Table of Contents
Overview
Key Concepts in Functional Genomics
Functional genomics is a field of study that focuses on understanding the function of genes and their roles in the overall biology of an organism. It encompasses several key concepts, including gene expression, which is the process by which the information encoded in a gene is converted into a functional product, such as a protein.[1.1] Additionally, functional genomics examines protein-protein interactions, which are crucial for many biological processes.[1.1] This discipline studies the relationships among various components of a biological system—such as genes, transcripts, proteins, and metabolites—and how these components work together to produce a given phenotype.[4.1] Unlike traditional genomics, which primarily concerns itself with sequencing and mapping genomes, functional genomics aims to explore how genes and their products, including proteins and RNA, collaborate to influence an organism's traits and health.[5.1] Functional genomics is a field of molecular biology that aims to describe the functions and interactions of genes and proteins, utilizing the extensive data generated by genomic and transcriptomic projects, such as genome sequencing and RNA sequencing.[3.1] This discipline employs high-throughput technologies, including microarrays and next-generation sequencing, to investigate various aspects of cellular function, such as DNA mutations, histone modifications, and chromatin dynamics.[2.1] By studying the relationships among genes, transcripts, proteins, and metabolites on a genome-wide scale, functional genomics elucidates how these components interact to produce specific phenotypes.[4.1] Ultimately, this approach provides insights into gene activity, regulation, and the biological consequences of genetic variation across organisms.[7.1] Functional genomics is a field dedicated to understanding the functions of genes and their roles in the overall biology of an organism. Unlike traditional genomics, which primarily focuses on sequencing and mapping genomes, functional genomics aims to explore how genes and their products, such as proteins and RNA, work together to influence an organism's traits and health.[5.1] By employing genome-wide techniques, functional genomics seeks to unravel the complexities of gene interactions and identify key regulatory elements.[6.1] Environmental factors, including drugs, chemicals, temperature, and light, can significantly influence gene expression, thereby affecting an organism's development and functioning.[10.1] For instance, chronic stress can lead to changes in histone acetylation and methylation, impacting genes involved in mood regulation.[8.1] These examples illustrate the intricate relationship between gene expression regulation and phenotype, highlighting the importance of both genetic and environmental factors in shaping biological outcomes. Moreover, the integration of functional genomics into personalized medicine is becoming increasingly significant. By tailoring treatment plans based on an individual's genetic profile, healthcare practitioners can enhance the efficacy of therapies and provide more targeted interventions.[16.1] This personalized approach recognizes the unique genetic makeup of each individual, which influences their health, disease susceptibility, and response to treatments.[16.1] Thus, functional genomics plays a crucial role in advancing our understanding of biology and improving patient care through personalized medicine.History
Milestones in Functional Genomics Research
The field of functional genomics has undergone significant advancements since its inception, marked by several key milestones. The completion of the Human Genome Project in 2003 was a pivotal moment, producing the first drafts of the human genome sequence and laying the groundwork for large-scale functional genomics studies aimed at understanding the molecular basis of biological traits encoded in the genome.[56.1] Following this, the National Human Genome Research Institute initiated the Advanced Sequencing Technology Program, which stimulated the development of new DNA sequencing methods to reduce costs and enhance genomic analysis capabilities.[57.1] The advent of high-throughput technologies, such as next-generation sequencing (NGS), has revolutionized functional genomics by significantly increasing the throughput and speed of DNA sequencing.[58.1] These advancements have enabled researchers to conduct genome-wide functional analyses, facilitating the exploration of gene functions and interactions on a genomic scale.[53.1] Moreover, the integration of computational tools and algorithms has further enhanced the understanding of genotype-phenotype relationships, allowing for the generation of important datasets that unravel complex genetic blueprints.[64.1] In recent years, the emergence of CRISPR-based functional genomics tools has provided scalable and multiplexable methods for characterizing genes and their regulatory elements.[66.1] This new era of functional genomics research promises to elucidate mechanisms of gene function and regulation, as well as how genes collaborate to influence complex phenotypes.[52.1] Overall, these milestones reflect the dynamic evolution of functional genomics, driven by technological innovations and a deeper understanding of genetic interactions.Techniques And Methodologies
High-Throughput Technologies
Recent advancements in high-throughput technologies have significantly transformed the field of functional genomics, enabling researchers to analyze gene functions and interactions on an unprecedented scale. High-throughput functional genomic technologies, such as quantitative real-time polymerase chain reaction, next-generation sequencing, and mass spectrometry, have been developed to facilitate genome-wide functional analysis, thereby filling gaps in knowledge about pathogenic pathways between genes, proteins, and their interaction networks.[99.1] These technologies allow for the simultaneous measurement of many or all gene products, such as mRNAs or proteins, within a biological sample, which is a departure from traditional gene-by-gene approaches.[93.1] The emergence of high-throughput technologies has ushered in a new era in functional genomics, allowing for scalable and multiplexable characterization of genes and their regulatory elements.[92.1] A notable advancement in this field is the integration of the RNA-guided CRISPR-associated Cas9 nuclease with genome-scale guide RNA libraries, which facilitates unbiased phenotypic screening in human cells.[118.1] This innovative approach enhances our understanding of gene function and regulation, as well as the interactions among genes that influence complex phenotypes.[92.1] Furthermore, high-throughput phenotyping techniques, such as Tn-seq, are enabling functional genomics across a diverse array of bacterial species, while whole genome sequencing is accelerating forward genetic screens.[100.1] Collectively, these developments are expanding the range of phenotypes that can be assayed on a global scale, thereby significantly advancing our comprehension of gene functions and networks.[100.1] High-throughput technologies have catalyzed a remarkable expansion in biological research across genomics, proteomics, and metabolomics, resulting in an unprecedented volume of data that presents both significant challenges and extraordinary opportunities.[115.1] However, researchers face several critical impediments when transitioning from traditional functional genomics methods to high-throughput technologies. These impediments include the lack of a reversible system, limitations to nonessential genes, the labor-intensive nature of developing genome-wide libraries, and the difficulty in constructing manageable-sized libraries for studying phenotypes under bottleneck conditions.[117.1] As the field evolves, the emergence of functional immunomics, driven by advancements in immunomic microarray technology, introduces new challenges that are both similar to and distinct from those encountered in functional genomics.[116.1] Addressing these challenges is crucial for researchers to effectively utilize high-throughput functional genomics and to navigate the complexities associated with these innovative technologies.Integration with Other "-Omics" Approaches
The integration of functional genomics with other "-omics" approaches, such as genomics, transcriptomics, and proteomics, is increasingly recognized as vital for advancing our understanding of biological systems and disease mechanisms. The genomics field is expanding its use of computational methods, particularly artificial intelligence (AI) and machine learning (ML), to uncover hidden patterns in large and complex datasets derived from both basic and clinical research projects.[103.1] These AI/ML-based computational tools are essential for handling, extracting, and interpreting valuable information from extensive data troves.[103.1] Furthermore, the combination of multilayered network analysis with machine learning techniques has shown significant promise in analyzing multiomics datasets. These approaches aim to identify associations between various data elements, continuously refining predictive models through iterative learning processes.[102.1] As a result, the convergence of genomic research and machine learning is becoming increasingly important, providing researchers with the necessary tools to derive meaningful insights from complex data.[103.1] Moreover, the application of CRISPR technology within functional genomics exemplifies the integration of genomic editing with other omics methodologies. CRISPR/Cas9 systems enable high-throughput screening of genetic perturbations, allowing for the exploration of genomic, transcriptomic, and epigenomic changes in a robust manner.[109.1] This capability is further enhanced by advances in sequencing technologies, which have significantly increased the volume of genomic data available for analysis. Such integration not only aids in understanding gene interactions but also in elucidating the roles of genetic variants in complex biological systems.[108.1] The integration of machine learning with functional genomics is becoming increasingly vital for analyzing diverse genomic data generated from various assays, including RNA-seq, microarray expression data, and chromatin accessibility assays such as DNase-seq and MNase-seq.[105.1] This evolution in machine learning applications is essential as it allows researchers to recognize patterns in complex datasets and utilize data from a variety of genomic and proteomic sources, including histone modification and transcription factor binding data.[105.1] The incorporation of genomics into clinical practice holds tremendous promise for advancing personalized medicine and improving patient outcomes; however, several challenges must be addressed to fully realize the potential of genomic medicine, particularly in the interpretation of complex genetic information.[96.1] As new technologies emerge, the demand for innovative machine learning methods and experts capable of applying these techniques to large-scale data sets will continue to grow, highlighting the need for ongoing development in this field.[105.1]In this section:
