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The plover neurotranscriptome assembly: transcriptomic analysis in an ecological model species without a reference genome
Author(s) -
Moghadam Hooman K.,
Harrison Peter W.,
Zachar Gergely,
Székely Tamás,
Mank Judith E.
Publication year - 2013
Publication title -
molecular ecology resources
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.96
H-Index - 136
eISSN - 1755-0998
pISSN - 1755-098X
DOI - 10.1111/1755-0998.12096
Subject(s) - biology , transcriptome , genome , contig , reference genome , sequence assembly , genetics , expressed sequence tag , de novo transcriptome assembly , gene , plover , computational biology , evolutionary biology , gene expression , ecology , habitat
We assembled a de novo transcriptome of short‐read Illumina RNA ‐Seq data generated from telencephalon and diencephalon tissue samples from the Kentish plover, C haradrius alexandrinus . This is a species of considerable interest in behavioural ecology for its highly variable mating system and parental behaviour, but it lacks genomic resources and is evolutionarily distant from the few available avian draft genome sequences. We assembled and identified over 21 000 transcript contigs with significant expression in our samples, showing high homology to exonic sequences in avian draft genomes. From these, we identified >31 000 high‐quality SNP s and > 2500 simple sequence repeats ( SSR s). We also analysed expression patterns in our data to identify potential candidate genes related to differences in male and female behaviour, identifying over 200 nonoverlapping putative autosomal transcripts that show significant expression differences between males and females. Gene ontology analysis revealed that female‐biased transcripts were significantly enriched for cerebral functions related to learning, cognition and memory, and male‐biased transcripts were mostly enriched for terms related to neural function such as neuron projection and synapses. This data set provides one of the first de novo transcriptome assemblies from non‐normalized short‐read next‐generation data and outlines an effective strategy for measuring sequence and expression variability simultaneously without the aid of a reference genome.