Genome Annotation Research Articles

Transposable element accumulation drives genome size increase in Hylesia metabus (Lepidoptera: Saturniidae), an urticating moth species from South America

Abstract We present the first nuclear genome assembly and a complete mitogenome for Hylesia metabus (Arthropoda; Insecta; Lepidoptera; Saturniidae). The assembled nuclear genome sequence is 1,271 Mb long, which is among the 10 largest lepidopteran genome assemblies published to date. It is scaffolded in 31 pseudo chromosomes, has a BUSCO score of 99.5%, and has a highly conserved synteny compared to phylogenetically close species. Repetitive elements make up 67% of the nuclear genome and are mainly located in intergenic regions, among which LINEs were predominant, with CR1-Zenon being the most abundant. Phylogenetic and comparative analyses of H. metabus assembly and 17 additional Saturniidae and Sphingidae assemblies suggested that an accumulation of repetitive elements likely led to the increased size of H. metabus’ genome. Gene annotation using Helixer identified 26,122 transcripts. The Z scaffold was identified using both a synteny analysis and variations of coverage for two resequenced male and female H. metabus. The H. metabus nuclear genome and mitogenome assemblies can be found and browsed on the BIPAA website and constitute useful resources for future population and comparative genomics studies.

A cross-tissue transcriptome-wide association study reveals GRK4 as a novel susceptibility gene for COPD

AbstractChronic obstructive pulmonary disease (COPD) is a prevalent respiratory disorder with environmental factors being the primary risk determinants. However, genetic factors also substantially contribute to the susceptibility and progression of COPD. Although genome-wide association studies (GWAS) have identified several loci associated with COPD susceptibility, the specific pathogenic genes underlying these loci, along with their biological functions and roles within regulatory networks, remain unclear. This lack of clarity constrains our ability to achieve a deeper understanding of the genetic basis of COPD. This study leveraged the FinnGen R11 genetic dataset, comprising 21,617 cases and 372,627 controls, along with GTEx V8 eQTLs data to conduct a cross-tissue transcriptome-wide association study (TWAS). Initially, we performed a cross-tissue TWAS analysis using the Unified Test for Molecular Signatures (UTMOST), followed by validation of the UTMOST findings in single tissues using the Functional Summary-based Imputation (FUSION) method and conditional and joint (COJO) analyses of the identified genes. Subsequently, candidate susceptibility genes were screened using Multi-marker Analysis of Genomic Annotation (MAGMA). The causal relationship between these candidate genes and COPD was further evaluated through summary data-based Mendelian randomization (SMR), colocalization analysis, and Mendelian randomization (MR). Additionally, the identified results were validated against the COPD dataset in the GWAS Catalog (GCST90399694). GeneMANIA was employed to further explore the functional significance of these susceptibility genes. In the cross-tissue TWAS analysis (UTMOST), we identified 17 susceptibility genes associated with COPD. Among these, a novel susceptibility gene, G protein-coupled receptor kinase 4 (GRK4), was validated through single-tissue TWAS (FUSION) and MAGMA analyses, with further confirmation via SMR, MR, and colocalization analyses. Moreover, GRK4 was validated in an independent dataset. This study identifies GRK4 as a potential novel susceptibility gene for COPD, which may influence disease risk by exacerbating inflammatory responses. The findings address gaps in previous single-tissue GWAS studies, revealing consistent expression and potential function of GRK4 across different tissues. However, considering the study’s limitations, further investigation and validation of GRK4’s role in COPD are warranted.

Time-course RNA sequencing reveals high similarity in mRNAome between hepatic stellate cells activated by agalactosyl IgG and TGF-β1.

