MOTIVATION: Splicing, a critical co-transcriptional process in eukaryotes, enhances transcriptome diversity by generating isoforms specific to cell types, tissues, or developmental stages. Recent advancements in splicing modulators have opened new avenues for targeting previously undruggable genes by inducing significant perturbations in splicing events. These developments underscore the need for comprehensive methods to accurately identify and compare splicing events. While several tools have been developed to detect local splice variants, inconsistencies across methods remain a significant challenge. To address this, we present SpliceHarmonization, an integrated approach that combines the strengths of rMATS, LeafCutter, and MAJIQ, enabling robust and reliable splicing analysis with event type annotations. RESULTS: In a comprehensive evaluation using diverse simulated datasets, SpliceHarmonization streamlined and standardized the outputs from three detection methods into a unified format, thereby improving splicing detection with event type annotation and outperforming individual methods. By integrating the outputs from rMATS, LeafCutter, and MAJIQ, our approach not only enhanced identification of a wide range of splicing events but also effectively mitigated method-specific discrepancies. This integration led to an accuracy exceeding 0.8 and a recall of up to 0.5, with an observed increase in AUC of up to 10%. Furthermore, SpliceHarmonization demonstrated high sensitivity in detecting low-abundance and complex splicing events, providing annotations including genomic coordinates and event type. AVAILABILITY AND IMPLEMENTATION: SpliceHarmonization is available at https://github.com/interactivereport/SpliceHarmonization.

SUMMARY: We developed Quickomics, a feature-rich R Shiny-powered tool to enable biologists to fully explore complex omics statistical analysis results and perform advanced analysis in an easy-to-use interactive interface. It covers a broad range of secondary and tertiary analytical tasks after primary analysis of omics data is completed. Each functional module is equipped with customizable options and generates both interactive and publication-ready plots to uncover biological insights from data. The modular design makes the tool extensible with ease. AVAILABILITY AND IMPLEMENTATION: Researchers can experience the functionalities with their own data or demo RNA-Seq and proteomics datasets by using the app hosted at http://quickomics.bxgenomics.com and following the tutorial, https://bit.ly/3rXIyhL. The source code under GPLv3 license is provided at https://github.com/interactivereport/Quickomics for local installation. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Chinese hamster ovary (CHO) cell lines are widely used in industry for biological drug production. During cell culture development, considerable effort is invested to understand the factors that greatly impact cell growth, specific productivity and product qualities of the biotherapeutics. While high-throughput omics approaches have been increasingly utilized to reveal cellular mechanisms associated with cell line phenotypes and guide process optimization, comprehensive omics data analysis and management have been a challenge. Here we developed CHOmics, a web-based tool for integrative analysis of CHO cell line omics data that provides an interactive visualization of omics analysis outputs and efficient data management. CHOmics has a built-in comprehensive pipeline for RNA sequencing data processing and multi-layer statistical modules to explore relevant genes or pathways. Moreover, advanced functionalities were provided to enable users to customize their analysis and visualize the output systematically and interactively. The tool was also designed with the flexibility to accommodate other types of omics data and thereby enabling multi-omics comparison and visualization at both gene and pathway levels. Collectively, CHOmics is an integrative platform for data analysis, visualization and management with expectations to promote the broader use of omics in CHO cell research.

Summary: We present a bioinformatics and systems biology visualization toolkit harmonizing real time interactive exploring and analyzing of big data, full-fledged customizing of look-n-feel and producing multi-panel publication-ready figures in PDF format simultaneously. Availability and implementation: Source code and detailed user guides are available at http://canvasxpress.org, https://baohongz.github.io/canvasDesigner and https://baohongz.github.io/canvasDesigner/demo_video.html. Supplementary information: Supplementary data are available at Bioinformatics online.

BACKGROUND: Genome-wide miRNA expression data can be used to study miRNA dysregulation comprehensively. Although many open-source tools for microRNA (miRNA)-seq data analyses are available, challenges remain in accurate miRNA quantification from large-scale miRNA-seq dataset. We implemented a pipeline called QuickMIRSeq for accurate quantification of known miRNAs and miRNA isoforms (isomiRs) from multiple samples simultaneously. RESULTS: QuickMIRSeq considers the unique nature of miRNAs and combines many important features into its implementation. First, it takes advantage of high redundancy of miRNA reads and introduces joint mapping of multiple samples to reduce computational time. Second, it incorporates the strand information in the alignment step for more accurate quantification. Third, reads potentially arising from background noise are filtered out to improve the reliability of miRNA detection. Fourth, sequences aligned to miRNAs with mismatches are remapped to a reference genome to further reduce false positives. Finally, QuickMIRSeq generates a rich set of QC metrics and publication-ready plots. CONCLUSIONS: The rich visualization features implemented allow end users to interactively explore the results and gain more insights into miRNA-seq data analyses. The high degree of automation and interactivity in QuickMIRSeq leads to a substantial reduction in the time and effort required for miRNA-seq data analysis.

MOTIVATION: MicroRNAs (miRNAs) are important regulatory molecules. A critical step in elucidating miRNA function is identifying potential miRNA targets. However, few reliable tools have been developed for identifying miRNA targets in plants. RESULTS: Here, we developed a Smith-Waterman-like alignment tool in order to accurately predict miRNA targets. Dynamic programming was used to build a score matrix based on the complementarity of nucleotides in order to trace the optimal local alignments. Important parameters, such as maximum mismatches and maximum consecutive mismatches between miRNAs and their targets, were also used for filtering the optimal local alignments. Almost all of the parameters in this alignment tool can be adjusted by users. Compared to other target prediction tools, Target-align exhibits strong sensitivity and accuracy for identifying miRNA targets. More importantly, Target-align can identify multi-target sites as well potential for non-cleaved targets sites by change the default settings. Windows, web and command-line versions were developed to better serve different users. AVAILABILITY: http://www.leonxie.com/targetAlign.php. CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.