http://lod.bco-dmo.org/id/dataset/854895 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 2021-06-29 ISO 19115-2 Geographic Information - Metadata - Part 2: Extensions for Imagery and Gridded Data ISO 19115-2:2009(E) Methodology information and links to data access for allele frequencies and FST estimates for 1,904,119 SNPs analyzed in five population samples of Atlantic silverside (Menidia menidia) collected along the east coast of North America between 2005 to 2007 2021-06-29 publication 2021-06-29 revision BCO-DMO Linked Data URI 2021-06-29 creation http://lod.bco-dmo.org/id/dataset/854895 Nina Overgaard Therkildsen Cornell University principalInvestigator Hannes Baumann University of Connecticut 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: Therkildsen, N. O., Baumann, H. (2021) Methodology information and links to data access for allele frequencies and FST estimates for 1,904,119 SNPs analyzed in five population samples of Atlantic silverside (Menidia menidia) collected along the east coast of North America between 2005 to 2007. Biological and Chemical Oceanography Data Management Office (BCO-DMO). (Version 1) Version Date 2021-06-29 [if applicable, indicate subset used]. http://lod.bco-dmo.org/id/dataset/854895 [access date] Methods and Sampling: &lt;p&gt;&lt;strong&gt;Methodology:&lt;/strong&gt;&lt;/p&gt; &lt;div&gt; &lt;p&gt;The following is an excerpt of the methods used to generate these data. Full details can be found in the associated publication and supplemental material of Wilder et al. (2020) and is also available at the Dryad repository where the allele frequencies and FST estimates&amp;nbsp;are stored (https://doi.org/10.5061/dryad.jm63xsj7w).&lt;/p&gt; &lt;p&gt;&lt;strong&gt;&lt;em&gt;Data generation read mapping and SNP calling:&lt;/em&gt;&lt;/strong&gt;&lt;/p&gt; &lt;p&gt;Atlantic silverside muscle samples collected from Georgia (GA), New York (NY), Gulf of Maine (GoM) and Gulf of St. Lawrence (GSL) were sequenced by Therkildsen &amp;amp; Palumbi (2017) to a final, average depth of 1.5x across the reference transcriptome. See Therkildsen &amp;amp; Palumbi (2017) and Therkildsen et al. (2019) for further detail. We added 47 additional samples from Oregon Inlet, North Carolina (NC) near Cape Hatteras. Individually barcoded sample libraries were prepared at Cornells Biotechnology Resource Center using similar methods to Therkildsen &amp;amp; Palumbi (2017), with a few minor modifications. Reagents from the Illumina Nextera kit (96 sample Nextera DNA Library Prep Kit) were used at 1/3 the recommended concentration in 1/10 the recommended volume (5ul instead of 50ul), with 2ng of input DNA. Individual libraries were pooled, and size-selected using a Pippin Prep to remove fragments &amp;lt;286 bp (150 bp insert plus 136 bp of Illumina adapters). Filtered mapped reads for the NC samples had a final average depth of 1.5x.&lt;/p&gt; &lt;p&gt;We generated paired-end 75 bp sequences on the NextSeq500. Filtered mapped reads for the NC samples had a final average depth of 1.5x.&lt;/p&gt; &lt;p&gt;Read adapters were trimmed from the paired-end reads using Trimmomatic v.0.3.6 (Bolger et al. 2014), and mapped to the reference transcriptome with Bowtie2 v2.2.3 (Langmead and Salzberg 2012), using the very-sensitive-local mode and retaining unpaired, orphaned and concordantly paired reads with mapping quality &amp;gt;20. Duplicate reads were removed with MarkDuplicates (PICARD Tools v1.139; http://broadinstitute.github.io/picard/). To avoid spurious SNP calls stemming from differences in read length in the NC sample (Leigh et al. 2018), we called SNPs across the original four population sample set (GA, NY, GoM and GSL). Downstream analyses of all samples (including NC) were performed on this set of SNPs. Because NC is located in the center of the distribution of the southern phylogeographic group (Mach et al. 2005; Lou et al. 2018), we do not expect to have missed variants private to that population by excluding it from the SNP calling. Biallelic SNPs were called in the program ANGSD v. 0.912 (Korneliussen et al. 2014). ANGSD estimates genotype likelihoods at each site based on the aligned reads and their mapping and sequencing quality scores. Using genotype likelihoods for low-coverage sequencing data allows for the incorporation of uncertainty in genotyping, providing more accurate population genetic inference (Li 2011; Nielsen et al. 2011). We called polymorphic sites across the four original populations (n = 189), at sites with a probability &amp;lt;1e-6 of being monomorphic, using the SAMtools model of genotype likelihoods in ANGSD. Bases with quality score &amp;lt;20 were excluded. We excluded sites with minor allele frequency (MAF) &amp;lt; 0.01, total read depth &amp;lt;75 and &amp;gt;759 (mean depth +1 standard deviation), or data from &amp;lt;75 individuals. ANGSD called 1,942,329 SNPs that passed the above filters. From these, we filtered out 38,210 SNPs within repetitive elements identified by RepeatMasker (Smit et al. 2017). The final dataset comprised 1,904,119 bialleleic SNPs across the ~52 MB transcriptome.