Applications In Disease Research
Identification of Genetic Markers
Advancements in functional genomics have significantly enhanced the identification of genetic markers associated with rare diseases. These powerful genomic technologies have the potential to rapidly accelerate the process of identifying genetic variants, particularly rare variants that are likely to be pathogenic, thereby dramatically reducing the time required for rare disease diagnosis.[162.1] The urgency of this need is underscored by the challenges faced by patients impacted by rare diseases, where timely diagnosis is critical.[163.1] The integration of large consortium initiatives and advancements in data analysis, including the application of artificial intelligence (AI) and machine learning (ML), has further propelled the field of rare disease research. These technologies assist in analyzing vast amounts of genomic and clinical data to identify disease patterns, predict treatment outcomes, and develop personalized therapies, ultimately improving diagnostic accuracy and advancing drug development.[165.1] Despite these advancements, significant barriers remain, such as difficulties in identifying causal variants and translating genomic findings into clinical practice.[165.1] Moreover, the role of CRISPR-based genome editing has emerged as a valuable tool in the functional study of genetic variants. Researchers have utilized CRISPR screening studies to introduce specific genetic modifications, which can aid in the identification of pathogenic variants across a wide array of human genes.[166.1] This innovative approach complements the ongoing efforts to understand the genetic underpinnings of rare diseases and enhances the potential for developing targeted therapeutic interventions.Role in Personalized Medicine
Functional genomics is a crucial field in the advancement of personalized medicine, focusing on how the genome and its products, such as RNA and proteins, function and interact to influence various biological processes, including health and disease.[128.1] Unlike traditional genomics, which primarily involves sequencing and mapping genomes, functional genomics aims to understand the roles of genes and their products in determining an organism's traits and responses to stimuli.[128.1] This approach enables the identification and investigation of genes and biological processes associated with diseases, which can serve as potential drug targets.[132.1] Recent advancements in human genome research, particularly in pharmacogenomics and functional genomics, have significantly contributed to personalized medicine, with state-of-the-art techniques like next-generation sequencing enhancing our understanding of genetic variations and their implications for tailored therapies.[68.1] Recent advancements in genome sequencing technologies have significantly reduced the cost of genetic analysis, making it feasible to analyze a large number of genetic variants in patients.[69.1] This capability is crucial for personalized medicine, as it allows healthcare providers to predict disease risks and customize treatment plans based on an individual's genetic profile.[70.1] For instance, functional genomics tools such as CRISPR-Cas9 enable researchers to modify genes to study their roles in disease, which can lead to the identification of novel drug targets and improve disease modeling.[132.1] The integration of functional genomics into personalized medicine is revolutionizing the understanding of the molecular basis of health and disease. State-of-the-art research on the human genome has produced remarkable achievements in pharmacogenomics and functional genomics, significantly contributing to the advancement of personalized medicine.[68.1] This emerging field aims to deconvolute the link between genotype and phenotype by utilizing large-scale omic data sets and next-generation gene and epigenome editing tools to perturb genes of interest.[131.1] Through these methodologies, researchers can identify and investigate genes and biological processes associated with diseases, which can be explored as potential drug targets.[132.1] Furthermore, the advancements in genomics and biotechnology are enabling more personalized approaches to medicine, allowing for the prediction of disease risks and the tailoring of treatments to individual genetic profiles.[70.1] By integrating genetic, molecular, and clinical data, personalized medicine enhances diagnostic accuracy and treatment targeting, ultimately leading to more effective disease management and improved patient outcomes.[70.1]In this section:
Recent Advancements
CRISPR and Genome Editing Technologies
Recent advancements in CRISPR and genome editing technologies have significantly impacted the field of functional genomics, particularly in the context of clinical applications for monogenic diseases. The CRISPR-Cas9 system is recognized as a versatile genomic editing tool that can manipulate nearly any target genomic sequence, facilitating the correction of mutations associated with these diseases, which is a critical step toward the application of stem cell-based gene therapy in clinical practice.[193.1] Furthermore, recent developments in CRISPR-based functional genomics have highlighted the advantages of prime editing (PE) technologies, which can directly rewrite target DNA sequences without inducing double-strand breaks (DSBs), offering a distinct benefit over traditional Cas nucleases and base editors.[194.1] These innovations underscore the potential of CRISPR technologies to enhance our understanding of genetic variants and their implications in precision medicine. As the integration of CRISPR technologies into functional genomics progresses, several ethical considerations have arisen. These include the implications of somatic versus germline editing, the distinction between treatment and enhancement, and the autonomy of individuals affected by such interventions, particularly in the context of reproductive choices.[195.1] Furthermore, the potential for unequal access to these advanced technologies raises societal concerns that must be addressed as they become more prevalent in healthcare.[195.1]Novel Functional Genomics Tools and Approaches
Recent advances in functional genomics have significantly enhanced our ability to gather genomic data, particularly through the development of novel tools and approaches. Despite these advancements, understanding the impact of genetic mutations, especially variants of uncertain significance (VUSs), remains a challenge in precision medicine.[174.1] Among the innovative technologies, prime editing (PE) has emerged as a notable advancement, offering a distinctive advantage over traditional Cas nucleases and base editors. PEs can directly rewrite target DNA without inducing double-strand breaks (DSBs), thereby facilitating a more accurate analysis of genetic variants.[174.1] This capability is crucial for the functional study of disease-associated genetic variants, as it allows researchers to investigate the implications of specific mutations with greater precision.[174.1] Additionally, the integration of next-generation sequencing (NGS) technologies has transformed functional genomics by increasing the throughput and speed of DNA sequencing. This has enabled researchers to conduct extensive genomic analyses across various fields, including clinical genomics, cancer research, and the study of infectious diseases.[175.1] The ability to rapidly sequence and analyze large volumes of genomic data has opened new avenues for understanding the complexities of gene interactions and their roles in biological processes. Machine learning (ML) and artificial intelligence (AI) have also emerged as powerful tools in functional genomics, particularly in analyzing large and complex datasets. These computational methods help identify hidden patterns within genomic data, facilitating the extraction of meaningful insights that can inform research and clinical applications.[103.1] For instance, ML approaches are being utilized to analyze single-cell transcriptomic and epigenomic data, allowing for the automatic extraction of information from vast datasets and enhancing our understanding of gene functions.[177.1] Moreover, the combination of functional genomics with induced pluripotent stem cells (hiPSCs) has provided a unique platform for modeling complex diseases. This approach allows researchers to investigate disease mechanisms that are challenging to study in vivo, offering insights into the pathological processes underlying conditions such as cystic fibrosis, Huntington's disease, and monogenic diabetes.[184.1] By leveraging these novel tools and approaches, functional genomics continues to advance our understanding of genetic regulation and its implications for personalized medicine.[183.1]In this section:
