TY - JOUR A1 - Ulrich, Jens-Uwe A1 - Lutfi, Ahmad A1 - Rutzen, Kilian A1 - Renard, Bernhard Y. T1 - ReadBouncer BT - precise and scalable adaptive sampling for nanopore sequencing JF - Bioinformatics N2 - 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. Y1 - 2022 U6 - https://doi.org/10.1093/bioinformatics/btac223 SN - 1367-4803 SN - 1460-2059 VL - 38 IS - SUPPL 1 SP - 153 EP - 160 PB - Oxford Univ. Press CY - Oxford ER - TY - JOUR A1 - Wittig, Alice A1 - Miranda, Fabio Malcher A1 - Hölzer, Martin A1 - Altenburg, Tom A1 - Bartoszewicz, Jakub Maciej A1 - Beyvers, Sebastian A1 - Dieckmann, Marius Alfred A1 - Genske, Ulrich A1 - Giese, Sven Hans-Joachim A1 - Nowicka, Melania A1 - Richard, Hugues A1 - Schiebenhoefer, Henning A1 - Schmachtenberg, Anna-Juliane A1 - Sieben, Paul A1 - Tang, Ming A1 - Tembrockhaus, Julius A1 - Renard, Bernhard Y. A1 - Fuchs, Stephan T1 - CovRadar BT - continuously tracking and filtering SARS-CoV-2 mutations for genomic surveillance JF - Bioinformatics N2 - 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. Y1 - 2022 U6 - https://doi.org/10.1093/bioinformatics/btac411 SN - 1367-4803 SN - 1367-4811 VL - 38 IS - 17 SP - 4223 EP - 4225 PB - Oxford Univ. Press CY - Oxford ER - TY - JOUR A1 - Hiort, Pauline A1 - Schlaffner, Christoph N. A1 - Steen, Judith A. A1 - Renard, Bernhard Y. A1 - Steen, Hanno T1 - multiFLEX-LF: a computational approach to quantify the modification stoichiometries in label-free proteomics data sets JF - Journal of proteome research N2 - 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. KW - bioinformatics tool KW - label-free quantification KW - LC-MS KW - MS KW - post-translational modification KW - modification stoichiometry KW - PTM KW - quantification Y1 - 2022 U6 - https://doi.org/10.1021/acs.jproteome.1c00669 SN - 1535-3893 SN - 1535-3907 VL - 21 IS - 4 SP - 899 EP - 909 PB - American Chemical Society CY - Washington ER - TY - JOUR A1 - Altenburg, Tom A1 - Giese, Sven Hans-Joachim A1 - Wang, Shengbo A1 - Muth, Thilo A1 - Renard, Bernhard Y. T1 - Ad hoc learning of peptide fragmentation from mass spectra enables an interpretable detection of phosphorylated and cross-linked peptides JF - Nature machine intelligence N2 - 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%. Y1 - 2022 U6 - https://doi.org/10.1038/s42256-022-00467-7 SN - 2522-5839 VL - 4 IS - 4 SP - 378 EP - 388 PB - Springer Nature Publishing CY - London ER - TY - JOUR A1 - Piro, Vitor C. A1 - Dadi, Temesgen H. A1 - Seiler, Enrico A1 - Reinert, Knut A1 - Renard, Bernhard Y. T1 - ganon BT - precise metagenomics classification against large and up-to-date sets of reference sequences JF - Bioinformatics N2 - 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. Y1 - 2020 U6 - https://doi.org/https://doi.org/10.1093/bioinformatics/btaa458 SN - 1367-4811 SN - 1367-4803 VL - 36 SP - 12 EP - 20 PB - Oxford Univ. Press CY - Oxford ER -