Comparing antimicrobial resistance prediction pipelines from bacterial whole genome sequencing data: An inter-laboratory study
Ontology highlight
ABSTRACT:
PROVIDER: PRJEB34513 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| ERR3535480_1.fastq.gz | Fastqsanger.gz | |||
| ERR3535480_2.fastq.gz | Fastqsanger.gz | |||
| ERR3535471_1.fastq.gz | Fastqsanger.gz | |||
| ERR3535471_2.fastq.gz | Fastqsanger.gz | |||
| ERR3535472_1.fastq.gz | Fastqsanger.gz |
Items per page: 5 1 - 5 of 20 |
Similar Datasets
Project description:<h4>Introduction</h4>Lipidomic profiling allows 100s if not 1000s of lipids in a sample to be detected and quantified. Modern lipidomics techniques are ultra-sensitive assays that enable the discovery of novel biomarkers in a variety of fields and provide new insight in mechanistic investigations. Despite much progress in lipidomics, there remains, as for all high throughput 'omics' strategies, the need to develop strategies to standardize and integrate quality control into studies in order to enhance robustness, reproducibility, and usability of studies within specific fields and beyond.<h4>Objectives</h4>We aimed to understand how much results from lipid profiling in the model organism Caenorhabditis elegans are influenced by different culture conditions in different laboratories.<h4>Methods</h4>In this work we have undertaken an inter-laboratory study, comparing the lipid profiles of N2 wild type C. elegans and daf-2(e1370) mutants lacking a functional insulin receptor. Sample were collected from worms grown in four separate laboratories under standardized growth conditions. We used an UPLC-UHR-ToF-MS system allowing chromatographic separation before MS analysis.<h4>Results</h4>We found common qualitative changes in several marker lipids in samples from the individual laboratories. On the other hand, even in this controlled experimental system, the exact fold-changes for each marker varied between laboratories.<h4>Conclusion</h4>Our results thus reveal a serious limitation to the reproducibility of current lipid profiling experiments and reveal challenges to the integration of such data from different laboratories.
2022-02-21 | MTBLS2016 | MetaboLights
Project description:162 FFPE samples, representing six different tumour types, were profiled in triplicate across three independent laboratories. OncoScan¬ FFPE assay data was then analysed for reproducibility of genome-wide copy number, loss of heterozygosity and somatic mutations.
2015-02-18 | E-MTAB-2914 | biostudies-arrayexpress
Project description:To facilitate collaborative research efforts between multi-investigator teams using DNA microarrays, we identified sources of error and data variability between laboratories and across microarray platforms and methods to accommodate this variability. RNA expression data were generated in seven laboratories, comparing two standard RNA samples using twelve microarray platforms. At least two standard microarray types (one spotted, one commercial) were used by all laboratories. Reproducibility for most platforms within any laboratory was typically good, but reproducibility between platforms and across laboratories was generally poor. Reproducibility between laboratories dramatically increased when standardized protocols were implemented for RNA labeling, hybridization, microarray processing, data acquisition and data normalization. Nonetheless, concordance could be found across different laboratories and platforms when data were analyzed in terms of enriched Gene Ontology categories. These findings indicate that microarray results generated by multiple sites and platforms can be comparable, and that multi-investigator teams will maximize data comparability by adopting a common platform and a common set of procedures to generate compatible data. Keywords: other
2005-04-21 | GSE2458 | GEO
Project description:To facilitate collaborative research efforts between multi-investigator teams using DNA microarrays, we identified sources of error and data variability between laboratories and across microarray platforms and methods to accommodate this variability. RNA expression data were generated in seven laboratories, comparing two standard RNA samples using twelve microarray platforms. At least two standard microarray types (one spotted, one commercial) were used by all laboratories. Reproducibility for most platforms within any laboratory was typically good, but reproducibility between platforms and across laboratories was generally poor. Reproducibility between laboratories dramatically increased when standardized protocols were implemented for RNA labeling, hybridization, microarray processing, data acquisition and data normalization. Nonetheless, concordance could be found across different laboratories and platforms when data were analyzed in terms of enriched Gene Ontology categories. These findings indicate that microarray results generated by multiple sites and platforms can be comparable, and that multi-investigator teams will maximize data comparability by adopting a common platform and a common set of procedures to generate compatible data. Keywords: other
