AI-Powered Software Set to Revolutionize Genomic Analysis in 2023
The next generation of genomic sequencing is revolutionizing the field of biology.
With the ability to sequence deoxyribonucleic acid (DNA) at unprecedented speeds and accuracy, researchers are now able to unlock the mysteries of the genome, leading to new insights and discoveries across various fields, such as biochemistry and anatomical research.
The wealth of data produced by advancing genomic sequencing and biomanufacturing technology is leading to new discoveries in fields such as medicine, genetics, and evolutionary biology.
However, handling and analyzing a vast amount of data becomes difficult manually, time-consuming, expensive, and error-prone.
To overcome these challenges, researchers are turning to artificial intelligence (AI) integrated software solutions, aiming to improve the speed, accuracy, and affordability of genomic sequencing.
This article explores the use of AI-integrated software in genomic sequencing and cell therapy as well as insights into the launch of a revolutionary product.
Use of AI-Powered Software in Genomic Sequencing
With the increasing number of therapies, such as AI in biomanufacturing, approved by regulatory and government organs such as the Food and Drug Administration (FDA), the global cell and gene therapy biomanufacturing market is expected to advance.
In accordance with the data insights from BIS research, the global cell and gene therapy biomanufacturing market was valued at $12.31 billion in 2022 and is anticipated to reach $29.76 billion by 2031, with a CAGR of 10.31% during the forecast period 2022–2031.
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By leveraging machine learning algorithms, AI-integrated software systems can quickly identify patterns and relationships within genomic data, leading to new discoveries and better patient outcomes.
The following are some of the ways in which AI-powered software is being used in genomic sequencing:
Genome Assembly: The process of genome assembly involves piecing together short fragments of DNA to reconstruct the complete genome.
This is a challenging task, as the genome is made up of billions of base pairs.
AI-powered software can be used to analyze the fragments of DNA and assemble them into a complete genome more accurately and efficiently than traditional methods.
Genome Annotation: Genome annotation involves identifying the different genes and functional elements within a genome.
AI-powered software can be used to predict the location of genes and other functional elements within the genome, allowing researchers to better understand the biological processes underlying various diseases.
Disease Diagnosis: AI-powered software can be used to analyze genomic data and identify the genetic mutations that are responsible for various diseases.
This can help doctors to make more accurate diagnoses and develop personalized treatment plans for patients.
Variant Calling: Variant calling is the process of identifying differences between the DNA sequences of different individuals or organisms.
AI-powered software can help automate the process by using machine learning algorithms to identify and classify variants in the genome.
Drug Discovery: AI-powered software can be used to analyze genomic data and identify potential drug targets for various diseases.
This can help pharmaceutical companies to develop more effective drugs that target the underlying genetic causes of diseases.
France-Based Biotech Firm Launches AI-Integrated Genomic Analysis Software
Genomic Vision, based in France, is a biotechnology company that specializes in the development and commercialization of diagnostic solutions based on DNA molecular combing.
On March 7th, 2023, Genomic Vision announced the launch of its innovative AI-based technology, FiberSmart, which automates the identification and quantification of fluorescent signals on combed DNA molecules.
The innovative technology was successfully tested, verified, and approved by AstraZeneca and the Fritz Lipmann Institute, rooted in Germany.
FiberSmart is specifically being used with a technique called Replication Combing Assay (RCA), which involves stretching out individual DNA molecules to observe their replication patterns.
By using FiberSmart, researchers can obtain a more detailed view of the replication parameters and conduct automated and faster analysis of their data.
FiberSmart is compatible with scanners from the FiberVision family and offers improved image reconstruction quality, resulting in better resolution of single-molecule DNA structures.
The user interface (UI) of the FiberSmart software is intuitive, ergonomic, and easy to use, which allows faster analysis of replicated DNA signals that help describe replication.
The software can visualize, detect, and analyze DNA replication kinetics up to three times more accurately and up to ten times faster than existing software solutions.
Furthermore, the FiberSmart software is compatible with the existing suite of scanners developed by Genomic Visions, such as FiberVision and FiberVision-S, to facilitate the process of replication combing assay.
FiberSmart can compare multiple groups of samples and highlight and quantify differences between them, which assists in studying the effects of experimental treatments or genetic mutations on DNA replication.
By comparing multiple groups, researchers can quickly identify significant differences in replication patterns and investigate the underlying causes.
Automated interactive report generation is another key feature of FiberSmart that allows researchers to easily generate customized reports summarizing their findings and sharing them with collaborators or publishing them in scientific journals.
Moreover, FiberSmart also provides visualization of main replication parameters, which enables researchers to gain valuable insights into the timing and regulation of DNA replication in their samples.
Aaron Bensimon, CEO of Genomic Vision, said, “The launch of FiberSmart is an important milestone for Genomic Vision as we bring the benefits of powerful AI technology to our users, who can now perform faster and more accurate genomic analysis seamlessly.”
In accordance with Bensimon’s statement, Genomic Vision’s DNA combing technique has many potential uses, especially in cell and gene therapy, where accurate genomic analysis is important for quality control.
Conclusion
The future potential of advanced cell and gene therapies is enormous, and biomanufacturing is expected to play a critical role in realizing this potential.
The success of these therapies depends on the ability to produce large quantities of high-quality cells or gene vectors in a cost-effective and scalable manner.
This requires the use of advanced technologies and processes, such as automation, bioreactors, and quality control systems, to optimize the production process.
Integration of machine learning and artificial intelligence is expected to aid this advancement of the biomanufacturing process.
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