NAR Genomics and Bioinformatics最新文献

Metallophores are secondary metabolites that enable bacterial growth in metal-limited environments such as the human nasal microbiome. While synthesis and uptake of metallophores in Staphylococcus aureus are well characterized, the diversity across the Staphylococcus genus remains unclear. We performed a comprehensive bioinformatic analysis of 77 representative species, as well as over 1800 strains, to map metallophore biosynthetic gene clusters (BGCs) and uptake systems. Staphyloferrin A (SF-A) biosynthesis was widely conserved, though disrupted loci were found in some species, with some of them appearing to have replaced SF-A with a newly discovered, still uncharacterized, BGC. In contrast, staphyloferrin B and staphylopine production were restricted to select species. Uptake systems were more broadly distributed, showing evidence of "cheating" species that lack biosynthesis, but retain the required lipoproteins for metallophore usage. Staphylococcus lugdunensis exemplifies this, encoding multiple uptake systems without producing known metallophores. Strain-level variation was also observed, particularly with specific cases of SF-A truncation, but also for the diversity of lipoprotein receptors. These findings highlight the diversity of metallophore systems, suggesting diverse metallophore-dependent cooperation and competition within the Staphylococcus genus. This work provides a foundation for future experimental studies to identify the role of metallophores in microbial community interactions.

Rare exonic variant studies have previously implicated overlapping risk genes and pathways for autism spectrum disorder (ASD), severe, undiagnosed developmental disorders (UDDs), intellectual disability (ID), congenital heart disease (CHD), and schizophrenia (SCZ). Here, we use a two-trait Bayesian integrative analysis approach on 43 287 ASD, UDD/ID, CHD, and SCZ case trios to increase statistical power for gene discovery and to identify shared risk genes. At a posterior probability > 0.80, we identified 180 candidate risk genes for ASD, 315 for UDD/ID, 49 for CHD, and 47 for SCZ, including genes not previously reported, and also detected shared risk genes in pair-wise analyses. Gene set enrichment analysis of the ASD-UDD/ID, ASD-SCZ, and UDD/ID-SCZ shared risk genes overwhelmingly implicated gene sets associated with the synapse and epigenetic modification, while CHD-ASD shared risk genes were enriched in cell cycle phase transition gene sets, and CHD-UDD/ID shared risk genes implicated cardiac development. ASD-UDD/ID risk genes had elevated expression in interneurons and pyramidal cells, while ASD-UDD/ID and CHD-UDD/ID shared risk genes showed elevated connectivity in protein-protein interaction networks. Leveraging information across disorders with genetic overlap, both to increase power for candidate risk gene discovery and also as a method to elucidate shared genetic mechanisms.

The recent expansion of prokaryotic genomes reveals many ortholog groups (OGs) whose function cannot be inferred from conventional, sequence similarity-based annotation methods, especially in metagenome-assembled genomes. Phylogenetic profiling is one of the promising methods to annotate these OGs, by identifying functional relationships of OGs using co- or anti-occurrence of OG distributions, not sequence similarity. Here, we proposed two new phylogenetic methods for large-scale data, Ancestral State Adjustment (ASA) and Simultaneous EVolution test (SEV), which consider the ancestral state of OG presence/absence. In evaluations using three distinct prokaryotic datasets, ASA and SEV showed better or comparable performance to both established and recently proposed methods for large-scale data. We compared the functionally related OGs detected by each method and found that SEV and its predecessor can identify slowly evolving OGs, such as housekeeping genes. In contrast, ASA and its predecessors can detect functionally related OGs that tend to be gained or lost in a fixed order, indicating a strong evolutionary constraint that provides clues for functional prediction. Using matrix multiplication, we also showed that SEV is scalable in the latest genome databases.

