Rapid alignment-free phylogenetic identification of metagenomic sequences

Scaling DEPP phylogenetic placement to ultra-large reference trees: a tree-aware ensemble approach

MOTIVATION

Phylogenetic placement of a query sequence on a backbone tree is increasingly used across biomedical sciences to identify the content of a sample from its DNA content. The accuracy of such analyses depends on the density of the backbone tree, making it crucial that placement methods scale to very large trees. Moreover, a new paradigm has been recently proposed to place sequences on the species tree using single-gene data. The goal is to better characterize the samples and to enable combined analyses of marker-gene (e.g., 16S rRNA gene amplicon) and genome-wide data. The recent method DEPP enables performing such analyses using metric learning. However, metric learning is hampered by a need to compute and save a quadratically growing matrix of pairwise distances during training. Thus, the training phase of DEPP does not scale to more than roughly 10 000 backbone species, a problem that we faced when trying to use our recently released Greengenes2 (GG2) reference tree containing 331 270 species.

RESULTS

This paper explores divide-and-conquer for training ensembles of DEPP models, culminating in a method called C-DEPP. While divide-and-conquer has been extensively used in phylogenetics, applying divide-and-conquer to data-hungry machine-learning methods needs nuance. C-DEPP uses carefully crafted techniques to enable quasi-linear scaling while maintaining accuracy. C-DEPP enables placing 20 million 16S fragments on the GG2 reference tree in 41 h of computation.

AVAILABILITY AND IMPLEMENTATION

The dataset and C-DEPP software are freely available at https://github.com/yueyujiang/dataset_cdepp/.

Robust expansion of phylogeny for fast-growing genome sequence data

Massive sequencing of SARS-CoV-2 genomes has urged novel methods that employ existing phylogenies to add new samples efficiently instead of de novo inference. 'TIPars' was developed for such challenge integrating parsimony analysis with pre-computed ancestral sequences. It took about 21 seconds to insert 100 SARS-CoV-2 genomes into a 100k-taxa reference tree using 1.4 gigabytes. Benchmarking on four datasets, TIPars achieved the highest accuracy for phylogenies of moderately similar sequences. For highly similar and divergent scenarios, fully parsimony-based and likelihood-based phylogenetic placement methods performed the best respectively while TIPars was the second best. TIPars accomplished efficient and accurate expansion of phylogenies of both similar and divergent sequences, which would have broad biological applications beyond SARS-CoV-2. TIPars is accessible from https://tipars.hku.hk/ and source codes are available at https://github.com/id-bioinfo/TIPars.

EPIK: precise and scalable evolutionary placement with informative k-mers

MOTIVATION

Phylogenetic placement enables phylogenetic analysis of massive collections of newly sequenced DNA, when de novo tree inference is too unreliable or inefficient. Assuming that a high-quality reference tree is available, the idea is to seek the correct placement of the new sequences in that tree. Recently, alignment-free approaches to phylogenetic placement have emerged, both to circumvent the need to align the new sequences and to avoid the calculations that typically follow the alignment step. A promising approach is based on the inference of k-mers that can be potentially related to the reference sequences, also called phylo-k-mers. However, its usage is limited by the time and memory-consuming stage of reference data preprocessing and the large numbers of k-mers to consider.

RESULTS

We suggest a filtering method for selecting informative phylo-k-mers based on mutual information, which can significantly improve the efficiency of placement, at the cost of a small loss in placement accuracy. This method is implemented in IPK, a new tool for computing phylo-k-mers that significantly outperforms the software previously available. We also present EPIK, a new software for phylogenetic placement, supporting filtered phylo-k-mer databases. Our experiments on real-world data show that EPIK is the fastest phylogenetic placement tool available, when placing hundreds of thousands and millions of queries while still providing accurate placements.

AVAILABILITY AND IMPLEMENTATION

IPK and EPIK are freely available at https://github.com/phylo42/IPK and https://github.com/phylo42/EPIK. Both are implemented in C++ and Python and supported on Linux and MacOS.

