Alessandro La Ferlita1,2,3, Salvatore Alaimo1, Sebastiano Di Bella4, Emanuele Martorana5, Georgios I Laliotis2, Francesco Bertoni6, Luciano Cascione6, Philip N Tsichlis2, Alfredo Ferro1, Roberta Bosotti4, Alfredo Pulvirenti7. 1. Department of Clinical and Experimental Medicine, Bioinformatics Unit, University of Catania, Catania, Italy. 2. Department of Cancer Biology and Genetics, The Ohio State University, Columbus, OH, USA. 3. Department of Physics and Astronomy, University of Catania, Catania, Italy. 4. Nerviano Medical Sciences, Nerviano, Milan, Italy. 5. Regional Referral Centre for Rare Lung Diseases, A. O. U. "Policlinico-Vittorio Emanuele", Department of Clinical and Experimental Medicine, University of Catania, Catania, Italy. 6. Institute of Oncology Research, Bellinzona, Switzerland. 7. Department of Clinical and Experimental Medicine, Bioinformatics Unit, University of Catania, Catania, Italy. alfredo.pulvirenti@unict.it.
Abstract
BACKGROUND: RNA-Seq is a well-established technology extensively used for transcriptome profiling, allowing the analysis of coding and non-coding RNA molecules. However, this technology produces a vast amount of data requiring sophisticated computational approaches for their analysis than other traditional technologies such as Real-Time PCR or microarrays, strongly discouraging non-expert users. For this reason, dozens of pipelines have been deployed for the analysis of RNA-Seq data. Although interesting, these present several limitations and their usage require a technical background, which may be uncommon in small research laboratories. Therefore, the application of these technologies in such contexts is still limited and causes a clear bottleneck in knowledge advancement. RESULTS: Motivated by these considerations, we have developed RNAdetector, a new free cross-platform and user-friendly RNA-Seq data analysis software that can be used locally or in cloud environments through an easy-to-use Graphical User Interface allowing the analysis of coding and non-coding RNAs from RNA-Seq datasets of any sequenced biological species. CONCLUSIONS: RNAdetector is a new software that fills an essential gap between the needs of biomedical and research labs to process RNA-Seq data and their common lack of technical background in performing such analysis, which usually relies on outsourcing such steps to third party bioinformatics facilities or using expensive commercial software.
BACKGROUND: RNA-Seq is a well-established technology extensively used for transcriptome profiling, allowing the analysis of coding and non-coding RNA molecules. However, this technology produces a vast amount of data requiring sophisticated computational approaches for their analysis than other traditional technologies such as Real-Time PCR or microarrays, strongly discouraging non-expert users. For this reason, dozens of pipelines have been deployed for the analysis of RNA-Seq data. Although interesting, these present several limitations and their usage require a technical background, which may be uncommon in small research laboratories. Therefore, the application of these technologies in such contexts is still limited and causes a clear bottleneck in knowledge advancement. RESULTS: Motivated by these considerations, we have developed RNAdetector, a new free cross-platform and user-friendly RNA-Seq data analysis software that can be used locally or in cloud environments through an easy-to-use Graphical User Interface allowing the analysis of coding and non-coding RNAs from RNA-Seq datasets of any sequenced biological species. CONCLUSIONS: RNAdetector is a new software that fills an essential gap between the needs of biomedical and research labs to process RNA-Seq data and their common lack of technical background in performing such analysis, which usually relies on outsourcing such steps to third party bioinformatics facilities or using expensive commercial software.
Authors: Jason S Cumbie; Jeffrey A Kimbrel; Yanming Di; Daniel W Schafer; Larry J Wilhelm; Samuel E Fox; Christopher M Sullivan; Aron D Curzon; James C Carrington; Todd C Mockler; Jeff H Chang Journal: PLoS One Date: 2011-10-06 Impact factor: 3.240
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Authors: Robert Buels; Eric Yao; Colin M Diesh; Richard D Hayes; Monica Munoz-Torres; Gregg Helt; David M Goodstein; Christine G Elsik; Suzanna E Lewis; Lincoln Stein; Ian H Holmes Journal: Genome Biol Date: 2016-04-12 Impact factor: 13.583