Biology subjects

Viehweger, A.

Publications and source records attributed to Viehweger, A..

13 recordsLinked to original sources

bioRxiv2023

Nanopore sequencing for accurate bacterial outbreak tracing

Our study investigated the effectiveness of Oxford Nanopore Technologies for accurate outbreak tracing by resequencing 33 isolates of a three-year-long Klebsiella pneumoniae outbreak with Illumina short read sequencing data as the point of reference. We detected considerable base errors through cgMLST and phylogenetic analysis of genomes sequenced with Oxford Nanopore Technologies, leading to the false exclusion of some outbreak-related strains from the outbreak cluster. Nearby methylation sites cause these errors and can also be found in other species besides K. pneumoniae. Based on this data, we explored PCR-based sequencing and a masking strategy, which both successfully addressed these inaccuracies and ensured accurate outbreak tracing. We offer our masking strategy as a bioinformatic workflow (MPOA is freely available on GitHub under the GNUv3 license: github.com/replikation/MPOA) to identify and mask problematic genome positions in a reference-free manner. Our research highlights limitations in using Oxford Nanopore Technologies for sequencing prokaryotic organisms, especially for investing outbreaks. For time-critical projects that cannot wait for further technological developments by Oxford Nanopore Technologies, our study recommends either PCR-based sequencing or using our provided bioinformatic workflow. We would advise that read mapping-based quality control of genomes should be provided when publishing results.

molecular biology↗

bioRxiv2023

Targeted decontamination of sequencing data with CLEAN

Many biological and medical questions are answered based on the analysis of sequence data. However, we can find contamination, artificial spike-ins, and overrepresented rRNA sequences in various read collections and assemblies. In particular, spike-ins used as controls, as those known from Illumina or Nanopore data, are often not considered as contaminants and also not appropriately removed during analyses. Additionally, removing human host DNA may be necessary for data protection and ethical considerations to ensure that individuals cannot be identified. We developed CLEAN, a pipeline to remove unwanted sequences from both long- and short-read sequencing techniques. While focusing on Illumina and Nanopore data with their technology-specific control sequences, the pipeline can also be used for host decontamination of metagenomic reads and assemblies, or the removal of rRNA from RNA-Seq data. The results are the purified sequences and sequences identified as contaminated with statistics summarized in a report. The output can be used directly in subsequent analyses, resulting in faster computations and improved results. Although decontamination seems mundane, many contaminants are routinely overlooked, cleaned by steps that are not fully reproducible or difficult to trace. CLEAN facilitates reproducible, platform-independent data analysis in genomics and transcriptomics and is freely available at https://github.com/rki-mf1/clean under a BSD3 license. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=93 SRC="FIGDIR/small/552089v3_ufig1.gif" ALT="Figure 1"> View larger version (16K): org.highwire.dtl.DTLVardef@133c638org.highwire.dtl.DTLVardef@1ab448org.highwire.dtl.DTLVardef@1320f21org.highwire.dtl.DTLVardef@17f7a9c_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2021

Evaluation of microbiome enrichment and host DNA depletion in human vaginal samples using Oxford Nanopore's adaptive sequencing

Metagenomic sequencing is promising for clinical applications to study microbial composition concerning disease or patient outcomes. Alterations of the vaginal microbiome are associated with adverse pregnancy outcomes, like preterm premature rupture of membranes and preterm birth. Methodologically these samples often have to deal with low relative amounts of prokaryotic DNA and high amounts of host DNA (> 90%), decreasing the overall microbial resolution. Nanopores adaptive sampling method offers selective DNA depletion or target enrichment to directly reject or accept DNA molecules during sequencing without specialized sample preparation. Here, we demonstrate how selective human host depletion resulted in a 1.70 fold ({+/-} 0.27 fold) increase in total sequencing depth, providing higher taxonomic profiling sensitivity. At the same time, the microbial composition remains consistent with the control experiments. The complete removal of all human host sequences is not yet possible and should be considered as an ethical approval statement might still be necessary. Adaptive sampling increased microbial sequencing yield in all 15 sequenced clinical routine vaginal samples, making it a valuable tool for clinical surveillance and medical-based research, which can be used in addition to other host depletion methods before sequencing.

