De novo transcriptome assembly and annotation for the desert rainbowfish (Melanotaenia splendida tatei) with comparison with candidate genes for future climates.
Mar Genomics
; 35: 63-68, 2017 Oct.
Article
in En
| MEDLINE
| ID: mdl-28545860
ABSTRACT
Transcriptomics via RNA-seq has rapidly emerged as a powerful tool for ecological and evolutionary studies, enabling genome-scale studies of adaptation via regulation of global gene expression. Here we present a de novo transcriptome for the desert rainbowfish (Melanotaenia splendida tatei) based on individuals sampled in the Lake Eyre Basin, Australia's arid zone river system. Recently developed methods in RNA-seq and bioinformatics were used for sequencing, assembling and annotating a high-quality liver transcriptome suitable for studies of ecology and adaptation in desert rainbowfish. Transcript annotation in UniprotKB using BLASTx assigned unique protein matches to ~47% of 116,092 Trinity genes, while BLASTp assigned unique protein matches to ~35% of 62,792 predicted protein-coding regions. A full Trinotate annotation report is provided for predicted genes and their corresponding transcripts. Annotations were compared with previously identified genes for transcriptional regulation and heritable plasticity in future climates in the subtropical rainbowfish (M. duboulayi), finding ~57% of these candidate genes present in the desert rainbowfish transcriptome. We discuss the utility of transcriptomics methods for ecological studies of adaptation, while emphasising a range of methodological considerations for dealing with transcriptome datasets. This newly assembled transcriptome is expected to help elucidate mechanisms for adaptation to high temperatures and a variable climate in desert aquatic fauna.
Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Climate Change
/
Fish Proteins
/
Molecular Sequence Annotation
/
Transcriptome
/
Fishes
Type of study:
Prognostic_studies
Limits:
Animals
Country/Region as subject:
Oceania
Language:
En
Journal:
Mar Genomics
Year:
2017
Document type:
Article
Affiliation country:
Australia