Methods, Tools and Algorithms in Evolutionary Bioinformatics

Evolutionary bioinformatics sits at the intersection of molecular evolution, algorithm design, and high throughput data science. Over the past decade, surges in sequencing throughput, long read technologies, single cell assays, and metagenomics have transformed the scale and diversity of evolutionary questions we can ask—from reconstructing deep phylogenies and inferring demographic histories to tracking real time pathogen evolution and functional adaptation. This expansion has sharpened the need for 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/paralogy delineation in the presence of duplication, loss, and horizontal gene transfer; and reconciliation of gene and species trees at genome scale. Concurrently, algorithmic advances—probabilistic models (e.g., context dependent and codon aware substitution), HMMs, coalescent and birth death frameworks, efficient likelihood approximations, and MCMC/variational methods—are being complemented by machine learning and differentiable programming to accelerate inference, guide model selection, and improve feature representation. Reproducibility and benchmarking remain central challenges, given heterogeneous data quality, batch effects, and the combinatorial complexity of analysis choices. Containerized workflows, provenance tracking, and open data standards are increasingly critical for trustworthy, reusable research.

This topic aims to clarify best practices, and chart priorities for robust, transparent, and scalable evolutionary inference across organisms and data modalities. This topic also aims to assemble authoritative reviews that evaluate methodological foundations, compare tools and algorithms, and establish best practice guidance for evolutionary bioinformatics. We seek critical syntheses that bridge theory, implementation, and applications, highlighting performance trade offs, reproducibility, and open resources to accelerate reliable evolutionary inference at scale.

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/HMM methods, alignment uncertainty and downstream impact.

o Models of molecular evolution: site heterogeneous and mixture models, codon/partition models, heterotachy, compositional bias, model adequacy and selection.

o Phylogenetic inference: distance, maximum likelihood, Bayesian and coalescent approaches; tree search heuristics; bootstrap/posterior support; gene–species tree reconciliation; phylogenomics at scale.

o Comparative genomics and gene family evolution: orthology/paralogy, duplication–loss–transfer, synteny aware methods, pan genome 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/FPGA acceleration, succinct data structures, approximate likelihoods.

o Machine learning in evolutionary analysis: representation learning for sequences/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.

Manuscript expectations: transparent methods, clear reporting standards, discussion of assumptions/limitations, comparative evaluations where feasible, and links to open data, code, and reproducible workflows.