&lt;/p&gt; &lt;p&gt;&lt;strong&gt;&lt;em&gt;Estimating allele frequencies, identifying FST outliers and functional annotation of SNPs&lt;/em&gt;&lt;/strong&gt;&lt;/p&gt; &lt;p&gt;At all globally variant sites, major and minor alleles were inferred based on global allele frequencies and minor allele frequencies (MAF) were estimated within populations from genotype likelihoods at sites with data for at least 10 individuals in the program ANGSD. We estimated pairwise FST at each SNP between population pairs using the 2-dimensional site frequency spectrum (2D SFS) as prior.&lt;/p&gt; &lt;p&gt;For each SNP, we estimated the global FST across all five populations as in the OutFLANK script FST functions.R, except that we made a slight modification to the script so that we could directly input allele frequencies and sample sizes estimated in ANGSD (which takes genotype likelihoods into account), rather than estimating allele frequencies from allele counts in OutFLANK. FST was then estimated by the procedure implemented in OutFLANK. We fit the X2 distribution to the pruned SNP set by trimming 5% from each side of the FST distribution and using q&amp;lt;0.05 to estimate the degrees of freedom and FSTbar of the X2 distribution in OutFLANK (Whitlock and Lotterhos 2015). We then applied the X2 fit to all SNPs across the transcriptome to identify FST outliers using q&amp;lt;0.05.&lt;/p&gt; &lt;p&gt;To understand the potential function of SNPs within the transcriptome, we used two programs, Transdecoder and GeneMarkS-T, to predict coding sequences (CDS) and untranslated regions (UTR). From these annotations, we used snpEff to predict the function (e.g., missense variant, synonymous variant, 3 UTR variant) of each SNP.&lt;/p&gt; &lt;p&gt;&lt;strong&gt;&lt;em&gt;Identifying LD blocks and ordering Atlantic silverside genes along medaka chromosomes&lt;/em&gt;&lt;/strong&gt;&lt;/p&gt; &lt;p&gt;We estimated linkage disequilibrium between all pairs of SNPs identified as FST outliers. Because the majority of these SNPs were fixed for opposite alleles at the extremes of the geographic distribution, precluding LD inference, we limited our LD analysis to the three sampling locations in the center of the range (NC, NY and GoM). Within each of these populations, we estimated LD across pairs of SNPs with MAF&amp;gt;0.1 in the program ngsLD. We summarized patterns by calculating an average LD between pairs of contigs that had at least 2 outlier SNPs with MAF&amp;gt;0.1. For each pair of these contigs, we calculated the mean D across all pairs of outlier SNPs between contigs. We used a hierarchical clustering approach to determine how contigs clustered into blocks within each population by generating dendrograms from the pairwise LD matrix using the Ward.D method in the hclust function in R (Murtagh and Legendre 2014).&lt;/p&gt; &lt;p&gt;We downloaded all medaka peptide sequences and used blastx to compare silversides contigs to medaka peptides. We then compared medaka peptides to silverside contigs using tblastn with soft masking and an e-value &amp;lt; 10-4. 19,230 of the 20,998 contigs had a reciprocal best hit to the medaka genome. Of these, 17,724 contigs mapped to one of the 24 medaka chromosomes.&lt;/p&gt; &lt;p&gt;&lt;strong&gt;Locations:&lt;/strong&gt;&lt;br /&gt; Five locations along the east coast of North America. Samples&amp;nbsp;originally collected for the analysis were presented in Hice et al. (2012).