Mining for Mitochondria: 68 Mitogenomes for Wrasses and Parrotfishes (F: Labridae) From Off-Target UCE Data

Area of Science:

• Genomics
• Marine Biology
• Evolutionary Biology

Background:

• Labridae (wrasses and parrotfishes) are ecologically diverse but underrepresented in mitochondrial genomic data.
• Complete mitochondrial genomes are crucial for population genetics, phylogenetics, species identification, and environmental DNA (eDNA) studies.
• Target-capture sequencing (TCS) generates off-target reads that can be utilized for mitogenome assembly.

Purpose of the Study:

• To assemble and annotate off-target reads from an ultraconserved elements (UCE) dataset to generate novel complete mitochondrial genomes for Labridae species.
• To significantly expand the taxonomic coverage of labrid mitogenomes in public databases.
• To investigate phylogenetic relationships within Labridae using the newly generated mitogenomic data.

Main Methods:

• Utilized off-target reads from a published ultraconserved elements (UCE) dataset for Labridae.
• Assembled and annotated these off-target reads to produce complete mitochondrial genomes.
• Performed phylogenetic reconstruction using the newly assembled mitogenomes.

Main Results:

• Successfully generated complete mitochondrial genomes for 68 Labridae species, with 54 novel to NCBI.
• Increased the representation of labrid mitogenomes on NCBI's RefSeq and Nucleotide databases by approximately 8% and 8%, respectively.
• Recovered partial mitochondrial genes for 191 additional species.
• Mitogenome lengths varied, with the D-loop region showing the most length variation.
• Phylogenetic reconstruction revealed topological discordances with previous nuclear phylogenies, particularly in the placement of the Cirrhilabrinae tribe.

Conclusions:

• Off-target reads from TCS datasets offer a cost-effective method for generating valuable genomic resources.
• The newly assembled mitogenomes enhance evolutionary and conservation research within the Labridae family.
• The study highlights the utility of leveraging existing sequencing data for broader genomic discovery.