2017
DOI: 10.1186/s12859-017-1494-2
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ATGC transcriptomics: a web-based application to integrate, explore and analyze de novo transcriptomic data

Abstract: BackgroundIn the last years, applications based on massively parallelized RNA sequencing (RNA-seq) have become valuable approaches for studying non-model species, e.g., without a fully sequenced genome. RNA-seq is a useful tool for detecting novel transcripts and genetic variations and for evaluating differential gene expression by digital measurements. The large and complex datasets resulting from functional genomic experiments represent a challenge in data processing, management, and analysis. This problem i… Show more

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Cited by 8 publications
(8 citation statements)
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“…With the rapid development of neural networks and sensor technologies based on the Internet of Things, we can more intelligently detect athletes' training status and physical conditions. Therefore, this paper proposes a wearable device smart football player health prediction algorithm [ 10 , 11 ] based on the recurrent neural network (RNN). Firstly, wearable sensors are used to obtain the health information data [ 12 14 ] of football players and to obtain the big data of the athletes' health [ 15 , 16 ].…”
Section: Introductionmentioning
confidence: 99%
“…With the rapid development of neural networks and sensor technologies based on the Internet of Things, we can more intelligently detect athletes' training status and physical conditions. Therefore, this paper proposes a wearable device smart football player health prediction algorithm [ 10 , 11 ] based on the recurrent neural network (RNN). Firstly, wearable sensors are used to obtain the health information data [ 12 14 ] of football players and to obtain the big data of the athletes' health [ 15 , 16 ].…”
Section: Introductionmentioning
confidence: 99%
“…In order to identify putative orthologs of these senescence-associated WRKYs in petunia, the predicted protein sequences of the AtWRKY genes were used to perform BLAST searches using a public transcriptomic leaf database of Petunia x hybrida cv. 'Mitchell Diploid' (Villarino et al 2014) loaded into a web tool developed by Gonzalez et al (2017). Of the 28 AtWRKY proteins, 20 cDNA sequences were obtained in P. hybrida (PhWRKYs) by using tBLASTn (Table 1 and Supplementary Table S3).…”
Section: Resultsmentioning
confidence: 99%
“…Twenty-eight WRKY genes of Arabidopsis were selected from published studies on the leaf transcriptome (Buchanan-Wollaston et al 2005;Wagstaff et al 2009;Breeze et al 2011), public repositories including Leaf Senescence DataBase (Liu et al 2011) and Arabidopsis eFP Browser (Winter et al 2007). A leaf transcriptome dataset of P. hybrida (Villarino et al 2014) was used to create a repository to run BLAST (Petunia Transcriptome Repository ATGC v1.0) (Gonzalez et al 2017). Putative P. hybrida orthologs in this repository and the parental genomes of P. hybrida (P. axillaris and P. in ata) available at SOL Genomics Network (http:// solgenomics.net) (Bombarely et al 2016) were searched by using tBLASTn.…”
Section: Identification Of Wrky Transcription Factors In P Hybridamentioning
confidence: 99%
“…To compare the different deep models with the proposed methodology, we use various performance measures such as precision, recall, and FScore along with accuracy, as accuracy alone cannot determine the effectiveness of a model [ 98 ]. During the experimentation, 75% of the dataset is for training the model, and 25% is for testing purposes.…”
Section: Methodsmentioning
confidence: 99%