DOI: 10.5061/dryad.rr4xgxdg4
title: Raw sequence data and analytical resources for: Mitochondrial genome structure and composition in 70 fishes: a key resource for fisheries management in the South Atlantic

description: Background Phylogenetic gaps of public databases of reference sequences
 are a major obstacle for comparative genomics and management of marine
 resources, particularly in the Global South, where economically important
 fisheries and conservation flagship species often lack closely-related
 references. We applied target-enrichment to obtain complete mitochondrial
 genomes of marine ichthyofauna from the Brazilian coast selected based on
 economic significance, conservation status and lack of
 phylogenetically-close references. These included sardines (Dorosomatidae,
 Alosidae), mackerels (Scombridae) croakers (Sciaenidae), groupers
 (Epinephelidae) and snappers (Lutjanidae). Results Custom baits were
 designed to enrich mitochondrial DNA across a broad phylogenetic range of
 fishes. Sequencing generated approximately 100k reads per sample, which
 were assembled in a total of 70 complete mitochondrial genomes and include
 fifty-two new additions to GenBank, including five species with no
 previous mitochondrial data. Departures from the typical gene content and
 order occurred in only three taxa and mostly involved tRNA gene
 duplications. Start-codons for all genes, except Cytochrome C Oxidase
 subunit I (COI), were consistently ATG, whilst a wide range of stop-codons
 deviated from the prevailing TAA. Phylogenetic analysis confirmed assembly
 accuracy and revealed signs of cryptic diversification within the Mullus
 genus. Lineage delimitation methods using Sardinella aurita and S.
 brasiliensis mitochondrial genomes support a single Operational Taxonomic
 Unit. Conclusions Target enrichment was highly efficient, providing
 complete novel mitochondrial genomes with little sequencing effort. These
 sequences are deposited in public databases to enable subsequent studies
 in population genetics and adaptation of Latin American fish species and
 serve as a vital resource for conservation and management programs that
 rely on molecular data for species and genus-level identification.
description: # Raw sequence data and analytical resources for "Mitochondrial
 genome structure and composition in 70 fishes: a key resource for
 fisheries management in the South Atlantic"
 This repository contains
 raw sequence data (Illumina MiSeq reads 150 bp) and scripts applied for
 all analyses performed in the paper "Mitochondrial genome structure
 and composition in 70 fishes: a key resource for fisheries management in
 the South Atlantic" Alvarenga & D'Elia et al. 2024. ##
 Analytical Resources 1: Genome Assembly, Mapping, and Pilon Pipeline (MA,
 FH, AD - September 2021 - September 2022): *UNIX-Scripts.sh* ### Section
 1: GENOME ASSEMBLY WITH NOVOPLASTY (MA - September 2021) * **About**:
 Assembly of mitochondrial genomes using NOVOPlasty. * **Requirements**:
 Reads (.fastq) and config files (.txt) in the working folder. * **Contig
 files**: The config files can be downloaded here
 ( *
 **Parameters**: K-mer = 39, Insert Size = 550, Optional parameters =
 Insert Range = 1.8, Insert Range strict = 1.3. ### Section 2: MAPPING AND
 PILON PIPELINE (FH & MA - September 2021) * **About**: Mapping
 mitochondrial genomes, correcting errors with Pilon, and re-mapping. *
 **Requirements**: Reference assembly (.fasta), reads (.fastq), and
 Mitofish results (.txt, .fas, \_genes.fa, etc). * Check that the sample
 names (prefix) of the refs and reads are identical * It will create a new
 folder “pilon_results” containing the remaped reads to the corrected fasta
 assembly ### Section 3: MAXIMUM LIKELIHOOD PHYLOGENETIC INFERENCE PIPELINE
 (AD - September 2022) * **About**: Maximum likelihood phylogenetic
 analysis using local BLAST and RAxML. * **Requirements**: Reference
 database (Mitofish_db) and assembled sequences after quality control
 (SAMPLES.fa). * It will create a new FASTA file (multifasta.fa) containing
 reference genomes and your .fa files * The new FASTA file will be used as
 input file for the ML inference ## Analytical Resources 2: Extract
 Coverage Stats and Alignment Rate (MA - April 2022): *Python-Scripts.py*
 ### Section 1: Extract Coverage Stats with COVERAGE-STATS.PY * **About**:
 Calculate basic coverage statistics. * **Requirements**:
 'Sample_ID.txt' with genome names and '$0.cov.txt'
 files from the mapping pipeline. ### Section 2: Extract Percentage of
 Mapping Stats with ALIGNMENT-RATE.PY * **About**: Extract alignment
 percentage from all genomes. * **Requirements**: 'Sample_ID.txt'
 with genome names and '$0.stat.txt' files from the mapping
 pipeline. ## Analytical Resources 3: Species Delimitation by GMYC/Splits
 Package (MA and AKPD - April-July 2022): *R-Scripts.R* ### Section 1:
 Species Delimitation by GMYC/splits package (AKPD - July 2022) *
 **Requirements**: Downloading the 'splits' R package and a
 bayesian tree file in NEXUS format.