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bioRxiv2023

FISHtoFigure: An easy-to-use tool for rapid, multi-target partitioning and analysis of sub-cellular mRNA transcripts in smFISH data

Single molecule fluorescence in situ hybridisation (smFISH) has become a valuable tool to investigate the mRNA expression of single cells. However, it requires a considerable amount of bioinformatic expertise to use currently available open-source analytical software packages to extract and analyse quantitative data about transcript expression. Here, we present FISHtoFigure, a new software tool developed specifically for the analysis of mRNA abundance and co-expression in QuPath-quantified, multi-labelled smFISH data. FISHtoFigure facilitates the automated spatial analysis of transcripts of interest, allowing users to analyse populations of cells positive for specific combinations of mRNA targets without the need for bioinformatics expertise. As a proof of concept and to demonstrate the capabilities of this new research tool, we have validated FISHtoFigure in multiple biological systems. We used FISHtoFigure to identify an upregulation of T-cells in the spleens of mice infected with influenza A virus, before analysing more complex data showing crosstalk between microglia and regulatory B-cells in the brains of mice infected with Trypanosoma brucei brucei. These analyses demonstrate the ease of analysing cell expression profiles using FISHtoFigure and the value of this new tool in the field of smFISH data analysis.

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bioRxiv2023

Synergizing Geometric Deep Learning and Data-Centric Methods for Improved Protein Structural Alignment

MotivationStructures are replacing the role of sequences. Traditional bioinformatics research focused on sequences because they were easily obtained. Advances in techniques like cryo-electron microscopy, molecular modeling, docking algorithms, and structure prediction software have shifted the focus to structures. Given the importance of deep learning in many of these breakthroughs, it makes sense to also explore how it can modernize classic bioinformatics tools. However, empirical findings have shown that machine learning based methods have many pitfalls resulting in overoptimistic conclusions, including data leakage between test and training data. Thus, there is a need for new innovations to make neural networks more intelligible. ResultsWe have developed vanGOGH, a geometric deep learning-based structural alignment approach that performs on par with the state-of-the-art without ever having been trained on a pair of naturally found homologs. We adopted a data-centric approach to address deep learning and data limitations by augmenting protein templates into synthetic homologs for training. Our method allowed us to supplement homolog data by knowledge-driven augmentation, self-learning of relevant structural features by supervised examples and protein alignment that is competitive with state-of-the art methods. AvailabilityGNN framework: https://github.com/DeepRank/deeprank-core/tree/main/deeprankcore ContactLi.Xue@radboudumc.nl

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bioRxiv2023

Scanpro: robust proportion analysis for single cell resolution data

In higher organisms, individual cells respond to signals and perturbations by epigenetic regulation such as adjustment of gene expression. However, in addition to shifting their transcriptional profile, the adaptive response of cells can also lead to shifts in the proportions of different cell types. Recent methods such as scRNA-seq allow for the interrogation of expression on the single cell level, and can quantify individual cell type clusters within complex tissue samples. In order to identify clusters showing differential composition between different biological conditions, differential proportion analysis has recently been introduced. However, bioinformatics tools for robust proportion analysis of both replicated and unreplicated single cell datasets are critically missing. In this manuscript, we present Scanpro, a modular tool for proportion analysis, seamlessly integrating into widely accepted frameworks in the Python environment. Scanpro is fast, accurate, supports datasets without replicates, and is intended to be used by bioinformatics experts and beginners alike.

