Literature DB >> 20238073

Genetic variation analysis for biomedical researchers: a primer.

Michael R Barnes1.   

Abstract

Biomedical researchers studying gene function should consider the impact of variation, even if genetics is not the primary objective of an investigation. Information on genetic variation can provide a valuable insight into the functional range and critical regions of a gene, protein or regulatory element. Genetic variants may be diverse in nature, ranging from single nucleotide variants, tandem repeats, small insertions or deletions to large copy number variants. Until recently, information on genetic variation was quite limited, but now a range of large scale surveys of variation have made plentiful data on common variation and a picture is beginning to emerge from the driving forces in human evolution and population diversification. Next-generation sequencing technologies are moving knowledge into a new phase focused on the individual genome and complete disclosure of individual variation, including the rarest of variants. The consequences of these advances in medicine are unresolved, but it is clear that biomedical researchers cannot afford to ignore this information. This review presents a broad overview of the in silico methods that will allow a researcher to quickly review known variation in a gene of interest, providing some pointers for further investigation.

Entities:  

Mesh:

Year:  2010        PMID: 20238073     DOI: 10.1007/978-1-60327-367-1_1

Source DB:  PubMed          Journal:  Methods Mol Biol        ISSN: 1064-3745


  5 in total

1.  The stability of myocilin olfactomedin domain variants provides new insight into glaucoma as a protein misfolding disorder.

Authors:  J Nicole Burns; Katherine C Turnage; Chandler A Walker; Raquel L Lieberman
Journal:  Biochemistry       Date:  2011-06-09       Impact factor: 3.162

2.  Identification of Copy Number Alterations from Next-Generation Sequencing Data.

Authors:  Sheida Nabavi; Fatima Zare
Journal:  Adv Exp Med Biol       Date:  2022       Impact factor: 2.622

3.  In silico SNP analysis of the breast cancer antigen NY-BR-1.

Authors:  Zeynep Kosaloglu; Julia Bitzer; Niels Halama; Zhiqin Huang; Marc Zapatka; Andreas Schneeweiss; Dirk Jäger; Inka Zörnig
Journal:  BMC Cancer       Date:  2016-11-18       Impact factor: 4.430

4.  Genetic variations analysis for complex brain disease diagnosis using machine learning techniques: opportunities and hurdles.

Authors:  Hala Ahmed; Louai Alarabi; Shaker El-Sappagh; Hassan Soliman; Mohammed Elmogy
Journal:  PeerJ Comput Sci       Date:  2021-09-20

5.  An evaluation of copy number variation detection tools for cancer using whole exome sequencing data.

Authors:  Fatima Zare; Michelle Dow; Nicholas Monteleone; Abdelrahman Hosny; Sheida Nabavi
Journal:  BMC Bioinformatics       Date:  2017-05-31       Impact factor: 3.169

  5 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.