Generation Of Genetic Data Research Articles

GENEVIC: GENetic data Exploration and Visualization via Intelligent interactive Console.

The vast generation of genetic data poses a significant challenge in efficiently uncovering valuable knowledge. Introducing GENEVIC, an AI-driven chat framework that tackles this challenge by bridging the gap between genetic data generation and biomedical knowledge discovery. Leveraging generative AI, notably ChatGPT, it serves as a biologist's "copilot." It automates the analysis, retrieval, and visualization of customized domain-specific genetic information, and integrates functionalities to generate protein interaction networks, enrich gene sets, and search scientific literature from PubMed, Google Scholar, and arXiv, making it a comprehensive tool for biomedical research. In its pilot phase, GENEVIC is assessed using a curated database that ranks genetic variants associated with Alzheimer's disease, schizophrenia, and cognition, based on their effect weights from the Polygenic Score (PGS) Catalog, thus enabling researchers to prioritize genetic variants in complex diseases. GENEVIC's operation is user-friendly, accessible without any specialized training, secured by Azure OpenAI's HIPAA-compliant infrastructure, and evaluated for its efficacy through real-time query testing. As a prototype, GENEVIC is set to advance genetic research, enabling informed biomedical decisions. GENEVIC is publicly accessible at https://genevicanath2024.streamlit.app. The underlying code is open-source and available via GitHub at https://github.com/bsml320/GENEVIC.git (also at https://github.com/anath2110/GENEVIC.git).

Microbial genomics: a potential toolkit for forensic investigations.

Microbial forensics is a new discipline of science that analyzes evidence related to biological crime through the uniqueness and abundance of microorganisms and their toxins. Microorganisms remain alive longer than any other trace of biological evidence, such as DNA, fingerprints, and fibers, because of the protective cell membrane or capsules. Microbiological research has opened up various possibilities for forensic investigations of microbial flora. Current molecular technologies, including DNA sequencing, whole-genome sequencing, metagenomics, DNA fingerprinting, and molecular phylogeny, provide valid results for forensic investigations. Recent advancements in genome sequencing technologies, genetic data generation, and bioinformatic tools have significantly improved microbial sampling methods and forensic analyses. In this review, we discuss the applications of microbial genomic tools and technologies in forensic investigations, including human identification, geolocation, and causes of death.

Microbial forensics: new breakthroughs and future prospects.

Recent advances in genetic data generation, through massive parallel sequencing (MPS), storage and analysis have fostered significant progresses in microbial forensics (or forensic microbiology). Initial applications in circumstances of biocrime, bioterrorism and epidemiology are now accompanied by the prospect of using microorganisms (i) as ancillary evidence in criminal cases; (ii) to clarify causes of death (e.g., drownings, toxicology, hospital-acquired infections, sudden infant death and shaken baby syndromes); (iii) to assist human identification (skin, hair and body fluid microbiomes); (iv) for geolocation (soil microbiome); and (v) to estimate postmortem interval (thanatomicrobiome and epinecrotic microbial community). When compared with classical microbiological methods, MPS offers a diverse range of advantages and alternative possibilities. However, prior to its implementation in the forensic context, critical efforts concerning the elaboration of standards and guidelines consolidated by the creation of robust and comprehensive reference databases must be undertaken.

A Bayesian Inference Framework to Reconstruct Transmission Trees Using Epidemiological and Genetic Data

The accurate identification of the route of transmission taken by an infectious agent through a host population is critical to understanding its epidemiology and informing measures for its control. However, reconstruction of transmission routes during an epidemic is often an underdetermined problem: data about the location and timings of infections can be incomplete, inaccurate, and compatible with a large number of different transmission scenarios. For fast-evolving pathogens like RNA viruses, inference can be strengthened by using genetic data, nowadays easily and affordably generated. However, significant statistical challenges remain to be overcome in the full integration of these different data types if transmission trees are to be reliably estimated. We present here a framework leading to a bayesian inference scheme that combines genetic and epidemiological data, able to reconstruct most likely transmission patterns and infection dates. After testing our approach with simulated data, we apply the method to two UK epidemics of Foot-and-Mouth Disease Virus (FMDV): the 2007 outbreak, and a subset of the large 2001 epidemic. In the first case, we are able to confirm the role of a specific premise as the link between the two phases of the epidemics, while transmissions more densely clustered in space and time remain harder to resolve. When we consider data collected from the 2001 epidemic during a time of national emergency, our inference scheme robustly infers transmission chains, and uncovers the presence of undetected premises, thus providing a useful tool for epidemiological studies in real time. The generation of genetic data is becoming routine in epidemiological investigations, but the development of analytical tools maximizing the value of these data remains a priority. Our method, while applied here in the context of FMDV, is general and with slight modification can be used in any situation where both spatiotemporal and genetic data are available.