Online tree expansion could help solve the problem of scalability in Bayesian phylogenetics

Bayesian phylogenetics is now facing a critical point. Over the last 20 years, Bayesian methods have reshaped phylogenetic inference and gained widespread popularity due to their high accuracy, the ability to quantify the uncertainty of inferences and the possibility of accommodating multiple aspects of evolutionary processes in the models that are used. Unfortunately, Bayesian methods are computationally expensive, and typical applications involve at most a few hundred sequences. This is problematic in the age of rapidly expanding genomic data and increasing scope of evolutionary analyses, forcing researchers to resort to less accurate but faster methods, such as maximum parsimony and maximum likelihood. Does this spell doom for Bayesian methods? Not necessarily. Here, we discuss some recently proposed approaches that could help scale up Bayesian analyses of evolutionary problems considerably. We focus on two particular aspects: online phylogenetics, where new data sequences are added to existing analyses, and alternatives to Markov chain Monte Carlo (MCMC) for scalable Bayesian inference. We identify 5 specific challenges and discuss how they might be overcome. We believe that online phylogenetic approaches and Sequential Monte Carlo hold great promise and could potentially speed up tree inference by orders of magnitude. We call for collaborative efforts to speed up the development of methods for real-time tree expansion through online phylogenetics.

Zeta diversity patterns in metabarcoded lotic algal assemblages as a tool for bioassessment

Assessments of the ecological health of algal assemblages in streams typically focus on measures of their local diversity and classify individuals by morphotaxonomy. Such assemblages are often connected through various ecological processes, such as dispersal, and may be more accurately assessed as components of regional-, rather than local-scale assemblages. With recent declines in the costs of sequencing and computation, it has also become increasingly feasible to use metabarcoding to more accurately classify algal species and perform regional-scale bioassessments. Recently, zeta diversity has been explored as a novel method of constructing regional bioassessments for groups of streams. Here, we model the use of zeta diversity to investigate whether stream health can be determined by the landscape diversity of algal assemblages. We also compare the use of DNA metabarcoding and morphotaxonomy classifications in these zeta diversity-based bioassessments of regional stream health. From 96 stream samples in California, we used various orders of zeta diversity to construct models of biotic integrity for multiple assemblages of diatoms, as well as hybrid assemblages of diatoms in combination with soft-bodied algae, using taxonomy data generated with both DNA sequencing as well as traditional morphotaxonomic approaches. We compared our ability to evaluate the ecological health of streams with the performance of multiple algal indices of biological condition. Our zeta diversity-based models of regional biotic integrity were more strongly correlated with existing indices for algal assemblages classified using metabarcoding compared to morphotaxonomy. Metabarcoding for diatoms and hybrid algal assemblages involved rbcL and 18S V9 primers, respectively. Importantly, we also found that these algal assemblages, independent of the classification method, are more likely to be assembled under a process of niche differentiation rather than stochastically. Taken together, these results suggest the potential for zeta diversity patterns of algal assemblages classified using metabarcoding to inform stream bioassessments.

SCAMPP: Scaling Alignment-Based Phylogenetic Placement to Large Trees

Phylogenetic placement, the problem of placing a "query" sequence into a precomputed phylogenetic "backbone" tree, is useful for constructing large trees, performing taxon identification of newly obtained sequences, and other applications. The most accurate current methods, such as pplacer and EPA-ng, are based on maximum likelihood and require that the query sequence be provided within a multiple sequence alignment that includes the leaf sequences in the backbone tree. This approach enables high accuracy but also makes these likelihood-based methods computationally intensive on large backbone trees, and can even lead to them failing when the backbone trees are very large (e.g., having 50,000 or more leaves). We present SCAMPP (SCaling AlignMent-based Phylogenetic Placement), a technique to extend the scalability of these likelihood-based placement methods to ultra-large backbone trees. We show that pplacer-SCAMPP and EPA-ng-SCAMPP both scale well to ultra-large backbone trees (even up to 200,000 leaves), with accuracy that improves on APPLES and APPLES-2, two recently developed fast phylogenetic placement methods that scale to ultra-large datasets. EPA-ng-SCAMPP and pplacer-SCAMPP are available at https://github.com/chry04/PLUSplacer.

How to optimally sample a sequence for rapid analysis

MOTIVATION

We face an increasing flood of genetic sequence data, from diverse sources, requiring rapid computational analysis. Rapid analysis can be achieved by sampling a subset of positions in each sequence. Previous sequence-sampling methods, such as minimizers, syncmers and minimally overlapping words, were developed by heuristic intuition, and are not optimal.

