Genomics Community Research Articles

MATES: a deep learning-based model for locus-specific quantification of transposable elements in single cell

Transposable elements (TEs) are crucial for genetic diversity and gene regulation. Current single-cell quantification methods often align multi-mapping reads to either ‘best-mapped’ or ‘random-mapped’ locations and categorize them at the subfamily levels, overlooking the biological necessity for accurate, locus-specific TE quantification. Moreover, these existing methods are primarily designed for and focused on transcriptomics data, which restricts their adaptability to single-cell data of other modalities. To address these challenges, here we introduce MATES, a deep-learning approach that accurately allocates multi-mapping reads to specific loci of TEs, utilizing context from adjacent read alignments flanking the TE locus. When applied to diverse single-cell omics datasets, MATES shows improved performance over existing methods, enhancing the accuracy of TE quantification and aiding in the identification of marker TEs for identified cell populations. This development facilitates the exploration of single-cell heterogeneity and gene regulation through the lens of TEs, offering an effective transposon quantification tool for the single-cell genomics community.

Abstract B093: Facilitating conversations on cancer genomic education: Development of a toolkit for community advisory boards

Abstract Community Advisory Boards are representative groups of individuals that reside in a particular area and serve an important role in the research process. Often CABs are the voice of the community, rather it be key community stakeholders, business, or community organizations. The purpose of this project is to describe the development of a cancer genomic toolkit and initial introduction of the toolkit to local community advisory boards to CAB members interested in cancer genomic topics to increase inclusion and awareness of emerging topics and activities around cancer genomic research. Existing CAB members from the Meharry Medical College/Vanderbilt-Ingram Cancer Center/Tennessee State University Cancer Partnership (MVTCP) and the Research Centers in Minority Institutions (RCMI) at Meharry Medical College were introduced to a cancer genomic toolkit that is in development. CAB members were presented the toolkit during a regularly scheduled CAB meeting for approximately 30 minutes and were allowed 10 minutes to answer questions. After the presentation of the cancer genomic toolkit, CAB members were sent a word document version of the toolkit to provide any feedback for improvement during the development stage. CAB members were allowed up to two months to review the cancer genomic toolkit. Directions for review were to use track changes and leave comments or provide direct feedback via email response. To date, one CAB member has provided individualized feedback. Group feedback included questions and open discussion around historic research participation. Next steps are to introduce the tool to a newly formed genomic community advisory board and begin to reiterate the toolkit for evaluation and implementation for other local CABs. Citation Format: Calandra G. Whitted. Facilitating conversations on cancer genomic education: Development of a toolkit for community advisory boards [abstract]. In: Proceedings of the 17th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2024 Sep 21-24; Los Angeles, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2024;33(9 Suppl):Abstract nr B093.

Abstract B115: Intensive Course in genetic cancer risk assessment: Empowering clinicians, expanding clinical knowledge, and elevating care in underserved populations

Abstract Background: Despite efforts to integrate genetic cancer risk assessment (GCRA) services into mainstream medicine, the dearth of healthcare providers adequately trained in clinical cancer genetics remains a significant barrier to care, particularly in underserved communities. Since 2001, City of Hope has helped address this need through a distance mediated lntensive Course (IC) and Clinical Cancer Genomics Community of Practice (CCGCoP), with a focus on training and supporting clinicians who deliver care to underserved patient populations. Here we report on the reach, education, and practice outcomes from the past five annual cohorts of the IC in its pursuit to expand GCRA care to underserved areas and minority populations. Methods: Practice catchment area and patient population data were collected on the course application. Cancer genetics knowledge was assessed at baseline and immediate post-course; professional self-efficacy and practice changes were measured at baseline, immediate, and 12 months post-course. Results: Of 762 participants from five IC cohorts (2020-2024), 566 (74%) indicated they provide all or part of their care in an underserved area and/or to minority populations; 55 (10%) practice outside the U.S. Of these 566 clinicians, mean pre-to-post course knowledge scores increased 59%, and mean pre-to-post self-efficacy scores increased by 38%. Of 195 participants who completed a one-year post-course practice survey, 68 (35%) did not practice GCRA at baseline, of whom 61 (88%) reported implementing GCRA into their practice one-year post-course. Of 126 participants who were practicing GCRA at baseline, 55 (44%) reported an increase in their GCRA patient volume at one-year post-course. Open-ended feedback revealed that participants highly valued IC training and ongoing CCGCoP support for building and sustaining their GCRA skills, resources, and practices. Conclusions:Results demonstrate the efficacy of distance-mediated training and ongoing practice support in enhancing clinician competencies and knowledge of practicing GCRA therefore increasing access to GCRA services for underserved and minority patient populations. Efforts are underway to leverage the resources of the IC and CCGCoP to develop a more flexible, cost-effective course model and collaborate with champions to increase access to best practices in GCRA in diverse underserved settings across the US and internationally. Citation Format: Dev Tanna, John Luna, Alex Capasso, Kathleen Blazer. Intensive Course in genetic cancer risk assessment: Empowering clinicians, expanding clinical knowledge, and elevating care in underserved populations [abstract]. In: Proceedings of the 17th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2024 Sep 21-24; Los Angeles, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2024;33(9 Suppl):Abstract nr B115.