molecular biology↗

bioRxiv2021

Adaptive nanopore sequencing on miniature flow cell detects extensive antimicrobial resistence

Rapid screening of hospital admissions to detect asymptomatic carriers of resistant bacteria can prevent pathogen outbreaks. However, the resulting isolates rarely have their genome sequenced due to cost constraints and long turn-around times to get and process the data, limiting their usefulness to the practitioner. Here we use real-time, on-device target enrichment ("adaptive") sequencing as a highly multiplexed assay covering 1,147 antimicrobial resistance genes. We compare its utility against standard and metagenomic sequencing, focusing on an isolate of Raoultella ornithinolytica harbouring three carbapenemases (NDM, KPC, VIM). Based on this experimental data, we then model the influence of several variables on the enrichment results and predict a large effect of nucleotide identity (higher is better) and read length (shorter is better). Lastly, we show how all relevant resistance genes are detected using adaptive sequencing on a miniature ("Flongle") flow cell, motivating its use in a clinical setting to monitor similar cases and their surroundings.

microbiology↗

bioRxiv2021

Context-aware genomic surveillance reveals hidden transmission of a carbapenemase-producing Klebsiella pneumoniae

Genomic surveillance can inform effective public health responses to pathogen outbreaks. However, integration of non-local data is rarely done. We investigate two large hospital outbreaks of a carbapenemase-carrying Klebsiella pneumoniae strain in Germany and show the value of contextual data. By screening more than ten thousand genomes, 500 thousand metagenomes, and two culture collections using in silico and in vitro methods, we identify a total of 415 closely related genomes reported in 28 studies. We identify the relationship between the two outbreaks through time-dated phylogeny, including their respective origin. One of the outbreaks presents extensive hidden transmission, with descendant isolates only identified in other studies. We then leverage the genome collection from this meta-analysis to identify genes under positive selection. We thereby identify an inner membrane transporter (ynjC) with a putative role in colistin resistance. Contextual data from other sources can thus enhance local genomic surveillance at multiple levels and should be integrated by default when available.

microbiology↗

bioRxiv2021

Chromosome-level genome assembly and transcriptome- based annotation of the oleaginous yeast Rhodotorula toruloides CBS 14

Rhodotorula toruloides is an oleaginous yeast with high biotechnological potential. In order to understand the molecular physiology of lipid synthesis in R. toruloides and to advance metabolic engineering, a high-resolution genome is required. We constructed a genome draft of R. toruloides CBS 14, using a hybrid assembly approach, consisting of short and long reads generated by Illumina and Nanopore sequencing, respectively. The genome draft consists of 23 contigs and 3 scaffolds, with a N50 length of 1,529,952 bp, thus largely representing chromosomal organization. The total size is 20,534,857 bp with a GC content of 61.83%. Transcriptomic data from different growth conditions was used to aid species-specific gene annotation. In total we annotated 9,464 genes and identified 11,691 transcripts. Furthermore, we demonstrated the presence of a potential plasmid, an extrachromosomal circular structure of about 11 kb with a copy number about three times as high as the other chromosomes. SignificanceThis obtained high-quality draft genome provides the suitable framework needed for genetic manipulations, and future studies of lipid metabolism and evolution of oleaginous yeasts. The identified extrachromosomal circular DNA may be useful for developing efficient episomal vectors for the manipulation of Rhodotorula yeasts. Data depositionThis project has been deposited at ENA under the accession PRJEB-40807.

microbiology↗

bioRxiv2020

DarkQ: Continuous genomic monitoring using message queues

MotivationNewly sequenced genomes are often not noticed by potential stakeholders because submission to public databases is delayed, and search options are limited. However, the discovery of genomes can be vital: In pathogen outbreaks, fast updates are essential to coordinate containment efforts and prevent spread. ResultsHere we introduce DarkQ, a message queue that allows for instant sharing and discovery of genomes. AvailabilityDarkQ is released under the BSD-2 license at github.com/phiweger/darkq.

