Enhort: a platform for deep analysis of genomic positions.
Ontology highlight
ABSTRACT:
SUBMITTER: Menzel M
PROVIDER: S-EPMC7924414 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:In Drosophila, pairing of maternal and paternal homologous chromosomes can permit trans-interactions between enhancers on one homolog and promoters on another, an example of transvection. Although trans-interactions have been observed at many loci in the Drosophila genome and in other organisms, the parameters that govern enhancer action in trans remain poorly understood. Using a transgenic reporter system, we asked whether enhancers and promoters at nonallelic, but nearby, genomic positions can communication in trans. Using one transgenic insertion carrying the synthetic enhancer GMR and another nearby insertion carrying the hsp70 promoter driving a fluorescent reporter, we show that transgenes separated by 2.6 kb of linear distance can support enhancer action in trans at the 53F8 locus. Furthermore, transvection between the nonallelic insertions can be augmented by a small deletion flanking one insert, likely via changes to the paired configuration of the homologs. Subsequent analyses of other insertions in 53F8 that carry different transgenic sequences demonstrate that the capacity to support transvection between nonallelic sites varies greatly, suggesting that factors beyond the linear distance between insertion sites play an important role. Finally, analysis of transvection between nearby nonallelic sites at other genomic locations shows evidence of position effects, where one locus supported GMR action in trans over a linear distance of over 10 kb, whereas another locus showed no evidence of transvection over a span <200 bp. Overall, our data demonstrate that transvection between nonallelic sites represents a complex interplay between genomic context, interallelic distance, and promoter identity.
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Project description:UnlabelledFusobacterium are anaerobic gram-negative bacteria that have been associated with a wide spectrum of human infections and diseases. As the biology of Fusobacterium is still not well understood, comparative genomic analysis on members of this species will provide further insights on their taxonomy, phylogeny, pathogenicity and other information that may contribute to better management of infections and diseases. To facilitate the ongoing genomic research on Fusobacterium, a specialized database with easy-to-use analysis tools is necessary. Here we present FusoBase, an online database providing access to genome-wide annotated sequences of Fusobacterium strains as well as bioinformatics tools, to support the expanding scientific community. Using our custom-developed Pairwise Genome Comparison tool, we demonstrate how differences between two user-defined genomes and how insertion of putative prophages can be identified. In addition, Pathogenomics Profiling Tool is capable of clustering predicted genes across Fusobacterium strains and visualizing the results in the form of a heat map with dendrogram.Database urlhttp://fusobacterium.um.edu.my.
| S-EPMC4141642 | biostudies-literature
Project description:BackgroundHelicobacter is a genus of Gram-negative bacteria, possessing a characteristic helical shape that has been associated with a wide spectrum of human diseases. Although much research has been done on Helicobacter and many genomes have been sequenced, currently there is no specialized Helicobacter genomic resource and analysis platform to facilitate analysis of these genomes. With the increasing number of Helicobacter genomes being sequenced, comparative genomic analysis on members of this species will provide further insights on their taxonomy, phylogeny, pathogenicity and other information that may contribute to better management of diseases caused by Helicobacter pathogens.DescriptionTo facilitate the ongoing research on Helicobacter, a specialized central repository and analysis platform for the Helicobacter research community is needed to host the fast-growing amount of genomic data and facilitate the analysis of these data, particularly comparative analysis. Here we present HelicoBase, a user-friendly Helicobacter resource platform with diverse functionality for the analysis of Helicobacter genomic data for the Helicobacter research communities. HelicoBase hosts a total of 13 species and 166 genome sequences of Helicobacter spp. Genome annotations such as gene/protein sequences, protein function and sub-cellular localisation are also included. Our web implementation supports diverse query types and seamless searching of annotations using an AJAX-based real-time searching system. JBrowse is also incorporated to allow rapid and seamless browsing of Helicobacter genomes and annotations. Advanced bioinformatics analysis tools consisting of standard BLAST for similarity search, VFDB BLAST for sequence similarity search against the Virulence Factor Database (VFDB), Pairwise Genome Comparison (PGC) tool for comparative genomic analysis, and a newly designed Pathogenomics Profiling Tool (PathoProT) for comparative pathogenomic analysis are also included to facilitate the analysis of Helicobacter genomic data.ConclusionsHelicoBase offers access to a range of genomic resources as well as tools for the analysis of Helicobacter genome data. HelicoBase can be accessed at http://helicobacter.um.edu.my.