Challenges And Limitations
Data Processing and Analysis
The integration and analysis of data from multiple 'omics' platforms present significant challenges in functional genomics and systems biology. One of the primary obstacles is the development of bioinformatics tools capable of comprehensively integrating and analyzing this diverse data to generate a holistic view of cellular systems and networks.[214.1] The reliance on cell lines and animal models, rather than primary cells or tissues derived from patients, further complicates the translation of functional genomics findings into applicable insights for human disease.[215.1] This limitation is particularly pronounced when tissue or primary cell-based models are used, as they often represent a narrow spectrum of individual variability.[218.1] Moreover, the complexity of host-pathogen interactions necessitates a shift from conventional experimental approaches that focus solely on individual molecular components. Such methods fail to provide a comprehensive understanding of these interactions, highlighting the need for functional genomics approaches that can address this complexity.[216.1] The application of large-scale functional genomics techniques to differentiated pluripotent stem cell (PSC) populations also encounters technical challenges, which must be navigated to enhance the efficacy of these studies.[217.1] Artificial intelligence (AI) and machine learning (ML) have emerged as crucial tools in addressing these challenges. These technologies facilitate the processing, analysis, modeling, and interpretation of large-scale genomic data, thereby enhancing the capabilities of bioinformatics tools.[221.1] The integration of explainable artificial intelligence (XAI) is particularly noteworthy, as it aids in understanding genomic regions and their roles in disease states, such as cancer, by providing predictions that are interpretable and grounded in biological principles.[220.1] Despite these advancements, there remains an urgent need for improved computational methods and software tools that can manage the vast volumes and complexities of multi-omics data. Addressing these challenges is essential for fully harnessing the potential of bioinformatics in multi-omics analysis and for unlocking critical insights into human health and disease.[236.1]Future Directions
Emerging Technologies
Recent advancements in functional genomics have been propelled by the development of single-cell CRISPR screening techniques, which are known as Perturb-Seq, CRISP-seq, or CROP-seq. These methodologies combine pooled CRISPR screening with single-cell RNA sequencing (scRNA-seq), enabling the efficient interrogation of multiple genes simultaneously, limited only by the sequencing capabilities.[271.1] This integration allows for the suppression of target genes while simultaneously measuring transcriptome-wide expression within individual cells.[270.1] Consequently, it has facilitated the mapping of gene circuits and the determination of causal relationships between gene expression and cellular functions.[270.1] Moreover, the application of single-cell CRISPR technologies has been categorized into four principal types: scCRISPR with RNA-seq, scCRISPR with ATAC-seq, scCRISPR with proteome probing, and imaging-based scCRISPR. These methodologies have proven effective in deciphering complex genotype-phenotype relationships and understanding genetic regulations, particularly in the context of tumor biology.[272.1] The integration of CRISPR-based functional genomics with human pluripotent stem cell (hPSC) differentiation technologies further enhances the ability to explore gene functions across diverse human cell types, thereby identifying mechanisms and therapeutic targets relevant to human diseases.[273.1] In addition to these integrations, emerging third-generation CRISPR technologies are being developed to meet critical needs in the field, such as achieving precise insertion of large genetic payloads and enabling gene regulation without direct genome editing through epigenome engineering.[274.1] These innovations, alongside multiplexed CRISPR technologies, are transforming genetic editing and transcriptional regulation, thereby expanding the capabilities of functional genomics.[275.1] Understanding gene regulation remains a pivotal aspect of functional genomics, as gene regulatory networks (GRNs) elucidate the intricate interactions among DNA, RNA, proteins, and other molecules that govern gene expression. Recent advancements in high-throughput technologies and computational biology have provided unprecedented insights into the dynamic and context-dependent nature of these networks.[276.1] The next few decades are expected to witness transformative advancements in genome and epigenome editing technologies, enabling precise corrections of genetic mutations and fine-tuning of gene expression.[277.1] Emerging technologies in genomics, particularly mobile genetic elements (MGEs) and epigenetic editors, are gaining recognition as effective alternatives for achieving precise large insertions or stable gene manipulation in mammalian cells, thereby paving the way for next-generation gene therapy developments.[278.1] The advancements in CRISPR-based functional genomics are particularly noteworthy, as they enable scalable and multiplexable characterization of genes and gene regulatory elements, which is essential for understanding complex phenotypes and gene interactions.[286.1] Furthermore, the integration of CRISPR technologies with novel molecular assays, such as single-cell sequencing and epigenetic profiling, has led to unprecedented insights into gene function and regulation.[286.1] As the field progresses, there is a growing emphasis on high-throughput screening technologies that focus on mammalian cell systems, which are expected to reveal new mechanisms of gene function and inform future directions in functional genomics research.[287.1]Potential Impact on Medicine and Biology
Recent advancements in functional genomics are poised to significantly impact medicine and biology, particularly through the integration of innovative technologies such as CRISPR and artificial intelligence (AI). The development of CRISPR-based screening technologies and next-generation sequencing platforms has enabled pooled screening methods that facilitate massively parallel and unbiased assessments of biological phenomena in human cells, thereby enhancing our understanding of gene function and regulation.[254.1] These advancements have led to the discovery of over 1,000 human microproteins essential for optimal cell proliferation, highlighting the potential for ongoing technological innovations to clarify the roles of these proteins in both normal physiology and disease.[253.1] Moreover, the integration of AI into genomic data analysis is transforming the landscape of personalized medicine. AI models are increasingly utilized to analyze genomic data, predicting patient susceptibility to diseases and responses to specific treatments, which allows for more effective and tailored therapeutic strategies.[258.1] The combination of AI with genomic and transcriptomic data enables researchers to uncover previously undetectable patterns, thereby improving diagnostics and drug discovery.[258.1] As collaborations between technology companies, research institutions, and healthcare providers expand, the role of AI in genomics is expected to become more integrated into clinical applications, despite ongoing challenges related to data privacy and ethical considerations.[257.1] The future of genomic data analysis is set to revolutionize healthcare, with technological innovations advancing at an exponential pace. The integration of cloud computing, AI-driven algorithms, and CRISPR gene-editing technologies will enhance the precision and accessibility of genomic services, facilitating faster discoveries and more personalized treatments.[258.1] Furthermore, the application of machine learning methods to optimize guide RNA (gRNA) design and predict both on-target and off-target activity in CRISPR systems exemplifies the potential of AI to refine genetic editing techniques.[262.1] Overall, these advancements in functional genomics are expected to redefine the boundaries of genetic research and medicine, paving the way for significant breakthroughs in understanding and treating complex diseases.In this section:
References
[1] 10 Functional Genomics Facts: Unlock Secrets - David Brown — Key Concepts in Functional Genomics Functional genomics involves several key concepts, including: * Gene expression: The process by which the information encoded in a gene is converted into a functional product, such as a protein. * Protein-protein interactions: The interactions between proteins that are crucial for many biological processes. * Gene regulation: The processes by which cells
[2] PDF — Approach: In this article, we summarize the technologies and concepts that form the basis of this functional genomics approach. Results: Microarray and next-generation sequencing technologies have allowed researchers to investigate many different aspects of the cell, including DNA mu-tations, histone modifications, DNA methylation, chromatin
[3] Functional genomics - Wikipedia — Functional genomics is a field of molecular biology that attempts to describe gene (and protein) functions and interactions. Functional genomics make use of the vast data generated by genomic and transcriptomic projects (such as genome sequencing projects and RNA sequencing).
