Notes

Pulmonary ramifications involving acetaminophen exposures outside of hepatic accumulation.
Further, 1345 and 1568 cell type-specific DEGs are identified by BSDE from datasets on pulmonary fibrosis and multiple sclerosis, among which the top findings are supported by previous results from the literature.

R package BSDE is freely available from doi.org/10.5281/zenodo.6332254. For real data analysis with the R package, see doi.org/10.5281/zenodo.6332566. These can also be accessed thorough GitHub at github.com/mqzhanglab/BSDE and github.com/mqzhanglab/BSDE_pipeline. The two single-cell sequencing datasets can be download with UCSC cell browser from cells.ucsc.edu/?ds=ms and cells.ucsc.edu/?ds=lung-pf-control.

Supplementary data are available at Bioinformatics online.
Supplementary data are available at Bioinformatics online.
When investigating gene expression profiles, determining important directed edges between genes can provide valuable insights in addition to identifying differentially expressed genes. In the subsequent functional enrichment analysis (EA), understanding how enriched pathways or genes in the pathway interact with one another can help infer the gene regulatory network (GRN), important for studying the underlying molecular mechanisms. However, packages for easy inference of the GRN based on EA are scarce. Here, we developed an R package, CBNplot, which infers the Bayesian network (BN) from gene expression data, explicitly utilizing EA results obtained from curated biological pathway databases. The core features include convenient wrapping for structure learning, visualization of the BN from EA results, comparison with reference networks, and reflection of gene-related information on the plot. As an example, we demonstrate the analysis of bladder cancer-related datasets using CBNplot, including probabilistic reasoning, which is a unique aspect of BN analysis. We display the transformability of results obtained from one dataset to another, the validity of the analysis as assessed using established knowledge and literature, and the possibility of facilitating knowledge discovery from gene expression datasets.

The library, documentation and web server are available at https//github.com/noriakis/CBNplot.

Supplementary data are available at Bioinformatics online.
Supplementary data are available at Bioinformatics online.
We previously developed the LDM for testing hypotheses about the microbiome that performs the test at both the community level and the individual taxon level. The LDM can be applied to relative abundance data and presence-absence data separately, which work well when associated taxa are abundant and rare, respectively. Here, we propose LDM-omni3 that combines LDM analyses at the relative abundance and presence-absence data scales, thereby offering optimal power across scenarios with different association mechanisms. The new LDM-omni3 test is available for the wide range of data types and analyses that are supported by the LDM.

The LDM-omni3 test has been added to the R package LDM, which is available on GitHub at https//github.com/yijuanhu/LDM.

Supplementary data are available at Bioinformatics online.
Supplementary data are available at Bioinformatics online.
In quantitative bottom-up mass spectrometry (MS)-based proteomics, the reliable estimation of protein concentration changes from peptide quantifications between different biological samples is essential. This estimation is not a single task but comprises the two processes of protein inference and protein abundance summarization. Furthermore, due to the high complexity of proteomics data and associated uncertainty about the performance of these processes, there is a demand for comprehensive visualization methods able to integrate protein with peptide quantitative data including their post-translational modifications. Hence, there is a lack of a suitable tool that provides post-identification quantitative analysis of proteins with simultaneous interactive visualization.

In this article, we present VIQoR, a user-friendly web service that accepts peptide quantitative data of both labeled and label-free experiments and accomplishes the crucial components protein inference and summarization and interactive visualization modules, including the novel VIQoR plot. We implemented two different parsimonious algorithms to solve the protein inference problem, while protein summarization is facilitated by a well-established factor analysis algorithm called fast-FARMS followed by a weighted average summarization function that minimizes the effect of missing values. In addition, summarization is optimized by the so-called Global Correlation Indicator (GCI). We test the tool on three publicly available ground truth datasets and demonstrate the ability of the protein inference algorithms to handle shared peptides. We furthermore show that GCI increases the accuracy of the quantitative analysis in datasets with replicated design.

VIQoR is accessible at http//computproteomics.bmb.sdu.dk/Apps/VIQoR/. The source code is available at https//bitbucket.org/veitveit/viqor/.

Supplementary data are available at Bioinformatics online.
Supplementary data are available at Bioinformatics online.
Live-cell microscopy has become an essential tool for analyzing dynamic processes in various biological applications. Thereby, high-throughput and automated tracking analyses allow the simultaneous evaluation of large numbers of objects. However, to critically assess the influence of individual objects on calculated summary statistics, and to detect heterogeneous dynamics or possible artifacts, such as misclassified or -tracked objects, a direct mapping of gained statistical information onto the actual image data would be necessary.