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bioRxiv2023

In-silico analysis reveals hub genes and enriched pathways in Type 2 Diabetes Mellitus

The worldwide prevalence of diabetes mellitus poses a significant hazard to human health and a substantial economic burden for modern society. Multiple genes and gene-environment interactions make up the complicated genetic landscape of type 2 diabetes (T2D). The majority of genetic studies concentrated on separate genes, while some put an excessive amount of emphasis on SNPs. The objective of this work was to analyze all known T2D-associated genes in a collective manner in order to identify candidate genes and assess their possible functional role using bioinformatic tools. Interactome networks, enrichment clustering, hub genes, enriched biological pathways, and miRNAs targeting hub genes were analyzed. From the pool of 490 genes implicated in type 2 diabetes, the 25 most prominent hub genes were identified using measures of betweenness centrality and degree of each node. From the interactome analysis 8 different MCODE clusters was observed with a highest number of 63 genes in cluster one. GO analyses revealed that candidate genes were significantly enriched in glucose homeostasis, carbohydrate homeostasis, regulation of type B pancreatic cell apoptotic process, and regulation of hormone secretion biological process. Insulin resistance, PI3K-Akt signaling pathway, AMPK signaling pathway, Neuroactive ligand-receptor interaction, and Adipocytokine signaling pathway were among the notable enriched pathways. Subsequently we found 54 miRNAs targeting 25 hub genes out of which IRS1 and IGF1 hub genes were targeted by seven different miRNA. This study contributes to a thorough bioinformatics investigation of genes, functions, and pathways that are relevant to the etiology of T2DM and may be useful in the development of precision medicine-based treatments.

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bioRxiv2023

Advancing Protein-DNA Binding Site Prediction: Integrating Sequence Models and Machine Learning Classifiers

Predicting protein-DNA binding sites is a challenging computational problem in the field of bioinformatics. Identifying the specific residues where proteins bind to DNA is of paramount importance, as it enables the modeling of their interactions and facilitates downstream studies. Nevertheless, the development of accurate and efficient computational methods for this task remains a persistent challenge. Accurate prediction of protein-DNA binding sites has far-reaching implications for understanding molecular mechanisms, disease processes, drug discovery, and synthetic biology applications. It helps bridge the gap between genomics and functional biology, enabling researchers to uncover the intricacies of cellular processes and advance our knowledge of the biological world. The method used to predict DNA binding residues in this study is a potent combination of conventional bioinformatics tools, protein language models, and cutting-edge machine learning and deep learning classifiers. On a dataset of protein-DNA binding sites, our model is meticulously trained, and it is then rigorously examined using several experiments. As indicated by higher predictive behavior with AUC values on two benchmark datasets, the results show superior performance when compared to existing models. The suggested model has a strong capacity for generalization and shows specificity for DNA-binding sites. We further demonstrated the adaptability of our model as a universal framework for binding site prediction by training it on a variety of protein-ligand binding site datasets. In conclusion, our innovative approach for predicting protein-DNA binding residues holds great promise in advancing our understanding of molecular interactions, thus paving the way for several groundbreaking applications in the field of molecular biology and genetics. Our approach demonstrated efficacy and versatility underscore its potential for driving transformative discoveries in biomolecular research.

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bioRxiv2023

Fine-Tuning GBS Data with Comparison of Reference and Mock Genome Approaches for Advancing Genomic Selection in Less Studied Farmed Species

BackgroundDiversifying animal cultivation demands efficient genotyping for enabling genomic selection, but non-model species lack efficient genotyping solutions. The aim of this study was to optimize a genotyping-by-sequencing (GBS) double-digest RAD-sequencing (ddRAD) pipeline. Bovine data was used to automate the bioinformatic analysis. The application of the optimization was demonstrated on non-model European whitefish data. ResultsDdRAD data generation was designed for a reliable estimation of relatedness and is scalable to up to 384 samples. The GBS sequencing yielded approximately one million reads for each of the around 100 assessed samples. Optimizing various strategies to create a de-novo reference genome for variant calling (mock reference) showed that using three samples outperformed other building strategies with single or very large number of samples. Adjustments to most pipeline tuning parameters had limited impact on high-quality data, except for the identity criterion for merging mock reference genome clusters. For each species, over 15k GBS variants based on the mock reference were obtained and showed comparable results with the ones called using an existing reference genome. Repeatability analysis showed high concordance over replicates, particularly in bovine while in European whitefish data repeatability did not exceed earlier observations. ConclusionsThe proposed cost-effective ddRAD strategy, coupled with an efficient bioinformatics workflow, enables broad adoption of ddRAD GBS across diverse farmed species. While beneficial, a reference genome is not obligatory. The integration of Snakemake streamlines the pipeline usage on computer clusters and supports customization. This user-friendly solution facilitates genotyping for both model and non-model species.