[4] Functional Genomics - an overview | ScienceDirect Topics — 2 Functional genomics Functional genomics is the science that studies, on a genome-wide scale, the relationships among the components of a biological system - genes, transcripts, proteins, metabolites, etc. - and how these components work together to produce a given phenotype.
[5] Introduction to Functional Genomics - Sheynkman Lab — Functional genomics is the field of study that focuses on understanding the function of genes and their roles in the overall biology of an organism. Unlike traditional genomics, which is mainly concerned with sequencing and mapping genomes, functional genomics aims to explore how genes and their products (like proteins and RNA) work together to influence an organism's traits, health
[6] Functional Genomics: The Science Behind Gene Interaction Magic — By employing genome-wide techniques, functional genomics seeks to unravel the complexities of this process and identify key regulatory elements. Comparing Genomics and Functional Genomics Genomics and functional genomics are closely related but they focus on different aspects. Genomics is primarily concerned with the DNA sequence.
[7] PDF — Summary Functional genomics describes an approach to investigating the activity of genes and gene products, how they are regulated and the consequences of variation on a genome-wide scale on an organism's biology. Using datasets from multiple high-throughput technologies (e.g. sequencing and mass
[8] Environmental Influences on Phenotype: Mechanisms and Examples — Environmental factors such as stress and exposure to pollutants can lead to changes in histone acetylation and methylation, impacting gene expression. For example, chronic stress has been shown to alter histone acetylation patterns in the brain, affecting genes involved in mood regulation and potentially contributing to the development of
[10] Environmental Influences on Gene Expression - Nature — Environmental Influences on Gene Expression | Learn Science at Scitable Similarly, drugs, chemicals, temperature, and light are among the external environmental factors that can determine which genes are turned on and off, thereby influencing the way an organism develops and functions. A second example of how chemical environments affect gene expression is the case of supplemental oxygen administration causing blindness in premature infants (Silverman, 2004). In addition to drugs and chemicals, temperature and light are external environmental factors that may influence gene expression in certain organisms. For example, Himalayan rabbits carry the C gene, which is required for the development of pigments in the fur, skin, and eyes, and whose expression is regulated by temperature (Sturtevant, 1913).
[16] How Functional Medicine Uses Genomics for Personalized Treatment — In recent years, functional medicine genomics has merged into healthcare, offering a revolutionary approach that emphasizes personalized treatment.t. This innovative methodology recognizes that each individual has a unique genetic makeup, influencing their health, disease susceptibility, and response to various treatments. By integrating genomics into functional medicine, healthcare practitioners can provide targeted, effective therapies tailored to each patient’s needs. Functional medicine practitioners can develop personalized treatment plans that align with each patient’s genetic profile by applying genomic information. By harnessing the power of genomics, functional medicine practitioners can more effectively identify genetic predispositions, tailor treatment plans, and monitor disease progression. Through the lens of functional medicine, the future of healthcare is increasingly focused on personalized treatment, recognizing the unique genetic tapestry that shapes each individual’s health journey.
[52] A new era in functional genomics screens - Nature — Advertisement View all journals Search Log in Explore content About the journal Publish with us Subscribe Sign up for alerts RSS feed nature nature reviews genetics review articles article Review Article Published: 20 September 2021 A new era in functional genomics screens Laralynne Przybyla ORCID: orcid.org/0000-0002-8598-47791,2 & Luke A. Gilbert ORCID: orcid.org/0000-0001-5854-08253,4,5 Nature Reviews Genetics volume 23, pages 89–103 (2022)Cite this article 34k Accesses 115 Citations 130 Altmetric Metrics details Subjects CRISPR-Cas systems Genetic interaction Genomic analysis Abstract The past 25 years of genomics research first revealed which genes are encoded by the human genome and then a detailed catalogue of human genome variation associated with many diseases. The advent of new CRISPR functional genomics tools allows for scalable and multiplexable characterization of genes and gene regulatory elements encoded by the human genome. These approaches promise to reveal mechanisms of gene function and regulation, and to enable exploration of how genes work together to modulate complex phenotypes.
[53] The most common technologies and tools for functional genome analysis - PMC — From the functional point of view, analysis of proteomics and interactomics is as vitally important as previously described analysis of genomics, epigenomics, and transcriptomics, because some studies show that gene expression at DNA or mRNA levels is substantially unchanged, although it affects the protein function and vice versa (46, 47). It is believed that successful functional genome analysis discovers genetic basic for human health by filling the gaps in knowledge about pathogenic pathways between genes, proteins, and their interaction network. Thus, in order to extend the limits of current techniques, some high-throughput technologies such as quantitative real-time polymerase chain reaction, next-generation sequencing or mass spectrometry have been developed, which provide an opportunity to perform genome-wide functional analysis.
[56] Recent advances in functional genome analysis - PMC — At the beginning of this century, the Human Genome Project produced the first drafts of the human genome sequence. Following this, large-scale functional genomics studies were initiated to understand the molecular basis underlying the translation of
[57] Cultivating DNA Sequencing Technology After the Human Genome Project — Consequently, the National Human Genome Research Institute of the National Institutes of Health created a program-widely known as the Advanced Sequencing Technology Program-that stimulated all stages of development of new DNA sequencing methods, from innovation to advanced manufacturing and production testing, with the goal of reducing the cost
[58] Next-Generation Sequencing Technology: Current Trends and Advancements — This review article provides an overview of NGS technology and its impact on various areas of research, such as clinical genomics, cancer, infectious diseases, and the study of the microbiome. These second-generation sequencing technologies have significantly increased the throughput and speed of DNA sequencing, enabling a wide range of applications in genomics research and clinical diagnostics . 192.Abel H.J., Duncavage E.J. Detection of structural DNA variation from next generation sequencing data: A review of informatic approaches. 203.Hajibabaei M., Shokralla S., Zhou X., Singer G.A.C., Baird D.J. Environmental Barcoding: A Next-Generation Sequencing Approach for Biomonitoring Applications Using River Benthos. 208.Minogue T.D., Koehler J.W., Stefan C.P., Conrad T.A. Next-Generation Sequencing for Biodefense: Biothreat Detection, Forensics, and the Clinic.
