@article{BartoszewiczNasriNowickaetal.2022, author = {Bartoszewicz, Jakub M. and Nasri, Ferdous and Nowicka, Melania and Renard, Bernhard Y.}, title = {Detecting DNA of novel fungal pathogens using ResNets and a curated fungi-hosts data collection}, series = {Bioinformatics}, volume = {38}, journal = {Bioinformatics}, publisher = {Oxford Univ. Press}, address = {Oxford}, issn = {1367-4803}, doi = {10.1093/bioinformatics/btac495}, pages = {ii168 -- ii174}, year = {2022}, abstract = {Background: Emerging pathogens are a growing threat, but large data collections and approaches for predicting the risk associated with novel agents are limited to bacteria and viruses. Pathogenic fungi, which also pose a constant threat to public health, remain understudied. Relevant data remain comparatively scarce and scattered among many different sources, hindering the development of sequencing-based detection workflows for novel fungal pathogens. No prediction method working for agents across all three groups is available, even though the cause of an infection is often difficult to identify from symptoms alone. Results: We present a curated collection of fungal host range data, comprising records on human, animal and plant pathogens, as well as other plant-associated fungi, linked to publicly available genomes. We show that it can be used to predict the pathogenic potential of novel fungal species directly from DNA sequences with either sequence homology or deep learning. We develop learned, numerical representations of the collected genomes and visualize the landscape of fungal pathogenicity. Finally, we train multi-class models predicting if next-generation sequencing reads originate from novel fungal, bacterial or viral threats. Conclusions: The neural networks trained using our data collection enable accurate detection of novel fungal pathogens. A curated set of over 1400 genomes with host and pathogenicity metadata supports training of machine-learning models and sequence comparison, not limited to the pathogen detection task.}, language = {en} } @article{PiroRenard2023, author = {Piro, Vitor C. and Renard, Bernhard Y.}, title = {Contamination detection and microbiome exploration with GRIMER}, series = {GigaScience}, volume = {12}, journal = {GigaScience}, publisher = {Oxford Univ. Press}, address = {Oxford}, issn = {2047-217X}, doi = {10.1093/gigascience/giad017}, pages = {13}, year = {2023}, abstract = {Background: Contamination detection is a important step that should be carefully considered in early stages when designing and performing microbiome studies to avoid biased outcomes. Detecting and removing true contaminants is challenging, especially in low-biomass samples or in studies lacking proper controls. Interactive visualizations and analysis platforms are crucial to better guide this step, to help to identify and detect noisy patterns that could potentially be contamination. Additionally, external evidence, like aggregation of several contamination detection methods and the use of common contaminants reported in the literature, could help to discover and mitigate contamination. Results: We propose GRIMER, a tool that performs automated analyses and generates a portable and interactive dashboard integrating annotation, taxonomy, and metadata. It unifies several sources of evidence to help detect contamination. GRIMER is independent of quantification methods and directly analyzes contingency tables to create an interactive and offline report. Reports can be created in seconds and are accessible for nonspecialists, providing an intuitive set of charts to explore data distribution among observations and samples and its connections with external sources. Further, we compiled and used an extensive list of possible external contaminant taxa and common contaminants with 210 genera and 627 species reported in 22 published articles. Conclusion: GRIMER enables visual data exploration and analysis, supporting contamination detection in microbiome studies. The tool and data presented are open source and available at https://gitlab.com/dacs-hpi/grimer.}, language = {en} } @article{GarrelsKhodabakhshRenardetal.2023, author = {Garrels, Tim and Khodabakhsh, Athar and Renard, Bernhard Y. and Baum, Katharina}, title = {LazyFox: fast and parallelized overlapping community detection in large graphs}, series = {PEERJ Computer Science}, volume = {9}, journal = {PEERJ Computer Science}, publisher = {PeerJ Inc.