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bioRxiv2023

TEclass2: Classification of transposable elements using Transformers

MotivationTransposable elements (TEs) are interspersed repetitive sequences that are major constituents of most eukaryotic genomes and are crucial for genome evolution. Despite the existence of multiple tools for their classification and annotation, none of them can achieve completely reliable results making it a challenge for genomic studies. In this work, we introduce TEclass2, a new software that uses a deep learning approach based upon a linear Transformer architecture with a k-mer to-kenizer and further adaptations to handle DNA sequences. This software has an easy configuration that allows training models on new datasets and the classification of TE models providing multiple metrics for a reliable evaluation of the results. ResultsThis work shows a successful adaptation of deep learning with Transformers for the classification of TE models from consensus sequences, and these results lay a foundation for novel methodologies in bioinformatics. We provide a tool for the training of models and the classification of consensus sequences from TE models on custom data and a web page interface with a pre-trained dataset based on curated and non-curated TE libraries allowing a fast and simple classification of TEs. Availabilityhttps://bioinformatics.uni-muenster.de/tools/teclass2/index.pl Contactwojmak@uni-muenster.de Supplementary informationSupplementary data are available at Bioinformatics online.

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bioRxiv2023

Random Walk with Restart on multilayer networks: from node prioritisation to supervised link prediction and beyond

BackgroundBiological networks have proven invaluable ability for representing biological knowledge. Multilayer networks, which gather different types of nodes and edges in multiplex, heterogeneous and bipartite networks, provide a natural way to integrate diverse and multi-scale data sources into a common framework. Recently, we developed MultiXrank, a Random Walk with Restart algorithm able to explore such multilayer networks. MultiXrank outputs scores reflecting the proximity between an initial set of seed node(s) and all the other nodes in the multilayer network. We illustrate here the versatility of bioinformatics tasks that can be performed using MultiXrank. ResultsWe first show that MultiXrank can be used to prioritise genes and drugs of interest by exploring multilayer networks containing interactions between genes, drugs, and diseases. In a second study, we illustrate how MultiXrank scores can also be used in a supervised strategy to train a binary classifier to predict gene-disease associations. The classifier performance are validated using outdated and novel gene-disease association for training and evaluation, respectively. Finally, we show that MultiXrank scores can be used to compute diffusion profiles and use them as disease signatures. We computed the diffusion profiles of more than 100 immune diseases using a multilayer network that includes cell-type specific genomic information. The clustering of the immune disease diffusion profiles reveals shared shared phenotypic characteristics. ConclusionOverall, we illustrate here diverse applications of MultiXrank to showcase its versatility. We expect that this can lead to further and broader bioinformatics applications.

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bioRxiv2023

VRP Assembler: haplotype-resolved de novo assembly of diploid and polyploid genomes using quantum computing

Precision medicines emphasis on individual genetic variants highlights the importance of haplotype-resolved assembly, a computational challenge in bioinformatics given its combinatorial nature. While classical algorithms have made strides in addressing this issue, the potential of quantum computing remains largely untapped. Here, we present the VRP assembler: a novel approach that transforms this task into a vehicle routing problem, a combinatorial optimization problem solvable on a quantum computer. We demonstrate its potential and feasibility through a proof of concept on short synthetic diploid and triploid genomes using a D-Wave quantum annealer. To tackle larger-scale assembly problems, we integrate the VRP assembler with Googles OR-Tools, achieving a haplotype-resolved assembly across the human major histocompatibility complex (MHC) region. Our results show encouraging performance compared to hifiasm with the phasing accuracy first approaching the theoretical limit, underscoring the promising future of quantum computing in bioinformatics.

