Genomic Applications Research Articles

CURRENT APPROACHES AND STRATEGIES APPLIED IN FIRST-IN-CLASS DRUG DISCOVERY.

First-in-class drug discovery (FICDD) offers novel therapies, new biological targets and mechanisms of action (MOAs) toward targeting various diseases and provides opportunities to understand unexplored biology and to target unmet diseases. Current screening approaches followed in FICDD for discovery of hit and lead molecules can be broadly categorized and discussed under phenotypic drug discovery (PDD) and target-based drug discovery (TBDD). Each category has been further classified and described with suitable examples from the literature outlining the current trends in screening approaches applied in small molecule drug discovery (SMDD). Similarly, recent applications of functional genomics, structural biology, artificial intelligence (AI), machine learning (ML), and other such advanced approaches in FICDD have also been highlighted in the article. Further, some of the current medicinal chemistry strategies applied during discovery of hits and optimization studies such as hit-to-lead (HTL) and lead optimization (LO) have been simultaneously overviewed in this article.

Mayfly developmental atlas: developmental temporal expression atlas of the mayfly, Ephemera vulgata, reveals short germ-specific hox gene activation

BackgroundOver the course of evolution, insects have seen drastic changes in their mode of development. While insects with derived modes of development have been studied extensively, information on ancestral modes of development is lacking. To address this, we selected a member of one of the earliest lineages of extant flying insects, serving as an outgroup to the modern winged insects, the short germ, non-model mayfly Ephemera vulgata Linnaeus (Insecta: Ephemeroptera, Ephemeridae). We document the embryonic morphology throughout its development and establish a global temporal expression atlas.ResultsDAPI staining was used to visualise developmental morphology to provide a frame of reference for the sequenced timepoints. A transcriptome was assembled from 3.2 billion Illumina RNAseq reads divided in 12 timepoints with 3 replicates per timepoint consisting of 35,091 putative genes. We identified 6,091 significantly differentially expressed genes (DEGs) and analysed them for broad expression patterns via gene ontology (GO) as well as for specific genes of interest. This revealed a U-shaped relationship between the sum of DEGs and developmental timepoints, over time, with the lowest number of DEGs at 72 hours after egg laying (hAEL). Based on a principal component analysis of sequenced timepoints, overall development could be divided into four stages, with a transcriptional turning point around katatrepsis. Expression patterns of zld and smg showed a persistent negative correlation and revealed the maternal-to-zygotic transition (MZT), occurring 24 hAEL. The onset of development of some major anatomical structures, including the head, body, respiratory system, limb, muscle, and eye, are reported. Finally, we show that the ancestral short germ sequential mode of segmentation translates to a sequential Hox gene activation and find diverging expression patterns for lab and pb. We incorporate these patterns and morphological observations to an overview of the developmental timeline.ConclusionsWith our comprehensive differential expression study, we demonstrate the versatility of our global temporal expression atlas. It has the capacity to contribute significantly to phylogenetic insights in early-diverging insect developmental biology and can be deployed in both molecular and genomic applications for future research.

Nucleotide Transformer: building and evaluating robust foundation models for human genomics.

The prediction of molecular phenotypes from DNA sequences remains a longstanding challenge in genomics, often driven by limited annotated data and the inability to transfer learnings between tasks. Here, we present an extensive study of foundation models pre-trained on DNA sequences, named Nucleotide Transformer, ranging from 50 million up to 2.5 billion parameters and integrating information from 3,202 human genomes and 850 genomes from diverse species. These transformer models yield context-specific representations of nucleotide sequences, which allow for accurate predictions even in low-data settings. We show that the developed models can be fine-tuned at low cost to solve a variety of genomics applications. Despite no supervision, the models learned to focus attention on key genomic elements and can be used to improve the prioritization of genetic variants. The training and application of foundational models in genomics provides a widely applicable approach for accurate molecular phenotype prediction from DNA sequence.

