scholarly journals Genomic variant annotation workflow for clinical applications

F1000Research ◽  
2016 ◽  
Vol 5 ◽  
pp. 1963 ◽  
Author(s):  
Thomas Thurnherr ◽  
Franziska Singer ◽  
Daniel J. Stekhoven ◽  
Niko Beerenwinkel
Keyword(s):  
Gene Interaction ◽  
Clinical Applications ◽  
Interaction Data ◽  
Bioconductor Package ◽  
Variant Annotation ◽  
Interaction Database ◽  
Genomic Aberrations ◽  
Genomic Variant ◽  
Different Levels ◽  
Generation Sequencing

Annotation and interpretation of DNA aberrations identified through next-generation sequencing is becoming an increasingly important task. Even more so in the context of data analysis pipelines for medical applications, where genomic aberrations are associated with phenotypic and clinical features. Here we describe a workflow to identify potential gene targets in aberrated genes or pathways and their corresponding drugs. To this end, we provide the R/Bioconductor package rDGIdb, an R wrapper to query the drug-gene interaction database (DGIdb). DGIdb accumulates drug-gene interaction data from 15 different resources and allows filtering on different levels. The rDGIdb package makes these resources and tools available to R users. Moreover, rDGIdb queries can be automated through incorporation of the rDGIdb package into NGS sequencing pipelines.

scholarly journals Genomic variant annotation workflow for clinical applications

F1000Research ◽  
2016 ◽  
Vol 5 ◽  
pp. 1963 ◽  
Author(s):  
Thomas Thurnherr ◽  
Franziska Singer ◽  
Daniel J. Stekhoven ◽  
Niko Beerenwinkel
Keyword(s):  
Gene Interaction ◽  
Clinical Applications ◽  
Interaction Data ◽  
Bioconductor Package ◽  
Variant Annotation ◽  
Interaction Database ◽  
Genomic Aberrations ◽  
Genomic Variant ◽  
Different Levels ◽  
Generation Sequencing

Annotation and interpretation of DNA aberrations identified through next-generation sequencing is becoming an increasingly important task. Even more so in the context of data analysis pipelines for medical applications, where genomic aberrations are associated with phenotypic and clinical features. Here we describe a workflow to identify potential gene targets in aberrated genes or pathways and their corresponding drugs. To this end, we provide the R/Bioconductor package rDGIdb, an R wrapper to query the drug-gene interaction database (DGIdb). DGIdb accumulates drug-gene interaction data from 15 different source databases and allows filtering on different levels. The rDGIdb package makes these resources and tools available to R users. Moreover, DGIdb queries can be automated through incorporation of the rDGIdb package into NGS sequencing pipelines.

scholarly journals Integration of the Drug–Gene Interaction Database (DGIdb 4.0) with open crowdsource efforts

2020 ◽  
Vol 49 (D1) ◽  
pp. D1144-D1151
Author(s):  
Sharon L Freshour ◽  
Susanna Kiwala ◽  
Kelsy C Cotto ◽  
Adam C Coffman ◽  
Joshua F McMichael ◽  
...  
Keyword(s):  
Interaction Network ◽  
Gene Interaction ◽  
Application Programming Interface ◽  
Application Framework ◽  
Interaction Data ◽  
Gene Interaction Network ◽  
Web Based ◽  
Web Resource ◽  
Interaction Database ◽  
Primary Focus

Abstract The Drug-Gene Interaction Database (DGIdb, www.dgidb.org) is a web resource that provides information on drug-gene interactions and druggable genes from publications, databases, and other web-based sources. Drug, gene, and interaction data are normalized and merged into conceptual groups. The information contained in this resource is available to users through a straightforward search interface, an application programming interface (API), and TSV data downloads. DGIdb 4.0 is the latest major version release of this database. A primary focus of this update was integration with crowdsourced efforts, leveraging the Drug Target Commons for community-contributed interaction data, Wikidata to facilitate term normalization, and export to NDEx for drug-gene interaction network representations. Seven new sources have been added since the last major version release, bringing the total number of sources included to 41. Of the previously aggregated sources, 15 have been updated. DGIdb 4.0 also includes improvements to the process of drug normalization and grouping of imported sources. Other notable updates include the introduction of a more sophisticated Query Score for interaction search results, an updated Interaction Score, the inclusion of interaction directionality, and several additional improvements to search features, data releases, licensing documentation and the application framework.

scholarly journals ngsReports: a Bioconductor package for managing FastQC reports and other NGS related log files

2019 ◽  
Vol 36 (8) ◽  
pp. 2587-2588 ◽  
Author(s):  
Christopher M Ward ◽  
Thu-Hien To ◽  
Stephen M Pederson
Keyword(s):  
Quality Control ◽  
R Package ◽  
Supplementary Information ◽  
Bioconductor Package ◽  
Supplementary Data ◽  
Large Sample ◽  
Log Files ◽  
Shiny App ◽  
Next Generation Sequencing Ngs ◽  
Generation Sequencing