2010-06-10 | E-GEOD-2458 | biostudies-arrayexpress
Project description:Microbial amplicon sequencing studies are an important tool in biological and biomedical research. Widespread 16S rRNA gene microbial surveys have shed light on the structure of many ecosystems inhabited by bacteria, including the human body. However, specialized software and algorithms are needed to convert raw sequencing data into biologically meaningful information (i.e. tables of bacterial counts). While different bioinformatic pipelines are available in a rapidly changing and improving field, users are often unaware of limitations and biases associated with individual pipelines and there is a lack of agreement regarding best practices. Here, we compared six bioinformatic pipelines for the analysis of amplicon sequence data: three OTU-level flows (QIIME-uclust, MOTHUR, and USEARCH-UPARSE) and three ASV-level (DADA2, Qiime2-Deblur, and USEARCH-UNOISE3). We tested workflows with different quality control options, clustering algorithms, and cutoff parameters on a mock community as well as on a large (N = 2170) recently published fecal sample dataset from the multi-ethnic HELIUS study. We assessed the sensitivity, specificity, and degree of consensus of the different outputs. DADA2 offered the best sensitivity, at the expense of decreased specificity compared to USEARCH-UNOISE3 and Qiime2-Deblur. USEARCH-UNOISE3 showed the best balance between resolution and specificity. OTU-level USEARCH-UPARSE and MOTHUR performed well, but with lower specificity than ASV-level pipelines. QIIME-uclust produced large number of spurious OTUs as well as inflated alpha-diversity measures and should be avoided in future studies. This study provides guidance for researchers using amplicon sequencing to gain biological insights.
| S-EPMC6964864 | biostudies-literature
Project description:Madin-Darby Canine Kidney (MDCK) cells are widely used to study epithelial cell functionality. Their low endogenous drug transporter protein levels make them an amenable system to investigate trans-epithelial permeation and drug transporter protein activity after their transfection. MDCK cells display diverse phenotypic traits and as such laboratory-to-laboratory variability in drug permeability assessments are observed. Consequently, In Vitro-In Vivo extrapolation (IVIVE) approaches using permeability and/or transporter activity data require calibration. A comprehensive proteomic quantification of eleven filter-grown parental or mock-transfected MDCK monolayers from eight different pharmaceutical laboratories using the Total Protein Approach (TPA) is provided. TPA enabled estimations of key morphometric parameters such as monolayer cellularity and volume. Overall, metabolic liability to xenobiotics is likely to be limited for MDCK cell due to the low expression of required enzymes. SLC16A1 (MCT1) was the highest abundance SLC transporter linked to xenobiotic activity, while ABCC4 (MRP4) was the highest abundant ABC transporter. Our data supports existing findings that Claudin-2 levels may be linked to tight junction modulation, thus impacting trans-epithelial resistance. This unique database provides data on more than 8000 proteins copy number and concentrations, thus allowing an in-depth appraisal of the control monolayers used in each laboratory.
2023-07-20 | PXD039552 | Pride
Project description:Reproducibility in molecular and cellular studies is fundamental to scientific discovery. To establish the reproducibility of a well-defined long term neuronal differentiation protocol, we repeated the cellular and molecular comparison of the same two iPSC lines across five distinct laboratories. Despite uncovering acceptable variability within individual laboratories, we detect poor cross-site reproducibility of the differential gene expression signature between these two lines. Factor analysis identifies the laboratory as the largest source of variation along with several variation-inflating confounds such as passaging effects and progenitor storage. Single cell transcriptomics shows substantial cellular heterogeneity underlying inter-laboratory variability and being responsible for biases in differential gene expression inference. Factor analysis-based normalization of the combined dataset can remove the nuisance technical effects, enabling the execution of robust hypothesis generating studies. Our study shows that multi-center collaborations can expose systematic biases and identify critical factors to be standardized when publishing novel protocols, contributing to increased cross-site reproducibility.