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bioRxiv2023

Fasten with Pipes

Fasten is a versatile bioinformatics tool suite developed to address gaps in the manipulation of sequence data files in the fastq format. Existing tools often lack support for interleaved fastq files and Unix-style pipe file control. Fasten provides a solution by allowing bioinformaticians to efficiently work with paired-end sequence data, facilitating data transformation, and offering competitive speeds. This suite, implemented in Rust, offers a comprehensive manual, continuous integration, and seamless integration into the command line with Unix pipes. Fasten is poised to become an invaluable resource for routine command-line bioinformatics tasks, enhancing the accessibility and flexibility of data processing in the field.

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bioRxiv2023

SuPreMo: a computational tool for streamlining in silico perturbation using sequence-based predictive models

Computationally editing genome sequences is a common bioinformatics task, but current approaches have limitations, such as incompatibility with structural variants, challenges in identifying responsible sequence perturbations, and the need for vcf file inputs and phased data. To address these bottlenecks, we present Sequence Mutator for Predictive Models (SuPreMo), a scalable and comprehensive tool for performing in silico mutagenesis. We then demonstrate how pairs of reference and perturbed sequences can be used with machine learning models to prioritize pathogenic variants or discover new functional sequences. Availability and ImplementationSuPreMo was written in Python, and can be run using only one line of code to generate both sequences and 3D genome disruption scores. The codebase, instructions for installation and use, and tutorials are on the Github page: https://github.com/ketringjoni/SuPreMo/tree/main. Contactkatherine.pollard@gladstone.ucsf.edu Supplementary informationSupplementary data are available at Bioinformatics online.

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bioRxiv2023

Differential quantification of alternative splicing events on spliced pangenome graphs

Pangenomes are becoming a powerful framework to perform many bioinformatics analyses taking into account the genetic variability of a population, thus reducing the bias introduced by a single reference genome. With the wider diffusion of pangenomes, integrating genetic variability with transcriptome diversity is becoming a natural extension that demands specific methods for its exploration. In this work, we extend the notion of spliced pangenomes to that of annotated spliced pangenomes; this allows us to introduce a formal definition of Alternative Splicing (AS) events on a graph structure. To investigate the usage of graph pangenomes for the quantification of AS events across conditions, we developed pantas, the first pangenomic method for the detection and differential analysis of AS events from short RNA-Seq reads. A comparison with state-of-the-art linear reference-based approaches proves that pantas achieves competitive accuracy, making spliced pangenomes effective for conducting AS events quantification and opening future directions for the analysis of population-based transcriptomes. pantas is open-source and freely available at github.com/algolab/pantas. Author summaryThe ever increasing availability of complete genomes is advancing our comprehension of many biological mechanisms and is enhancing the knowledge we can extract from sequencing data. Pange3PM ESTnome graphs are a convenient way to represent multiple genomes and the genetic variability within a population. Integrating genetic variability with transcriptome diversity can improve our understanding of alternative splicing, a regulation mechanism which allows a single gene to code for multiple proteins. However, many unanswered questions are limiting our comprehension of the relationship between genetic and trancriptomic variations. With this work, we start to fill this gap by introducing pantas, the first approach based on pangenome graphs for the detection and differential quantification of alternative splicing events. A comparison with state-of-the-art approaches based on linear genome prove that pangenome graphs can be effectively used to perform such an analysis. By integrating genetic and transcriptome variability in a single structure, pantas can pave the way to next generation bioinformatic approaches for the accurate analysis of the relations between genetic variations and alternative splicing aberrations.