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Project description:Distinct bacteria are able to cope with highly diverse lifestyles; for instance, they can be free living or host-associated. Thus, these organisms must possess a large and varied genomic arsenal to withstand different environmental conditions. To facilitate the identification of genomic features that might influence bacterial adaptation to a specific niche, we introduce LifeStyle-Specific-Islands (LiSSI). LiSSI combines evolutionary sequence analysis with statistical learning (Random Forest with feature selection, model tuning and robustness analysis). In summary, our strategy aims to identify conserved consecutive homology sequences (islands) in genomes and to identify the most discriminant islands for each lifestyle.
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Project description:Background. The gram-negative Neisseria is associated with two of the most potent human epidemic diseases: meningococcal meningitis and gonorrhoea. In both cases, disease is caused by bacteria colonizing human mucosal membrane surfaces. Overall, the genus shows great diversity and genetic variation mainly due to its ability to acquire and incorporate genetic material from a diverse range of sources through horizontal gene transfer. Although a number of databases exist for the Neisseria genomes, they are mostly focused on the pathogenic species. In this present study we present the freely available NeisseriaBase, a database dedicated to the genus Neisseria encompassing the complete and draft genomes of 15 pathogenic and commensal Neisseria species. Methods. The genomic data were retrieved from National Center for Biotechnology Information (NCBI) and annotated using the RAST server which were then stored into the MySQL database. The protein-coding genes were further analyzed to obtain information such as calculation of GC content (%), predicted hydrophobicity and molecular weight (Da) using in-house Perl scripts. The web application was developed following the secure four-tier web application architecture: (1) client workstation, (2) web server, (3) application server, and (4) database server. The web interface was constructed using PHP, JavaScript, jQuery, AJAX and CSS, utilizing the model-view-controller (MVC) framework. The in-house developed bioinformatics tools implemented in NeisseraBase were developed using Python, Perl, BioPerl and R languages. Results. Currently, NeisseriaBase houses 603,500 Coding Sequences (CDSs), 16,071 RNAs and 13,119 tRNA genes from 227 Neisseria genomes. The database is equipped with interactive web interfaces. Incorporation of the JBrowse genome browser in the database enables fast and smooth browsing of Neisseria genomes. NeisseriaBase includes the standard BLAST program to facilitate homology searching, and for Virulence Factor Database (VFDB) specific homology searches, the VFDB BLAST is also incorporated into the database. In addition, NeisseriaBase is equipped with in-house designed tools such as the Pairwise Genome Comparison tool (PGC) for comparative genomic analysis and the Pathogenomics Profiling Tool (PathoProT) for the comparative pathogenomics analysis of Neisseria strains. Discussion. This user-friendly database not only provides access to a host of genomic resources on Neisseria but also enables high-quality comparative genome analysis, which is crucial for the expanding scientific community interested in Neisseria research. This database is freely available at http://neisseria.um.edu.my.
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Project description:BackgroundThe availability of the human genome sequence as well as the large number of physically accessible oligonucleotides, cDNA, and BAC clones across the entire genome has triggered and accelerated the use of several platforms for analysis of DNA copy number changes, amongst others microarray comparative genomic hybridization (arrayCGH). One of the challenges inherent to this new technology is the management and analysis of large numbers of data points generated in each individual experiment.ResultsWe have developed arrayCGHbase, a comprehensive analysis platform for arrayCGH experiments consisting of a MIAME (Minimal Information About a Microarray Experiment) supportive database using MySQL underlying a data mining web tool, to store, analyze, interpret, compare, and visualize arrayCGH results in a uniform and user-friendly format. Following its flexible design, arrayCGHbase is compatible with all existing and forthcoming arrayCGH platforms. Data can be exported in a multitude of formats, including BED files to map copy number information on the genome using the Ensembl or UCSC genome browser.ConclusionArrayCGHbase is a web based and platform independent arrayCGH data analysis tool, that allows users to access the analysis suite through the internet or a local intranet after installation on a private server. ArrayCGHbase is available at http://medgen.ugent.be/arrayCGHbase/.
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Project description:DANCE is the first standard, generic, and extensible benchmark platform for accessing and evaluating computational methods across the spectrum of benchmark datasets for numerous single-cell analysis tasks. Currently, DANCE supports 3 modules and 8 popular tasks with 32 state-of-art methods on 21 benchmark datasets. People can easily reproduce the results of supported algorithms across major benchmark datasets via minimal efforts, such as using only one command line. In addition, DANCE provides an ecosystem of deep learning architectures and tools for researchers to facilitate their own model development. DANCE is an open-source Python package that welcomes all kinds of contributions.
