arXiv subjects

Jeet Sukumaran

Publications and source records attributed to Jeet Sukumaran.

4 recordsLinked to original sources

arXiv2026

PhyloFrame: A DataFrame-based Library for Fast, Flexible Phylogenetic Computation

PhyloFrame is a Python library for phylogenetic computation targeting the gap between specialist, compiler-optimized operations and flexible, script-based workflows -- with emphasis on fast, memory-efficient operations for very large tree sizes (e.g., $\geq$ 300,000 taxa). PhyloFrame is built around a DataFrame-based tree representation, where each row corresponds to a node and columns record ancestor relationships, branch lengths, taxon labels, and any user-defined attributes. Crucial for scalability, such array-backed storage allows both library and end-user code alike to seamlessly harness Just-in-Time (JIT) compilation (e.g., Numba) and vectorized execution (e.g., NumPy, Polars). At large tree sizes, performance generally matches or exceeds Python libraries backed by native code -- notably, achieving strong performance in topological-order traversals and Newick I/O. DataFrame-based representation affords several additional conveniences, including: - succinct bulk operations (e.g., NumPy); - powerful queries and transformations (e.g., Polars expressions, Pandas indexing, SQL-style joins and merges); - compatibility with modern tabular data formats that are compression-friendly, type-aware, nullable, and highly portable (e.g., Parquet); and - broad interoperation with table-oriented data science tools (e.g., Seaborn, Plotly, Vega-Altair, tidyverse, Excel). Current library features include tree input/output, synthetic tree generation, taxon-based queries, tree traversals, tree metrics, tree manipulation, tree downsampling, and tree comparison. Most functionality supports both Pandas and Polars DataFrames, and is available through programmatic and CLI-based interfaces.

q-bio.PE↗

arXiv2026

Population-scale Ancestral Recombination Graphs with tskit 1.0

Ancestral recombination graphs (ARGs) are an increasingly important component of population and statistical genetics. The tskit library has become key infrastructure for the field, providing an expressive and general representation of ARGs together with a suite of efficient fundamental operations. In this note, we announce tskit version 1.0, describe its underlying rationale, and document its stability guarantees. These guarantees provide a foundation for durable computational artefacts and support long-term reproducibility of code and analyses.

q-bio.PE↗

arXiv2024

DendroPy 5: a mature Python library for phylogenetic computing

Contemporary bioinformatics has seen in profound new visibility into the composition, structure, and history of the natural world around us. Arguably, the central pillar of bioinformatics is phylogenetics -- the study of hereditary relatedness among organisms. Insight from phylogenetic analysis has touched nearly every corner of biology. Examples range across natural history, population genetics and phylogeography, conservation biology, public health, medicine, in vivo and in silico experimental evolution, application-oriented evolutionary algorithms, and beyond. High-throughput genetic and phenotypic data has realized groundbreaking results, in large part, through conjunction with open-source software used to process and analyze it. Indeed, the preceding decades have ushered in a flourishing ecosystem of bioinformatics software applications and libraries. Over the course of its nearly fifteen-year history, the DendroPy library for phylogenetic computation in Python has established a generalist niche in serving the bioinformatics community. Here, we report on the recent major release of the library, DendroPy version 5. The software release represents a major milestone in transitioning the library to a sustainable long-term development and maintenance trajectory. As such, this work positions DendroPy to continue fulfilling a key supporting role in phyloinformatics infrastructure.

q-bio.PE↗

arXiv2014

Implications of uniformly distributed, empirically informed priors for phylogeographical model selection: A reply to Hickerson et al

Establishing that a set of population-splitting events occurred at the same time can be a potentially persuasive argument that a common process affected the populations. Oaks et al. (2013) assessed the ability of an approximate-Bayesian method (msBayes) to estimate such a pattern of simultaneous divergence across taxa, to which Hickerson et al. (2014) responded. Both papers agree the method is sensitive to prior assumptions and often erroneously supports shared divergences; the papers differ about the explanation and solution. Oaks et al. (2013) suggested the method's behavior is caused by the strong weight of uniform priors on divergence times leading to smaller marginal likelihoods of models with more divergence-time parameters (Hypothesis 1); they proposed alternative priors to avoid strongly weighted posteriors. Hickerson et al. (2014) suggested numerical approximation error causes msBayes analyses to be biased toward models of clustered divergences (Hypothesis 2); they proposed using narrow, empirical uniform priors. Here, we demonstrate that the approach of Hickerson et al. (2014) does not mitigate the method's tendency to erroneously support models of clustered divergences, and often excludes the true parameter values. Our results also show that the tendency of msBayes analyses to support models of shared divergences is primarily due to Hypothesis 1. This series of papers demonstrate that if our prior assumptions place too much weight in unlikely regions of parameter space such that the exact posterior supports the wrong model of evolutionary history, no amount of computation can rescue our inference. Fortunately, more flexible distributions that accommodate prior uncertainty about parameters without placing excessive weight in vast regions of parameter space with low likelihood increase the method's robustness and power to detect temporal variation in divergences.

q-bio.PE↗