scholarly journals NetR and AttR, Two New Bioinformatic Tools to Integrate Diverse Datasets into Cytoscape Network and Attribute Files

Genes ◽  
2019 ◽  
Vol 10 (6) ◽  
pp. 423
Author(s):  
Armen Halajyan ◽  
Natalie Weingart ◽  
Mirza Yeahia ◽  
Mariano Loza-Coll
Keyword(s):  
High Throughput ◽  
Genetic Interaction ◽  
Model Systems ◽  
Published Data ◽  
Data Generation ◽  
Bioinformatic Tools ◽  
Genome Wide ◽  
Genome Wide Data ◽  
Experimental Model Systems ◽  
Genetic Interaction Data

High-throughput technologies have allowed researchers to obtain genome-wide data from a wide array of experimental model systems. Unfortunately, however, new data generation tends to significantly outpace data re-utilization, and most high throughput datasets are only rarely used in subsequent studies or to generate new hypotheses to be tested experimentally. The reasons behind such data underutilization include a widespread lack of programming expertise among experimentalist biologists to carry out the necessary file reformatting that is often necessary to integrate published data from disparate sources. We have developed two programs (NetR and AttR), which allow experimental biologists with little to no programming background to integrate publicly available datasets into files that can be later visualized with Cytoscape to display hypothetical networks that result from combining individual datasets, as well as a series of published attributes related to the genes or proteins in the network. NetR also allows users to import protein and genetic interaction data from InterMine, which can further enrich a network model based on curated information. We expect that NetR/AttR will allow experimental biologists to mine a largely unexploited wealth of data in their fields and facilitate their integration into hypothetical models to be tested experimentally.

scholarly journals Regulation of Cytochrome c Oxidase by Natural Compounds Resveratrol, (–)-Epicatechin, and Betaine

Cells ◽  
2021 ◽  
Vol 10 (6) ◽  
pp. 1346
Author(s):  
Icksoo Lee
Keyword(s):  
Cytochrome C ◽  
Cytochrome C Oxidase ◽  
Model Systems ◽  
Health Improvement ◽  
Published Data ◽  
Mitochondrial Electron Transport Chain ◽  
Naturally Occurring ◽  
Transport Chain ◽  
Wide Range ◽  
Experimental Model Systems

Numerous naturally occurring molecules have been studied for their beneficial health effects. Many compounds have received considerable attention for their potential medical uses. Among them, several substances have been found to improve mitochondrial function. This review focuses on resveratrol, (–)-epicatechin, and betaine and summarizes the published data pertaining to their effects on cytochrome c oxidase (COX) which is the terminal enzyme of the mitochondrial electron transport chain and is considered to play an important role in the regulation of mitochondrial respiration. In a variety of experimental model systems, these compounds have been shown to improve mitochondrial biogenesis in addition to increased COX amount and/or its enzymatic activity. Given that they are inexpensive, safe in a wide range of concentrations, and effectively improve mitochondrial and COX function, these compounds could be attractive enough for possible therapeutic or health improvement strategies.

scholarly journals Mutant resources for functional genomics in Dictyostelium discoideum using REMI-seq technology

BMC Biology ◽  
2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Nicole Gruenheit ◽  
Amy Baldwin ◽  
Balint Stewart ◽  
Sarah Jaques ◽  
Thomas Keller ◽  
...  
Keyword(s):  
Dictyostelium Discoideum ◽  
High Throughput ◽  
Gene Function ◽  
Evolutionary Biology ◽  
Large Scale ◽  
Function Analysis ◽  
Model Systems ◽  
Genome Wide ◽  
The Social ◽  
Drug Resistance Marker