[64] Enhancing recognition and interpretation of functional phenotypic ... — Background Decoding human genomic sequences requires comprehensive analysis of DNA sequence functionality. Through computational and experimental approaches, researchers have studied the genotype-phenotype relationship and generate important datasets that help unravel complicated genetic blueprints. Thus, the recently developed artificial intelligence methods can be used to interpret the
[66] A new era in functional genomics screens - Nature — Advertisement View all journals Search Log in Explore content About the journal Publish with us Subscribe Sign up for alerts RSS feed nature nature reviews genetics review articles article Review Article Published: 20 September 2021 A new era in functional genomics screens Laralynne Przybyla ORCID: orcid.org/0000-0002-8598-47791,2 & Luke A. Gilbert ORCID: orcid.org/0000-0001-5854-08253,4,5 Nature Reviews Genetics volume 23, pages 89–103 (2022)Cite this article 34k Accesses 115 Citations 130 Altmetric Metrics details Subjects CRISPR-Cas systems Genetic interaction Genomic analysis Abstract The past 25 years of genomics research first revealed which genes are encoded by the human genome and then a detailed catalogue of human genome variation associated with many diseases. The advent of new CRISPR functional genomics tools allows for scalable and multiplexable characterization of genes and gene regulatory elements encoded by the human genome. These approaches promise to reveal mechanisms of gene function and regulation, and to enable exploration of how genes work together to modulate complex phenotypes.
[68] Advances in Personalized Medicines along with Functional Genomics and ... — State-of-the-art research on the human genome has produced remarkable research achievements in pharmacogenomics and functional genomics, and these research results are making an invaluable contribution to the advancement of personalized medicine. Advances in human genome research are being made from highly integrated next generation sequencing
[69] Bioinformatics challenges for personalized medicine - PMC — Consumers can already get 500 000–1 000 000 variant markers analyzed with associated trait information (Hindorff et al., 2009), and soon full genome sequencing will cost less than $1000 (Drmanac et al., 2010). PhenoPred detects gene–disease associations using the human PPI network, known gene–disease associations, protein sequences and protein functional information at the molecular level (Radivojac et al., 2008). The Skippy algorithm (Woolfe et al., 2010) analyzes the genomic region surrounding the variant to predict severe effects on gene function through disruption of splicing. Systems biology and network approaches address to the problem of complexity by integrating molecular data at multiple levels of biology including genomes, transcriptomes, metabolomes, proteomes and functional and regulatory networks (Kohl et al., 2010).
[70] Revolutionizing Personalized Medicine: Synergy with Multi-Omics Data ... — The history of personalized medicine is punctuated by significant milestones in genetics, technology, and clinical applications, shifting healthcare from a one-size-fits-all approach to a more individualized understanding of the molecular basis of health and disease and effective treatment strategies . Advances in genomics and biotechnology in the 21st century are enabling more personalized approaches to medicine, predicting disease risks, and tailoring treatments to individual genetic profiles. Personalized medicine leverages these images in conjunction with genetic data to gain deeper insights into disease mechanisms in individual patients, enhancing precision in diagnosis and treatment strategies . Through the integration of genetic, molecular, and clinical data, personalized medicine enables more accurate diagnosis, precise treatment targeting, and effective disease management.
[92] A new era in functional genomics screens - Nature — Advertisement View all journals Search Log in Explore content About the journal Publish with us Subscribe Sign up for alerts RSS feed nature nature reviews genetics review articles article Review Article Published: 20 September 2021 A new era in functional genomics screens Laralynne Przybyla ORCID: orcid.org/0000-0002-8598-47791,2 & Luke A. Gilbert ORCID: orcid.org/0000-0001-5854-08253,4,5 Nature Reviews Genetics volume 23, pages 89–103 (2022)Cite this article 34k Accesses 115 Citations 130 Altmetric Metrics details Subjects CRISPR-Cas systems Genetic interaction Genomic analysis Abstract The past 25 years of genomics research first revealed which genes are encoded by the human genome and then a detailed catalogue of human genome variation associated with many diseases. The advent of new CRISPR functional genomics tools allows for scalable and multiplexable characterization of genes and gene regulatory elements encoded by the human genome. These approaches promise to reveal mechanisms of gene function and regulation, and to enable exploration of how genes work together to modulate complex phenotypes.
[93] PDF — The field of functional genomics attempts to describe the functions and interactions of genes and pro-teins by making use of genome-wide approaches, in contrast to the gene-by-gene approach of classical molec-ular biology techniques. It combines data derived from the various pro-cesses related to DNA sequence, gene expression, and protein
[96] PDF — The integration of genomics into clinical practice holds tremendous promise for advancing personalized medicine and improving patient outcomes. However, several challenges must be addressed to realize the full potential of genomic medicine, including the interpretation of complex genetic
[99] The most common technologies and tools for functional genome analysis — From the functional point of view, analysis of proteomics and interactomics is as vitally important as previously described analysis of genomics, epigenomics, and transcriptomics, because some studies show that gene expression at DNA or mRNA levels is substantially unchanged, although it affects the protein function and vice versa (46, 47). It is believed that successful functional genome analysis discovers genetic basic for human health by filling the gaps in knowledge about pathogenic pathways between genes, proteins, and their interaction network. Thus, in order to extend the limits of current techniques, some high-throughput technologies such as quantitative real-time polymerase chain reaction, next-generation sequencing or mass spectrometry have been developed, which provide an opportunity to perform genome-wide functional analysis.
[100] High-throughput bacterial functional genomics in the sequencing era — High-throughput phenotyping accelerates understanding of gene function and network. Tn-seq is enabling functional genomics in a diverse set of bacteria. Whole genome sequencing is accelerating forward genetic screens. New approaches are expanding the types of phenotypes assayed on a global scale. Acknowledgments
[102] A new era in plant functional genomics - ScienceDirect — Combining multilayered network analysis and machine learning approach to analyze multiomics data sets for functional genomics is highly promising. Machine learning and deep learning approaches attempt to find associations between data elements to derive a mathematical equation, which keeps on getting self-refined by using a subset of input data
[103] Artificial Intelligence, Machine Learning and Genomics — The Big Picture The genomics field continues to expand the use of computational methods such as artificial intelligence and machine learning to improve our understanding of hidden patterns in large and complex genomics data sets from basic and clinical research projects. NHGRI is identifying and shaping its unique role in the convergence of genomic and machine learning research. This is why genomics researchers need AI/ML-based computational tools that can handle, extract and interpret the valuable information hidden within this large trove of data. These are just a few ways by which AI/ML methods are helping predict and identify hidden patterns in genomic data.
[105] Machine learning in genetics and genomics - PMC - PubMed Central (PMC) — In addition to learning to recognize patterns in DNA sequences, machine learning can take as input data generated by other genomic assays, such as microarray or RNA-seq expression data, chromatin accessibility assays such as DNase-seq, MNase-seq, and FAIRE, or histone modification, transcription factor (TF) binding ChIP-seq data, etc. Sections 3–5 describe strategies a researcher can use to guide a machine learning model, through prior knowledge, means of integrating heterogeneous data sets and feature selection. As new technologies for generating large genomic and proteomic data sets emerge, pushing beyond DNA sequencing to mass spectrometry, flow cytometry and high-resolution imaging methods, demand will increase not only for new machine learning methods but also for experts capable of applying and adapting them to big data sets.