Previous studies have demonstrated the clinical relevance of aberrant serum immunoglobulin G (IgG) N-glycomic profiles in liver fibrosis and the pathogenic effects of agalactosyl IgG on activating hepatic stellate cells (HSCs). However, the dynamics of gene expression changes during HSC activation by agalactosyl IgG remain poorly understood. We performed RNA sequencing to analyze the mRNAome of human LX-2 HSCs at multiple time points after treatment with agalactosyl IgG and then compared these results with those obtained after normal IgG and transforming growth factor (TGF)-β1 treatments. Gene expression changes were significantly pronounced on day 5 and subsided by day 11 after HSC activation. A high degree of similarity in gene expression patterns between HSCs treated with agalactosyl IgG and TGF-β1 was observed, of which 1796 and 1785 differentially expressed genes (DEGs) were identified, respectively. Disease ontology analyses revealed that 114 and 105 DEGs in activated HSCs following agalactosyl IgG and TGF-β1 treatments, respectively, were linked to liver cirrhosis, hepatitis, fatty liver disease, hepatitis B, and alcoholic hepatitis, with CCL5 and FAS being the most commonly affected genes. DEGs associated with liver fibrosis or aforementioned liver diseases involved in gene annotation, physiological functions, and signaling pathways regarding secretion of cytokines and chemokines, expression of fibrosis-related growth factors and their receptors, modification of extracellular matrices, and regulation of cell viability in activated HSCs. In conclusion, this study characterized the dynamics of mRNAome and gene networks and identified the liver fibrosis-related DEGs during HSC activation by agalactosyl IgG and TGF-β1.

Identification of RppSLN from an Elite Landrace: A Major Locus Conferring Resistance to Southern Corn Rust in Maize (Zea mays L.)

Southern corn rust (SCR) is one of the most destructive foliar diseases in maize (Zea mays L.), resulting in significant yield losses. Therefore, the continuous identification of disease-resistant germplasm and the deployment of resistant hybrids is essential for durably controlling SCR. The objective of this research was to identify and characterize resistance loci against SCR in maize to expand disease management strategies. Here, we identified a maize landrace with high resistance to SCR ‘Silunuo’ (SLN) approaching complete immunity. We backcrossed it with a susceptible inbred line, N531, to generate a stable SCR-resistant introgression line N531_R. By crossing it with F35 (a susceptible inbred line), we created a large F2 segregating population and mapped a major SCR-resistant locus on chromosome 10, known as RppSLN. Based on the genome assembly and annotation, we found that RppSLN harbors two NBS-LRR (nucleotide binding site–leucine-rich repeat) genes, namely Zmays10G000430 and Zmays10G000440. These NBS-LRR genes were significantly induced during artificial inoculation with Puccinia polysora, suggesting that they might be candidate genes collectively contributing to the resistance level at this locus. In conclusion, this study identified a major SCR resistance locus directly isolated from a landrace, providing valuable support and information for expanding new disease-resistant germplasms and promoting the utilization of landraces.

High-quality genome assembly and annotation of the crested gecko (Correlophus ciliatus).

Correlophus ciliatus, or the crested gecko, is widely kept as a pet in many countries around the world due to its ease to care and bred and its high survival rate. However, there is limited number of genomic studies on the crested gecko. In this study, we generated a high-quality chromosome-level genome assembly of the crested gecko by combining Nanopore, Illumina, and Hi-C data. The genome assemble has a size of 1.66 Gb, with scaffold N50 of 109.97 Mb, and 99.52% of the scaffold anchored on 19 chromosomes. The BUSCO analysis indicated a gene completeness of 90.3% (n=7,480), including 6,673 (89.2%) single-copy genes and 84 (1.1%) duplicated genes. Additionally, we identified 21,065 protein-coding genes using the MAKER3 annotation toolkit, with 41.98% (697.51 Mb) consisting of repetitive elements. Among these, 21,037 genes were validated through InterProScan5. Our study is the first to report a chromosome-level genome for the crested gecko. It provides valuable genomic resources for understanding molecular mechanisms under many interesting traits of the species.

Shared genetic architecture between COVID-19 and irritable bowel syndrome: a large-scale genome-wide cross-trait analysis

BackgroundIt has been reported that COVID-19 patients have an increased risk of developing IBS; however, the underlying genetic mechanisms of these associations remain largely unknown. The aim of this study was to investigate potential shared SNPs, genes, proteins, and biological pathways between COVID-19 and IBS by assessing pairwise genetic correlations and cross-trait genetic analysis.Materials and methodsWe assessed the genetic correlation between three COVID-19 phenotypes and IBS using linkage disequilibrium score regression (LDSC) and high-definition likelihood (HDL) methods. Two different sources of IBS data were combined using METAL, and the Multi-trait analysis of GWAS (MTAG) method was applied for multi-trait analysis to enhance statistical robustness and discover new genetic associations. Independent risk loci were examined using genome-wide complex trait analysis (GCTA)-conditional and joint analysis (COJO), multi-marker analysis of genomic annotation (MAGMA), and functional mapping and annotation (FUMA), integrating various QTL information and methods to further identify risk genes and proteins. Gene set variation analysis (GSVA) was employed to compute pleiotropic gene scores, and combined with immune infiltration algorithms, IBS patients were categorized into high and low immune infiltration groups.ResultsWe found a positive genetic correlation between COVID-19 infection, COVID-19 hospitalization, and IBS. Subsequent multi-trait analysis identified nine significantly associated genomic loci. Among these, eight genetic variants were closely related to the comorbidity of IBS and COVID-19. The study also highlighted four genes and 231 proteins associated with the susceptibility to IBS identified through various analytical strategies and a stratification approach for IBS risk populations.ConclusionsOur study reveals a shared genetic architecture between these two diseases, providing new insights into potential biological mechanisms and laying the groundwork for more effective interventions.

