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Extensible quality control dashboard built around FASTQ assessment. FQC is designed to better group FastQC result data across groups where each group is comprised of FASTQs related to an experiment or sequencing batch. Individual samples are grouped into paired-end sets when available and the dashboard's extensibility allows a user to add plots or tables as desired.
Libra is a Python package that extends support of its parent package, SQLAlchemy. Libra’s primary functionality supports the dynamic creation of object-oriented analogs of SQL tables from a variety of user-defined, text-based schema definition formats and provides quality control and analysis tools and methods
The SpacerExtractor tool is meant to robustly identify and extract CRISPR spacers from metagenome short reads. Working from a database of known CRISPR repeats, SpacerExtractor quickly scans short reads for the corresponding repeat sequences, extract the potential spacer between two repeats, apply several quality control, denoising, and clustering steps, and provides a full non-redundant complement of spacers for each detected repeat. Because of the high variability observed at CRISPR loci, this read mining approach typically recovers a much larger diversity of spacers than can be found in assembled contigs. SpacerExtractor also includes commands to run CRISPR-Cas Typer on a new set of genomes or MAGs, and add newly predicted repeats to the repeat database.
The NLR Data Processing Pipeline for MADIS software package is for downloading, processing, and performing QA/QC on MADIS data. Designed to handle the following steps: 1) Download all MADIS data as compressed netcdf files for a given time period. 2) Unpack netcdf files into timeseries csvs for each coordinate within the given bounding box. 3) Process the csvs to filter according to quality control checks and convert variables to correct units. 4) Write processed csvs to a single nc file.
Every observationally based research program needs a way to collect data from instruments, convert the data from its raw format into a more usable format, apply quality control, process it into higher-order data products, store the data, and make the data available to its scientific community. This data flow is illustrated pictorially in Fig. 11-1. These are the basic requirements of any scientific data system, and ARM’s data system would have to address these requirements and more. This research provides one view of the development of the ARM data system, which includes the ARM Data Archive, and some of the notable decisions that were made along the way.
The Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), updates NASA’s previous satellite-era (1980 onward) reanalysis system to include additional observations and improvements to the Goddard Earth Observing System, version 5 (GEOS-5), Earth system model. As a major step toward a full Integrated Earth Systems Analysis (IESA), in addition to meteorological observations, MERRA-2 now includes assimilation of aerosol optical depth (AOD) from various ground- and space-based remote sensing platforms. Here, in the first of a pair of studies, the MERRA-2 aerosol assimilation is documented, including a description of the prognostic model (GEOS-5 coupled to the GOCART aerosol module), aerosol emissions, and the quality control of ingested observations. Initial validation and evaluation of the analyzed AOD fields are provided using independent observations from ground, aircraft, and shipborne instruments. The positive impact of the AOD assimilation on simulated aerosols is demonstrated by comparing MERRA-2 aerosol fields to an identical control simulation that does not include AOD assimilation. Fianlly, after showing the AOD evaluation, this paper takes a first look at aerosol–climate interactions by examining the shortwave, clear-sky aerosol direct radiative effect. The companion paper (Part II) evaluates and validates available MERRA-2 aerosol properties not directly impacted by the AOD assimilation (e.g., aerosol vertical distribution and absorption). Importantly, while highlighting the skill of the MERRA-2 aerosol assimilation products, both studies point out caveats that must be considered when using this new reanalysis product for future studies of aerosols and their interactions with weather and climate.
Here, this study compares six evapotranspiration ET products for Canada's landmass, namely, eddy covariance EC measurements; surface water budget ET; remote sensing ET from MODIS; and land surface model (LSM) ET from the Community Land Model (CLM), the Ecological Assimilation of Land and Climate Observations (EALCO) model, and the Variable Infiltration Capacity model (VIC). The ET climatology over the Canadian landmass is characterized and the advantages and limitations of the datasets are discussed. The EC measurements have limited spatial coverage, making it difficult for model validations at the national scale. Water budget ET has the largest uncertainty because of data quality issues with precipitation in mountainous regions and in the north. MODIS ET shows relatively large uncertainty in cold seasons and sparsely vegetated regions. The LSM products cover the entire landmass and exhibit small differences in ET among them. Annual ET from the LSMs ranges from small negative values to over 600 mm across the landmass, with a countrywide average of 256 ± 15 mm. Seasonally, the countrywide average monthly ET varies from a low of about 3 mm in four winter months (November-February) to 67 ± 7 mm in July. The ET uncertainty is scale dependent. Larger regions tend to have smaller uncertainties because of the offset of positive and negative biases within the region. More observation networks and better quality controls are critical to improving ET estimates. Future techniques should also consider a hybrid approach that integrates strengths of the various ET products to help reduce uncertainties in ET estimation.