Abstract Background Genomes can be sequenced with relative ease, but ascribing gene function remains a major challenge. Genetically tractable model systems are crucial to meet this challenge. One powerful model is the social amoeba Dictyostelium discoideum, a eukaryotic microbe widely used to study diverse questions in the cell, developmental and evolutionary biology. Results We describe REMI-seq, an adaptation of Tn-seq, which allows high throughput, en masse, and quantitative identification of the genomic site of insertion of a drug resistance marker after restriction enzyme-mediated integration. We use REMI-seq to develop tools which greatly enhance the efficiency with which the sequence, transcriptome or proteome variation can be linked to phenotype in D. discoideum. These comprise (1) a near genome-wide resource of individual mutants and (2) a defined pool of ‘barcoded’ mutants to allow large-scale parallel phenotypic analyses. These resources are freely available and easily accessible through the REMI-seq website that also provides comprehensive guidance and pipelines for data analysis. We demonstrate that integrating these resources allows novel regulators of cell migration, phagocytosis and macropinocytosis to be rapidly identified. Conclusions We present methods and resources, generated using REMI-seq, for high throughput gene function analysis in a key model system.

scholarly journals Efficient strategies for screening large-scale genetic interaction networks

2017 ◽  
Author(s):  
Raamesh Deshpande ◽  
Justin Nelson ◽  
Scott W. Simpkins ◽  
Michael Costanzo ◽  
Jeff S. Piotrowski ◽  
...  
Keyword(s):  
Large Scale ◽  
Optimal Algorithm ◽  
Genetic Interaction ◽  
Genetic Screens ◽  
Chemical Genetic ◽  
Genome Wide ◽  
Powerful Approach ◽  
Interaction Screening ◽  
Genetic Interaction Data

Large-scale genetic interaction screening is a powerful approach for unbiased characterization of gene function and understanding systems-level cellular organization. While genome-wide screens are desirable as they provide the most comprehensive interaction profiles, they are resource and time-intensive and sometimes infeasible, depending on the species and experimental platform. For these scenarios, optimal methods for more efficient screening while still producing the maximal amount of information from the resulting profiles are of interest.To address this problem, we developed an optimal algorithm, called COMPRESS-GI, which selects a small but informative set of genes that captures most of the functional information contained within genome-wide genetic interaction profiles. The utility of this algorithm is demonstrated through an application of the approach to define a diagnostic mutant set for large-scale chemical genetic screens, where more than 13,000 compound screens were achieved through the increased throughput enabled by the approach. COMPRESS-GI can be broadly applied for directing genetic interaction screens in other contexts, including in species with little or no prior genetic-interaction data.

scholarly journals fluff: exploratory analysis and visualization of high-throughput sequencing data

PeerJ ◽  
2016 ◽  
Vol 4 ◽  
pp. e2209 ◽  
Author(s):  
Georgios Georgiou ◽  
Simon J. van Heeringen
Keyword(s):  
High Throughput ◽  
High Throughput Sequencing ◽  
Developmental Stages ◽  
Command Line ◽  
Clustering Methods ◽  
Sequencing Data ◽  
Link Type ◽  
High Throughput Sequencing Data ◽  
Genome Wide ◽  
Genome Wide Data

Summary.In this article we describe fluff, a software package that allows for simple exploration, clustering and visualization of high-throughput sequencing data mapped to a reference genome. The package contains three command-line tools to generate publication-quality figures in an uncomplicated manner using sensible defaults. Genome-wide data can be aggregated, clustered and visualized in a heatmap, according to different clustering methods. This includes a predefined setting to identify dynamic clusters between different conditions or developmental stages. Alternatively, clustered data can be visualized in a bandplot. Finally, fluff includes a tool to generate genomic profiles. As command-line tools, the fluff programs can easily be integrated into standard analysis pipelines. The installation is straightforward and documentation is available athttp://fluff.readthedocs.org.Availability.fluff is implemented in Python and runs on Linux. The source code is freely available for download athttps://github.com/simonvh/fluff.

scholarly journals Extensive Ethnolinguistic Diversity in Vietnam Reflects Multiple Sources of Genetic Diversity