&lt;/p&gt; &lt;div&gt;USA:Jekyll Island,Georgia 31.02 -81.43&lt;br /&gt; USA:Oregon Inlet,NorthCarolina 35.77 -75.52&lt;br /&gt; USA:Minas Basin,Gulf of Maine 45.2 -64.38&lt;br /&gt; USA:Patchogue,NewYork 40.75 -73.00&lt;br /&gt; Canada:Magdalen Island, Gulf of St. Lawrence 47.4 -61.85&lt;/div&gt; &lt;/div&gt; Funding provided by NSF Division of Ocean Sciences (NSF OCE) Award Number: OCE-1756316 Award URL: https://www.nsf.gov/awardsearch/show-award?AWD_ID=1756316 Funding provided by NSF Division of Ocean Sciences (NSF OCE) Award Number: OCE-1756751 Award URL: https://www.nsf.gov/awardsearch/show-award?AWD_ID=1756751 completed Nina Overgaard Therkildsen Cornell University 607-255-2014 Department of Natural Resources 208 Fernow Hall Ithaca NY 14853 USA nt246@cornell.edu pointOfContact Hannes Baumann University of Connecticut 860-405-9297 Department of Marine Sciences 1080 Shennecossett Road Groton CT 06340-6048 USA hannes.baumann@uconn.edu pointOfContact asNeeded Dataset Version: 1 Unknown Illumina HiSeq 2000 Illumina NextSeq500 theme None, User defined Automated DNA Sequencer Automated DNA Sequencer instrument BCO-DMO Standard Instruments 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. Collaborative research: The genomic underpinnings of local adaptation despite gene flow along a coastal environmental cline https://befel.marinesciences.uconn.edu/2018/03/07/research-news-new-nsf-grant-to-study-silverside-genes/ Collaborative research: The genomic underpinnings of local adaptation despite gene flow along a coastal environmental cline <p>NSF Abstract:</p> <p>Oceans are large, open habitats, and it was previously believed that their lack of obvious barriers to dispersal would result in extensive mixing, preventing organisms from adapting genetically to particular habitats. It has recently become clear, however, that many marine species are subdivided into multiple populations that have evolved to thrive best under contrasting local environmental conditions. Nevertheless, we still know very little about the genomic mechanisms that enable divergent adaptations in the face of ongoing intermixing. This project focuses on the Atlantic silverside (Menidia menidia), a small estuarine fish that exhibits a remarkable degree of local adaptation in growth rates and a suite of other traits tightly associated with a climatic gradient across latitudes. Decades of prior lab and field studies have made Atlantic silverside one of the marine species for which we have the best understanding of evolutionary tradeoffs among traits and drivers of selection causing adaptive divergence. Yet, the underlying genomic basis is so far completely unknown. The investigators will integrate whole genome sequencing data from wild fish sampled across the distribution range with breeding experiments in the laboratory to decipher these genomic underpinnings. This will provide one of the most comprehensive assessments of the genomic basis for local adaptation in the oceans to date, thereby generating insights that are urgently needed for better predictions about how species can respond to rapid environmental change. The project will provide interdisciplinary training for a postdoc as well as two graduate and several undergraduate students from underrepresented minorities. The findings will also be leveraged to develop engaging teaching and outreach materials (e.g. a video documentary and popular science articles) to promote a better understanding of ecology, evolution, and local adaptation among science students and the general public.</p> <p>The goal of the project is to characterize the genomic basis and architecture underlying local adaptation in M. menidia and examine how the adaptive divergence is shaped by varying levels of gene flow and maintained over ecological time scales. The project is organized into four interconnected components. Part 1 examines fine-scale spatial patterns of genomic differentiation along the adaptive cline to a) characterize the connectivity landscape, b) identify genomic regions under divergent selection, and c) deduce potential drivers and targets of selection by examining how allele frequencies vary in relation to environmental factors and biogeographic features. Part 2 maps key locally adapted traits to the genome to dissect their underlying genomic basis. Part 3 integrates patterns of variation in the wild (part 1) and the mapping of traits under controlled conditions (part 2) to a) examine how genomic architectures underlying local adaptation vary across gene flow regimes and b) elucidating the potential role of chromosomal rearrangements and other tight linkage among adaptive alleles in facilitating adaptation. Finally, part 4 examines dispersal - selection dynamics over seasonal time scales to a) infer how selection against migrants and their offspring maintains local adaptation despite homogenizing connectivity and b) validate candidate loci for local adaptation. Varying levels of gene flow across the species range create a natural experiment for testing general predictions about the genomic mechanisms that enable adaptive divergence in the face of gene flow. The findings will therefore have broad implications and will significantly advance our understanding of the role genomic architecture plays in modifying the gene flow - selection balance within coastal environments.