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Maximum likelihood estimation in generalized broken‐line regression
Our spatiotemporal transcriptome atlas provides a comprehensive resource for investigating the function of coding genes and noncoding RNAs during crucial stages of early neurogenesis. Tobacco and alcohol use are leading causes of mortality that influence risk for many complex diseases and disorders1.
They are heritable2,3 and etiologically related4,5 behaviors that have been resistant to gene discovery efforts In sample sizes up to 1. Smoking phenotypes were positively genetically correlated with many health conditions, whereas alcohol use was negatively correlated with these conditions, such that increased genetic risk for alcohol use is associated with lower disease risk.
We report evidence for the involvement of many systems in tobacco and alcohol use, including genes involved in nicotinic, dopaminergic, and glutamatergic neurotransmission. The results provide a solid starting point to evaluate the effects of these loci in model organisms and more precise substance use measures. The aberrant activities of transcription factors such as the androgen receptor AR underpin prostate cancer development. While the AR cis-regulation has been extensively studied in prostate cancer, information pertaining to the spatial architecture of the AR transcriptional circuitry remains limited.
In this paper, we propose a novel framework to profile long-range chromatin interactions associated with AR and its collaborative transcription factor, erythroblast transformation-specific related gene ERG , using chromatin interaction analysis by paired-end tag ChIA-PET. We identified ERG-associated long-range chromatin interactions as a cooperative component in the AR-associated chromatin interactome, acting in concert to achieve coordinated regulation of a subset of AR target genes. Through multifaceted functional data analysis, we found that AR-ERG interaction hub regions are characterized by distinct functional signatures, including bidirectional transcription and cotranscription factor binding.
Finally, we found strong enrichment of prostate cancer genome-wide association study GWAS single nucleotide polymorphisms SNPs at AR-ERG co-binding sites participating in chromatin interactions and gene regulation, suggesting GWAS target genes identified from chromatin looping data provide more biologically relevant findings than using the nearest gene approach.
Taken together, our results revealed an AR-ERG-centric higher-order chromatin structure that drives coordinated gene expression in prostate cancer progression and the identification of potential target genes for therapeutic intervention. Genome-wide epigenomic maps have revealed millions of putative enhancers and promoters, but experimental validation of their function and high-resolution dissection of their driver nucleotides remain limited. Here, we present HiDRA High-resolution Dissection of Regulatory Activity , a combined experimental and computational method for high-resolution genome-wide testing and dissection of putative regulatory regions.
By design, fragments are highly overlapping in densely-sampled accessible regions, enabling us to pinpoint driver regulatory nucleotides by exploiting differences in activity between partially-overlapping fragments using a machine learning model SHARPR-RE. These are enriched for regulatory motifs, evolutionarily-conserved nucleotides, and disease-associated genetic variants from genome-wide association studies. Overall, HiDRA provides a high-throughput, high-resolution approach for dissecting regulatory regions and driver nucleotides. Seventeen years after the sequencing of the human genome, the human proteome is still under revision.
We have carried out an in-depth investigation on the genes classified as coding by one or more sets of manual curators and not coding by others. Data from large-scale genetic variation analyses suggests that most are not under protein-like purifying selection and so are unlikely to code for functional proteins. A further genes annotated as coding in all three reference sets have characteristics that are typical of non-coding genes or pseudogenes. These potential non-coding genes also appear to be undergoing neutral evolution and have considerably less supporting transcript and protein evidence than other coding genes.
We believe that the three reference databases currently overestimate the number of human coding genes by at least , complicating and adding noise to large-scale biomedical experiments. Determining which potential non-coding genes do not code for proteins is a difficult but vitally important task since the human reference proteome is a fundamental pillar of most basic research and supports almost all large-scale biomedical projects. Protein arginylation mediated by arginyltransferase ATE1 is a key regulatory process essential for mammalian embryogenesis, cell migration, and protein regulation.
