A method of alignment masking for refining the phylogenetic signal of multiple sequence alignments

Diversity and distribution of Symbiodiniaceae detected on coral reefs of Lombok, Indonesia using environmental DNA metabarcoding

Background

Dinoflagellates of family Symbiodiniaceae are important to coral reef ecosystems because of their contribution to coral health and growth; however, only a few studies have investigated the function and distribution of Symbiodiniaceae in Indonesia. Understanding the distribution of different kinds of Symbiodiniaceae can improve forecasting of future responses of various coral reef systems to climate change. This study aimed to determine the diversity of Symbiodiniaceae around Lombok using environmental DNA (eDNA).

Methods

Seawater and sediment samples were collected from 18 locations and filtered to obtain fractions of 0.4-12 and >12 µm. After extraction, molecular barcoding polymerase chain reaction was conducted to amplify the primary V9-SSU 18S rRNA gene, followed by sequencing (Illumina MiSeq). BLAST, Naïve-fit-Bayes, and maximum likelihood routines were used for classification and phylogenetic reconstruction. We compared results across sampling sites, sample types (seawater/sediment), and filter pore sizes (fraction).

Results

Phylogenetic analyses resolved the amplicon sequence variants into 16 subclades comprising six Symbiodiniaceae genera (or genera-equivalent clades) as follows: Symbiodinium, Breviolum, Cladocopium, Durusdinium, Foraminifera Clade G, and Halluxium. Comparative analyses showed that the three distinct lineages within Cladocopium, Durusdinium, and Foraminifera Clade G were the most common. Most of the recovered sequences appeared to be distinctive of different sampling locations, supporting the possibility that eDNA may resolve regional and local differences among Symbiodiniaceae genera and species.

Conclusions

eDNA surveys offer a rapid proxy for evaluating Symbiodiniaceae species on coral reefs and are a potentially useful approach to revealing diversity and relative ecological dominance of certain Symbiodiniaceae organisms. Moreover, Symbiodiniaceae eDNA analysis shows potential in monitoring the local and regional stability of coral-algal mutualisms.

Build a better bootstrap and the RAWR shall beat a random path to your door: phylogenetic support estimation revisited

Motivation

The standard bootstrap method is used throughout science and engineering to perform general-purpose non-parametric resampling and re-estimation. Among the most widely cited and widely used such applications is the phylogenetic bootstrap method, which Felsenstein proposed in 1985 as a means to place statistical confidence intervals on an estimated phylogeny (or estimate ‘phylogenetic support’). A key simplifying assumption of the bootstrap method is that input data are independent and identically distributed (i.i.d.). However, the i.i.d. assumption is an over-simplification for biomolecular sequence analysis, as Felsenstein noted.

Results

In this study, we introduce a new sequence-aware non-parametric resampling technique, which we refer to as RAWR (‘RAndom Walk Resampling’). RAWR consists of random walks that synthesize and extend the standard bootstrap method and the ‘mirrored inputs’ idea of Landan and Graur. We apply RAWR to the task of phylogenetic support estimation. RAWR’s performance is compared to the state-of-the-art using synthetic and empirical data that span a range of dataset sizes and evolutionary divergence. We show that RAWR support estimates offer comparable or typically superior type I and type II error compared to phylogenetic bootstrap support. We also conduct a re-analysis of large-scale genomic sequence data from a recent study of Darwin’s finches. Our findings clarify phylogenetic uncertainty in a charismatic clade that serves as an important model for complex adaptive evolution.

Availability and implementation

Data and software are publicly available under open-source software and open data licenses at: https://gitlab.msu.edu/liulab/RAWR-study-datasets-and-scripts.

LMAP_S: Lightweight Multigene Alignment and Phylogeny eStimation

Background

Recent advances in genome sequencing technologies and the cost drop in high-throughput sequencing continue to give rise to a deluge of data available for downstream analyses. Among others, evolutionary biologists often make use of genomic data to uncover phenotypic diversity and adaptive evolution in protein-coding genes. Therefore, multiple sequence alignments (MSA) and phylogenetic trees (PT) need to be estimated with optimal results. However, the preparation of an initial dataset of multiple sequence file(s) (MSF) and the steps involved can be challenging when considering extensive amount of data. Thus, it becomes necessary the development of a tool that removes the potential source of error and automates the time-consuming steps of a typical workflow with high-throughput and optimal MSA and PT estimations.

