The Playbook Workflow Builder (PWB) is a web-based platform to dynamically construct and execute bioinformatics workflows by utilizing a growing network of input datasets, semantically annotated API endpoints, and data visualization tools contributed by an ecosystem of collaborators. Via a user-friendly user interface, workflows can be constructed from contributed building-blocks without technical expertise. The output of each step of the workflow is added into reports containing textual descriptions, figures, tables, and references. To construct workflows, users can click on cards that represent each step in a workflow, or construct workflows via a chat interface that is assisted by a large language model (LLM). Completed workflows are compatible with Common Workflow Language (CWL) and can be published as research publications, slideshows, and posters. To demonstrate how the PWB generates meaningful hypotheses that draw knowledge from across multiple resources, we present several use cases. For example, one of these use cases prioritizes drug targets for individual cancer patients using data from the NIH Common Fund programs GTEx, LINCS, Metabolomics, GlyGen, and ExRNA. The workflows created with PWB can be repurposed to tackle similar use cases using different inputs. The PWB platform is available from: https://playbook-workflow-builder.cloud/.

SUMMARY: We present LipidFinder 2.0, incorporating four new modules that apply artefact filters, remove lipid and contaminant stacks, in-source fragments and salt clusters, and a new isotope deletion method which is significantly more sensitive than available open-access alternatives. We also incorporate a novel false discovery rate method, utilizing a target-decoy strategy, which allows users to assess data quality. A renewed lipid profiling method is introduced which searches three different databases from LIPID MAPS and returns bulk lipid structures only, and a lipid category scatter plot with color blind friendly pallet. An API interface with XCMS Online is made available on LipidFinder's online version. We show using real data that LipidFinder 2.0 provides a significant improvement over non-lipid metabolite filtering and lipid profiling, compared to available tools. AVAILABILITY AND IMPLEMENTATION: LipidFinder 2.0 is freely available at https://github.com/ODonnell-Lipidomics/LipidFinder and http://lipidmaps.org/resources/tools/lipidfinder. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

SUMMARY: We present LipidFinder online, hosted on the LIPID MAPS website, as a liquid chromatography/mass spectrometry (LC/MS) workflow comprising peak filtering, MS searching and statistical analysis components, highly customized for interrogating lipidomic data. The online interface of LipidFinder includes several innovations such as comprehensive parameter tuning, a MS search engine employing in-house customized, curated and computationally generated databases and multiple reporting/display options. A set of integrated statistical analysis tools which enable users to identify those features which are significantly-altered under the selected experimental conditions, thereby greatly reducing the complexity of the peaklist prior to MS searching is included. LipidFinder is presented as a highly flexible, extensible user-friendly online workflow which leverages the lipidomics knowledge base and resources of the LIPID MAPS website, long recognized as a leading global lipidomics portal. AVAILABILITY AND IMPLEMENTATION: LipidFinder on LIPID MAPS is available at: http://www.lipidmaps.org/data/LF.

BACKGROUND: Macrophage activation by lipopolysaccharide and adenosine triphosphate (ATP) has been studied extensively because this model system mimics the physiological context of bacterial infection and subsequent inflammatory responses. Previous studies on macrophages elucidated the biological roles of caspase-1 in post-translational activation of interleukin-1β and interleukin-18 in inflammation and apoptosis. However, the results from these studies focused only on a small number of factors. To better understand the host response, we have performed a high-throughput study of Kdo2-lipid A (KLA)-primed macrophages stimulated with ATP. RESULTS: The study suggests that treating mouse bone marrow-derived macrophages with KLA and ATP produces 'synergistic' effects that are not seen with treatment of KLA or ATP alone. The synergistic regulation of genes related to immunity, apoptosis and lipid metabolism is observed in a time-dependent manner. The synergistic effects are produced by nuclear factor kappa-light-chain-enhancer of activated B cells (NF-kB) and activator protein (AP)-1 through regulation of their target cytokines. The synergistically regulated cytokines then activate signal transducer and activator of transcription (STAT) factors that result in enhanced immunity, apoptosis and lipid metabolism; STAT1 enhances immunity by promoting anti-microbial factors; and STAT3 contributes to downregulation of cell cycle and upregulation of apoptosis. STAT1 and STAT3 also regulate glycerolipid and eicosanoid metabolism, respectively. Further, western blot analysis for STAT1 and STAT3 showed that the changes in transcriptomic levels were consistent with their proteomic levels. In summary, this study shows the synergistic interaction between the toll-like receptor and purinergic receptor signaling during macrophage activation on bacterial infection. AVAILABILITY: Time-course data of transcriptomics and lipidomics can be queried or downloaded from http://www.lipidmaps.org. CONTACT: [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: Currently available methods for the prediction of subcellular location of mitochondrial proteins rely largely on the presence of mitochondrial targeting signals in the protein sequences. However, a large fraction of mitochondrial proteins lack such signals, making those tools ineffective for genome-scale prediction of mitochondria-targeted proteins. Here, we propose a method for genome-scale prediction of nucleus-encoded mitochondrial proteins. The new method, MITOPRED, is based on the Pfam domain occurrence patterns and the amino acid compositional differences between mitochondrial and non-mitochondrial proteins. RESULTS: MITOPRED could predict mitochondrial proteins with 100% specificity at a 44% sensitivity rate and with 67% specificity at 99% sensitivity. Additionally, it was sufficiently robust to predict mitochondrial proteins across different eukaryotic species with similar accuracy. Based on Matthews correlation coefficient measure, the prediction performance of MITOPRED is clearly superior (0.73) to those of the two popular methods TargetP (0.51) and PSORT (0.53). Using this method, we predicted the nucleus-encoded mitochondrial proteins from six complete genomes (three invertebrate, two vertebrate and one plant species) and estimated the total number in each genome. In human, our method estimated the existence of 1362 mitochondrial proteins corresponding to 4.8% of the total proteome. AVAILABILITY: MITOPRED program is freely accessible at http://mitopred.sdsc.edu. Source code is available on request from the authors. SUPPLEMENTARY INFORMATION: Training data sets are also available at http://mitopred.sdsc.edu