Biology subjects

O'Brien, A.

Publications and source records attributed to O'Brien, A..

18 recordsLinked to original sources

bioRxiv2026

Open-world fungal ITS embeddings improve higher-rank placement and cross-view retrieval, but percent identity is stronger on a matched ITS2 novelty benchmark

1. Learned sequence embeddings are increasingly proposed for fungal ITS classification and novelty detection, but are usually evaluated against weaker versions of themselves or default-configured alignment, by AUROC alone, and with queries treated as independent. We asked whether a purpose-trained encoder outperforms percent identity when both score identical queries under a firewalled open-world design, and which evaluation choices decide the answer. 2. From the UNITE release of 19 February 2025 we built ITS-core, ITS1 and ITS2 views and a genus-separated split with sealed calibration and test partitions. A convolutional encoder trained with genus-proxy, cross-view, hierarchical and episodic objectives was frozen and hash-sealed before test data were opened. We compared it with exhaustive, coverage-filtered VSEARCH identity on the same queries and references using genus-clustered paired intervals, and logged every post-opening correction before computing corrected metrics. 3. Three evaluation choices changed the comparison. Default VSEARCH heuristics returned a lower-identity hit than exhaustive search for 48.3% of benchmark queries; without a coverage filter, identity placed only 64.9% of novel ITS-core queries in the correct class, against 98.7% with it, because the conserved 5.8S let partial alignments win; and the encoder's embeddings depended on inference batching. Once corrected, identity exceeded the encoder in known-genus accuracy and novel-family placement at every view (family 80.0% to 84.6% against 53.8% to 64.8%) and detected more novel genera at a lower false-novelty rate. The encoder's novelty AUROC was within 0.023 of identity's, and no genus-clustered interval excluded zero. Identity's own development-to-test gap exceeded the encoder's, so that gap reflects partition composition rather than selection. Of the genera held out by a historical benchmark, 85% had been present in training; on a leakage-safe version, identity's AUROC advantage was 0.065, with a genus-clustered interval excluding zero. 4. Correctly configured alignment matched or exceeded the learned embedding throughout. The decisive results came from the evaluation, not the representation: baseline configuration, batch invariance, genus-level uncertainty, a parameter-free control for selection and a leakage audit each changed a conclusion, and each is inexpensive to apply to any learned barcode method.

bioinformatics↗

bioRxiv2026

A Framework for Benchmarking Pathway Reconstruction Algorithms

Cells coordinate diverse biological processes through interactions among thousands of molecules, but mapping these interactions comprehensively and systematically remains an open problem. Pathway reconstruction algorithms address this problem by linking molecules of interest, identified from high-throughput omics experiments, using prior knowledge encoded as background interaction networks. This process recovers intermediate molecules and interactions that were not directly measured in the experimental data but plausibly connect the observed molecules. It generates testable hypotheses about interactions that drive cell behavior and informs the choice of follow-up experiments. Many algorithms have been created over decades, each optimizing different computational objectives and relying on different assumptions. The resulting heterogeneity has made benchmarking challenging, limiting systematic comparisons. Therefore, selecting an algorithm for a given biological context remains a non-trivial and poorly informed task. This registered report presents a large-scale benchmark of pathway reconstruction algorithms, evaluating 14 algorithms across 822 datasets from four biological settings. To enable this benchmark, we introduce Signaling Pathway Reconstruction Analysis Streamliner (SPRAS), which standardizes algorithm inputs, outputs, and execution into a formal framework, enabling systematic comparison that was previously infeasible. We will assess each algorithm on reconstruction performance against gold standard pathways, algorithm similarity, and computational performance across different biological contexts. Together, these evaluations will provide quantitative evidence for understanding pathway reconstruction algorithm behavior and guiding algorithm selection.

bioinformatics↗

bioRxiv2026

Pretrained deep-learning ITS classifiers read the flanking regions, not the ITS2 barcode, and so fail on the amplicon that environmental fungal surveys sequence