Genomic Insights into Stutzerimonas kunmingensis TFRC-KFRI-1 Isolated from Manila Clam (Ruditapes philippinarum): Functional and Phylogenetic Analysis

Stutzerimonas kunmingensis TFRC-KFRI-1, isolated from the gut of Manila Clam in the sediment of the West Sea of Korea, was investigated for its potential as a probiotic bacterium. This strain, belonging to the family Pseudomonadaceae, was previously classified as Pseudomonas kunmingensis but later reclassified to the genus Stutzerimonas, known for species with bioremediation and probiotic properties. To evaluate its genomic features and potential applications, we performed draft-genome sequencing and analysis. The genome of S. kunmingensis TFRC-KFRI-1 was assembled into a 4,756,396 bp sequence with a 62.8% GC content. Genomic analysis suggested potential genes for carbohydrate degradation and lactic acid production. The strain exhibited high average nucleotide identity (ANI) and 16S rRNA similarity with S. kunmingensis HL22-2T, further supporting its potential as a probiotic. This genome sequence provides valuable insights into the functional capabilities of S. kunmingensis TFRC-KFRI-1 and its potential applications in various industries, including aquaculture and food biotechnology. The genome sequence is available under GenBank accession number JBGJJB000000000.1, with related project information under BioProject PRJNA1147901 and Bio-Sample SAMN43173893.

Physiological and Molecular Mechanisms of Lepidopteran Insects: Genomic Insights and Applications of Genome Editing for Future Research.

Lepidopteran insects are a major threat to global agriculture, causing significant crop losses and economic damage. Traditional pest control methods are becoming less effective due to the rapid evolution of insecticide resistance. This study explores the current status and genomic characteristics of 1315 Lepidopteran records, alongside an overview of relevant research, utilizing advanced functional genomics techniques, including RNA-seq and CRISPR/Cas9 gene-editing technologies to uncover the molecular mechanisms underlying insecticide resistance. Our genomic analysis revealed significant variability in genome size, assembly quality, and chromosome number, which may influence species' biology and resistance mechanisms. We identified key resistance-associated genes and pathways, including detoxification and metabolic pathways, which help these insects evade chemical control. By employing CRISPR/Cas9 gene-editing techniques, we directly manipulated resistance-associated genes to confirm their roles in resistance, demonstrating their potential for targeted interventions in pest management. These findings emphasize the value of integrating genomic data into the development of effective and sustainable pest control strategies, reducing reliance on chemical insecticides and promoting environmentally friendly integrated pest management (IPM) approaches. Our study highlights the critical role of functional genomics in IPM and its potential to provide long-term solutions to the growing challenge of Lepidopteran resistance.

Advances in genomics analysis for microbial soil remediation

Abstract. Soil pollution is a global environmental problem that poses a threat to ecological balance and human health. Microbial soil remediation is an eco-friendly technology that utilizes microorganisms to degrade or transform soil pollutants. Due to some drawbacks of traditional soil remediation methods, microbial remediation method has become a hot research topic nowadays. In recent years, the development of genomics analysis technology has greatly promoted the progress of microbial remediation research, and the use of genomics approach is free from the technical constraints of traditional research and can efficiently obtain the relative abundance of microbial communities and other information. In this paper, we reviewed the application of genomics in microbial soil remediation, and discussed the genomic analysis of several examples of heavy metal contaminated soil remediation, which provides a reference for researchers to quickly screen analytical methods in the field of microbial soil remediation, and reveals the biological significance of genomics analytical techniques.

ReaL-MGE is a tool for enhanced multiplex genome engineering and application to malonyl-CoA anabolism

The complexities encountered in microbial metabolic engineering continue to elude prediction and design. Unravelling these complexities requires strategies that go beyond conventional genetics. Using multiplex mutagenesis with double stranded (ds) DNA, we extend the multiplex repertoire previously pioneered using single strand (ss) oligonucleotides. We present ReaL-MGE (Recombineering and Linear CRISPR/Cas9 assisted Multiplex Genome Engineering). ReaL-MGE enables precise manipulation of numerous large DNA sequences as demonstrated by the simultaneous insertion of multiple kilobase-scale sequences into E. coli, Schlegelella brevitalea and Pseudomonas putida genomes without any off-target errors. ReaL-MGE applications to enhance intracellular malonyl-CoA levels in these three genomes achieved 26-, 20-, and 13.5-fold elevations respectively, thereby promoting target polyketide yields by more than an order of magnitude. In a further round of ReaL-MGE, we adapt S. brevitalea to malonyl-CoA elevation utilizing a restricted carbon source (lignocellulose from straw) to realize production of the anti-cancer secondary metabolite, epothilone from lignocellulose. Multiplex mutagenesis with dsDNA enables the incorporation of lengthy segments that can fully encode additional functions. Additionally, the utilization of PCR to generate the dsDNAs brings flexible design advantages. ReaL-MGE presents strategic options in microbial metabolic engineering.

