Farmerborg7003

Genomes are spatiotemporally organized within the cell nucleus. Genome-wide chromosome conformation capture (Hi-C) technologies have uncovered the 3D genome organization. Furthermore, live-cell imaging experiments have revealed that genomes are functional in 4D. Although computational modeling methods can convert 2D Hi-C data into population-averaged static 3D genome models, exploring 4D genome nature based on 2D Hi-C data remains lacking. Here, we describe a 4D simulation method, PHi-C (polymer dynamics deciphered from Hi-C data), that depicts 4D genome features from 2D Hi-C data by polymer modeling. PHi-C allows users to interpret 2D Hi-C data as physical interaction parameters within single chromosomes. The physical interaction parameters can then be used in the simulations and analyses to demonstrate dynamic characteristics of genomic loci and chromosomes as observed in live-cell imaging experiments. PHi-C is available at https//github.com/soyashinkai/PHi-C.Recent RNA knockdown experiments revealed that a dozen divergent long noncoding RNAs (lncRNAs) positively regulate the transcription of genes in cis. Here, to understand the regulatory mechanism of divergent lncRNAs, we proposed a computational model IRDL (Identify the Regulatory Divergent LncRNAs) to associate divergent lncRNAs with target genes. IRDL took advantage of the cross-tissue paired expression and chromatin accessibility data in ENCODE and a dozen experimentally validated divergent lncRNA target genes. IRDL integrated sequence similarity, co-expression and co-accessibility features, battled the scarcity of gold standard datasets with an increasingly learning framework and identified 446 and 977 divergent lncRNA-gene regulatory associations for mouse and human, respectively. We found that the identified divergent lncRNAs and target genes correlated well in expression and chromatin accessibility. The functional and pathway enrichment analysis suggests that divergent lncRNAs are strongly associated with developmental regulatory transcription factors. The predicted loop structure validation and canonical database search indicate a scaffold regulatory model for divergent lncRNAs. Furthermore, we computationally revealed the tissue/cell-specific regulatory associations considering the specificity of lncRNA. In conclusion, IRDL provides a way to understand the regulatory mechanism of divergent lncRNAs and hints at hundreds of tissue/cell-specific regulatory associations worthy for further biological validation.Although bioluminescent bacteria are the most abundant and widely distributed of all light-emitting organisms, the biological role and evolutionary history of bacterial luminescence are still shrouded in mystery. Bioluminescence has so far been observed in the genomes of three families of Gammaproteobacteria in the form of canonical lux operons that adopt the CDAB(F)E(G) gene order. LuxA and luxB encode the two subunits of bacterial luciferase responsible for light-emission. Our deep exploration of public marine environmental databases considerably expands this view by providing a catalog of new lux homolog sequences, including 401 previously unknown luciferase-related genes. It also reveals a broader diversity of the lux operon organization, which we observed in previously undescribed configurations such as CEDA, CAED and AxxCE. learn more This expanded operon diversity provides clues for deciphering lux operon evolution and propagation within the bacterial domain. Leveraging quantitative tracking of marine bacterial genes afforded by planetary scale metagenomic sampling, our study also reveals that the novel lux genes and operons described herein are more abundant in the global ocean than the canonical CDAB(F)E(G) operon.The revolution in new sequencing technologies is greatly leading to new understandings of the relations between genotype and phenotype. To interpret and analyze data that are grouped according to a phenotype of interest, methods based on statistical enrichment became a standard in biology. However, these methods synthesize the biological information by a priori selecting the over-represented terms and may suffer from focusing on the most studied genes that represent a limited coverage of annotated genes within a gene set. Semantic similarity measures have shown great results within the pairwise gene comparison by making advantage of the underlying structure of the Gene Ontology. We developed GSAn, a novel gene set annotation method that uses semantic similarity measures to synthesize a priori Gene Ontology annotation terms. The originality of our approach is to identify the best compromise between the number of retained annotation terms that has to be drastically reduced and the number of related genes that has to be as large as possible. Moreover, GSAn offers interactive visualization facilities dedicated to the multi-scale analysis of gene set annotations. Compared to enrichment analysis tools, GSAn has shown excellent results in terms of maximizing the gene coverage while minimizing the number of terms.Using the presently available datasets of annotated missense variants, we ran a protein family-specific benchmarking of tools for predicting the pathogenicity of single amino acid variants. We find that despite the high overall accuracy of all tested methods, each tool has its Achilles heel, i.e. protein families in which its predictions prove unreliable (expected accuracy does not exceed 51% in any method). As a proof of principle, we show that choosing the optimal tool and pathogenicity threshold at a protein family-individual level allows obtaining reliable predictions in all Pfam domains (accuracy no less than 68%). A functional analysis of the sets of protein domains annotated exclusively by neutral or pathogenic mutations indicates that specific protein functions can be associated with a high or low sensitivity to mutations, respectively. The highly sensitive sets of protein domains are involved in the regulation of transcription and DNA sequence-specific transcription factor binding, while the domains that do not result in disease when mutated are responsible for mediating immune and stress responses. These results suggest that future predictors of pathogenicity and especially variant prioritization tools may benefit from considering functional annotation.