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1. Genome-wide alignment-free phylogenetic distance estimation under a no strand-bias model

   https://doi.org/10.1093/bioadv/vbac055  

   Balaban, Metin; Bristy, Nishat Anjum; Faisal, Ahnaf; Bayzid, Md Shamsuzzoha; Mirarab, Siavash; Lengauer, ed., Thomas (August 2022, Bioinformatics Advances)

   Abstract  Summary: 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 implementationOur software is available open source at https://github.com/nishatbristy007/NSB. Supplementary informationSupplementary data are available at Bioinformatics Advances online. 

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2. Minmers are a generalization of minimizers that enable unbiased local Jaccard estimation

   https://doi.org/10.1093/bioinformatics/btad512  

   Kille, Bryce; Garrison, Erik; Treangen, Todd J; Phillippy, Adam M (September 2023, Bioinformatics)

   Robinson, Peter (Ed.)

   Abstract MotivationThe Jaccard similarity on k-mer sets has shown to be a convenient proxy for sequence identity. By avoiding expensive base-level alignments and comparing reduced sequence representations, tools such as MashMap can scale to massive numbers of pairwise comparisons while still providing useful similarity estimates. However, due to their reliance on minimizer winnowing, previous versions of MashMap were shown to be biased and inconsistent estimators of Jaccard similarity. This directly impacts downstream tools that rely on the accuracy of these estimates. ResultsTo address this, we propose the minmer winnowing scheme, which generalizes the minimizer scheme by use of a rolling minhash with multiple sampled k-mers per window. We show both theoretically and empirically that minmers yield an unbiased estimator of local Jaccard similarity, and we implement this scheme in an updated version of MashMap. The minmer-based implementation is over 10 times faster than the minimizer-based version under the default ANI threshold, making it well-suited for large-scale comparative genomics applications. Availability and implementationMashMap3 is available at https://github.com/marbl/MashMap. 

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3. Estimating error models for whole genome sequencing using mixtures of Dirichlet-multinomial distributions

   https://doi.org/10.1093/bioinformatics/btx133  

   Wu, Steven H.; Schwartz, Rachel S.; Winter, David J.; Conrad, Donald F.; Cartwright, Reed A.; Stegle, ed., Oliver (March 2017, Bioinformatics)

   Abstract MotivationAccurate identification of genotypes is an essential part of the analysis of genomic data, including in identification of sequence polymorphisms, linking mutations with disease and determining mutation rates. Biological and technical processes that adversely affect genotyping include copy-number-variation, paralogous sequences, library preparation, sequencing error and reference-mapping biases, among others. ResultsWe modeled the read depth for all data as a mixture of Dirichlet-multinomial distributions, resulting in significant improvements over previously used models. In most cases the best model was comprised of two distributions. The major-component distribution is similar to a binomial distribution with low error and low reference bias. The minor-component distribution is overdispersed with higher error and reference bias. We also found that sites fitting the minor component are enriched for copy number variants and low complexity regions, which can produce erroneous genotype calls. By removing sites that do not fit the major component, we can improve the accuracy of genotype calls. Availability and ImplementationMethods and data files are available at https://github.com/CartwrightLab/WuEtAl2017/ (doi:10.5281/zenodo.256858). Supplementary informationSupplementary data is available at Bioinformatics online. 

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4. WITCH-NG: efficient and accurate alignment of datasets with sequence length heterogeneity

   https://doi.org/10.1093/bioadv/vbad024  

   Liu, Baqiao; Warnow, Tandy; Lengauer, ed., Thomas (March 2023, Bioinformatics Advances)

   Abstract SummaryMultiple sequence alignment is a basic part of many bioinformatics pipelines, including in phylogeny estimation, prediction of structure for both RNAs and proteins, and metagenomic sequence analysis. Yet many sequence datasets exhibit substantial sequence length heterogeneity, both because of large insertions and deletions in the evolutionary history of the sequences and the inclusion of unassembled reads or incompletely assembled sequences in the input. A few methods have been developed that can be highly accurate in aligning datasets with sequence length heterogeneity, with UPP one of the first methods to achieve good accuracy, and WITCH a recent improvement on UPP for accuracy. In this article, we show how we can speed up WITCH. Our improvement includes replacing a critical step in WITCH (currently performed using a heuristic search) by a polynomial time exact algorithm using Smith–Waterman. Our new method, WITCH-NG (i.e. ‘next generation WITCH’) achieves the same accuracy but is substantially faster. WITCH-NG is available at https://github.com/RuneBlaze/WITCH-NG. Availability and implementationThe datasets used in this study are from prior publications and are freely available in public repositories, as indicated in the Supplementary Materials. Supplementary informationSupplementary data are available at Bioinformatics Advances online. 

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5. UPP2: fast and accurate alignment of datasets with fragmentary sequences

   https://doi.org/10.1093/bioinformatics/btad007  

   Park, Minhyuk; Ivanovic, Stefan; Chu, Gillian; Shen, Chengze; Warnow, Tandy; Marschall, ed., Tobias (January 2023, Bioinformatics)

   Abstract MotivationMultiple sequence alignment (MSA) is a basic step in many bioinformatics pipelines. However, achieving highly accurate alignments on large datasets, especially those with sequence length heterogeneity, is a challenging task. Ultra-large multiple sequence alignment using Phylogeny-aware Profiles (UPP) is a method for MSA estimation that builds an ensemble of Hidden Markov Models (eHMM) to represent an estimated alignment on the full-length sequences in the input, and then adds the remaining sequences into the alignment using selected HMMs in the ensemble. Although UPP provides good accuracy, it is computationally intensive on large datasets. ResultsWe present UPP2, a direct improvement on UPP. The main advance is a fast technique for selecting HMMs in the ensemble that allows us to achieve the same accuracy as UPP but with greatly reduced runtime. We show that UPP2 produces more accurate alignments compared to leading MSA methods on datasets exhibiting substantial sequence length heterogeneity and is among the most accurate otherwise. Availability and implementationhttps://github.com/gillichu/sepp. Supplementary informationSupplementary data are available at Bioinformatics online. 

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