Volume II: Methods, Tools and Algorithms in Evolutionary Bioinformatics

Building on the success of the first volume, this Research Topic continues to map the methodological frontier of evolutionary bioinformatics—the field where molecular evolution, algorithm design, and high throughput data science meet. The first volume assembled authoritative reviews on alignment, phylogenetic inference, comparative genomics, selection, phylodynamics, and reproducibility. Volume 2 extends that work, capturing the rapid progress made since and addressing the questions that remain open.The pace of change has not slowed. Sequencing throughput, long read and ultra long read platforms, single cell and spatial assays, and ever larger metagenomic and pangenomic datasets keep expanding the scale and diversity of evolutionary questions we can ask—from reconstructing deep phylogenies and demographic histories to tracking real time pathogen evolution and functional adaptation. This growth sharpens a persistent need: rigorous, scalable methods that integrate sequence data with structural, ecological, and phenotypic information while quantifying uncertainty and bias.Foundational problems remain active frontiers: multiple sequence alignment under indel rich regimes; robust phylogenetic inference under model misspecification and heterotachy; orthology and paralogy delineation amid duplication, loss, and horizontal gene transfer; and reconciliation of gene and species trees at genome scale. Alongside these, algorithmic advances—probabilistic models, HMMs, coalescent and birth–death frameworks, efficient likelihood approximations, and MCMC and variational methods—are increasingly complemented by machine learning, foundation models for biological sequences, and differentiable programming that accelerate inference, guide model selection, and improve feature representation. Reproducibility and benchmarking stay central, given heterogeneous data quality, batch effects, and the combinatorial complexity of analysis choices. Containerized workflows, provenance tracking, and open data standards remain critical for trustworthy, reusable research.This volume aims to clarify best practices and chart priorities for robust, transparent, and scalable evolutionary inference across organisms and data modalities. As in the first volume, we seek to assemble authoritative reviews that evaluate methodological foundations, compare tools and algorithms, and establish best practice guidance. We welcome critical syntheses that bridge theory, implementation, and application, highlighting performance trade-offs, reproducibility, and open resources to accelerate reliable evolutionary inference at scale. New and emerging contributors, as well as authors from the first volume, are encouraged to take part.We invite the following article types: mini reviews (focused perspectives), systematic reviews (protocol driven syntheses with explicit criteria), and full reviews (comprehensive, critical overviews).Suggested subthemes:o Sequence alignment and homology inference: indel models, structure aware MSA, profile and HMM methods, alignment uncertainty and downstream impact.o Models of molecular evolution: site heterogeneous and mixture models, codon and partition models, heterotachy, compositional bias, model adequacy and selection.o Phylogenetic inference: distance, maximum likelihood, Bayesian and coalescent approaches; tree search heuristics; bootstrap and posterior support; gene–species tree reconciliation; phylogenomics at scale.o Comparative and pangenomics and gene family evolution: orthology and paralogy, duplication–loss–transfer, synteny aware methods, pangenome and metagenome contexts.o Selection and adaptation: dN/dS frameworks, branch site tests, polymorphism–divergence integration, convergence detection, epistasis and fitness landscapes.o Phylodynamics and epidemiology: birth–death and coalescent models, temporal signal, sampling biases, real time pathogen tracking.o Algorithmic and computational advances: probabilistic programming, variational inference, GPU and FPGA acceleration, succinct data structures, approximate likelihoods.o Machine learning and foundation models in evolutionary analysis: representation learning for sequences and structures, hybrid ML–mechanistic models, calibration and interpretability.o Reproducibility and benchmarking: gold standard datasets, simulation frameworks, workflow systems, containers, provenance, FAIR data and software. Topic editors: Jack M Craig, Sayaka Miura, Denis Jacob Machado Source: https://www.frontiersin.org/research-topics/83824/volume-ii-methods-tools-and-algorithms-in-evolutionary-bioinformatics