scholarly journals SVGenes: a library for rendering genomic features in scalable vector graphic format

2013 ◽  
Vol 29 (15) ◽  
pp. 1890-1892 ◽  
Author(s):  
G. J. Etherington ◽  
D. MacLean
Keyword(s):  
Scalable Vector Graphic ◽  
Genomic Features ◽  
Vector Graphic

Genome sequence of Novosphingobium malaysiense strain MUSC 273T, novel alpha-proteobacterium isolated from intertidal soil

2018 ◽  
Vol 1 (1) ◽  
Author(s):  
Hooi-Leng Ser ◽  
Wai-Fong Yin ◽  
Kok-Gan Chan ◽  
Nurul-Syakima Ab Mutalib ◽  
Learn-Han Lee
Keyword(s):  
Secondary Metabolites ◽  
Secondary Metabolite ◽  
Genome Sequence ◽  
Catalase Activity ◽  
Gene Clusters ◽  
Rrna Genes ◽  
Gram Negative ◽  
Genomic Features

Novosphingobium malaysiense strain MUSC 273T is a recently identified Gram-negative, aerobic alpha-proteobacterium. The strain was isolated from intertidal soil with strong catalase activity. The genome sequence comprises 5,027,021 bp, with 50 tRNA and 3 rRNA genes. Further analysis identified presence of secondary metabolite gene clusters within genome of MUSC 273T. Knowledge of the genomic features of the strain may allow further biotechnological exploitation, particularly for production of secondary metabolites as well as production of industrially important enzymes

Sequence-Based Deep Learning Frameworks on Enhancer-Promoter Interactions Prediction

2020 ◽  
Vol 26 ◽  
Author(s):  
Xiaoping Min ◽  
Fengqing Lu ◽  
Chunyan Li
Keyword(s):  
Gene Expression ◽  
Deep Learning ◽  
Dna Sequences ◽  
Experimental Methods ◽  
Learning Methods ◽  
Comprehensive Review ◽  
Genomic Features ◽  
Disease Mechanisms ◽  
Evaluation Strategies ◽  
Learning Frameworks

: Enhancer-promoter interactions (EPIs) in the human genome are of great significance to transcriptional regulation which tightly controls gene expression. Identification of EPIs can help us better deciphering gene regulation and understanding disease mechanisms. However, experimental methods to identify EPIs are constrained by the fund, time and manpower while computational methods using DNA sequences and genomic features are viable alternatives. Deep learning methods have shown promising prospects in classification and efforts that have been utilized to identify EPIs. In this survey, we specifically focus on sequence-based deep learning methods and conduct a comprehensive review of the literatures of them. We first briefly introduce existing sequence-based frameworks on EPIs prediction and their technique details. After that, we elaborate on the dataset, pre-processing means and evaluation strategies. Finally, we discuss the challenges these methods are confronted with and suggest several future opportunities.

scholarly journals Genomic diversity of Mycobacterium avium subsp. paratuberculosis: pangenomic approach for highlighting unique genomic features with newly constructed complete genomes

2021 ◽  
Vol 52 (1) ◽  
Author(s):  
Jaewon Lim ◽  
Hong-Tae Park ◽  
Seyoung Ko ◽  
Hyun-Eui Park ◽  
Gyumin Lee ◽  
...  
Keyword(s):  
Mycobacterium Avium ◽  
Phenotypic Diversity ◽  
Control Strategies ◽  
Genomic Diversity ◽  
Phenotypic Traits ◽  
Whole Genome ◽  
Genomic Features ◽  
Phenotypic Differences ◽  
Mycobacterium Avium Subsp Paratuberculosis ◽  
Type Strains

AbstractMycobacterium avium subsp. paratuberculosis (MAP) is a causative agent of Johne’s disease, which is a chronic granulomatous enteropathy in ruminants. Determining the genetic diversity of MAP is necessary to understand the epidemiology and biology of MAP, as well as establishing disease control strategies. In the present study, whole genome-based alignment and comparative analysis were performed using 40 publicly available MAP genomes, including newly sequenced Korean isolates. First, whole genome-based alignment was employed to identify new genomic structures in MAP genomes. Second, the genomic diversity of the MAP population was described by pangenome analysis. A phylogenetic tree based on the core genome and pangenome showed that the MAP was differentiated into two major types (C- and S-type), which was in keeping with the findings of previous studies. However, B-type strains were discriminated from C-type strains. Finally, functional analysis of the pangenome was performed using three virulence factor databases (i.e., PATRIC, VFDB, and Victors) to predict the phenotypic diversity of MAP in terms of pathogenicity. Based on the results of the pangenome analysis, we developed a real-time PCR technique to distinguish among S-, B- and C-type strains. In conclusion, the results of our study suggest that the phenotypic differences between MAP strains can be explained by their genetic polymorphisms. These results may help to elucidate the diversity of MAP, extending from genomic features to phenotypic traits.

scholarly journals Is the dinoflagellate Amoebophrya really missing a mtDNA?

2021 ◽  
Author(s):  
Ehsan Kayal ◽  
David R Smith
Keyword(s):  
Mitochondrial Dna ◽  
Single Line ◽  
Genomic Features ◽  
Transcriptomic Data ◽  
Recent Suggestion

Abstract Mitochondrial DNA (mtDNA) is a universal hallmark of aerobic eukaryotes. That is why the recent suggestion by John et al. (2019) that the aerobic dinoflagellate Amoebophrya sp. strain AT5 (Syndiniales) lacks mtDNA was so remarkable. Here, by reanalysing recently published genomic and transcriptomic data from three Amoebophrya strains, we provide evidence of a cryptic, highly reduced mtDNA in this clade. More work is needed before one can definitively say if Amoebophrya has or does not have a mtDNA, but for now the data are pointing towards the existence of one. Ultimately, we urge caution when basing supposedly absent genomic features on single line evidences.

scholarly journals scMET: Bayesian modeling of DNA methylation heterogeneity at single-cell resolution

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Chantriolnt-Andreas Kapourani ◽  
Ricard Argelaguet ◽  
Guido Sanguinetti ◽  
Catalina A. Vallejos
Keyword(s):  
Single Cell ◽  
Bayesian Modeling ◽  
Bayesian Model ◽  
Regulation Of Gene Expression ◽  
Differential Methylation ◽  
Hierarchical Bayesian Model ◽  
Genomic Features ◽  
Distinct Cell ◽  
Biological Heterogeneity

AbstractHigh-throughput single-cell measurements of DNA methylomes can quantify methylation heterogeneity and uncover its role in gene regulation. However, technical limitations and sparse coverage can preclude this task. scMET is a hierarchical Bayesian model which overcomes sparsity, sharing information across cells and genomic features to robustly quantify genuine biological heterogeneity. scMET can identify highly variable features that drive epigenetic heterogeneity, and perform differential methylation and variability analyses. We illustrate how scMET facilitates the characterization of epigenetically distinct cell populations and how it enables the formulation of novel hypotheses on the epigenetic regulation of gene expression. scMET is available at https://github.com/andreaskapou/scMET.

Sign in / Sign up

Export Citation Format

Share Document