2018-08-20 | GSE118734 | GEO
Project description:Reproducibility in molecular and cellular studies is fundamental to scientific discovery. To establish the reproducibility of a well-defined long term neuronal differentiation protocol, we repeated the cellular and molecular comparison of the same two iPSC lines across five distinct laboratories. Despite uncovering acceptable variability within individual laboratories, we detect poor cross-site reproducibility of the differential gene expression signature between these two lines. Factor analysis identifies the laboratory as the largest source of variation along with several variation-inflating confounds such as passaging effects and progenitor storage. Single cell transcriptomics shows substantial cellular heterogeneity underlying inter-laboratory variability and being responsible for biases in differential gene expression inference. Factor analysis-based normalization of the combined dataset can remove the nuisance technical effects, enabling the execution of robust hypothesis generating studies. Our study shows that multi-center collaborations can expose systematic biases and identify critical factors to be standardized when publishing novel protocols, contributing to increased cross-site reproducibility.
2018-08-20 | GSE118733 | GEO
Project description:Motivation:The secretome denotes the collection of secreted proteins exported outside of the cell. The functional roles of secreted proteins include the maintenance and remodelling of the extracellular matrix as well as signalling between host and non-host cells. These features make secretomes rich reservoirs of biomarkers for disease classification and host-pathogen interaction studies. Common biomarkers are extracellular proteins secreted via classical pathways that can be predicted from sequence by annotating the presence or absence of N-terminal signal peptides. Several heterogeneous command line tools and web-interfaces exist to identify individual motifs, signal sequences and domains that are either characteristic or strictly excluded from secreted proteins. However, a single flexible secretome-prediction workflow that combines all analytic steps is still missing. Results:To bridge this gap the SecretSanta package implements wrapper and parser functions around established command line tools for the integrative prediction of extracellular proteins that are secreted via classical pathways. The modularity of SecretSanta enables users to create tailored pipelines and apply them across the whole tree of life to facilitate comparison of secretomes across multiple species or under various conditions. Availability and implementation:SecretSanta is implemented in the R programming language and is released under GPL-3 license. All functions have been optimized and parallelized to allow large-scale processing of sequences. The open-source code, installation instructions and vignette with use case scenarios can be downloaded from https://github.com/gogleva/SecretSanta. Supplementary information:Supplementary data are available at Bioinformatics online.
| S-EPMC6022548 | biostudies-literature
Project description:Antimicrobial resistance (AMR) poses a threat to public health. Clinical microbiology laboratories typically rely on culturing bacteria for antimicrobial-susceptibility testing (AST). As the implementation costs and technical barriers fall, whole-genome sequencing (WGS) has emerged as a 'one-stop' test for epidemiological and predictive AST results. Few published comparisons exist for the myriad analytical pipelines used for predicting AMR. To address this, we performed an inter-laboratory study providing sets of participating researchers with identical short-read WGS data from clinical isolates, allowing us to assess the reproducibility of the bioinformatic prediction of AMR between participants, and identify problem cases and factors that lead to discordant results. We produced ten WGS datasets of varying quality from cultured carbapenem-resistant organisms obtained from clinical samples sequenced on either an Illumina NextSeq or HiSeq instrument. Nine participating teams ('participants') were provided these sequence data without any other contextual information. Each participant used their choice of pipeline to determine the species, the presence of resistance-associated genes, and to predict susceptibility or resistance to amikacin, gentamicin, ciprofloxacin and cefotaxime. We found participants predicted different numbers of AMR-associated genes and different gene variants from the same clinical samples. The quality of the sequence data, choice of bioinformatic pipeline and interpretation of the results all contributed to discordance between participants. Although much of the inaccurate gene variant annotation did not affect genotypic resistance predictions, we observed low specificity when compared to phenotypic AST results, but this improved in samples with higher read depths. Had the results been used to predict AST and guide treatment, a different antibiotic would have been recommended for each isolate by at least one participant. These challenges, at the final analytical stage of using WGS to predict AMR, suggest the need for refinements when using this technology in clinical settings. Comprehensive public resistance sequence databases, full recommendations on sequence data quality and standardization in the comparisons between genotype and resistance phenotypes will all play a fundamental role in the successful implementation of AST prediction using WGS in clinical microbiology laboratories.
| S-EPMC7067211 | biostudies-literature