New Advances in NGS Technologies

D’Agaro, Edo

doi:10.5772/66924

Cited by 4 publications

(2 citation statements)

References 29 publications

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“…The use of plant-derived ingredients in diets may also change the microbial populations living in the fish gut [24]. Metagenomic analysis, which is performed by nextgeneration sequencing (NGS) techniques, allows the identification of the main bacterial species of the fish gut microbiome [25]. In France, rainbow trout populations have been selected to efficiently digest diets containing some plant-based ingredients [26].…”

Section: New Trends In Aquaculture Feed Productionmentioning

confidence: 99%

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Recent Trends and Economic Aspects in the Rainbow Trout (Oncorhynchus mykiss) Sector

D’Agaro

Gibertoni²,

Esposito³

2022

Applied Sciences

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In recent decades, the global aquaculture sector has become more complex and dynamic due to several factors, such as strong demand at the retail level, diversification of farmed species, outsourcing of product processing, and synergies among producers, processors, and retailers. Globally, the fish food sector has become very important to meet the growing human demand for food. In 2020, 89% of fish production was used for direct human consumption (17% of the total protein consumed). Global fish consumption has increased (excluding algae) at an annual rate of 3% since 1960. In 2030, the total fish production is expected to reach 202 million tons (MT) and the aquaculture production 106 MT. The United Nations has estimated that, between now and 2030, the global demand for fish products will increase by at least 40 MT each year. This need cannot be satisfied by fishing alone, given that fishing practices have reached an intensity that is unsustainable, and that climate change and pollution are worsening the problems of marine ecosystems. European and Italian aquaculture is characterised by a diversification scenario in terms of production systems, technologies adopted, and species bred. According to an EU report, Italian aquaculture production has grown by 4% in the last five years (fourth place in Europe). In Italy, rainbow trout is the most widely farmed species. The production of rainbow trout contributes approximately 25.5% of the total value and 69.9% of the volume of farmed fish. Over the past ten years, trout production has increased by 8.5% overall with an average annual growth rate of 2.5%. The purpose of this review is to provide an overview of the economic situation of the rainbow trout production and consumption sectors in Italy using a supply chain approach. In particular, in this brief excursus, the main production trends and the economic and organisational relationships between the various companies and associations in the aquaculture sector are analysed.

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Section: New Trends In Aquaculture Feed Productionmentioning

confidence: 99%

“…As a result, in farmed trout, the content of long-chain omega-3 fatty acids has decreased. Genetic selection and marker-assisted selection have also been used to improve fish growth and health [25]. For example, selected trout show 10-15% weight gain per generation with feeds based on plant ingredients.…”

Section: Recent and Future Trends Of The Sectormentioning

confidence: 99%

Recent Trends and Economic Aspects in the Rainbow Trout (Oncorhynchus mykiss) Sector

D’Agaro

Gibertoni²,

Esposito³

2022

Applied Sciences

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Genomic selection in salmonids: new discoveries and future perspectives

et al. 2021

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Over the past 20 years, the introduction of new molecular techniques has given a new impetus to genetic and genomic studies of fishes. The main traits selected in the aquaculture sector conform to the polygenic model, and, thus far, effective breeding programmes based on genome-wide association studies (GWAS) and marker-assisted selection (MAS) have been applied to simple traits (e.g. disease resistance and sexual maturation of salmonids) and known Quantitative Trait Loci (QTLs). Genomic selection uses the genomic relationships between candidate loci and SNPs distributed over the entire genome and in tight linkage disequilibrium (LD) with genes that encode the traits. SNP (low and high density) arrays are used for genotyping thousands of genetic markers (single nucleotide polymorphisms, SNPs). The genomic expected breeding value (GEBV) of selection candidates is usually calculated by means of the GBLUP or ssGBLUP (single step) methods. In recent years, in several aquaculture breeding programmes, the genomic selection method has been applied to different fish and crustacean species. While routine implementation of genomic selection is now largely carried out in Atlantic salmon (Salmo salar) and rainbow trout (Oncorhynchus mykiss), it is expected that, in the near future, this method will progressively spread to other fish species. However, genomic selection is an expensive method, so it will be relevant mostly for traits of high economic value. In several studies (using different salmonid species), the accuracy of the GEBVs varied from 0.10 to 0.80 for different traits (e.g. growth rate and disease resistance) compared to traditional breeding methods based on geneology. Genomic selection applied to aquaculture species has the potential to improve selection programmes substantially and to change ongoing fish breeding systems. In the long term, the ability to use low-pass genome sequencing methods, low-cost genotyping and novel phenotyping techniques will allow genomic selection to be applied to thousands of animals directly at the farm level.

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Integration of Artificial Intelligence, Machine Learning and Deep Learning Techniques in Genomics: Review on Computational Perspectives for NGS Analysis of DNA and RNA Seq Data

K.,

Niranjan,

Vishal

et al. 2024

CBIO

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In the current state of genomics and biomedical research, the utilization of Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning (DL) have emerged as paradigm shifters. While traditional NGS DNA and RNA sequencing analysis pipelines have been sound in decoding genetic information, the sequencing data’s volume and complexity have surged. There is a demand for more efficient and accurate methods of analysis. This has led to dependency on AI/ML and DL approaches. This paper highlights these tool approaches to ease combat the limitations and generate better results, with the help of pipeline automation and integration of these tools into the NGS DNA and RNA-seq pipeline we can improve the quality of research as large data sets can be processed using Deep Learning tools. Automation helps reduce labor-intensive tasks and helps researchers to focus on other frontiers of research. In the traditional pipeline all tasks from quality check to the variant identification in the case of SNP detection take a huge amount of computational time and manually the researcher has to input codes to prevent manual human errors, but with the power of automation, we can run the whole process in comparatively lesser time and smoother as the automated pipeline can run for multiple files instead of the one single file observed in the traditional pipeline. In conclusion, this review paper sheds light on the transformative impact of DL's integration into traditional pipelines and its role in optimizing computational time. Additionally, it highlights the growing importance of AI-driven solutions in advancing genomics research and enabling data-intensive biomedical applications.

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New Advances in NGS Technologies

Cited by 4 publications

References 29 publications

Recent Trends and Economic Aspects in the Rainbow Trout (Oncorhynchus mykiss) Sector

Recent Trends and Economic Aspects in the Rainbow Trout (Oncorhynchus mykiss) Sector

Genomic selection in salmonids: new discoveries and future perspectives

Integration of Artificial Intelligence, Machine Learning and Deep Learning Techniques in Genomics: Review on Computational Perspectives for NGS Analysis of DNA and RNA Seq Data

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