Background. Deep-learning classifiers for the fungal internal transcribed spacer (ITS) report accuracies above 90% and are increasingly proposed for environmental metabarcoding. That application differs from the benchmarks in two ways: surveys sequence a primer- bounded subregion, most often ITS2, rather than the full-length reference sequences the models were trained and tested on, and much of what they recover belongs to genera absent from any reference. We tested whether the reported accuracies transfer. Design. Five methods were scored on the same 5,222 queries, one sequence per genus, at two loci: the full-length ITS record, and the ITS2 subregion of that identical record. The methods are the two pretrained deep-learning classifiers distributed with MycoAI, a convolutional network and a transformer sharing a training corpus of 5.23M sequences; two reference-based methods, best-hit alignment and SINTAX; and HiTaC, a hierarchical logistic regression over k-mer counts, which we fitted ourselves to the reference the other two consult. Queries were stratified by whether the query's genus lies in the pretrained models' label space, recovered from the distributed models themselves. A novel genus is one outside that label space, and outside the reference by the same rule, so no method here can return its correct name; seen genera are the rest (2.2). Results. The design favours the pretrained models, whose queries come from the public dataset they were trained on. Even so, on full-length ITS the other three methods exceed both of them at every rank and in both strata, best-hit alignment recovering the correct family for 92.3% of seen-genus queries against 77.9% and 76.5%. Restricting the identical records to ITS2 costs the reference-based methods under four percentage points of seen-genus family accuracy and costs the two pretrained models 49.6 and 58.0, reducing them to 28.3% and 18.5%; HiTaC refitted at that locus recovers 89.2%, so neither learned classification nor the amplicon is what fails. An ablation identifies the cause. Grafting each query's unaltered ITS2 between the flanking regions of a donor record from a different phylum returns the donor's family for 34.4% of queries in the convolutional model and 63.7% in the transformer, against the query's own for 4.2% and 0.8%, from a baseline near zero where no donor is present. The predictions therefore follow the flanking regions rather than the ITS2 barcode, which accounts for the collapse and predicts the same failure for any subregion amplicon. Compounding this, on ITS2 the classifiers' confidence score all but ceases to separate novel from seen genera (AUROC 0.541 and 0.503, the latter at chance, against 0.866 for alignment identity), and the convolutional model is in addition substantially overconfident there, so for that model the failure is not detectable from its own output at all. Recommendation. Reported accuracies for such models should specify the amplicon region of the evaluation, state the length distribution of the training corpus, and be accompanied by a same-query classical baseline.

bioinformatics↗

bioRxiv2026

A calibrated novelty flag for fungal ITS metabarcoding: choosing the error rate at which sequences are declared new

O_LIEnvironmental fungal surveys routinely recover internal transcribed spacer (ITS) sequences that cannot be assigned at fine taxonomic ranks, the so-called fungal "dark matter." Such sequences are set aside by thresholding a similarity or confidence score at a conventional value. Those conventions do abstain, but the error rate a threshold implies is neither stated nor selectable, and a threshold defined for one kind of score does not transfer to another. C_LIO_LIWe present a conformal novelty flag that supplies what is missing: each query receives a p-value with a distribution-free guarantee that the rate of falsely declaring a known sequence novel is bounded by a user-chosen . We evaluate it on a leave-one-genus-out benchmark built from UNITE, on alignment identity, a k-mer bootstrap consensus and two neural classifiers output probabilities, and on a soil fungal dataset. C_LIO_LIThe flag holds its nominal rate across two orders of magnitude in , so an operating point can be chosen rather than inherited: at = 0.05 it fires on 4.3% of known-genus queries and recovers 19.2% of genuinely novel genera. The cutoff holding a 5% error rate here is 64.8% identity, nowhere near the customary 97%, showing how little a threshold carries its error rate between datasets. Coverage transferred across eleven settings spanning those four scores, two amplicon regions and a sevenfold change in reference size, all within 1.1 percentage points of nominal, while detection ranged from 5.2% to 53.3%: the guarantee is on the error rate and not on power, and two of our settings are valid but uninformative. Applied to soil data the flag identifies 20.7% of amplicon sequence variants as novel at a controlled 5% error rate, 16.3% under an abundance filter. Half of those recur near-identically among GlobalFungis unnamed environmental variants while fewer than one in ten matches a named species hypothesis, a sixfold skew towards the uncatalogued against 1.9-fold for sequences the flag passes. C_LIO_LIThe flag turns an arbitrary cutoff into a decision with a stated error rate, and in doing so converts dark matter from a residue into a set of prioritizable targets for formal description. C_LI

bioinformatics↗

bioRxiv2026

SSUplex: fast, both-strand extraction and origin-sorting of small-subunit rRNA for environmental DNA metabarcoding