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bioRxiv2023

Role of RIPK1 in Diffuse Gliomas pathology

PurposeThe aim of the present work was to investigate the role of Receptor-interacting protein kinase 1 (RIPK1) both in mutated and wild type isocitrate dehydrogenase (IDH) Diffuse Gliomas (DG). Patients and MethodsWe analyzed RIPK1 mRNA expression in DG databases from The Cancer Genome Atlas (TCGA) containing clinical, genomic and transcriptomic information from 661 patients. Transcriptomic studies (mRNA expression levels, correlation heatmaps, survival plots and Gene Ontology and meta-analysis of immune gene signatures) were performed with USC Xena and R. Statistical significance was set at p-values less than 0.05. ResultsThe results showed a lower survival probability in patients belonging to the high RIPK1 expression subgroup compared to those samples with low RIPK1 expression. We also observed a higher expression of RIPK1 in wtIDH samples compared to those with mIDH. In order to further characterize the role of RIPK1 in DG, we performed a Gene Ontology and Pathway Enrichment Analysis using the Xena platforms differential expression tool. The results showed that RIPK1 is involved in inflammatory and immune responses. Hence, the expression levels of some of the genes involved in the following molecular processes crucial for cancer progression were studied: proliferation, epithelial-mesenchymal transition, immune cell infiltration and cell death pathways. Briefly, the results showed significant differences in genes related to increased cellular dedifferentiation, proinflammatory cell death pathways and tumor infiltrating immune cells gene signatures (Welchs t-test). ConclusionRIPK1 over-expression is associated with a poor prognosis in DG. This fact, together with our results suggest that RIPK1 may play a crucial role in glioma pathogenesis highlighting the need to take into account RIPK1 expression levels for decision making when choosing or designing therapeutic alternatives. CONTEXT SUMMARYO_ST_ABSKey ObjectiveC_ST_ABSEvaluate the role of the Receptor-interacting protein kinase 1 (RIPK1) in Diffuse Gliomas (DG) pathology through an exhaustive in silico patient database analysis. Knowledge generatedWe demonstrated that RIPK1 is overexpressed in more aggressive DG and correlates with clinical attributes associated with poor prognosis. In addition, our analyses showed that high RIPK1 expression correlates with key genes involved in pro inflammatory cell death pathways and an increased expression of immune gene signatures suggesting greater immunological infiltration in the tumor. RelevanceOur results from patient database analyses propose RIPK1 as a new relevant molecular prognosis marker for DG. Our findings are in concordance with different preclinical studies and provide additional information that can be useful for decision making when choosing therapeutic strategies and for the development of novel therapeutic approaches such as gene or immunotherapy. This work was presented inXIII Argentine Congress of Bioinformatics and Computational Biology (XIII CAB2C), XIII International Conference of the Iberoamerican Society of Bioinformatics (XIII SoIBio) and III Annual Meeting of the Ibero-American Artificial Intelligence Network for Big BioData (III RiaBio).

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bioRxiv2023

gNOMO2: a comprehensive and modular pipeline for integrated multi-omics analyses of microbiomes

BackgroundOver the past few years, the rise of omics technologies has offered an exceptional chance to gain a deeper insight into the structural and functional characteristics of microbial communities. As a result, there is a growing demand for user friendly, reproducible, and versatile bioinformatic tools that can effectively harness multi-omics data to offer a holistic understanding of microbiomes. Previously, we introduced gNOMO, a bioinformatic pipeline specifically tailored to analyze microbiome multi-omics data in an integrative manner. In response to the evolving demands within the microbiome field and the growing necessity for integrated multi-omics data analysis, we have implemented substantial enhancements to the gNOMO pipeline. ResultsHere, we present gNOMO2, a comprehensive and modular pipeline that can seamlessly manage various omics combinations, ranging from two to four distinct omics data types including 16S rRNA gene amplicon sequencing, metagenomics, metatranscriptomics, and metaproteomics. Furthermore, gNOMO2 features a specialized module for processing 16S rRNA gene amplicon sequencing data to create a protein database suitable for metaproteomics investigations. Moreover, it incorporates new differential abundance, integration and visualization approaches, all aimed at providing a more comprehensive toolkit and insightful analysis of microbiomes. The functionality of these new features is showcased through the use of four microbiome multi-omics datasets encompassing various ecosystems and omics combinations. gNOMO2 not only replicated most of the primary findings from these studies but also offered further valuable perspectives. ConclusionsgNOMO2 enables the thorough integration of taxonomic and functional analyses in microbiome multi-omics data, opening up avenues for novel insights in the field of both host associated and free-living microbiome research. gNOMO2 is available freely at https://github.com/muzafferarikan/gNOMO2.