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Project description:The oral streptococci are spherical Gram-positive bacteria categorized under the phylum Firmicutes which are among the most common causative agents of bacterial infective endocarditis (IE) and are also important agents in septicaemia in neutropenic patients. The Streptococcus mitis group is comprised of 13 species including some of the most common human oral colonizers such as S. mitis, S. oralis, S. sanguinis and S. gordonii as well as species such as S. tigurinus, S. oligofermentans and S. australis that have only recently been classified and are poorly understood at present. We present StreptoBase, which provides a specialized free resource focusing on the genomic analyses of oral species from the mitis group. It currently hosts 104 S. mitis group genomes including 27 novel mitis group strains that we sequenced using the high throughput Illumina HiSeq technology platform, and provides a comprehensive set of genome sequences for analyses, particularly comparative analyses and visualization of both cross-species and cross-strain characteristics of S. mitis group bacteria. StreptoBase incorporates sophisticated in-house designed bioinformatics web tools such as Pairwise Genome Comparison (PGC) tool and Pathogenomic Profiling Tool (PathoProT), which facilitate comparative pathogenomics analysis of Streptococcus strains. Examples are provided to demonstrate how StreptoBase can be employed to compare genome structure of different S. mitis group bacteria and putative virulence genes profile across multiple streptococcal strains. In conclusion, StreptoBase offers access to a range of streptococci genomic resources as well as analysis tools and will be an invaluable platform to accelerate research in streptococci. Database URL: http://streptococcus.um.edu.my.
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Project description:Medical image analysis and computer-assisted intervention problems are increasingly being addressed with deep-learning-based solutions. Established deep-learning platforms are flexible but do not provide specific functionality for medical image analysis and adapting them for this domain of application requires substantial implementation effort. Consequently, there has been substantial duplication of effort and incompatible infrastructure developed across many research groups. This work presents the open-source NiftyNet platform for deep learning in medical imaging. The ambition of NiftyNet is to accelerate and simplify the development of these solutions, and to provide a common mechanism for disseminating research outputs for the community to use, adapt and build upon.The NiftyNet infrastructure provides a modular deep-learning pipeline for a range of medical imaging applications including segmentation, regression, image generation and representation learning applications. Components of the NiftyNet pipeline including data loading, data augmentation, network architectures, loss functions and evaluation metrics are tailored to, and take advantage of, the idiosyncracies of medical image analysis and computer-assisted intervention. NiftyNet is built on the TensorFlow framework and supports features such as TensorBoard visualization of 2D and 3D images and computational graphs by default.We present three illustrative medical image analysis applications built using NiftyNet infrastructure: (1) segmentation of multiple abdominal organs from computed tomography; (2) image regression to predict computed tomography attenuation maps from brain magnetic resonance images; and (3) generation of simulated ultrasound images for specified anatomical poses.The NiftyNet infrastructure enables researchers to rapidly develop and distribute deep learning solutions for segmentation, regression, image generation and representation learning applications, or extend the platform to new applications.
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Project description:With the decreasing cost of next-generation sequencing, whole-genome sequence-based bacterial genome comparisons are expected to become a mainstream process in the public health domain. Extended multilocus sequence typing (MLST) methods are becoming increasingly popular for use in comparing bacterial genetic relatedness in epidemiological investigations. Several extended MLST schemes based on biological signatures have been reported. Among them, whole-genome MLST (wgMLST) has gradually become one of the most widely used approaches for bacterial strain typing. In addition to using bacterial typing, many researchers aim to identify differences in the genes of compared strains. Because these differences might provide insights into critical bacterial functions, such as virulence and pathogenicity, researchers usually study these genes that differ between strains. Hence, we designed a web service tool based on wgMLST-constructed tree topology coupled with the feature selection method to create the "canonical wgMLST (cano-wgMLST) tree." The genes that differ between strains are shown at each split of the tree, thereby directly providing information for performing a comparative genomic analysis for each strain pair. We demonstrated that this web service tool could be operated efficiently on two datasets consisting of 22 Campylobacter jejuni isolates and 59 S. Heidelberg isolates, respectively. We imposed this tool on a designated web server, cano-wgMLST_BacCompare, to enable users to create a wgMLST tree and canonical wgMLST tree automatically from their uploaded bacterial genomes for not only epidemiological investigation but also comparative genomic analysis. Additionally, detailed information on how to use this service is provided. The cano-wgMLST_BacCompare is available at http://baccompare.imst.nsysu.edu.tw.
| S-EPMC6668299 | biostudies-literature