Gene Prediction- Importance and Methods

Gene Prediction- Importance and Methods

One of the main topics in bioinformatics is the prediction of genes using computer-assisted methods for the localization of protein-coding regions.

Predicting genes essentially means locating genes throughout a genome. Also called gene discovery, it refers to the process of identifying regions of genomic DNA that code for genes.

These include genes that code for proteins, RNA genes, and other functional elements, such as regulatory genes.

Gene Prediction- Importance and Methods

What are the Importance of Gene Prediction?

Helps comment on large, consistent sequences

It helps to identify fundamental and essential elements of the genome such as functional genes, introns, exons, splice sites, regulatory sites, genes that encode known proteins, motifs, ESTs, ACRs, etc.

Differentiate between coding and non-coding regions of a genome

Prediction of complete exon-intron structures of protein-coding regions

Describe individual genes according to their function.

It has wide application in structural genomics, functional genomics, metabolomics, transcriptomics, proteomics, genomics, and other studies related to genetics, including the detection, treatment, and prevention of genetic disorders.

Bioinformatics and the Prediction of Genes

As databases of human DNA sequences and models grow rapidly over time, it has become nearly impossible to perform conventional meticulous experiments on living cells and organisms to predict genes.

Previously, statistical analysis of the homologous recombination rates of several different genes could determine their order on a given chromosome, and information from many of these experiments could be combined to create a genetic map that specified the approximate position of known genes to each other.

However, the limits of current bioinformatics research make it increasingly possible to predict the function of such a gene stream based solely on its sequence.

What are the Methods of Gene Prediction?

Two classes of methods are generally adopted:

  1. Similarity-based searches

This is a method based on sequence similarity searches.

This is a conceptually simple approach based on finding similarities in gene sequences between ESTs (expressed sequence tags), proteins, or other genomes with the input genome.

This approach is based on the assumption that functional regions (exons) are evolutionarily more conserved than non-functional regions (intergenic or intronic regions).

Once a similarity exists between a particular genomic region and an EST, DNA, or protein, the similarity information can be used to infer the structure or function of the gene for that region.

Local alignment and global alignment are two methods based on searches for similarities. The most common local alignment tool is the BLAST family of programs, which recognize sequence similarities to known genes, proteins, or ESTs.

Two other types of software, PROCRUSTES, and GeneWise use the global alignment of a homologous protein to translate ORFs into a genomic sequence for gene prediction.

A new heuristic method based on pairwise genome comparisons was implemented in the CSTfinder software.

Gene Prediction- Importance and Methods
  1. Ab- initio prediction

It is a method based on genetic structure and signal-based research.

It uses the structure of the gene as a template to detect genes.

Ab initio gene predictions are based on two types of sequence information: signal sensors and content sensors.

Signal sensors relate to short sequence motifs such as splice sites, branch points, polypyrimidine bundles, start codons, and stop codons.

Content sensors, for their part, relate to species-specific codon usage patterns and allow coding sequences to be distinguished from surrounding non-coding sequences using statistical recognition algorithms. Exon detection must rely on content sensors.

Research with this method is therefore based on the main character that is present in genes.

Many algorithms are used to model the structure of genes, such as B. dynamic programming, linear discriminant analysis, linguistic methods, hidden Markov model, and neural networks.

Several ab initio gene prediction programs have been developed based on these models. Some of the most used are GeneID, FGENESH, GeneParser, GlimmerM, GENSCAN, etc.

Gene Prediction- Importance and Methods

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Gene Prediction- Importance and Methods

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