[108] PDF — REVIEW ARTICLE OPEN Recent advances in CRISPR-based functional genomics for the study of disease-associated genetic variants Heon Seok Kim 1,2,3, Jiyeon Kweon 4 and Yongsub Kim 4,5✉ © The Author(s) 2024 Advances in sequencing technology have greatly increased our ability to gather genomic data, yet understanding the impact of genetic mutations, particularly variants of uncertain significance (VUSs), remains a challenge in precision medicine. Compared to Cas nucleases or base editors, PEs have a distinctive advantage: they can directly rewrite a target DNA without inducing DSBs or Fig. 1 A schematic overview of genome engineering strategies for the functional study of disease-associated genetic variants. 79 DDX3X (12,776 nucleotide variants) Cas9 mediated HDR Point mutations N/A Illumina short-read Direct, gDNA sequencing Cellular fitness Custom R script 3432 functionally abnormal variants Radford et al.
[109] Functional Genomics via CRISPR-Cas - PMC — Facile single guide RNA (sgRNA) library synthesis allows CRISPR-Cas screening to rapidly investigate the functional consequences of genomic, transcriptomic, and epigenomic perturbations. Harnessing CRISPR-Cas systems effectively allows for a library of perturbations (sgRNA targeting a particular locus) to be performed in a cell population either in the arrayed or pooled format via typically lentiviral transduction. (b) CRISPR-Cas9 has made multiplexed functional screening with single cell resolution more robust than ever before. Facile single guide RNA (sgRNA) library synthesis allows CRISPR-Cas screening to rapidly investigate the functional consequences of genomic, transcriptomic, and epigenomic perturbations. Genetic screens in human cells using the CRISPR/Cas9 system. Genome-Scale CRISPR-Cas9 Knockout Screening in Human Cells. High-throughput screening of a CRISPR/Cas9 library for functional genomics in human cells.
[115] High-throughput data analysis in systems biology: Techniques ... — Recent years have witnessed an explosion in biological research driven by high-throughput technologies in genomics, proteomics, and metabolomics. These advances have generated an unprecedented volume of data, presenting both challenges and extraordinary opportunities.
[116] From Functional Genomics to Functional Immunomics: New Challenges, Old ... — With the ongoing development of immunomic microarray technology—a spatially addressable, large-scale technology for measurement of specific immunological response—the new challenge of functional immunomics is emerging, which bears similarities to but is also significantly different from functional genomics.
[117] rise and future of CRISPR-based approaches for high-throughput genomics ... — Important impediments in many high-throughput genetic screening methods include: (i) the lack of a reversible system, (ii) the limitation to nonessential genes, (iii) the labour-intensive nature of developing genome-wide libraries, and (iv) the challenge of constructing manageable-sized libraries to study phenotypes under bottleneck conditions
[118] High-throughput functional genomics using CRISPR-Cas9 — Recently, the RNA-guided CRISPR (clustered regularly interspaced short palindromic repeat)-associated Cas9 nuclease has been combined with genome-scale guide RNA libraries for unbiased, phenotypic screening. References 44 and 45 describe the development of lentiviral genome-scale sgRNA libraries and the application for positive and negative selection genetic screening in human cells. Genome-wide recessive genetic screening in mammalian cells with a lentiviral CRISPR-guide RNA library. High-throughput screening of a CRISPR/Cas9 library for functional genomics in human cells. This paper describes structure-guided engineering of a robust Cas9-based transcriptional activator and the development of a genome-scale sgRNA library for gain-of-function genetic screening. Whole-genome sequencing analysis reveals high specificity of CRISPR/Cas9 and TALEN-based genome editing in human iPSCs. Cell Stem Cell 15, 12–13 (2014).
[128] Introduction to Functional Genomics - Sheynkman Lab — Unlike traditional genomics, which is mainly concerned with sequencing and mapping genomes, functional genomics aims to explore how genes and their products (like proteins and RNA) work together to influence an organism's traits, health, development, and responses to various stimuli.
[131] Applications of Functional Genomics for Drug Discovery — Applications of Functional Genomics for Drug Discovery - ScienceDirect Applications of Functional Genomics for Drug Discovery Functional genomics is an emerging field of research that aims to deconvolute the link between genotype and phenotype by making use of large -omic data sets and next-generation gene and epigenome editing tools to perturb genes of interest. Here we review how functional genomic tools can be used to better understand the biological interplay between genes, improve disease modeling, and identify novel drug targets. 기능 유전체학(Functional genomics)은 대규모의 오믹(omic) 데이터 세트와 차세대 유전자 및 후생유전자(epigenome) 편집도구를 사용하여 관심 대상 유전자를 교란시킴으로써 유전자형과 표현형 간의 연결 해체를 목표로 하는 새로운 연구분야이다. Next article in issue For all open access content, the relevant licensing terms apply.
[132] Functional genomics - AstraZeneca — Using functional genomics, we can identify and investigate genes and biological processes that are associated with diseases and which can be explored as potential drug targets. Genome editing is a key tool in functional genomics, making it possible to delete or change genes in cells to understand their roles in disease.
[162] Expediting rare disease diagnosis: a call to bridge the gap between ... — These powerful genomic technologies have the potential to rapidly accelerate the process of identifying genetic variants, particularly rare variants, that are likely to be pathogenic and could dramatically reduce the amount of time required for rare disease diagnosis.
[163] Expediting rare disease diagnosis: a call to bridge the gap between ... — Reducing the time required for disease diagnosis is among the most critical needs of patients impacted by a rare disease. In this perspective we describe current challenges associated with rare disease diagnosis and discuss several cutting-edge functional genomic screening technologies that have the potential to rapidly accelerate the process
[165] Rare disease genomics and precision medicine - Genomics & Informatics — This review explores the role of genomics in rare disease research, the impact of large consortium initiatives, advancements in extensive data analysis, the integration of artificial intelligence (AI) and machine learning (ML), and the therapeutic implications in precision medicine. Although big data has been integrated in rare disease genomics, major barriers still need to be addressed, including difficulties in identifying causal variants and translating findings into clinical practice. AI and ML technologies contribute to rare disease research by assisting the analysis of vast amounts of genomic and clinical data to identify disease patterns, predict treatment outcomes, and develop personalized therapies, ultimately improving diagnostic accuracy and advancing drug development . The field of rare disease research has undergone significant advancements, driven by technological innovations in genomic sequencing, big data analytics, and AI.
[166] Recent advances in CRISPR-based functional genomics for the study of ... — Advantages of CRISPR-based genome editing for the functional study of genetic variants Researchers have conducted CRISPR screening studies utilizing cytosine base editors, which enable C-to-T substitutions and can introduce PTCs. Genome-wide analyses have demonstrated that cytosine base editors with the NGG PAM can potentially introduce PTCs into approximately 17,000 human genes50,51 (Table 1). Third, the efficiency of gene KO achieved by base editors is generally lower than the efficiency of introducing SNVs. Therefore the presence of a gRNA in a cell does not guarantee the successful introduction of the intended SNVs. To overcome the challenges associated with interpreting CRISPR base editor screen data, two research groups recently introduced a reporter-assisted base editor screen method92,93.
