Characterization of the transcriptome of an ecologically important avian species, the Vinous-throated Parrotbill Paradoxornis webbianus bulomachus (Paradoxornithidae; Aves)

Abstract

BackgroundAdaptive divergence driven by environmental heterogeneity has long been a fascinating topic in ecology and evolutionary biology. The study of the genetic basis of adaptive divergence has, however, been greatly hampered by a lack of genomic information. The recent development of transcriptome sequencing provides an unprecedented opportunity to generate large amounts of genomic data for detailed investigations of the genetics of adaptive divergence in non-model organisms. Herein, we used the Illumina sequencing platform to sequence the transcriptome of brain and liver tissues from a single individual of the Vinous-throated Parrotbill, Paradoxornis webbianus bulomachus, an ecologically important avian species in Taiwan with a wide elevational range of sea level to 3100 m.ResultsOur 10.1 Gbp of sequences were first assembled based on Zebra Finch (Taeniopygia guttata) and chicken (Gallus gallus) RNA references. The remaining reads were then de novo assembled. After filtering out contigs with low coverage (<10X), we retained 67,791 of 487,336 contigs, which covered approximately 5.3% of the P. w. bulomachus genome. Of 7,779 contigs retained for a top-hit species distribution analysis, the majority (about 86%) were matched to known Zebra Finch and chicken transcripts. We also annotated 6,365 contigs to gene ontology (GO) terms: in total, 122 GO-slim terms were assigned, including biological process (41%), molecular function (32%), and cellular component (27%). Many potential genetic markers for future adaptive genomic studies were also identified: 8,589 single nucleotide polymorphisms, 1,344 simple sequence repeats and 109 candidate genes that might be involved in elevational or climate adaptation.ConclusionsOur study shows that transcriptome data can serve as a rich genetic resource, even for a single run of short-read sequencing from a single individual of a non-model species. This is the first study providing transcriptomic information for species in the avian superfamily Sylvioidea, which comprises more than 1,000 species. Our data can be used to study adaptive divergence in heterogeneous environments and investigate other important ecological and evolutionary questions in parrotbills from different populations and even in other species in the Sylvioidea.

The venom-gland transcriptome of the eastern coral snake (Micrurus fulvius) reveals high venom complexity in the intragenomic evolution of venoms

“…Toxin transcripts also possessed a much higher SNP density than nontoxins. SNP density was calculated as the number of SNPs per 1,000 bases (kb) in the coding regions of toxin and nontoxin transcripts containing SNPs [35]. Nontoxin transcripts had a SNP density of 0.96 SNPs/kb while toxins had a SNP density of 1.93 SNPs/kb.…”

“…SNPs were only considered if they occurred in the coding sequence of full-length, annotated transcripts, had a SNP% ranging from 40–60%, and at least 20-fold coverage with maximum coverage bounded at 20,000-fold. These parameters are more stringent than previous SNP identification approaches [35] and provide a conservative estimation of variable sites in the coding regions of annotated transcripts. The frequency of toxin versus nontoxin heterozygous loci, relative to the number of transcripts belonging to each class identified in the transcriptome, was compared to a χ 2 distribution with one degree of freedom.…”

De Novo Assembly and Characterization of the Invasive Northern Pacific Seastar Transcriptome

“…Consequently, the similarity between some matches to sequences of different species may not represent true orthology. A filtered species list is proposed to be better able to reconcile interspecific contig homology as only longer alignments with a high sequence similarity are retained [ 57 ]. As such, we filtered the species-hit distribution to remove lower similarity BLASTX matches, retaining only those with similarity of > 60% and where a contig contains > 100 amino acid residues (n = 8,544)( Fig 2C ).…”

Representing virus-host interactions and other multi-organism processes in the Gene Ontology

“…GO ‘slims’ - subsets of the GO tailored for a particular application - have been employed widely in the analysis of large datasets e.g. [ 48 – 50 ]. With the volume of data arising from metagenomics and metatranscriptomics studies increasing apace, and the interest in the study of the microbiome and viriome of different environments, it is ever more important that bioinformatics tools be available to analyse these data.…”