2020 ◽  
Vol 37 (9) ◽  
pp. 2503-2519 ◽  
Author(s):  
Dang Liu ◽  
Nguyen Thuy Duong ◽  
Nguyen Dang Ton ◽  
Nguyen Van Phong ◽  
Brigitte Pakendorf ◽  
...  
Keyword(s):  
Genetic Diversity ◽  
Southeast Asia ◽  
Linguistic Diversity ◽  
Published Data ◽  
Multiple Sources ◽  
Austronesian Languages ◽  
Genome Wide ◽  
Genome Wide Data ◽  
History Of ◽  
Mainland Southeast Asia

Abstract Vietnam features extensive ethnolinguistic diversity and occupies a key position in Mainland Southeast Asia. Yet, the genetic diversity of Vietnam remains relatively unexplored, especially with genome-wide data, because previous studies have focused mainly on the majority Kinh group. Here, we analyze newly generated genome-wide single-nucleotide polymorphism data for the Kinh and 21 additional ethnic groups in Vietnam, encompassing all five major language families in Mainland Southeast Asia. In addition to analyzing the allele and haplotype sharing within the Vietnamese groups, we incorporate published data from both nearby modern populations and ancient samples for comparison. In contrast to previous studies that suggested a largely indigenous origin for Vietnamese genetic diversity, we find that Vietnamese ethnolinguistic groups harbor multiple sources of genetic diversity that likely reflect different sources for the ancestry associated with each language family. However, linguistic diversity does not completely match genetic diversity: There have been extensive interactions between the Hmong-Mien and Tai-Kadai groups; different Austro-Asiatic groups show different affinities with other ethnolinguistic groups; and we identified a likely case of cultural diffusion in which some Austro-Asiatic groups shifted to Austronesian languages during the past 2,500 years. Overall, our results highlight the importance of genome-wide data from dense sampling of ethnolinguistic groups in providing new insights into the genetic diversity and history of an ethnolinguistically diverse region, such as Vietnam.

scholarly journals Extensive ethnolinguistic diversity in Vietnam reflects multiple sources of genetic diversity

2019 ◽  
Author(s):  
Dang Liu ◽  
Nguyen Thuy Duong ◽  
Nguyen Dang Ton ◽  
Nguyen Van Phong ◽  
Brigitte Pakendorf ◽  
...  
Keyword(s):  
Genetic Diversity ◽  
Linguistic Diversity ◽  
Published Data ◽  
Multiple Sources ◽  
Austronesian Languages ◽  
Snp Data ◽  
Genome Wide ◽  
Genome Wide Data ◽  
History Of ◽  
Dense Sampling

AbstractVietnam features extensive ethnolinguistic diversity and occupies a key position in Mainland Southeast Asia (MSEA). Yet, the genetic diversity of Vietnam remains relatively unexplored, especially with genome-wide data, because previous studies have focused mainly on the majority Kinh group. Here we analyze newly-generated genome-wide SNP data for the Kinh and 21 additional ethnic groups in Vietnam, encompassing all five major language families in MSEA. In addition to analyzing the allele and haplotype sharing within the Vietnamese groups, we incorporate published data from both nearby modern populations and ancient samples for comparison. We find that the Vietnamese ethnolinguistic groups harbor multiple sources of genetic diversity that are associated with heterogeneous ancestry sharing profiles in each language family. However, linguistic diversity does not completely match genetic diversity; there have been extensive interactions between the Hmong-Mien and Tai-Kadai groups, and a likely case of cultural diffusion in which some Austro-Asiatic groups shifted to Austronesian languages. Overall, our results highlight the importance of genome-wide data from dense sampling of ethnolinguistic groups in providing new insights into the genetic diversity and history of an ethnolinguistically-diverse region, such as Vietnam.

scholarly journals High-Throughput ChIPmentation: freely scalable, single day ChIPseq data generation from very low cell-numbers