Exonic enrichment of histone marks hints at their role in regulating alternative splicing. This study aims to connect the transcriptome and epigenome in the context of splicing outcomes in embryonic cell lines. The tools rMATS and MANorm were used to obtain estimates of differential inclusion of exons and differential enrichment of epigenetic signals, respectively. Two classes of alternative exons were identified in embryonic cell lines: those differentially co-occurring with at least one mark among H3K27ac, H3K27me3, H3K36me3, H3K9me3, and H3K4me3, and those marked by neither of these marks. Binary classifiers were trained using RNA-binding protein (RBP) binding affinities on the flanking regions of these exons. This resulted in a set of RBPs, whose putative binding was predicted to associate local chromatin modification marking an exon with its differential inclusion, some of which have been experimentally shown to interact with histone mark reader proteins. We speculate that sequence signals harbored at exon-intron flanks regulate differential splicing of exons, marked by at least one of the five epigenetic signatures. Finally, eCLIP data from ENCODE for the HepG2 and K562 cell lines support TIA1 and U2AF2 as potential episplicing RBPs, as predicted by our model in the embryonic cell lines.

Horizontal gene transfer (HGT) accelerates the spread of antimicrobial resistance (AMR) via mobile genetic elements allowing pathogens to acquire resistance genes across species. This process drives the evolution of multidrug-resistant "superbugs" in clinical settings. Detection of HGT is critical to mitigating AMR, but traditional methods based on sequence assembly or comparative genomics lack resolution for complex transfer events. While machine learning (ML) promises improved detection, several studies in other domains have demonstrated that data representations will strongly influence its performance. There is, however, no clear recommendation on the best data representation for HGT detection. Here, we evaluated 44 genomic data representations using five ML models across four data sets. We demonstrate that ML performance is highly dependent on the genomic data representation. The RCKmer-based representation (k = 7) paired with a support vector machine is found to be optimal (F1: 0.959; MCC: 0.908), outperforming other approaches. Moreover, models trained on multi-species data sets are shown to generalize better. Our findings suggest that genomic surveillance benefits from task-specific genome data representations. This work provides state-of-the-art, fine-tuned models for identifying and annotating genomic islands that will enable proper detection of transfer of AMR-related genes between species.

India, home to the world's largest cattle population, hosts native dairy breeds essential to its agricultural economy because of their adaptability and resilience. This study characterizes the genomes of five prominent breeds-Gir, Kankrej, Red Sindhi, Sahiwal, and Tharparkar-highlighting their unique genomic characteristics. The de novo assemblies ranged from 2.70 to 2.78 Gb in size, with 90% of the genomes assembled in just 56 to 1663 scaffolds. The use of reference-guided scaffolding further enhanced these genomes, resulting in 93.3%-96.7% pseudomolecule coverage with strong BUSCO scores (94.1%-95.5%). Comparative analyses revealed 87%-95% synteny with the Brahman genome and identified 19.84-153.16 Mb of structural rearrangements per genome, including inversions, translocations, and duplications. Synteny diversity analysis uncovered 10 643 perfectly collinear regions spanning 87.3 Mb and 6622 hotspots of rearrangement (HOT regions) covering 55.18 Mb. These HOT regions, characterized by high synteny diversity, were significantly enriched with immune-related genes. Moreover, immune-related gene clusters, including major histocompatibility complex, natural killer complex, and leukocyte receptor complex, were identified within HOT regions in the desi reference genome. Our findings provide valuable insights into the genetic diversity of desi cattle breeds. The high-quality genome assemblies generated in this study will serve as valuable resources for future research in genetic improvement, disease resistance, and environmental adaptation.

Haplotype blocks in the genome are informative of evolutionary processes and they play a pivotal role in describing the genomic variability across human populations and susceptibility/resistance to diseases. Several software have been developed for haplotype block detection, but they do not distinguish between the impacts of major and minor single nucleotides polymorphism (SNP) alleles. In this study, we present a powerful haploblock detection software, specifically designed for identifying haploblocks associated with SNP minor allele haploblocks (MiA-haploblocks). These haploblocks are particularly important as they can significantly influence phenotypic traits, offering a novel approach for studying genetic associations and complex traits. HaploExplore operates on VCF files containing phased data, exhibiting rapid processing times, and generating user-friendly outputs. Results converge when analyzing populations of 100 individuals or more. A comparative analysis of HaploExplore against other haploblock detection software revealed its superiority in terms of either simplicity, flexibility, or speed, with the unique capability to target minor alleles. HaploExplore will be very useful for evolutionary genomics and for GWAS analysis in human diseases, given that the effects of genetic associations may accumulate within a specific haploblock.

