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Figure 1 in Mitochondrial genomes of four pierid butterfly species (Lepidoptera: Pieridae) with assessments about Pieridae phylogeny upon multiple mitogenomic datasets

Figure 1. Circular map of Baltia butleri, Talbotia naganum, Pontia callidice, Pontia daplidice mitochondrial genome. COI, COII, and COIII refer to the cytochrome oxidase subunits; CytB refers to cytochrome B; ATP6 and ATP8 refer to subunits 6 and 8 of F0 ATPase; ND1-6 refers to the components of NADH dehydrogenase. The tRNAs locations are marked by the color blocks and labeled by the IUPAC-IUB single letter amino acid code. L1, L2, S1, and S2 denote tRNALeu (CUN), tRNALeu (UUR), tRNASer (AGN), and tRNASer (UCN), respectively. The non-underlined genes are transcribed on the majority strand whereas the underlined genes are transcribed on the minority strand.

Figure 16 in Mitochondrial genomes of four pierid butterfly species (Lepidoptera: Pieridae) with assessments about Pieridae phylogeny upon multiple mitogenomic datasets

Figure 16. Bayesian inference (BI) and Maximum likehood (ML) phylogenetic trees inferred from mitochondrial genomes of pierid family based on 22tRNA genes.

Figure 3 in Mitochondrial genomes of four pierid butterfly species (Lepidoptera: Pieridae) with assessments about Pieridae phylogeny upon multiple mitogenomic datasets

Figure 3. Relative Synonymous Codon Usage (RSCU) of the four pierid butterfly mitogenomes newly determined in this study.

Figure 15 in Mitochondrial genomes of four pierid butterfly species (Lepidoptera: Pieridae) with assessments about Pieridae phylogeny upon multiple mitogenomic datasets

Figure 15. Bayesian inference (BI) and Maximum likehood (ML) phylogenetic trees inferred from mitochondrial genomes of pierid family based on four datasets (13PCGs, 13PCGs+2rRNAs, 2rRNAs, 2rRNAs+22tRNAs).

opencc-by-4.0Aug 2021View details →

Data from: Refining the evolutionary time machine: an assessment of whole genome amplification using single historical Daphnia eggs

This dataset contains the original raw sequence files used in the associated publication "Refining the evolutionary time machine: an assessment of whole genome amplification using single historical Daphnia eggs" by O'Grady, Dhandapani, Colbourne & Frisch in Molecular Ecology Resources. DOI:10.1111/1755-0998.13524   (filename > name used in associated publication) DF1_ATTACTC-GGCTCTG_L008_R1_001.fastq.gz -> DM1 DF2_TCCGGAG-GGCTCTG_L008_R1_001.fastq.gz -> DM2 DF3_CGCTCAT-GGCTCTG_L008_R1_001.fastq.gz -> DM3 DF4_GAGATTC-GGCTCTG_L008_R1_001.fastq.gz -> DM4 DF5_CTGAAGC-GGCTCTG_L008_R1_001.fastq.gz -> DM5 DF11_CTGAAGC-AGGCGAA_L008_R1_001.fastq.gz -> DM6 DF12_TAATGCG-AGGCGAA_L008_R1_001.fastq.gz -> DM7 DF6_TAATGCG-GGCTCTG_L008_R1_001.fastq.gz -> DP1 DF7_ATTACTC-AGGCGAA_L008_R1_001.fastq.gz -> DP4 DF8_TCCGGAG-AGGCGAA_L008_R1_001.fastq.gz -> DP5 DF9_CGCTCAT-AGGCGAA_L008_R1_001.fastq.gz -> DP2 DF10_GAGATTC-AGGCGAA_L008_R1_001.fastq.gz -> DP3 170426_E00397_0064_BHJ2JWALXX_2_TP-D7-004_1.fastq.gz -> DP6 (fw) 170426_E00397_0064_BHJ2JWALXX_2_TP-D7-004_2.fastq.gz -> DP6 (rv) 170426_E00397_0064_BHJ2JWALXX_2_TP-D7-010_1.fastq.gz -> DP7 (fw) 170426_E00397_0064_BHJ2JWALXX_2_TP-D7-010_2.fastq.gz -> DP7 (rv) 170426_E00397_0064_BHJ2JWALXX_2_TP-D7-005_1.fastq.gz -> DP8 (fw) 170426_E00397_0064_BHJ2JWALXX_2_TP-D7-005_2.fastq.gz -> DP8 (rv) 170426_E00397_0064_BHJ2JWALXX_2_TP-D7-006_1.fastq.gz -> DP9 (fw) 170426_E00397_0064_BHJ2JWALXX_2_TP-D7-006_2.fastq.gz -> DP9 (rv)