Systematic analysis of SARS-CoV-2 Omicron subvariants' impact on B and T cell epitopes.

Epitopes are specific structures in antigens that are recognized by the immune system. They are widely used in the context of immunology-related applications, such as vaccine development, drug design, and diagnosis / treatment / prevention of disease. The SARS-CoV-2 virus has represented the main point of interest within the viral and genomic surveillance community in the last four years. Its ability to mutate and acquire new characteristics while it reorganizes into new variants has been analyzed from many perspectives. Understanding how epitopes are impacted by mutations that accumulate on the protein level cannot be underrated. With a focus on Omicron-named SARS-CoV-2 lineages, including the last WHO-designated Variants of Interest, we propose a workflow for data retrieval, integration, and analysis pipeline for conducting a database-wide study on the impact of lineages' characterizing mutations on all T cell and B cell linear epitopes collected in the Immune Epitope Database (IEDB) for SARS-CoV-2. Our workflow allows us to showcase novel qualitative and quantitative results on 1) coverage of viral proteins by deposited epitopes; 2) distribution of epitopes that are mutated across Omicron variants; 3) distribution of Omicron characterizing mutations across epitopes. Results are discussed based on the type of epitope, the response frequency of the assays, and the sample size. Our proposed workflow can be reproduced at any point in time, given updated variant characterizations and epitopes from IEDB, thereby guaranteeing to observe a quantitative landscape of mutations' impact on demand. A big data-driven analysis such as the one provided here can inform the next genomic surveillance policies in combatting SARS-CoV-2 and future epidemic viruses.

A genome assembly and transcriptome atlas of the inbred Babraham pig to illuminate porcine immunogenetic variation.

The inbred Babraham pig serves as a valuable biomedical model for research due to its high level of homozygosity, including in the major histocompatibility complex (MHC) loci and likely other important immune-related gene complexes, which are generally highly diverse in outbred populations. As the ability to control for this diversity using inbred organisms is of great utility, we sought to improve this resource by generating a long-read whole genome assembly and transcriptome atlas of a Babraham pig. The genome was de novo assembled using PacBio longreads and error-corrected using Illumina shortreads. Assembled contigs were then mapped to the porcine reference assembly, Sscrofa11.1, to generate chromosome-level scaffolds. The resulting TPI_Babraham_pig_v1 assembly is nearly as contiguous as Sscrofa11.1 with a contig N50 of 34.95 Mb and contig L50 of 23. The remaining sequence gaps are generally the result of poor assembly across large and highly repetitive regions such as the centromeres and tandemly duplicated gene families, including immune-related gene complexes, that often vary in gene content between haplotypes. We also further confirm homozygosity across the Babraham MHC and characterize the allele content and tissue expression of several other immune-related gene complexes, including the antibody and T cell receptor loci, the natural killer complex, and the leukocyte receptor complex. The Babraham pig genome assembly provides an alternate highly contiguous porcine genome assembly as a resource for the livestock genomics community. The assembly will also aid biomedical and veterinary research that utilizes this animal model such as when controlling for genetic variation is critical.

Genomics and synthetic community experiments uncover the key metabolic roles of acetic acid bacteria in sourdough starter microbiomes.