RESULTS

We present a sequence-sampling approach that provably optimizes sensitivity for a whole class of sequence comparison methods, for randomly evolving sequences. It is likely near-optimal for a wide range of alignment-based and alignment-free analyses. For real biological DNA, it increases specificity by avoiding simple repeats. Our approach generalizes universal hitting sets (which guarantee to sample a sequence at least once) and polar sets (which guarantee to sample a sequence at most once). This helps us understand how to do rapid sequence analysis as accurately as possible.

AVAILABILITY AND IMPLEMENTATION

Source code is freely available at https://gitlab.com/mcfrith/noverlap.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Recent progress on methods for estimating and updating large phylogenies

With the increased availability of sequence data and even of fully sequenced and assembled genomes, phylogeny estimation of very large trees (even of hundreds of thousands of sequences) is now a goal for some biologists. Yet, the construction of these phylogenies is a complex pipeline presenting analytical and computational challenges, especially when the number of sequences is very large. In the past few years, new methods have been developed that aim to enable highly accurate phylogeny estimations on these large datasets, including divide-and-conquer techniques for multiple sequence alignment and/or tree estimation, methods that can estimate species trees from multi-locus datasets while addressing heterogeneity due to biological processes (e.g. incomplete lineage sorting and gene duplication and loss), and methods to add sequences into large gene trees or species trees. Here we present some of these recent advances and discuss opportunities for future improvements. This article is part of a discussion meeting issue 'Genomic population structures of microbial pathogens'.

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

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.

Distance-Based Phylogenetic Placement with Statistical Support

Phylogenetic identification of unknown sequences by placing them on a tree is routinely attempted in modern ecological studies. Such placements are often obtained from incomplete and noisy data, making it essential to augment the results with some notion of uncertainty. While the standard likelihood-based methods designed for placement naturally provide such measures of uncertainty, the newer and more scalable distance-based methods lack this crucial feature. Here, we adopt several parametric and nonparametric sampling methods for measuring the support of phylogenetic placements that have been obtained with the use of distances. Comparing the alternative strategies, we conclude that nonparametric bootstrapping is more accurate than the alternatives. We go on to show how bootstrapping can be performed efficiently using a linear algebraic formulation that makes it up to 30 times faster and implement this optimized version as part of the distance-based placement software APPLES. By examining a wide range of applications, we show that the relative accuracy of maximum likelihood (ML) support values as compared to distance-based methods depends on the application and the dataset. ML is advantageous for fragmentary queries, while distance-based support values are more accurate for full-length and multi-gene datasets. With the quantification of uncertainty, our work fills a crucial gap that prevents the broader adoption of distance-based placement tools.

Metagenomic Analysis Using Phylogenetic Placement-A Review of the First Decade

Phylogenetic placement refers to a family of tools and methods to analyze, visualize, and interpret the tsunami of metagenomic sequencing data generated by high-throughput sequencing. Compared to alternative (e. g., similarity-based) methods, it puts metabarcoding sequences into a phylogenetic context using a set of known reference sequences and taking evolutionary history into account. Thereby, one can increase the accuracy of metagenomic surveys and eliminate the requirement for having exact or close matches with existing sequence databases. Phylogenetic placement constitutes a valuable analysis tool per se, but also entails a plethora of downstream tools to interpret its results. A common use case is to analyze species communities obtained from metagenomic sequencing, for example via taxonomic assignment, diversity quantification, sample comparison, and identification of correlations with environmental variables. In this review, we provide an overview over the methods developed during the first 10 years. In particular, the goals of this review are 1) to motivate the usage of phylogenetic placement and illustrate some of its use cases, 2) to outline the full workflow, from raw sequences to publishable figures, including best practices, 3) to introduce the most common tools and methods and their capabilities, 4) to point out common placement pitfalls and misconceptions, 5) to showcase typical placement-based analyses, and how they can help to analyze, visualize, and interpret phylogenetic placement data.