Scoping the requirements for a genomic competency framework to inform the integration of genomics into pharmacy undergraduate curricula

Abstract Introduction The application of genomics is due to have a significant impact on the role of pharmacy within future healthcare systems. Since 2021, the updated GPhC education standards have included a requirement to apply the principles of genomics,1 but a lack of consistency across UK pharmacy programmes has led to inequity of genomics literacy for pharmacy students.1 National UK genomic workforce strategies include a pharmacy undergraduate indicative syllabus focussing on upskilling the NHS workforce in readiness for the wider implementation of genomics.2,3 There is a lack of UK research exploring the undergraduate genomic education requirements of pharmacy professionals. Aim Scope the requirements of a genomic competency framework for undergraduate pharmacy curricula. Methods Ethics approval was obtained from Swansea University ethics committee in July 2023. Fifteen multi-professional stakeholders from across the UK, perceived as genomic experts and/or pharmacy educationalists from each devolved nation, were selected using purposive sampling and all were invited to participate within a virtual semi-structured interview (SSI) and a consecutive focus group. SSIs and the focus group were facilitated and recorded using Microsoft® Teams and pre-designed open question topic frameworks were used. All data was anonymously transcribed verbatim and analysed using thematic analysis. Results Twelve participants consented and SSIs were held between September/October 2023. Five participants attended the consecutive focus group in December 2023. Five SSI themes were identified: Competency development - where the need for pharmacists to have comprehensive knowledge of the aspects and factors affecting the use of genomic testing within clinical practice (e.g. research and consent) was identified, whilst others described the need for pharmacists to focus on pharmacogenomics only; Curriculum approach – where there were variations in the depth of general genomic training and appropriate topics needed by pharmacists and the requirement for genomics to be scattered throughout undergraduate programmes; Future practice roles - the current postgraduate pharmacogenomic module could be used to establish the future undergraduate requirements and the need for pharmacy curricula to focus on the application of genomics to medicines rather than as a diagnostic tool; Genomic topics - participants acknowledged the complex nature of determining the genomic undergraduate curricula requirements without routine mainstream use of genomics within clinical practice but suggested pharmacy genomic competencies needing further refinement; Level of knowledge - participants highlighted the need for overall genomic pharmacy knowledge but a deeper level of pharmacogenomic knowledge and recommended that the competency framework should be relevant and achievable across healthcare sectors. Discussion and conclusion Genomic competencies should be established for pharmacy professionals as appropriate to their job roles. Once genomics is widely implemented, clarity around genomic competence requirements will develop, following workforce upskilling. The varied backgrounds of study participants impacted their opinions which were potentially influenced by their knowledge of the pharmacist role across sectors and their lack of familiarity with current pharmacy training. Study limitations are small participant numbers and low data generalisability. A genomic competency framework is needed to standardise pharmacy training and study findings will be used to develop a UK consensus for UK genomic undergraduate pharmacy training.

Decoding the Genetic Code: Scientific Exploration of the Telomere-totelomere (T2T) Genome

Abstract: With the development of third-generation sequencing technology, genome assembly has entered a new era. The combination of multiple sequencing methods can combine the strengths of each, resulting in higher-quality assembly results. Telomere-to-Telomere (T2T) genome refers to a zero gap genome that is assembled at the T2T level of one or more chromosomes through the combination of multiple sequencing technologies, such as Pacific Biosciences High-Fidelity (PacBio HiFi), Oxford Nanopore Technologies (ONT) Ultra-long, High-throughput Chromosome Conformation Capture (Hi-C), and others. High-quality reference genomes are the basis for genomics research, and the T2T genome has enabled the exploration of unknown areas of the genome, such as telomeres and centromeres, which has provided a more in-depth direction for research. This review provides a comprehensive overview of the T2T genome, reviewing the development of sequencing technologies and then outlining sequencing strategies, assembly methods, quality assessment processes, and analysis software for the T2T genome with practical examples. Representative T2T genomic species of plants, animals, and microorganisms (e.g., human, Arabidopsis, brewer's yeast, and so on) are presented separately. A summary of the potential and challenges of current T2T genomic applications is provided, along with an outlook for future developments.

