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146 results for “pattern sequence”
Data from: Complex population structure and haplotype patterns in Western Europe honey bee from sequencing a large panel of haploid drones
<p>This vcf file contains 7.023.689 SNPs and 870 honey bee samples, as described in the paper "Complex population structure and haplotype patterns in Western Europe honey bee from sequencing a large panel of haploid drones" by Wragg et al., available at https://doi.org/10.1101/2021.09.20.460798 as preprint.</p> <p>Eight hundred and seventy haploid drone samples from several honey bee subspecies hybrids were sequenced and aligned to the HAv3.1 reference genome. Sequence read alignment and genotyping quality filters were used to obtain a selection of 7.023.689 high-quality SNPs. The file Diversity_Study_629_Samples.txt corresponds to the 629 unique samples that were used for the diversity study described in the paper and can be used to recreate the restricted diversity dataset using bcftools or an equivalent software.</p> <p>Having sequenced haploid drones, heterozygous SNPs resulting from duplicated regions could be filtered out and the data is phased.</p>
Figure 5 in Genetic difference between two Schistosoma japonicum isolates with contrasting cercarial shedding patterns revealed by whole genome sequencing
Figure 5. Gene ontology enrichment analysis of genes from genome regions with strong selective signals.
Figure 2 in Genetic difference between two Schistosoma japonicum isolates with contrasting cercarial shedding patterns revealed by whole genome sequencing
Figure 2. Box plots of diversity, Tajima's D of S. japonicum and their FST. (A) Nucleotide diversity estimated in 100-kb windows sliding in 10-kb steps throughout the genome. (B) Tajima's D estimated within a nonoverlapping 100-kb window throughout the genome. (C) pairwise FST computed in 100-kb windows sliding in 10-kb steps throughout the genome.
Figure 4 in Genetic difference between two Schistosoma japonicum isolates with contrasting cercarial shedding patterns revealed by whole genome sequencing
Figure 4. Distribution of p ratios (pST/pHX) and FST values calculated in 100-kb windows sliding in 10-kb steps throughout the genome. Data points colored red and blue were identified as selected regions in ST (red dots) and in HX (blue dots), respectively. These points correspond to the 5% left and right tails of the empirical p ratio distribution, where the p ratios are 0.092 and 1.643, respectively (vertical dashed lines), and the 5% right tail of the empirical FST distribution, where FST is 0.965 (horizontal dashed line).
Figure 3 in Genetic difference between two Schistosoma japonicum isolates with contrasting cercarial shedding patterns revealed by whole genome sequencing
Figure 3. Box plots of diversity and relationship of two sample groups. (A) Nucleotide diversity estimated in 100-kb windows sliding in 10-kb steps throughout the genome. (B) Tajima's D estimated within a nonoverlapping 100-kb window throughout the genome. (C) FST between computed in 100-kb windows sliding in 10-kb steps throughout the genome.
BRAIN Journal-Two new software behavioral design patterns: Obligation Link and History Reminder-Figure 5. The Sequence Diagram of the "History Reminder" design pattern
<p>Analyzing the two main classes HistoryReminder and HistoryReminderOriginator of the “History Reminder” design pattern, we find that these two classes act as actors of the whole sequence of operations. The sequence diagram of the “History Reminder” design pattern is given below in Figure 5.</p>
BRAIN Journal-Two new software behavioral design patterns: Obligation Link and History Reminder-Figure 1. The sequence diagram of the "Obligation Link" design pattern
<p>A sequence diagram is used to model the flow of the logic of the system. A sequence diagram represents perhaps the most popular Unified Modelling Language (UML) diagram that describes the behavior of a scheme. Figure 1 shows the sequence diagram of “Obligation Link” design pattern.</p>
A codon model for associating phenotypic traits with altered selective patterns of sequence evolution
