grenepipe: a flexible, scalable and reproducible pipeline to automate variant calling from sequence reads.

SUMMARY: We developed grenepipe, an all-in-one Snakemake workflow to streamline the data processing from raw high-throughput sequencing data of individuals or populations to genotype variant calls. Our pipeline offers a range of popular software tools within a single configuration file, automatically installs software dependencies, is highly optimized for scalability in cluster environments and runs with a single command. AVAILABILITY AND IMPLEMENTATION: grenepipe is published under the GPLv3 and freely available at github.com/moiexpositoalonsolab/grenepipe.

Interpreting phylogenetic placements for taxonomic assignment of environmental DNA.

Taxonomic assignment of operational taxonomic units (OTUs) is an important bioinformatics step in analyzing environmental sequencing data. Pairwise alignment and phylogenetic-placement methods represent two alternative approaches to taxonomic assignments, but their results can differ. Here we used available colpodean ciliate OTUs from forest soils to compare the taxonomic assignments of VSEARCH (which performs pairwise alignments) and EPA-ng (which performs phylogenetic placements). We showed that when there are differences in taxonomic assignments between pairwise alignments and phylogenetic placements at the subtaxon level, there is a low pairwise similarity of the OTUs to the reference database. We then showcase how the output of EPA-ng can be further evaluated using GAPPA to assess the taxonomic assignments when there exist multiple equally likely placements of an OTU, by taking into account the sum over the likelihood weights of the OTU placements within a subtaxon, and the branch distances between equally likely placement locations. We also inferred the evolutionary and ecological characteristics of the colpodean OTUs using their placements within subtaxa. This study demonstrates how to fully analyze the output of EPA-ng, by using GAPPA in conjunction with knowledge of the taxonomic diversity of the clade of interest.

Phylogenetic Analysis of SARS-CoV-2 Data Is Difficult.

Numerous studies covering some aspects of SARS-CoV-2 data analyses are being published on a daily basis, including a regularly updated phylogeny on nextstrain.org. Here, we review the difficulties of inferring reliable phylogenies by example of a data snapshot comprising a quality-filtered subset of 8,736 out of all 16,453 virus sequences available on May 5, 2020 from gisaid.org. We find that it is difficult to infer a reliable phylogeny on these data due to the large number of sequences in conjunction with the low number of mutations. We further find that rooting the inferred phylogeny with some degree of confidence either via the bat and pangolin outgroups or by applying novel computational methods on the ingroup phylogeny does not appear to be credible. Finally, an automatic classification of the current sequences into subclasses using the mPTP tool for molecular species delimitation is also, as might be expected, not possible, as the sequences are too closely related. We conclude that, although the application of phylogenetic methods to disentangle the evolution and spread of COVID-19 provides some insight, results of phylogenetic analyses, in particular those conducted under the default settings of current phylogenetic inference tools, as well as downstream analyses on the inferred phylogenies, should be considered and interpreted with extreme caution.

Swarm v3: towards tera-scale amplicon clustering.

MOTIVATION: Previously we presented swarm, an open-source amplicon clustering programme that produces fine-scale molecular operational taxonomic units (OTUs) that are free of arbitrary global clustering thresholds. Here, we present swarm v3 to address issues of contemporary datasets that are growing towards tera-byte sizes. RESULTS: When compared with previous swarm versions, swarm v3 has modernized C++ source code, reduced memory footprint by up to 50%, optimized CPU-usage and multithreading (more than 7 times faster with default parameters), and it has been extensively tested for its robustness and logic. AVAILABILITY AND IMPLEMENTATION: Source code and binaries are available at https://github.com/torognes/swarm. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

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.

Quartet-Based Computations of Internode Certainty Provide Robust Measures of Phylogenetic Incongruence.

