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arxiv: 2606.25584 · v1 · pith:WOST6EHKnew · submitted 2026-06-24 · 🧬 q-bio.PE

ML-MAWS: Alignment-Free Maximum Likelihood Phylogeny Estimation Using Minimal Absent Words

Papri Saha , Sudipta Kumar Das , Anonnya Sarkar and , Md. Manzurul Hasan This is my paper

Pith reviewed 2026-06-25 19:28 UTC · model grok-4.3

classification 🧬 q-bio.PE
keywords alignment-free phylogenyminimal absent wordsmaximum likelihoodLewis Mkv modelbinary character matrixbacterial genomesviral genomesphylogenetic signal
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The pith

Encoding minimal absent words as binary characters enables maximum likelihood tree estimation on unaligned genomes.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper presents ML-MAWS, which turns minimal absent words extracted from genomes into a binary presence-absence matrix and then runs maximum likelihood inference under the Lewis Mkv model with ascertainment correction. Three filtering steps—strand-aware combination of forward and reverse-complement words, entropy maximization across lengths, and retention of only parsimony-informative columns—aim to preserve phylogenetic signal in this coarse encoding. On fourteen benchmark collections spanning bacteria, mitochondria, viruses and simulated sequences, the resulting trees recover splits close to published reference topologies while also returning per-branch bootstrap-style support and a full probabilistic model. This combination is presented as an advance over distance-only alignment-free methods that lack statistical machinery. The approach therefore claims both competitive topological accuracy and the added benefit of rigorous inference without requiring multiple sequence alignment.

Core claim

ML-MAWS recovers near-correct splits on bacterial, mitochondrial, viral and simulated genome benchmarks by encoding minimal absent words as a binary character matrix and estimating trees under the Lewis Mkv model with ascertainment bias correction; the method supplies per-branch statistical support and a probabilistic framework that distance-based alignment-free approaches lack.

What carries the argument

Binary presence/absence matrix of minimal absent words, filtered by strand awareness, entropy selection across lengths, and parsimony-informative capping, then analysed under the Lewis Mkv model with ascertainment bias correction.

If this is right

  • Trees produced by ML-MAWS carry per-branch statistical support values unavailable from distance-only alignment-free methods.
  • The method supplies a full probabilistic model that can be extended to model testing or ancestral-state reconstruction.
  • Computation remains linear in sequence length because minimal absent words are extracted via suffix-automaton traversal.
  • The same binary matrix can be re-analysed under alternative substitution models once the Lewis Mkv baseline is established.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Because the input is a character matrix rather than pairwise distances, the framework could be combined with existing coalescent or multispecies models that operate on character data.
  • The entropy-based length selection step might generalise to other k-mer or word-based features beyond minimal absent words.
  • If the binary signal proves robust, the approach could be applied to very large metagenomic assemblies where alignment is impractical.

Load-bearing premise

The binary presence or absence of minimal absent words still carries enough phylogenetic signal to produce trees whose topological accuracy matches or exceeds that of continuous distance methods after the described filtering.

What would settle it

A new benchmark collection in which the Robinson-Foulds or matching-split distance of ML-MAWS trees to the reference is substantially larger than that of published continuous distance baselines on the same sequences.

Figures

Figures reproduced from arXiv: 2606.25584 by Anonnya Sarkar and, Md. Manzurul Hasan, Papri Saha, Sudipta Kumar Das.

Figure 1
Figure 1. Figure 1: Workflow diagram of the ML-MAWS pipeline. Genomic sequences are processed through suffix automaton–based MAW extraction with [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: ML-MAWS phylogenetic tree for the Fish mtDNA dataset (25 species). Branch labels indicate bootstrap support (%). The tree recovers major [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Normalized Robinson–Foulds distance (nRF) across benchmark datasets and methods. Lower values indicate better topological accuracy. [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Matching Split Distance (MSD) across datasets. MSD provides partial credit for near-correct bipartitions, offering a more nuanced view of [PITH_FULL_IMAGE:figures/full_fig_p007_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Ablation study comparing strand-aware (IQ-TREE) and no-strand variants across datasets: (a) nRF, (b) nQD, and (c) bootstrap support. [PITH_FULL_IMAGE:figures/full_fig_p008_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Effect of increasing horizontal gene transfer (HGT) on nRF [PITH_FULL_IMAGE:figures/full_fig_p008_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Log-scale comparison of (a) running time and (b) peak memory across all methods. ML-MAWS trades computational resources for statistical [PITH_FULL_IMAGE:figures/full_fig_p009_7.png] view at source ↗
read the original abstract

Alignment-free methods in phylogenetic tree construction have major benefits in computational efficiency over alignment-based methods, but most sacrifice sequence information to pairwise distances, losing the statistical power of maximum likelihood (ML) inference. We describe ML-MAWS, an algorithm that fills this gap by encoding Minimal Absent Words (MAWs) as a binary presence/absence character matrix and estimating using an ML tree under the Lewis Mkv model using ascertainment bias correction. MAWs are obtained in linear time through the traversal of a suffix automaton. Three new elements contribute to the phylogenetic signal: strand-aware filtering combines forward and reverse complement MAW sets to eliminate compositional artifacts; entropy-based multi-length selection uses Shannon entropy maximization to select the most informative lengths of MAWs; and parsimony-informative character capping only retains the most discriminative columns. We tested ML-MAWS on 14 benchmark datasets of bacterial, mitochondrial, viral, and simulated genomes with normalized Robinson Foulds distances and matching split distances, against published reference trees. The results show that the coarse binary encoding of MAWs can lead to higher topological errors than continuous-valued distance baselines, while ML-MAWS can successfully recover near-correct splits and can uniquely provide per-branch statistical confidence as well as a rigorous probabilistic framework that is lacking in these methods.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The manuscript proposes ML-MAWS, an alignment-free phylogenetic method that extracts minimal absent words (MAWs) in linear time via suffix automata, encodes them as a binary presence/absence character matrix after strand-aware filtering, entropy-based length selection, and parsimony capping, and infers trees by maximum likelihood under the Lewis Mkv model with ascertainment bias correction. On 14 bacterial, mitochondrial, viral, and simulated datasets, it reports higher normalized Robinson-Foulds and matching-split distances than continuous distance baselines yet claims recovery of near-correct splits together with per-branch statistical supports and a probabilistic framework unavailable to distance methods.