<p>Detecting the signature of selection in coding sequences and associating it with shifts in phenotypic states can unveil genes underlying complex traits. Of the various signatures of selection exhibited at the molecular level, changes in the pattern of selection at protein coding genes have been of main interest. To this end, phylogenetic branch-site codon models are routinely applied to detect changes in selective patterns along specific branches of the phylogeny. Many of these methods rely on a pre-specified partition of the phylogeny to branch categories, thus treating the course of trait evolution as fully resolved and assuming that phenotypic transitions have occurred only at speciation events. Here we present TraitRELAX, a new phylogenetic model that alleviates these strong assumptions by explicitly accounting for the uncertainty in the evolution of both trait and coding sequences. This joint statistical framework enables the detection of changes in selection intensity upon repeated trait transitions. We evaluated the performance of TraitRELAX using simulations and then applied it to two case studies. Using TraitRELAX, we found an intensification of selection in the primate SEMG2 gene in polygynandrous species compared to species of other mating forms, as well as changes in the intensity of purifying selection operating on sixteen bacterial genes upon transitioning from a free-living to an endosymbiotic lifestyle.</p>
Molecular sequencing and morphological identification reveal similar patterns in native bee communities across private and public grasslands of eastern North Dakota
<p>Bees play a key role in the functioning of human-modified and natural ecosystems by pollinating agricultural crops and wild plant communities. Global pollinator conservation efforts need large-scale and long-term monitoring to detect changes in species' demographic patterns and shifts in bee community structure. The objective of this project was to test a molecular sequencing pipeline that would utilize a commonly used locus, produce accurate and precise identifications consistent with morphological identifications, and generate data that are both qualitative and quantitative. We applied this amplicon sequencing pipeline to native bee communities sampled across Conservation Reserve Program (CRP) lands and native grasslands in eastern North Dakota. We found the 28S LSU locus to be more capable of discriminating between species than the 18S SSU rRNA locus, and in some cases even resolved instances of cryptic species or morphologically ambiguous species complexes. Overall, we found the amplicon sequencing method to be a qualitatively accurate representation of the sampled bee community richness and species identity, especially when a well-curated database of known 28S LSU sequences is available. Both morphological identification and molecular sequencing revealed similar patterns in native bee community structure across CRP lands and native prairie. Additionally, a genetic algorithm approach to compute taxon-specific correction factors using a small subset of the most concordant samples demonstrated that a high level of quantitative accuracy could be possible if the specimens are fresh and processed soon after collection. Here we provide a first step to a molecular pipeline for identifying insect pollinator communities. This tool should prove useful for future national monitoring efforts as use of molecular tools becomes more affordable and as numbers of 28S LSU sequences for pollinator species increase in publicly-available databases.</p>
Contrasting patterns of sequence variation in steelhead populations reflect distinct evolutionary processes
<p>Multiple evolutionary processes influence genome‐wide allele frequencies and quantifying effects of genetic drift, and multiple forms of selection remain challenging in natural populations. Here, we investigate variation at major effect loci in contrast to patterns of neutral drift across a wide collection of steelhead (<em>Oncorhynchus mykiss</em>) populations that have declined in abundance due to anthropogenic impacts. Whole‐genome resequencing of 74 populations of steelhead revealed genome‐wide patterns (~8 million SNPs) consistent with expected neutral population structure. However, allelic variation at major effect loci associated with adult migration timing (chromosome 28: GREB1L/ROCK1) and age at maturity (chromosome 25: SIX6) reflected how selection has acted on phenotypic variation in contrast with neutral structure. Variation at major effect loci was influenced by evolutionary processes with differing signals between the strongly divergent Coastal and Inland lineages, while allele frequencies within and among populations within the Inland lineage have been driven by local natural selection as well as recent anthropogenic influences. Recent anthropogenic effects appeared to have influenced the frequency of major effect alleles including artificial selection for specific traits in hatchery stocks with subsequent gene flow into natural populations. Selection from environmental factors at various scales has also likely influenced variation for major effect alleles. These results reveal evolutionary mechanisms that influence allele frequencies at major effect loci that are critical for conservation of phenotypic traits and life history variation of this protected species.</p>
Control over Conformational Landscapes of Polypeptoids by Monomer Sequence Patterning
