EpiDope: Prediction of B-cell epitopes from amino acid sequences
Features
- Fast genome wide search
- Interactive graphical results
- Docker support
Download
- Download from GitHub
Reference
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![[DOI]](https://i0.wp.com/www.rna.uni-jena.de/wp-content/plugins/papercite/img/external.png?resize=10%2C10&ssl=1)
M. Collatz, F. Mock, E. Barth, M. Hölzer, K. Sachse, and M. Marz, “EpiDope: a deep neural network for linear B-cell epitope prediction,” Bioinformatics, 2020.
[Bibtex]@Article{Collatz:20, author = {Maximilian Collatz and Florian Mock and Emanuel Barth and Martin Hölzer and Konrad Sachse and Manja Marz}, journal = {Bioinformatics}, title = {{EpiDope}: A Deep Neural Network for linear {B}-cell epitope prediction}, year = {2020}, doi = {10.1093/bioinformatics/btaa773}, editor = {Lenore Cowen}, publisher = {Oxford University Press ({OUP})}, }
VIDHOP: virus host prediction
VIDHOP is a fast and accurate deep learning approach for viral host prediction, which is based on the viral genome sequence only. VIDHOP allows highly accurate predictions while using only fractions (100–400 bp) of the viral genome sequences. VIDHOP also allows the user to train and use models for other viruses.
Features
- From input fasta to prediction of host in seconds.
Download
- Download from GitHub
Reference
- F. Mock, A. Viehweger, E. Barth, and M. Marz, “VIDHOP, viral host prediction with deep learning,” Bioinformatics, 2020.
[Bibtex]@Article{Mock:20, author = {Florian Mock and Adrian Viehweger and Emanuel Barth and Manja Marz}, title = {{VIDHOP}, viral host prediction with Deep Learning}, journal = {Bioinformatics}, year = {2020}, doi = {10.1093/bioinformatics/btaa705}, editor = {Jinbo Xu}, publisher = {Oxford University Press ({OUP})}, }
RNAflow: Simple RNA-Seq differential gene expression pipeline using Nextflow
RNA-Seq enables the identification and quantification of RNA molecules, often with the aim of detecting differentially expressed genes (DEGs). Although RNA-Seq evolved into a standard technique, there is no universal gold standard for these data’s computational analysis. On top of that, previous studies proved the irreproducibility of RNA-Seq studies.
RNAflow is a portable, scalable, and parallelizable Nextflow RNA-Seq pipeline to detect DEGs, which assures a high level of reproducibility. The pipeline automatically takes care of common pitfalls, such as ribosomal RNA removal and low abundance gene filtering. Apart from various visualizations for the DEG results, we incorporated downstream pathway analysis for common species as Homo sapiens and Mus musculus.
Download
- Download from GitHub
Reference
- M. Lataretu and M. Hölzer, “RNAflow: an effective and simple RNA-seq differential gene expression pipeline using Nextflow,” Genes, vol. 11, iss. 12, p. 1487, 2020.
[Bibtex]@Article{Lataretu:20, author = {Marie Lataretu and Martin Hölzer}, title = {{RNAflow}: An Effective and Simple {RNA}-Seq Differential Gene Expression Pipeline Using {N}extflow}, journal = {Genes}, year = {2020}, volume = {11}, number = {12}, pages = {1487}, doi = {10.3390/genes11121487}, publisher = {{MDPI} {AG}}, }
SIM: SilentMutations
SilentMutations (SIM) can analyze the effect of multiple point mutations on the secondary structures of two interacting viral RNAs. It simulates destructive and compensatory mutants of two key regions from a single-stranded RNA, which can then be utilized for the combinatorial in vitro analysis of RNA-RNA interactions.
Download
- Download from GitHub
Reference
- D. Desiro, M. Hölzer, B. Ibrahim, and M. Marz, “SilentMutations (SIM): a tool for analyzing long-range RNA-RNA interactions in viral genomes and structured RNAs,” Virus Res, 2018.
[Bibtex]@Article{Desiro:18, author = {Desiro, Daniel and H\"{o}lzer, Martin and Ibrahim, Bashar and Marz, Manja}, title = {{SilentMutations} ({SIM}): a tool for analyzing long-range {RNA-RNA} interactions in viral genomes and structured {RNA}s}, journal = {{Virus Res}}, year = {2018}, abstract = {A single nucleotide change in the coding region can alter the amino acid sequence of a protein. In consequence, natural or artificial sequence changes in viral RNAs may have various effects not only on protein stability, function and structure but also on viral replication. In recent decades, several tools have been developed to predict the effect of mutations in structured RNAs such as viral genomes or non-coding RNAs. Some tools use multiple point mutations and also take coding regions into account. However, none of these tools was designed to specifically simulate the effect of mutations on viral long-range interactions. Here, we developed SilentMutations (SIM), an easy-to-use tool to analyze the effect of multiple point mutations on the secondary structures of two interacting viral RNAs. The tool can simulate disruptive and compensatory mutants of two interacting single-stranded RNAs. This allows a fast and accurate assessment of key regions potentially involved in functional long-range RNA-RNA interactions and will eventually help virologists and RNA-experts to design appropriate experiments. SIM only requires two interacting single-stranded RNA regions as input. The output is a plain text file containing the most promising mutants and a graphical representation of all interactions. We applied our tool on two experimentally validated influenza A virus and hepatitis C virus interactions and we were able to predict potential double mutants for in vitro validation experiments. The source code and documentation of SIM are freely available at github.com/desiro/silentMutations.}, doi = {10.1016/j.virusres.2018.11.005}, keywords = {Codon Mutation; Double-mutant; RNA secondary structure; RNA virus; Virology; Virus Bioinformatics; silent mutation}, pmid = {30439394}, }
PCAGO: Principal component analysis for RNA-Seq read counts
PCAGO is an interactive web service that helps you analyze your RNA-Seq read counts with principal component analysis (PCA) and clustering.
