Identifying Genomic Regions Targeted During Eggplant Domestication Using Transcriptome Data.
Ontology highlight
ABSTRACT:
SUBMITTER: Page AML
PROVIDER: S-EPMC8634079 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:Eggplant (Solanum melongena L.) is an economically and nutritionally important fruit crop of the Solanaceae family, which was domesticated in India and southern China. However, the genome regions subjected to selective sweeps in eggplant remain unknown. In the present study, we performed comparative transcriptome analysis of cultivated and wild eggplant species with emphasis on the selection pattern during domestication. In total, 44,073 (S. sisymbriifolium) to 58,677 (S. melongena cultivar S58) unigenes were generated for the six eggplant accessions with total lengths of 36.6-46 Mb. The orthologous genes were assessed using the ratio of nonsynonymous (K a) to synonymous (K s) nucleotide substitutions to characterize selective patterns during eggplant domestication. We identified 19 genes under positive selection across the phylogeny that were classified into four groups. The gene (OG12205) under positive selection was possibly associated with fruit-related traits in eggplant, which may have resulted from human manipulation. Eight positive selected genes were potentially involved in stress tolerance or disease resistance, suggesting that environmental changes and biotic stresses were important selective pressures in eggplant domestication. Taken together, our results shed light on the effects of artificial and natural selection on the transcriptomes of eggplant and its wild relatives. Identification of the selected genes will facilitate the understanding of genetic architecture of domesticated-related traits and provide resources for resistant breeding in eggplant.
| S-EPMC6532321 | biostudies-literature
Project description:BACKGROUND:Dogs [Canis lupus familiaris] were the first animal species to be domesticated and continue to occupy an important place in human societies. Recent studies have begun to reveal when and where dog domestication occurred. While much progress has been made in identifying the genetic basis of phenotypic differences between dog breeds we still know relatively little about the genetic changes underlying the phenotypes that differentiate all dogs from their wild progenitors, wolves [Canis lupus]. In particular, dogs generally show reduced aggression and fear towards humans compared to wolves. Therefore, selection for tameness was likely a necessary prerequisite for dog domestication. With the increasing availability of whole-genome sequence data it is possible to try and directly identify the genetic variants contributing to the phenotypic differences between dogs and wolves. RESULTS:We analyse the largest available database of genome-wide polymorphism data in a global sample of dogs 69 and wolves 7. We perform a scan to identify regions of the genome that are highly differentiated between dogs and wolves. We identify putatively functional genomic variants that are segregating or at high frequency [>?=?0.75 Fst] for alternative alleles between dogs and wolves. A biological pathways analysis of the genes containing these variants suggests that there has been selection on the 'adrenaline and noradrenaline biosynthesis pathway', well known for its involvement in the fight-or-flight response. We identify 11 genes with putatively functional variants fixed for alternative alleles between dogs and wolves. The segregating variants in these genes are strong candidates for having been targets of selection during early dog domestication. CONCLUSIONS:We present the first genome-wide analysis of the different categories of putatively functional variants that are fixed or segregating at high frequency between a global sampling of dogs and wolves. We find evidence that selection has been strongest around non-synonymous variants. Strong selection in the initial stages of dog domestication appears to have occurred on multiple genes involved in the fight-or-flight response, particularly in the catecholamine synthesis pathway. Different alleles in some of these genes have been associated with behavioral differences between modern dog breeds, suggesting an important role for this pathway at multiple stages in the domestication process.
| S-EPMC4710014 | biostudies-literature
Project description:Large-scale genetic screens in Arabidopsis are a powerful approach for molecular dissection of complex signaling networks. However, map-based cloning can be time-consuming or even hampered due to low chromosomal recombination. Current strategies using next generation sequencing for molecular identification of mutations require whole genome sequencing and advanced computational devises and skills, which are not readily accessible or affordable to every laboratory. We have developed a streamlined method using parallel massive sequencing for mutant identification in which only targeted regions are sequenced. This targeted parallel sequencing (TPSeq) method is more cost-effective, straightforward enough to be easily done without specialized bioinformatics expertise, and reliable for identifying multiple mutations simultaneously. Here, we demonstrate its use by identifying three novel nitrate-signaling mutants in Arabidopsis.