The SITE-100 Project: Site-Based Biodiversity Genomics for Species Discovery, Community Ecology, and a Global Tree-of-Life

Most insect communities are composed of evolutionarily diverse lineages, but detailed phylogenetic analyses of whole communities are lacking, in particular in species-rich tropical faunas. Likewise, our knowledge of the Tree-of-Life to document evolutionary diversity of organisms remains highly incomplete and especially requires the inclusion of unstudied lineages from species-rich ecosystems. Here we present the SITE-100 program, which is an attempt at building the Tree-of-Life from whole-community sampling of high-biodiversity sites around the globe. Combining the local site-based sets into a global tree produces an increasingly comprehensive estimate of organismal phylogeny, while also re-tracing evolutionary history of lineages constituting the local community. Local sets are collected in bulk in standardized passive traps and imaged with large-scale high-resolution cameras, which is followed by a parataxonomy step for the preliminary separation of morphospecies and selection of specimens for phylogenetic analysis. Selected specimens are used for individual DNA extraction and sequencing, usually to sequence mitochondrial genomes. All remaining specimens are bulk extracted and subjected to metabarcoding. Phylogenetic analysis on the mitogenomes produces a reference tree to which short barcode sequences are added in a secondary analysis using phylogenetic placement methods or backbone constrained tree searches. However, the approach may be hampered because (1) mitogenomes are limited in phylogenetic informativeness, and (2) site-based sampling may produce poor taxon coverage which causes challenges for phylogenetic inference. To mitigate these problems, we first assemble nuclear shotgun data from taxonomically chosen lineages to resolve the base of the tree, and add site-based mitogenome and DNA barcode data in three hierarchical steps. We posit that site-based sampling, though not meeting the criterion of “taxon-completeness,” has great merits given preliminary studies showing representativeness and evenness of taxa sampled. We therefore argue in favor of site-based sampling as an unorthodox but logistically efficient way to construct large phylogenetic trees.

Fast and accurate distance-based phylogenetic placement using divide and conquer

Phylogenetic placement of query samples on an existing phylogeny is increasingly used in molecular ecology, including sample identification and microbiome environmental sampling. As the size of available reference trees used in these analyses continues to grow, there is a growing need for methods that place sequences on ultra-large trees with high accuracy. Distance-based placement methods have recently emerged as a path to provide such scalability while allowing flexibility to analyse both assembled and unassembled environmental samples. In this study, we introduce a distance-based phylogenetic placement method, APPLES-2, that is more accurate and scalable than existing distance-based methods and even some of the leading maximum-likelihood methods. This scalability is owed to a divide-and-conquer technique that limits distance calculation and phylogenetic placement to parts of the tree most relevant to each query. The increased scalability and accuracy enables us to study the effectiveness of APPLES-2 for placing microbial genomes on a data set of 10,575 microbial species using subsets of 381 marker genes. APPLES-2 has very high accuracy in this setting, placing 97% of query genomes within three branches of the optimal position in the species tree using 50 marker genes. Our proof-of-concept results show that APPLES-2 can quickly place metagenomic scaffolds on ultra-large backbone trees with high accuracy as long as a scaffold includes tens of marker genes. These results pave the path for a more scalable and widespread use of distance-based placement in various areas of molecular ecology.

App-SpaM: phylogenetic placement of short reads without sequence alignment

Motivation

Phylogenetic placement is the task of placing a query sequence of unknown taxonomic origin into a given phylogenetic tree of a set of reference sequences. A major field of application of such methods is, for example, the taxonomic identification of reads in metabarcoding or metagenomic studies. Several approaches to phylogenetic placement have been proposed in recent years. The most accurate of them requires a multiple sequence alignment of the references as input. However, calculating multiple alignments is not only time-consuming but also limits the applicability of these approaches.

Results

Herein, we propose Alignment-free phylogenetic placement algorithm based on Spaced-word Matches (App-SpaM), an efficient algorithm for the phylogenetic placement of short sequencing reads on a tree of a set of reference sequences. App-SpaM produces results of high quality that are on a par with the best available approaches to phylogenetic placement, while our software is two orders of magnitude faster than these existing methods. Our approach neither requires a multiple alignment of the reference sequences nor alignments of the queries to the references. This enables App-SpaM to perform phylogenetic placement on a broad variety of datasets.