The frontier of precision medicine: application of single-cell multi-omics in preimplantation genetic diagnosis.

The advent of single-cell multi-omics technologies has revolutionized the landscape of preimplantation genetic diagnosis (PGD), offering unprecedented insights into the genetic, transcriptomic, and proteomic profiles of individual cells in early-stage embryos. This breakthrough holds the promise of enhancing the accuracy, efficiency, and scope of PGD, thereby significantly improving outcomes in assisted reproductive technologies (ARTs) and genetic disease prevention. This review provides a comprehensive overview of the importance of PGD in the context of precision medicine and elucidates how single-cell multi-omics technologies have transformed this field. We begin with a brief history of PGD, highlighting its evolution and application in detecting genetic disorders and facilitating ART. Subsequently, we delve into the principles, methodologies, and applications of single-cell genomics, transcriptomics, and proteomics in PGD, emphasizing their role in improving diagnostic precision and efficiency. Furthermore, we review significant recent advances within this domain, including key experimental designs, findings, and their implications for PGD practices. The advantages and limitations of these studies are analyzed to assess their potential impact on the future development of PGD technologies. Looking forward, we discuss the emerging research directions and challenges, focusing on technological advancements, new application areas, and strategies to overcome existing limitations. In conclusion, this review underscores the pivotal role of single-cell multi-omics in PGD, highlighting its potential to drive the progress of precision medicine and personalized treatment strategies, thereby marking a new era in reproductive genetics and healthcare.

What are the challenges in the Health Technology Assessment of genetic and genomic applications?

Abstract Introduction Evaluating genetic/genomic applications (GGAs) is crucial for their implementation in clinical practice but hindered by several issues, such as rapid development processes and unclear benefits. Since these challenges lack comprehensive discussion, this study aims to identify and analyze all barriers emerged in the Health Technology Assessment (HTA) of genetic/genomic tests, filling a gap in the current literature. Methods PubMed, Scopus and Web of Science were searched to identify studies that specifically discussed any challenge or barrier in the HTA evaluation of GGAs. No restriction was applied on evaluation aspect or study type. Challenges/barriers were then grouped into the domains outlined in the EUnetHTA Core Model. A narrative synthesis of the main findings was performed. This study was supported by the EC and MUR- PNRR-M4C2-I1.3 Project PE_00000019 ‘HEAL ITALIA’. Results 19 articles were included: one third involved experts from different countries, and about 50% were author perspectives. Articles either focused on one aspect only (37%) or were more general (47%). The most challenging domain was economic aspects (69%), followed by clinical effectiveness (47%) and social impact (42%), but issues were found in all domains. The lack of a standardized HTA approach, the paucity of evidence on clinical outcomes, the challenges in capturing all health benefits, and the difficulties in identifying the healthcare pathways triggered by the test were consistently mentioned across the HTA domains. Conclusions Our study systematically summarized challenges in the HTA evaluation of GGAS, providing a thorough analysis and categorization of these issues. Various challenges surfaced, notably related to the identification of costs, as well as clinical and non-clinical benefits. There is a need for exhaustive discussion on potential solutions to facilitate the assessment process of these technologies to ultimately foster their implementation in clinical practice. Key messages • There are multiple challenges in the HTA evaluation of genetic and genomic tests in clinical practice that impact every HTA domain. • There is a need for concrete efforts in generating further evidence in relation to the definition of costs and benefits of the healthcare pathways triggered by genetic/genomic applications.