</p> <p>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.</p> GenomAdapt largerWorkCitation project eng; USA oceans -81.43 -61.85 31.02 47.4 2005-01-01 2007-01-01 Eastern coastline of North America 0 BCO-DMO catalogue of parameters from Methodology information and links to data access for allele frequencies and FST estimates for 1,904,119 SNPs analyzed in five population samples of Atlantic silverside (Menidia menidia) collected along the east coast of North America between 2005 to 2007 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 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 https://osprey.bco-dmo.org/dataset/854895/data/download download onLine dataset &lt;p&gt;&lt;strong&gt;Methodology:&lt;/strong&gt;&lt;/p&gt; &lt;div&gt; &lt;p&gt;The following is an excerpt of the methods used to generate these data. Full details can be found in the associated publication and supplemental material of Wilder et al. (2020) and is also available at the Dryad repository where the allele frequencies and FST estimates&amp;nbsp;are stored (https://doi.org/10.5061/dryad.jm63xsj7w).&lt;/p&gt; &lt;p&gt;&lt;strong&gt;&lt;em&gt;Data generation read mapping and SNP calling:&lt;/em&gt;&lt;/strong&gt;&lt;/p&gt; &lt;p&gt;Atlantic silverside muscle samples collected from Georgia (GA), New York (NY), Gulf of Maine (GoM) and Gulf of St. Lawrence (GSL) were sequenced by Therkildsen &amp;amp; Palumbi (2017) to a final, average depth of 1.5x across the reference transcriptome. See Therkildsen &amp;amp; Palumbi (2017) and Therkildsen et al. (2019) for further detail. We added 47 additional samples from Oregon Inlet, North Carolina (NC) near Cape Hatteras. Individually barcoded sample libraries were prepared at Cornells Biotechnology Resource Center using similar methods to Therkildsen &amp;amp; Palumbi (2017), with a few minor modifications. Reagents from the Illumina Nextera kit (96 sample Nextera DNA Library Prep Kit) were used at 1/3 the recommended concentration in 1/10 the recommended volume (5ul instead of 50ul), with 2ng of input DNA. Individual libraries were pooled, and size-selected using a Pippin Prep to remove fragments &amp;lt;286 bp (150 bp insert plus 136 bp of Illumina adapters). Filtered mapped reads for the NC samples had a final average depth of 1.5x.&lt;/p&gt; &lt;p&gt;We generated paired-end 75 bp sequences on the NextSeq500. Filtered mapped reads for the NC samples had a final average depth of 1.5x.&lt;/p&gt; &lt;p&gt;Read adapters were trimmed from the paired-end reads using Trimmomatic v.0.3.6 (Bolger et al. 2014), and mapped to the reference transcriptome with Bowtie2 v2.2.3 (Langmead and Salzberg 2012), using the very-sensitive-local mode and retaining unpaired, orphaned and concordantly paired reads with mapping quality &amp;gt;20. Duplicate reads were removed with MarkDuplicates (PICARD Tools v1.139; http://broadinstitute.github.io/picard/). To avoid spurious SNP calls stemming from differences in read length in the NC sample (Leigh et al. 2018), we called SNPs across the original four population sample set (GA, NY, GoM and GSL). Downstream analyses of all samples (including NC) were performed on this set of SNPs. Because NC is located in the center of the distribution of the southern phylogeographic group (Mach et al. 2005; Lou et al. 2018), we do not expect to have missed variants private to that population by excluding it from the SNP calling. Biallelic SNPs were called in the program ANGSD v. 0.912 (Korneliussen et al. 2014). ANGSD estimates genotype likelihoods at each site based on the aligned reads and their mapping and sequencing quality scores. Using genotype likelihoods for low-coverage sequencing data allows for the incorporation of uncertainty in genotyping, providing more accurate population genetic inference (Li 2011; Nielsen et al. 2011). We called polymorphic sites across the four original populations (n = 189), at sites with a probability &amp;lt;1e-6 of being monomorphic, using the SAMtools model of genotype likelihoods in ANGSD. Bases with quality score &amp;lt;20 were excluded. We excluded sites with minor allele frequency (MAF) &amp;lt; 0.01, total read depth &amp;lt;75 and &amp;gt;759 (mean depth +1 standard deviation), or data from &amp;lt;75 individuals. ANGSD called 1,942,329 SNPs that passed the above filters. From these, we filtered out 38,210 SNPs within repetitive elements identified by RepeatMasker (Smit et al. 2017). The final dataset comprised 1,904,119 bialleleic SNPs across the ~52 MB transcriptome.