Features & Facts
- read count normalization
- download annotations and GO terms for your genes
- tool to find gene variance cut-off for PCA
Download
- Go to web server
- Source code @GitHub
- Run PCAGO as standalone desktop application using the Electron framework
Reference
- R. Gerst and M. Hölzer, “PCAGO: an interactive web service to analyze RNA-seq data with principal component analysis,” bioRxiv, p. 433078, 2018.
[Bibtex]@Article{Gerst:18, author = {Ruman Gerst and Martin H\"{o}lzer}, title = {{PCAGO}: An interactive web service to analyze {RNA}-Seq data with principal component analysis}, journal = {{bioRxiv}}, year = {2018}, pages = {433078}, doi = {10.1101/433078}, publisher = {Cold Spring Harbor Laboratory}, }
LRIscan: Long range RNA-RNA interactions
LRIscan is no longer maintained.
Download
Reference
- M. Fricke and M. Marz, “Prediction of conserved long-range RNA-RNA interactions in full viral genomes,” Bioinformatics, vol. 32, p. 2928–2935, 2016.
[Bibtex]@Article{Fricke:16, author = {Fricke, Markus and Marz, Manja}, title = {Prediction of conserved long-range {RNA-RNA} interactions in full viral genomes}, journal = {Bioinformatics}, year = {2016}, volume = {32}, pages = {2928--2935}, abstract = {Long-range RNA-RNA interactions (LRIs) play an important role in viral replication, however, only a few of these interactions are known and only for a small number of viral species. Up to now, it has been impossible to screen a full viral genome for LRIs experimentally or in silico Most known LRIs are cross-reacting structures (pseudoknots) undetectable by most bioinformatical tools. We present LRIscan, a tool for the LRI prediction in full viral genomes based on a multiple genome alignment. We confirmed 14 out of 16 experimentally known and evolutionary conserved LRIs in genome alignments of HCV, Tombusviruses, Flaviviruses and HIV-1. We provide several promising new interactions, which include compensatory mutations and are highly conserved in all considered viral sequences. Furthermore, we provide reactivity plots highlighting the hot spots of predicted LRIs. Source code and binaries of LRIscan freely available for download at http://www.rna.uni-jena.de/en/supplements/lriscan/, implemented in Ruby/C ++ and supported on Linux and Windows. manja@uni-jena.de Supplementary data are available at Bioinformatics online.}, doi = {10.1093/bioinformatics/btw323}, issue = {19}, keywords = {Computer Simulation; Genome, Viral; RNA, Viral; Sequence Analysis, RNA; Software}, pmid = {27288498}, }
RNAgraphdist: Graph distance between to bases
RNAgraphdist is no longer maintained.
Features & Facts
- Handles thousands of input constraints
- Plots all results with gnuplot
- Optimized for multi-cores
- Runtime complexity: O(n log n)
Download
References
- J. Qin, M. Fricke, M. Marz, P. F. Stadler, and R. Backofen, “Graph-distance distribution of the Boltzmann ensemble of RNA secondary structures,” Algorithms Mol Biol, vol. 9, p. 19, 2014.
[Bibtex]@Article{Qin:14, author = {Qin, Jing and Fricke, Markus and Marz, Manja and Stadler, Peter F and Backofen, Rolf}, title = {Graph-distance distribution of the {B}oltzmann ensemble of {RNA} secondary structures}, journal = {{Algorithms Mol Biol}}, year = {2014}, volume = {9}, pages = {19}, abstract = {Large RNA molecules are often composed of multiple functional domains whose spatial arrangement strongly influences their function. Pre-mRNA splicing, for instance, relies on the spatial proximity of the splice junctions that can be separated by very long introns. Similar effects appear in the processing of RNA virus genomes. Albeit a crude measure, the distribution of spatial distances in thermodynamic equilibrium harbors useful information on the shape of the molecule that in turn can give insights into the interplay of its functional domains. Spatial distance can be approximated by the graph-distance in RNA secondary structure. We show here that the equilibrium distribution of graph-distances between a fixed pair of nucleotides can be computed in polynomial time by means of dynamic programming. While a naïve implementation would yield recursions with a very high time complexity of O(n (6) D (5)) for sequence length n and D distinct distance values, it is possible to reduce this to O(n (4)) for practical applications in which predominantly small distances are of of interest. Further reductions, however, seem to be difficult. Therefore, we introduced sampling approaches that are much easier to implement. They are also theoretically favorable for several real-life applications, in particular since these primarily concern long-range interactions in very large RNA molecules. The graph-distance distribution can be computed using a dynamic programming approach. Although a crude approximation of reality, our initial results indicate that the graph-distance can be related to the smFRET data. The additional file and the software of our paper are available from http://www.rna.uni-jena.de/RNAgraphdist.html.}, doi = {10.1186/1748-7188-9-19}, keywords = {Boltzmann distribution; Graph-distance; Partition function; Pre-mRNA splicing; smFRET}, pmid = {25285153}, } - R. Backofen, M. Fricke, M. Marz, J. Qin, and P. F. Stadler, “Distribution of graph-distances in Boltzmann ensembles of RNA secondary structures,” in International Workshop on Algorithms in Bioinformatics, 2013, p. 112–125.
[Bibtex]@InProceedings{Backofen:13, author = {Backofen, Rolf and Fricke, Markus and Marz, Manja and Qin, Jing and Stadler, Peter F}, title = {Distribution of graph-distances in {B}oltzmann ensembles of {RNA} secondary structures}, booktitle = {{International Workshop on Algorithms in Bioinformatics}}, year = {2013}, pages = {112--125}, }