The Genomic SSR Millets Database (GSMDB): enhancing genetic resources for sustainable agriculture.

The global population surge demands increased food production and nutrient-rich options to combat rising food insecurity. Climate-resilient crops are vital, with millets emerging as superfoods due to nutritional richness and stress tolerance. Given limited genomic information, a comprehensive genetic resource is crucial to advance millet research. Whole-genome sequencing provides an unprecedented opportunity, and molecular genetic methodologies, particularly simple sequence repeats (SSRs), play a pivotal role in DNA fingerprinting, constructing linkage maps, and conducting population genetic studies. SSRs are composed of repetitive DNA sequences where one to six nucleotides are repeated in tandem and distributed throughout the genome. Different millet species exhibit genomic variations attributed to the presence of SSRs. While SSRs have been identified in a few millet species, the existing information only covers some of the sequenced genomes. Moreover, there is an absence of complete gene annotation and visualization features for SSRs. Addressing this disparity and leveraging the de-novo millet genome assembly available from the NCBI, we have developed the Genomic SSR Millets Database (GSMDB; https://bioinfo.icgeb.res.in/gsmdb/). This open-access repository provides a web-based tool offering search functionalities and comprehensive details on 6.747645 million SSRs mined from the genomic sequences of seven millet species. The database, featuring unrestricted public access and JBrowse visualization, is a pioneering resource for the research community dedicated to advancing millet cultivars and related species. GSMDB holds immense potential to support myriad studies, including genetic diversity assessments, genetic mapping, marker-assisted selection, and comparative population investigations aiming to facilitate the millet breeding programs geared toward ensuring global food security. Database URL: https://bioinfo.icgeb.res.in/gsmdb/.

Abstract A001: HNRNPH1 regulates the alternative splicing of transcripts expressed by cancer-associated genes