Ribosomal RNA metabarcoding sits at the center of how we characterize microbial and eukaryotic communities in environmental samples, and long-read sequencing has made full-length small-subunit (SSU; 16S/18S) profiling routine. The broadly conserved primers that make rRNA such a convenient marker are also its liability: by design they co-amplify organellar (mitochondrial, chloroplast) and cross-domain SSU alongside the intended target. Left unsorted before taxonomic assignment, these passengers are systematically misclassified, and the error propagates straight into estimates of community composition and diversity. Reads must therefore be detected, extracted, and sorted by origin before they ever reach a classifier. We present SSUplex, an open-source tool that detects SSU rRNA, assigns each read to one of five origins (bacteria, archaea, eukaryota, mitochondria, chloroplast), and extracts the SSU region for downstream classification. SSUplex reimplements the extraction-and-origin logic of the widely used Metaxa2 in the Rust programming language, scans both strands, and ships as a single dependency-light binary suited to long-read (Oxford Nanopore, PacBio HiFi) and short-read data. Benchmarked against Metaxa2 on public data, SSUplex reproduces Metaxa2 origin calls on full-length reads (96.8% concordance) and matches its extraction speed on small inputs, then pulls away to run up to [~]3.4x faster with [~]35% lower peak memory at 200,000 reads, the per-sample scale a long-read amplicon run typically reaches. We are candid about a genuine, measured trade-off in the origin-ranking statistic, and we pinpoint the bacteria-versus-mitochondria boundary as the methods one intrinsically lower-confidence edge. For the now-common workflow in which origin-sorted reads are handed to a dedicated classifier rather than classified in place, SSUplex is a fast, reproducible, embeddable stand-in for Metaxa2s extraction role. Source code and a benchmark harness that regenerates every result from public data are available under the MIT license at https://github.com/ayobi/ssuplex.

bioinformatics↗

bioRxiv2026

EMITS: expectation-maximization abundance estimation for fungal ITS communities from long-read sequencing

As long-read amplicon sequencing becomes routine for fungal metabarcoding, species-level abundance estimation from ITS amplicons remains limited by naive best-hit classification, which misattributes reads among closely related species sharing similar ITS sequences and fragments abundance across redundant database entries. Expectation-maximization (EM) approaches developed for full-length 16S rRNA, notably EMU [Curry et al., 2022], have recently been benchmarked for fungal ITS metabarcoding [Graetz et al., 2025], but applying EMU to ITS requires custom reference database construction and uses parameters originally tuned for 16S. Here we present EMITS, a Rust-based tool that applies EM to iteratively resolve ambiguous read-to-reference mappings from minimap2 alignments against the UNITE database, producing probabilistic species-level abundance estimates. EMITS provides UNITE-native header parsing with automatic accession aggregation, empirically tuned platform presets for current Oxford Nanopore (R10.4.1, R9.4.1, Duplex) and PacBio HiFi chemistries, and integration with ITSxRust [OBrien et al., 2026] for upstream ITS region extraction. We validated EMITS using three complementary approaches and benchmarked it against both naive best-hit counting and EMU (with a UNITE-formatted reference database). In controlled simulations, EM reduced L1 error by 80- 92% compared to naive counting under realistic noise conditions. On the ATCC fungal ITS mock community, EMITS provided superior within-genus species resolution in taxonomically challenging genera: it correctly identified Trichophyton mentagrophytes (2.21%) where EMU misattributed substantial abundance to T. tonsurans (1.54%); it suppressed Penicillium rubens false positives (0.002% vs. EMU 0.58%); and it more accurately consolidated Nakaseomyces glabratus abundance across UNITE accessions (12.40% vs. EMU 9.95%). On a 21-species synthetic UNITE community lacking substantial within-genus difficulty, all three methods detected expected species at 100% sensitivity, with aggregate L1 errors of 8.64% (naive), 7.48% (EMITS), and 6.71% (EMU). Together with ITSxRust for upstream ITS extraction, EMITS provides a complete pipeline tuned for long-read fungal amplicon profiling.

