GigaScience最新文献

Structural variations (SVs) play a significant role in speciation and adaptation in many species, yet few studies have explored the prevalence and impact of different categories of SVs. We conducted a comparative analysis of long-read assembled reference genomes of closely related Eucalyptus species to identify candidate SVs potentially influencing speciation and adaptation. Interspecies SVs can be either fixed differences or polymorphic in one or both species. To describe SV patterns, we employed short-read whole-genome sequencing on over 600 individuals of Eucalyptus melliodora and Eucalyptus sideroxylon, along with recent high-quality genome assemblies. We aligned reads and genotyped interspecies SVs predicted between species reference genomes. Our results revealed that 49,756 of 58,025 and 39,536 of 47,064 interspecies SVs could be typed with short reads in E. melliodora and E. sideroxylon, respectively. Focusing on inversions and translocations, symmetric SVs that are readily genotyped within both populations, 24 were found to be structural divergences, 2,623 structural polymorphisms, and 928 shared structural polymorphisms. We assessed the functional significance of fixed interspecies SVs by examining differences in estimated recombination rates and genetic differentiation between species, revealing a complex history of natural selection. Shared structural polymorphisms displayed enrichment of potentially adaptive genes. Understanding how different classes of genetic mutations contribute to genetic diversity and reproductive barriers is essential for understanding how organisms enhance fitness, adapt to changing environments, and diversify. Our findings reveal the prevalence of interspecies SVs and elucidate their role in genetic differentiation, adaptive evolution, and species divergence within and between populations.

Viral helicases are promising targets for the development of antiviral therapies. Given their vital function of unwinding double-stranded nucleic acids, inhibiting them blocks the viral replication cycle. Previous studies have elucidated key structural details of these helicases, including the location of substrate binding sites, flexible domains, and the discovery of potential inhibitors. Here we present a series of new Galaxy tools and workflows for performing and analyzing molecular dynamics simulations of viral helicases. We first validate them by demonstrating recapitulation of data from previous simulations of Zika (NS3) and SARS-CoV-2 (NSP13) helicases in apo and complex with inhibitors. We further demonstrate the utility and generalizability of these Galaxy workflows by applying them to new cases, proving their usefulness as a widely accessible method for exploring antiviral activity.

Background: Cohort studies increasingly collect biosamples for molecular profiling and are observing molecular heterogeneity. High-throughput RNA sequencing is providing large datasets capable of reflecting disease mechanisms. Clustering approaches have produced a number of tools to help dissect complex heterogeneous datasets, but selecting the appropriate method and parameters to perform exploratory clustering analysis of transcriptomic data requires deep understanding of machine learning and extensive computational experimentation. Tools that assist with such decisions without prior field knowledge are nonexistent. To address this, we have developed Omada, a suite of tools aiming to automate these processes and make robust unsupervised clustering of transcriptomic data more accessible through automated machine learning-based functions.

Findings: The efficiency of each tool was tested with 7 datasets characterized by different expression signal strengths to capture a wide spectrum of RNA expression datasets. Our toolkit's decisions reflected the real number of stable partitions in datasets where the subgroups are discernible. Within datasets with less clear biological distinctions, our tools either formed stable subgroups with different expression profiles and robust clinical associations or revealed signs of problematic data such as biased measurements.

Conclusions: In conclusion, Omada successfully automates the robust unsupervised clustering of transcriptomic data, making advanced analysis accessible and reliable even for those without extensive machine learning expertise. Implementation of Omada is available at http://bioconductor.org/packages/omada/.

Sex role differentiation is a widespread phenomenon. Sex pheromones are often associated with sex roles and convey sex-specific information. In Lepidoptera, females release sex pheromones to attract males, which evolve sophisticated olfactory structures to relay pheromone signals. However, in some primitive moths, sex role differentiation becomes diverged. Here, we introduce the chromosome-level genome assembly from ancestral Himalaya ghost moths, revealing a unique olfactory evolution pattern and sex role parity among Lepidoptera. These olfactory structures of the ghost moths are characterized by a dense population of trichoid sensilla, both larger male and female antennal entry parts of brains, compared to the evolutionary later Lepidoptera. Furthermore, a unique tandem of 34 odorant receptor 19 homologs in Thitarodes xiaojinensis (TxiaOr19) has been identified, which presents overlapped motifs with pheromone receptors (PRs). Interestingly, the expanded TxiaOr19 was predicted to have unconventional tuning patterns compared to canonical PRs, with nonsexual dimorphic olfactory neuropils discovered, which contributes to the observed equal sex roles in Thitarodes adults. Additionally, transposable element activity bursts have provided traceable loci landscapes where parallel diversifications occurred between TxiaOr19 and PRs, indicating that the Or19 homolog expansions were diversified to PRs during evolution and thus established the classic sex roles in higher moths. This study elucidates an olfactory prototype of intermediate sex communication from Himalaya ghost moths.

Living in the intertidal environment, littorinid snails are excellent models for understanding genetic mechanisms underlying adaptation to harsh fluctuating environments. Furthermore, the karyotypes of littorinid snails, with the same chromosome number as the presumed bilaterian ancestor, make them valuable for investigating karyotype evolution from the bilaterian ancestor to mollusks. Here, we generated high-quality, chromosome-scale genome assemblies for 2 littorinid marine snails, Littorina brevicula (927.94 Mb) and Littoraria sinensis (882.51 Mb), with contig N50 of 3.43 Mb and 2.31 Mb, respectively. Comparative genomic analyses identified 92 expanded gene families and 85 positively selected genes as potential candidates possibly associated with intertidal adaptation in the littorinid lineage, which were functionally enriched in stimulus responses, innate immunity, and apoptosis process regulation and might be involved in cellular homeostasis maintenance in stressful intertidal environments. Genome macrosynteny analyses indicated that 4 fissions and 4 fusions led to the evolution from the 17 presumed bilaterian ancestral chromosomes to the 17 littorinid chromosomes, implying that the littorinid snails have a highly conserved karyotype with the bilaterian ancestor. Based on the most parsimonious reconstruction of the common ancestral karyotype of scallops and littorinid snails, 3 chromosomal fissions and 1 chromosomal fusion from the bilaterian ancient linkage groups were shared by the bivalve scallop and gastropoda littorinid snails, indicating that the chromosome-scale ancient gene linkages were generally preserved in the mollusk genomes for over 500 million years. The highly conserved karyotype makes the littorinid snail genomes valuable resources for understanding early bilaterian evolution and biology.

