http://lod.bco-dmo.org/id/dataset/686981
eng; USA
utf8
dataset
Highest level of data collection, from a common set of sensors or instrumentation, usually within the same research project
Biological and Chemical Oceanography Data Management Office (BCO-DMO)
Unavailable
508-289-2009
WHOI MS#36
Woods Hole
MA
02543
USA
info@bco-dmo.org
http://www.bco-dmo.org
Monday - Friday 8:00am - 5:00pm
For questions regarding this resource, please contact BCO-DMO via the email address provided.
pointOfContact
2017-04-06
ISO 19115-2 Geographic Information - Metadata - Part 2: Extensions for Imagery and Gridded Data
ISO 19115-2:2009(E)
Atlantic silverside (Menidia menidia) cDNA transcriptome and TSA accessions from specimens collected at Poquott Beach, New York in June of 2013 (Fishery Genome Changes project)
2017-04-06
publication
2017-04-06
revision
BCO-DMO Linked Data URI
2017-04-06
creation
http://lod.bco-dmo.org/id/dataset/686981
Stephen R. Palumbi
Stanford University
principalInvestigator
Biological and Chemical Oceanography Data Management Office (BCO-DMO)
Unavailable
508-289-2009
WHOI MS#36
Woods Hole
MA
02543
USA
info@bco-dmo.org
http://www.bco-dmo.org
Monday - Friday 8:00am - 5:00pm
For questions regarding this resource, please contact BCO-DMO via the email address provided.
publisher
Cite this dataset as: Palumbi, S. R. (2017) Atlantic silverside (Menidia menidia) cDNA transcriptome and TSA accessions from specimens collected at Poquott Beach, New York in June of 2013 (Fishery Genome Changes project). Biological and Chemical Oceanography Data Management Office (BCO-DMO). Version Date 2017-04-06 [if applicable, indicate subset used]. http://lod.bco-dmo.org/id/dataset/686981 [access date]
Atlantic silverside (Menidia menidia) cDNA transcriptome and TSA accessions Dataset Description: <p>The data include Atlantic silverside (Menidia menidia) genetic accession information at the National Center for Biotechnology Information (NCBI). Accessions in this dataset are&nbsp;for cDNA transcriptome or a Transcriptome Shotgun Assembly (TSA). Links to the accession at NCBI are provided along with accession number and accession type. &nbsp;Atlantic silversides were collected at Poquott Beach, New York on June 20th and 21st, 2013.</p> Methods and Sampling: <p><strong>Raw cDNA transcriptome sequence reads:</strong></p>
<p>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</p>
<p>Species: Atlantic silverside (Menidia menidia)<br />
Sample type: mRNA from mix of tissues<br />
RNA extraction method: Qiagen RNeasy Plus Universal Tissue Mini Kit<br />
Library preparation: Illumina’s TruSeq RNA sample prep kit v2<br />
Sequencing instrument: Illumina HiSeq 2000</p>
<p><strong>Assembled </strong><strong>Atlantic silverside transcriptome:</strong></p>
<p>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&lt;10e-2) blastn hit to the RBH contig set (and therefore are non-redundant).</p>
<p><strong>Methods are also published in:</strong></p>
<p>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:&nbsp;<a href="http://doi.org/10.1111/1755-0998.12593" target="_blank">10.1111/1755-0998.12593</a></p>
Funding provided by NSF Division of Ocean Sciences (NSF OCE) Award Number: OCE-1434325 Award URL: https://www.nsf.gov/awardsearch/show-award?AWD_ID=1434325
completed
Stephen R. Palumbi
Stanford University
831-655-6214
Hopkins Marine Station 120 Ocean View Blvd
Pacific Grove
CA
93950
USA
spalumbi@stanford.edu
pointOfContact
asNeeded
Unknown
accession_id
accession_link
sample_type
BioProject
species
sample_site
sample_state
lat_approx
lon_approx
Illumina HiSeq 2000
theme
None, User defined
accession number
external_link
sample type
species scientific name (binomial)
site
latitude
longitude
featureType
BCO-DMO Standard Parameters
Automated DNA Sequencer
instrument
BCO-DMO Standard Instruments
Palumbi_silversides
service
Deployment Activity
NY, GA, Nova Scotia, & Quebec
place
Locations
otherRestrictions
otherRestrictions
Access Constraints: none. Use Constraints: Please follow guidelines at: http://www.bco-dmo.org/terms-use Distribution liability: Under no circumstances shall BCO-DMO be liable for any direct, incidental, special, consequential, indirect, or punitive damages that result from the use of, or the inability to use, the materials in this data submission. If you are dissatisfied with any materials in this data submission your sole and exclusive remedy is to discontinue use.