Background: With cancer as one of the leading causes of death worldwide, accurate primary tumor type prediction is critical in identifying genetic factors that can inhibit or slow tumor progression. There have been efforts to categorize primary tumor types with gene expression data using machine learning, and more recently with deep learning, in the last several years. Methods In this paper, we developed four 1-dimensional (1D) Convolutional Neural Network (CNN) models to classify RNA-seq count data as one of 17 highly represented primary tumor types or 32 primary tumor types regardless of imbalanced representation. Additionally, we adapted the models to take as input either all Ensembl genes (60,483) or protein coding genes only (19,758). Unlike previous work, we avoided selection bias by not filtering genes based on expression values. RNA-seq count data expressed as FPKM-UQ of 9,025 and 10,940 samples from The Cancer Genome Atlas (TCGA) were downloaded from the Genomic Data Commons (GDC) corresponding to 17 and 32 primary tumor types respectively for training and validating the models. Results: All 4 1D-CNN models had an overall accuracy of 94.7% to 97.6% on the test dataset. Further evaluation indicates that the models with protein coding genes only as features performed with better accuracy compared to the models with all Ensembl genes for both 17 and 32 primary tumor types. For all models, the accuracy by primary tumor type was above 80% for most primary tumor types. Conclusions: We packaged all 4 models as a Python-based deep learning classification tool called TULIP (TUmor CLassIfication Predictor) for performing quality control on primary tumor samples and characterizing cancer samples of unknown tumor type. Further optimization of the models is needed to improve the accuracy of certain primary tumor types.
Currently, the use of advanced high strength steels (AHSSs) is the most cost effective means of reducing vehicle body weight and maintaining structural integrity at the same time. However, AHSSs present a big challenge to the traditional resistance spot welding (RSW) widely applied in automotive industries because the rapid heating and cooling procedures during RSW produce hardened weld microstructures, which lower the ductility and fatigue properties of welded joints and raise the probability of interfacial failure under external loads. Changing process parameters or post-weld heat treatment may reduce the weld brittleness, but those traditional quality control methods also increase energy consumption and prolong cycle time. In recent years, a magnetically assisted RSW (MA-RSW) method was proposed, in which an externally applied magnetic field would interact with the conduction current to produce a Lorentz force that would affect weld nugget formation. This paper is a review of an experimental MA-RSW platform, the mode of the external magnetic field and the mechanism that controls nugget shape, weld microstructures and joint performance. In conclusion, the advantages of the MA-RSW method in improving the weldability of AHSSs are given, a recent application of the MA-RSW process to light metals is described and the outlook for the MA-RSW process is presented.
Abstract Background Single-cell RNA sequencing (scRNA-seq) has revolutionized the study of transcriptomes, arising as a powerful tool for discovering and characterizing cell types and their developmental trajectories. However, scRNA-seq analysis is complex, requiring a continuous, iterative process to refine the data and uncover relevant biological information. A diversity of tools has been developed to address the multiple aspects of scRNA-seq data analysis. However, an easy-to-use web application capable of conducting all critical steps of scRNA-seq data analysis is still lacking. Summary We present Asc-Seurat, a feature-rich workbench, providing an user-friendly and easy-to-install web application encapsulating tools for an all-encompassing and fluid scRNA-seq data analysis. Asc-Seurat implements functions from the Seurat package for quality control, clustering, and genes differential expression. In addition, Asc-Seurat provides a pseudotime module containing dozens of models for the trajectory inference and a functional annotation module that allows recovering gene annotation and detecting gene ontology enriched terms. We showcase Asc-Seurat’s capabilities by analyzing a peripheral blood mononuclear cell dataset. Conclusions Asc-Seurat is a comprehensive workbench providing an accessible graphical interface for scRNA-seq analysis by biologists. Asc-Seurat significantly reduces the time and effort required to analyze and interpret the information in scRNA-seq datasets.
