Biology subjects

Czech, L.

Publications and source records attributed to Czech, L..

4 recordsLinked to original sources

bioRxiv2018

Scalable Methods for Post-Processing, Visualizing, and Analyzing Phylogenetic Placements

The exponential decrease in molecular sequencing cost generates unprecedented amounts of data. Hence, scalable methods to analyze these data are required. Phylogenetic (or Evolutionary) Placement methods identify the evolutionary provenance of anonymous sequences with respect to a given reference phylogeny. This increasingly popular method is deployed for scrutinizing metagenomic samples from environments such as water, soil, or the human gut.\n\nHere, we present novel and, more importantly, highly scalable methods for analyzing phylogenetic placements of metagenomic samples. More specifically, we introduce methods for visualizing differences between samples and their correlation with associated meta-data on the reference phylogeny, as well as for clustering similar samples using a variant of the k-means method.\n\nTo demonstrate the scalability and utility of our methods, as well as to provide exemplary interpretations of our methods, we applied them to 3 publicly available datasets comprising 9782 samples with a total of approximately 168 million sequences. The results indicate that new biological insights can be attained via our methods.

bioinformatics↗

bioRxiv2018

Methods for Automatic Reference Trees and Multilevel Phylogenetic Placement

MotivationIn most metagenomic sequencing studies, the initial analysis step consists in assessing the evolutionary provenance of the sequences. Phylogenetic (or Evolutionary) Placement methods can be employed to determine the evolutionary position of sequences with respect to a given reference phylogeny. These placement methods do however face certain limitations: The manual selection of reference sequences is labor-intensive; the computational effort to infer reference phylogenies is substantially larger than for methods that rely on sequence similarity; the number of taxa in the reference phylogeny should be small enough to allow for visually inspecting the results.\n\nResultsWe present algorithms to overcome the above limitations. First, we introduce a method to automatically construct representative sequences from databases to infer reference phylogenies. Second, we present an approach for conducting large-scale phylogenetic placements on nested phylogenies. Third, we describe a preprocessing pipeline that allows for handling huge sequence data sets. Our experiments on empirical data show that our methods substantially accelerate the workflow and yield highly accurate placement results.\n\nImplementationFreely available under GPLv3 at http://github.com/lczech/gappa.\n\nContactlucas.czech@h-its.org\n\nSupplementary InformationSupplementary data are available at Bioinformatics online.

bioinformatics↗

bioRxiv2018

EPA-ng: Massively Parallel Evolutionary Placement of Genetic Sequences

Next Generation Sequencing (NGS) technologies have led to a ubiquity of molecular sequence data. This data avalanche is particularly challenging in metagenetics, which focuses on taxonomic identification of sequences obtained from diverse microbial environments. To achieve this, phylogenetic placement methods determine how these sequences fit into an evolutionary context. Previous implementations of phylogenetic placement algorithms, such as the Evolutionary Placement Algorithm (EPA) included in RAxML, or O_SCPLOWPPLACERC_SCPLOW, are being increasingly used for this purpose. However, due to the steady progress in NGS technologies, the current implementations face substantial scalability limitations. Here we present EPA-O_SCPLOWNGC_SCPLOW, a complete reimplementation of the EPA that is substantially faster, offers a distributed memory parallelization, and integrates concepts from both, RAxML-EPA, and O_SCPLOWPPLACERC_SCPLOW. EPA-O_SCPLOWNGC_SCPLOW can be executed on standard shared memory, as well as on distributed memory systems (e.g., computing clusters). To demonstrate the scalability of EPA-O_SCPLOWNGC_SCPLOW we placed 1 billion metagenetic reads from the Tara Oceans Project onto a reference tree with 3,748 taxa in just under 7 hours, using 2,048 cores. Our performance assessment shows that EPA-O_SCPLOWNGC_SCPLOW outperforms RAxML-EPA and O_SCPLOWPPLACERC_SCPLOW by up to a factor of 30 in sequential execution mode, while attaining comparable parallel efficiency on shared memory systems. We further show that the distributed memory parallelization of EPA-O_SCPLOWNGC_SCPLOW scales well up to 3,520 cores. EPA-O_SCPLOWNGC_SCPLOW is available under the AGPLv3 license: https://github.com/Pbdas/epa-ng

bioinformatics↗

bioRxiv2017

Quartet-based computations of internode certainty provide accurate and robust measures of phylogenetic incongruence

Incongruence, or topological conflict, is prevalent in genome-scale data sets but relatively few measures have been developed to quantify it. Internode Certainty (IC) and related measures were recently introduced to explicitly quantify the level of incongruence of a given internode (or internal branch) among a set of phylogenetic trees and complement regular branch support statistics in assessing the confidence of the inferred phylogenetic relationships. Since most phylogenomic studies contain data partitions (e.g., genes) with missing taxa and IC scores stem from the frequencies of bipartitions (or splits) on a set of trees, the calculation of IC scores requires adjusting the frequencies of bipartitions from these partial gene trees. However, when the proportion of missing data is high, current approaches that adjust bipartition frequencies in partial gene trees tend to overestimate IC scores and alternative adjustment approaches differ substantially from each other in their scores. To overcome these issues, we developed three new measures for calculating internode certainty that are based on the frequencies of quartets, which naturally apply to both comprehensive and partial trees. Our comparison of these new quartet-based measures to previous bipartition-based measures on simulated data shows that: 1) on comprehensive trees, both types of measures yield highly similar IC scores; 2) on partial trees, quartet-based measures generate more accurate IC scores; and 3) quartet-based measures are more robust to the absence of phylogenetic signal and errors in the phylogenetic relationships to be assessed. Additionally, analysis of 15 empirical phylogenomic data sets using our quartet-based measures suggests that numerous relationships remain unresolved despite the availability of genome-scale data. Finally, we provide an efficient open-source implementation of these quartet-based measures in the program QuartetScores, which is freely available at https://github.com/algomaus/QuartetScores.

evolutionary biology↗