| S-EPMC3348062 | biostudies-literature
Project description:Barley (Hordeum vulgare) is an established model to study domestication of the Fertile Crescent cereals. Recent molecular data suggested that domesticated barley genomes consist of the ancestral blocks descending from multiple wild barley populations. However, the relationship between the mosaic ancestry patterns and the process of domestication itself remained unclear. To address this knowledge gap, we identified candidate domestication genes using selection scans based on targeted resequencing of 433 wild and domesticated barley accessions. We conducted phylogenetic, population structure, and ancestry analyses to investigate the origin of the domesticated barley haplotypes separately at the neutral and candidate domestication loci. We discovered multiple selective sweeps that occurred on all barley chromosomes during domestication in the background of several ancestral wild populations. The ancestry analyses demonstrated that, although the ancestral blocks of the domesticated barley genomes were descended from all over the Fertile Crescent, the candidate domestication loci originated specifically in its eastern and western parts. These findings provided the first molecular evidence implicating multiple wild or protodomesticated lineages in the process of barley domestication initiated in the Levantine and Zagros clusters of the origin of agriculture.
| S-EPMC5947139 | biostudies-literature
Project description:The placenta serves as a connection between the mother and the fetus during pregnancy, providing the fetus with oxygen, nutrients, and growth hormones. However, the regulatory mechanisms and dynamic gene interaction networks underlying early placental development are understudied. Here, we generated RNA-sequencing data from mouse fetal placenta at embryonic days 7.5, 8.5, and 9.5 to identify genes with timepoint-specific expression, then inferred gene interaction networks to analyze highly connected network modules. We determined that timepoint-specific gene network modules were associated with distinct developmental processes, and with similar expression profiles to specific human placental cell populations. From each module, we identified hub genes and their direct neighboring genes, which were predicted to govern placental functions. We confirmed that four novel candidate regulators identified through our analyses regulate cell migration in the HTR-8/SVneo cell line. Overall, we predicted several novel regulators of placental development expressed in specific placental cell types using network analysis of bulk RNA-sequencing data. Our findings and analysis approaches will be valuable for future studies investigating the transcriptional landscape of early development.
| S-EPMC9748866 | biostudies-literature
Project description:BackgroundCultivated barley (Hordeum vulgare) is widely used in animal feed, beverages, and foods and has become a model crop for molecular evolutionary studies. Few studies have examined the evolutionary fates of different types of genes in barley during the domestication process.ResultsThe rates of nonsynonymous substitution (Ka) to synonymous substitution (Ks) were calculated by comparing orthologous genes in different barley groups (wild vs. landrace and landrace vs. improved cultivar). The rates of evolution, properties, expression patterns, and diversity of positively selected genes (PSGs) and negatively selected genes (NSGs) were compared. PSGs evolved more rapidly, possessed fewer exons, and had lower GC content than NSGs; they were also shorter and had shorter intron, exon, and first exon lengths. Expression levels were lower, the tissue specificity of expression was higher, and codon usage bias was weaker for PSGs than for NSGs. Nucleotide diversity analysis revealed that PSGs have undergone a more severe genetic bottleneck than NSGs. Several candidate PSGs were involved in plant growth and development, which might make them as excellent targets for the molecular breeding of barley.ConclusionsOur comprehensive analysis of the evolutionary, structural, and functional divergence between PSGs and NSGs in barley provides new insight into the evolutionary trajectory of barley during domestication. Our findings also aid future functional studies of PSGs in barley.
| S-EPMC9158214 | biostudies-literature
Project description:MotivationCancer subtype classification has the potential to significantly improve disease prognosis and develop individualized patient management. Existing methods are limited by their ability to handle extremely high-dimensional data and by the influence of misleading, irrelevant factors, resulting in ambiguous and overlapping subtypes.ResultsTo address the above issues, we proposed a novel approach to disentangling and eliminating irrelevant factors by leveraging the power of deep learning. Specifically, we designed a deep-learning framework, referred to as DeepType, that performs joint supervised classification, unsupervised clustering and dimensionality reduction to learn cancer-relevant data representation with cluster structure. We applied DeepType to the METABRIC breast cancer dataset and compared its performance to state-of-the-art methods. DeepType significantly outperformed the existing methods, identifying more robust subtypes while using fewer genes. The new approach provides a framework for the derivation of more accurate and robust molecular cancer subtypes by using increasingly complex, multi-source data.Availability and implementationAn open-source software package for the proposed method is freely available at http://www.acsu.buffalo.edu/~yijunsun/lab/DeepType.html.Supplementary informationSupplementary data are available at Bioinformatics online.