Implementation of polygenic risk scores in secondary prevention of (breast) cancer

Abstract Utilizing polygenic risk scores to inform secondary prevention of breast cancer via population screening programs is one of the pioneering applications of genomics in public health applications. Over the last decade, polygenic risk scores for breast cancer have been developed and validated in various European populations (AUC ± 0.63, OR per Z unit ± 1.6, depending on the population). In some countries, the polygenic risk score has been integrated with decision modeling tools, such as CanRisk in the Netherlands, and low-level implementation into health and care systems has started. Implementation strategies vary, but generally consider initial implementation into high-risk populations, such as breast cancer families, in specialized clinical setting, such as academic medical centres, or specialized cancer clinics, referring relatives of patients to population screening based on comprehensive risk modelling, including polygenic risk scores. This route may be followed by other disease fields as well. The presentations outlines the current evidence and considerations on breast cancer genetic risk modelling, clinical pilot studies and the outline of step-wise implementation of such models to benefit secondary prevention programs. We start from the perspective of a single hospital, to the needed steps to upscale to national populations, both in diversity of these populations, as the throughput of genetic testing and counselling, until the harmonization and recalibration of various European healthcare models. We draw from examples of the Genotyping on all patients (GOALL), building cancer health platforms (CanHeal) and Genome of Europe (GoE) consortia.

Essentials of genomics in nursing undergraduate education: A discussion paper

AimTo map the 2021 American Association of Colleges of Nursing Essentials to the American Nurses Association Essentials of Genomic Nursing for all nurses and provide resources for nursing faculty to support the seamless integration of genomics into existing undergraduate curricula. BackgroundSince the completion of the Human Genome Project in 2003, rapid advancements in genomic science leading to practical applications of genomics have revolutionized all areas of healthcare. Nursing is built on foundational life sciences, including genomics. As the largest segment of the healthcare workforce, who spend the most time with patients and families, nurses play a critical role in healthcare teams integrating genomic knowledge into patient care to improve health and well-being. Consequently, nurses must be equipped with foundational genomic knowledge and skills during their undergraduate education. However, there is wide variability in whether and how nursing programs have incorporated genomics into their curricula. Additionally, nursing faculty may have limited knowledge of foundational genomic concepts and lack confidence in teaching genomics. DesignDiscussion paper MethodsWe aligned domains from the American Association of Colleges of Nursing Essentials and American Nurses Association Essentials of Genomic Nursing. ResultsA map illustrating alignment in multiple areas, which provide examples of ways to integrate genomics into existing nursing curricula. ConclusionAlthough based on domains developed in the United States, the map, curricular resources, example learning outcomes, and clinical vignettes can be used by nursing faculty globally to prepare future nurses who are competent in providing genomics-informed nursing care on entry-to-practice.

Reflections on Genomic Applications to Fisheries: Notes from the Annual Symposium of the Fisheries Society of the British Isles

Reflections on Genomic Applications to Fisheries: Notes from the Annual Symposium of the Fisheries Society of the British Isles

MitoSort: Robust Demultiplexing of Pooled Single-cell Genomics Data Using Endogenous Mitochondrial Variants.

Multiplexing across donors has emerged as a popular strategy to increase throughput, reduce costs, overcome technical batch effects, and improve doublet detection in single-cell genomic studies. To eliminate additional experimental steps, endogenous nuclear genome variants are used for demultiplexing pooled single-cell RNA sequencing (scRNA-seq) data by several computational tools. However, these tools have limitations when applied to single-cell sequencing methods that do not cover nuclear genomic regions well, such as single-cell assay for transposase-accessible chromatin with sequencing (scATAC-seq). Here, we demonstrate that mitochondrial germline variants are an alternative, robust, and computationally efficient endogenous barcode for sample demultiplexing. We propose MitoSort, a tool that uses mitochondrial germline variants to assign cells to their donor of origin and identify cross-genotype doublets in single-cell genomics datasets. We evaluate its performance by using in silico pooled mitochondrial scATAC-seq (mtscATAC-seq) libraries and experimentally multiplexed data with cell hashtags. MitoSort achieves high accuracy and efficiency in genotype clustering and doublet detection for mtscATAC-seq data, addressing the limitations of current computational techniques tailored for scRNA-seq data. Moreover, MitoSort exhibits versatility and can be applied to various single-cell sequencing approaches beyond mtscATAC-seq, provided the mitochondrial variants are reliably detected. Furthermore, we demonstrate the application of MitoSort in a case study where B cells from eight donors were pooled and assayed by single-cell multi-omics sequencing. Altogether, our results demonstrate the accuracy and efficiency of MitoSort, which enables reliable sample demultiplexing in various single-cell genomic applications. MitoSort is available at https://github.com/tangzhj/MitoSort.