<p>This dataset accompanies the article "Control Over Conformational Landscapes of Polypeptoids by Monomer Sequence Patterning" by Audra J. DeStefano, Shawn D. Mengel, Morgan W. Bates, Sally Jiao, M. Scott Shell, Songi Han, and Rachel A. Segalman in <em>Macromolecules </em>in 2024. The article demonstrates how patterning of hydrophobic residues along a polymer backbone can tune the distribution of end-to-end distances and that these effects can be predicted by a simple bead-spring simulation. This dataset contains the necessary experimental data to reproduce the main text and supporting figures. High performance liquid chromatographs, mass spectra, double electron-electron resonance time domain signals, and simulated end-to-end distance distributions are included.</p>
Data from: Genomic footprint of cladogenesis revealed through RADseq and Sanger sequencing demonstrates congruent patterns in the velvet worm Peripatopsis sedgwicki species complex (Onychophora: Peripatopsidae)
<p>In the present study, first generation DNA sequencing (mitochondrial cytochrome c oxidase subunit one, <em>COI</em>) and reduced-representative genomic RADseq data were used to understand the patterns and processes of diversification of the velvet worm, <em>Peripatopsis sedgwicki</em> species complex across its distribution range in South Africa. For the RADseq data, three datasets (two primary and one supplementary) were generated corresponding to 1259 - 11,468 SNPs, in order to assess the species diversity and phylogeographic of the species complex. Tree topologies for the two primary datasets were inferred using maximum likelihood and Bayesian inferences methods. Phylogenetic analyses using the <em>COI </em>datasets retrieved four distinct, statistically well-supported clades within the species complex. Five species delimitation methods applied to the <em>COI </em>data (ASAP, bPTP, bGMYC, STACEY, and iBPP) all showed support for the distinction of the Fort Fordyce Nature Reserve specimens. In the main <em>P. sedgwicki </em>species complex, the species delimitation methods revealed a variable number of operational taxonomic units and overestimated the number of putative taxa. Divergence time estimates coupled with the geographic exclusivity of species and phylogeographic results suggest recent cladogenesis during the Plio/Pleistocene. The RADseq were subjected to a principal components analysis and a discriminant analysis of principal components, under a maximum-likelihood framework. The latter results corroborate the four main clades observed using the <em>COI</em> data, however, applying additional filtering revealed additional diversity. The high overall congruence observed between the RADseq and <em>COI </em>data suggests that first generation sequence data remain a cheap and effective method for evolutionary studies, although RADseq does provide a far greater resolution of contemporary temporo-spatial patterns. </p>
Deep-sequence phylogenetics to quantify patterns of HIV transmission in the context of a universal testing and treatment trial – BCPP/ Ya Tsie trial
<p><strong>Background:</strong> Mathematical models predict that community-wide access to HIV testing-and-treatment can rapidly and substantially reduce new HIV infections. Yet several large universal test-and-treat HIV prevention trials in high-prevalence epidemics demonstrated variable reduction in population-level incidence.</p> <p><strong>Methods:</strong> To elucidate patterns of HIV spread in universal test-and-treat trials we quantified the contribution of geographic-location, gender, age and randomized-HIV-intervention to HIV transmissions in the 30-community Ya Tsie trial in Botswana (estimated trial population: 175,664).</p> <p><strong>Results:</strong> Deep-sequence phylogenetic analysis revealed that most inferred HIV transmissions within the trial occurred within the same or between neighboring communities, and between similarly-aged partners. Transmissions into intervention communities from control communities were more common than the reverse post-baseline (30% [12.2 – 56.7] versus 3% [0.1 – 27.3]) than at baseline (7% [1.5 – 25.3] versus 5% [0.9 – 22.9]) compatible with a benefit from treatment-as-prevention.</p> <p><strong>Conclusion:</strong> Our findings suggest that population mobility patterns are fundamental to HIV transmission dynamics and to the impact of HIV control strategies.</p> <p><strong>Funding:</strong> This study was supported by the National Institute of General Medical Sciences (U54GM088558); the Fogarty International Center (FIC) of the U.S. National Institutes of Health (D43 TW009610); and the President's Emergency Plan for AIDS Relief through the Centers for Disease Control and Prevention (CDC) (Cooperative agreements U01 GH000447 and U2G GH001911).</p>
Sequencing of individual barcoded cDNAs on Pacific Biosciences and Oxford Nanopore technologies reveals platform-specific error patterns (repository for Genome Research paper, 2022)