Abstract Background Variation in omics data due to intrinsic biological stochasticity is often viewed as a challenging and undesirable feature of complex systems analyses. In fact, numerous statistical methods are utilized to minimize the variation among biological replicates. Results We demonstrate that the common statistics relative standard deviation (RSD) and coefficient of variation (CV), which are often used for quality control or part of a larger pipeline in omics analyses, can also be used as a metric of a physiological stress response. Using an approach we term Replicate Variation Analysis (RVA), we demonstrate that acute physiological stress leads to feature-wide canalization of CV profiles of metabolomes and proteomes across biological replicates. Canalization is the repression of variation between replicates, which increases phenotypic similarity. Multiple in-house mass spectrometry omics datasets in addition to publicly available data were analyzed to assess changes in CV profiles in plants, animals, and microorganisms. In addition, proteomics data sets were evaluated utilizing RVA to identify functionality of reduced CV proteins. Conclusions RVA provides a foundation for understanding omics level shifts that occur in response to cellular stress. This approach to data analysis helps characterize stress response and recovery, and could be deployed to detect populations under stress, monitor health status, and conduct environmental monitoring.
BACKGROUND: Next generation sequencing (NGS) has become a universal practice in modern molecular biology. As the throughput of sequencing experiments increases, the preparation of conventional multiplexed libraries becomes more labor intensive. Conventional library preparation typically requires quality control (QC) testing for individual libraries such as amplification success evaluation and quantification, none of which occur until the end of the library preparation process. RESULTS: In this study, we address the need for a more streamlined high-throughput NGS workflow by tethering real-time quantitative PCR (qPCR) to conventional workflows to save time and implement single tube and single reagent QC. We modified two distinct library preparation workflows by replacing PCR and quantification with qPCR using SYBR Green I. qPCR enabled individual library quantification for pooling in a single tube without the need for additional reagents. Additionally, a melting curve analysis was implemented as an intermediate QC test to confirm successful amplification. Sequencing analysis showed comparable percent reads for each indexed library, demonstrating that pooling calculations based on qPCR allow for an even representation of sequencing reads. To aid the modified workflow, a software toolkit was developed and used to generate pooling instructions and analyze qPCR and melting curve data. CONCLUSIONS: We successfully applied fluorescent amplification for next generation sequencing (FA-NGS) library preparation to both plasmids and bacterial genomes. As a result of using qPCR for quantification and proceeding directly to library pooling, the modified library preparation workflow has fewer overall steps. Therefore, we speculate that the FA-NGS workflow has less risk of user error. The melting curve analysis provides the necessary QC test to identify and troubleshoot library failures prior to sequencing. While this study demonstrates the value of FA-NGS for plasmid or gDNA libraries, we speculate that its versatility could lead to successful application across other library types.
Chloroplasts respond to stress and changes in the environment by producing reactive oxygen species (ROS) that have specific signaling abilities. The ROS singlet oxygen ( 1 O 2 ) is unique in that it can signal to initiate cellular degradation including the selective degradation of damaged chloroplasts. This chloroplast quality control pathway can be monitored in the Arabidopsis thaliana mutant plastid ferrochelatase two ( fc2 ) that conditionally accumulates chloroplast 1 O 2 under diurnal light cycling conditions leading to rapid chloroplast degradation and eventual cell death. The cellular machinery involved in such degradation, however, remains unknown. Recently, it was demonstrated that whole damaged chloroplasts can be transported to the central vacuole via a process requiring autophagosomes and core components of the autophagy machinery. The relationship between this process, referred to as chlorophagy, and the degradation of 1 O 2 -stressed chloroplasts and cells has remained unexplored. Results To further understand 1 O 2 -induced cellular degradation and determine what role autophagy may play, the expression of autophagy-related genes was monitored in 1 O 2 -stressed fc2 seedlings and found to be induced. Although autophagosomes were present in fc2 cells, they did not associate with chloroplasts during 1 O 2 stress. Mutations affecting the core autophagy machinery ( atg5 , atg7 , and atg10 ) were unable to suppress 1 O 2 -induced cell death or chloroplast protrusion into the central vacuole, suggesting autophagosome formation is dispensable for such 1 O 2 –mediated cellular degradation. However, both atg5 and atg7 led to specific defects in chloroplast ultrastructure and photosynthetic efficiencies, suggesting core autophagy machinery is involved in protecting chloroplasts from photo-oxidative damage. Finally, genes predicted to be involved in microautophagy were shown to be induced in stressed fc2 seedlings, indicating a possible role for an alternate form of autophagy in the dismantling of 1 O 2 -damaged chloroplasts. Conclusions Our results support the hypothesis that 1 O 2 -dependent cell death is independent from autophagosome formation, canonical autophagy, and chlorophagy. Furthermore, autophagosome-independent microautophagy may be involved in degrading 1 O 2 -damaged chloroplasts. At the same time, canonical autophagy may still play a role in protecting chloroplasts from 1 O 2 -induced photo-oxidative stress. Together, this suggests chloroplast function and degradation is a complex process utilizing multiple autophagy and degradation machineries, possibly depending on the type of stress or damage incurred.