This study is a comprehensive genomic and ecological survey of acetic acid bacteria (AAB) isolated from sourdough starters. By combining comparative genomics with manipulative experiments using synthetic microbiomes, we demonstrate that even strains with >97% average nucleotide identity can shift important microbiome functions, underscoring the importance of species and strain diversity in microbial systems. We also demonstrate the utility of sourdough starters as a model system to understand the consequences of genomic diversity at the strain and species level on multispecies communities. These results are also relevant to industrial and home-bakers as we uncover the importance of AAB in shaping properties of sourdough starters that have direct impacts on sensory notes and the quality of sourdough bread.

Prevalence and spectrum of germline pathogenic variants in cancer susceptibility genes among mexican patients with exocrine pancreatic cancer

BackgroundAlthough universal germline genetic testing is recommended for patients with exocrine pancreatic cancer (PC), access to genetic testing remains limited in low- and middle-income countries. This study aims to narrow the gap in our understanding of the spectrum of germline pathogenic and likely pathogenic variants (PVs) in cancer susceptibility genes in the Mexican population. MethodsThe landscape of PVs in cancer susceptibility genes was identified by next-generation sequencing multigene panel assays among patients with PC who were enrolled in the Clinical Cancer Genomics Community Research Network prospective registry in Mexico City. ResultsFrom August 2019 to April 2023, 137 patients underwent genetic testing. The median age at diagnosis was 60 years (range 36–85), 58.4 % were women, and 38.7 % were metastatic at diagnosis. The frequency of germline PVs was 16 % (n = 22): ATM 36.4 % (n = 8), CDKN2A/p16INK4A 27.3 % (n = 6), BRCA2 9.1 % (n = 2), PALB2 9.1 % (n = 2), CHEK2 9.1 % (n = 2), TP53 4.5 % (n = 1), and NF1 4.5 % (n = 1). Additionally, 2 carriers of monoallelic germline variants in MUTYH were identified. No significant differences were observed between carriers and non-carriers in terms of family history of pancreatic cancer. ConclusionsWe identified a significant frequency of actionable germline PVs in Mexicans with PC, wherein the majority were in a broad spectrum of genes associated with the homologous recombination DNA repair mechanism. Most pancreatic cancer associated PVs were detected in non-BRCA genes, so our findings support the recommendation of multigene panel testing for genetic cancer risk assessment of Mexican individuals with PC.

Comprehensive analysis of Novel mutations in CCM1/KRIT1 and CCM2/MGC4607 and their clinical implications in Cerebral Cavernous malformations

BackgroundCerebral Cavernous Malformations (CCM) is a genetic disease characterized by vascular abnormalities in the brain and spinal cord, affecting 0.4-0.5 % of the population. We identified two novel pathogenic mutations, CCM1/KRIT1 c.811delT (p.Trp271GlyfsTer5) and CCM2/MGC4607 c.613_614insGG p.Glu205GlyfsTer31), which disrupt crucial protein domains and potentially alter disease progression. ObjectiveThe study aims to comprehensively analyze a Brazilian cohort of CCM patients, integrating genetic, clinical, and structural aspects. Specifically, we sought to identify novel mutations within the CCM complex, and explore their potential impact on disease progression. MethodsWe conducted a detailed examination of neuroradiological and clinical features in both symptomatic and asymptomatic CCM patients, performing genetic analyses through sequencing of the CCM1/KRIT1, CCM2/MGC4607, and CCM3/PDCD10 genes In silico structural predictions were carried out using PolyPhen-2, SIFT, and Human Genomics Community tools. Protein-protein interactions and docking analyses were explored using the STRING database. ResultsGenetic analysis identifies 6 pathogenic mutations, 4 likely pathogenic, 1 variants of uncertain significance, and 7 unclassified mutations, including the novel mutations in CCM1 c.811delT and CCM2 c.613_614insGG. In silico structural analysis revealed significant alterations in protein structure, supporting their pathogenicity. Protein-protein interaction analysis indicated nuanced impacts on cellular processes. Clinically, we observed a broad spectrum of symptoms, including seizures and focal neurological deficits. However, no statistically significant differences were found in lesion burden, age of first symptom onset, or sex between the identified CCM1/KRIT1 and CCM2/MGC4607 mutations among all patients studied. ConclusionThis study enhances the understanding of CCM by linking clinical variability, genetic mutations, and structural effects. The identification of these novel mutations opens new avenues for research and potential therapeutic strategies.