2018 ◽  
Author(s):  
Charlotte Gustafsson ◽  
Ayla De Paepe ◽  
Christian Schmidl ◽  
Robert Månsson
Keyword(s):  
Transcription Factor ◽  
Histone Modifications ◽  
High Throughput ◽  
Chromatin Immunoprecipitation ◽  
Dna Purification ◽  
Data Generation ◽  
Cell Numbers ◽  
Factor Binding ◽  
Genome Wide ◽  
A Genome

AbstractChromatin immunoprecipitation coupled to sequencing (ChIP-seq) is widely used to map histone modifications and transcription factor binding on a genome-wide level. Here, we present high-throughput ChIPmentation (HT-ChIPmentation) that eliminates the need for DNA purification prior to library amplification and reduces reverse-crosslinking time from hours to minutes. The resulting workflow is easily established, extremely rapid, and compatible with requirements for very low numbers of FACS sorted cells, high-throughput applications and single day data generation.

scholarly journals fluff: exploratory analysis and visualization of high-throughput sequencing data

2016 ◽  
Author(s):  
Georgios Georgiou ◽  
Simon J. van Heeringen
Keyword(s):  
High Throughput ◽  
High Throughput Sequencing ◽  
Developmental Stages ◽  
Command Line ◽  
Clustering Methods ◽  
Sequencing Data ◽  
Link Type ◽  
High Throughput Sequencing Data ◽  
Genome Wide ◽  
Genome Wide Data

AbstractSummaryIn this application note we describe fluff, a software package that allows for simple exploration, clustering and visualization of high-throughput sequencing data mapped to a reference genome. The package contains three command-line tools to generate publication-quality figures in an uncomplicated manner using sensible defaults. Genome-wide data can be aggregated, clustered and visualized in a heatmap, according to different clustering methods. This includes a predefined setting to identify dynamic clusters between different conditions or developmental stages. Alternatively, clustered data can be visualized in a bandplot. Finally, fluff includes a tool to generate genomic profiles. As command-line tools, the fluff programs can easily be integrated into standard analysis pipelines. The installation is straightforward and documentation is available at http://fluff.readthedocs.org.Availabilityfluff is implemented in Python and runs on Linux. The source code is freely available for download at http://github.com/simonvh/[email protected]

scholarly journals Molecular phenotyping of infection-associated small non-coding RNAs

2016 ◽  
Vol 371 (1707) ◽  
pp. 20160081 ◽  
Author(s):  
Lars Barquist ◽  
Alexander J. Westermann ◽  
Jörg Vogel
Keyword(s):  
Virulence Factors ◽  
High Throughput ◽  
Genetic Manipulation ◽  
Genetic Interaction ◽  
Forward Genetics ◽  
Model Systems ◽  
Transposon Insertion ◽  
Culture Models ◽  
Molecular Phenotypes ◽  
Transposon Insertion Sequencing

Infection is a complicated balance, with both pathogen and host struggling to tilt the result in their favour. Bacterial infection biology has relied on forward genetics for many of its advances, defining phenotype in terms of replication in model systems. However, many known virulence factors fail to produce robust phenotypes, particularly in the systems most amenable to genetic manipulation, such as cell-culture models. This has particularly been limiting for the study of the bacterial regulatory small RNAs (sRNAs) in infection. We argue that new sequencing-based technologies can work around this problem by providing a ‘molecular phenotype’, defined in terms of the specific transcriptional dysregulation in the infection system induced by gene deletion. We illustrate this using the example of our recent study of the PinT sRNA using dual RNA-seq, that is, simultaneous RNA sequencing of host and pathogen during infection. We additionally discuss how other high-throughput technologies, in particular genetic interaction mapping using transposon insertion sequencing, may be used to further dissect molecular phenotypes. We propose a strategy for how high-throughput technologies can be integrated in the study of non-coding regulators as well as bacterial virulence factors, enhancing our ability to rapidly generate hypotheses with regards to their function. This article is part of the themed issue ‘The new bacteriology’.

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