&lt;/p&gt; &lt;p&gt;&lt;strong&gt;&lt;em&gt;Estimating allele frequencies, identifying FST outliers and functional annotation of SNPs&lt;/em&gt;&lt;/strong&gt;&lt;/p&gt; &lt;p&gt;At all globally variant sites, major and minor alleles were inferred based on global allele frequencies and minor allele frequencies (MAF) were estimated within populations from genotype likelihoods at sites with data for at least 10 individuals in the program ANGSD. We estimated pairwise FST at each SNP between population pairs using the 2-dimensional site frequency spectrum (2D SFS) as prior.&lt;/p&gt; &lt;p&gt;For each SNP, we estimated the global FST across all five populations as in the OutFLANK script FST functions.R, except that we made a slight modification to the script so that we could directly input allele frequencies and sample sizes estimated in ANGSD (which takes genotype likelihoods into account), rather than estimating allele frequencies from allele counts in OutFLANK. FST was then estimated by the procedure implemented in OutFLANK. We fit the X2 distribution to the pruned SNP set by trimming 5% from each side of the FST distribution and using q&amp;lt;0.05 to estimate the degrees of freedom and FSTbar of the X2 distribution in OutFLANK (Whitlock and Lotterhos 2015). We then applied the X2 fit to all SNPs across the transcriptome to identify FST outliers using q&amp;lt;0.05.&lt;/p&gt; &lt;p&gt;To understand the potential function of SNPs within the transcriptome, we used two programs, Transdecoder and GeneMarkS-T, to predict coding sequences (CDS) and untranslated regions (UTR). From these annotations, we used snpEff to predict the function (e.g., missense variant, synonymous variant, 3 UTR variant) of each SNP.&lt;/p&gt; &lt;p&gt;&lt;strong&gt;&lt;em&gt;Identifying LD blocks and ordering Atlantic silverside genes along medaka chromosomes&lt;/em&gt;&lt;/strong&gt;&lt;/p&gt; &lt;p&gt;We estimated linkage disequilibrium between all pairs of SNPs identified as FST outliers. Because the majority of these SNPs were fixed for opposite alleles at the extremes of the geographic distribution, precluding LD inference, we limited our LD analysis to the three sampling locations in the center of the range (NC, NY and GoM). Within each of these populations, we estimated LD across pairs of SNPs with MAF&amp;gt;0.1 in the program ngsLD. We summarized patterns by calculating an average LD between pairs of contigs that had at least 2 outlier SNPs with MAF&amp;gt;0.1. For each pair of these contigs, we calculated the mean D across all pairs of outlier SNPs between contigs. We used a hierarchical clustering approach to determine how contigs clustered into blocks within each population by generating dendrograms from the pairwise LD matrix using the Ward.D method in the hclust function in R (Murtagh and Legendre 2014).&lt;/p&gt; &lt;p&gt;We downloaded all medaka peptide sequences and used blastx to compare silversides contigs to medaka peptides. We then compared medaka peptides to silverside contigs using tblastn with soft masking and an e-value &amp;lt; 10-4. 19,230 of the 20,998 contigs had a reciprocal best hit to the medaka genome. Of these, 17,724 contigs mapped to one of the 24 medaka chromosomes.&lt;/p&gt; &lt;p&gt;&lt;strong&gt;Locations:&lt;/strong&gt;&lt;br /&gt; Five locations along the east coast of North America. Samples&amp;nbsp;originally collected for the analysis were presented in Hice et al. (2012).&lt;/p&gt; &lt;div&gt;USA:Jekyll Island,Georgia 31.02 -81.43&lt;br /&gt; USA:Oregon Inlet,NorthCarolina 35.77 -75.52&lt;br /&gt; USA:Minas Basin,Gulf of Maine 45.2 -64.38&lt;br /&gt; USA:Patchogue,NewYork 40.75 -73.00&lt;br /&gt; Canada:Magdalen Island, Gulf of St. Lawrence 47.4 -61.85&lt;/div&gt; &lt;/div&gt; 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 PI Supplied Instrument Description:Illumina HiSeq 2000 with paired-end 125 bp (for samples from GA, NY, GoM, and GSL) 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. Illumina NextSeq500 Illumina NextSeq500 PI Supplied Instrument Name: Illumina NextSeq500 PI Supplied Instrument Description:Illumina NextSeq500 paired-end 75 bp (for samples from NC). 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.