DNASimCLR: a contrastive learning-based deep learning approach for gene sequence data classification

BackgroundThe rapid advancements in deep neural network models have significantly enhanced the ability to extract features from microbial sequence data, which is critical for addressing biological challenges. However, the scarcity and complexity of labeled microbial data pose substantial difficulties for supervised learning approaches. To address these issues, we propose DNASimCLR, an unsupervised framework designed for efficient gene sequence data feature extraction.ResultsDNASimCLR leverages convolutional neural networks and the SimCLR framework, based on contrastive learning, to extract intricate features from diverse microbial gene sequences. Pre-training was conducted on two classic large scale unlabelled datasets encompassing metagenomes and viral gene sequences. Subsequent classification tasks were performed by fine-tuning the pretrained model using the previously acquired model. Our experiments demonstrate that DNASimCLR is at least comparable to state-of-the-art techniques for gene sequence classification. For convolutional neural network-based approaches, DNASimCLR surpasses the latest existing methods, clearly establishing its superiority over the state-of-the-art CNN-based feature extraction techniques. Furthermore, the model exhibits superior performance across diverse tasks in analyzing biological sequence data, showcasing its robust adaptability.ConclusionsDNASimCLR represents a robust and database-agnostic solution for gene sequence classification. Its versatility allows it to perform well in scenarios involving novel or previously unseen gene sequences, making it a valuable tool for diverse applications in genomics.

Genomics and Bioinformatics in One Health: Transdisciplinary Approaches for Health Promotion and Disease Prevention.

The One Health concept underscores the interconnectedness of human, animal, and environmental health, necessitating an integrated, transdisciplinary approach to tackle contemporary health challenges. This perspective paper explores the pivotal role of genomics and bioinformatics in advancing One Health initiatives. By leveraging genomic technologies and bioinformatics tools, researchers can decode complex biological data, enabling comprehensive insights into pathogen evolution, transmission dynamics, and host-pathogen interactions across species and environments (or ecosystems). These insights are crucial for predicting and mitigating zoonotic disease outbreaks, understanding antimicrobial resistance patterns, and developing targeted interventions for health promotion and disease prevention. Furthermore, integrating genomic data with environmental and epidemiological information enhances the precision of public health responses. Here we discuss case studies demonstrating successful applications of genomics and bioinformatics in One Health contexts, such as including data integration, standardization, and ethical considerations in genomic research. By fostering collaboration among geneticists, bioinformaticians, epidemiologists, zoologists, and data scientists, the One Health approach can harness the full potential of genomics and bioinformatics to safeguard global health. This perspective underscores the necessity of continued investment in interdisciplinary education, research infrastructure, and policy frameworks to effectively employ these technologies in the service of a healthier planet.

Single nucleus DNA sequencing of flow sorted archived frozen and formalin fixed paraffin embedded solid tumors.

Archived samples, including frozen and formalin fixed paraffin embedded (FFPE) tissues, are a vast resource of clinically annotated materials for the application of high-definition genomics to improve patient management and provide a molecular basis for the delivery of personalized cancer therapeutics. Notably, FFPE tissues are stable, provide repeat sampling of tissues of interest, and can be stored indefinitely at ambient temperature. The development of single cell DNA sequencing (scDNA-seq) technologies provides an unparalleled opportunity for the study of tumor heterogeneity and the identification of often rare subclonal cell populations that drive tumor evolution and progression to advanced therapy resistant disease. However, major limitations to the use of archived tissues for scDNA-seq include the low yields of intact cells in the presence of high levels of subcellular debris in biopsies, and the highly variable quantity and quality of the DNA extracted from samples of interest. The latter is of high significance for the use of FFPE tissues due to the presence of DNA-protein crosslinks. In addition, many samples, notably tumors arising in solid tissues, contain admixtures of reactive stroma, inflammatory cells, and necrosis in immediate contact with tumor cells. To expand their use for translational studies, we optimized flow sorting and sequencing of single nuclei from archived fresh frozen (FF) and FFPE tumor tissues. Our methods, which include isolation of intact nuclei suitable for library preparations, quality control (QC) metrics for each step, and a single cell sequencing bioinformatic processing and analysis pipeline, were validated with flow sorted nuclei from matching FF and FFPE ovarian cancer surgical samples and a sequencing panel of 553 amplicons targeting single nucleotide and copy number variants in genes of interest. Our flow sorting based protocol provides intact nuclei suitable for snDNA-seq from archival FF and FFPE tissues. Furthermore, we have developed QC steps that optimize the preparation and selection of samples for deep single cell clonal profiling. Our data processing pipeline captures rare subclones in tumors with highly variable genomes based on variants in genes of interest.