bioinformatics↗

bioRxiv2026

ITSxRust: ITS region extraction with partial-chain recovery and structured diagnostics for long-read amplicon sequencing

As long-read amplicon sequencing (e.g., Oxford Nanopore and PacBio HiFi) becomes routine for fungal metabarcoding, identifying and extracting ITS subregions at scale has become a throughput and robustness bottleneck. The nuclear ribosomal internal transcribed spacer (ITS) region is the formal DNA barcode for fungi and is widely used for taxonomic profiling of fungal communities [Schoch et al., 2012]. Standard preprocessing locates conserved ribosomal flanks with hidden Markov profile models (profile-HMMs) to extract ITS1, 5.8S, ITS2, or the full ITS, as implemented in ITSx [Bengtsson-Palme et al., 2013] and ITSxpress [Rivers et al., 2018, Einarsson and Rivers, 2024]. Here we describe ITSxRust, a Rust-based ITS extractor designed for long-read scale. ITSxRust coordinates HMMER searches with efficient Rust-native I/O and sequence processing, optionally reduces redundant searches via dereplication, provides ONT and HiFi parameter presets, and emits structured failure diagnostics and QC summaries. On an Oxford Nanopore ITS dataset (54,659 reads), ITSxRust extracted the full ITS region from 75.3% of reads, exceeding both ITSx (69.9%) and ITSxpress v2 (41.4%), while running 4.6x faster than ITSx. In addition, a partial-chain fallback strategy that extracts subregions using two-anchor pairs when the full four-anchor chain is unavailable recovered an additional 10,725 reads that would otherwise be discarded.

bioinformatics↗

bioRxiv2025

Observing adaptation of duckweeds to their local microbiome depends on local pondwater

Populations can locally adapt to both the biotic and abiotic factors of an environment, but observing adaptation to biotic factors can sometimes depend on the abiotic conditions, and vice versa. One important aspect of the biotic environment is the microbiome: interactions between microbiomes and their hosts are critical for host fitness and trait expression. Hosts may adapt to their local microbiomes, and hosts may depend on interactions with microbes for adaptation to the local environment. Using Lemna minor (duckweed) as a model host organism, we examined differences in host fitness when grown in local and nonlocal microbiomes and in local and nonlocal water. We experimentally recombined duckweeds, microbes, and water from 4 different ponds around Durham, New Hampshire in well-plate microcosms in a growth chamber. Duckweed, microbe, and water source all affected microbial and duckweed growth, as well as duckweed traits. However, we observed only weak positive local adaptation that resulted in higher frond final area when duckweeds were paired with their local water and microbes. We also found that microbial growth was reduced when duckweeds were paired with microbes or water from their local site.

ecology↗

bioRxiv2024

Reciprocal links between methionine metabolism, DNA repair and therapy resistance in glioblastoma

