Whole genome amplification (WGA), and in particular multiple displacement amplification (MDA), has become a key technique for genomic sequencing of microscopic organisms, yet it introduces artefacts such as palindromic (inverted chimeric) reads that may compromise downstream analyses. We assessed how pervasive palindromic reads generated by MDA impact the assembly of tardigrade (Acutuncus giovanniniae and A. mecnuffi) mitogenomes sequenced with Oxford Nanopore technology. We show that the MDA produces a high proportion of palindromic reads, often exceeding one-third of mitochondrial reads and frequently exhibiting complex multi-inversion structures. These artefacts severely impair long-read assembly, leading to low success rates and inconsistent genome reconstruction. To solve this issue, a strategy based on in silico fragmentation of long reads into short, high-quality fragments, followed by short-read assembly, consistently produced complete and accurate circularised mitochondrial genomes. Our results demonstrate that palindromic read formation can be, in some cases, a limitation of MDA coupled with long-read sequencing, but this issue can be mitigated through read fragmentation. This approach provides a simple, robust and scalable solution for mitogenome assembly from data heavily affected by amplification artefacts, particularly in microscopic taxa where whole genome amplification is often unavoidable.
The choice of optimality criterion is a key consideration in phylogenetic studies. Recent work challenges the notion that more computationally demanding optimisation objectives result in better phylogenetic trees. This finding underscores the importance of comparing trees across optimisation objectives in addition to different models of evolution and data partitions. It is currently cumbersome to optimise trees for alternative objectives because multiple programmes must be used, each with its own optimisation framework. Here, I introduce Treeline for optimising balanced minimum evolution, maximum likelihood and maximum parsimony trees. Treeline explores the optimisation landscape using a new strategy based on perturbing the patristic distance matrix used to initialize candidate trees, which is shown to be particularly effective for the balanced minimum evolution objective. Tests suggest Treeline can be more accurate, memory efficient or faster than existing programmes designed for a single optimisation objective. Treeline's unified nature facilitates comparison of phylogenetic trees across distinct optimisation objectives and models of sequence evolution. Consistent with prior studies, the balanced minimum evolution objective resulted in gene trees that were more consistent with species trees than maximum likelihood or maximum parsimony objectives. With a case study of species from the family Hominidae, including Homo sapiens, I show how Treeline simplifies the process of constructing trees under alternative optimisation objectives and models of evolution. Treeline is part of the DECIPHER package for R and is available from Bioconductor and online (https://DECIPHER.codes/).
Given the high levels of endemism, diversity, and increasing anthropogenic impacts in tropical regions, studies on species interactions and ecological networks are urgently needed to understand community responses to environmental change. Increasingly, molecular methods are used to monitor biodiversity and identify species interactions. In this study, we developed an optimised DNA metabarcoding protocol to construct quantitative dung beetle-vertebrate trophic networks in a heterogenous tropical forest. We show that while the highest probability of detection is within 3 h of feeding, vertebrate DNA can still be detected in most beetles after 24 h, including around 50% of beetles with visually empty guts. Using the vertebrate taxa detected from DNA in the gut contents, we constructed dung beetle-vertebrate networks across 50 forest sites. Our networks displayed high trophic generalism and nestedness. By using group-specific primers, we documented interactions between dung beetles and multiple vertebrate species, including amphibians, birds, and rare mammals, such as the Sunda slow loris and Sunda pangolin. Our optimised molecular techniques contribute towards the development of new methods for comprehensive biodiversity assessments and ecological network studies that are urgently needed to monitor biodiversity in the hyper-diverse tropics.
Metabarcoding sequence data from environmental DNA (eDNA) is rapidly expanding as a powerful method for biodiversity surveys. In order to interpret these data, tools are needed that account for the uncertainty associated with eDNA sampling, sequencing and analysis. The data resulting from eDNA marker gene analysis differ from many traditional methods of biodiversity surveys because they are highly complex, sparse and compositional. Methodological biases produce uncertainty at every step of the sampling and sequencing process. Thus, it is critical that users have a way of interpreting eDNA results that accounts for their compositional nature and models the uncertainty resulting from factors like patchy sampling, PCR amplification biases and variable sequencing depth. Here, we introduce MAMBO: Metabarcoding Analysis using Modeled Bayesian Occurrences. MAMBO simulates in silico replication and models the uncertainty surrounding the sequencing and analysis process. Further, it uses these modelled sequence count data to correlate two sets of marker genes with a Bayesian regression, facilitating the linkage of different groups targeted by these assays. Compared with correlational network analyses, MAMBO overcomes many of the limitations to robust statistical analyses of eDNA marker gene data and provides an opportunity for new insight into ecological patterns over space and time.
Analysing large population genomic datasets requires an interdisciplinary skillset. Beyond a knowledge base in genetics and population biology, population genomic analyses involve computer science and statistics, representing a barrier for researchers without experience in those fields. PopGenHelpR seeks to lower this barrier by enabling researchers to perform population genomic analyses and generate near-publication quality figures in a streamlined and informed fashion. PopGenHelpR allows users to estimate genetic diversity within populations as well as differentiation among populations and individuals from single nucleotide polymorphism data. PopGenHelpR includes commonly used measures such as observed heterozygosity and FST. PopGenHelpR also provides five previously unavailable measures of heterozygosity, including the proportion of heterozygous loci and homozygosity by locus. Additionally, PopGenHelpR integrates widely used visualization tools in population genomics that normally require additional software packages, such as ancestry bar charts, piechart maps, and genetic differentiation heatmaps. Moreover, PopGenHelpR is minimally dependent on other R packages, reducing its chance of being removed from public networks. The PopGenHelpR website also provides tutorials and educational resources. Altogether, PopGenHelpR provides resources for informed analyses and effective visualization, making PopGenHelpR a valuable tool for many researchers. PopGenHelpR is available on the Comprehensive R Archive Network and GitHub (https://github.com/kfarleigh/PopGenHelpR).