Availability and implementation

The source code of App-SpaM is freely available on Github at https://github.com/matthiasblanke/App-SpaM together with detailed instructions for installation and settings. App-SpaM is furthermore available as a Conda-package on the Bioconda channel.

Contact

matthias.blanke@biologie.uni-goettingen.de.

Supplementary information

Supplementary data are available at Bioinformatics Advances online.

PEWO: a collection of workflows to benchmark phylogenetic placement

MOTIVATION

Phylogenetic placement (PP) is a process of taxonomic identification for which several tools are now available. However, it remains difficult to assess which tool is more adapted to particular genomic data or a particular reference taxonomy. We developed Placement Evaluation WOrkflows (PEWO), the first benchmarking tool dedicated to PP assessment. Its automated workflows can evaluate PP at many levels, from parameter optimization for a particular tool, to the selection of the most appropriate genetic marker when PP-based species identifications are targeted. Our goal is that PEWO will become a community effort and a standard support for future developments and applications of PP.

AVAILABILITY AND IMPLEMENTATION

https://github.com/phylo42/PEWO.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

OMAmer: tree-driven and alignment-free protein assignment to subfamilies outperforms closest sequence approaches

MOTIVATION

Assigning new sequences to known protein families and subfamilies is a prerequisite for many functional, comparative and evolutionary genomics analyses. Such assignment is commonly achieved by looking for the closest sequence in a reference database, using a method such as BLAST. However, ignoring the gene phylogeny can be misleading because a query sequence does not necessarily belong to the same subfamily as its closest sequence. For example, a hemoglobin which branched out prior to the hemoglobin alpha/beta duplication could be closest to a hemoglobin alpha or beta sequence, whereas it is neither. To overcome this problem, phylogeny-driven tools have emerged but rely on gene trees, whose inference is computationally expensive.

RESULTS

Here, we first show that in multiple animal and plant datasets, 18-62% of assignments by closest sequence are misassigned, typically to an over-specific subfamily. Then, we introduce OMAmer, a novel alignment-free protein subfamily assignment method, which limits over-specific subfamily assignments and is suited to phylogenomic databases with thousands of genomes. OMAmer is based on an innovative method using evolutionarily informed k-mers for alignment-free mapping to ancestral protein subfamilies. Whilst able to reject non-homologous family-level assignments, we show that OMAmer provides better and quicker subfamily-level assignments than approaches relying on the closest sequence, whether inferred exactly by Smith-Waterman or by the fast heuristic DIAMOND.

AVAILABILITYAND IMPLEMENTATION

OMAmer is available from the Python Package Index (as omamer), with the source code and a precomputed database available at https://github.com/DessimozLab/omamer.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Rapid screening and detection of inter-type viral recombinants using Phylo-K-Mers

Motivation

Novel recombinant viruses may have important medical and evolutionary significance, as they sometimes display new traits not present in the parental strains. This is particularly concerning when the new viruses combine fragments coming from phylogenetically distinct viral types. Here, we consider the task of screening large collections of sequences for such novel recombinants. A number of methods already exist for this task. However, these methods rely on complex models and heavy computations that are not always practical for a quick scan of a large number of sequences.

Results

We have developed SHERPAS, a new program to detect novel recombinants and provide a first estimate of their parental composition. Our approach is based on the precomputation of a large database of ‘phylogenetically-informed k-mers’, an idea recently introduced in the context of phylogenetic placement in metagenomics. Our experiments show that SHERPAS is hundreds to thousands of times faster than existing software, and enables the analysis of thousands of whole genomes, or long-sequencing reads, within minutes or seconds, and with limited loss of accuracy.

Availability and implementation

The source code is freely available for download at https://github.com/phylo42/sherpas.

Supplementary information

Supplementary data are available at Bioinformatics online.

