Table of Contents

• Coverage
• Dataset Description
  • Methods & Sampling
  • Data Processing Description
• Data Files
• Parameters
• Instruments
• Deployments
• Project Information
• Funding

The data include Atlantic silverside (Menidia menidia) genetic accession information at the National Center for Biotechnology Information (NCBI). Accessions in this dataset are for cDNA transcriptome or a Transcriptome Shotgun Assembly (TSA). Links to the accession at NCBI are provided along with accession number and accession type.  Atlantic silversides were collected at Poquott Beach, New York on June 20th and 21st, 2013.

Raw cDNA transcriptome sequence reads:

The raw sequence data are deposited in the NCBI Sequence Read Archive (SRA) with accession numbers SRR3990241- SRR3990248 associated with BioProject PRJNA330848 and BioSamples SAMN05427525 - SAMN05427532

Species: Atlantic silverside (Menidia menidia)
Sample type: mRNA from mix of tissues
RNA extraction method: Qiagen RNeasy Plus Universal Tissue Mini Kit
Library preparation: Illumina’s TruSeq RNA sample prep kit v2
Sequencing instrument: Illumina HiSeq 2000

Assembled Atlantic silverside transcriptome:

An assembled Atlantic silverside transcriptome is deposited in the NCBI GenBank Transcriptome Shotgun Assembly Sequence Database (TSA). This version of the project (01) has the accession number GEVY01000000, and consists of sequences GEVY01000001-GEVY01020998. The cleaned RNA-seq reads from all samples were de no assembled with two different programs: CLC Genomic Workbench v6.0.2 (both with an automatically optimized word size of 25 and a longer word size of 40) and Trinity v. r20131110 (with default settings, but retaining only the isoform with the highest mapped read depth within each subcomponent). We saw that each assembly contained a substantial set of unique transcripts not present in the other assemblies and therefore merged all three to maximize the gene space coverage in our final contig set. To reduce redundancy, we used cd-hit-est v4.5.4 to collapse the contig set into the longest representative for each unique sequence, and CAP3 v12/21/07 to meta-assemble partial assemblies of the same transcript. Following these procedures, we broke up likely chimeric contigs with the method by Yang and Smith (2013, BMC Genomics 14:328). Because we wanted to reduce our contig set to only include a single representative transcript for each silverside gene, we used a reciprocal best hit blast approach to extract non-redundant putative orthologs to the gene sets in three related species: platyfish (Xiphophorus maculatus), medaka (Oryzias latipes), and Nile tilapia (Oreochromis niloticus). We compared our contig set against the full peptide set for each reference species (downloaded from Ensemble release 75) with blastx, and then compared the peptide sequences for each species to our contig set with tblastn, in both cases using soft masking and an e-value cut-off of 10e-4. For each reference species, we recorded reciprocal best hits (RBHs) when a contig and a protein had a best match to each other. We used a sequential approach to select putative orthologs. We first extracted the contigs that were RBHs to platyfish proteins (since this species yielded the highest number of RBHs). We also added additional contigs that had a best hit to a portion of an RBH protein not covered by the RBH contig (secondary hits (maximum overlap of 10 amino acids allowed)), under the assumption that these contigs represented transcript fragments. We then added contigs that were RBHs (and the associated secondary non-overlapping hits to the same proteins) to medaka proteins that were non-redundant to the platyfish proteins. Medaka proteins were considered non-redundant if they did not have a RBH to the previously extracted RBH platyfish protein set (in a direct blastp comparison of the two protein set) or was annotated to the same zebrafish gene (ZFIN ID) as an RBH platyfish protein. We similarly added contigs that were RBH or associated secondary hits to tilipia proteins that were non-redundant to the proteins included from the other species. To recover additional high quality non-redundant transcripts, we used TransDecoder to predict coding regions in our redundancy-reduced contig set on the basis of nucleotide composition, open reading frame (ORF) length and Pfam domain content. Of the contigs predicted to contain a complete ORF, we retained the subset which did not have a significant (e-value<10e-2) blastn hit to the RBH contig set (and therefore are non-redundant).

Methods are also published in:

Therkildsen, N. O., and S. R. Palumbi.2016. Practical low-coverage genomewide sequencing of hundreds of individually barcoded samples for population and evolutionary genomics in nonmodel species. Molecular Ecology Resources. doi: 10.1111/1755-0998.12593

Assembly Method :: CLC Genomics Workbench 6.0.2; Trinity r20131110; CAP3 v12/21/07 

BCO-DMO Data Manager Processing Notes:
* added a conventional header with dataset name, PI name, version date
* modified parameter names to conform with BCO-DMO naming conventions
* broke sampling location description into sample_location and sample_state
* added lat/lon of sample site to dataset

Description from NSF award abstract:
One of the strongest impacts there is on ocean species is reduction in population size due to fishing. Even for species that are not overfished, harvest takes a large fraction of the biggest and fastest growing individuals. As a result, fishing exerts strong natural selection on fish population, selecting for slow growing, small individuals. Experiments in artificial fishing have shown rapid evolution of growth rates, maturation size and other traits for lab populations. The classic Conover-Munsch experiments ten years ago showed the power of fishing to generate rapid evolution. However, no analysis of the genetic impact of fishing under such controlled conditions has been done, and no investigation of the way whole genomes respond to strong fisheries evolution has been attempted. Luckily, the Conover-Munsch samples - from the fish used in their classic experiment - have been preserved, and modern genomic techniques are now available that can analyze the way fisheries-induced evolution shaped the genetic diversity and genome architecture of these populations. Strong selection is known in other systems to leave a legacy of deleterious changes in the genome. The results of this study will be important components of understanding the long-term effects of fishing because they will for the first time allow a mechanistic understanding of how natural selection works on fish populations. The project will also compare the changes that occur after the relaxation of fishing pressure to estimate if there is a legacy of deleterious genome changes in fished species that impedes their recovery. The data from this study will show how fishing creates change at loci under selection, and also how this strong selection generates other, non-adaptive shifts because of genetic hitchhiking and inbreeding.

The investigators will use next generation DNA sequencing to sequence the protein coding regions of the Conover-Munsch fish samples. They will discover, document and compare genetic variants across the genome in lines selected for large size, in lines selected for small size, and in the original populations in order to chart evolutionary changes at the genomic level imposed by fishing. They will use outlier analyses to pinpoint loci at which strong selection has acted to change allele frequencies. They will compare these changes to changes in fish populations after the relaxation of fishing in order to distinguish evolutionary changes that are easily reversible from those that are not. In addition, they will also compare genetic changes induced by fishing to those that occur naturally along an environmental gradient on the US east coast. Preliminary data indicate that many of the genetic variants selected for in the fisheries experiment are old variants - estimated by patterns of linkage disequilibrium - already present in the original population. Working on the genetics of the natural gradient will show which of these variants have been selected by evolutionary forces in the native environment, and subsequently been favored by the novel evolutionary pressure of fishing.