Excluding embryos with two novel mutations in FREM2 gene by the next-generation sequencing-based single nucleotide polymorphism haplotyping.
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ABSTRACT:
SUBMITTER: Zhou Y
PROVIDER: S-EPMC8660615 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Here, we evaluate the applicability of a new method that combines targeted next-generation sequencing (NGS) with targeted haplotyping in identifying PKD2 gene mutations in human preimplantation embryos in vitro. To achieve this goal, a proband family with a heterozygous deletion of c.595_595?+?14delGGTAAGAGCGCGCGA in exon 1 of the PKD2 gene was studied. A total of 10 samples were analyzed, including 7 embryos. An array-based gene chip was designed to capture all of the exons of 21 disease-related genes, including PKD2. We performed Sanger sequencing combined with targeted haplotyping to evaluate the feasibility of this new method. A total of 7.09?G of data were obtained from 10 samples by NGS. In addition, 24,142 informative single-nucleotide polymorphisms (SNPs) were identified. Haplotyping analysis of several informative SNPs of PKD2 that we selected revealed that embryos 3, 5, and 6 did not inherit the mutation haplotypes of the PKD2 gene, a finding that was 100% accurate and was consistent with Sanger sequencing. Our results demonstrate that targeted NGS combined with targeted haplotyping can be used to identify PKD2 gene mutations in human preimplantation embryos in vitro with high sensitivity, fidelity, throughput and speed.
| S-EPMC4858692 | biostudies-literature
Project description:BACKGROUND: Genetic information based on molecular markers has increasingly being used in cattle breeding improvement programmes, as a mean to improve conventionally phenotypic selection. Advances in molecular genetics have led to the identification of several genetic markers associated with genes affecting economic traits. Until recently, the identification of the causative genetic variants involved in the phenotypes of interest has remained a difficult task. The advent of novel sequencing technologies now offers a new opportunity for the identification of such variants. Despite sequencing costs plummeting, sequencing whole-genomes or large targeted regions is still too expensive for most laboratories. A transcriptomic-based sequencing approach offers a cheaper alternative to identify a large number of polymorphisms and possibly to discover causative variants. In the present study, we performed a gene-based single nucleotide polymorphism (SNP) discovery analysis in bovine Longissimus thoraci, using RNA-Seq. To our knowledge, this represents the first study done in bovine muscle. RESULTS: Messenger RNAs from Longissimus thoraci from three Limousin bull calves were subjected to high-throughput sequencing. Approximately 36-46 million paired-end reads were obtained per library. A total of 19,752 transcripts were identified and 34,376 different SNPs were detected. Fifty-five percent of the SNPs were found in coding regions and ~22% resulted in an amino acid change. Applying a very stringent SNP quality threshold, we detected 8,407 different high-confidence SNPs, 18% of which are non synonymous coding SNPs. To analyse the accuracy of RNA-Seq technology for SNP detection, 48 SNPs were selected for validation by genotyping. No discrepancies were observed when using the highest SNP probability threshold. To test the usefulness of the identified SNPs, the 48 selected SNPs were assessed by genotyping 93 bovine samples, representing mostly the nine major breeds used in France. Principal component analysis indicates a clear separation between the nine populations. CONCLUSIONS: The RNA-Seq data and the collection of newly discovered coding SNPs improve the genomic resources available for cattle, especially for beef breeds. The large amount of variation present in genes expressed in Limousin Longissimus thoracis, especially the large number of non synonymous coding SNPs, may prove useful to study the mechanisms underlying the genetic variability of meat quality traits.
| S-EPMC3751807 | biostudies-literature
Project description:Nasopharyngeal carcinoma (NPC) is a head and neck cancer with high incidence in South China and East Asia. To provide a theoretical basis for NPC risk screening and early prevention, we conducted a meta-analysis of relevant literature on the association of single nucleotide polymorphisms (SNP)s with NPC susceptibility. Further, expression of 15 candidate SNPs identified in the meta-analysis was evaluated in a cohort of NPC patients and healthy volunteers using next-generation sequencing technology. Among the 15 SNPs detected in the meta-analysis, miR-146a (rs2910164, C>G), HCG9 (rs3869062, A>G), HCG9 (rs16896923, T>C), MMP2 (rs243865, C>T), GABBR1 (rs2076483, T>C), and TP53 (rs1042522, C>G) were associated with decreased susceptibility to NPC, while GSTM1 (+/DEL), IL-10 (rs1800896, A>G), MDM2 (rs2279744, T>G), MDS1-EVI1 (rs6774494, G>A), XPC (rs2228000, C>T), HLA-F (rs3129055, T>C), SPLUNC1 (rs2752903, T>C; and rs750064, A>G), and GABBR1 (rs29232, G>A) were associated with increased susceptibility to NPC. In our case-control study, an association with increased risk for NPC was found for the AG vs AA genotype in HCG9 (rs3869062, A>G). In addition, heterozygous deletion of the GSTM1 allele was associated with increased susceptibility to NPC, while an SNP in GABBR1 (rs29232, G>A) was associated with decreased risk, and might thus have a protective role on NPC carcinogenesis. This work provides the first comprehensive assessment of SNP expression and its relationship to NPC risk. It suggests the need for well-designed, larger confirmatory studies to validate its findings.