The large amount and diversity of viral genomic datasets generated by next-generation sequencing technologies poses a set of challenges for computational data analysis workflows, including rigorous quality control, scaling to large sample sizes, and tailored steps for specific applications. Here, we present V-pipe 3.0, a computational pipeline designed for analyzing next-generation sequencing data of short viral genomes. It is developed to enable reproducible, scalable, adaptable, and transparent inference of genetic diversity of viral samples. By presenting 2 large-scale data analysis projects, we demonstrate the effectiveness of V-pipe 3.0 in supporting sustainable viral genomic data science.

Commelinales belongs to the commelinids clade, which also comprises Poales that includes the most important monocot species, such as rice, wheat, and maize. No reference genome of Commelinales is currently available. Water hyacinth (Pontederia crassipes or Eichhornia crassipes), a member of Commelinales, is one of the devastating aquatic weeds, although it is also grown as an ornamental and medical plant. Here, we present a chromosome-scale reference genome of the tetraploid water hyacinth with a total length of 1.22 Gb (over 95% of the estimated size) across 8 pseudochromosome pairs. With the representative genomes, we reconstructed a phylogeny of the commelinids, which supported Zingiberales and Commelinales being sister lineages of Arecales and shed lights on the controversial relationship of the orders. We also reconstructed ancestral karyotypes of the commelinids clade and confirmed the ancient commelinids genome having 8 chromosomes but not 5 as previously reported. Gene family analysis revealed contraction of disease-resistance genes during polyploidization of water hyacinth, likely a result of fitness requirement for its role as a weed. Genetic diversity analysis using 9 water hyacinth lines from 3 continents (South America, Asia, and Europe) revealed very closely related nuclear genomes and almost identical chloroplast genomes of the materials, as well as provided clues about the global dispersal of water hyacinth. The genomic resources of P. crassipes reported here contribute a crucial missing link of the commelinids species and offer novel insights into their phylogeny.

Background: Dynamic functional connectivity (dFC) has become an important measure for understanding brain function and as a potential biomarker. However, various methodologies have been developed for assessing dFC, and it is unclear how the choice of method affects the results. In this work, we aimed to study the results variability of commonly used dFC methods.

Methods: We implemented 7 dFC assessment methods in Python and used them to analyze the functional magnetic resonance imaging data of 395 subjects from the Human Connectome Project. We measured the similarity of dFC results yielded by different methods using several metrics to quantify overall, temporal, spatial, and intersubject similarity.

Results: Our results showed a range of weak to strong similarity between the results of different methods, indicating considerable overall variability. Somewhat surprisingly, the observed variability in dFC estimates was found to be comparable to the expected functional connectivity variation over time, emphasizing the impact of methodological choices on the final results. Our findings revealed 3 distinct groups of methods with significant intergroup variability, each exhibiting distinct assumptions and advantages.

Conclusions: Overall, our findings shed light on the impact of dFC assessment analytical flexibility and highlight the need for multianalysis approaches and careful method selection to capture the full range of dFC variation. They also emphasize the importance of distinguishing neural-driven dFC variations from physiological confounds and developing validation frameworks under a known ground truth. To facilitate such investigations, we provide an open-source Python toolbox, PydFC, which facilitates multianalysis dFC assessment, with the goal of enhancing the reliability and interpretability of dFC studies.

Background: Advanced whole-genome sequencing techniques enable covering nearly all genome nucleotide variations and thus can provide deep insights into protecting endangered species. However, the use of genomic data to make conservation strategies is still rare, particularly for endangered plants. Here we performed comprehensive conservation genomic analysis for Malania oleifera, an endangered tree species with a high amount of nervonic acid. We used whole-genome resequencing data of 165 samples, covering 16 populations across the entire distribution range, to investigate the formation reasons of its extremely small population sizes and to evaluate the possible genomic offsets and changes of ecology niche suitability under future climate change.

Results: Although M. oleifera maintains relatively high genetic diversity among endangered woody plants (θπ = 3.87 × 10-3), high levels of inbreeding have been observed, which have reduced genetic diversity in 3 populations (JM, NP, and BM2) and caused the accumulation of deleterious mutations. Repeated bottleneck events, recent inbreeding (∼490 years ago), and anthropogenic disturbance to wild habitats have aggravated the fragmentation of M. oleifera and made it endangered. Due to the significant effect of higher average annual temperature, populations distributed in low altitude exhibit a greater genomic offset. Furthermore, ecological niche modeling shows the suitable habitats for M. oleifera will decrease by 71.15% and 98.79% in 2100 under scenarios SSP126 and SSP585, respectively.

Conclusions: The basic realizations concerning the threats to M. oleifera provide scientific foundation for defining management and adaptive units, as well as prioritizing populations for genetic rescue. Meanwhile, we highlight the importance of integrating genomic offset and ecological niche modeling to make targeted conservation actions under future climate change. Overall, our study provides a paradigm for genomics-directed conservation.