SUMMARY: Complex tabular datasets comprising many diverse features can require specific expertise to interpret, posing a barrier to researchers with minimal data science experience. EDAmame is an interactive tool that simplifies initial analysis and visualization of these datasets, providing insights into data quality and feature relationships. By leveraging open-source machine learning frameworks in R, EDAmame allows researchers to perform effective exploratory data analysis without command-line or coding requirements. AVAILABILITY AND IMPLEMENTATION: A limited online version can be accessed at https://edamame.org.au/ or can be downloaded from https://doi.org/10.5281/zenodo.15356492. The app is developed in R Shiny and implements tidyverse and tidymodels packages.

This study compares the relative utility of deep learning models as automated phenotypic classifiers, built with features of peripheral blood cell populations assayed with flow cytometry. We report surprisingly good sensitively for the task of distinguishing male from female mice solely from attributes of cells present in peripheral blood samples. This novel application of cytometry technology is easily validated and represents an example use that is not reliably possible with current visual analysis techniques. It serves as a useful test case with known ground truth labels of a subtle cellular phenotype and indicates the potential of this approach to more biologically meaningful phenotypes. We evaluate three different deep neural network architectures that vary in how the high-dimensional flow cytometry data is processed as input: 1) a convolutional neural network applied to the raw, per-cell marker intensity values, 2) a multi-layer perceptron taking per-channel histograms representing the range of intensities for a given marker, and 3) a multi-layer perceptron using a hypervoxel representation of the set of marker intensities for each cell in a given input sample. Our results show the histogram-based multi-layer perceptron achieves the highest classification accuracy of 91% on 2300 mouse blood samples for a mutant phenotype classification task. This indicates the value of deep learning models for detection of subtle signals that are not readily observed in high dimensions by visual analysis. These results demonstrate the effectiveness of deep learning for automated analysis of flow cytometry data on a challenging and biologically complex classification task.

SUMMARY: Missense mutations that change protein stability are strongly associated with human genetic disease. With the recent availability of predicted structures for all human proteins generated using the AlphaFold2 prediction model, genome-wide assessment of the stability effects of genetic variation can, for the first time, be easily performed. This facilitates the interrogation of personal genetic variation for potentially pathogenic effects through the application of stability metrics. Here, we present a novel tool to prioritize variants predicted to cause strong instability in essential proteins. We show that by filtering by ΔΔG values and then prioritizing by StabilitySort Z-scores, we are able to more accurately discriminate pathogenic, protein-destabilizing mutations from population variation, compared with other mutation effect predictors. AVAILABILITY AND IMPLEMENTATION: StabilitySort is available as a web service (https://www.stabilitysort.org), as a data download for integration with other tools (https://www.stabilitysort.org/download) or can be deployed as a standalone system from source code (https://gitlab.com/baaron/StabilitySort). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BACKGROUND: Amplified fragment length polymorphism (AFLP) is a PCR-based technique that involves restriction of genomic DNA followed by ligation of adaptors to the fragments generated and selective PCR amplification of a subset of these fragments. The amplified fragments are separated on a sequencing gel and visualized by autoradiography or fluorescent sequencing equipment. AFLP allows high-resolution genotyping but the lack of a format for databasing and comparison of AFLP fingerprint profiles limits its wider applications in profiling large numbers of biological samples. RESULTS: A scheme is described to represent a DNA fingerprint profile with a nucleotide sequence-like format in which the information line contains the minimal necessary details to interpret an AFLP DNA fingerprint profile. They include technique used, information on restriction enzymes, primer combination, biological source for DNA materials, fragment sizing and annotation. The bodylines contain information on size and relative intensity of DNA fragments by a string of defined alphabets or symbols. Algorithms for normalizing raw data, binning of fragments and comparing AFLP DNA fingerprint profiles are described. Firstly, the peak heights are normalized against their average and then represented by five symbols according to their relative intensities. Secondly, a binning algorithm based loosely on common springs and rubber bands is applied, which positions sequence fragments into their best possible integer approximations. A BLAST-like reward-penalty concept is used to compare AFLP fingerprint profiles by matching peaks using two metrics: score and percentage of similarity. A software package was developed based on our scheme and proposed algorithms. Example of use this software is given in evaluating novelty of a new tropical orchid cultivar by comparing its AFLP fingerprint profile against those of related commercial cultivars in a database. CONCLUSIONS: AFLP DNA fingerprint profiles can be databased and compared effectively with software developed based on our scheme and algorithms. It will facilitate wider use of this DNA fingerprinting technique in areas such as forensic study, intellectual property protection for biological materials and biodiversity management. Moreover, the same concepts can be applied to databasing and comparing DNA fingerprint profiles obtained with other DNA fingerprint techniques.