Strengthening bioremediation potential: Enterobacter ludwigii ES2 for combined nicosulfuron and Cd contamination through whole genome and microbial diversity community analysis

Nicosulfuron and Cd are common pollutants that pose significant threats to the environment and human health, particularly under combined stress. This study is the first to remediate environmental nicosulfuron and Cd under combined stress using microbiological techniques. Enterobacter ludwigii ES2 was isolated, characterized, and demonstrated to degrade 93.80 % of nicosulfuron and remove 59.64 % of Cd within 4 d. Potential functional genes, including nicosulfuron degradation genes gstA, gstB, glnQ, glnP, mreB, and sixA, and Cd tolerance/removal-related genes mntA, mntB, mntH, dnaK, znuA, and zupt, were predicted by sequencing the whole genome of strain ES2, and their expression was verified by qRT-PCR. Strain ES2 managed oxidative stress induced by Cd through superoxide dismutase, glutathione, catalase, peroxidase, and malondialdehyde. Furthermore, to repair compound stress, up to 90.48 % of nicosulfuron and 67.74 % of Cd were removed. The community structure analysis indicated that Enterobacteriaceae, Sphingomonadaceae, and Gemmatimonadaceae were dominant populations, with ES2 stably colonizing and becoming the dominant bacterium. In summary, ES2 demonstrated significant potential in remediating nicosulfuron and Cd pollution from various perspectives, providing a solid theoretical foundation.

Genetics healthcare providers' experiences counseling patients with results from consumer genomic testing.

Consumer genomic testing (CGT), including direct-to-consumer and consumer-initiated testing, is increasingly widespread yet has limited regulatory oversight. To assess the current state, we surveyed genetics healthcare providers' experiences with CGT. A retrospective survey about experiences counseling on CGT results was completed by 139 respondents recruited from the National Society of Genetic Counselors, Clinical Cancer Genomics Community of Practice, and genetics professional societies. Among respondents, 41% disagreed with the statement that potential benefits of CGT outweigh harms, 21% agreed, and 38% were undecided. A total of 94% encountered ≥1 challenge counseling CGT patients, including adverse psychosocial events (76%), incorrect variant interpretation (68%), and unconfirmed results (69%); unconfirmed results were more common among oncology providers (p = 0.03). Providers reporting higher total challenge scores (p = 0.004) or more psychosocial or interpretation challenges (p ≤ 0.01) were more likely to indicate CGT harms outweigh benefits. Those with higher CGT clinical volume were more likely to indicate benefits outweigh harms (p = 0.003). Additional CGT challenges included patient understanding and communication of results, false negatives, incorrect testing/care, and financial costs; seven respondents (6%) documented positive outcomes. Providers counseling CGT patients encounter psychosocial and medical challenges. Collaborations between regulators, CGT laboratories, providers, and consumers may help mitigate risks.

A multiomic characterization of the leukemia cell line REH using short- and long-read sequencing.

The B-cell acute lymphoblastic leukemia (ALL) cell line REH, with the t(12;21) ETV6::RUNX1 translocation, is known to have a complex karyotype defined by a series of large-scale chromosomal rearrangements. Taken from a 15-yr-old at relapse, the cell line offers a practical model for the study of pediatric B-ALL. In recent years, short- and long-read DNA and RNA sequencing have emerged as a complement to karyotyping techniques in the resolution of structural variants in an oncological context. Here, we explore the integration of long-read PacBio and Oxford Nanopore whole-genome sequencing, IsoSeq RNA sequencing, and short-read Illumina sequencing to create a detailed genomic and transcriptomic characterization of the REH cell line. Whole-genome sequencing clarified the molecular traits of disrupted ALL-associated genes including CDKN2A, PAX5, BTG1, VPREB1, and TBL1XR1, as well as the glucocorticoid receptor NR3C1 Meanwhile, transcriptome sequencing identified seven fusion genes within the genomic breakpoints. Together, our extensive whole-genome investigation makes high-quality open-source data available to the leukemia genomics community.