| S-EPMC8215925 | biostudies-literature
Project description:BackgroundDNA methylation is essential for regulating gene expression, and the changes of DNA methylation status are commonly discovered in disease. Therefore, identification of differentially methylation patterns, especially differentially methylated regions (DMRs), in two different groups is important for understanding the mechanism of complex diseases. Few tools exist for DMR identification through considering features of methylation data, but there is no comprehensive integration of the characteristics of DNA methylation data in current methods.ResultsAccounting for the characteristics of methylation data, such as the correlation characteristics of neighboring CpG sites and the high heterogeneity of DNA methylation data, we propose a data-driven approach for DMR identification through evaluating the energy of single site using modified 1D Ising model. Applied to both simulated and publicly available datasets, our approach is compared with other popular methods in terms of performance. Simulated results show that our method is more sensitive than competing methods. Applied to the real data, our method can identify more common DMRs than DMRcate, ProbeLasso, and Wang's methods with a high overlapping ratio. Also, the necessity of integrating the heterogeneity and correlation characteristics in identifying DMR is shown through comparing results with only considering mean or variance signals and without considering relationship of neighboring CpG sites, respectively. Through analyzing the number of DMRs identified in real data located in different genomic regions, we find that about 90% DMRs are located in CGI which always regulates the expression of genes. It may help us understand the functional effect of DNA methylation on disease.
| S-EPMC6276520 | biostudies-literature
Project description:Background: Analysis of scATAC-seq data has been recently scaled to thousands of cells. While processing of other types of single cell data was boosted by the implementation of alignment-free techniques, pipelines available to process scATAC-seq data still require large computational resources. We propose here an approach based on pseudoalignment, which reduces the execution times and hardware needs at little cost for precision. Methods: Public data for 10k PBMC were downloaded from 10x Genomics web site. Reads were aligned to various references derived from DNase I Hypersensitive Sites (DHS) using kallisto and quantified with bustools. We compared our results with the ones publicly available derived by cellranger-atac. We subsequently tested our approach on scATAC-seq data for K562 cell line. Results: We found that kallisto does not introduce biases in quantification of known peaks; cells groups identified are consistent with the ones identified from standard method. We also found that cell identification is robust when analysis is performed using DHS-derived reference in place of de novo identification of ATAC peaks. Lastly, we found that our approach is suitable for reliable quantification of gene activity based on scATAC-seq signal, thus allows for efficient labelling of cell groups based on marker genes. Conclusions: Analysis of scATAC-seq data by means of kallisto produces results in line with standard pipelines while being considerably faster; using a set of known DHS sites as reference does not affect the ability to characterize the cell populations.
| S-EPMC7308914 | biostudies-literature
Project description:When a selective sweep occurs in the chromosomal region around a target gene in two populations that have recently separated, it produces three dramatic genomic consequences: 1) decreased multi-locus heterozygosity in the region; 2) elevated or diminished genetic divergence (F(ST)) of multiple polymorphic variants adjacent to the selected locus between the divergent populations, due to the alternative fixation of alleles; and 3) a consequent regional increase in the variance of F(ST) (S(2)F(ST)) for the same clustered variants, due to the increased alternative fixation of alleles in the loci surrounding the selection target. In the first part of our study, to search for potential targets of directional selection, we developed and validated a resampling-based computational approach; we then scanned an array of 31 different-sized moving windows of SNP variants (5-65 SNPs) across the human genome in a set of European and African American population samples with 183,997 SNP loci after correcting for the recombination rate variation. The analysis revealed 180 regions of recent selection with very strong evidence in either population or both. In the second part of our study, we compared the newly discovered putative regions to those sites previously postulated in the literature, using methods based on inspecting patterns of linkage disequilibrium, population divergence and other methodologies. The newly found regions were cross-validated with those found in nine other studies that have searched for selection signals. Our study was replicated especially well in those regions confirmed by three or more studies. These validated regions were independently verified, using a combination of different methods and different databases in other studies, and should include fewer false positives. The main strength of our analysis method compared to others is that it does not require dense genotyping and therefore can be used with data from population-based genome SNP scans from smaller studies of humans or other species.
| S-EPMC2248624 | biostudies-literature