Abstract Motivation High throughput next generation sequencing (NGS) has become exceedingly cheap, facilitating studies to be undertaken containing large sample numbers. Quality control (QC) is an essential stage during analytic pipelines and the outputs of popular bioinformatics tools such as FastQC and Picard can provide information on individual samples. Although these tools provide considerable power when carrying out QC, large sample numbers can make inspection of all samples and identification of systemic bias a challenge. Results We present ngsReports, an R package designed for the management and visualization of NGS reports from within an R environment. The available methods allow direct import into R of FastQC reports along with outputs from other tools. Visualization can be carried out across many samples using default, highly customizable plots with options to perform hierarchical clustering to quickly identify outlier libraries. Moreover, these can be displayed in an interactive shiny app or HTML report for ease of analysis. Availability and implementation The ngsReports package is available on Bioconductor and the GUI shiny app is available at https://github.com/UofABioinformaticsHub/shinyNgsreports. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Comparative Study of NGS Platform Ion Torrent Personal Genome Machine and Therascreen Rotor-Gene Q for the Detection of Somatic Variants in Cancer

2020 ◽  
Vol 9 (1) ◽  
pp. 4
Author(s):  
Angela Lombardi ◽  
Margherita Russo ◽  
Amalia Luce ◽  
Floriana Morgillo ◽  
Virginia Tirino ◽  
...  
Keyword(s):  
Target Genes ◽  
Personal Genome ◽  
Analysis Method ◽  
Wild Type ◽  
Specific Analysis ◽  
Current Therapies ◽  
Genomic Aberrations ◽  
Melanoma Patients ◽  
Next Generation Sequencing Ngs ◽  
Generation Sequencing

Molecular profiling of a tumor allows the opportunity to design specific therapies which are able to interact only with cancer cells characterized by the accumulation of several genomic aberrations. This study investigates the usefulness of next-generation sequencing (NGS) and mutation-specific analysis methods for the detection of target genes for current therapies in non-small-cell lung cancer (NSCLC), metastatic colorectal cancer (mCRC), and melanoma patients. We focused our attention on EGFR, BRAF, KRAS, and BRAF genes for NSCLC, melanoma, and mCRC samples, respectively. Our study demonstrated that in about 2% of analyzed cases, the two techniques did not show the same or overlapping results. Two patients affected by mCRC resulted in wild-type (WT) for BRAF and two cases with NSCLC were WT for EGFR according to PGM analysis. In contrast, these samples were mutated for the evaluated genes using the therascreen test on Rotor-Gene Q. In conclusion, our experience suggests that it would be appropriate to confirm the WT status of the genes of interest with a more sensitive analysis method to avoid the presence of a small neoplastic clone and drive the clinician to correct patient monitoring.

scholarly journals Dissecting Genomic Aberrations in Myeloproliferative Neoplasms by Multiplex-PCR and Next Generation Sequencing

PLoS ONE ◽  
2015 ◽  
Vol 10 (4) ◽  
pp. e0123476 ◽  
Author(s):  
Martin M. J. Kirschner ◽  
Mirle Schemionek ◽  
Claudia Schubert ◽  
Nicolas Chatain ◽  
Stephanie Sontag ◽  
...  
Keyword(s):  
Next Generation Sequencing ◽  
Multiplex Pcr ◽  
Myeloproliferative Neoplasms ◽  
Next Generation ◽  
Genomic Aberrations ◽  
Generation Sequencing

scholarly journals Effects of Life Stage, Site, and Species on the Dragonfly Gut Microbiome

2020 ◽  
Vol 8 (2) ◽  
pp. 183 ◽  
Author(s):  
Sarah Nobles ◽  
Colin R. Jackson
Keyword(s):  
Gut Microbiome ◽  
Host Species ◽  
Life Stage ◽  
Rrna Gene ◽  
Life Stages ◽  
Aquatic Habitats ◽  
Surrounding Environment ◽  
Different Levels ◽  
Generation Sequencing ◽  
Bacterial 16S Rrna Gene

Insects that undergo metamorphosis from juveniles to adults provide an intriguing opportunity to examine the effects of life stage, species, and the environment on their gut microbiome. In this study, we surveyed the gut microbiomes of 13 species of dragonfly collected from five different locations subject to different levels of human impact. Juveniles were collected as nymphs from aquatic habitats while airborne adults were caught at the same locations. The gut microbiome was characterized by next generation sequencing of the bacterial 16S rRNA gene. Life stage was an important factor, with the gut microbiomes of dragonfly nymphs differing from those of adult dragonflies. Gut microbiomes of nymphs were influenced by sample site and, to a lesser extent, host species. Neither sample location nor host species had a strong effect on the gut microbiome of dragonfly adults. Regardless of life stage, gut microbiomes were dominated by members of the Proteobacteria, with members of the Bacteroidetes (especially in adults), Firmicutes, and Acidobacteria (especially in nymphs) also being proportionally abundant. These results demonstrate that different life stages of metamorphosing insects can harbor very different gut microbiomes and differ in how this microbiome is influenced by the surrounding environment.

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