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bioRxiv2023

Democratizing computational pathology: optimized Whole Slide Image representations for The Cancer Genome Atlas

Automatic analysis of hematoxylin and eosin (H&E) stained Whole Slide Images (WSI) bears great promise for computer assisted diagnosis and biomarker discovery. However, scarcity of annotated datasets leads to underperforming models. Furthermore, the size and complexity of the image data limit their integration into bioinformatic workflows and thus their adoption by the bioinformatics community. Here, we present Giga-SSL, a self-supervised method for learning WSI representations without any annotation. We show that applying a simple linear classifier on the Giga-SSL representations improves classification performance over the fully supervised alternative on five benchmarked tasks and across different datasets. Moreover, we observe a substantial performance increase for small datasets (average gain of 7 AUC point) and a doubling of the number of mutations predictable from WSIs in a pan-cancer setting (from 45 to 93). We make the WSI representations available, compressing the TCGA-FFPE images from 12TB to 23MB and enabling fast analysis on a laptop CPU. We hope this resource will facilitate multimodal data integration in order to analyze WSI in their genomic and transcriptomic context.

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bioRxiv2023

Diverse Genetic Contexts of HicA Toxin Domains Propose a Role in Anti-Phage Defense

Toxin - antitoxin (TA) modules are prevalent in prokaryotic genomes, often in substantial numbers. For instance, the Mycobacterium tuberculosis genome alone harbors close to 100 TA modules, half of which belong to a singular type. Traditionally ascribed multiple biological roles, recent insights challenge these notions and instead indicate a predominant function in phage defense. TAs are often located within Defense Islands, genomic regions that encode various defense systems. The analysis of genes within Defense Islands have unveiled a wide array of systems, including TAs that serve in anti-phage defence. Prokaryotic cells are equipped with anti-phage Viperins that, analogous to their mammalian counterparts, inhibit viral RNA transcription. Additionally, bacterial Structural Maintenance of Chromosome (SMC) proteins combat plasmid intrusion by recognizing foreign DNA signatures. This study undertakes a comprehensive bioinformatics analysis of genetic elements encoding the HicA double-stranded RNA-binding domain, complemented by protein structure modeling. The HicA toxin domains are found in at least 14 distinct contexts and thus exhibit a remarkable genetic diversity. Traditional bicistronic TA operons represent eight of these contexts, while four are characterized by monocistronic operons encoding fused HicA domains. Two contexts involve hicA adjacent to genes that encode bacterial Viperins. Notably, genes encoding RelE toxins are also adjacent to Viperin genes in some instances. This configuration hints at a synergistic enhancement of Viperin-mediated anti-phage action by HicA and RelE toxins. The discovery of a HicA domain merged with an SMC domain is compelling, prompting further investigation into its potential roles. ImportanceProkaryotic organisms harbor a multitude of Toxin - Antitoxin (TA) systems, which have long puzzled scientists as "genes in search for a function". Recent scientific advancement have shed light on a primary role of TAs as anti-phage defense mechanisms. To gain an overview of TAs it is important to analyze their genetic contexts that can give hints on function and guide future experimental inquiries. This manuscript describes a thorough bioinformatics examination of genes encoding the HicA toxin domain, revealing its presence in no fewer than 14 unique genetic arrangements. Some configurations notably align with anti-phage activities, underscoring potential roles in microbial immunity. These insights robustly reinforce the hypothesis that HicA toxins are integral components of the prokaryotic anti-phage defense repertoire. The elucidation of these genetic contexts not only advances our understanding of TAs but also contributes to a paradigm shift in how we perceive their functionality within the microbial world.