Abstract The RNA binding protein HNRNPH1 is a critical regulator of alternative splicing (AS). We have previously demonstrated that HNRNPH1 mediates an exon exclusion event required for the expression of the mRNA encoding the fusion oncoprotein expressed in a subset (∼37%) of Ewing sarcomas. Furthermore, studies by others have shown that HNRNPH1 regulates a mutually exclusive exon (MXE) AS event (exon-18a versus exon-18b) that defines the expression of TCF3 transcript variants, which, when dysregulated, promotes tumorigenesis in Burkitt’s lymphoma. In this study, we have interrogated the transcriptome-wide effects of depleting HNRNPH1 by RNAi to further explore its regulation of transcripts expressed by cancer-associated genes. We performed whole transcriptome RNA sequencing (RNAseq; short-read, Illumina; long-read, PacBio IsoSeq) of two cell lines (HEK-293T and HT-1080) post 48-hour transfection with a control siRNA (siNeg) or an siRNA targeting HNRNPH1 (siHNRNPH1). We then assembled a workflow to quantify changes in individual AS events (percent-spliced-in, ΔPSI=±0.1) using rMATS and MAJIQ, and differential transcript expression (DTE) using RSEM (pValue≤0.05) and EBSeq (FDR≤0.05). RNAseq analyses were validated using PCR assays. The rMATS analysis revealed skipped exons (SE) as the most frequent event effected in HEK-293T and HT-1080 cells following HNRNPH1 silencing, with 1069 SE or MXE events from 607 genes common to both. Gene annotation (Metascape) of these 607 genes identified 49 as cancer-associated. Consistent with previous studies, we detected a significant change in the TCF3-exon-18a/18b MXE event using rMATS (decreased PSI for 18a) and MAJIQ (negative ΔPSI for 18a and positive ΔPSI for 18b) following HNRNPH1 silencing in both cell lines. Furthermore, DTE revealed a corresponding decrease in 18a-containing TCF3-transcripts and an increase in 18b-containing TCF3-transcripts, validating our analytical pipeline. Next, we extended our analysis to uncharacterized HNRNPH1-regulated AS events, beginning with examination of transcripts encoding the mitotic kinase AURKA. Previous studies have shown that AS of AURKA transcripts localizes to the untranslated regions (UTRs), particularly the 5’UTR. However, regulators of these AS events are unknown, and it is unclear how these UTR variants affect AURKA protein expression. Long-read RNAseq of AURKA mRNAs from both cell lines revealed extensive 5’UTR AS, and the rMATS and MAJIQ analyses showed decreased PSI and negative ΔPSI for a specific 5’UTR-exon following HNRNPH1 silencing. DTE revealed corresponding decreases in abundance of AURKA transcripts containing this exon (AURKA-203, -205, -209, and -213) upon HNRNPH1 depletion, which we confirmed with variant-specific RT-PCR. Furthermore, immunoblot analysis showed a decrease in AURKA protein expression following the silencing of HNRNPH1. Our assessment of AURKA AS suggests HNRNPH1 mediates the inclusion of a specific AURKA-5’UTR-exon, providing evidence for a previously uncharacterized post-transcriptional regulatory mechanism of AURKA expression. Citation Format: Tayvia Brownmiller, Patricio Pichling, Tamara L Jones, Ioannis Grammatikakis, Soumya Sundara Rajan, Erica C Pehrsson. HNRNPH1 regulates the alternative splicing of transcripts expressed by cancer-associated genes [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: RNAs as Drivers, Targets, and Therapeutics in Cancer; 2024 Nov 14-17; Bellevue, Washington. Philadelphia (PA): AACR; Mol Cancer Ther 2024;23(11_Suppl):Abstract nr A001.

The secondary metabolism collaboratory: a database and web discussion portal for secondary metabolite biosynthetic gene clusters.

Secondary metabolites are small molecules produced by all corners of life, often with specialized bioactive functions with clinical and environmental relevance. Secondary metabolite biosynthetic gene clusters (BGCs) can often be identified within DNA sequences by various sequence similarity tools, but determining the exact functions of genes in the pathway and predicting their chemical products can often only be done by careful, manual comparative analysis. To facilitate this, we report the first release of the secondary metabolism collaboratory (SMC), which aims to provide a comprehensive, tool-agnostic repository of BGC sequence data drawn from all publicly available and user-submitted bacterial and archaeal genome and contig sources. On the website, users are provided a searchable catalog of putative BGCs identified from each source, along with visualizations of gene and domain annotations derived from multiple sequence analysis tools. SMC's data is also available through publicly-accessible application programming interface (API) endpoints to facilitate programmatic access. Users are encouraged to share their findings (and search for others') through comment posts on BGC and source pages. At the time of writing, SMC is the largest repository of BGC information, holding 13.1M BGC regions from 1.3M source sequences and growing, and can be found at https://smc.jgi.doe.gov.

Mining contextually meaningful subgraphs from a vertex-attributed graph.

Networks have emerged as a natural data structure to represent relations among entities. Proteins interact to carry out cellular functions and protein-Protein interaction network analysis has been employed for understanding the cellular machinery. Advances in genomics technologies enabled the collection of large data that annotate proteins in interaction networks. Integrative analysis of interaction networks with gene expression and annotations enables the discovery of context-specific complexes and improves the identification of functional modules and pathways. Extracting subnetworks whose vertices are connected and have high attribute similarity have applications in diverse domains. We present an enumeration approach for mining sets of connected and cohesive subgraphs, where vertices in the subgraphs have similar attribute profile. Due to the large number of cohesive connected subgraphs and to overcome the overlap among these subgraphs, we propose an algorithm for enumerating a set of representative subgraphs, the set of all closed subgraphs. We propose pruning strategies for efficiently enumerating the search tree without missing any pattern or reporting duplicate subgraphs. On a real protein-protein interaction network with attributes representing the dysregulation profile of genes in multiple cancers, we mine closed cohesive connected subnetworks and show their biological significance. Moreover, we conduct a runtime comparison with existing algorithms to show the efficiency of our proposed algorithm.