Significance. If the central modeling assumptions hold, the work would supply the first explicit maximum-likelihood treatment of alignment-free data, a genuine methodological advance over purely distance-based approaches. The linear-time MAW extraction and the three filtering heuristics are computationally attractive and directly address known compositional artifacts. The explicit acknowledgment that topological accuracy remains inferior to distance baselines is a strength of the presentation, as it correctly frames the contribution around the availability of calibrated supports rather than raw accuracy.

major comments (2)
  1. [Abstract] Abstract: the claim that ML-MAWS 'can successfully recover near-correct splits' while simultaneously reporting higher topological error than distance baselines is load-bearing for the central contribution; the manuscript must define a quantitative threshold (e.g., normalized RF < 0.05 or matching-split distance < 0.10) and report per-dataset values to substantiate 'near-correct' rather than leaving the interpretation to the reader.
  2. [Methods] Methods (Lewis Mkv application and filtering pipeline): the model treats MAW columns as conditionally independent given the tree and branch lengths, yet each MAW is a deterministic function of the same input string; a single substitution can create or destroy multiple MAWs. The strand-aware union, entropy maximization, and parsimony capping steps are described but no diagnostic (e.g., pairwise character correlation matrix or simulation under a sequence evolution model) is provided to show that residual dependence is negligible. Because the per-branch supports and likelihood comparisons rest on this independence assumption, its violation directly undermines the claimed 'rigorous probabilistic framework'.
minor comments (2)
  1. The 14 benchmark datasets are referenced only generically; explicit accession numbers, sequence lengths, and reference tree sources should be tabulated to permit exact reproduction.
  2. Figure legends should state the exact normalization used for Robinson-Foulds distances and whether the reported values are means or medians across replicates.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive and detailed report. We address each major comment below and commit to revisions that directly respond to the concerns raised.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the claim that ML-MAWS 'can successfully recover near-correct splits' while simultaneously reporting higher topological error than distance baselines is load-bearing for the central contribution; the manuscript must define a quantitative threshold (e.g., normalized RF < 0.05 or matching-split distance < 0.10) and report per-dataset values to substantiate 'near-correct' rather than leaving the interpretation to the reader.

    Authors: We agree that the phrase 'near-correct splits' requires a quantitative definition to be interpretable. In the revised manuscript we will explicitly define 'near-correct' using the thresholds suggested (normalized RF < 0.05 or matching-split distance < 0.10) and add a table (or supplementary table) that reports the exact normalized RF and matching-split distances for each of the 14 datasets. The abstract will be updated to reference these concrete values rather than the current qualitative statement. revision: yes

  2. Referee: [Methods] Methods (Lewis Mkv application and filtering pipeline): the model treats MAW columns as conditionally independent given the tree and branch lengths, yet each MAW is a deterministic function of the same input string; a single substitution can create or destroy multiple MAWs. The strand-aware union, entropy maximization, and parsimony capping steps are described but no diagnostic (e.g., pairwise character correlation matrix or simulation under a sequence evolution model) is provided to show that residual dependence is negligible. Because the per-branch supports and likelihood comparisons rest on this independence assumption, its violation directly undermines the claimed 'rigorous probabilistic framework'.

    Authors: The referee correctly notes that MAWs are not strictly independent. The Lewis Mkv model is applied here as a practical approximation for binary characters, analogous to its use in other morphological or presence/absence datasets; the three filtering steps are intended to mitigate redundancy. No explicit diagnostic for residual dependence appears in the submitted manuscript. In revision we will add (i) a pairwise correlation matrix computed on the final character matrices of the real datasets and (ii) a small simulation study under a sequence evolution model to quantify the degree of dependence and its influence on support values. Results and any resulting caveats will be reported. revision: yes

Circularity Check

0 steps flagged

No significant circularity; derivation relies on external standards

full rationale

The paper encodes MAWs via standard suffix automata (linear-time traversal, no author-specific prior), applies the published Lewis Mkv model with ascertainment correction, and uses explicitly described filtering heuristics (strand-aware union, entropy maximization, parsimony capping). No equations, fitted parameters, or self-citations reduce the reported trees, likelihoods, or supports to quantities defined by the authors' own inputs or prior work. Evaluation uses external benchmark datasets and reference trees. The central claim therefore remains self-contained against independent components.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The central claim rests on the assumption that MAW presence/absence forms a valid character matrix for the Lewis Mkv model and that the three filtering heuristics preserve phylogenetic signal. No free parameters are explicitly named in the abstract. No new entities are postulated.

axioms (2)
  • standard math Suffix automaton traversal yields all minimal absent words in linear time
    Invoked in the description of MAW extraction; standard result in string algorithms.
  • domain assumption Lewis Mkv model with ascertainment bias correction is appropriate for binary character matrices derived from sequence features
    Core modeling choice stated in the abstract.

pith-pipeline@v0.9.1-grok · 5770 in / 1470 out tokens · 21341 ms · 2026-06-25T19:28:02.894303+00:00 · methodology

discussion (0)

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Reference graph

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