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Gene Therapy and Gene Correction
Published in Yashwant V. Pathak, Gene Delivery Systems, 2022
Manish P. Patel, Sagar A. Popat, Jayvadan K. Patel
The very first challenge encountered in treating genetically heterogeneous diseases is identifying the specific genes responsible for the underlying disease. With advancement and hard work, a great deal of progress has been made to allow for cheaper and more approachable methods to identify particular disease-causing mutations. The high-throughput sequencing method has reduced the cost of whole-genome and exome sequencing to $1,000 (Reuter et al. 2015). After target gene identification, there are many process that a gene undergoes before reaching the patient. Starting with gene modification, identifying the perfect vector for pre-clinical trials, clinical trials, various data and many more. The cost and difficulty level of testing gene therapy is as high as with a new drug molecule. The cost of clinical trials and other regulatory required data findings to bring a new drug to market ranges between $161 million and $2 billion (Sertkaya et al. 2014).
The Future of Systems Metabolic Engineering
Published in Jean F. Challacombe, Metabolic Pathway Engineering, 2021
Genomics-enabled approaches to cellular engineering will continue to be important for designing microbial systems that produce chemicals and natural products in the near future [2]. One promising approach for the future is exome sequencing, which enables more focused data mining and pathway analysis than whole genome approaches. Exome-based methods focus only on the exons of protein coding genes in the genome and these represent functions that are expressed in the organism under experimental conditions [3]. Focusing on exomes significantly reduces the amount of sequencing data that needs to be produced and empowers more in-depth analyses of biochemical pathways, such as identification of enzymes that are expressed under particular conditions. However, exome sequencing should probably be used together with whole genome sequencing and analysis, as identifying all of the enzyme constituents of an organism’s biochemical pathways (not just those that are expressed) is critical for informing enzyme engineering efforts to design and develop microbial cell factories that produce specific products [4].
Artificial Intelligence in Systems Biology
Published in P. Kaliraj, T. Devi, Artificial Intelligence Theory, Models, and Applications, 2021
S. Dhivya, S. Hari Priya, R. Sathishkumar
Genetic variants and various human diseases were detected using high-throughput genome sequencing technology like Whole-Exome Sequencing (WES). In humans, missense variants cause genetic disorders like cancer, Mendelian diseases, and other unidentified ailments. Experimental validation of these genetic diseases is quite complex, which is laborious and needs huge resources, while this problem can be solved using computational approaches like analysis of sequence similarity, phylogenetic relationship, and 3D protein structural homology. Apart from this, some of the common methods used for predicting genetic diseases are shown in Figure 7.11.
A pilot study of exome sequencing in a diverse New Zealand cohort with undiagnosed disorders and cancer
Published in Journal of the Royal Society of New Zealand, 2018
Colina McKeown, Samantha Connors, Rachel Stapleton, Tim Morgan, Ian Hayes, Katherine Neas, Joanne Dixon, Kate Gibson, David M. Markie, Peter Tsai, Cherie Blenkiron, Sandra Fitzgerald, Paula Shields, Patrick Yap, Ben Lawrence, Cristin Print, Stephen P. Robertson
The search space within which to find a specific genetic explanation for monogenic disorders is becoming well-refined. There are approximately 19,000 protein-coding genes in the human genome, covering approximately 50 Mb of DNA sequence, and it is in this portion of the genome—the exome—that an estimated 85% of Mendelian molecular diagnoses will be found (Blackburn et al. 2015; Chong et al. 2015; Valencia et al. 2015). Whole exome sequencing (WES) is increasingly being used worldwide with studies of its utility demonstrating diagnostic rates of 16%–57% (de Ligt et al. 2012; Soden et al. 2014; Yang et al. 2014; Chong et al. 2015; Valencia et al. 2015; Wright et al. 2015; Monroe et al. 2016; Stark et al. 2016). Currently, mutations in 3849 genes have been proven to be responsible for 6121 Mendelian phenotypes (OMIM Gene Map Statistics 2017).