}, address = {London}, issn = {2376-5992}, doi = {10.7717/peerj-cs.1291}, pages = {30}, year = {2023}, abstract = {The detection of communities in graph datasets provides insight about a graph's underlying structure and is an important tool for various domains such as social sciences, marketing, traffic forecast, and drug discovery. While most existing algorithms provide fast approaches for community detection, their results usually contain strictly separated communities. However, most datasets would semantically allow for or even require overlapping communities that can only be determined at much higher computational cost. We build on an efficient algorithm, FOX, that detects such overlapping communities. FOX measures the closeness of a node to a community by approximating the count of triangles which that node forms with that community. We propose LAZYFOX, a multi-threaded adaptation of the FOX algorithm, which provides even faster detection without an impact on community quality. This allows for the analyses of significantly larger and more complex datasets. LAZYFOX enables overlapping community detection on complex graph datasets with millions of nodes and billions of edges in days instead of weeks. As part of this work, LAZYFOX's implementation was published and is available as a tool under an MIT licence at https://github.com/TimGarrels/LazyFox.}, language = {en} } @article{PiroDadiSeileretal.2020, author = {Piro, Vitor C. and Dadi, Temesgen H. and Seiler, Enrico and Reinert, Knut and Renard, Bernhard Y.}, title = {ganon}, series = {Bioinformatics}, volume = {36}, journal = {Bioinformatics}, publisher = {Oxford Univ. Press}, address = {Oxford}, issn = {1367-4811}, doi = {https://doi.org/10.1093/bioinformatics/btaa458}, pages = {12 -- 20}, year = {2020}, abstract = {Motivation: The exponential growth of assembled genome sequences greatly benefits metagenomics studies. However, currently available methods struggle to manage the increasing amount of sequences and their frequent updates. Indexing the current RefSeq can take days and hundreds of GB of memory on large servers. Few methods address these issues thus far, and even though many can theoretically handle large amounts of references, time/memory requirements are prohibitive in practice. As a result, many studies that require sequence classification use often outdated and almost never truly up-to-date indices. Results: Motivated by those limitations, we created ganon, a k-mer-based read classification tool that uses Interleaved Bloom Filters in conjunction with a taxonomic clustering and a k-mer counting/filtering scheme. Ganon provides an efficient method for indexing references, keeping them updated. It requires <55 min to index the complete RefSeq of bacteria, archaea, fungi and viruses. The tool can further keep these indices up-to-date in a fraction of the time necessary to create them. Ganon makes it possible to query against very large reference sets and therefore it classifies significantly more reads and identifies more species than similar methods. When classifying a high-complexity CAMI challenge dataset against complete genomes from RefSeq, ganon shows strongly increased precision with equal or better sensitivity compared with state-of-the-art tools. With the same dataset against the complete RefSeq, ganon improved the F1-score by 65\% at the genus level. It supports taxonomy- and assembly-level classification, multiple indices and hierarchical classification.}, language = {en} } @article{UlrichLutfiRutzenetal.2022, author = {Ulrich, Jens-Uwe and Lutfi, Ahmad and Rutzen, Kilian and Renard, Bernhard Y.}, title = {ReadBouncer}, series = {Bioinformatics}, volume = {38}, journal = {Bioinformatics}, number = {SUPPL 1}, publisher = {Oxford Univ. Press}, address = {Oxford}, issn = {1367-4803}, doi = {10.1093/bioinformatics/btac223}, pages = {153 -- 160}, year = {2022}, abstract = {Motivation: Nanopore sequencers allow targeted sequencing of interesting nucleotide sequences by rejecting other sequences from individual pores. This feature facilitates the enrichment of low-abundant sequences by depleting overrepresented ones in-silico. Existing tools for adaptive sampling either apply signal alignment, which cannot handle human-sized reference sequences, or apply read mapping in sequence space relying on fast graphical processing units (GPU) base callers for real-time read rejection. Using nanopore long-read mapping tools is also not optimal when mapping shorter reads as usually analyzed in adaptive sampling applications. Results: Here, we present a new approach for nanopore adaptive sampling that combines fast CPU and GPU base calling with read classification based on Interleaved Bloom Filters. ReadBouncer improves the potential enrichment of low abundance sequences by its high read classification sensitivity and specificity, outperforming existing tools in the field. It robustly removes even reads belonging to large reference sequences while running on commodity hardware without GPUs, making adaptive sampling accessible for in-field researchers. Readbouncer also provides a user-friendly interface and installer files for end-users without a bioinformatics background.