Incongruence, or topological conflict, is prevalent in genome-scale data sets. Internode certainty (IC) and related measures were recently introduced to explicitly quantify the level of incongruence of a given internal branch among a set of phylogenetic trees and complement regular branch support measures (e.g., bootstrap, posterior probability) that instead assess the statistical confidence of inference. 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, IC score calculation typically requires adjusting the frequencies of bipartitions from these partial gene trees. However, when the proportion of missing taxa is high, the scores yielded by current approaches that adjust bipartition frequencies in partial gene trees differ substantially from each other and tend to be overestimates. To overcome these issues, we developed three new IC measures based on the frequencies of quartets, which naturally apply to both complete and partial trees. Comparison of our new quartet-based measures to previous bipartition-based measures on simulated data shows that: (1) on complete data sets, both quartet-based and bipartition-based measures yield very similar IC scores; (2) IC scores of quartet-based measures on a given data set with and without missing taxa are more similar than the scores of bipartition-based measures; and (3) quartet-based measures are more robust to the absence of phylogenetic signal and errors in phylogenetic inference than bipartition-based measures. Additionally, the analysis of an empirical mammalian phylogenomic data set using our quartet-based measures reveals the presence of substantial levels of incongruence for numerous internal branches. An efficient open-source implementation of these quartet-based measures is freely available in the program QuartetScores (https://github.com/lutteropp/QuartetScores).

The Windblown: Possible Explanations for Dinophyte DNA in Forest Soils.

Dinophytes are widely distributed in marine- and fresh-waters, but have yet to be conclusively documented in terrestrial environments. Here, we evaluated the presence of these protists from an environmental DNA metabarcoding dataset of Neotropical rainforest soils. Using a phylogenetic placement approach with a reference alignment and tree, we showed that the numerous sequencing reads that were phylogenetically placed as dinophytes did not correlate with taxonomic assignment, environmental preference, nutritional mode, or dormancy. All the dinophytes in the soils are rather windblown dispersal units of aquatic species and are not biologically active residents of terrestrial environments.

Methods for automatic reference trees and multilevel phylogenetic placement.

MOTIVATION: In 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. RESULTS: We 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 datasets. Our experiments on empirical data show that our methods substantially accelerate the workflow and yield highly accurate placement results. AVAILABILITY AND IMPLEMENTATION: Freely available under GPLv3 at http://github.com/lczech/gappa. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

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. 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 PPLACER, are being increasingly used for this purpose. However, due to the steady progress in NGS technologies, the current implementations face substantial scalability limitations. Herein, we present EPA-NG, a complete reimplementation of the EPA that is substantially faster, offers a distributed memory parallelization, and integrates concepts from both, RAxML-EPA and PPLACER. EPA-NG can be executed on standard shared memory, as well as on distributed memory systems (e.g., computing clusters). To demonstrate the scalability of EPA-NG, we placed $1$ billion metagenetic reads from the Tara Oceans Project onto a reference tree with 3748 taxa in just under $7$ h, using 2048 cores. Our performance assessment shows that EPA-NG outperforms RAxML-EPA and PPLACER 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-NG scales well up to 2048 cores. EPA-NG is available under the AGPLv3 license: https://github.com/Pbdas/epa-ng.

A Critical Review on the Use of Support Values in Tree Viewers and Bioinformatics Toolkits.

Phylogenetic trees are routinely visualized to present and interpret the evolutionary relationships of species. Most empirical evolutionary data studies contain a visualization of the inferred tree with branch support values. Ambiguous semantics in tree file formats can lead to erroneous tree visualizations and therefore to incorrect interpretations of phylogenetic analyses. Here, we discuss problems that arise when displaying branch values on trees after rerooting. Branch values are typically stored as node labels in the widely-used Newick tree format. However, such values are attributes of branches. Storing them as node labels can therefore yield errors when rerooting trees. This depends on the mostly implicit semantics that tools deploy to interpret node labels. We reviewed ten tree viewers and ten bioinformatics toolkits that can display and reroot trees. We found that 14 out of 20 of these tools do not permit users to select the semantics of node labels. Thus, unaware users might obtain incorrect results when rooting trees. We illustrate such incorrect mappings for several test cases and real examples taken from the literature. This review has already led to improvements in eight tools. We suggest tools should provide options that explicitly force users to define the semantics of node labels.

Clarifying the Relationships between Microsporidia and Cryptomycota.

Some protists with microsporidian-like cell biological characters, including Mitosporidium, Paramicrosporidium, and Nucleophaga, have SSU rRNA gene sequences that are much less divergent than canonical Microsporidia. We analysed the phylogenetic placement and environmental diversity of microsporidian-like lineages that group near the base of the fungal radiation and show that they group in a clade with metchnikovellids and canonical microsporidians, to the exclusion of the clade including Rozella, in line with what is currently known of their morphology and cell biology. These results show that the phylogenetic scope of Microsporidia has been greatly underestimated. We propose that much of the lineage diversity previously thought to be cryptomycotan/rozellid is actually microsporidian, offering new insights into the evolution of the highly specialized parasitism of canonical Microsporidia. This insight has important implications for our understanding of opisthokont evolution and ecology, and is important for accurate interpretation of environmental diversity. Our analyses also demonstrate that many opisthosporidian (aphelid+rozellid+microsporidian) SSU V4 OTUs from Neotropical forest soils group with the short-branching Microsporidia, consistent with the abundance of their protist and arthropod hosts in soils. This novel diversity of Microsporidia provides a unique opportunity to investigate the evolutionary origins of a highly specialized clade of major animal parasites.