<p>Simulated ONT and PacBio RNA-Seq data for "Sequencing of individual barcoded cDNAs on Pacific Biosciences and Oxford Nanopore technologies reveals platform-specific error patterns" paper (Mikheenko et al., Genome Research, 2022). All details can be found in the Methods section of the paper.</p> <p><strong>PacBio.simulated_uniform_coverage.fasta.gz</strong> and <strong>ONT.simulated_uniform_coverage.fasta.gz files</strong> were used in Supplemental Note “Benchmarking of the read-to-isoform assignment algorithm”.</p> <p><strong>ONT.simulated_real_expression.fasta.gz</strong> file and all GTF files were used in the Section "Splice site correction improves transcript discovery precision". <strong>mouse.gencode.M26.spatial.15percent.reduced.gtf</strong> was used as the annotation file for all tools. <strong>mouse.gencode.M26.spatial.15percent.expressed.gtf </strong>contains the set of all expressed isoforms. <strong>mouse.gencode.M26.spatial.15percent.expressed_kept.gtf</strong> contains those of the isoforms that are in presented in the annotation file ("known" transcripts), <strong>mouse.gencode.M26.spatial.15percent.reduced.gtf</strong> contains expressed isoforms that were removed from the annotation ("novel" transcripts).</p>
Data supporting publication "Metagenomic Immunoglobulin Sequencing (MIG-Seq) Exposes Patterns of IgA Antibody Binding in the Healthy Human Gut Microbiome"
<p>Data supporting publication "Metagenomic Immunoglobulin Sequencing (MIG-Seq) Exposes Patterns of IgA Antibody Binding in the Healthy Human Gut Microbiome"</p>
Figure 1 in Genetic difference between two Schistosoma japonicum isolates with contrasting cercarial shedding patterns revealed by whole genome sequencing
Figure 1. Map of geographical locations of research sites.
Data from: Individual Movement - Sequence Analysis Method (IM-SAM): characterising spatio-temporal patterns of animal trajectories across scales and landscapes
<p>Dataset included in Zenodo supports the analyses performed in "<em>Individual Movement - Sequence Analysis Methods (IM-SAM) characterising spatio-temporal patterns of animal trajectories across scales and landscapes.</em>"</p> <p>The dataset includes one RDS file, that can be easily loaded into R using the readRDS function. The RDS file consists out of a list including two objects per animal:</p> <ul> <li>Object 1 contains a data frame with the real and simulated sequences for an animal. e.g., ls[[1]][[1]] </li> <li>Object 2 contains the home range in raster format of an animal. e.g., ls[[1]][[2]]</li> </ul> <p>The data frames in object 1 contain real habitat use sequences and corresponding simulated habitat use sequences generated in the home range of the specific individual (900 simulated sequences: 6 habitat selection rules x 3 selection coefficients x 50 repetitions). Open and closed habitats are respectively encoded by 0 and 1. The first 96 columns of each row in a data frame represent a 16-day habitat use sequence, with a fixed 4-hour relocation interval (0, 4, 8, 12, 16 and 20h). Column names are named as follows: Day_1_0h, Day_1_4h,..., Day_16_20h. In the next columns we provide the selection coefficients (columns 97-99), the habitat selection rules (or pattern, columns 100-102) and the number of missing values (mvs, columns, 103-104) for each of the real and simulated sequences. Note that simulated sequences have no missing values (i.e. values are always 0.00) and for real sequences there is no selection coefficient or habitat selection rule (i.e. values are always xxx).</p> <p>Rownames of simulated sequences are composed out of the habitat selection rule (c, o, a24, a33, a42 and u), the selection coefficient (5, 10, 50) and the replicate (1 to 50), separated by dashes. For example, the first simulated sequence in the first data frame (ls[[1]][[1]][1,]) is described as a24_10_1. The rownames of real sequences instead are composed out of the individuals' identifier, the biweekly period (1 to 23) and the year. For example, the first real sequence in the first data frame (ls[[1]][[1]][901,]) is described as 1_5_2006.</p> <p><br> </p>
Long-read sequencing of diagnosis and post-therapy medulloblastoma reveals complex rearrangement patterns and epigenetic signatures
<p>Imaging data related to the "Long-read sequencing of diagnosis and post-therapy medulloblastoma reveals complex rearrangement patterns and epigenetic signatures" manuscript</p>
Deep-sequence phylogenetics to quantify patterns of HIV transmission in the context of a universal testing and treatment trial – BCPP/ Ya Tsie trial
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Control over Conformational Landscapes of Polypeptoids by Monomer Sequence Patterning
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OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.