Eleanor Wedell

Bio: Eleanor Wedell is an academic researcher from University of Illinois at Urbana–Champaign. The author has an hindex of 1, co-authored 2 publications receiving 2 citations.

Scalable and Accurate Phylogenetic Placement Using pplacer-XR

Abstract: Phylogenetic placement, the problem of placing a 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 method, pplacer, performs the placement using maximum likelihood but fails frequently on backbone trees with 5000 sequences. We show a simple technique, pplacer-XR (pplacer-eXtra Range), that extends pplacer to large datasets. We show, using challenging large datasets, that pplacer-XR provides the accuracy of pplacer and the scalability to ultra-large datasets of a leading fast phylogenetic placmement method, APPLES. pplacer-XR is available in open source form on github.

Center-Periphery Structure in Communities: Extracellular Vesicles.

Abstract: Clustering and community detection in networks are of broad interest and have been the subject of extensive research that spans several fields. We are interested in the relatively narrow question of detecting communities of scientific publications that are linked by citations. These publication communities can be used to identify scientists with shared interests who form communities of researchers. Building on the well-known k-core algorithm, we have developed a modular pipeline to find publication communities. We compare our approach to communities discovered by the widely used Leiden algorithm for community finding. Using a quantitative and qualitative approach, we evaluate community finding results on a citation network consisting of over 14 million publications relevant to the field of extracellular vesicles.

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

Abstract: 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.

SCAMPP: Scaling Alignment-Based Phylogenetic Placement to Large Trees

Abstract: 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 .

ppIacerDC: a new scalable phylogenetic placement method

Abstract: Motivation: Phylogenetic placement (i.e., the insertion of a sequence into a phylogenetic tree) is a basic step in several bioinformatics pipelines, including taxon identification in metagenomic analysis and large scale phylogeny estimation. The most accurate current method is pplacer, which attempts to optimize the placement using maximum likelihood, but it frequently fails on datasets where the phylogenetic tree has 5000 leaves. APPLES is the current most scalable method, and EPA-ng, although more scalable than pplacer and more accurate than APPLES, also fails on many 50,000-taxon trees. Here we describe pplacerDC, a divide-and-conquer approach that enables pplacer to be used when the phylogenetic tree is very large. Results: Our study shows that pplacerDC has excellent accuracy and scalability, matching pplacer where pplacer can run, improving accuracy compared to APPLES and EPA-ng, and is able to run on datasets with up to 100,000 sequences. Availability: The pplacerDC code is available on GitHub at https://github.com/kodingkoning/pplacerDC.

SCAMPP: Scaling Alignment-Based Phylogenetic Placement to Large Trees

Abstract: 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.

Scaling deep phylogenetic embedding to ultra-large reference trees: a tree-aware ensemble approach

Abstract: 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, DEPP (or any distance-based method) does not scale to more than roughly ten thousand species, a problem that we faced when trying to use our recently released Greengenes2 (GG2) reference tree containing 331,270 species. Scalability problems can be addressed in phylogenetics using divide- and-conquer. However, applying divide- and-conquer to data-hungry machine learning methods needs nuance. This paper explores divide- and-conquer for training ensembles of DEPP models, culminating in a method called C-DEPP that uses carefully crafted techniques to enable quasi-linear scaling while maintaining accuracy. C-DEPP enables placing twenty million 16S fragments on the GG2 reference tree in 41 hours of computation.