Phylogeny Estimation Given Sequence Length Heterogeneity

Phylogeny estimation is a major step in many biological studies, and has many well known challenges. With the dropping cost of sequencing technologies, biologists now have increasingly large datasets available for use in phylogeny estimation. Here we address the challenge of estimating a tree given large datasets with a combination of full-length sequences and fragmentary sequences, which can arise due to a variety of reasons, including sample collection, sequencing technologies, and analytical pipelines. We compare two basic approaches: (1) computing an alignment on the full dataset and then computing a maximum likelihood tree on the alignment, or (2) constructing an alignment and tree on the full length sequences and then using phylogenetic placement to add the remaining sequences (which will generally be fragmentary) into the tree. We explore these two approaches on a range of simulated datasets, each with 1000 sequences and varying in rates of evolution, and two biological datasets. Our study shows some striking performance differences between methods, especially when there is substantial sequence length heterogeneity and high rates of evolution. We find in particular that using UPP to align sequences and RAxML to compute a tree on the alignment provides the best accuracy, substantially outperforming trees computed using phylogenetic placement methods. We also find that FastTree has poor accuracy on alignments containing fragmentary sequences. Overall, our study provides insights into the literature comparing different methods and pipelines for phylogenetic estimation, and suggests directions for future method development. [Phylogeny estimation, sequence length heterogeneity, phylogenetic placement.]

Genesis and Gappa: processing, analyzing and visualizing phylogenetic (placement) data

SUMMARY

We present genesis, a library for working with phylogenetic data, and gappa, an accompanying command-line tool for conducting typical analyses on such data. The tools target phylogenetic trees and phylogenetic placements, sequences, taxonomies and other relevant data types, offer high-level simplicity as well as low-level customizability, and are computationally efficient, well-tested and field-proven.

AVAILABILITY AND IMPLEMENTATION

Both genesis and gappa are written in modern C++11, and are freely available under GPLv3 at http://github.com/lczech/genesis and http://github.com/lczech/gappa.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Uncovering Effects from the Structure of Metabarcode Sequences for Metagenetic and Microbiome Analysis

The advent of next-generation sequencing has allowed for higher-throughput determination of which species live within a specific location. Here we establish that three analysis methods for estimating diversity within samples—namely, Operational Taxonomic Units; the newer Amplicon Sequence Variants; and a method commonly found in sequence analysis, minhash—are affected by various properties of these sequence data. Using simulations we show that the presence of Single Nucleotide Polymorphisms and the depth of coverage from each species affect the correlations between these approaches. Through this analysis, we provide insights which would affect the decisions on the application of each method. Specifically, the presence of sequence read errors and variability in sequence read coverage deferentially affects these processing methods.

The number of k-mer matches between two DNA sequences as a function of k and applications to estimate phylogenetic distances

We study the number Nk of length-k word matches between pairs of evolutionarily related DNA sequences, as a function of k. We show that the Jukes-Cantor distance between two genome sequences-i.e. the number of substitutions per site that occurred since they evolved from their last common ancestor-can be estimated from the slope of a function F that depends on Nk and that is affine-linear within a certain range of k. Integers kmin and kmax can be calculated depending on the length of the input sequences, such that the slope of F in the relevant range can be estimated from the values F(kmin) and F(kmax). This approach can be generalized to so-called Spaced-word Matches (SpaM), where mismatches are allowed at positions specified by a user-defined binary pattern. Based on these theoretical results, we implemented a prototype software program for alignment-free sequence comparison called Slope-SpaM. Test runs on real and simulated sequence data show that Slope-SpaM can accurately estimate phylogenetic distances for distances up to around 0.5 substitutions per position. The statistical stability of our results is improved if spaced words are used instead of contiguous words. Unlike previous alignment-free methods that are based on the number of (spaced) word matches, Slope-SpaM produces accurate results, even if sequences share only local homologies.

Read-SpaM: assembly-free and alignment-free comparison of bacterial genomes with low sequencing coverage

BACKGROUND

In many fields of biomedical research, it is important to estimate phylogenetic distances between taxa based on low-coverage sequencing reads. Major applications are, for example, phylogeny reconstruction, species identification from small sequencing samples, or bacterial strain typing in medical diagnostics.

RESULTS

We adapted our previously developed software program Filtered Spaced-Word Matches (FSWM) for alignment-free phylogeny reconstruction to take unassembled reads as input; we call this implementation Read-SpaM.

CONCLUSIONS

Test runs on simulated reads from semi-artificial and real-world bacterial genomes show that our approach can estimate phylogenetic distances with high accuracy, even for large evolutionary distances and for very low sequencing coverage.