Metadata functional requirements for genomic data practice and curation

Introduction. The rapid accumulation of genomic data and their widespread reuse in clinical and scientific practice demands more effective description and organization of genomic information, which poses new challenges in developing metadata schemas. Based on a previously developed taxonomy of metadata requirements, this paper reports the results from a survey that addresses these new challenges. Method. The survey gathered empirical data from 156 genomics scientists to identify context-sensitive metadata functional models for genome curation. The study further investigated the metadata elements from four well-known genomic metadata schemes against the functional requirements for genome metadata. Analysis. The survey data were employed the statistical package STATA, to produce descriptive statistics, factor analysis, Fisher’s exact test, and related reports. Results. Analysis of the empirical results revealed that genomics scientists recognize specific sets of criteria for metadata needs in the genome-curation context. Twenty one metadata requirements were reduced to five factor constructs. The ranking of these constructs in decreasing order is: portability, reusability, manipulability, sufficiency, interoperability, extensibility, and modularity. The Fisher exact test results revealed that the genomic community required rich context and technical related metadata elements to facilitate data exchanges and experimental operations in genome curation. Conclusion. The findings indicated that genomics scientists developed metadata to meet the needs in genome curation activities related to data wrangling, integrations across platforms and databases, and data reuse. Architectural layout as flat file needs extra administrative metadata to support data sharing and documentation. The resulting metadata requirement model can serve as valuable resources to genome scientists, data curators and administrators for designing metadata schema and developing data-curation policies.

The Immunopeptidomics Ontology (ImPO).

The adaptive immune response plays a vital role in eliminating infected and aberrant cells from the body. This process hinges on the presentation of short peptides by major histocompatibility complex Class I molecules on the cell surface. Immunopeptidomics, the study of peptides displayed on cells, delves into the wide variety of these peptides. Understanding the mechanisms behind antigen processing and presentation is crucial for effectively evaluating cancer immunotherapies. As an emerging domain, immunopeptidomics currently lacks standardization-there is neither an established terminology nor formally defined semantics-a critical concern considering the complexity, heterogeneity, and growing volume of data involved in immunopeptidomics studies. Additionally, there is a disconnection between how the proteomics community delivers the information about antigen presentation and its uptake by the clinical genomics community. Considering the significant relevance of immunopeptidomics in cancer, this shortcoming must be addressed to bridge the gap between research and clinical practice. In this work, we detail the development of the ImmunoPeptidomics Ontology, ImPO, the first effort at standardizing the terminology and semantics in the domain. ImPO aims to encapsulate and systematize data generated by immunopeptidomics experimental processes and bioinformatics analysis. ImPO establishes cross-references to 24 relevant ontologies, including the National Cancer Institute Thesaurus, Mondo Disease Ontology, Logical Observation Identifier Names and Codes and Experimental Factor Ontology. Although ImPO was developed using expert knowledge to characterize a large and representative data collection, it may be readily used to encode other datasets within the domain. Ultimately, ImPO facilitates data integration and analysis, enabling querying, inference and knowledge generation and importantly bridging the gap between the clinical proteomics and genomics communities. As the field of immunogenomics uses protein-level immunopeptidomics data, we expect ImPO to play a key role in supporting a rich and standardized description of the large-scale data that emerging high-throughput technologies are expected to bring in the near future. Ontology URL: https://zenodo.org/record/10237571 Project GitHub: https://github.com/liseda-lab/ImPO/blob/main/ImPO.owl.

FungiRegEx: A Tool for Pattern Identification in Fungal Proteomic Sequences Using Regular Expressions

In the context of proteomic-scale research, it is imperative to automatically analyze numerous species and subspecies to discern distinctive characteristics present in multiple species of the fungi kingdom that contain sequences of interest that could fulfill a specific biological function. To achieve this, complex sequences must be recognized within an organism’s entire set of proteomes. Our study presents FungiRegEx, a piece of software that facilitates the identification of regular expressions of proteomes of fungal organisms and uses real-time data retrieval of the different species from the JGI Mycocosm database without the need to download any file. Integrating a graphical user interface that makes it easy to use, the tool offers regular expression searches on 2402 fungal species from the JGI Mycocosm portal. The tool was validated with the AXSXG sequence and the RXRL effector, demonstrating the effectiveness of FungiRegEx in identifying user-defined patterns in the recovered sequences. This tool allows customization and filtering, and it can save results if required, combining speed, adaptability, and ease of use. It provides an experience without a console and programming, displaying the results in a GUI and making them easier to read. Its architecture guarantees optimized use of resources, time consumption, and implementation flexibility, allowing the customization of specific software parameters for resource management. The tool’s potential for future research and exploration is emphasized, providing a nuanced perspective on its practical use within the fungal genomics community. The tools are available at the addresses mentioned in the text.

