is one of the most common applications of RNA-sequencing (RNA-seq) data. This process allows for the elucidation of differentially expressed genes across two or more conditions and is widely used in many applications of RNA-seq data analysis.

STRING is a database of known and predicted protein-protein interactions. The interactions include direct (physical) and indirect (functional) associations; they stem from computational prediction, from knowledge transfer between organisms, and from interactions aggregated from other (primary) databases.

The STRING database aims to integrate all known and predicted associations between proteins, including both physical interactions as well as functional associations.

Characterizing protein–protein interactions through methods such as co-immunoprecipitation (co-IP), pull-down assays, crosslinking, label transfer, and far–western blot analysis is critical to understand protein function and the biology of the cell.

Differential protein expression analysis determines the relative abundances (molar ratios) of identical proteins in two or more samples (multi-protein mixtures) representing different conditions (groups) – e.g. control vs patient, treated vs untreated, etc.

What is identification of DEGs? ›

Identification of DEGs

Hierarchical clustering analysis was applied to categorize the data into two groups of different expression patterns. Significance analysis by Student's t-test and fold-change (FC) in the expression of genes between each pair of cancerous and normal tissues were jointly used to identify DEGs.

Methods. Gene set enrichment analysis uses a priori gene sets that have been grouped together by their involvement in the same biological pathway, or by proximal location on a chromosome. A database of these predefined sets can be found at the Molecular signatures database (MSigDB).

New undesired protein interactions are the main causes of several diseases, including Huntington's disease (see introduction), cystic fibrosis, and Alzheimer's disease.

Proteins bind to each other through a combination of hydrophobic bonding, van der Waals forces, and salt bridges at specific binding domains on each protein. These domains can be small binding clefts or large surfaces and can be just a few peptides long or span hundreds of amino acids.

Abstract. Protein–protein interactions (PPIs) participate in all important biological processes in living organisms, such as catalyzing metabolic reactions, DNA replication, DNA transcription, responding to stimuli and transporting molecules from one location to another.

Differential gene expression is often associated with methylation of DNA, and until recently the prevailing idea was that increased cytosine methylation nearby and within genes correlated with reduced gene expression.

Gene expression is important because a specific protein can be produced only when its gene is turned on. But it takes more than one step to get from gene to protein, and the process of building proteins is a key step in the gene expression pathway that can be altered in cancer.

Gene expression is primarily controlled at the level of transcription, largely as a result of binding of proteins to specific sites on DNA.

differential gene expression. the process by which cells become different from one another based upon the unique combo of genes that are active or expressed. Expressing genes -> express different proteins -> different cell types. 1.

STRING relies on the phylogeny to infer the functional interaction (protein networks) with direct interaction to score nodes, while GeneMANIA uses functional genomic data with label propagation to score nodes and generate gene networks (Figure 3).

What does string score mean? ›

In STRING, each protein-protein interaction is annotated with one or more 'scores'. Importantly, these scores do not indicate the strength or the specificity of the interaction. Instead, they are indicators of confidence, i.e. how likely STRING judges an interaction to be true, given the available evidence.

In computer programming, a string is traditionally a sequence of characters, either as a literal constant or as some kind of variable. The latter may allow its elements to be mutated and the length changed, or it may be fixed (after creation).

String manipulation is an important skill for novice programmers to master as most applications deal with text and/or interact with the user. The analysis shows most novice program- mers (88%) were able to sketch their own programming plan to print a word in pyramid style.

STRING, for example, suggests thresholds of 0.15 (low confidence), 0.40 (medium confidence), 0.70 (high confidence) or 0.90 (highest confidence), whereas HitPredict identifies all interactions scoring below 0.28 as medium-high confidence, and interactions scoring above as high confidence.

The most frequently used methods for measuring protein content in foods include the Kjeldahl method, Dumas method, direct measurement methods using UV-spectroscopy and refractive index measurement.

The Lowry method has been widely used for protein determination for many decades, due to its simplicity and availability. However, besides aromatic amino acids, a wide range of other compounds react with the Folin–Ciocalteu reagent [30].