}, language = {en} } @article{HiortSchlaffnerSteenetal.2022, author = {Hiort, Pauline and Schlaffner, Christoph N. and Steen, Judith A. and Renard, Bernhard Y. and Steen, Hanno}, title = {multiFLEX-LF: a computational approach to quantify the modification stoichiometries in label-free proteomics data sets}, series = {Journal of proteome research}, volume = {21}, journal = {Journal of proteome research}, number = {4}, publisher = {American Chemical Society}, address = {Washington}, issn = {1535-3893}, doi = {10.1021/acs.jproteome.1c00669}, pages = {899 -- 909}, year = {2022}, abstract = {In liquid-chromatography-tandem-mass-spectrometry-based proteomics, information about the presence and stoichiometry ofprotein modifications is not readily available. To overcome this problem,we developed multiFLEX-LF, a computational tool that builds uponFLEXIQuant, which detects modified peptide precursors and quantifiestheir modification extent by monitoring the differences between observedand expected intensities of the unmodified precursors. multiFLEX-LFrelies on robust linear regression to calculate the modification extent of agiven precursor relative to a within-study reference. multiFLEX-LF cananalyze entire label-free discovery proteomics data sets in a precursor-centric manner without preselecting a protein of interest. To analyzemodification dynamics and coregulated modifications, we hierarchicallyclustered the precursors of all proteins based on their computed relativemodification scores. We applied multiFLEX-LF to a data-independent-acquisition-based data set acquired using the anaphase-promoting complex/cyclosome (APC/C) isolated at various time pointsduring mitosis. The clustering of the precursors allows for identifying varying modification dynamics and ordering the modificationevents. Overall, multiFLEX-LF enables the fast identification of potentially differentially modified peptide precursors and thequantification of their differential modification extent in large data sets using a personal computer. Additionally, multiFLEX-LF candrive the large-scale investigation of the modification dynamics of peptide precursors in time-series and case-control studies.multiFLEX-LF is available athttps://gitlab.com/SteenOmicsLab/multiflex-lf.}, language = {en} } @article{AltenburgGieseWangetal.2022, author = {Altenburg, Tom and Giese, Sven Hans-Joachim and Wang, Shengbo and Muth, Thilo and Renard, Bernhard Y.}, title = {Ad hoc learning of peptide fragmentation from mass spectra enables an interpretable detection of phosphorylated and cross-linked peptides}, series = {Nature machine intelligence}, volume = {4}, journal = {Nature machine intelligence}, number = {4}, publisher = {Springer Nature Publishing}, address = {London}, issn = {2522-5839}, doi = {10.1038/s42256-022-00467-7}, pages = {378 -- 388}, year = {2022}, abstract = {Fragmentation of peptides leaves characteristic patterns in mass spectrometry data, which can be used to identify protein sequences, but this method is challenging for mutated or modified sequences for which limited information exist. Altenburg et al. use an ad hoc learning approach to learn relevant patterns directly from unannotated fragmentation spectra. Mass spectrometry-based proteomics provides a holistic snapshot of the entire protein set of living cells on a molecular level. Currently, only a few deep learning approaches exist that involve peptide fragmentation spectra, which represent partial sequence information of proteins. Commonly, these approaches lack the ability to characterize less studied or even unknown patterns in spectra because of their use of explicit domain knowledge. Here, to elevate unrestricted learning from spectra, we introduce 'ad hoc learning of fragmentation' (AHLF), a deep learning model that is end-to-end trained on 19.2 million spectra from several phosphoproteomic datasets. AHLF is interpretable, and we show that peak-level feature importance values and pairwise interactions between peaks are in line with corresponding peptide fragments. We demonstrate our approach by detecting post-translational modifications, specifically protein phosphorylation based on only the fragmentation spectrum without a database search. AHLF increases the area under the receiver operating characteristic curve (AUC) by an average of 9.4\% on recent phosphoproteomic data compared with the current state of the art on this task. Furthermore, use of AHLF in rescoring search results increases the number of phosphopeptide identifications by a margin of up to 15.1\% at a constant false discovery rate. To show the broad applicability of AHLF, we use transfer learning to also detect cross-linked peptides, as used in protein structure analysis, with an AUC of up to 94\%.}, language = {en} } @article{WittigMirandaHoelzeretal.2022, author = {Wittig, Alice and Miranda, Fabio Malcher and H{\"o}lzer, Martin and Altenburg, Tom and Bartoszewicz, Jakub Maciej and Beyvers, Sebastian and Dieckmann, Marius Alfred and Genske, Ulrich and Giese, Sven Hans-Joachim and Nowicka, Melania and Richard, Hugues and Schiebenhoefer, Henning and Schmachtenberg, Anna-Juliane and Sieben, Paul and Tang, Ming and Tembrockhaus, Julius and Renard, Bernhard Y. and Fuchs, Stephan}, title = {CovRadar}, series = {Bioinformatics}, volume = {38}, journal = {Bioinformatics}, number = {17}, publisher = {Oxford Univ. Press}, address = {Oxford}, issn = {1367-4803}, doi = {10.1093/bioinformatics/btac411}, pages = {4223 -- 4225}, year = {2022}, abstract = {The ongoing pandemic caused by SARS-CoV-2 emphasizes the importance of genomic surveillance to understand the evolution of the virus, to monitor the viral population, and plan epidemiological responses. Detailed analysis, easy visualization and intuitive filtering of the latest viral sequences are powerful for this purpose. We present CovRadar, a tool for genomic surveillance of the SARS-CoV-2 Spike protein. CovRadar consists of an analytical pipeline and a web application that enable the analysis and visualization of hundreds of thousand sequences. First, CovRadar extracts the regions of interest using local alignment, then builds a multiple sequence alignment, infers variants and consensus and finally presents the results in an interactive app, making accessing and reporting simple, flexible and fast.