title: Using deep RNA sequencing for the structural annotation of the Laccaria bicolor mycorrhizal transcriptome.
abstract: BACKGROUND
  Accurate structural annotation is important for prediction of function and required for in vitro approaches to characterize or validate the gene expression products. Despite significant efforts in the field, determination of the gene structure from genomic data alone is a challenging and inaccurate process. The ease of acquisition of transcriptomic sequence provides a direct route to identify expressed sequences and determine the correct gene structure.
  
  
  METHODS
  We developed methods to utilize RNA-seq data to correct errors in the structural annotation and extend the boundaries of current gene models using assembly approaches. The methods were validated with a transcriptomic data set derived from the fungus Laccaria bicolor, which develops a mycorrhizal symbiotic association with the roots of many tree species. Our analysis focused on the subset of 1501 gene models that are differentially expressed in the free living vs. mycorrhizal transcriptome and are expected to be important elements related to carbon metabolism, membrane permeability and transport, and intracellular signaling. Of the set of 1501 gene models, 1439 (96%) successfully generated modified gene models in which all error flags were successfully resolved and the sequences aligned to the genomic sequence. The remaining 4% (62 gene models) either had deviations from transcriptomic data that could not be spanned or generated sequence that did not align to genomic sequence. The outcome of this process is a set of high confidence gene models that can be reliably used for experimental characterization of protein function.
  
  
  CONCLUSIONS
  69% of expressed mycorrhizal JGI "best" gene models deviated from the transcript sequence derived by this method. The transcriptomic sequence enabled correction of a majority of the structural inconsistencies and resulted in a set of validated models for 96% of the mycorrhizal genes. The method described here can be applied to improve gene structural annotation in other species, provided that there is a sequenced genome and a set of gene models.
authors.affiliation: Biosciences Division, Argonne National Laboratory, Lemont, Illinois, United States of America.
authors.forename: Peter E
authors.initials: PE
authors.lastname: Larsen
authors.forename: Geetika
authors.initials: G
authors.lastname: Trivedi
authors.forename: Avinash
authors.initials: A
authors.lastname: Sreedasyam
authors.forename: Vincent
authors.initials: V
authors.lastname: Lu
authors.forename: Gopi K
authors.initials: GK
authors.lastname: Podila
authors.forename: Frank R
authors.initials: FR
authors.lastname: Collart
mesh_terms: D020869:Gene Expression Profiling / Q000379:methods*; D005800:Genes, Fungal / Q000235:genetics; D055399:Laccaria / Q000235:genetics*; D012313:RNA / Q000235:genetics; D017423:Sequence Analysis, RNA / Q000379:methods*
chemical_list: D012313:RNA