The quality of a protein is vital when considering the nutritional benefits that it can provide. Determining the quality of a protein is determined by assessing its essential amino acid composition, digestibility and bioavailability of amino acids (FAO/WHO, 1990).

Identification of causal mutations typically begins with genetic mapping, followed by candidate gene sequencing and complementation studies using transformation.

Gene expression measurement is usually achieved by quantifying levels of the gene product, which is often a protein. Two common techniques used for protein quantification include Western blotting and enzyme-linked immunosorbent assay or ELISA.

There are many genetic tests that require a sample of your blood, skin, hair, amniotic fluid or tissues to identify changes to your genes, chromosomes or proteins. Genetic testing can locate mutated genes or chromosomes that cause genetic conditions.

Why is gene enrichment analysis important? ›

Gene set enrichment analysis (GSEA) is a powerful tool for the interpretation of high-throughput expression studies such as mass spectrometry-based proteomics or Next-Generation Sequencing, in order to identify insights into biological processes or pathways underlying a given phenotype.

Currently the most widely used techniques are qPCR, expression microarrays, and RNAseq for the transcriptome analysis. In this chapter, these techniques will be reviewed. Keywords: Gene expression; Microarrays; RNA sequencing; Transcriptome; qPCR.

A technique called mass spectrometry permits scientists to sequence the amino acids in a protein. After a sequence is known, comparing its amino acid sequence with databases allows scientists to discover if there are related proteins whose function is already known.

The gold standard of analyzing CRISPR editing involves targeted next-generation sequencing to perform deep sequencing on the region of interest. Targeted NGS is an extremely sensitive method of detecting editing outcomes, and the high-throughput sequence-based data provides a comprehensive view of the indels generated.

RNA-seq data analysis typically involves several steps: trimming, alignment, counting and normalization of the sequenced reads, and, very often, differential expression (DE) analysis across conditions.

The standard workflow for gene expression analysis in cell cultures is a multistep process that requires harvesting of cells, isolation of RNA, removal of contaminating DNA, cDNA synthesis, and finally qPCR, where cell harvesting and RNA isolation are the rate-limiting steps.

To perform this test, viral RNA is first isolated and extracted from patient samples. Then, the purified RNA is subjected to reverse transcription quantitative polymerase chain reaction (RT-qPCR), which converts the virus RNA to DNA and amplifies the DNA to produce millions of copies.

Scientists are studying CRISPR for many conditions, including high cholesterol, HIV, and Huntington's disease. Researchers have also used CRISPR to cure muscular dystrophy in mice. Most likely, the first disease CRISPR helps cure will be caused by just one flaw in a single gene, like sickle cell disease.

DNA replacement in human embryos (germline genome therapy) The most controversial usage of CRISPR-Cas9 is the modification of human embryo DNA, or, in other words, its use for germline genome therapy.

RNA-Seq allows researchers to detect both known and novel features in a single assay, enabling the identification of transcript isoforms, gene fusions, single nucleotide variants, and other features without the limitation of prior knowledge.

Can RNA-seq identify mutations? ›

Detection of mutations by RNA-Seq was more successful for mutations present at a high allelic frequency. Mutations were more often missed by RNA-Seq when present at low frequency or when tested on FNA samples. All TERT mutations were missed by RNA-Seq.

Heatmaps are commonly used to visualize RNA-Seq results. They are useful for visualizing the expression of genes across the samples.

In addition to Northern blot tests and SAGE analyses, there are several other techniques for analyzing gene expression. Most of these techniques, including microarray analysis and reverse transcription polymerase chain reaction (RT-PCR), work by measuring mRNA levels.

RNA sequencing (RNA-Seq), which directly sequences and counts the mRNA molecules in the whole transcriptome, can be selected to measure gene expression to detect genomic changes in disease states.

Microarray helps in analyzing large amount of samples which have either been recorded previously or new samples; it even helps to test the incidence of a particular marker in tumors.

A microarray is a special genetic test that looks in detail at a person's chromosomes to see if there are any extra or missing sections which might account for problems they have been experiencing.

Microarray experiments allow description of genome-wide expression changes in health and disease. The results of such experiments are expected to change the methods employed in the diagnosis and prognosis of disease in obstetrics and gynecology.