@article{GuptaSrivastavaOsmanogluetal.2020,
  author    = {Gupta, Shishir K. and Srivastava, Mugdha and Osmanoglu, Oezge and Dandekar, Thomas},
  title     = {Genome-wide inference of the Camponotus floridanus protein-protein interaction network using homologous mapping and interacting domain profile pairs},
  series = {Scientific Reports},
  volume    = {10},
  journal   = {Scientific Reports},
  number    = {1},
  doi       = {10.1038/s41598-020-59344-1},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-229406},
  year      = {2020},
  abstract  = {Apart from some model organisms, the interactome of most organisms is largely unidentified. High-throughput experimental techniques to determine protein-protein interactions (PPIs) are resource intensive and highly susceptible to noise. Computational methods of PPI determination can accelerate biological discovery by identifying the most promising interacting pairs of proteins and by assessing the reliability of identified PPIs. Here we present a first in-depth study describing a global view of the ant Camponotus floridanus interactome. Although several ant genomes have been sequenced in the last eight years, studies exploring and investigating PPIs in ants are lacking. Our study attempts to fill this gap and the presented interactome will also serve as a template for determining PPIs in other ants in future. Our C. floridanus interactome covers 51,866 non-redundant PPIs among 6,274 proteins, including 20,544 interactions supported by domain-domain interactions (DDIs), 13,640 interactions supported by DDIs and subcellular localization, and 10,834 high confidence interactions mediated by 3,289 proteins. These interactions involve and cover 30.6\% of the entire C. floridanus proteome.},
  language  = {en}
}
@article{KunzLiangNillaetal.2016,
  author    = {Kunz, Meik and Liang, Chunguang and Nilla, Santosh and Cecil, Alexander and Dandekar, Thomas},
  title     = {The drug-minded protein interaction database (DrumPID) for efficient target analysis and drug development},
  series = {Database},
  volume    = {2016},
  journal   = {Database},
  doi       = {10.1093/database/baw041},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-147369},
  pages     = {baw041},
  year      = {2016},
  abstract  = {The drug-minded protein interaction database (DrumPID) has been designed to provide fast, tailored information on drugs and their protein networks including indications, protein targets and side-targets. Starting queries include compound, target and protein interactions and organism-specific protein families. Furthermore, drug name, chemical structures and their SMILES notation, affected proteins (potential drug targets), organisms as well as diseases can be queried including various combinations and refinement of searches. Drugs and protein interactions are analyzed in detail with reference to protein structures and catalytic domains, related compound structures as well as potential targets in other organisms. DrumPID considers drug functionality, compound similarity, target structure, interactome analysis and organismic range for a compound, useful for drug development, predicting drug side-effects and structure-activity relationships.},
  language  = {en}
}
@article{WolfKuonenDandekaretal.2015,
  author    = {Wolf, Beat and Kuonen, Pierre and Dandekar, Thomas and Atlan, David},
  title     = {DNAseq workflow in a diagnostic context and an example of a user friendly implementation},
  series = {BioMed Research International},
  journal   = {BioMed Research International},
  number    = {403497},
  doi       = {10.1155/2015/403497},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-144527},
  year      = {2015},
  abstract  = {Over recent years next generation sequencing (NGS) technologies evolved from costly tools used by very few, to a much more accessible and economically viable technology. Through this recently gained popularity, its use-cases expanded from research environments into clinical settings. But the technical know-how and infrastructure required to analyze the data remain an obstacle for a wider adoption of this technology, especially in smaller laboratories. We present GensearchNGS, a commercial DNAseq software suite distributed by Phenosystems SA. The focus of GensearchNGS is the optimal usage of already existing infrastructure, while keeping its use simple. This is achieved through the integration of existing tools in a comprehensive software environment, as well as custom algorithms developed with the restrictions of limited infrastructures in mind. This includes the possibility to connect multiple computers to speed up computing intensive parts of the analysis such as sequence alignments. We present a typical DNAseq workflow for NGS data analysis and the approach GensearchNGS takes to implement it. The presented workflow goes from raw data quality control to the final variant report. This includes features such as gene panels and the integration of online databases, like Ensembl for annotations or Cafe Variome for variant sharing.},
  language  = {en}
}