UniEuk: Time to Speak a Common Language in Protistology!

Universal taxonomic frameworks have been critical tools to structure the fields of botany, zoology, mycology, and bacteriology as well as their large research communities. Animals, plants, and fungi have relatively solid, stable morpho-taxonomies built over the last three centuries, while bacteria have been classified for the last three decades under a coherent molecular taxonomic framework. By contrast, no such common language exists for microbial eukaryotes, even though environmental '-omics' surveys suggest that protists make up most of the organismal and genetic complexity of our planet's ecosystems! With the current deluge of eukaryotic meta-omics data, we urgently need to build up a universal eukaryotic taxonomy bridging the protist -omics age to the fragile, centuries-old body of classical knowledge that has effectively linked protist taxa to morphological, physiological, and ecological information. UniEuk is an open, inclusive, community-based and expert-driven international initiative to build a flexible, adaptive universal taxonomic framework for eukaryotes. It unites three complementary modules, EukRef, EukBank, and EukMap, which use phylogenetic markers, environmental metabarcoding surveys, and expert knowledge to inform the taxonomic framework. The UniEuk taxonomy is directly implemented in the European Nucleotide Archive at EMBL-EBI, ensuring its broad use and long-term preservation as a reference taxonomy for eukaryotes.

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.

Wide-ranging consequences of priority effects governed by an overarching factor.

Priority effects, where arrival order and initial relative abundance modulate local species interactions, can exert taxonomic, functional, and evolutionary influences on ecological communities by driving them to alternative states. It remains unclear if these wide-ranging consequences of priority effects can be explained systematically by a common underlying factor. Here, we identify such a factor in an empirical system. In a series of field and laboratory studies, we focus on how pH affects nectar-colonizing microbes and their interactions with plants and pollinators. In a field survey, we found that nectar microbial communities in a hummingbird-pollinated shrub, Diplacus (formerly Mimulus) aurantiacus, exhibited abundance patterns indicative of alternative stable states that emerge through domination by either bacteria or yeasts within individual flowers. In addition, nectar pH varied among D. aurantiacus flowers in a manner that is consistent with the existence of these alternative stable states. In laboratory experiments, Acinetobacter nectaris, the bacterium most commonly found in D. aurantiacus nectar, exerted a strongly negative priority effect against Metschnikowia reukaufii, the most common nectar-specialist yeast, by reducing nectar pH. This priority effect likely explains the mutually exclusive pattern of dominance found in the field survey. Furthermore, experimental evolution simulating hummingbird-assisted dispersal between flowers revealed that M. reukaufii could evolve rapidly to improve resistance against the priority effect if constantly exposed to A. nectaris-induced pH reduction. Finally, in a field experiment, we found that low nectar pH could reduce nectar consumption by hummingbirds, suggesting functional consequences of the pH-driven priority effect for plant reproduction. Taken together, these results show that it is possible to identify an overarching factor that governs the eco-evolutionary dynamics of priority effects across multiple levels of biological organization.

Genetic diversity loss in the Anthropocene.

Anthropogenic habitat loss and climate change are reducing species' geographic ranges, increasing extinction risk and losses of species' genetic diversity. Although preserving genetic diversity is key to maintaining species' adaptability, we lack predictive tools and global estimates of genetic diversity loss across ecosystems. We introduce a mathematical framework that bridges biodiversity theory and population genetics to understand the loss of naturally occurring DNA mutations with decreasing habitat. By analyzing genomic variation of 10,095 georeferenced individuals from 20 plant and animal species, we show that genome-wide diversity follows a mutations-area relationship power law with geographic area, which can predict genetic diversity loss from local population extinctions. We estimate that more than 10% of genetic diversity may already be lost for many threatened and nonthreatened species, surpassing the United Nations' post-2020 targets for genetic preservation.

SCRAPP: A tool to assess the diversity of microbial samples from phylogenetic placements.