genomics↗

bioRxiv2020

What the Phage: A scalable workflow for the identification and analysis of phage sequences

Phages are among the most abundant and diverse biological entities on earth. Phage prediction from sequence data is a crucial first step to understanding their impact on the environment. A variety of bacteriophage prediction tools have been developed over the years. They differ in algorithmic approach, results, and ease of use. We, therefore, developed "What the Phage" (WtP), an easy-to-use and parallel multitool approach for phage prediction combined with an annotation and classification downstream strategy, thus, supporting the users decision-making process by summarizing the results of the different prediction tools in charts and tables. WtP is reproducible and scales to thousands of datasets through a workflow manager (Nextflow). WtP is freely available under a GPL-3.0 license (https://github.com/replikation/What_the_Phage).

bioinformatics↗

bioRxiv2020

Metagenomics workflow for hybrid assembly, differential coverage binning, transcriptomics and pathway analysis (MUFFIN)

Metagenomics has redefined many areas of microbiology. However, metagenome-assembled genomes (MAGs) are often fragmented, primarily when sequencing was performed with short reads. Recent long-read sequencing technologies promise to improve genome reconstruction. However, the integration of two different sequencing modalities makes downstream analyses complex. We, therefore, developed MUFFIN, a complete metagenomic workflow that uses short and long reads to produce high-quality bins and their annotations. The workflow is written by using Nextflow, a workflow orchestration software, to achieve high reproducibility and fast and straightforward use. This workflow also produces the taxonomic classification and KEGG pathways of the bins and can be further used by providing RNA-Seq data (optionally) for quantification and annotation. We tested the workflow using twenty biogas reactor samples and assessed the capacity of MUFFIN to process and output relevant files needed to analyze the microbial community and their function. MUFFIN produces functional pathway predictions and if provided de novo transcript annotations across the metagenomic sample and for each bin. Author SummaryRVD did the development and design of MUFFIN and wrote the first draft; BM and EBR did the critical reading and correction of the manuscript; MH did the critical reading of the manuscript and the general adjustments for the metagenomic workflow; AV did the critical reading of the manuscript and adjustments for the taxonomic classifications. CB supervised the project, did the workflow design, helped with the implementation, and revised the manuscript.

bioinformatics↗

bioRxiv2019

Addressing dereplication crisis: Taxonomy-free reduction of massive genome collections using embeddings of protein content

Many recent microbial genome collections curate hundreds of thousands of genomes. This volume complicates many genomic analyses such as taxon assignment because the associated computational burden is substantial. However, the number of representatives of each species is highly skewed towards human pathogens and model organisms. Thus many genomes contain little additional information and could be removed. We created a frugal dereplication method that can reduce massive genome collections based on genome sequence alone, without the need for manual curation nor taxonomic information. We recently created a genome representation for bacteria and archaea called "nanotext". This method embeds each genome in a low-dimensional vector of numbers. Extending nanotext, our proposed algorithm called "thinspace" uses these vectors to group and dereplicate similar genomes. We dereplicated the Genome Taxonomy Database (GTDB) from about 150 thousand genomes to less than 22 thousand. The resulting collection increases the percent of classified reads in a metagenomic dataset by a factor of 5 compared to NCBI RefSeq and performs equal to both a larger as well as a manually curated GTDB subset. With thinspace, massive genome collections can be dereplicated on regular hardware, without affecting downstream results. It is released under a BSD-3 license (github.com/phiweger/thinspace).