Despite decades of studies, very little is known about the specificity of ATE1-mediated target site recognition.checkout.midtrans.com/donde-conocer-chicos-de-gaucn.php
Here, we used in vitro assays and computational analysis to dissect target site specificity of mouse arginyltransferases and gain insights into the complexity of the mammalian arginylome. We found that the four ATE1 isoforms have different, only partially overlapping target site specificity that includes more variability in the target residues than previously believed.
Based on all the available data, we generated an algorithm for identifying potential arginylation consensus motif and used this algorithm for global prediction of proteins arginylated in vivo on the N-terminal D and E. Our analysis reveals multiple proteins with potential ATE1 target sites and expand our understanding of the biological complexity of the intracellular arginylome.
The accurate identification and description of the genes in the human and mouse genomes is a fundamental requirement for high quality analysis of data informing both genome biology and clinical genomics. Over the last 15 years, the GENCODE consortium has been producing reference quality gene annotations to provide this foundational resource.
Specifically, we generate primary data, create bioinformatics tools and provide analysis to support the work of expert manual gene annotators and automated gene annotation pipelines. In addition, manual and computational annotation workflows use any and all publicly available data and analysis, along with the research literature to identify and characterise gene loci to the highest standard.
Great strides in gene discovery have been made using a multitude of methods to associate phenotypes with genetic variants, but there still remains a substantial gap between observed symptoms and identified genetic defects. Herein, we use the convergence of various genetic and genomic techniques to investigate the underpinnings of a constellation of phenotypes that include prostate cancer PCa and sensorineural hearing loss SNHL in a human subject. Through interrogation of the subject's de novo, germline, balanced chromosomal translocation, we first identify a correlation between his disorders and a poorly annotated gene known as lipid droplet associated hydrolase LDAH.
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Using data repositories of both germline and somatic variants, we identify convergent genomic evidence that substantiates a correlation between loss of LDAH and PCa. By leveraging convergent evidence in emerging genomic data, we hypothesize that loss of LDAH is involved in PCa and other phenotypes observed in support of a genotype-phenotype association in an n-of-one human subject. Large-scale deep-coverage whole-genome sequencing WGS is now feasible and offers potential advantages for locus discovery. Common variant association yields known loci except for few variants previously poorly imputed.
Rare coding variant association yields known Mendelian dyslipidemia genes but rare non-coding variant association detects no signals. At these sample sizes and for these phenotypes, the incremental value of WGS for discovery is limited but WGS permits simultaneous assessment of monogenic and polygenic models to severe hypercholesterolemia.
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To assess the impact of genetic variation in regulatory loci on human health, we constructed a high-resolution map of allelic imbalances in DNA methylation, histone marks, and gene transcription in 71 epigenomes from 36 distinct cell and tissue types from 13 donors. Deep whole-genome bisulfite sequencing of 49 methylomes revealed sequence-dependent CpG methylation imbalances at thousands of heterozygous regulatory loci. Such loci are enriched for stochastic switching, which is defined as random transitions between fully methylated and unmethylated states of DNA.
The methylation imbalances at thousands of loci are explainable by different relative frequencies of the methylated and unmethylated states for the two alleles. Further analyses provided a unifying model that links sequence-dependent allelic imbalances of the epigenome, stochastic switching at gene regulatory loci, and disease-associated genetic variation. However, knowledge of the global structure of the transcriptome is limited to cellular systems at steady state, thus hindering the understanding of RNA structure dynamics during biological transitions and how it influences gene function.
Here, we characterized mRNA structure dynamics during zebrafish development. We observed that on a global level, translation guides structure rather than structure guiding translation. We detected a decrease in structure in translated regions and identified the ribosome as a major remodeler of RNA structure in vivo.
In contrast, we found that 3' untranslated regions UTRs form highly folded structures in vivo, which can affect gene expression by modulating microRNA activity. Furthermore, dynamic 3'-UTR structures contain RNA-decay elements, such as the regulatory elements in nanog and ccna1, two genes encoding key maternal factors orchestrating the maternal-to-zygotic transition. These results reveal a central role of RNA structure dynamics in gene regulatory programs. Oocytes have a remarkable ability to reactivate silenced genes in somatic cells.
However, it is not clear how the chromatin architecture of somatic cells affects this transcriptional reprogramming.