[174] PDF — REVIEW ARTICLE OPEN Recent advances in CRISPR-based functional genomics for the study of disease-associated genetic variants Heon Seok Kim 1,2,3, Jiyeon Kweon 4 and Yongsub Kim 4,5✉ © The Author(s) 2024 Advances in sequencing technology have greatly increased our ability to gather genomic data, yet understanding the impact of genetic mutations, particularly variants of uncertain significance (VUSs), remains a challenge in precision medicine. Compared to Cas nucleases or base editors, PEs have a distinctive advantage: they can directly rewrite a target DNA without inducing DSBs or Fig. 1 A schematic overview of genome engineering strategies for the functional study of disease-associated genetic variants. 79 DDX3X (12,776 nucleotide variants) Cas9 mediated HDR Point mutations N/A Illumina short-read Direct, gDNA sequencing Cellular fitness Custom R script 3432 functionally abnormal variants Radford et al.
[175] Next-Generation Sequencing Technology: Current Trends and Advancements ... — This review article provides an overview of NGS technology and its impact on various areas of research, such as clinical genomics, cancer, infectious diseases, and the study of the microbiome. These second-generation sequencing technologies have significantly increased the throughput and speed of DNA sequencing, enabling a wide range of applications in genomics research and clinical diagnostics . 192.Abel H.J., Duncavage E.J. Detection of structural DNA variation from next generation sequencing data: A review of informatic approaches. 203.Hajibabaei M., Shokralla S., Zhou X., Singer G.A.C., Baird D.J. Environmental Barcoding: A Next-Generation Sequencing Approach for Biomonitoring Applications Using River Benthos. 208.Minogue T.D., Koehler J.W., Stefan C.P., Conrad T.A. Next-Generation Sequencing for Biodefense: Biothreat Detection, Forensics, and the Clinic.
[177] Machine learning for single-cell genomics data analysis — In this review, we survey recent advances in ML approaches developed to analyse single-cell transcriptomic and epigenomic data, focusing mainly on articles published in the last two years (2019-2020). This period witnessed active developments of new methods, in particular, based on deep learning, to automatically extract information from large sets of single-cell data, tackling important
[183] Functional Genomics: The Science Behind Gene Interaction Magic — Functional Genomics in Disease Research. ... For example, functional genomics can help identify which patients are more likely to respond to certain cancer therapies. It allows for the customization of drug combinations based on the genetic makeup of both the individual and the tumor, enhancing the precision of medical interventions.
[184] Functional genomics and the future of iPSCs in disease modeling — Examples include cystic fibrosis, Rett syndrome, Huntington's disease (HD), monogenic diabetes, and polycystic kidney disease. The combination of functional genomics and hiPSCs can shed light on disease mechanisms that are difficult to model in in vivo systems and provide information on possible pathological mechanisms and key controllers of
[193] Advances in therapeutic application of CRISPR-Cas9 — Briefings in Functional Genomics, Volume 19, Issue 3, May 2020, Pages 164-174, https://doi ... studies leaded to a critical step toward further application of stem cell-based gene therapy to monogenic diseases in clinical practice. ... As a genomic editing tool, CRISPR-Cas9 can manipulate almost any target genomic sequence and correct
[194] PDF — REVIEW ARTICLE OPEN Recent advances in CRISPR-based functional genomics for the study of disease-associated genetic variants Heon Seok Kim 1,2,3, Jiyeon Kweon 4 and Yongsub Kim 4,5✉ © The Author(s) 2024 Advances in sequencing technology have greatly increased our ability to gather genomic data, yet understanding the impact of genetic mutations, particularly variants of uncertain significance (VUSs), remains a challenge in precision medicine. Compared to Cas nucleases or base editors, PEs have a distinctive advantage: they can directly rewrite a target DNA without inducing DSBs or Fig. 1 A schematic overview of genome engineering strategies for the functional study of disease-associated genetic variants. 79 DDX3X (12,776 nucleotide variants) Cas9 mediated HDR Point mutations N/A Illumina short-read Direct, gDNA sequencing Cellular fitness Custom R script 3432 functionally abnormal variants Radford et al.
[195] CRISPR & Ethics - Innovative Genomics Institute (IGI) — In this chapter, we survey some of the ethical issues that span uses of genome editing, and then dive into some of the key questions and concerns that are specific to the the use of CRISPR in human health, agriculture, and the environment. In addition to the ethical issues mentioned above, the potential to edit the human genome raises questions around appropriate use of editing (somatic versus germline editing, treatment versus prevention versus enhancement), reproductive autonomy and the autonomy of the child/fetus, unequal access, and societal effects. With transgenic technologies and CRISPR genome editing, instead of relying on random DNA changes, research can make specific changes to individual plants and generate new plants with specific traits.
[214] Functional genomics, challenges and perspectives for the future — Perhaps the biggest challenge facing functional genomics and systems biology to date is the development of bioinformatics tools that will integrate and analyze data obtained from multiple 'omics' platforms in a comprehensive way to generate a holistic view of cellular systems and networks. Another challenge is to integrate different
[215] PDF — Limitations and challenges The use of cell lines and animal models in many multi-omic studies instead of primary cells/tissues (i.e. derived from patients) presents a major challenge in translating functional genomics findings for application in human disease. Where tissue/primary cell-based models have been used they are
[216] Challenges and Opportunities in Understanding Genetics of Fungal ... — (iii) Overcoming limitations: the introduction of functional genomics approaches. Given the complexity of host-pathogen interactions, conventional experimental approaches that study only individual molecular components (either of the host or pathogen) cannot provide a comprehensive picture of these interactions.
[217] Functional genomics in stem cell models: considerations and ... — However, many technical challenges remain when applying large-scale functional genomics approaches to differentiated PSC populations. Here we review current technologies in the field of iPSC disease modeling and CRISPR-based functional genomics screens and practical considerations for implementation across a range of modalities, applications
[218] PDF — Summary Functional genomics describes an approach to investigating the activity of genes and gene ... presents a major challenge in translating functional genomics findings for application in human disease. Where tissue/primary cell-based models have been used they are representative of relatively few individuals - i.e. different tissues from
[220] An overview of artificial intelligence in the field of genomics - Springer — This paper highlights the need for eXplainable Artificial Intelligence (XAI) in the field of genomics and how the understanding of genomic regions, specifically the non-coding regulatory region of genomes (i.e., enhancers), can help uncover underlying molecular principles of disease states, in particular cancer in humans. These tools use histone modification and high-throughput sequencing assay data as a training data set and based on the extracted features predict the enhancers in genomes. Both models are trained on genomic data, The opaque models give non-traceable predictions compared to XAI, which provides predictions along with the IF–THEN rule base that is understandable to layman
[221] Artificial intelligence-driven biomedical genomics - ScienceDirect — Artificial intelligence (AI), which encompasses machine learning (ML), computer vision, neural networks, and natural language processing, has emerged as an indispensable tool for addressing these challenges. It empowers the processing, analysis, modeling, and interpretation of large-scale genomic data.