Results

We introduce LMAP_S (Lightweight Multigene Alignment and Phylogeny eStimation), a user-friendly command-line and interactive package, designed to handle an improved alignment and phylogeny estimation workflow: MSF preparation, MSA estimation, outlier detection, refinement, consensus, phylogeny estimation, comparison and editing, among which file and directory organization, execution, manipulation of information are automated, with minimal manual user intervention. LMAP_S was developed for the workstation multi-core environment and provides a unique advantage for processing multiple datasets. Our software, proved to be efficient throughout the workflow, including, the (unlimited) handling of more than 20 datasets.

Conclusions

We have developed a simple and versatile LMAP_S package enabling researchers to effectively estimate multiple datasets MSAs and PTs in a high-throughput fashion. LMAP_S integrates more than 25 software providing overall more than 65 algorithm choices distributed in five stages. At minimum, one FASTA file is required within a single input directory. To our knowledge, no other software combines MSA and phylogeny estimation with as many alternatives and provides means to find optimal MSAs and phylogenies. Moreover, we used a case study comparing methodologies that highlighted the usefulness of our software. LMAP_S has been developed as an open-source package, allowing its integration into more complex open-source bioinformatics pipelines. LMAP_S package is released under GPLv3 license and is freely available at https://lmap-s.sourceforge.io/.

Effect of CO2 Concentration on Uptake and Assimilation of Inorganic Carbon in the Extreme Acidophile Acidithiobacillus ferrooxidans

This study was motivated by surprising gaps in the current knowledge of microbial inorganic carbon (Ci) uptake and assimilation at acidic pH values (pH < 3). Particularly striking is the limited understanding of the differences between Ci uptake mechanisms in acidic versus circumneutral environments where the Ci predominantly occurs either as a dissolved gas (CO2) or as bicarbonate (HCO3 -), respectively. In order to gain initial traction on the problem, the relative abundance of transcripts encoding proteins involved in Ci uptake and assimilation was studied in the autotrophic, polyextreme acidophile Acidithiobacillus ferrooxidans whose optimum pH for growth is 2.5 using ferrous iron as an energy source, although they are able to grow at pH 5 when using sulfur as an energy source. The relative abundance of transcripts of five operons (cbb1-5) and one gene cluster (can-sulP) was monitored by RT-qPCR and, in selected cases, at the protein level by Western blotting, when cells were grown under different regimens of CO2 concentration in elemental sulfur. Of particular note was the absence of a classical bicarbonate uptake system in A. ferrooxidans. However, bioinformatic approaches predict that sulP, previously annotated as a sulfate transporter, is a novel type of bicarbonate transporter. A conceptual model of CO2 fixation was constructed from combined bioinformatic and experimental approaches that suggests strategies for providing ecological flexibility under changing concentrations of CO2 and provides a portal to elucidating Ci uptake and regulation in acidic conditions. The results could advance the understanding of industrial bioleaching processes to recover metals such as copper at acidic pH. In addition, they may also shed light on how chemolithoautotrophic acidophiles influence the nutrient and energy balance in naturally occurring low pH environments.

Multiple Sequence Alignment Averaging Improves Phylogeny Reconstruction

The classic methodology of inferring a phylogenetic tree from sequence data is composed of two steps. First, a multiple sequence alignment (MSA) is computed. Then, a tree is reconstructed assuming the MSA is correct. Yet, inferred MSAs were shown to be inaccurate and alignment errors reduce tree inference accuracy. It was previously proposed that filtering unreliable alignment regions can increase the accuracy of tree inference. However, it was also demonstrated that the benefit of this filtering is often obscured by the resulting loss of phylogenetic signal. In this work we explore an approach, in which instead of relying on a single MSA, we generate a large set of alternative MSAs and concatenate them into a single SuperMSA. By doing so, we account for phylogenetic signals contained in columns that are not present in the single MSA computed by alignment algorithms. Using simulations, we demonstrate that this approach results, on average, in more accurate trees compared to 1) using an unfiltered MSA and 2) using a single MSA with weights assigned to columns according to their reliability. Next, we explore in which regions of the MSA space our approach is expected to be beneficial. Finally, we provide a simple criterion for deciding whether or not the extra effort of computing a SuperMSA and inferring a tree from it is beneficial. Based on these assessments, we expect our methodology to be useful for many cases in which diverged sequences are analyzed. The option to generate such a SuperMSA is available at http://guidance.tau.ac.il.