The Pfam protein families database: embracing AI/ML.

The Pfam protein families database is a comprehensive collection of protein domains and families used for genome annotation and protein structure and function analysis (https://www.ebi.ac.uk/interpro/). This update describes major developments in Pfam since 2020, including decommissioning the Pfam website and integration with InterPro, harmonization with the ECOD structural classification, and expanded curation of metagenomic, microprotein and repeat-containing families. We highlight how AlphaFold structure predictions are being leveraged to refine domain boundaries and identify new domains. New families discovered through large-scale sequence similarity analysis of AlphaFold models are described. We also detail the development of Pfam-N, which uses deep learning to expand family coverage, achieving an 8.8% increase in UniProtKB coverage compared to standard Pfam. We discuss plans for more frequent Pfam releases integrated with InterPro and the potential for artificial intelligence to further assist curation. Despite recent advances, many protein families remain to be classified, and Pfam continues working toward comprehensive coverage of the protein universe.

Abstract B017: Discovery of cancer-specific small RNAs using a new unbiased analysis of cohort sequence data

Abstract It is well-established that small RNAs such as microRNAs can have important roles as oncogenes or tumor suppressors. The majority of studies of small RNAs in cancer have focused on examining the often subtle deregulation of specific, well-characterized molecules, aiming to understand their role in tumorigenesis. However, these efforts have predominantly concentrated on known, well-annotated RNA sequences, potentially overlooking novel, cancer-specific molecules. In this study, we sought to apply a new computational approach to uncover small RNA molecules that are uniquely associated with cancer and are completely absent from all non- cancerous tissues. To achieve this, we developed an unbiased approach that can detect cancer- specific sequences even if they are lowly abundant, do not overlap with existing genome annotations and even if they do not map to the human reference genome. We applied our framework to hundreds of small RNA-seq samples across a diverse range of cancer types from two large-scale public resources: The Cancer Genome Atlas (TCGA) and the Clinical Proteomic Tumor Analysis Consortium (CPTAC). Through this comprehensive analysis, we identified a series of small RNA molecules that are exclusively present in tumor samples and completely absent in adjacent normal tissues across multiple cancer types. Furthermore, these molecules were not detected in a large cohort comprising over 2,500 non-cancerous public samples, underscoring their specificity to cancerous conditions. Some of these molecules are also associated with different patient metadata. For example, one of these candidates exhibited significant correlation with poor patient survival in both Lung Adenocarcinoma and Endometrial Carcinoma within both the TCGA and CPTAC datasets. The discovery of such cancer-specific small RNAs holds immense potential for clinical applications. These molecules could serve as highly specific diagnostic and prognostic biomarkers, aiding in the early detection and stratification of cancers. Moreover, their unique presence in cancer cells, positions them as promising targets for the development of novel therapeutic interventions. In summary, our findings highlight the importance of exploring the full spectrum of small RNAs in cancer research. By moving beyond the constraints of known annotations, we have uncovered previously unknown molecules that could improve cancer diagnostics, prognostics, and treatment. Citation Format: Panagiotis Kalogeropoulos, Maria Alejandra Ulloa, Marc Friedländer. Discovery of cancer-specific small RNAs using a new unbiased analysis of cohort sequence data [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: RNAs as Drivers, Targets, and Therapeutics in Cancer; 2024 Nov 14-17; Bellevue, Washington. Philadelphia (PA): AACR; Mol Cancer Ther 2024;23(11_Suppl):Abstract nr B017.

The genome sequence of bronze sap hoverfly, Ferdinandea cuprea (Scopoli, 1763)

We present a genome assembly from an individual Ferdinandea cuprea (bronze sap hoverfly; Arthropoda; Insecta; Diptera; Syrphidae). The genome sequence has a total length of 560.20 megabases. Most of the assembly (99.92%) is scaffolded into 5 chromosomal pseudomolecules. The mitochondrial genome has also been assembled and is 16.0 kilobases in length. Gene annotation of this assembly on Ensembl identified 12,662 protein-coding genes.

High-quality sika deer omics data and integrative analysis reveal genic and cellular regulation of antler regeneration.