[236] Bioinformatics Databases and Tools for Analysis of Multi-omics - Springer — Furthermore, there is an urgent need for improved computational methods and software tools capable of handling huge volumes of data and the complexity of multi-omics data. Addressing these challenges will be critical in harnessing the full power of bioinformatics databases in multi-omics analysis and unlocking the secrets of the human
[253] Finding functional microproteins: Trends in Genetics - Cell Press — This review highlights recent advances in functional genomics that have led to the discovery of >1000 human microproteins required for optimal cell proliferation. Ongoing technological innovations will continue to clarify the roles and mechanisms of microproteins in both normal physiology and disease, potentially opening new avenues for
[254] A new era in functional genomics screens - Nature — Advertisement View all journals Search Log in Explore content About the journal Publish with us Subscribe Sign up for alerts RSS feed nature nature reviews genetics review articles article Review Article Published: 20 September 2021 A new era in functional genomics screens Laralynne Przybyla ORCID: orcid.org/0000-0002-8598-47791,2 & Luke A. Gilbert ORCID: orcid.org/0000-0001-5854-08253,4,5 Nature Reviews Genetics volume 23, pages 89–103 (2022)Cite this article 34k Accesses 115 Citations 130 Altmetric Metrics details Subjects CRISPR-Cas systems Genetic interaction Genomic analysis Abstract The past 25 years of genomics research first revealed which genes are encoded by the human genome and then a detailed catalogue of human genome variation associated with many diseases. The advent of new CRISPR functional genomics tools allows for scalable and multiplexable characterization of genes and gene regulatory elements encoded by the human genome. These approaches promise to reveal mechanisms of gene function and regulation, and to enable exploration of how genes work together to modulate complex phenotypes.
[257] AI in genomics and genetic analysis - Artificial Intelligence — AI in genomics and genetic analysis - Artificial Intelligence + AI in genomics and genetic analysis Discover how AI in genomics and genetic analysis revolutionizes genome sequencing, research, and personalized medicine. AI in genomics and genetic analysis is revolutionizing the future of biological research and medical advancements. The integration of AI in genetic research has revolutionized the way scientists approach genomic data. As collaborations between tech companies, research institutions, and healthcare providers continue to grow, the role of AI in genomics and genetic analysis will likely become more integrated into everyday clinical applications. While challenges surrounding data privacy and ethical considerations remain significant, the future of AI in genomics promises groundbreaking advances that will redefine the boundaries of genetic research and medicine.
[258] 2025 and Beyond: The Future of Genomic Data Analysis and Innovations in ... — In personalized medicine, AI models analyze genomic data to predict a patient’s susceptibility to specific diseases or their response to certain drugs, leading to more effective and tailored treatments. AI is also crucial in the integration of multi-omic data, enabling the discovery of complex relationships across genomic, transcriptomic, proteomic, and metabolic layers of biological data. The future of genomic data analysis is set to revolutionize healthcare, research, and personalized medicine, with technological innovations advancing at an exponential pace. The integration of cloud computing, AI-driven algorithms, and CRISPR gene-editing technologies will further enhance the precision and accessibility of genomic services, enabling faster discoveries and more tailored treatments. Qian, W., (2024) 2025 and Beyond: The Future of Genomic Data Analysis and Innovations in Genomics Services - Crown Bioscience.
[262] AI applications in functional genomics - ScienceDirect — Various ML methods and DL methods have been developed to optimize gRNA design and predict both on-target and off-target activity, including: CRISTA , an RF-based regression model that scores the propensity of a genomic site to be cleaved by a given gRNA; DeepCRISPR , a computational platform that uses data augmentation technique to expand the training dataset of experimentally validated gRNA sequences and feeds two CNNs (one for on- and one for off-target activity prediction), with gRNA representations produced by pre-trained autoencoders; CROTON , an end-to-end framework based on deep multi-task CNNs and neural architecture search to predicting CRISPR-Cas9 editing outcomes; and the complementary tools CRISPR-ONT and CRISPR-OFFT , attention-based CNNs trained to predict gRNA on- and off-target activities, respectively.
[270] Simultaneous CRISPR screening and spatial transcriptomics reveal ... — More recently, pooled screening has been combined with single-cell RNA sequencing (scRNA-seq) to combine suppression of target genes with transcriptome-wide expression measurements in the same cell. 4, 5, 6 This has allowed for mapping gene circuits within cells and for determining causal connections between the expression of a gene and that of
[271] CRISPR Screening in Single Cells - PubMed — The combination of single-cell RNA-seq and CRISPR allows for efficient interrogation of possibly any number of genes, only limited by the sequencing capability. Here we describe the current protocols for CRISPR screening in single cells, from cloning and virus production to generating sequencing dat …
[272] Massively Parallel CRISPR‐Based Genetic Perturbation Screening at ... — According to the integration of distinct single‐cell technologies, these methods principally fall into four categories: scCRISPR with RNA‐seq, scCRISPR with ATAC‐seq, scCRISPR with proteome probing, and imaging‐based scCRISPR. scCRISPR has deciphered genotype-phenotype relationships, genetic regulations, tumor biological issues, and
[273] CRISPR-based functional genomics screening in human-pluripotent-stem ... — The integration of CRISPR-based functional genomics screening with hPSC differentiation technology opens up new opportunities to unravel gene function in diverse human cell types and identify mechanisms and therapeutic targets for human diseases in more physiologically relevant contexts.
[274] Past, present, and future of CRISPR genome editing technologies — Emerging (third-generation) CRISPR technologies are being developed to address two major unmet needs in the field: achieving precise insertion of large (gene-sized) payloads, and gene regulation without any genome editing through epigenome engineering.
[275] Multiplexed CRISPR technologies for gene editing and ... - Nature — Multiplexed CRISPR technologies have recently emerged as powerful approaches for genetic editing and transcriptional regulation. Here the authors review this emerging technology and discuss
[276] Gene Regulatory Network: New Insights for Modern Biology — Understanding gene regulation is crucial for deciphering cellular function, development, and disease. Gene regulatory networks (GRNs) describe the complex interactions between DNA, RNA, proteins, and other molecules that control gene expression. Advances in high-throughput technologies and computational biology have provided unprecedented insights, revealing their dynamic and context-dependent
[277] Rapidly evolving genome and epigenome editing technologies - Cell Press — The next few decades are poised to witness transformative advancements in genome and epigenome editing technologies, allowing for precise correction of genetic mutations, fine-tuning of gene expression, and site-specific insertion or deletion of large DNA sequences.
[278] Recent advances in therapeutic gene editing technologies — Beyond the CRISPR system, mobile genetic elements (MGEs) and epigenetic editors are emerging as efficient alternatives for precise large insertions or stable gene manipulation in mammalian cells. These advances collectively set the stage for next-generation gene therapy development.
[286] High throughput CRISPR approaches: New solutions for challenging ... — Summing up, high-throughput CRISPR based approaches are a step forward towards functional genomics that combine CRISPR technologies with novel molecular assays. These include single-cell sequencing to address cellular heterogeneity and epigenetic profiling by targeting coding and noncoding elements and have led to unprecedented insights into
[287] A new age in functional genomics using CRISPR/Cas9 in arrayed library ... — In this review, we will highlight recent developments in the area of CRISPR-based functional genomics and discuss potential future directions, with a special focus on mammalian cell systems and arrayed library screening. Keywords: CRISPR, Cas9/sgRNA, high-throughput screening, high-content imaging, knockdown, siRNA. Introduction