MOTIVATION: Oxford Nanopore sequencing enables long-read analysis for diverse applications, but artefacts introduced by Nanopore barcoding are poorly characterized and can compromise demultiplexing accuracy and downstream analyses. RESULTS: Using a rapid barcoding experiment on 66 diagnostic samples, we found that only 83% of reads followed the expected single-barcode configuration, while 17% showed complex barcode attachments. We observed similar patterns in public datasets, and also in native barcoding datasets where only 30%-70% of the reads had barcodes on both ends. Widely used demultiplexers, including Dorado, fail to resolve these cases, leaving ∼10% of our rapid barcoding reads partially trimmed and contaminated with adapter fragments. We developed Barbell, a pattern-aware demultiplexer that is designed to detect complex barcode configurations. Barbell reduced contaminated reads from >400 000 (Dorado/Flexiplex) to 166 (99.96% reduction), minimized barcode bleeding, and supports custom experimental designs such as dual-end barcodes and shorter barcodes (e.g. Illumina barcodes). We further show that such contamination is widespread in public databases, with Nanopore sequences detected in hundreds of NCBI entries, some of which are responsible for artificial taxonomic connections. AVAILABILITY AND IMPLEMENTATION: Barbell is open source and available at https://github.com/rickbeeloo/barbell.

MOTIVATION: The microbes that live in an environment can be identified from the combined genomic material, also referred to as the metagenome. Sequencing a metagenome can result in large volumes of sequencing reads. A promising approach to reduce the size of metagenomic datasets is by clustering reads into groups based on their overlaps. Clustering reads are valuable to facilitate downstream analyses, including computationally intensive strain-aware assembly. As current read clustering approaches cannot handle the large datasets arising from high-throughput metagenome sequencing, a novel read clustering approach is needed. In this article, we propose OGRE, an Overlap Graph-based Read clustEring procedure for high-throughput sequencing data, with a focus on shotgun metagenomes. RESULTS: We show that for small datasets OGRE outperforms other read binners in terms of the number of species included in a cluster, also referred to as cluster purity, and the fraction of all reads that is placed in one of the clusters. Furthermore, OGRE is able to process metagenomic datasets that are too large for other read binners into clusters with high cluster purity. CONCLUSION: OGRE is the only method that can successfully cluster reads in species-specific clusters for large metagenomic datasets without running into computation time- or memory issues. AVAILABILITYAND IMPLEMENTATION: Code is made available on Github (https://github.com/Marleen1/OGRE). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BACKGROUND: The field of viromics has greatly benefited from recent developments in metagenomics, with significant efforts focusing on viral discovery. However, functional annotation of the increasing number of viral genomes is lagging behind. This is highlighted by the degree of annotation of the protein clusters in the prokaryotic Virus Orthologous Groups (pVOGs) database, with 83% of its current 9518 pVOGs having an unknown function. RESULTS: In this study we describe a machine learning approach to explore potential functional associations between pVOGs. We measure seven genomic features and use them as input to a Random Forest classifier to predict protein-protein interactions between pairs of pVOGs. After systematic evaluation of the model's performance on 10 different datasets, we obtained a predictor with a mean accuracy of 0.77 and Area Under Receiving Operation Characteristic (AUROC) score of 0.83. Its application to a set of 2,133,027 pVOG-pVOG interactions allowed us to predict 267,265 putative interactions with a reported probability greater than 0.65. At an expected false discovery rate of 0.27, we placed 95.6% of the previously unannotated pVOGs in a functional context, by predicting their interaction with a pVOG that is functionally annotated. CONCLUSIONS: We believe that this proof-of-concept methodology, wrapped in a reproducible and automated workflow, can represent a significant step towards obtaining a more complete picture of bacteriophage biology.

MOTIVATION: Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease caused by aberrations in the genome. While several disease-causing variants have been identified, a major part of heritability remains unexplained. ALS is believed to have a complex genetic basis where non-additive combinations of variants constitute disease, which cannot be picked up using the linear models employed in classical genotype-phenotype association studies. Deep learning on the other hand is highly promising for identifying such complex relations. We therefore developed a deep-learning based approach for the classification of ALS patients versus healthy individuals from the Dutch cohort of the Project MinE dataset. Based on recent insight that regulatory regions harbor the majority of disease-associated variants, we employ a two-step approach: first promoter regions that are likely associated to ALS are identified, and second individuals are classified based on their genotype in the selected genomic regions. Both steps employ a deep convolutional neural network. The network architecture accounts for the structure of genome data by applying convolution only to parts of the data where this makes sense from a genomics perspective. RESULTS: Our approach identifies potentially ALS-associated promoter regions, and generally outperforms other classification methods. Test results support the hypothesis that non-additive combinations of variants contribute to ALS. Architectures and protocols developed are tailored toward processing population-scale, whole-genome data. We consider this a relevant first step toward deep learning assisted genotype-phenotype association in whole genome-sized data. AVAILABILITY AND IMPLEMENTATION: Our code will be available on Github, together with a synthetic dataset (https://github.com/byin-cwi/ALS-Deeplearning). The data used in this study is available to bona-fide researchers upon request. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