Glioblastoma (GBM) is uniformly lethal due to profound treatment resistance. Altered cellular metabolism is a key mediator of GBM treatment resistance. Uptake of the essential sulfur-containing amino acid methionine is drastically elevated in GBMs compared to normal cells, however, it is not known how this methionine is utilized or whether it relates to GBM treatment resistance. Here, we find that radiation acutely increases the levels of methionine-related metabolites in a variety of treatment-resistant GBM models. Stable isotope tracing studies further revealed that radiation acutely activates methionine to S-adenosyl methionine (SAM) conversion through an active signaling event mediated by the kinases of the DNA damage response. In vivo tumor SAM synthesis increases after radiation, while normal brain SAM production remains unchanged, indicating a tumor- specific metabolic alteration to radiation. Pharmacological and dietary strategies to block methionine to SAM conversion slowed DNA damage response and increased cell death following radiation in vitro. Mechanistically, these effects are due to depletion of DNA repair proteins and are reversed by SAM supplementation. These effects are selective to GBMs lacking the methionine salvage enzyme methylthioadenosine phosphorylase. Pharmacological inhibition of SAM synthesis hindered tumor growth in flank and orthotopic in vivo GBM models when combined with radiation. By contrast, methionine depletion does not reduce tumor SAM levels and fails to radiosensitize intracranial models, indicating depleting SAM, as opposed to simply lowering methionine, is critical for hindering tumor growth in intracranial models of GBM. These results highlight a new signaling link between DNA damage and SAM synthesis and define the metabolic fates of methionine in GBM in vivo. Inhibiting radiation-induced SAM synthesis slows DNA repair and augments radiation efficacy in GBM. Using MAT2A inhibitors to deplete SAM may selectively overcome treatment resistance in GBMs with defective methionine salvage while sparing normal brain.

cancer biology↗

bioRxiv2024

Elevated type I interferon signaling defines the proliferative advantage of ARF and p53 mutant tumor cells

The tumor suppressors p53 and ARF collaborate to prevent unwarranted cell proliferation and as such are two of the most frequently mutated genes in human cancer. Concomitant loss of functional p53 and ARF leads to massive gains in cell proliferation and transformation and is often observed in some of the most aggressive human cancer subtypes. These phenotypic gains are preceded by increased type I interferon (IFN) signaling that involves canonical STAT1 activation and a subsequent IFN-stimulated gene (ISG) signature. Here, we show that cells lacking p53 and ARF require active JAK1 to phosphorylate STAT1 on Y701 to maintain their high rate of proliferation. In fact, the use of selective JAK1 inhibitors ruxolitinib or baricitinib inhibited the induction of ISGs and the proliferation of p53 and ARF deleted cells. We identify a group of solid human tumors that lack functional p53 and ARF, show an expression signature of the upregulated type I IFN response genes, and are sensitive to selective JAK1 inhibitors. These data suggest that the type I IFN response acts as a positive driver of proliferation in the absence of p53 and ARF and, as such, presents itself as a potential therapeutic target in aggressive solid tumors.

cancer biology↗

bioRxiv2024

ProteinWeaver: A Webtool to Visualize Ontology-Annotated Protein Networks

Molecular interaction networks are a vital tool for studying biological systems. While many tools exist that visualize a protein or a pathway within a network, no tool provides the ability for a researcher to consider a proteins position in a network in the context of a specific biological process or pathway. We developed ProteinWeaver, a web-based tool designed to visualize and analyze non-human protein interaction networks by integrating known biological functions. ProteinWeaver provides users with an intuitive interface to situate a user-specified protein in a user-provided biological context (as a Gene Ontology term) in five model organisms. Protein-Weaver also reports the presence of physical and regulatory network motifs within the queried subnetwork and statistics about the proteins distance to the biological process or pathway within the network. These insights can help researchers generate testable hypotheses about the proteins potential role in the process or pathway under study. Two cell biology case studies demonstrate ProteinWeavers potential to generate hypotheses from the queried subnetworks. ProteinWeaver is available at https://proteinweaver.reedcompbio.org/.

systems biology↗

bioRxiv2023

PEPhub: a database, web interface, and API for editing, sharing, and validating biological sample metadata

BackgroundAs biological data increases, we need additional infrastructure to share it and promote interoperability. While major effort has been put into sharing data, relatively less emphasis is placed on sharing metadata. Yet, sharing metadata is also important, and in some ways has a wider scope than sharing data itself. ResultsHere, we present PEPhub, an approach to improve sharing and interoperability of biological metadata. PEPhub provides an API, natural language search, and user-friendly web-based sharing and editing of sample metadata tables. We used PEPhub to process more than 100,000 published biological research projects and index them with fast semantic natural language search. PEPhub thus provides a fast and user-friendly way to finding existing biological research data, or to share new data. Availabilityhttps://pephub.databio.org