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Data from: Genome wide assessment of genetic variation and population distinctiveness of the pig family in South Africa

Open the record for dataset details and reuse information.

publicJul 2020View details →

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Assessing changes in genomic divergence following a century of human mediated secondary contact among wild and captive-bred ducks

Along with manipulating habitat, the direct release of domesticated individuals into the wild is a practice used world-wide to augment wildlife populations. We test between possible outcomes of human-mediated secondary contact using genomic techniques at both historical and contemporary time scales for two iconic duck species. First, we sequence several thousand ddRAD-seq loci for contemporary mallards (Anas platyrhynchos) throughout North America, and two domestic mallard-types (i.e., known game-farm mallards and feral Khaki Campbell's). We show that North American mallards may well be becoming a hybrid swarm due to interbreeding with domesticated game-farm mallards released for hunting. Next, to attain a historical perspective, we applied a bait-capture array targeting thousands of loci in century-old (1842-1915) and contemporary (2009-2010) mallard and American black duck (A. rubripes) specimens. We conclude that American black ducks and mallards have always been closely related, with a divergence time of ~600,000 years before present, and likely evolved through prolonged isolation followed by limited bouts of gene flow (i.e., secondary contact). They continue to maintain genetic separation, a finding that overturns decades of prior research and speculation suggesting the genetic extinction of the American black duck due to contemporary interbreeding with mallards. Thus, despite having high rates of hybridization, actual gene flow is limited between mallards and American black ducks. Conversely, our historical and contemporary data confirm that the intensive stocking of game-farm mallards during the last ~100 years has fundamentally changed the genetic integrity of North America's wild mallard population, especially in the east. It thus becomes of great interest to ask whether the iconic North American mallard is declining in the wild due to introgression of maladaptive traits from domesticated forms. Moreover, we hypothesize that differential gene flow from domestic game-farm mallards into the wild mallard population may explain the overall temporal increase in differentiation between wild black ducks and mallards, as well as the uncoupling of genetic diversity and effective population size estimates across time in our results. Finally, our findings highlight how genomic methods can recover complex population histories by capturing DNA preserved in traditional museum specimens.

opencc-zeroJan 2020View details →

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VCF files and regression analyses for: Assessing fine-scale pondscape connectivity with amphibian eyes: an integrative approach using genomic and capture-mark-recapture data

In the face of habitat loss, preserving functional connectivity is essential to maintain genetic diversity and the demographic dynamics required for the viability of biotic communities. This requires knowledge of the dispersal behavior of target species, which can be modeled as kernels, or probability density functions of dispersal distances at increasing geographic distances. We present an integrative approach to investigate the relationships between genetic connectivity and demographic parameters in organisms with low vagility focusing on five syntopic pond-breeding amphibians. We genotyped 1,056 individuals of two anuran and three urodele species (1,732–3,913 SNPs per species) from populations located in a landscape comprising 64 ponds to characterize fine-scale genetic structure in a comparative framework and combined this genetic data with information obtained in a previous two-year capture-mark-recapture (CMR) study. Specifically, we contrasted graphs reconstructed from genomic data with connectivity graphs based on dispersal kernels and demographic information obtained from CMR data from previous studies and assessed the effects of population size, population density, geographical distances, inverse movement probabilities and the presence of habitat patches potentially functioning as stepping stones on genetic differentiation. Our results suggest a significant influence of local population sizes on patterns of genetic connectivity at small spatial scales. In addition, movement records and cluster-derived kernels provide robust inferences on most likely dispersal paths that are consistent with genomic inferences on genetic connectivity. The integration of genetic and CMR data holds great potential for understanding genetic connectivity at spatial scales relevant to individual organisms, with applications for the implementation of management actions at the landscape level.