Aligning DNA sequences retrieved from fossils or other paleontological artifacts, referred to as ancient DNA (aDNA), is particularly challenging due to the short sequence length and chemical damage which creates a specific pattern of substitution (C[Formula: see text]T and G[Formula: see text]A) in addition to the heightened divergence between the sample and the reference genome thus exacerbating reference bias. This bias can be mitigated by aligning to pangenome graphs to incorporate documented organismic variation, but this approach still suffers from substitution patterns due to chemical damage. We introduce a novel methodology introducing the RYmer index, a variant of the commonly used minimizer index which represents purines (A,G) and pyrimidines (C,T) as R and Y, respectively. This creates an indexing scheme robust to the aforementioned chemical damage. We implemented SAFARI (Sensitive Alignments From A RYmer Index), an aDNA damage-aware version of the pangenome aligner vg giraffe, which uses RYmers to rescue alignments containing deaminated seeds. For highly damaged samples, the recovery rate could be upwards of 10%, an amount which could well affect downstream results. We show that our approach produces more correct alignments from aDNA sequences than current approaches while maintaining a tolerable rate of spurious alignments. In addition, we demonstrate that our algorithm improves the estimate of the rate of aDNA damage, especially for highly damaged samples. Crucially, we show that this improved alignment can directly translate into better insights gained from the data by showcasing its integration with a number of extant pangenome tools.

Yeast is commonly utilized in molecular and cell biology research, and Yarrowia lipolytica is favored by bioengineers due to its ability to produce copious amounts of lipids, chemicals, and enzymes for industrial applications. Y. lipolytica is a dimorphic yeast that can proliferate in aerobic and hydrophobic environments conducive to industrial use. However, there is limited knowledge about the basic molecular biology of this yeast, including how the genome is duplicated and how gene silencing occurs. Genome sequences of Y. lipolytica strains have offered insights into this yeast species and have facilitated the development of new industrial applications. Although previous studies have reported the genome sequence of a few Y. lipolytica strains, it is of value to have more precise sequences and annotation, particularly for studies of the biology of this yeast. To further study and characterize the molecular biology of this microorganism, a high-quality reference genome assembly and annotation has been produced for two related Y. lipolytica strains of the opposite mating type, strain E122 (MATA) and 22301-5 (MATB). The combination of short-read and long-read sequencing of genome DNA and short-read and long-read sequencing of transcript cDNAs allowed the genome assembly and a comparison with a distantly related Yarrowia strain.

Removing unwanted variation (RUV) is key for accurate biological interpretation in high-throughput sequencing studies. However, no standardized approach exists for pseudobulked single-cell RNA-sequencing (scRNA-seq) data. Improper implementation of RUV methods may remove biological information, jeopardizing power and false positive control in differential expression analysis. We evaluate the impact of three implementation strategies ('trails') in three RUV methods (RUV2, RUVIII, RUV4) using simulated and real biological signals in pseudobulked scRNA-seq data. Effects of technical noise under confounding and model misspecification conditions are also considered. Additionally, we introduce a novel strategy, RUVIII PBPS, to remove unwanted variation in pseudobulk differential expression analyses with insufficient technical replicates or negative control genes. Our analysis demonstrates that removing unwanted variation per cell type with RUV2 or RUVIII extracts factors associated with technical noise and controls the false discovery rate (FDR), even in the presence of confounding. RUVIII PBPS successfully controls the FDR when other standard RUV methods cannot be used due to missing technical replicates, dependence between the factor of interest and the sources of unwanted variation, and lack of plausible negative control genes.