High resolution genome changes during evolution in a classic fisheries experiment
https://osprey.bco-dmo.org/project/516279
High resolution genome changes during evolution in a classic fisheries experiment
<p><em>Description from NSF award abstract:</em><br />
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.</p>
<p>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.</p>
Fishery Genome Changes
largerWorkCitation
project
eng; USA
oceans
NY, GA, Nova Scotia, & Quebec
-73.1025
-73.1025
40.9475
40.9475
2017-04-06
The East coast of North America
0
BCO-DMO catalogue of parameters from Atlantic silverside (Menidia menidia) cDNA transcriptome and TSA accessions from specimens collected at Poquott Beach, New York in June of 2013 (Fishery Genome Changes project)
Biological and Chemical Oceanography Data Management Office (BCO-DMO)
Unavailable
508-289-2009
WHOI MS#36
Woods Hole
MA
02543
USA
info@bco-dmo.org
http://www.bco-dmo.org
Monday - Friday 8:00am - 5:00pm
For questions regarding this resource, please contact BCO-DMO via the email address provided.
pointOfContact
http://lod.bco-dmo.org/id/dataset-parameter/686995.rdf
Name: accession_id
Units: unitless
Description: <p>Accession identifier at National Center for Biotechnology Information (NCBI)</p>
http://lod.bco-dmo.org/id/dataset-parameter/686996.rdf
Name: accession_link
Units: unitless
Description: <p>Accession link at NCBI</p>
http://lod.bco-dmo.org/id/dataset-parameter/686997.rdf
Name: sample_type
Units: unitless
Description: <p>Sample description</p>
http://lod.bco-dmo.org/id/dataset-parameter/686998.rdf
Name: BioProject
Units: unitless
Description: <p>BioProject identifer at NCBI</p>
http://lod.bco-dmo.org/id/dataset-parameter/686999.rdf
Name: species
Units: unitless
Description: <p>Scientific name of specimen</p>
http://lod.bco-dmo.org/id/dataset-parameter/687000.rdf
Name: sample_site
Units: unitless
Description: <p>Location specimens were sampled</p>
http://lod.bco-dmo.org/id/dataset-parameter/687001.rdf
Name: sample_state
Units: unitless
Description: <p>state specimens were sampled</p>
http://lod.bco-dmo.org/id/dataset-parameter/687002.rdf
Name: lat_approx
Units: decimal degrees
Description: <p>Approximate latitude of sampling site; South is negative</p>
http://lod.bco-dmo.org/id/dataset-parameter/687003.rdf
Name: lon_approx
Units: decimal degrees
Description: <p>Approximate longitude of samling site; West is negative</p>
GB/NERC/BODC > British Oceanographic Data Centre, Natural Environment Research Council, United Kingdom
Biological and Chemical Oceanography Data Management Office (BCO-DMO)
Unavailable
508-289-2009
WHOI MS#36
Woods Hole
MA
02543
USA
info@bco-dmo.org
http://www.bco-dmo.org
Monday - Friday 8:00am - 5:00pm
For questions regarding this resource, please contact BCO-DMO via the email address provided.
pointOfContact
1672
https://datadocs.bco-dmo.org/file/3YY3gl7uXOQpRo/accessions.csv
accessions.csv
Primary data file for dataset ID 686981
download
https://osprey.bco-dmo.org/dataset/686981/data/download
download
onLine
dataset
<p><strong>Raw cDNA transcriptome sequence reads:</strong></p>
<p>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</p>
<p>Species: Atlantic silverside (Menidia menidia)<br />
Sample type: mRNA from mix of tissues<br />
RNA extraction method: Qiagen RNeasy Plus Universal Tissue Mini Kit<br />
Library preparation: Illumina’s TruSeq RNA sample prep kit v2<br />
Sequencing instrument: Illumina HiSeq 2000</p>
<p><strong>Assembled </strong><strong>Atlantic silverside transcriptome:</strong></p>
<p>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&lt;10e-2) blastn hit to the RBH contig set (and therefore are non-redundant).</p>
<p><strong>Methods are also published in:</strong></p>
<p>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:&nbsp;<a href="http://doi.org/10.1111/1755-0998.12593" target="_blank">10.1111/1755-0998.12593</a></p>
Specified by the Principal Investigator(s)
<p>Assembly Method :: CLC Genomics Workbench 6.0.2; Trinity r20131110; CAP3 v12/21/07&nbsp;</p>
<p>BCO-DMO Data Manager Processing Notes:<br />
* added a conventional header with dataset name, PI name, version date<br />
* modified parameter names to conform with BCO-DMO naming conventions<br />
* broke sampling location description into sample_location and sample_state<br />
* added lat/lon of sample site to dataset</p>
Specified by the Principal Investigator(s)
asNeeded
7.x-1.1
Biological and Chemical Oceanography Data Management Office (BCO-DMO)
Unavailable
508-289-2009
WHOI MS#36
Woods Hole
MA
02543
USA
info@bco-dmo.org
http://www.bco-dmo.org
Monday - Friday 8:00am - 5:00pm
For questions regarding this resource, please contact BCO-DMO via the email address provided.
pointOfContact
Illumina HiSeq 2000
Illumina HiSeq 2000
PI Supplied Instrument Name: Illumina HiSeq 2000 Instrument Name: Automated DNA Sequencer Instrument Short Name:Automated Sequencer Instrument Description: A DNA sequencer is an instrument that determines the order of deoxynucleotides in deoxyribonucleic acid sequences.
Deployment: Palumbi_silversides
Palumbi_silversides
Poquott Beach
shoreside
Palumbi_silversides
Stephen R. Palumbi
Stanford University
Poquott Beach
shoreside