bioinformatics↗

bioRxiv2019

Viral host prediction with Deep Learning

MotivationZoonosis, the natural transmission of infections from animals to humans, is a far-reaching global problem. The recent outbreaks of Zika virus, Ebola virus and Corona virus are examples of viral zoonosis, which occur more frequently due to globalization. In the case of a virus outbreak, it is helpful to know which host organism was the original carrier of the virus. Once the reservoir or intermediate host is known, it can be isolated to prevent further spreading of the viral infection. Recent approaches aim to predict a viral host based on the viral genome, often in combination with the potential host genome and arbitrarily selected features. These methods have a clear limitation in either the number of different hosts they can predict or the accuracy of their prediction. ResultsHere, we present a fast and accurate deep learning approach for viral host prediction, which is based on the viral genome sequence only. To ensure a high prediction accuracy, we developed an effective selection approach for the training data to avoid biases due to a highly unbalanced number of known sequences per virus-host combinations. We tested our deep neural network on three different virus species (influenza A, rabies lyssavirus, rotavirus A). We reached for each virus species an AUG between 0.93 and 0.98, outperforming previous approaches and allowing highly accurate predictions while only using fractions (100-400 bp) of the viral genome sequences. We show that deep neural networks are suitable to predict the host of a virus, even with a limited amount of sequences and highly unbalanced available data. The deep neural networks trained for this approach build the core of the virus-host predicting tool VIDHOP (Virus Deep learning HOst Prediction). AvailabilityThe trained models for the prediction of the host for the viruses influenza A, rabies lyssavirus, rotavirus A are implemented in the tool VIDHOP. This tool is freely available under https://github.com/flomock/vidhop. Supplementary informationSupplementary data are available at DOI 10.17605/OSF.IO/UXT7N

bioinformatics↗

bioRxiv2019

Distributed representations of protein domains and genomes and their compositionality

Learning algorithms have at their disposal an ever-growing number of metagenomes for biomining and the study of microbial functions. We propose a novel representation of function called nanotext that scales to very large data sets while capturing precise functional relationships.\n\nThese relationships are learned from a corpus of 32 thousand genome assemblies with 145 million protein domains. We treat the protein domains in a genome like words in a document, assuming that protein domains in a similar context have similar \"meaning\". This meaning can be distributed by the Word2Vec embedding algorithm over a vector of numbers. These vectors not only encode function but can be used to predict even complex genomic features and phenotypes.\n\nWe apply nanotext to data from the Tara ocean expedition to predict plausible culture media and growth temperatures for microorganisms from their metagenome assembled genomes (MAGs) alone. nanotext is freely released under a BSD licence (https://github.com/phiweger/nanotext).

genomics↗

bioRxiv2018

Nanopore direct RNA sequencing reveals modification in full-length coronavirus genomes

Sequence analyses of RNA virus genomes remain challenging due to the exceptional genetic plasticity of these viruses. Because of high mutation and recombination rates, genome replication by viral RNA-dependent RNA polymerases leads to populations of closely related viruses, so-called quasispecies. Standard (short-read) sequencing technologies are ill-suited to reconstruct large numbers of full-length haplotypes of (i) RNA virus genomes and (ii) subgenome-length (sg) RNAs comprised of noncontiguous genome regions. Here, we used a full-length, direct RNA sequencing (DRS) approach based on nanopores to characterize viral RNAs produced in cells infected with a human coronavirus.\n\nUsing DRS, we were able to map the longest ([~]26 kb) contiguous read to the viral reference genome. By combining Illumina and nanopore sequencing, we reconstructed a highly accurate consensus sequence of the human coronavirus (HCoV) 229E genome (27.3 kb). Furthermore, using long reads that did not require an assembly step, we were able to identify, in infected cells, diverse and novel HCoV-229E sg RNAs that remain to be characterized. Also, the DRS approach, which circumvents reverse transcription and amplification of RNA, allowed us to detect methylation sites in viral RNAs. Our work paves the way for haplotype-based analyses of viral quasispecies by demonstrating the feasibility of intra-sample haplotype separation.\n\nEven though several technical challenges remain to be addressed to exploit the potential of the nanopore technology fully, our work illustrates that direct RNA sequencing may significantly advance genomic studies of complex virus populations, including predictions on long-range interactions in individual full-length viral RNA haplotypes.

genomics↗