We present VisuStatR as a platform independent software package that allows the direct visualization of time-resolved summary statistics of morphological characteristics or motility dynamics onto raw images. The software contains several display modes to compare user-defined summary statistics and the underlying image data in various levels of detail.

VisuStatR is a free and open-source R-package, containing a user-friendly graphical-user interface and is available via GitHub at https//github.com/grrchrr/VisuStatR/ under the MIT+ license.

Supplementary data are available at Bioinformatics online.
Supplementary data are available at Bioinformatics online.
In the process of discovery and optimization of lead compounds, it is difficult for non-expert pharmacologists to intuitively determine the contribution of substructure to a particular property of a molecule.

In this work, we develop a user-friendly web service, named interpretable-absorption, distribution, metabolism, excretion and toxicity (ADMET), which predict 59 ADMET-associated properties using 90 qualitative classification models and 28 quantitative regression models based on graph convolutional neural network and graph attention network algorithms. In interpretable-ADMET, there are 250729 entries associated with 59 kinds of ADMET-associated properties for 80167 chemical compounds. In addition to making predictions, interpretable-ADMET provides interpretation models based on gradient-weighted class activation map for identifying the substructure, which is important to the particular property. check details Interpretable-ADMET also provides an optimize module to automatically generate a set of novel virtual candidates based on matched molecular pair rules. We believe that interpretable-ADMET could serve as a useful tool for lead optimization in drug discovery.

Interpretable-ADMET is available at http//cadd.pharmacy.nankai.edu.cn/interpretableadmet/.

Supplementary data are available at Bioinformatics online.
Supplementary data are available at Bioinformatics online.
We present MitoVisualize, a new tool for analysis of the human mitochondrial DNA (mtDNA). MitoVisualize enables visualization of (i) the position and effect of variants in mitochondrial transfer RNA and ribosomal RNA secondary structures alongside curated variant annotations, (ii) data across RNA structures, such as to show all positions with disease-associated variants or with post-transcriptional modifications and (iii) the position of a base, gene or region in the circular mtDNA map, such as to show the location of a large deletion. All visualizations can be easily downloaded as figures for reuse. MitoVisualize can be useful for anyone interested in exploring mtDNA variation, though is designed to facilitate mtDNA variant interpretation in particular.

MitoVisualize can be accessed via https//www.mitovisualize.org/. The source code is available at https//github.com/leklab/mito_visualize/.

Supplementary data are available at Bioinformatics online.
Supplementary data are available at Bioinformatics online.
Recombination is one of the essential genetic processes for sexually reproducing organisms, which can happen more frequently in some regions, called recombination hotspots. Although several factors, such as PRDM9 binding motifs, are known to be related to the hotspots, their contributions to the recombination hotspots have not been quantified, and other determinants are yet to be elucidated. Here, we propose a computational method, RHSNet, based on deep learning and signal processing, to identify and quantify the hotspot determinants in a purely data-driven manner, utilizing datasets from various studies, populations, sexes and species.

RHSNet can significantly outperform other sequence-based methods on multiple datasets across different species, sexes and studies. In addition to being able to identify hotspot regions and the well-known determinants accurately, more importantly, RHSNet can quantify the determinants that contribute significantly to the recombination hotspot formation in the relation between PRDM9 binding motif, histone modification and GC content. Further cross-sex, cross-population and cross-species studies suggest that the proposed method has the generalization power and potential to identify and quantify the evolutionary determinant motifs.

https//github.com/frankchen121212/RHSNet.

Supplementary data are available at Bioinformatics online.
Supplementary data are available at Bioinformatics online.Stream restoration is a popular approach for managing nitrogen (N) in degraded, flashy urban streams. Here, we investigated the long-term effects of stream restoration involving floodplain reconnection on riparian and in-stream N transport and transformation in an urban stream in the Chesapeake Bay watershed. We examined relationships between hydrology, chemistry, and biology using a Before/After-Control/Impact (BACI) study design to determine how hydrologic flashiness, nitrate (NO3 -) concentrations (mg/L), and N flux, both NO3 - and total N (kg/yr), changed after the restoration and floodplain hydrologic reconnection to its stream channel. We examined two independent surface water and groundwater data sets (EPA and USGS) collected from 2002-2012 at our study sites in the Minebank Run watershed. Restoration was completed during 2004 and 2005. Afterward, the monthly hydrologic flashiness index, based on mean monthly discharge, decreased over time from 2002 and 2008. However, from 2008-2012 hydrologic flashiness returned to pre-restoration levels.
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