Despite an abundance of online databases providing access to chemical data, there is increasing demand for highquality, structure-curated, open data to meet the various needs of the environmental sciences and computational toxicology communities. The U.S. Environmental Protection Agency’s (EPA) web-based CompTox Chemistry Dashboard is addressing these needs by integrating diverse types of relevant domain data through a cheminformatics layer, built upon a database of curated substances linked to chemical structures. These data include physicochemical, environmental fate and transport, exposure, usage, in vivo toxicity, and in vitro bioassay data, surfaced through an integration hub with link-outs to additional EPA data and public domain online resources. Batch searching allows for direct chemical identifer (ID) mapping and downloading of multiple data streams in several diferent formats. This facilitates fast access to available structure, property, toxicity, and bioassay data for collections of chemicals (hundreds to thousands at a time). Advanced search capabilities are available to support, for example, non-targeted analysis and identifcation of chemicals using mass spectrometry. The contents of the chemistry database, presently containing ~ 760,000 substances, are available as public domain data for download. The chemistry content underpinning the Dashboard has been aggregated over the past 15 years by both manual and auto-curation techniques within EPA’s DSSTox project. DSSTox chemical content is subject to strict quality controls to enforce consistency among chemical substance-structure identifers, as well as list curation review to ensure accurate linkages of DSSTox substances to chemical lists and associated data. The Dashboard, publicly launched in April 2016, has expanded considerably in content and user trafc over the past year. It is continuously evolving with the growth of DSSTox into high-interest or data-rich domains of interest to EPA, such as chemicals on the Toxic Substances Control Act listing, while providing the user community with a fexible and dynamic web-based platform for integration, processing, visualization and delivery of data and resources. The Dashboard provides support for a broad array of research and regulatory programs across the worldwide community of toxicologists and environmental scientists.
This work investigates and applies machine learning paradigms seldom seen in analytical spectroscopy for quantification of gallium in cerium matrices via processing of laser-plasma spectra. Ensemble regressions, support vector machine regressions, Gaussian kernel regressions, and artificial neural network techniques are trained and tested on cerium-gallium pellet spectra. A thorough hyperparameter optimization experiment is conducted initially to determine the best design features for each model. The optimized models are evaluated for sensitivity and precision using the limit of detection (LoD) and root mean-squared error of prediction (RMSEP) metrics, respectively. Gaussian kernel regression yields the superlative predictive model with an RMSEP of 0.33% and an LoD of 0.015% for quantification of Ga in a Ce matrix. This study concludes that these machine learning methods could yield robust prediction models for rapid quality control analysis of plutonium alloys.
Analytical spectroscopy methods have shown many possible uses for nuclear material diagnostics and measurements in recent studies. In particular, the application potential for various atomic spectroscopy techniques is uniquely diverse and generates interest across a wide range of nuclear science areas. Over the last decade, techniques such as laser-induced breakdown spectroscopy, Raman spectroscopy, and x-ray fluorescence spectroscopy have yielded considerable improvements in the diagnostic analysis of nuclear materials, especially with machine learning implementations. These techniques have been applied for analytical solutions to problems concerning nuclear forensics, nuclear fuel manufacturing, nuclear fuel quality control, and general diagnostic analysis of nuclear materials. The data yielded from atomic spectroscopy methods provide innovative solutions to problems surrounding the characterization of nuclear materials, particularly for compounds with complex chemistry. Implementing these optical spectroscopy techniques can provide comprehensive new insights into the chemical analysis of nuclear materials. In particular, recent advances coupling machine learning methods to the processing of atomic emission spectra have yielded novel, robust solutions for nuclear material characterization. This review paper will provide a summation of several of these recent advances and will discuss key experimental studies that have advanced the use of analytical atomic spectroscopy techniques as active tools for nuclear diagnostic measurements.
Laser powder bed fusion (LPBF) is a type of metal additive manufacturing which could benefit from improved process monitoring to improve quality control. We demonstrate, for the first time to our knowledge, the coaxial monitoring of melt track formation in steel powder with spatial frequency modulation imaging (SPIFI), an enhanced-resolution imaging technique which uses a photodiode to record one-dimensional images. Using a custom live-display software and a high-speed SPIFI geometry, we offset the SPIFI field of view from the fusing beam to monitor different regions of the LPBF melt pool and surrounding area. This demonstrates the potential of SPIFI to monitor spatial features within the melt pool in real-time with increased data efficiency.