| S-EPMC5581063 | biostudies-literature
Project description:Next-generation DNA sequencing promises to revolutionize clinical medicine and basic research. However, while this technology has the capacity to generate hundreds of billions of nucleotides of DNA sequence in a single experiment, the error rate of ~1% results in hundreds of millions of sequencing mistakes. These scattered errors can be tolerated in some applications but become extremely problematic when "deep sequencing" genetically heterogeneous mixtures, such as tumors or mixed microbial populations. To overcome limitations in sequencing accuracy, we have developed a method termed Duplex Sequencing. This approach greatly reduces errors by independently tagging and sequencing each of the two strands of a DNA duplex. As the two strands are complementary, true mutations are found at the same position in both strands. In contrast, PCR or sequencing errors result in mutations in only one strand and can thus be discounted as technical error. We determine that Duplex Sequencing has a theoretical background error rate of less than one artifactual mutation per billion nucleotides sequenced. In addition, we establish that detection of mutations present in only one of the two strands of duplex DNA can be used to identify sites of DNA damage. We apply the method to directly assess the frequency and pattern of random mutations in mitochondrial DNA from human cells.
| S-EPMC3437896 | biostudies-other
Project description:NGPS is a method for de-novo, full-length protein sequencing in high throughput. The method is based on cleavage of the protein at semi-random sites by microwave-assisted acid hydrolysis (MAAH), enrichment of LC-MS/MS amenable peptides from the hydrolysate by solid-phase-extraction, LC-MS/MS analysis, de-novo long peptide tag sequencing of resulting peptides and assembly of peptide tags into consensus contigs.
2017-04-03 | PXD003804 | Pride
Project description:Experimental evolution is an important research method that allows for the study of evolutionary processes occurring in microorganisms. Here we present a novel approach to experimental evolution that is based on application of next generation sequencing. Under this approach population level sequencing is applied to an evolving population in which multiple first-step beneficial mutations occur concurrently. As a result, frequencies of multiple beneficial mutations are observed in each replicate of an experiment. For this new type of data we develop methods of statistical inference. In particular, we propose a method for imputing selection coefficients of first-step beneficial mutations. The imputed selection coefficient are then used for testing the distribution of first-step beneficial mutations and for estimation of mean selection coefficient. In the case when selection coefficients are uniformly distributed, collected data may also be used to estimate the total number of available first-step beneficial mutations.
| S-EPMC5563372 | biostudies-other
Project description:BackgroundThe rapid development of next generation sequencing (NGS) technology provides a novel avenue for genomic exploration and research. Single nucleotide variants (SNVs) inferred from next generation sequencing are expected to reveal gene mutations in cancer. However, NGS has lower sequence coverage and poor SNVs detection capability in the regulatory regions of the genome. Post probabilistic based methods are efficient for detection of SNVs in high coverage regions or sequencing data with high depth. However, for data with low sequencing depth, the efficiency of such algorithms remains poor and needs to be improved.ResultsA new tool SNVHMM basing on a discrete hidden Markov model (HMM) was developed to infer the genotype for each position on the genome. We incorporated the mapping quality of each read and the corresponding base quality on the reads into the emission probability of HMM. The context information of the whole observation as well as its confidence were completely utilized to infer the genotype for each position on the genome in study. Therefore, more probability power can be gained over the Bayes based methods, which is very useful for SNVs detection for data with low sequencing depth. Moreover, our model was verified by testing against two sets of lobular breast tumor and Myelodysplastic Syndromes (MDS) data each. Comparing against a recently published SNVs calling algorithm SNVMix2, our model improved the performance of SNVMix2 largely when the sequencing depth is low and also outperformed SNVMix2 when SNVMix2 is well trained by large datasets.ConclusionsSNVHMM can detect SNVs from NGS cancer data efficiently even if the sequence depth is very low. The training data size can be very small for SNVHMM to work. SNVHMM incorporated the base quality and mapping quality of all observed bases and reads, and also provides the option for users to choose the confidence of the observation for SNVs prediction.
