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While alignment has been the dominant approach for determining homology prior to phylogenetic inference, alignment-free methods can simplify the analysis, especially when analyzing genome-wide data. Furthermore, alignment-free methods present the only option for emerging forms of data, such as genome skims, which do not permit assembly. Despite the appeal, alignment-free methods have not been competitive with alignment-based methods in terms of accuracy. One limitation of alignment-free methods is their reliance on simplified models of sequence evolution such as Jukes-Cantor. If we can estimate frequencies of base substitutions in an alignment-free setting, we can compute pairwise distances under more complex models. However, since the strand of DNA sequences is unknown for many forms of genome-wide data, which arguably present the best use case for alignment-free methods, the most complex models that one can use are the so-called no strand-bias models. We show how to calculate distances under a four-parameter no strand-bias model called TK4 without relying on alignments or assemblies. The main idea is to replace letters in the input sequences and recompute Jaccard indices between k-mer sets. However, on larger genomes, we also need to compute the number of k-mer mismatches after replacement due to random chance as opposed to homology. We show in simulation that alignment-free distances can be highly accurate when genomes evolve under the assumed models and study the accuracy on assembled and unassembled biological data., Availability and Implementation: Our software is available open source at https://github.com/nishatbristy007/NSB., Supplementary Information: Supplementary data are available at Bioinformatics Advances online., (© The Author(s) 2022. Published by Oxford University Press.)

Understanding cell differentiation-the process of generation of distinct cell-types-plays a pivotal role in developmental and evolutionary biology. Transcriptomic information and epigenetic marks are useful to elucidate hierarchical developmental relationships among cell-types. Standard phylogenetic approaches such as maximum parsimony, maximum likelihood and neighbor joining have previously been applied to ChIP-Seq histone modification data to infer cell-type trees, showing how diverse types of cells are related. In this study, we demonstrate the applicability and suitability of quartet-based phylogenetic tree estimation techniques for constructing cell-type trees. We propose two quartet-based pipelines for constructing cell phylogeny. Our methods were assessed for their validity in inferring hierarchical differentiation processes of various cell-types in H3K4me3, H3K27me3, H3K36me3, and H3K27ac histone mark data. We also propose a robust metric for evaluating cell-type trees., Competing Interests: The authors have declared that no competing interests exist.