Project description:Human peripheral blood natural killer cells are grouped in various ways according to phenotypic and functional characteristics. We sorted peripheral blood natural killer cells from healthy donors into seven surface phenotype-defined populations, hashtagged them, and sequenced them. This analysis permitted simultaneous identification of transcriptional clusters and traceback of the components to their phenotypic groups.
Project description:Although localized haploid phasing can be achieved using long read genome sequencing without parental data, reliable chromosome-scale phasing remains a great challenge. Given that sperm is a natural haploid cell, single-sperm genome sequencing can provide a chromosome-wide phase signal. Due to the limitation of read length, current short-read-based single-sperm genome sequencing methods can only achieve SNP haplotyping and come with difficulties in detecting and haplotyping structural variations (SVs) in complex genomic regions. To overcome these limitations, we developed a long-read-based single-sperm genome sequencing method and a corresponding data analysis pipeline that can accurately identify crossover events and chromosomal level aneuploidies in single sperm and efficiently detect SVs within individual sperm cells. Importantly, without parental genome information, our method can accurately conduct de novo phasing of heterozygous SVs as well as SNPs from male individuals at the whole chromosome scale. The accuracy for phasing of SVs was as high as 98.59% using 100 single sperm cells, and the accuracy for phasing of SNPs was as high as 99.95%. Additionally, our method reliably enabled deduction of the repeat expansions of haplotype-resolved STRs/VNTRs in single sperm cells. Our method provides a new opportunity for studying haplotype-related genetics in mammals.
| S-EPMC10450174 | biostudies-literature
Project description:Identification of full-sibling families from natural single-tree ash progenies based on SSR markers and genome-wide SNPs
Project description:The role natural selection plays in governing the locations and early evolution of copy number mutations remains largely unexplored. Here we employ high-density full-genome tiling arrays to create a fine-scale genomic map of copy number polymorphisms (CNPs) in Drosophila melanogaster. We inferred a total of 2,658 independent CNPs, 56% of which overlap genes. These include CNPs likely to be under positive selection, most notably high frequency duplications encompassing toxin-response genes. The locations and frequencies of CNPs are strongly shaped by purifying selection with deletions under stronger purifying selection than duplications. Among duplications, those overlapping exons or introns and those falling on the X-chromosome seem to be subject to the strongest purifying selection. In order to characterize copy number polymorphisms (CNPs) in Drosophila malanogaster, we applied comparative genome hybridization (CGH) using tiling arrays covering the full euchromatic genome of Drosophila melanogaster. We inferred copy number changes with a Hidden Markov Model (HMM) that returned the posterior probabilities for copy number by comparing DNA hybridization intensities between natural isolates and the reference genome strain. Training data for copy number changes were obtained via hybridization with a line known to contain a ~200kb homozygous duplication and from a set of 52 validated homozygous deletions. The probabilities of mutation were parsed to make CNP calls. Key words: comparative genomic hybridization, CGH, copy number polymorphism, CNP, copy number variation, CNV, duplication, deletion
Project description:Molecular genetic research relies heavily on the ability to detect polymorphisms in DNA. Single nucleotide polymorphisms (SNPs) are the most frequent form of DNA variation in the genome. In combination with a PCR assay, the corresponding SNP can be analyzed as a derived cleaved amplified polymorphic sequence (dCAPS) marker. The dCAPS method exploits the well-known specificity of a restriction endonuclease for its recognition site and can be used to virtually detect any SNP. Here, we describe the use of the dCAPS method for detecting single-nucleotide changes by means of a barley EST, CK569932, PCR-based marker.
Project description:The study of natural genetic variation for plant disease resistance responses is a complementary approach to utilizing mutants to elucidate genetic pathways. While some key genes involved in pathways controlling disease resistance, and signaling intermediates such as salicylic acid (SA) and jasmonic acid (JA), have been identified through mutational analyses, the use of genetic variation in natural populations permits the identification of change-of-function alleles, which likely act in a quantitative manner. Whole genome microarrays, such as Affymetrix GeneChips, allow for molecular characterization of the disease response at a genomics level and characterization of differences in gene expression due to natural variation. Differences in the level of gene expression, or expression level polymorphisms (ELPs), can be mapped in a segregating population to identify regulatory quantitative trait loci (expression QTLs, e-QTLs) affecting host resistance responses. We assessed Arabidopsis accessions Bayreuth-0 (Bay-0) and Shahdara (Sha) for natural variation in the response to JA. We treated vegetatively grown plants with either JA or a control solution (Silwet), and harvested the plants 4, 28, or 52 hours after chemical treatment. We present Affymetrix GeneChip microarray expression data for 2 biological replications of the control (Silwet) samples for Bay-0 and Sha. These GeneChips were used to generate genetic markers which allowed the development of high-density haplotype maps of a Bay-0 x Sha RIL population.
Project description:The study of natural genetic variation for plant disease resistance responses is a complementary approach to utilizing mutants to elucidate genetic pathways. While some key genes involved in pathways controlling disease resistance, and signaling intermediates such as salicylic acid (SA) and jasmonic acid (JA), have been identified through mutational analyses, the use of genetic variation in natural populations permits the identification of change-of-function alleles, which likely act in a quantitative manner. Whole genome microarrays, such as Affymetrix GeneChips, allow for molecular characterization of the disease response at a genomics level and characterization of differences in gene expression due to natural variation. Differences in the level of gene expression, or expression level polymorphisms (ELPs), can be mapped in a segregating population to identify regulatory quantitative trait loci (expression QTLs, e-QTLs) affecting host resistance responses. We assessed Arabidopsis accessions Bayreuth-0 (Bay-0) and Shahdara (Sha) for natural variation in the response to SA. We treated vegetatively grown plants with either SA or a control solution (Silwet), and harvested the plants 4, 28, or 52 hours after chemical treatment. We present Affymetrix GeneChip microarray expression data for 2 biological replications of the control (Silwet) samples for Bay-0 and Sha. These GeneChips were used to generate genetic markers which allowed the development of high-density haplotype maps of a Bay-0 x Sha RIL population.
Project description:The present study aimed at studying the rainbow trout egg transcriptome using 9152-cDNA microarrays after natural or controlled ovulation. The analysis of egg transcriptome after natural or controlled ovulation led to the identification of 26 genes. We observed that both hormonal induction and photoperiod control of ovulation induced significant changes in the egg mRNA abundance of specific genes. We demonstrate that hormonal induction of ovulation has an impact on the egg mRNA abundance of specific genes even though the resulting effects on the developmental potential of the egg is so far unknown. In addition, we also identified 1 gene exhibiting a differential mRNA abundance in eggs of varying developmental potential. Analysis of egg transcriptome after natural ovulation (4 samples), photoperiod-controlled ovulation (14 samples), and hormonally-induced ovulation (11 samples).