Microbial ecology research is currently driven by the continuously decreasing cost of DNA sequencing and the improving accuracy of data analysis methods. One such analysis method is phylogenetic placement, which establishes the phylogenetic identity of the anonymous environmental sequences in a sample by means of a given phylogenetic reference tree. However, assessing the diversity of a sample remains challenging, as traditional methods do not scale well with the increasing data volumes and/or do not leverage the phylogenetic placement information. Here, we present scrapp, a highly parallel and scalable tool that uses a molecular species delimitation algorithm to quantify the diversity distribution over the reference phylogeny for a given phylogenetic placement of the sample. scrapp employs a novel approach to cluster phylogenetic placements, called placement space clustering, to efficiently perform dimensionality reduction, so as to scale on large data volumes. Furthermore, it uses the phylogeny-aware molecular species delimitation method mPTP to quantify diversity. We evaluated scrapp using both, simulated and empirical data sets. We use simulated data to verify our approach. Tests on an empirical data set show that scrapp-derived metrics can classify samples by their diversity-correlated features equally well or better than existing, commonly used approaches. scrapp is available at https://github.com/pbdas/scrapp.

Long-read metabarcoding of the eukaryotic rDNA operon to phylogenetically and taxonomically resolve environmental diversity.

High-throughput DNA metabarcoding of amplicon sizes below 500 bp has revolutionized the analysis of environmental microbial diversity. However, these short regions contain limited phylogenetic signal, which makes it impractical to use environmental DNA in full phylogenetic inferences. This lesser phylogenetic resolution of short amplicons may be overcome by new long-read sequencing technologies. To test this idea, we amplified soil DNA and used PacBio Circular Consensus Sequencing (CCS) to obtain an ~4500-bp region spanning most of the eukaryotic small subunit (18S) and large subunit (28S) ribosomal DNA genes. We first treated the CCS reads with a novel curation workflow, generating 650 high-quality operational taxonomic units (OTUs) containing the physically linked 18S and 28S regions. To assign taxonomy to these OTUs, we developed a phylogeny-aware approach based on the 18S region that showed greater accuracy and sensitivity than similarity-based methods. The taxonomically annotated OTUs were then combined with available 18S and 28S reference sequences to infer a well-resolved phylogeny spanning all major groups of eukaryotes, allowing us to accurately derive the evolutionary origin of environmental diversity. A total of 1,019 sequences were included, of which a majority (58%) corresponded to the new long environmental OTUs. The long reads also allowed us to directly investigate the relationships among environmental sequences themselves, which represents a key advantage over the placement of short reads on a reference phylogeny. Together, our results show that long amplicons can be treated in a full phylogenetic framework to provide greater taxonomic resolution and a robust evolutionary perspective to environmental DNA.

Scalable methods for analyzing and visualizing phylogenetic placement of metagenomic samples.

BACKGROUND: 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. NOVEL METHODS: Here, we present novel and, more importantly, highly scalable methods for analyzing phylogenetic placements of metagenomic samples. More specifically, we introduce methods for (a) visualizing differences between samples and their correlation with associated meta-data on the reference phylogeny, (b) clustering similar samples using a variant of the k-means method, and (c) finding phylogenetic factors using an adaptation of the Phylofactorization method. These methods enable to interpret metagenomic data in a phylogenetic context, to find patterns in the data, and to identify branches of the phylogeny that are driving these patterns. RESULTS: To 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.

Parasites dominate hyperdiverse soil protist communities in Neotropical rainforests.

High animal and plant richness in tropical rainforest communities has long intrigued naturalists. It is unknown if similar hyperdiversity patterns are reflected at the microbial scale with unicellular eukaryotes (protists). Here we show, using environmental metabarcoding of soil samples and a phylogeny-aware cleaning step, that protist communities in Neotropical rainforests are hyperdiverse and dominated by the parasitic Apicomplexa, which infect arthropods and other animals. These host-specific parasites potentially contribute to the high animal diversity in the forests by reducing population growth in a density-dependent manner. By contrast, too few operational taxonomic units (OTUs) of Oomycota were found to broadly drive high tropical tree diversity in a host-specific manner under the Janzen-Connell model. Extremely high OTU diversity and high heterogeneity between samples within the same forests suggest that protists, not arthropods, are the most diverse eukaryotes in tropical rainforests. Our data show that protists play a large role in tropical terrestrial ecosystems long viewed as being dominated by macroorganisms.