Antler is the only organ that can fully regenerate annually in mammals. However, the regulatory pattern and mechanism of gene expression and cell differentiation during this process remain largely unknown. Here, we obtain comprehensive assembly and gene annotation of the sika deer (Cervus nippon) genome. Together with large-scale chromatin accessibility and gene expression data, we construct gene regulatory networks involved in antler regeneration, identifying four transcription factors, MYC, KLF4, NFE2L2, and JDP2 with high regulatory activity across whole regeneration process. Comparative studies and luciferase reporter assay suggest the MYC expression driven by a cervid-specific regulatory element might be important for antler regenerative ability. We further develop a model called cTOP which integrates single-cell data with bulk regulatory networks and find PRDM1, FOSL1, BACH1, and NFATC1 as potential pivotal factors in antler stem cell activation and osteogenic differentiation. Additionally, we uncover interactions within and between cell programs and pathways during the regeneration process. These findings provide insights into the gene and cell regulatory mechanisms of antler regeneration, particularly in stem cell activation and differentiation.

Identification and validation of two quantitative trait loci showing pleiotropic effect on multiple grain-related traits in bread wheat (Triticum aestivum L.).

QKl/Tgw/Gns.yaas-2D associates with KL, TGW, and GNS, and QKl/Tgw.yaas-5A associates with KL and TGW. Significantly pleiotropic and additive effects of these two QTL were validated. The YM5 allele both at QKl/Tgw/Gns.yaas-2D and QKl/Tgw.yaas-5A was proved to be the best allelic combination for improving yield potential. Kernel length (KL), kernel width (KW), thousand grain weight (TGW), and grain number per spike (GNS) play important roles in the yield improvement of wheat. In this study, one recombinant inbred line (RIL) derived from a cross between Yangmai 5 (YM5) and Yanzhan 1 (YZ1) was used to identify quantitative trait loci (QTL) associated with KL, KW, TGW, and GNS across three years. Two pleiotropic QTL namely QKl/Tgw/Gns.yaas-2D and QKl/Tgw.yaas-5A were located in two genomic regions on chromosomes 2D and 5A, respectively. Breeder-friendly Kompetitive Allele-Specific PCR (KASP) markers for QKl/Tgw/Gns.yaas-2D and QKl/Tgw.yaas-5A were developed and validated in a set of 246 wheat cultivars/lines. Analysis of allelic combinations indicated that the YM5 allele both at QKl/Tgw/Gns.yaas-2D and QKl/Tgw.yaas-5A is probably the best one to promote TGW, GNS, and grain weight per spike. Based on the analysis of gene annotation, sequence variations, expression patterns, and GO enrichment, twenty-five and twenty-four candidate genes of QKl/Tgw/Gns.yaas-2D and QKl/Tgw.yaas-5A, respectively, were identified. These results provide the basis of fine-mapping the target QTL and marker-assisted selection in wheat yield-breeding programs.

Haplotype-resolved genome and population genomics of the threatened garden dormouse in Europe.

Genomic resources are important for evaluating genetic diversity and supporting conservation efforts. The garden dormouse (Eliomys quercinus) is a small rodent that has experienced one of the most severe modern population declines in Europe. We present a high-quality haplotype-resolved reference genome for the garden dormouse, and combine comprehensive short and long-read transcriptomics data sets with homology-based methods to generate a highly complete gene annotation. Demographic history analysis of the genome reveal a sharp population decline since the last interglacial, indicating an association between colder climates and population declines before anthropogenic influence. Using our genome and genetic data from 100 individuals, largely sampled in a citizen-science project across the contemporary range, we conduct the first population genomic analysis for this species. We find clear evidence for population structure across the species' core Central European range. Notably, our data show that the Alpine population, characterized by strong differentiation and reduced genetic diversity, is reproductively isolated from other regions and likely represents a differentiated evolutionary significant unit (ESU). The predominantly declining Eastern European populations also show signs of recent isolation, a pattern consistent with a range expansion from Western to Eastern Europe during the Holocene, leaving relict populations now facing local extinction. Overall, our findings suggest that garden dormouse conservation may be enhanced in Europe through the designation of ESUs.

OrthoDB and BUSCO update: annotation of orthologs with wider sampling of genomes.

