INSTRAL: Discordance-aware Phylogenetic Placement using Quartet Scores

Rabiee, Maryam; Mirarab, Siavash

doi:10.1101/432906

Cited by 4 publications

(7 citation statements)

References 49 publications

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“…Both DEPP and APPLES+JC are more accurate than INSTRAL for low numbers of genes (≤ 4) but not for larger numbers of genes. In fact, as the number of genes increases to 32, INSTRAL starts to have the best accuracy, a result consistent with the theory as INSTRAL is statistically consistent under conditions simulated here [18].…”

Section: Resultssupporting

confidence: 87%

“…INSTRAL. This method updates a species tree given input gene trees (already updated to include the query) and accounts for discordance by maximizing the quartet score [18]. Here, input gene tree are inferred using FastTree-II [65].…”

Section: Epa-ngmentioning

confidence: 99%

“…In these high-throughput applications, phylogenetic placement [15][16][17][18][19] -adding a new sequence onto an existing reference tree -is sufficient, and the more challenging de novo reconstruction is neither necessary nor always desirable [20]. Phylogenetic placement has a long history [21][22][23], and maximum likelihood placement methods [15][16][17] are widely-used in microbiome analyses.…”

Section: Introductionmentioning

confidence: 99%

“…For example, species identification using marker genes such as 16S or COI [27,28] implicitly assumes that the gene tree is close enough to the species tree to allow accurate identification of the species by placement on the gene tree. More recently, inserting new genomes onto a species tree by minimizing quartet distance has become possible [18], but this approach requires genome-wide data and complicated analyses pipelines.…”

Section: Introductionmentioning

confidence: 99%

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DEPP: Deep Learning Enables Extending Species Trees using Single Genes

Jiang

Balaban

Zhu

et al. 2021

Preprint

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Identifying samples in an evolutionary context is a fundamental step in the study of microbiome, and more broadly, biodiversity. Extending a reference phylogeny by placing new query sequences onto it has been increasingly used for sample identification and other applications. Existing phylogenetic placement methods have assumed that the query sequence is homologous to the data used to infer the reference phylogeny. Thus, they are designed to place data from a single gene onto a gene tree (e.g., they can place 16S sequences onto a 16S gene tree). While this assumption is reasonable, ultimately, sample identification is a question of identifying the species not individual genes. The placement of single gene data on a gene tree is therefore used as a proxy for a more ambitious goal: extending a species tree given sequence data from one or more gene. This goal poses difficult algorithmic questions. Nevertheless, a sufficiently accurate solution would not only improve sample identification using marker genes, it would also help achieving the long-standing goal of combining 16S and metagenomic data. We approach this problem using deep neural networks (DNN) and introduce a method called DEPP. Given a reference species tree and sequence data from one (or a handful of) genes, DEPP learns how to extend the species tree to include new species. DEPP does not rely on pre-specified models of sequence evolution or gene tree discordance; instead, it uses highly parameterized DNNs to learn both aspects from the data. We test DEPP both in simulations and on real microbial data and show high accuracy.

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Section: Resultssupporting

confidence: 87%

Section: Epa-ngmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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DEPP: Deep Learning Enables Extending Species Trees using Single Genes

Jiang

Balaban

Zhu

et al. 2021

Preprint

Self Cite

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“…From an algorithmic perspective, constrained search can be considered an extension of the phylogenetic placement problem [37][38][39][40]. Unlike placement, constrained search also infers the relationship between query genomes and hence is more informative.…”

Section: Discussionmentioning

confidence: 99%

Forcing external constraints on tree inference using ASTRAL

Rabiee

Mirarab

2020

BMC Genomics

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Background: To account for genome-wide discordance among gene trees, several widely-used methods seek to find a species tree with the minimum distance to input gene trees. To efficiently explore the large space of species trees, some of these methods, including ASTRAL, use dynamic programming (DP). The DP paradigm can restrict the search space, and thus, ASTRAL and similar methods use heuristic methods to define a restricted search space. However, arbitrary constraints provided by the user on the output tree cannot be trivially incorporated into such restrictions. The ability to infer trees that honor user-defined constraints is needed for many phylogenetic analyses, but no solution currently exists for constraining the output of ASTRAL. Results: We introduce methods that enable the ASTRAL dynamic programming to infer constrained trees in an effective and scalable manner. To do so, we adopt a recently developed tree completion algorithm and extend it to allow multifurcating input and output trees. In simulation studies, we show that the approach for honoring constraints is both effective and fast. On real data, we show that constrained searches can help interrogate branches not recovered in the optimal ASTRAL tree to reveal support for alternative hypotheses. Conclusions:The new algorithm is added ASTRAL to all user-provided constraints on the species tree. BackgroundPhylogeny inference requires solving an optimization problem over the space of all trees. The super-exponential growth of the tree topology space makes examining all trees impossible, even for moderately large datasets. As a result, tree inference algorithms have adopted several heuristics strategies, including iterative search (e.g., hill-climbing), used by most maximum parsimony and maximum likelihood methods.An increasingly popular alternative is searching the tree space using dynamic programming (DP). For an

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Fast and Accurate Distance-based Phylogenetic Placement using Divide and Conquer

Balaban

Roush

Zhu

et al. 2021

Preprint

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Phylogenetic placement of query samples on an existing phylogeny is increasingly used in microbiome analyses and other biological studies. As the size of available reference trees used in microbiome analyses continues to grow, there is a growing need for methods that place sequences on ultra-large trees with high accuracy.In this paper, we build on our previous work and introduce APPLES-2, a distance-based phylogenetic placement method. In extensive studies, we show that APPLES-2 is more accurate and much more scalable than APPLES 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 dataset 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. As a proof of concept, we also tested APPLES-2 for placing scaffolds produced by metagenomic assembly. It was able to place 3318 scaffolds on the backbone tree with 10575 taxa in less than 3 hours. The accuracy of these placement was high as long as a scaffold included tens of marker genes.

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INSTRAL: Discordance-aware Phylogenetic Placement using Quartet Scores

Cited by 4 publications

References 49 publications

DEPP: Deep Learning Enables Extending Species Trees using Single Genes

DEPP: Deep Learning Enables Extending Species Trees using Single Genes

Forcing external constraints on tree inference using ASTRAL

Fast and Accurate Distance-based Phylogenetic Placement using Divide and Conquer

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