This protocol details Dataset Readiness Assessment for Training (DRAFT), a systematic method for determining if a high-dimensional biological dataset is suitable for developing reliable and equitable machine learning models. Standard model validation often fails to detect when performance is driven by spurious correlations within the dataset, leading to irreproducible findings. DRAFT provides a step-by-step methodology to perform a multiaxis assessment of a dataset's integrity before extensive modeling begins. The assessment comprises three basic protocols to probe the dataset's characteristics: (1) evaluating its potential for generalization by testing baseline model performance and stability under rigorous cross-validation; (2) auditing for inherent biases by analyzing model performance and calibration across demographic subgroups; and (3) measuring its capacity for scientific utility by assessing the stability of predictive features. The Cancer Genome Atlas Lung Adenocarcinoma (TCGA-LUAD) dataset serves as a primary case study to demonstrate DRAFT's ability to reveal a dataset's potential to produce fragile, inequitable, and scientifically uninformative models, even when preliminary modeling achieves high performance metrics. This framework provides a necessary template for vetting datasets, ensuring that subsequent computational models built upon them are robust, equitable, and capable of generating true scientific insight. © 2025 Wiley Periodicals LLC. Support Protocol 1: Environment and software installation via Conda Support Protocol 2: TCGA-LUAD case study: Data acquisition and preparation Basic Protocol 1: Generalization audit: Assessing for overfitting and illusory performance Basic Protocol 2: Equity audit: Assessing for hidden stratification and algorithmic bias Basic Protocol 3: Stability audit: Assessing for spurious discoveries and scientific utility.
JBrowse 2 is an open-source genome browser that provides unique features for visualizing syntenic relationships between multiple genomes. This article describes a protocol for setting up synteny views in JBrowse 2, using an assembly-to-assembly whole-genome alignment example. We detail data preparation steps, including the generation and formatting of whole-genome alignment data into formats compatible with JBrowse 2's synteny visualization capabilities, and show the GUI-driven process for setting up interactive synteny views and generating publication-quality figures. This protocol establishes methods for using JBrowse 2 to explore conserved sequences across multiple genomes. © 2025 The Author(s). Current Protocols published by Wiley Periodicals LLC. Basic Protocol: Using JBrowse 2 to show genomic synteny.
BioModelAnalyzer (BMA) is an open-source graphical tool for the development of executable models of protein and gene networks within cells. Based upon the Qualitative Networks formalism, the user can rapidly construct large networks, either manually or by connecting motifs selected from a built-in library. After the appropriate functions for each variable are defined, the user has access to three analysis engines to test the model. In addition to standard simulation tools, BMA includes an interface to the stability-testing algorithm and to a graphical Linear Temporal Logic (LTL) editor and analysis tool. Alongside this, we have developed a novel ChatBot to aid users constructing LTL queries and to explain the interface and run through tutorials. Here we present worked examples of model construction and testing via the interface. As an initial example, we discuss fate decisions in Dictyostelium discoidum and cAMP signaling. We go on to describe the workflow leading to the construction of a published model of the germline of C. elegans. Finally, we demonstrate how to construct simple models from the built-in network motif library. © 2020 by John Wiley & Sons, Inc. Basic Protocol 1: Modeling the signaling network of Dictyostelium discoidum Basic Protocol 2: Modeling the germline progression of Caenorhabditis elegans Basic Protocol 3: Constructing a model of the cell cycle using motifs.
MetaBridge is a web-based tool designed to facilitate the integration of metabolomics with other "omics" data types such as transcriptomics and proteomics. It uses data from the MetaCyc metabolic pathway database and the Kyoto Encyclopedia of Genes and Genomes (KEGG) to map metabolite compounds to directly interacting upstream or downstream enzymes in enzymatic reactions and metabolic pathways. The resulting list of enzymes can then be integrated with transcriptomics or proteomics data via protein-protein interaction networks to perform integrative multi-omics analyses. MetaBridge was developed to be intuitive and easy to use, requiring little to no prior computational experience. The protocols described here detail all steps involved in the use of MetaBridge, from preparing input data and performing metabolite mapping to utilizing the results to build a protein-protein interaction network. © 2020 by John Wiley & Sons, Inc. Basic Protocol 1: Mapping metabolite data using MetaCyc identifiers Basic Protocol 2: Mapping metabolite data using KEGG identifiers Support Protocol 1: Converting compound names to HMDB IDs Support Protocol 2: Submitting mapped genes produced by MetaBridge for protein-protein interaction (PPI) network construction.
Non-coding RNAs are essential for all life and carry out a wide range of functions. Information about these molecules is distributed across dozens of specialized resources. RNAcentral is a database of non-coding RNA sequences that provides a unified access point to non-coding RNA annotations from >40 member databases and helps provide insight into the function of these RNAs. This article describes different ways of accessing the data, including searching the website and retrieving the data programmatically over web APIs and a public database. We also demonstrate an example Galaxy workflow for using RNAcentral for RNA-seq differential expression analysis. RNAcentral is available at https://rnacentral.org. © 2020 The Authors. Basic Protocol 1: Viewing RNAcentral sequence reports Basic Protocol 2: Using RNAcentral text search to explore ncRNA sequences Basic Protocol 3: Using RNAcentral sequence search Basic Protocol 4: Using RNAcentral FTP archive Support Protocol 1: Using web APIs for programmatic data access Support Protocol 2: Using public Postgres database to export large datasets Support Protocol 3: Analyze non-coding RNA in RNA-seq datasets using RNAcentral and Galaxy.