| S-EPMC3718670 | biostudies-literature
Project description:Next-generation sequencing (NGS) has enabled whole genome and transcriptome single nucleotide variant (SNV) discovery in cancer. NGS produces millions of short sequence reads that, once aligned to a reference genome sequence, can be interpreted for the presence of SNVs. Although tools exist for SNV discovery from NGS data, none are specifically suited to work with data from tumors, where altered ploidy and tumor cellularity impact the statistical expectations of SNV discovery.We developed three implementations of a probabilistic Binomial mixture model, called SNVMix, designed to infer SNVs from NGS data from tumors to address this problem. The first models allelic counts as observations and infers SNVs and model parameters using an expectation maximization (EM) algorithm and is therefore capable of adjusting to deviation of allelic frequencies inherent in genomically unstable tumor genomes. The second models nucleotide and mapping qualities of the reads by probabilistically weighting the contribution of a read/nucleotide to the inference of a SNV based on the confidence we have in the base call and the read alignment. The third combines filtering out low-quality data in addition to probabilistic weighting of the qualities. We quantitatively evaluated these approaches on 16 ovarian cancer RNASeq datasets with matched genotyping arrays and a human breast cancer genome sequenced to >40x (haploid) coverage with ground truth data and show systematically that the SNVMix models outperform competing approaches.Software and data are available at http://compbio.bccrc.casshah@bccrc.ca SUPPLEMANTARY INFORMATION: Supplementary data are available at Bioinformatics online.
| S-EPMC2832826 | biostudies-other
Project description:BackgroundWe evaluated the performance of single-nucleotide polymorphism (SNP) genotyping arrays OncoScan (Thermo Fisher Scientific, San Diego, CA) and Infinium CytoSNP-850K (CytoSNP; Illumina, Waltham, MA) for assessing homologous recombination deficiency (HRD) genomic instability.MethodsDNA (pretreatment samples) across 20 tumor types was evaluated with OncoScan, CytoSNP, and the clinically validated HRD test. Copy number variation (CNV) and loss of heterozygosity (LOH) analyses were performed with ASCATv2.5.1. Aggregate HRD genomic metrics included LOH, telomeric-allelic imbalance number (TAI), and large-scale state transition (LST). Associations between BRCA mutation (BRCAm) status and the clinically validated HRD test metric (dichotomized at a clinical cut-off) were evaluated using area under the receiver operating characteristic (AUROC); Spearman ρ was calculated for continuous metrics. CNV segmentation and HRD genomic metrics were calculated (n = 120, n = 106, and n = 126 for OncoScan, CytoSNP and clinically validated HRD test, respectively).ResultsWhen assessed by SNP arrays, the genomic metric demonstrated good association with BRCAm (AUROC of HRD: OncoScan, 0.87; CytoSNP, 0.75) and the clinically validated test (cut-off, 42; AUROC of HRD: OncoScan, 0.92; CytoSNP, 0.91). The genomic metrics demonstrated good correlation with the clinically validated aggregate HRD test metric (ρ: OncoScan, 0.82; CytoSNP, 0.81) and for each component (ρ: OncoScan, 0.68 [LOH], 0.76 [TAI], and 0.78 [LST]; CytoSNP, 0.59 [LOH], 0.77 [TAI], and 0.82 [LST]). HRD assessed by SNP genotyping arrays and the clinically validated test showed good correlation.ConclusionOncoScan and CytoSNP may potentially identify most HRD-positive tumors with appropriate clinically relevant cut-offs.
| S-EPMC9749332 | biostudies-literature
Project description:Introduction: Methotrexate (MTX) is one of the most important drugs included in the first-line protocols to treat high-grade osteosarcoma (HGOS). Although several polymorphisms have been reported to be associated with drug response or MTX-related toxicity in pharmacogenetic studies, their role in the development of MTX resistance in HGOS is still unclear. Methods: Therefore, in this study, 22 single nucleotide polymorphisms (SNPs) in 4 genes of the folate metabolism, 7 MTX transporter genes, and 2 SNPs of the tumor protein p53 (TP53) gene were investigated using a custom multimodal-targeted next-generation sequencing (mmNGS) approach in 8 MTX-resistant and 12 MTX-sensitive human HGOS cell lines. The panel was validated by TaqMan genotyping assays. Results: High instability of TP53 rs1642785 was observed in all U-2OS/MTX variants. Allele changes of the solute carrier family 19 member 1/replication factor C subunit 1 (SLC19A1, previously known as RFC1) and rs1051266 were identified in all Saos-2/MTX-resistant variants in both DNA- and RNA- derived libraries compared to the parental Saos-2 cell line. Allele changes of methylenetetrahydrofolate reductase (MTHFR) rs1801133 were identified only in the RNA-derived libraries of the two U2OS variants with the highest MTX resistance level. Significantly upregulated gene expression associated with the development of MTX resistance was revealed for dihydrofolate reductase (DHFR) whereas SLC19A1 was downregulated. In addition, a fusion transcript of DHFR (ex4) and MutS Homolog 3 (MSH3) (ex9) was identified in the RNA libraries derived from the two U-2OS variants with the highest MTX resistance level. Conclusion: This innovative mmNGS approach enabled the simultaneous exploration of SNPs at DNA and RNA levels in human HGOS cell lines, providing evidence of the functional involvement of allele changes associated with the development of MTX resistance.
| S-EPMC10698553 | biostudies-literature