bioinformatics↗

bioRxiv2023

Predicting CRISPR-Cas12a guide efficiency for targeting using Machine Learning

Genome editing through the development of CRISPR (Clustered Regularly Interspaced Short Palindromic Repeat) - Cas technology has revolutionized many fields in biology. Beyond Cas9 nucleases, Cas12a (formerly Cpf1) has emerged as a promising alternative to Cas9 for editing AT-rich genomes. Despite the promises, guide RNA efficiency prediction through computational tools search still lacks accuracy. Through a computational meta-analysis, here we report that Cas12a target and off-target cleavage behaviour are factor of nucleotide bias combined with nucleotide mismatches relative to the protospacer adjacent motif (PAM) site. These features helped to train a machine learning random forest algorithm to improve the accuracy by at least 15% to existing algorithms to predict guide RNA efficiency for Cas12a enzyme. Despite the progresses, our report underscores the need for more representative datasets and further benchmarking to reliably and accurately predict guide RNA efficiency and off-target effects for Cas12a enzymes.

bioinformatics↗

bioRxiv2022

Having the 'right' microbiome matters for host trait expression and the strength of mutualism between duckweeds and microbes

An organisms phenotypes and fitness often depend on interactive effects of its genome (Ghost), microbiome (Gmicrobe), and environment (E). These G xG, G xE, and G xG xE effects fundamentally shape host-microbiome (co)evolution and may be widespread, but are rarely compared within a single experiment. We collected and cultured Lemna minor (duckweed) and its associated microbiome from 10 sites across an urban-to-rural ecotone. We factorially manipulated host genotype and microbiome in two environments (low and high zinc, an urban aquatic stressor) in an experiment with 200 treatments: 10 host genotypes x 10 microbiomes x 2 environments. Host genotype explained the most variation in L. minor fitness and traits, while microbiome effects depended on host genotype (G xG) or environment (G xE). Hosts had higher fitness and microbes grew fastest when tested microbiomes more closely matched field microbiomes, suggesting some local adaptation between hosts and their microbiota. High microbiome similarity also led to more predictable host trait expression. In contrast, although zinc decreased host fitness, we observed no local adaptation of urban duckweed or microbes to high-zinc conditions. Thus, we found that host fitness and trait expression are contingent on microbiome composition, with implications for microbiome engineering and host-microbiome evolution.

evolutionary biology↗

bioRxiv2021

A highly sensitive cell-based luciferase assay for high-throughput automated screening of SARS-CoV-2 nsp5/3CLpro inhibitors

Effective drugs against SARS-CoV-2 are urgently needed to treat severe cases of infection and for prophylactic use. The main viral protease (nsp5 or 3CLpro) represents an attractive and possibly broad-spectrum target for drug development as it is essential to the virus life cycle and highly conserved among betacoronaviruses. Sensitive and efficient high-throughput screening methods are key for drug discovery. Here we report the development of a gain-of-signal, highly sensitive cell-based luciferase assay to monitor SARS-CoV-2 nsp5 activity and show that it is suitable for high-throughput screening of compounds in a 384-well format. A benefit of miniaturisation and automation is that screening can be performed in parallel on a wild-type and a catalytically inactive nsp5, which improves the selectivity of the assay. We performed molecular docking-based screening on a set of 14,468 compounds from an in-house chemical database, selected 359 candidate nsp5 inhibitors and tested them experimentally. We identified four molecules, including the broad-spectrum antiviral merimepodib/VX-497, which show anti-nsp5 activity and inhibit SARS-CoV-2 replication in A549-ACE2 cells with IC50 values in the 4-21 {micro}M range. The here described assay will allow the screening of large-scale compound libraries for SARS-CoV-2 nsp5 inhibitors. Moreover, we provide evidence that this assay can be adapted to other coronaviruses and viruses which rely on a viral protease.

microbiology↗

bioRxiv2020

A common contaminant shifts impacts of climate change on a plant-microbe mutualism: effects of temperature, CO2 and leachate from tire wear particles