Metagenomic dissection of the intestinal microbiome in the giant river prawn Macrobrachium rosenbergii infected with Decapod iridescent virus 1

Microbiome in the intestines of aquatic invertebrates plays pivotal roles in maintaining intestinal homeostasis, especially when the host is exposed to pathogen invasion. Decapod iridescent virus 1 (DIV1) is a devastating virus seriously affecting the productivity and success of crustacean aquaculture. In this study, a metagenomic analysis was conducted to investigate the genomic sequences, community structure and functional characteristics of the intestinal microbiome in the giant river prawn Macrobrachiumrosenbergii infected with DIV1. The results showed that DIV1 infection could significantly reduce the diversity and richness of intestinal microbiome. Proteobacteria represented the largest taxon at the phylum level, and at the species level, the abundance of Gonapodya prolifera and Solemya velum gill symbiont increased significantly following DIV1 infection. In the infected prawns, four metabolic pathways related to purine metabolism, pyrimidine metabolism, glycerophospholipid metabolism, and pentose phosphate pathway, and five pathways related to nucleotide excision repair, homologous recombination, mismatch repair, base excision repair, and DNA replication were significantly enriched. Moreover, several immune response related pathways, such as shigellosis, bacterial invasion of epithelial cells, Salmonella infection, and Vibrio cholerae infection were repressed, indicating that secondary infection in M. rosenbergii may be inhibited via the suppression of these immune related pathways. DIV1 infection led to the induction of microbial carbohydrate enzymes such as the glycoside hydrolases (GHs), and reduced the abundance and number of antibiotic-resistant ontologies (AROs). A variety of AROs were identified from the microbiota, and mdtF and lrfA appeared as the dominant genes in the detected AROs. In addition, antibiotic efflux, antibiotic inactivation, and antibiotic target alteration were the main antibiotic resistance mechanisms. Collectively, the data would enable a deeper understanding of the molecular response of intestinal microbiota to DIV1, and offer more insights into its roles in prawn resistance to DIVI infection.

VARista: a free web platform for streamlined whole-genome variant analysis across T2T, hg38, and hg19.

With the increasing importance of genomic data in understanding genetic diseases, there is an essential need for efficient and user-friendly tools that simplify variant analysis. Although multiple tools exist, many present barriers such as steep learning curves, limited reference genome compatibility, or costs. We developed VARista, a free web-based tool, to address these challenges and provide a streamlined solution for researchers, particularly those focusing on rare monogenic diseases. VARista offers a user-centric interface that eliminates much of the technical complexity typically associated with variant analysis. The tool directly supports VCF files generated using reference genomes hg19, hg38, and the emerging T2T, with seamless remapping capabilities between them. Features such as gene summaries and links, tissue and cell-specific gene expression data for both adults and fetuses, as well as automated PCR design and integration with tools such as SpliceAI and AlphaMissense, enable users to focus on the biology and the case itself. As we demonstrate, VARista proved effective in narrowing down potential disease-causing variants, prioritizing them effectively, and providing meaningful biological context, facilitating rapid decision-making. VARista stands out as a freely available and comprehensive tool that consolidates various aspects of variant analysis into a single platform that embraces the forefront of genomic advancements. Its design inherently supports a shift in focus from technicalities to critical thinking, thereby promoting better-informed decisions in genetic disease research. Given its unique capabilities and user-centric design, VARista has the potential to become an essential asset for the genomic research community. https://VARista.link.

Literature mining discerns latent disease-gene relationships.

