ATGC Transcriptomics: An Application to Explore and Analyze de novo Transcriptomic Data

M. RIVAROLA; S. GONZÁLEZ; P. MORENO; B. CLAVIJO; P. FERNANDEZ; M. FARBER; N. PANIEGO

San Diego

Conferencia; Plant & Animal Genome XXII Conference; 2014

http://www.intlpag.org/2014/connect/organizing-committee

Massively  parallelized  RNA  sequencing  technology  (RNA­seq)  in  non­model  organisms  with  no  complete  genome  assembly  has  added  a  valuable  tool  to  characterize  genomic  orphan species, giving  also  the  possibility  of different conditions assays in  evaluating  transcription  events and  digital measure  of differential  gene  expression.  The analysis  of  transcriptome  data without a reference genome involves the integration of information from assembly, structural and functional annotation. One approach, here taken, is to combine the outputs from different in­silico analysis and merge the results so as to check the reliability and view the merged­output. In this work, we present an approach using an ontology based database and a web interface to store, visualize, analyze and share de novo transcriptomic data. We also include information associated to each feature represented in the database and create relationships between them. For example genetic markers (features) are associated to a transcript feature and information from expression assays and/or the presence of alternative splicing are all inter­connected. To accomplish  our  approach,  we  use  a  customized  version  of  the  Chado  schema  from  GMOD  (Generic  Model  Organism  Database,  http://gmod.org  [1]).  The  main  goals  for  ATGC transcriptomics are the integration of data and the ability to query such data utilizing controlled vocabulary and ontologies, i.e., Gene Ontology and Sequence Ontology in a user friendly way. Moreover, using the relationship between terms predefined in the ontologies allows users to move through the ontologies graphs structure and perform searches to form new hypothesis from data exploration.