Biology subjects

Hölzer, M.

Publications and source records attributed to Hölzer, M..

21 records · Page 2Linked to original sources

bioRxiv2019

A comprehensive annotation and differential expression analysis of short and long non-coding RNAs in 16 bat genomes

Although bats are increasingly becoming the focus of scientific studies due to their unique properties, these exceptional animals are still among the least studied mammals. Assembly quality and completeness of bat genomes vary a lot and especially non-coding RNA (ncRNA) annotations are incomplete or simply missing. Accordingly, standard bioinformatics pipelines for gene expression analysis often ignore ncRNAs such as microRNAs or long antisense RNAs. The main cause of this problem is the use of incomplete genome annotations. We present a complete screening for ncRNAs within 16 bat genomes. NcRNAs affect a remarkable variety of vital biological functions, including gene expression regulation, RNA processing, RNA interference and, as recently described, regulatory processes in viral infections. Within all investigated bat assemblies we annotated 667 ncRNA families including 162 snoRNAs and 193 miRNAs as well as rRNAs, tRNAs, several snRNAs and IncRNAs, and other structural ncRNA elements. We validated our ncRNA candidates by six RNA-Seq data sets and show significant expression patterns that have never been described before in a bat species on such a large scale. Our annotations will be usable as a resource (Electronic Supplement) for deeper studying of bat evolution, ncRNAs repertoire, gene expression and regulation, ecology, and important host-virus interactions. Supplementary informationis available at rna.uni-jena.de/supplements/bats, the Open Science Framework (doi.org/10.17605/OSF.IO/4CMDN), and GitHub (github.com/rnajena/bats_ncrna).

bioinformatics↗

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↗

bioRxiv2018

PCAGO: An interactive web service to analyze RNA-Seq data with principal component analysis

The initial characterization and clustering of biological samples is a critical step in the analysis of any transcriptomic study. In many studies, principal component analysis (PCA) is the clustering algorithm of choice to predict the relationship of samples or cells based solely on differential gene expression. In addition to the pure quality evaluation of the data, a PCA can also provide initial insights into the biological background of an experiment and help researchers to interpret the data and design the subsequent computational steps accordingly. However, to avoid misleading clusterings and interpretations, an appropriate selection of the underlying gene sets to build the PCA and the choice of the most fitting principal components for the visualization are crucial parts. Here, we present PCAGO, an easy-to-use and interactive web service to analyze gene quantification data derived from RNA sequencing (RNA-Seq) experiments with PCA. The tool includes features such as read-count normalization, filtering of read counts by gene annotation, and various visualization options. Additionally, PCAGO helps to select appropriate parameters such as the number of genes and principal components to create meaningful visualizations. Availability and implementationThe web service is implemented in R and freely available at pcago.bioinf.uni-jena.de. Contactmartin.hoelzer@uni-jena.de

bioinformatics↗