OrthoDB (https://www.orthodb.org) offers evolutionary and functional annotations of orthologous genes in the widest sampling of eukaryotes, prokaryotes, and viruses, extending experimental gene function knowledge to newly sequenced genomes. We collect gene annotations, delineate hierarchical gene orthologyand annotate the orthologous groups (OGs) with functional and evolutionary traits. OrthoDB is the leading resource for species diversity, striving to sample the most diverse and well-researched organisms with the highest quality genomic data. This update expands to include 5827 eukaryotic genomes. We have also added coding DNA sequences (CDSs) and gene loci coordinates. OrthoDB can be browsed, downloaded, or accessed using REST API, SPARQL/RDFand now also via API packages for Python and R Bioconductor. OrthoLoger (https://orthologer.ezlab.org), the tool used for inferring orthologs in OrthoDB, is now available as a Conda package and through BioContainers. ODB-mapper, a component of OrthoLoger, streamlines annotation of genes from newly sequenced genomes with OrthoDB evolutionary and functional descriptors. The benchmarking sets of universal single-copy orthologs (BUSCO), derived from OrthoDB, had correspondingly a major update. The BUSCO tool (https://busco.ezlab.org) has become a standard in genomics, uniquely capable of assessing both eukaryotic and prokaryotic species. It is applicable to gene sets, transcriptomes, genome assembliesand metagenomic bins.

Deciphering the tissue-specific functional effect of Alzheimer risk SNPs with deep genome annotation.

Alzheimer's disease (AD) is a highly heritable brain dementia, along with substantial failure of cognitive function. Large-scale genome-wide association studies (GWASs) have led to a set of SNPs significantly associated with AD and related traits. GWAS hits usually emerge as clusters where a lead SNP with the highest significance is surrounded by other less significant neighboring SNPs. Although functionality is not guaranteed even with the strongest associations in GWASs, lead SNPs have historically been the focus of the field, with the remaining associations inferred to be redundant. Recent deep genome annotation tools enable the prediction of function from a segment of a DNA sequence with significantly improved precision, which allows in-silico mutagenesis to interrogate the functional effect of SNP alleles. In this project, we explored the impact of top AD GWAS hits around APOE region on chromatin functions and whether it will be altered by the genetic context (i.e., alleles of neighboring SNPs). Our results showed that highly correlated SNPs in the same LD block could have distinct impacts on downstream functions. Although some GWAS lead SNPs showed dominant functional effects regardless of the neighborhood SNP alleles, several other SNPs did exhibit enhanced loss or gain of function under certain genetic contexts, suggesting potential additional information hidden in the LD blocks.

Structural variation in the pangenome of wild and domesticated barley.

Pangenomes are collections of annotated genome sequences of multiple individuals of a species1. The structural variants uncovered by these datasets are a major asset to genetic analysis in crop plants2. Here we report a pangenome of barley comprising long-read sequence assemblies of 76 wild and domesticated genomes and short-read sequence data of 1,315 genotypes. An expanded catalogue of sequence variation in the crop includes structurally complex loci that are rich in gene copy number variation. To demonstrate the utility of the pangenome, we focus on four loci involved in disease resistance, plant architecture, nutrient release and trichome development. Novel allelic variation at a powdery mildew resistance locus and population-specific copy number gains in a regulator of vegetative branching were found. Expansion of a family of starch-cleaving enzymes in elite malting barleys was linked to shifts in enzymatic activity in micro-malting trials. Deletion of an enhancer motif is likely to change the developmental trajectory of the hairy appendages on barley grains. Our findings indicate that allelic diversity at structurally complex loci may have helped crop plantsto adapt to new selective regimes in agricultural ecosystems.

Saccharomyces Genome Database: Advances in Genome Annotation, Expanded Biochemical Pathways, and Other Key Enhancements.

Budding yeast (Saccharomyces cerevisiae) is the most extensively characterized eukaryotic model organism and has long been used to gain insight into the fundamentals of genetics, cellular biology, and the functions of specific genes and proteins. The Saccharomyces Genome Database (SGD) is a scientific resource that provides information about the genome and biology of S. cerevisiae. For more than 30 years, SGD has maintained the genetic nomenclature, chromosome maps, and functional annotation for budding yeast along with search and analysis tools to explore these data. Here we describe recent updates at SGD, including the two most recent reference genome annotation updates, expanded biochemical pathways representation, changes to SGD search and data files, and other enhancements to the SGD website and user interface. These activities are part of our continuing effort to promote insights gained from yeast to enable the discovery of functional relationships between sequence and gene products in fungi and higher eukaryotes.