Anthropogenic stressors, such as climate change or chemical pollution, affect individual species and alter species interactions. Moreover, species interactions can modify effects of anthropogenic stressors on interacting species - a process which may vary amongst stressors or stressor combinations. Most ecotoxicological work focuses on single stressors on single species. Here, we test hypotheses about multiple stressors (climate change and tire wear particles) and interacting species, and whether species interactions modify responses. We use duckweed and its microbiome to model responses of plant-microbe interactions. Climate change is occurring globally, and with increasing urbanization, tire wear particles increasingly contaminate road runoff. Their leachate is associated with zinc, PAHs, plastic additives, and other toxic compounds. We crossed perpendicular gradients of temperature and CO2 in a well plate with factorial manipulation of leachate from tire wear particles and presence of duckweed microbiomes. We measured duckweed and microbial growth, duckweed greenness, and plant-microbe growth correlations. We found that tire leachate and warmer temperatures enhanced duckweed and microbial growth, but microbes diminished positive responses in duck-weed, meaning microbiomes became costly for duckweed. These costs of microbiomes were less-than-additive with warming and leachate, and might be caused by leachate-disrupted endocrine signaling in duckweed. We observed reduced greenness at higher CO2 without tire leachate, suggesting a relative increase in plant nutrient demand, and possibly underlying positive plant-microbe growth correlations in these conditions, as microbes presumably increase nutrient availability. However, with tire leachate, growth correlations were never positive, and shifted negative at lower CO2, further suggesting leachate favors mutualism disruption. In summary, while individual stressors of global change can affect individual species, in ecology we know species interact; and in ecotoxicology, we know stressors interact. Our results demonstrate this complexity: multiple stressors can affect species interactions, and species interactions can alter effects of multiple stressors.

ecology↗

bioRxiv2019

Resilience to multiple stressors in an aquatic plant and its microbiome

PremiseEnvironments are changing rapidly, and outcomes of species interactions, especially mutualisms, are notoriously dependent on the environment. A growing number of studies have investigated responses of mutualisms to anthropogenic changes, yet most studies have focused on nutrient pollution or climate change, and tested single stressors. Relatively little is known about impacts of simultaneous chemical contaminants, which may differ fundamentally from nutrient or climate stressors, and are especially widespread in aquatic habitats.\n\nMethodsWe investigated the impacts of two common contaminants on interactions between the common duckweed Lemna minor and its microbiome. Sodium chloride (salt) and benzotriazole (a corrosion inhibitor) negatively affect aquatic organisms individually, yet commonly co-occur in runoff to duckweed-inhabited sites. We tested three L. minor genotypes with and without the culturable portion of their microbiome across field realistic gradients of salt (3 levels) and benzotriazole (4 levels) in a fully factorial experiment (72 treatments), and measured plant and microbial growth.\n\nKey ResultsWe found that stressors had conditional effects. Salt decreased both plant and microbial growth, but decreased plant survival more as benzotriazole concentrations increased. In contrast, benzotriazole did not affect microbial abundance, and benefited plants when salt and microbes were absent, perhaps due to the biotrans-formation we observed without salt. Microbes did not ameliorate duckweed stressors, as microbial inoculation increased plant growth, but not at high salt concentrations.\n\nConclusionsOur results suggest that multistressor effects matter when predicting responses of mutualisms to global change, but that mutualisms may not buffer organisms from stressors.

plant biology↗

bioRxiv2019

VariantSpark, A Random Forest Machine Learning Implementation for Ultra High Dimensional Data

The demands on machine learning methods to cater for ultra high dimensional datasets, datasets with millions of features, have been increasing in domains like life sciences and the Internet of Things (IoT). While Random Forests are suitable for \"wide\" datasets, current implementations such as Googles PLANET lack the ability to scale to such dimensions. Recent improvements by Yggdrasil begin to address these limitations but do not extend to Random Forest. This paper introduces CursedForest, a novel Random Forest implementation on top of Apache Spark and part of the VariantSpark platform, which parallelises processing of all nodes over the entire forest. CursedForest is 9 and up to 89 times faster than Googles PLANET and Yggdrasil, respectively, and is the first method capable of scaling to millions of features.

bioinformatics↗