}, language = {en} } @inproceedings{HiortHugoZeinertetal.2022, author = {Hiort, Pauline and Hugo, Julian and Zeinert, Justus and M{\"u}ller, Nataniel and Kashyap, Spoorthi and Rajapakse, Jagath C. and Azuaje, Francisco and Renard, Bernhard Y. and Baum, Katharina}, title = {DrDimont: explainable drug response prediction from differential analysis of multi-omics networks}, series = {Bioinformatics}, volume = {38}, booktitle = {Bioinformatics}, publisher = {Oxford Univ. Press}, address = {Oxford}, issn = {1367-4803}, doi = {10.1093/bioinformatics/btac477}, pages = {ii113 -- ii119}, year = {2022}, abstract = {Motivation: While it has been well established that drugs affect and help patients differently, personalized drug response predictions remain challenging. Solutions based on single omics measurements have been proposed, and networks provide means to incorporate molecular interactions into reasoning. However, how to integrate the wealth of information contained in multiple omics layers still poses a complex problem. Results: We present DrDimont, Drug response prediction from Differential analysis of multi-omics networks. It allows for comparative conclusions between two conditions and translates them into differential drug response predictions. DrDimont focuses on molecular interactions. It establishes condition-specific networks from correlation within an omics layer that are then reduced and combined into heterogeneous, multi-omics molecular networks. A novel semi-local, path-based integration step ensures integrative conclusions. Differential predictions are derived from comparing the condition-specific integrated networks. DrDimont's predictions are explainable, i.e. molecular differences that are the source of high differential drug scores can be retrieved. We predict differential drug response in breast cancer using transcriptomics, proteomics, phosphosite and metabolomics measurements and contrast estrogen receptor positive and receptor negative patients. DrDimont performs better than drug prediction based on differential protein expression or PageRank when evaluating it on ground truth data from cancer cell lines. We find proteomic and phosphosite layers to carry most information for distinguishing drug response.}, language = {en} } @article{TauschLokaSchulzeetal.2022, author = {Tausch, Simon H. and Loka, Tobias P. and Schulze, Jakob M. and Andrusch, Andreas and Klenner, Jeanette and Dabrowski, Piotr Wojciech and Lindner, Martin S. and Nitsche, Andreas and Renard, Bernhard Y.}, title = {PathoLive-real-time pathogen identification from metagenomic illumina datasets}, series = {Life}, volume = {12}, journal = {Life}, number = {9}, publisher = {MDPI}, address = {Basel}, issn = {2075-1729}, doi = {10.3390/life12091345}, pages = {17}, year = {2022}, abstract = {Over the past years, NGS has become a crucial workhorse for open-view pathogen diagnostics. Yet, long turnaround times result from using massively parallel high-throughput technologies as the analysis can only be performed after sequencing has finished. The interpretation of results can further be challenged by contaminations, clinically irrelevant sequences, and the sheer amount and complexity of the data. We implemented PathoLive, a real-time diagnostics pipeline for the detection of pathogens from clinical samples hours before sequencing has finished. Based on real-time alignment with HiLive2, mappings are scored with respect to common contaminations, low-entropy areas, and sequences of widespread, non-pathogenic organisms. The results are visualized using an interactive taxonomic tree that provides an easily interpretable overview of the relevance of hits. For a human plasma sample that was spiked in vitro with six pathogenic viruses, all agents were clearly detected after only 40 of 200 sequencing cycles. For a real-world sample from Sudan, the results correctly indicated the presence of Crimean-Congo hemorrhagic fever virus. In a second real-world dataset from the 2019 SARS-CoV-2 outbreak in Wuhan, we found the presence of a SARS coronavirus as the most relevant hit without the novel virus reference genome being included in the database. For all samples, clinically irrelevant hits were correctly de-emphasized. Our approach is valuable to obtain fast and accurate NGS-based pathogen identifications and correctly prioritize and visualize them based on their clinical significance: PathoLive is open source and available on GitLab and BioConda.}, language = {en} }