title: samExploreR: exploring reproducibility and robustness of RNA-seq results based on SAM files.
abstract: Data from RNA-seq experiments provide us with many new possibilities to gain insights into biological and disease mechanisms of cellular functioning. However, the reproducibility and robustness of RNA-seq data analysis results is often unclear. This is in part attributed to the two counter acting goals of (i) a cost efficient and (ii) an optimal experimental design leading to a compromise, e.g. in the sequencing depth of experiments.
  
  
  
  We introduce an R package called samExploreR that allows the subsampling (m out of n bootstraping) of short-reads based on SAM files facilitating the investigation of sequencing depth related questions for the experimental design. Overall, this provides a systematic way for exploring the reproducibility and robustness of general RNA-seq studies. We exemplify the usage of samExploreR by studying the influence of the sequencing depth and the annotation on the identification of differentially expressed genes.
  
  
  
  samExploreR is available as an R package from Bioconductor.
  
  
  
  v@bio-complexity.comSupplementary information: Supplementary data are available at Bioinformatics online.
authors.affiliation: Computational Biology and Machine Learning Laboratory, Center for Cancer Research and Cell Biology, School of Medicine, Dentistry and Biomedical Sciences, Faculty of Medicine Health and Life Sciences, Queen's University Belfast, BT9 7AE Belfast, UK.
authors.forename: Alexey
authors.initials: A
authors.lastname: Stupnikov
authors.affiliation: Computational Biology and Machine Learning Laboratory, Center for Cancer Research and Cell Biology, School of Medicine, Dentistry and Biomedical Sciences, Faculty of Medicine Health and Life Sciences, Queen's University Belfast, BT9 7AE Belfast, UK School of Mathematics and Physics, Queen's University Belfast, BT7 1NN Belfast, UK.
authors.forename: Shailesh
authors.initials: S
authors.lastname: Tripathi
authors.affiliation: Computational Biology and Machine Learning Laboratory, Center for Cancer Research and Cell Biology, School of Medicine, Dentistry and Biomedical Sciences, Faculty of Medicine Health and Life Sciences, Queen's University Belfast, BT9 7AE Belfast, UK.
authors.forename: Ricardo
authors.initials: R
authors.lastname: de Matos Simoes
authors.affiliation: Northern Ireland Molecular Pathology Laboratory, Centre for Cancer Research and Cell Biology, Queen's University Belfast, BT9 7AE Belfast, UK.
authors.forename: Darragh
authors.initials: D
authors.lastname: McArt
authors.affiliation: Northern Ireland Molecular Pathology Laboratory, Centre for Cancer Research and Cell Biology, Queen's University Belfast, BT9 7AE Belfast, UK.
authors.forename: Manuel
authors.initials: M
authors.lastname: Salto-Tellez
authors.affiliation: Division of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR 72205, USA.
authors.forename: Galina
authors.initials: G
authors.lastname: Glazko
authors.affiliation: Department of Biomedical Computer Science and Mechatronics, UMIT, Hall in Tirol, Austria College of Computer and Control Engineering, Nankai University, Tianjin, P.R. China.
authors.forename: Matthias
authors.initials: M
authors.lastname: Dehmer
authors.affiliation: Computational Biology and Machine Learning Laboratory, Center for Cancer Research and Cell Biology, School of Medicine, Dentistry and Biomedical Sciences, Faculty of Medicine Health and Life Sciences, Queen's University Belfast, BT9 7AE Belfast, UK Predictive Medicine and Analytics Lab, Department of Signal Processing, Tampere University of Technology, 33720 Tampere, Finland Institute of Biosciences and Medical Technology, 33720 Tampere, Finland.
authors.forename: Frank
authors.initials: F
authors.lastname: Emmert-Streib
mesh_terms: D012313:RNA / Q000235:genetics*; D015203:Reproducibility of Results; D012107:Research Design; D017423:Sequence Analysis, RNA; D012984:Software
chemical_list: D012313:RNA