GEDI: An R Package for Integration of Transcriptomic Data from Multiple Platforms for Bioinformatics Applications.

Transcriptomic data is often expensive and difficult to generate in large cohorts relative to genomic data; therefore, it is often important to integrate multiple transcriptomic datasets from both microarray- and next generation sequencing (NGS)-based transcriptomic data across similar experiments or clinical trials to improve analytical power and discovery of novel transcripts and genes. However, transcriptomic data integration presents a few challenges including reannotation and batch effect removal. We developed the Gene Expression Data Integration (GEDI) R package to enable transcriptomic data integration by combining existing R packages. With just four functions, the GEDI R package makes constructing a transcriptomic data integration pipeline straightforward. Together, the functions overcome the complications in transcriptomic data integration by automatically reannotating the data and removing the batch effect. The removal of the batch effect is verified with principal component analysis and the data integration is verified using a logistic regression model with forward stepwise feature selection. To demonstrate the functionalities of the GEDI package, we integrated five bovine endometrial transcriptomic datasets from the NCBI Gene Expression Omnibus. These transcriptomic datasets were from multiple high-throughput platforms, namely, array-based Affymetrix and Agilent platforms, and NGS-based Illumina paired-end RNA-seq platform. Furthermore, we compared the GEDI package to existing tools and found that GEDI is the only tool that provides a full transcriptomic data integration pipeline including verification of both batch effect removal and data integration for downstream genomic and bioinformatics applications. © 2024 The Author(s). Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: ReadGE, a function to import gene expression datasets Basic Protocol 2: GEDI, a function to reannotate and merge gene expression datasets Basic Protocol 3: BatchCorrection, a function to remove batch effects from gene expression data Basic Protocol 4: VerifyGEDI, a function to confirm successful integration of gene expression data.

Evaluating deep neural networks in optimizing drug discovery and precision medicine: A review

Introduction/Background: Deep neural networks have shown great promise in advancing drug discovery and precision medicine. By leveraging large amounts of complex biomedical and chemical data, deep learning approaches can identify novel targets, predict drug-target and drug-drug interactions, generate new molecular structures, and assist in personalized treatment selection and development. However, fully utilizing deep learning techniques for optimization across the drug development pipeline remains an ongoing challenge. Materials and Methods: A comprehensive literature review was conducted using major bibliographic databases including PubMed, Web of Science, and Scopus. Search terms included combinations of "deep learning", "drug discovery", "precision medicine", "biomedical data", and "neural networks". Over 200 papers published between 2010-2023 related to deep learning applications in pharmacology and genomics were identified and reviewed. Results: Deep learning has been widely applied at various stages of the drug discovery process including target identification/prioritization, lead generation/optimization, and prediction of molecular properties. Convolutional neural networks are commonly used for the representation and classification of biological sequence and image data for tasks such as gene expression analysis and pathogen detection from microscopy images. Graph neural networks effectively model compound structures and interactome networks to predict molecular bindings and disease associations. Multi-modal neural networks integrate diverse data types for personalized treatment response prediction and biomarker discovery. Challenges remain around data and model interpretation, generalization to new targets/diseases, and integration across domains. Discussion: While deep learning has shown promise, rigorous benchmarking and validation on real-world clinical endpoints are still needed to establish usefulness in decision-making. Data and model transparency must be improved to enable scientific insights. Privacy and security risks accompanying "real world" biomedical big data will require ethical practices. Standardization and sharing of resources/protocols could accelerate progress by enabling comparison of techniques. Combining deep learning with other AI paradigms like causal inference may further improve utility in drug discovery and precision healthcare. Conclusion: Deep neural networks demonstrate potential for optimizing drug development and precision medicine applications. Continued advancement relies on addressing challenges around data, models, validation, and ethics. Multi-disciplinary collaborations integrating machine learning, molecular biology, medicine, and other domains are needed to fully realize benefits to patients.