• Our aim is to advance our understanding of biological systems,

    ranging from single species to multi-species systems and ecosystems,

    based on data from large-scale bioanalytical methods.

  • We develop, improve and apply

    computational methods

    for the interpretation of molecular information in biology.

  • We establish and analyse

    quantitative mathematical models.


Latest publications

Plasmid DNA contaminant in molecular reagents.

Background noise in metagenomic studies is often of high importance and its removal requires extensive post-analytic, bioinformatics filtering. This is relevant as significant signals may be lost due to a low signal-to-noise ratio. The presence of plasmid residues, that are frequently present in reagents as contaminants, has not been investigated so far, but may pose a substantial bias. Here we show that plasmid sequences from different sources are omnipresent in molecular biology reagents. Using a metagenomic approach, we identified the presence of the (pol) of equine infectious anemia virus in human samples and traced it back to the expression plasmid used for generation of a commercial reverse transcriptase. We found fragments of multiple other expression plasmids in human samples as well as commercial polymerase preparations. Plasmid contamination sources included production chain of molecular biology reagents as well as contamination of reagents from environment or human handling of samples and reagents. Retrospective analyses of published metagenomic studies revealed an inaccurate signal-to-noise differentiation. Hence, the plasmid sequences that seem to be omnipresent in molecular biology reagents may misguide conclusions derived from genomic/metagenomics datasets and thus also clinical interpretations. Critical appraisal of metagenomic data sets for the possibility of plasmid background noise is required to identify reliable and significant signals.

Wally N, Schneider M, Thannesberger J, Kastner MT, Bakonyi T, Indik S, Rattei T, Bedarf J, Hildebrand F, Law J, Jovel J, Steininger C
2019 - Sci Rep, 1: 1652

Assessment of urban microbiome assemblies with the help of targeted in silico gold standards.

Microbial communities play a crucial role in our environment and may influence human health tremendously. Despite being the place where human interaction is most abundant we still know little about the urban microbiome. This is highlighted by the large amount of unclassified DNA reads found in urban metagenome samples. The only in silico approach that allows us to find unknown species, is the assembly and classification of draft genomes from a metagenomic dataset. In this study we (1) investigate the applicability of an assembly and binning approach for urban metagenome datasets, and (2) develop a new method for the generation of in silico gold standards to better understand the specific challenges of such datasets and provide a guide in the selection of available software.
We applied combinations of three assembly (Megahit, SPAdes and MetaSPAdes) and three binning tools (MaxBin, MetaBAT and CONCOCT) to whole genome shotgun datasets from the CAMDA 2017 Challenge. Complex in silico gold standards with a simulated bacterial fraction were generated for representative samples of each surface type and city. Using these gold standards, we found the combination of SPAdes and MetaBAT to be optimal for urban metagenome datasets by providing the best trade-off between the number of high-quality genome draft bins (MIMAG standards) retrieved, the least amount of misassemblies and contamination. The assembled draft genomes included known species like Propionibacterium acnes but also novel species according to respective ANI values.
In our work, we showed that, even for datasets with high diversity and low sequencing depth from urban environments, assembly and binning-based methods can provide high-quality genome drafts. Of vital importance to retrieve high-quality genome drafts is sequence depth but even more so a high proportion of the bacterial sequence fraction too achieve high coverage for bacterial genomes. In contrast to read-based methods relying on database knowledge, genome-centric methods as applied in this study can provide valuable information about unknown species and strains as well as functional contributions of single community members within a sample. Furthermore, we present a method for the generation of sample-specific highly complex in silico gold standards.
This article was reviewed by Craig Herbold, Serghei Mangul and Yana Bromberg.

Gerner SM, Rattei T, Graf AB
2018 - Biol. Direct, 1: 22

Minimum Information about an Uncultivated Virus Genome (MIUViG).

We present an extension of the Minimum Information about any (x) Sequence (MIxS) standard for reporting sequences of uncultivated virus genomes. Minimum Information about an Uncultivated Virus Genome (MIUViG) standards were developed within the Genomic Standards Consortium framework and include virus origin, genome quality, genome annotation, taxonomic classification, biogeographic distribution and in silico host prediction. Community-wide adoption of MIUViG standards, which complement the Minimum Information about a Single Amplified Genome (MISAG) and Metagenome-Assembled Genome (MIMAG) standards for uncultivated bacteria and archaea, will improve the reporting of uncultivated virus genomes in public databases. In turn, this should enable more robust comparative studies and a systematic exploration of the global virosphere.

Roux S, Adriaenssens EM, Dutilh BE, Koonin EV, Kropinski AM, Krupovic M, Kuhn JH, Lavigne R, Brister JR, Varsani A, Amid C, Aziz RK, Bordenstein SR, Bork P, Breitbart M, Cochrane GR, Daly RA, Desnues C, Duhaime MB, Emerson JB, Enault F, Fuhrman JA, Hingamp P, Hugenholtz P, Hurwitz BL, Ivanova NN, Labonté JM, Lee KB, Malmstrom RR, Martinez-Garcia M, Mizrachi IK, Ogata H, Páez-Espino D, Petit MA, Putonti C, Rattei T, Reyes A, Rodriguez-Valera F, Rosario K, Schriml L, Schulz F, Steward GF, Sullivan MB, Sunagawa S, Suttle CA, Temperton B, Tringe SG, Thurber RV, Webster NS, Whiteson KL, Wilhelm SW, Wommack KE, Woyke T, Wrighton KC, Yilmaz P, Yoshida T, Young MJ, Yutin N, Allen LZ, Kyrpides NC, Eloe-Fadrosh EA
2018 - Nat. Biotechnol., in press

Lecture series

Mining sequence data — GTDB taxonomy

Phil Hugenholtz
Australian Center for Ecogenomics, University of Queensland, Brisbane, Australia
18:00 h
Aula, Campus of the University of Vienna, Vienna, Austria

Biobanking BBMRI-ERIC

Kurt Zatloukal
Medical University of Graz, Austria
18:00 h
Aula, Campus of the University of Vienna, Vienna, Austria

Is predicting function from meta-omics data possible?

Michael Wagner
Division of Microbial Ecology, Centre for Microbiology and Environmental Systems Science, University of Vienna, Austria
18:00 h
Aula, Campus of the University of Vienna, Vienna, Austria