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Because it promotes systemic body growth and controls cell growth and development, Insulin-like Growth Factor 1 (IGF1) research has received a lot of interest. An analysis of the Insulin-like Growth Factor 1 gene in chicken, turkey, and quail was done using bioinformatics.

Using a variety of software tools (Clustal W, MEGA 6, dnaSP, BLAST, phyre2, ExPASy GORIV, and Rasmol software), the 15 insulin-like growth factor 1 nucleotide sequences and their corresponding proteins were examined to ascertain the percentage of identity and similarities in function of the IGF 1 gene, genetic diversity, evolutionary relationship, protein structure prediction, and physiochemical properties.

Genebank is a public domain protein database. The results collected demonstrated that the IGF1 gene in avians had a percent identity and similarity range of 86-99% and was similar in function.

Within each species of avian, there was considerable genetic variety (1.000 in turkey, 0.900 in chicken, and 0.900 in quail). However, chicken got the greatest haplotype number value (four), indicating that it has more variety in its IGF1 gene sequence than do turkey and quail.

The IGF1 gene sequences of avian species were classified into the same taxon, according to phylogenetic analysis, and chicken and quail were more closely linked to one another than the IGF1 gene of turkey.

The alpha helix structure of the IGF1 gene sequences was found to occupy 20.92%, 21.57%, and 20.92% of the secondary structures of the chicken, turkey, and quail, respectively, according to the GORIV (Garnier-Osguthorpe-Robson IV) software tool’s analysis of secondary structures.

The results of the secondary and tertiary structure of the IGF1 protein predictions demonstrated the stability and correct formation of the avian IGF genes. IGF1 protein from chicken, turkey, and quail had an isoelectric potential (theoretical pI) of 9.25 and an estimated half-life of 30 hours, according to the physiochemical characteristics.

In conclusion, the IGF1 gene is highly effective in promoting growth and controlling cellular activities due to its high percentage of identity and similarity in function, high genetic diversity observed, relative relatedness in the phylogenetic study, and high alpha helix in the protein structure.


IGF1 is a naturally occurring protein that has the ability to promote cellular growth, proliferation, and differentiation. IGF1 proteins, according to Hegarty et al. (2006), are crucial for controlling a number of cellular functions.

It increases glucose absorption, stimulates myogenesis, inhibits cell cycle genes, increases lipid synthesis, and stimulates the production of progesterone in the synthesis of DNA, RNA, and protein (Etherton, 2004).

Insulin-like growth factor-1 is a mediator of many biological effects. IGF1 is being evaluated as a candidate gene for predicting growth and meat quality attributes in the animal genetic development scheme because of these biological functions (Andrade et al., 2008).

IGF1 is largely produced by the liver in target tissues in a paracrine or autocrine way as an endocrine hormone (Kemp, 2007). Growth hormone promotes its production, which can be inhibited by malnutrition, growth insensitivity, or a deficiency in growth hormone receptors (Flier and Underhill, 2006).

The anterior pituitary gland produces growth hormone, which is subsequently released into the bloodstream and causes the liver to create IGF1 (Akinfenwa et al., 2011).

Then, IGF1 promotes growth in nearly all body cells and encourages systemic body growth (Yilmaz et al., 2011). Therefore, decreased stature is caused by a deficiency in either growth hormone or IGF1 (Akinfenwa et al., 2011).

Many livestock species and laboratory animals have shown a correlation between growth characteristic and circulating IGF1 concentration (Bertlett and Tom, 2005; Bunter et al., 2005; Hegarty et al., 2006).

The purpose of bioinformatics is to assist the agriculture and health care sectors, with a few spinoffs, by discovering, developing, and implementing computer algorithms and software tools that facilitate an understanding of biological processes (Albert et al., 2011).

In a developing nation like Nigeria, bioinformatics can be used to analyse cattle genomic and proteomic data, which can be extremely helpful in creating genetic modifications.

By using the knowledge already available to comprehend the biological function of unidentified proteins, computational analysis significantly aids in understanding the molecular basis of the biological function of proteins.

The development of computational approaches offers the possibility to achieve several advancements far more quickly and effectively than would be attainable using laboratory techniques (Zimin et al., 2009).

According to, the study of methods for storing, retrieving, and analysing biological data, such as protein and nucleic acid sequences, structures, functions, pathways, and genetic interactions, falls under the category of bioinformatics.

Deoxyribonucleic acids (DNA) and ribonucleic acids (RNA) are the molecules that hold an organism’s genetic information. With the use of bioinformatics tools and databases, biologists may investigate the fixed structure of these macromolecules.

Gene Bank from the National Centre for Biotechnology Information, Swiss port from the Swiss Institute of Bioinformatics, and protein information Resources (PIR) ( are a few well-known data sources.
Understanding the genetic underpinnings of phenotypic variability within and between species is one of the biggest challenges in animal breeding.

Only the diversity of phenotypes observed across species in nature can compare to that produced by the relative breeding of domestic animals over thousands of years.

The majority of cattle in Nigeria have been chosen with little to no understanding of a sequence of cellular and molecular responses. Instead than focusing on the gene itself, selection has focused on the gene’s influence (Akinbiyi, 2014). Single gene activities or a mixture of several gene actions control traits.

In a developing nation like Nigeria where molecular genetics and bioinformatics are still under study and poorly documented, the study of the IGF1 gene on avian aims to better inform farmers and breeders about the significance of molecular components of genes in selection.

In comparison to most species, Mahmoud et al. (2014) found that the IGF1 gene in chickens has been shown to act as a better candidate gene for growth and other metabolic processes (proliferation and cellular differentiation).

In order to inform scientists and farmers about which species’ IGF1 gene can best serve as a molecular maker and as a growth promoter to improve farm animals’ production features, three bird species’ roles for IGF1 were identified and compared in this study.

According to Toro et al. (2008), molecular data on genetic variation within and between breeds is crucial for the efficient management of farm animals’ genetic resources.

According to FAO (2000), genetic diversity in livestock enables farmers to choose stocks or create new breeds in response to alterations in the environment, the threat of disease, fresh insights into the needs of human nutrition, shifting market conditions, and societal demands.
The study’s main goal was to use bioinformatics to learn more about the genes encoding three different avian species’ insulin-like growth factors.
The study’s specific goals are to:

I. ascertain the percentage of identity and functional similarity between the insulin-like growth factor 1 gene protein sequences of three bird species;

II. ascertain the genetic diversity of the IGF1 gene among three bird species;

III. ascertain the evolutionary relationship between the species; and

IV. ascertain the secondary and tertiary structures of the three avian species’ IGF1 protein;

Given the IGF1 gene’s widespread distribution among species, individuals of the same species, and cells of single multicellular animals, the genetics of IGF1 gene diversity in birds is an important research.

Understanding the genetic diversity of the IGF1 gene and its morphological characterization will help one determine which avian species’ IGF1 gene has undergone mutation, high natural selection,

and high genetic variation (allowing species to change over time and survive changing environmental conditions). To put it another way, having more genetic diversity can increase resilience.

To determine whether the variation and polymorphism among Gallus gallus, Meleagris gallopavo, and Coturnix coturnix are due to convergent or divergent evolution or by chance,

and to predict the secondary and tertiary structure of the insulin-like growth factor 1 gene of avians, it is important to study the IGF1 gene of birds through bioinformatics in Nigeria. It is also important to determine whether a specific mutation in the IGF1gene

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