qHTSWaterfall: 3-dimensional visualization software for quantitative high-throughput screening (qHTS) data

High throughput screening (HTS) is widely used in drug discovery and chemical biology to identify and characterize agents having pharmacologic properties often by evaluation of large chemical libraries. Standard HTS data can be simply plotted as an x-y graph usually represented as % activity of a compound tested at a single concentration vs compound ID, whereas quantitative HTS (qHTS) data incorporates a third axis represented by concentration. By virtue of the additional data points arising from the compound titration and the incorporation of logistic fit parameters that define the concentration-response curve, such as EC50 and Hill slope, qHTS data has been challenging to display on a single graph. Here we provide a flexible solution to the rapid plotting of complete qHTS data sets to produce a 3-axis plot we call qHTS Waterfall Plots. The software described here can be generally applied to any 3-axis dataset and is available as both an R package and an R shiny application.

Response of the critically endangered Przewalski's gazelle (Procapra przewalskii) to selenium deprived environment

Selenium (Se) is an essential mineral nutrient for animals. Se deprivation can lead to many disorders and even death. This study investigated the response of Przewalski's gazelle (P. przewalskii) to Se-deprived environment. We found that Se deprivation in soil and forage not only influenced the mineral contents of the blood and hair in P. przewalskii, but also severely disrupted their blood parameters. We identified significant changes in the abundance of 146 proteins and 25 metabolites (P < 0.05) in serum, including the selenoproteins L8IG93 (glutathione peroxidase) and F4YD09 (Cu/Zn superoxide dismutase). Furthermore, the major known proteins and metabolites associated with the Se stress response in P. przewalskii were Cu/Zn superoxide dismutase, the vitamin K-dependent protein C, the C4b-binding protein alpha chain, complement component C7, lipase linoleic acid, peptidase D, thymidine, pseudo-uridine, L-phenylalanine, L-glutamine, PGA1, and 15-deoxy-delta-12,14-PGJ2. The main signaling pathways involved included complement and coagulation cascades, metabolic pathways, and stress granule formation. Our results indicate that the intake of Se-deficient forage elicited an oxidative stress response in P. przewalskii. These findings provide insights into the response mechanisms of this threatened gazelle to Se stress, and enable the development of conservation strategies to protect populations on the Qinghai-Tibetan Plateau. SIGNIFICANCE: This study is the first to point out the presence of oxidative stress in P. przewalskii in selenium-deficient areas through proteomics and metabolomics studies. These findings should prove helpful for conservation efforts aimed at P. przewalskii populations and maintenance of the integrity of their ecological environment.

Issues Of Z-factor and an approach to avoid them for quality control in high-throughput screening studies

MOTIVATION

High throughput screening (HTS) is a vital automation technology in biomedical research in both industry and academia. The well-known z-factor has been widely used as a gatekeeper to assure assay quality in an HTS study. However, many researchers and users may not have realized that z-factor has major issues.

RESULTS

In this article, the following four major issues are explored and demonstrated so that researchers may use the z-factor appropriately. First, the z-factor violates the Pythagorean Theorem of Statistics. Second, there is no adjustment of sampling error in the application of the z-factor for quality control (QC) in HTS studies. Third, the expectation of the sample-based z-factor does not exist. Fourth, the thresholds in the z-factor based criterion lack a theoretical basis. Here, an approach to avoid these issues was proposed and new QC criteria under homoscedasticity were constructed so that researchers can choose a statistically grounded criterion for QC in the HTS studies. We implemented this approach in an R package and demonstrated its utility in multiple CRISPR/CAS9 or siRNA HTS studies.

AVAILABILITY

The R package qcSSMDhomo is freely available from GitHub: https://github.com/Karena6688/qcSSMDhomo. The file qcSSMDhomo_1.0.0.tar.gz (for Windows) containing qcSSMDhomo is also available at Bioinformatics online. qcSSMDhomo is distributed under the GNU General Public License.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Classification of Sputum Sounds Using Artificial Neural Network and Wavelet Transform

Sputum sounds are biological signals used to evaluate the condition of sputum deposition in a respiratory system. To improve the efficiency of intensive care unit (ICU) staff and achieve timely clearance of secretion in patients with mechanical ventilation, we propose a method consisting of feature extraction of sputum sound signals using the wavelet transform and classification of sputum existence using artificial neural network (ANN). Sputum sound signals were decomposed into the frequency subbands using the wavelet transform. A set of features was extracted from the subbands to represent the distribution of wavelet coefficients. An ANN system, trained using the Back Propagation (BP) algorithm, was implemented to recognize the existence of sputum sounds. The maximum precision rate of automatic recognition in texture of signals was as high as 84.53%. This study can be referred to as the optimization of performance and design in the automatic technology for sputum detection using sputum sound signals.

Dereplication: racing to speed up the natural products discovery process

Covering: 1993-2014 (July)To alleviate the dereplication holdup, which is a major bottleneck in natural products discovery, scientists have been conducting their research efforts to add tools to their "bag of tricks" aiming to achieve faster, more accurate and efficient ways to accelerate the pace of the drug discovery process. Consequently dereplication has become a hot topic presenting a huge publication boom since 2012, blending multidisciplinary fields in new ways that provide important conceptual and/or methodological advances, opening up pioneering research prospects in this field.

COPASutils: an R package for reading, processing, and visualizing data from COPAS large-particle flow cytometers

The R package COPASutils provides a logical workflow for the reading, processing, and visualization of data obtained from the Union Biometrica Complex Object Parametric Analyzer and Sorter (COPAS) or the BioSorter large-particle flow cytometers. Data obtained from these powerful experimental platforms can be unwieldy, leading to difficulties in the ability to process and visualize the data using existing tools. Researchers studying small organisms, such as Caenorhabditis elegans, Anopheles gambiae, and Danio rerio, and using these devices will benefit from this streamlined and extensible R package. COPASutils offers a powerful suite of functions for the rapid processing and analysis of large high-throughput screening data sets.

SbacHTS: spatial background noise correction for high-throughput RNAi screening

MOTIVATION

High-throughput cell-based phenotypic screening has become an increasingly important technology for discovering new drug targets and assigning gene functions. Such experiments use hundreds of 96-well or 384-well plates, to cover whole-genome RNAi collections and/or chemical compound files, and often collect measurements that are sensitive to spatial background noise whose patterns can vary across individual plates. Correcting these position effects can substantially improve measurement accuracy and screening success.

RESULT

We developed SbacHTS (Spatial background noise correction for High-Throughput RNAi Screening) software for visualization, estimation and correction of spatial background noise in high-throughput RNAi screens. SbacHTS is supported on the Galaxy open-source framework with a user-friendly open access web interface. We find that SbacHTS software can effectively detect and correct spatial background noise, increase signal to noise ratio and enhance statistical detection power in high-throughput RNAi screening experiments.

AVAILABILITY

http://www.galaxy.qbrc.org/