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 is especially significant for small research groups working with non-model species.ResultsWe developed a web-based application, called ATGC transcriptomics, with a flexible and adaptable interface that allows users to work with new generation sequencing (NGS) transcriptomic analysis results using an ontology-driven database. This new application simplifies data exploration, visualization, and integration for a better comprehension of the results.ConclusionsATGC transcriptomics provides access to non-expert computer users and small research groups to a scalable storage option and simple data integration, including database administration and management. The software is freely available under the terms of GNU public license at http://atgcinta.sourceforge.net.
Highlights
In 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
Functional genomic analysis of species lacking a reference genome involves the integration of heterogeneous data sources including transcriptome assembly and annotation, along with gene expression and genetic variants
We present the ATGC transcriptomics, a webbased application that arose from the need for genomic data management of small research groups
Summary
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. A typical de novo RNA-seq pipeline involves; (i) Sequence quality control (ii) Transcript assembly, (iii) Functional and structural annotation of those transcripts, (iv) Discovery of molecular markers (microsatellites (SSRs) and single nucleotide polymorphisms (SNPs), and (v) Differential expression of transcripts between the conditions assayed (See Conesa, A. et al, 2016 for a detailed review: [3]). All these steps produce large and complex structured results which need to be handled properly. Most of the applications performing these steps in a de novo RNA-seq pipeline greatly aided small research groups but, usually, these same groups lack data management capabilities and know how
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