Dysregulation of a gene's function, either due to mutations or impairments in regulatory networks, often triggers pathological states in the affected tissue. Comprehensive mapping of these apparent gene-pathology relationships is an ever-daunting task, primarily due to genetic pleiotropy and lack of suitable computational approaches. With the advent of high throughput genomics platforms and community scale initiatives such as the Human Cell Landscape project, researchers have been able to create gene expression portraits of healthy tissues resolved at the level of single cells. However, a similar wealth of knowledge is currently not at our finger-tip when it comes to diseases. This is because the genetic manifestation of a disease is often quite diverse and is confounded by several clinical and demographic covariates. To circumvent this, we mined ∼18 million PubMed abstracts published till May 2019 and automatically selected ∼4.5 million of them that describe roles of particular genes in disease pathogenesis. Further, we fine-tuned the pretrained bidirectional encoder representations from transformers (BERT) for language modeling from the domain of natural language processing to learn vector representation of entities such as genes, diseases, tissues, cell-types, etc., in a way such that their relationship is preserved in a vector space. The repurposed BERT predicted disease-gene associations that are not cited in the training data, thereby highlighting the feasibility of in silico synthesis of hypotheses linking different biological entities such as genes and conditions. PathoBERT pretrained model: https://github.com/Priyadarshini-Rai/Pathomap-Model. BioSentVec-based abstract classification model: https://github.com/Priyadarshini-Rai/Pathomap-Model. Pathomap R package: https://github.com/Priyadarshini-Rai/Pathomap.

Fingerprint Finder: Identifying Genomic Fingerprint Sites in Cotton Cohorts for Genetic Analysis and Breeding Advancement.

Genomic data in Gossypium provide numerous data resources for the cotton genomics community. However, to fill the gap between genomic analysis and breeding field work, detecting the featured genomic items of a subset cohort is essential for geneticists. We developed FPFinder v1.0 software to identify a subset of the cohort's fingerprint genomic sites. The FPFinder was developed based on the term frequency-inverse document frequency algorithm. With the short-read sequencing of an elite cotton pedigree, we identified 453 pedigree fingerprint genomic sites and found that these pedigree-featured sites had a role in cotton development. In addition, we applied FPFinder to evaluate the geographical bias of fiber-length-related genomic sites from a modern cotton cohort consisting of 410 accessions. Enriching elite sites in cultivars from the Yangtze River region resulted in the longer fiber length of Yangze River-sourced accessions. Apart from characterizing functional sites, we also identified 12,536 region-specific genomic sites. Combining the transcriptome data of multiple tissues and samples under various abiotic stresses, we found that several region-specific sites contributed to environmental adaptation. In this research, FPFinder revealed the role of the cotton pedigree fingerprint and region-specific sites in cotton development and environmental adaptation, respectively. The FPFinder can be applied broadly in other crops and contribute to genetic breeding in the future.

Future-proofing genomic data and consent management: a comprehensive review of technology innovations.

Genomic information is increasingly used to inform medical treatments and manage future disease risks. However, any personal and societal gains must be carefully balanced against the risk to individuals contributing their genomic data. Expanding our understanding of actionable genomic insights requires researchers to access large global datasets to capture the complexity of genomic contribution to diseases. Similarly, clinicians need efficient access to a patient's genome as well as population-representative historical records for evidence-based decisions. Both researchers and clinicians hence rely on participants to consent to the use of their genomic data, which in turn requires trust in the professional and ethical handling of this information. Here, we review existing and emerging solutions for secure and effective genomic information management, including storage, encryption, consent, and authorization that are needed to build participant trust. We discuss recent innovations in cloud computing, quantum-computing-proof encryption, and self-sovereign identity. These innovations can augment key developments from within the genomics community, notably GA4GH Passports and the Crypt4GH file container standard. We also explore how decentralized storage as well as the digital consenting process can offer culturally acceptable processes to encourage data contributions from ethnic minorities. We conclude that the individual and their right for self-determination needs to be put at the center of any genomics framework, because only on an individual level can the received benefits be accurately balanced against the risk of exposing private information.

Environmental processes and health implications potentially mediated by dust-borne bacteria.

Understanding microbial migration and survival mechanisms in dust events (DEs) can elucidate genetic and metabolic exchange between environments and help predict the atmospheric pathways of ecological and health-related microbial stressors. Dust-borne microbial communities have been previously characterized, but the impact and interactions between potentially active bacteria within transported communities remain limited. Here, we analysed samples collected during DEs in Israel, using amplicon sequencing of the 16S rRNA genes and transcripts. Different air trajectories and wind speeds were associated not only with the genomic microbial community composition variations but also with specific 16S rRNA bacterial transcripts. Potentially active dust-borne bacteria exhibited positive interactions, including carbon and nitrogen cycling, biotransformation of heavy metals, degradation of organic compounds, biofilm formation, and the presence of pathogenic taxa. This study provides insights into the potential interactive relationships and survival strategies of microorganisms within the extreme dust environment.