@article{SchwarzTamuriKultysetal.2016,
  author    = {Schwarz, Roland F. and Tamuri, Asif U. and Kultys, Marek and King, James and Godwin, James and Florescu, Ana M. and Schultz, J{\"o}rg and Goldman, Nick},
  title     = {ALVIS: interactive non-aggregative visualization and explorative analysis of multiple sequence alignments},
  series = {Nucleic Acids Research},
  volume    = {44},
  journal   = {Nucleic Acids Research},
  number    = {8},
  doi       = {10.1093/nar/gkw022},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-166374},
  pages     = {e77},
  year      = {2016},
  abstract  = {Sequence Logos and its variants are the most commonly used method for visualization of multiple sequence alignments (MSAs) and sequence motifs. They provide consensus-based summaries of the sequences in the alignment. Consequently, individual sequences cannot be identified in the visualization and covariant sites are not easily discernible. We recently proposed Sequence Bundles, a motif visualization technique that maintains a one-to-one relationship between sequences and their graphical representation and visualizes covariant sites. We here present Alvis, an open-source platform for the joint explorative analysis of MSAs and phylogenetic trees, employing Sequence Bundles as its main visualization method. Alvis combines the power of the visualization method with an interactive toolkit allowing detection of covariant sites, annotation of trees with synapomorphies and homoplasies, and motif detection. It also offers numerical analysis functionality, such as dimension reduction and classification. Alvis is user-friendly, highly customizable and can export results in publication-quality figures. It is available as a full-featured standalone version (http://www.bitbucket.org/rfs/alvis) and its Sequence Bundles visualization module is further available as a web application (http://science-practice.com/projects/sequence-bundles).},
  language  = {en}
}
@article{UrbanRemmeleDittrichetal.2020,
  author    = {Urban, Lara and Remmele, Christian W. and Dittrich, Marcus and Schwarz, Roland F. and M{\"u}ller, Tobias},
  title     = {covRNA: discovering covariate associations in large-scale gene expression data},
  series = {BMC Reserach Notes},
  volume    = {13},
  journal   = {BMC Reserach Notes},
  doi       = {10.1186/s13104-020-04946-1},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-229258},
  year      = {2020},
  abstract  = {Objective The biological interpretation of gene expression measurements is a challenging task. While ordination methods are routinely used to identify clusters of samples or co-expressed genes, these methods do not take sample or gene annotations into account. We aim to provide a tool that allows users of all backgrounds to assess and visualize the intrinsic correlation structure of complex annotated gene expression data and discover the covariates that jointly affect expression patterns. Results The Bioconductor package covRNA provides a convenient and fast interface for testing and visualizing complex relationships between sample and gene covariates mediated by gene expression data in an entirely unsupervised setting. The relationships between sample and gene covariates are tested by statistical permutation tests and visualized by ordination. The methods are inspired by the fourthcorner and RLQ analyses used in ecological research for the analysis of species abundance data, that we modified to make them suitable for the distributional characteristics of both, RNA-Seq read counts and microarray intensities, and to provide a high-performance parallelized implementation for the analysis of large-scale gene expression data on multi-core computational systems. CovRNA provides additional modules for unsupervised gene filtering and plotting functions to ensure a smooth and coherent analysis workflow.},
  language  = {en}
}