Search bioRxiv⌕ Search

bioRxiv · 10.1101/697243

Machine-learning classification suggests that many alphaproteobacterial prophages may instead be gene transfer agents

Abstract

Many of the sequenced bacterial and archaeal genomes encode regions of viral provenance. Yet, not all of these regions encode bona fide viruses. Gene transfer agents (GTAs) are thought to be former viruses that are now maintained in genomes of some bacteria and archaea and are hypothesized to enable exchange of DNA within bacterial populations. In Alphaproteobacteria, genes homologous to the head-tail gene cluster that encodes structural components of the Rhodobacter capsulatus GTA (RcGTA) are found in many taxa, even if they are only distantly related to Rhodobacter capsulatus. Yet, in most genomes available in GenBank RcGTA-like genes have annotations of typical viral proteins, and therefore are not easily distinguished from their viral homologs without additional analyses. Here, we report a support vector machine classifier that quickly and accurately distinguishes RcGTA-like genes from their viral homologs by capturing the differences in the amino acid composition of the encoded proteins. Our open-source classifier is implemented in Python and can be used to scan homologs of the RcGTA genes in newly sequenced genomes. The classifier can also be trained to identify other types of GTAs, or even to detect other elements of viral ancestry. Using the classifier trained on a manually curated set of homologous viruses and GTAs, we detected RcGTA-like head-tail gene clusters in 57.5% of the 1,423 examined alphaproteobacterial genomes. We also demonstrated that more than half of the in silico prophage predictions are instead likely to be GTAs, suggesting that in many alphaproteobacterial genomes the RcGTA-like elements remain unrecognized.\n\nData depositionSequence alignments and phylogenetic trees are available in a FigShare repository at DOI 10.6084/m9.figshare.8796419. The Python source code of the described classifier and additional scripts used in the analyses are available via a GitHub repository at https://github.com/ecg-lab/GTA-Hunter-v1

Source connections

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Kogay, R., Neely, T. B., Birnbaum, D. P., Hankel, C. R., Shakya, M., Zhaxybayeva, O.. 2019-07-11. Machine-learning classification suggests that many alphaproteobacterial prophages may instead be gene transfer agents. https://doi.org/10.1101/697243

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

Time-resolved single-cell screening reveals the dynamic regulatory architecture of human cardiogenesis

Congenital heart disease (CHD) originates during early embryogenesis, yet when disease genes act cannot be determined from existing data. Human fetal heart atlases lack critical time points, and perturbation screens that systematically assess gene function currently profile at most a few time points. Here we combined single-cell CRISPR screening of 400 disease-relevant genes in cardioids, sampled daily over seven developmental days (~975,000 cells), with a matched multiome atlas. Perturbation phenotypes were strongly dependent on developmental stage. Perturbations that reshaped cell-fate allocation or gene expression were enriched for CHD-linked genes, and most acted already during progenitor stages. Timing was largely independent of the target gene's own expression, marking instead when a cell state became functionally dependent on it, and some effects persisted after expression declined. Consensus gene regulatory networks integrating perturbation and multiome data recovered stage-specific regulators and nominated HOXB2 and HOXB3 as candidate cardiac regulators. Many perturbations converged on shared downstream programmes, which were reorganised through differentiation. Effects correlated in progenitors were maintained in differentiated cells, split into distinct programmes, converged with other perturbations or disappeared entirely, indicating that gene function is highly context dependent. Beyond advancing our understanding of the developmental regulatory logic underlying CHD, our work provides a resource for developing context-dependent models for in silico prediction of perturbation effects.

genomics↗

A Y-specific translocated gsdf copy is a candidate master sex-determining gene in common dab (Limanda limanda)

Sex-determining systems in flatfishes are highly diverse, and even closely related species can differ in both sex-determining loci and heterogametic systems. In common dab (Limanda limanda), the genetic basis of sex determination has not yet been established. Here, we investigated sex determination using pooled whole-genome sequencing combined with long-read genome sequencing of females and males from Iceland and Norway. Genome-wide allele-frequency analyses identified sex-associated regions consistent with male heterogamety (XX/XY), with the strongest signal on chromosome 16. Comparison of the existing reference assembly, which represents an X haplotype at this locus, with a haplotype-resolved male PacBio HiFi assembly revealed a [~]21.5 kb Y-specific insertion on chromosome 16. The insertion showed complete coverage in male pools but little or no coverage in female pools from either population. It contained a translocated genomic segment including a copy of gonadal soma-derived factor (gsdf) and a predicted gene of unknown function. Comparative genomic analyses were consistent with this segment originating from an autosomal gsdf-containing region on chromosome 1. Given the recurrent recruitment of gsdf as a sex-determining factor in teleosts, we designate the Y-linked copy gsdfY and identify it as the strongest candidate for the master sex-determining gene in common dab. Additional broad sex-associated differentiation on chromosome 19 was restricted to the Icelandic samples and may reflect population-specific expansion of the sex-linked region, although its structural basis remains unresolved. Together, these findings provide the first genomic characterization of sex determination in common dab and demonstrate how gene duplication and translocation can contribute to the evolution of novel sex-determining systems in flatfishes.

genomics↗

A population-scale isoform atlas enables isoform-based trait association and regulatory analysis in lettuce

Genomic variation shapes phenotypic diversity, yet how genetic variation contributes to transcript isoform diversity remains poorly understood at the population scale. Here, we integrated transcriptomic data from 240 lettuce accessions with a lettuce pangenome to construct a comprehensive pan isoform atlas comprising 531,657 isoforms from 49,729 transcribed loci. Isoform presence absence variation (iPAV) captured extensive transcript diversification during domestication and horticultural differentiation, providing additional resolution beyond locus-level analyses, particularly within loci shared across the population. To facilitate the systematic analysis of isoform trait associations, we developed ISONET-GWAS, a computational framework that integrates isoform-based association mapping, functional annotation, and network-based candidate prioritization. ISONET-GWAS recovered the known chlorophyll regulator LsGLK, identified 455 metabolite isoform associations across 77 metabolites, and prioritized candidate regulators of chlorophyll metabolism supported by functional validation. Genome-wide splicing quantitative trait locus (sQTL) mapping identified 10,498 sQTL associations, revealing widespread distal genetic associations with alternative splicing and candidate trans-sQTL hotspots. Notably, we identified a trans-sQTL hotspot overlapping LsPPR, which encodes a pentatricopeptide repeat RNA-binding protein. Perturbation of LsPPR resulted in widespread changes in alternative splicing of floweing-related genes, accompanied by delayed bolting and flowering and altered splicing of flowering-related genes. Together, our study establishes a population-scale pan-isoform resource and a broadly applicable analytical strategy for isoform-level trait association, candidate prioritization, and complementary analysis of the genetic regulation of alternative splicing in crops.

genomics↗