SUMMARY: Analyzing the functional profile of a microbial community from unannotated shotgun sequencing reads is one of the important goals in metagenomics. Functional profiling has valuable applications in biological research because it identifies the abundances of the functional genes of the organisms present in the original sample, answering the question what they can do. Currently, available tools do not scale well with increasing data volumes, which is important because both the number and lengths of the reads produced by sequencing platforms keep increasing. Here, we introduce SUPER-FOCUS, SUbsystems Profile by databasE Reduction using FOCUS, an agile homology-based approach using a reduced reference database to report the subsystems present in metagenomic datasets and profile their abundances. SUPER-FOCUS was tested with over 70 real metagenomes, the results showing that it accurately predicts the subsystems present in the profiled microbial communities, and is up to 1000 times faster than other tools. AVAILABILITY AND IMPLEMENTATION: SUPER-FOCUS was implemented in Python, and its source code and the tool website are freely available at https://edwards.sdsu.edu/SUPERFOCUS. CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BACKGROUND: Protein families participating in protein-protein interactions may contain sub-families that have different binding characteristics, ranging from right binding to showing no interaction at all. Composition differences at the sequence level in these sub-families are often decisive to their differential functional interaction. Methods to predict interface sites from protein sequences typically exploit conservation as a signal. Here, instead, we provide proof of concept that the sequence specificity between interacting versus non-interacting groups can be exploited to recognise interaction sites. RESULTS: We collected homodimeric and monomeric proteins and formed homologous groups, each having an interacting (homodimer) subgroup and a non-interacting (monomer) subgroup. We then compiled multiple sequence alignments of the proteins in the homologous groups and identified compositional differences between the homodimeric and monomeric subgroups for each of the alignment positions. Our results show that this specificity signal distinguishes interface and other surface residues with 40.9% recall and up to 25.1% precision. CONCLUSIONS: To our best knowledge, this is the first large scale study that exploits sequence specificity between interacting and non-interacting homologs to predict interaction sites from sequence information only. The performance obtained indicates that this signal contains valuable information to identify protein-protein interaction sites.

Abstract Motivation: Metagenomes are often characterized by high levels of unknown sequences. Reads derived from known microorganisms can easily be identified and analyzed using fast homology search algorithms and a suitable reference database, but the unknown sequences are often ignored in further analyses, biasing conclusions. Nevertheless, it is possible to use more data in a comparative metagenomic analysis by creating a cross-assembly of all reads, i.e. a single assembly of reads from different samples. Comparative metagenomics studies the interrelationships between metagenomes from different samples. Using an assembly algorithm is a fast and intuitive way to link (partially) homologous reads without requiring a database of reference sequences. Results: Here, we introduce crAss, a novel bioinformatic tool that enables fast simple analysis of cross-assembly files, yielding distances between all metagenomic sample pairs and an insightful image displaying the similarities. Availability and implementation: crAss is available as a web server at http://edwards.sdsu.edu/crass/, and the Perl source code can be downloaded to run as a stand-alone command line tool. Contact: [email protected] Supplementary information: Supplementary data are available at Bioinformatics online.

MOTIVATION: The intensification of DNA sequencing will increasingly unveil uncharacterized species with potential alternative genetic codes. A total of 0.65% of the DNA sequences currently in Genbank encode their proteins with a variant genetic code, and these exceptions occur in many unrelated taxa. RESULTS: We introduce FACIL (Fast and Accurate genetic Code Inference and Logo), a fast and reliable tool to evaluate nucleic acid sequences for their genetic code that detects alternative codes even in species distantly related to known organisms. To illustrate this, we apply FACIL to a set of mitochondrial genomic contigs of Globobulimina pseudospinescens. This foraminifer does not have any sequenced close relative in the databases, yet we infer its alternative genetic code with high confidence values. Results are intuitively visualized in a Genetic Code Logo. AVAILABILITY AND IMPLEMENTATION: FACIL is available as a web-based service at http://www.cmbi.ru.nl/FACIL/ and as a stand-alone program.

MOTIVATION: Most microbial species can not be cultured in the laboratory. Metagenomic sequencing may still yield a complete genome if the sequenced community is enriched and the sequencing coverage is high. However, the complexity in a natural population may cause the enrichment culture to contain multiple related strains. This diversity can confound existing strict assembly programs and lead to a fragmented assembly, which is unnecessary if we have a related reference genome available that can function as a scaffold. RESULTS: Here, we map short metagenomic sequencing reads from a population of strains to a related reference genome, and compose a genome that captures the consensus of the population's sequences. We show that by iteration of the mapping and assembly procedure, the coverage increases while the similarity with the reference genome decreases. This indicates that the assembly becomes less dependent on the reference genome and approaches the consensus genome of the multi-strain population. CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: Phylogenomics integrates the vast amount of phylogenetic information contained in complete genome sequences, and is rapidly becoming the standard for reliably inferring species phylogenies. There are, however, fundamental differences between the ways in which phylogenomic approaches like gene content, superalignment, superdistance and supertree integrate the phylogenetic information from separate orthologous groups. Furthermore, they all depend on the method by which the orthologous groups are initially determined. Here, we systematically compare these four phylogenomic approaches, in parallel with three approaches for large-scale orthology determination: pairwise orthology, cluster orthology and tree-based orthology. RESULTS: Including various phylogenetic methods, we apply a total of 54 fully automated phylogenomic procedures to the fungi, the eukaryotic clade with the largest number of sequenced genomes, for which we retrieved a golden standard phylogeny from the literature. Phylogenomic trees based on gene content show, relative to the other methods, a bias in the tree topology that parallels convergence in lifestyle among the species compared, indicating convergence in gene content. CONCLUSIONS: Complete genomes are no guarantee for good or even consistent phylogenies. However, the large amounts of data in genomes enable us to carefully select the data most suitable for phylogenomic inference. In terms of performance, the superalignment approach, combined with restrictive orthology, is the most successful in recovering a fungal phylogeny that agrees with current taxonomic views, and allows us to obtain a high-resolution phylogeny. We provide solid support for what has grown to be a common practice in phylogenomics during its advance in recent years. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.