Biology subjects

Korchmaros, A.

Publications and source records attributed to Korchmaros, A..

4 recordsLinked to original sources

bioRxiv2025

REvolutionH-tl: A Fast and Robust Tool for Decoding Evolutionary Gene Histories

REvolutionH-tl is a fast, scalable, and integrated software platform for inferring orthology relationships, gene trees, species trees, and reconciled evolutionary scenarios directly from sequence data. Built upon the formal framework of best match graphs (BMGs), REvolutionH-tl predicts orthogroups and orthologous gene pairs with high accuracy, requiring neither precomputed trees nor multiple external tools. The software reconstructs event-labeled gene and species trees, seamlessly integrating reconciliation to produce fast, accurate, and biologically insightful evolutionary scenarios. Through extensive benchmarking on synthetic datasets with known ground truth, REvolutionH-tl outperforms or matches the accuracy of established tools such as OrthoFinder, Proteinortho, RAxML, GeneRax, and RANGER-DTL, while achieving significantly lower runtimes. A key innovation of REvolutionH-tl is its built-in support for detailed, publication-ready visualizations, which allow users to explore genome evolution dynamics, orthogroup composition, and reconciliation results with clarity and ease. These visual features position REvolutionH-tl as the first platform of its kind to combine analytical precision with intuitive interpretability. The software is open-source, cross-platform, and freely available at https://pypi.org/project/revolutionhtl/, providing a robust solution for large-scale evolutionary analyses in comparative genomics. Author summaryComparative genomics relies on understanding how genes evolve across species. This involves identifying groups of related genes, reconstructing their evolutionary trees, and aligning them with the evolutionary history of species. These steps are typically performed using multiple tools, often requiring manual integration and technical expertise. We present REvolutionH-tl, an open-source software that automates the entire evolutionary reconstruction process--starting from protein sequences and producing gene trees, species trees, orthology assignments, and reconciled evolutionary scenarios. Unlike existing tools, REvolutionH-tl also includes built-in, high-quality visualizations that help users interpret complex evolutionary events such as gene duplications and losses. We evaluated REvolutionH-tl on simulated genomes with known evolutionary histories and found that it matches or exceeds the performance of widely used tools, while being significantly faster. Its visual output makes evolutionary analysis more accessible and interpretable, offering a valuable resource for researchers studying genome evolution.

bioinformatics↗

bioRxiv2024

Partial Fitch Graphs: Characterization, Satisfiability and Complexity

Horizontal gene transfer is an important contributor to evolution. Following Walter M. Fitch, two genes are xenologs if at least one HGT separates them. More formally, the directed Fitch graph has a set of genes as its vertices, and directed edges (x, y) for all pairs of genes x and y for which y has been horizontally transferred at least once since it diverged from the last common ancestor of x and y. Subgraphs of Fitch graphs can be inferred by comparative sequence analysis. In many cases, however, only partial knowledge about the "full" Fitch graph can be obtained. Here, we characterize Fitch-satisfiable graphs that can be extended to a biologically feasible "full" Fitch graph and derive a simple polynomial-time recognition algorithm. We then proceed to show that several versions of finding the Fitch graph with total maximum (confidence) edge-weights are NP-hard. In addition, we provide a greedy-heuristic for "optimally" recovering Fitch graphs from partial ones. Somewhat surprisingly, even if [~] 80% of information of the underlying input Fitch-graph G is lost (i.e., the partial Fitch graph contains only [~] 20% of the edges of G), it is possible to recover [~] 90% of the original edges of G on average.

bioinformatics↗

bioRxiv2020

U-Net Model for Brain Extraction on Non-human Primates

Brain extraction (a.k.a. skull stripping) is a fundamental step in the neuroimaging pipeline as it can affect the accuracy of downstream preprocess such as image registration, tissue classification, etc. Most brain extraction tools have been designed for and applied to human data and are often challenged by non-human primates (NHP) data. Amongst recent attempts to improve performance on NHP data, deep learning models appear to outperform the traditional tools. However, given the minimal sample size of most NHP studies and notable variations in data quality, the deep learning models are very rarely applied to multi-site samples in NHP imaging. To overcome this challenge, we used a transfer-learning framework that leverages a large human imaging dataset to pretrain a convolutional neural network (i.e. U-Net Model), and then transferred this to NHP data using a small NHP training sample. The resulting transfer-learning model converged faster and achieved more accurate performance than a similar U-Net Model trained exclusively on NHP samples. We improved the generalizability of the model by upgrading the transfer-learned model using additional training datasets from multiple research sites in the Primate Data-Exchange (PRIME-DE) consortium. Our final model outperformed brain extraction routines from popular MRI packages (AFNI, FSL, and FreeSurfer) across a heterogeneous sample from multiple sites in the PRIME-DE with less computational cost (20s~10min). We also demonstrated the transfer-learning process enables the macaque model to be updated for use with scans from chimpanzees, marmosets, and other mammals (e.g. pig). Our model, code, and the skull-stripped mask repository of 136 macaque monkeys are publicly available for unrestricted use by the neuroimaging community at https://github.com/HumanBrainED/NHP-BrainExtraction.

neuroscience↗

bioRxiv2020

Impact of Concatenating fMRI Data on Reliability for Functional Connectomics

Compelling evidence suggests the need for more data per individual to reliably map the functional organization of the human connectome. As the notion that more data is better emerges as a golden rule for functional connectomics, researchers find themselves grappling with the challenges of how to obtain the desired amounts of data per participant in a practical manner, particularly for retrospective data aggregation. Increasingly, the aggregation of data across all fMRI scans available for an individual is being viewed as a solution, regardless of scan condition (e.g., rest, task, movie). A number of open questions exist regarding the aggregation process and the impact of different decisions on the reliability of resultant aggregate data. We leveraged the availability of highly sampled test-retest datasets to systematically examine the impact of data aggregation strategies on the reliability of cortical functional connectomics. Specifically, we compared functional connectivity estimates derived after concatenating from: 1) multiple scans under the same state, 2) multiple scans under different states (i.e. hybrid or general functional connectivity), and 3) subsets of one long scan. We also varied connectivity processing (i.e. global signal regression, ICA-FIX, and task regression) and estimation procedures. When the total number of time points is equal, and the scan state held constant, concatenating multiple shorter scans had a clear advantage over a single long scan. However, this was not necessarily true when concatenating across different fMRI states (i.e. task conditions), where the reliability from the aggregate data varied across states. Concatenating fewer numbers of states that are more reliable tends to yield higher reliability. Our findings provide an overview of multiple dependencies of data concatenation that should be considered to optimize reliability in analysis of functional connectivity data.

neuroscience↗