opencc-zeroNov 2023View details →

opencc-by-4.0Jan 2024View details →

Data and code for: Assessing the Reliability of Point Mutation as Data Augmentation for Deep Learning with Genomic Data

Data and code for the paper "Assessing the Reliability of Point Mutation as Data Augmentation for Deep Learning with Genomic Data".

Figure 5 in Mitochondrial genomes of four pierid butterfly species (Lepidoptera: Pieridae) with assessments about Pieridae phylogeny upon multiple mitogenomic datasets

Figure 5. Predicted secondary structures of Pontia callidice and Pontia daplidice 22 tRNA genes.

Figure 10 in Mitochondrial genomes of four pierid butterfly species (Lepidoptera: Pieridae) with assessments about Pieridae phylogeny upon multiple mitogenomic datasets

Figure 10. Predicted secondary structure of Pontia callidice srRNA gene.

Figure 8 in Mitochondrial genomes of four pierid butterfly species (Lepidoptera: Pieridae) with assessments about Pieridae phylogeny upon multiple mitogenomic datasets

Figure 8. Predicted secondary structure of Talbotia naganum srRNA gene.

Figure 6 in Mitochondrial genomes of four pierid butterfly species (Lepidoptera: Pieridae) with assessments about Pieridae phylogeny upon multiple mitogenomic datasets

Figure 6. Predicted secondary structure of Baltia butleri srRNA gene.

Figure 4 in Mitochondrial genomes of four pierid butterfly species (Lepidoptera: Pieridae) with assessments about Pieridae phylogeny upon multiple mitogenomic datasets

Figure 4. Predicted secondary structures of Baltia butleri and Talbotia naganum 22 tRNA genes.

Figure 12 in Mitochondrial genomes of four pierid butterfly species (Lepidoptera: Pieridae) with assessments about Pieridae phylogeny upon multiple mitogenomic datasets

Figure 12. Predicted secondary structure of Pontia daplidice srRNA gene.

opencc-zeroJun 2022View details →

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Assessing population structure and genetic diversity in U.S. Suffolk sheep to define a framework for genomic selection

Long-term sustainability of breeds depends on having sufficient genetic diversity for adaptability to change, whether driven by climatic conditions or by priorities in breeding programs. Genetic diversity in Suffolk sheep in the U.S. was evaluated in four ways: 1) using genetic relationships from pedigree data [(n=64,310 animals recorded in the U.S. National Sheep Improvement Program (NSIP)]; 2) using molecular data (n=304 Suffolk genotyped with the OvineHD BeadChip); 3) comparing Australian (n=109) and Irish (n=55) Suffolk sheep to those in the U.S. using molecular data; and 4) assessing genetic relationships (connectedness) among active Suffolk flocks (n=18) in NSIP. By characterizing genetic diversity, a goal was to define the structure of a reference population for use for genomic selection strategies in this breed. Pedigree-based mean inbreeding level for the most recent year of available data was 5.5%. Ten animals defined 22.8% of the current gene pool. The effective population size (Ne) ranged from 27.5 to 244.2 based on pedigree and was 79.5 based on molecular data. Expected (HE) and observed (HO) heterozygosity were 0.317 and 0.306, respectively. Model-based population structure included 7 subpopulations. From Principal Component Analysis, countries separated into distinct populations. Within the U.S. population, flocks formed genetically disconnected clusters. A decline in genetic diversity over time was observed from both pedigree and genomic-based derived measures with evidence of population substructure as measured by FST. Using these measures of genetic diversity, a framework for establishing a genomic reference population in U.S. Suffolk sheep engaged in NSIP was proposed.