The Lab for Functional Genomics: Genetic Variants are Functionally Validated

Christopher Amos^*
*Correspondence: Christopher Amos, Department of Biomedical Data Science, University of Clinical and Translational Research, Houston, USA, Email:

Keywords

Genetic variants • Protein function • Gene expression analysis

Introduction

One of the most commonly used techniques is gene expression analysis. This involves measuring the levels of mRNA transcripts from different genes in various tissues or cell types. By comparing the expression levels of different genes between healthy and diseased individuals or between individuals with different genetic variants, researchers can identify genes that are dysregulated in disease or that are affected by specific genetic variants. Another common technique used in functional genomics laboratories is protein analysis. This involves studying the structure, function, and interactions of proteins encoded by different genes. By examining the effects of genetic variants on protein structure and function, researchers can determine whether these variants have a functional impact on the protein and its associated biological processes.

Literature Review

One of the major challenges in validating the functional impact of genetic variants is the sheer number of variants that are present in the human genome. There are millions of SNPs and CNVs in the human genome, many of which are rare or have unknown functional significance. To address this challenge, functional genomics laboratories often use large-scale genome-wide association studies (GWAS) to identify genetic variants that are associated with specific diseases or traits. GWAS involve comparing the genomes of thousands or even millions of individuals to identify genetic variants that are more common in individuals with a particular disease or trait than in healthy individuals. Once these variants are identified, functional genomics laboratories can then study their functional impact in more detail. Functional genomics laboratories also collaborate with other research groups to validate the functional impact of genetic variants in vivo. This involves using animal models such as mice, zebrafish, and fruit flies to study the effects of genetic variants on disease development and progression. By studying the effects of genetic variants in animal models, researchers can gain insights into the mechanisms underlying disease and develop new therapies that target these mechanisms [1,2].

Discussion

In addition to gene expression and protein analysis, functional genomics laboratories also use other techniques such as chromatin immunoprecipitation sequencing (ChIP-seq), which is used to identify protein-DNA interactions and histone modifications, and CRISPR/Cas9 gene editing, which is used to introduce specific genetic variants into cells and study their functional impact. Functional genomics laboratories also use bioinformatics tools and techniques to analyze large datasets generated by these experiments. This involves using statistical methods and machine learning algorithms to identify patterns and correlations between genetic variants and specific biological processes or diseases [3-6].

Conclusion

In conclusion, the functional genomics laboratory plays a critical role in validating the functional impact of genetic variants. By using a variety of techniques and approaches, including gene expression analysis, protein analysis, ChIP-seq, CRISPR/Cas9 gene editing, and bioinformatics, functional genomics laboratories can identify and study the effects of genetic variants on specific biological processes and disease pathways. This information can then be used to develop new diagnostic and therapeutic strategies that target these pathways and improve patient outcomes.

Acknowledgement

None

Conflict of Interest

None

References

1. Dang, Cach N., María N. Moreno-García and Fernando De la Prieta. "Using hybrid deep learning models of sentiment analysis and item genres in recommender systems for streaming services." Electronics 10 (2021): 2459.

Google Scholar, Crossref, Indexed at

2. Liu, Qi, Gengzhong Feng, Weibo Zheng and Jun Tian. "Managing data quality of cooperative information systems: Model and algorithm." Expert Syst Appl 189 (2022): 116074.

Google Scholar, Crossref, Indexed at

3. Mohan, I and M. Moorthi. "Topic flexible aspect based sentiment analysis using minimum spanning tree with Cuckoo search." JAIHC 12 (2021): 7399-7406.

Google Scholar, Crossref, Indexed at

4. Medhat, Walaa, Ahmed Hassan and Hoda Korashy. "Sentiment analysis algorithms and applications: A survey." Ain Shams Eng J 5 (2014): 1093-1113.

Google Scholar, Crossref, Indexed at

5. Tayarani-N, Mohammad-H., Xin Yao, and Hongming Xu. "Meta-heuristic algorithms in car engine design: A literature survey." IEEE Trans Evol Comput 19 (2014): 609-629.

Google Scholar, Crossref, Indexed at

6. Ghadimi, Noradin, Adel Akbarimajd, Hossein Shayeghi and Oveis Abedinia. "Two stage forecast engine with feature selection technique and improved meta-heuristic algorithm for electricity load forecasting." Energy 161 (2018): 130-142.

Google Scholar, Crossref, Indexed at