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bioRxiv2023

Database size positively correlates with the loss of species-level taxonomic resolution for the 16S rRNA and other prokaryotic marker genes

For decades, the 16S rRNA gene has been used to taxonomically classify prokaryotic species and to taxonomically profile microbial communities. The 16S rRNA gene has been criticized for being too conserved to differentiate between distinct species. We argue that the inability to differentiate between species is not a unique feature of the 16S rRNA gene. Rather, we observe the gradual loss of species-level resolution for other marker genes as the number of gene sequences increases in reference databases. We demonstrate this effect through the analysis of three commonly used databases of nearly-universal prokaryotic marker genes: the SILVA 16S rRNA gene database, the Genome Taxonomy Database (GTDB), and a set of 40 taxonomically-informative single-copy genes. Our results reflect a more fundamental property of the taxonomies themselves and have broad implications for bioinformatic analyses beyond taxonomic classification. Effective solutions for fine-level taxonomic classification require a more precise, and operationally-relevant, definition of the taxonomic labels being sought, and the use of combinations of genomic markers in the classification process. ImportanceThe use of reference databases for assigning taxonomic labels to genomic and metagenomic sequences is a fundamental bioinformatic task in the characterization of microbial communities. The increasing accessibility of high throughput sequencing has led to a rapid increase in the size and number of sequences in databases. This has been beneficial for improving our understanding of the global microbial genetic diversity. However, there is evidence that as the microbial diversity is more densely sampled, increasingly longer genomic segments are needed to differentiate between distinct species. The scientific community needs to be aware of this issue and needs to develop methods that better account for it when assigning taxonomic labels to metagenomic sequences from microbial communities.

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bioRxiv2023

Eukfinder: a pipeline to retrieve microbial eukaryote genomes from metagenomic sequencing data.

Whole-genome shotgun (WGS) metagenomic sequencing of microbial communities allows us to discover the functions, physiologies, and evolutionary histories of microbial prokaryote and eukaryote members of diverse ecosystems. Despite their importance, metagenomic studies of microbial eukaryotes lag behind those of prokaryotes, due to the difficulty in identifying and assembling high-quality eukaryotic genomes from WGS data. To address this problem, we have developed Eukfinder, a bioinformatics pipeline that recovers and assembles nuclear and mitochondrial genomes of eukaryotic microbes from WGS metagenomics data. As part of its workflow, it utilizes two specialized databases to classify reads based on taxonomy which can be customized to the dataset or environment of interest. We applied Eukfinder to human gut microbiome WGS metagenomic sequencing data to recover genomes from the protistan parasite Blastocystis sp., a highly prevalent colonizer of the gastrointestinal tract of humans and animals. We tested Eukfinder using both a series of simulated gut microbiome datasets, which included varying numbers of Blastocystis reads combined with bacterial reads and by using real metagenomic gut samples containing Blastocystis. We compared the results of Eukfinder with other published workflows. With sufficient reads, Eukfinder efficiently assembles high-quality near-complete nuclear and mitochondrial genomes from diverse Blastocystis subtypes from metagenomic data without the aid of a reference genome. Furthermore, with sufficient depth of sequence sampling, Eukfinder outperforms similar tools used to recover eukaryotic genomes from metagenomic data. Eukfinder will be a useful tool for reference-independent and cultivation-free study of eukaryotic microbial genomes from environmental metagenomic sequencing samples. IMPORTANCERapid advancements in next-generation sequencing technologies have made whole-genome shotgun (WGS) metagenomic sequencing an efficient method for de novo reconstruction of microbial genomes from samples taken from different environments. So far, thousands of new prokaryotic genomes have been characterized from strains or species that were unknown to science. However, the relatively large size and complexity of protistan genomes has, until recently, precluded the use of the WGS metagenomic approach to sample microbial eukaryotic diversity. The bioinformatics pipeline we developed, Eukfinder, can recover eukaryotic microbial genomes from environmental WGS metagenomic samples. By retrieving high-quality protistan genomes from diverse metagenomic samples, we can increase numbers of reference genomes available to aid future metagenomic investigations into the functions, physiologies, and evolutionary histories of eukaryotic microbes in the gut microbiome and a variety of other ecosystems.

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