Similar Datasets
Project description:Exome sequencing identified mutations in clpex mouse mutants
| PRJNA545319 | ENA
Project description:10 LMP1 expressing mouse lymphomas were sorted and sequenced for exome mutations
2020-06-02 | GSE136073 | GEO
Project description:TRIP4 is one of the subunits of the transcriptional coregulator ASC-1, a ribonucleoprotein complex that participates in transcriptional coactivation and RNA processing events. Recessive variants in the TRIP4 gene have been associated with spinal muscular atrophy with bone fractures as well as a severe form of congenital muscular dystrophy. Here we present the diagnostic journey of a patient with cerebellar hypoplasia and spinal muscular atrophy (PCH1) and congenital bone fractures. Initial exome sequencing analysis revealed no candidate variants. Reanalysis of the exome data by inclusion in the Solve-RD project resulted in the identification of a homozygous stop-gain variant in the TRIP4 gene, previously reported as disease-causing. This highlights the importance of analysis reiteration and improved and updated bioinformatic pipelines. Proteomic profile of the patient’s fibroblasts showed altered RNA-processing and impaired exosome activity supporting the pathogenicity of the detected variant. In addition, we identified a novel genetic form of PCH1, further strengthening the link of this characteristic phenotype with altered RNA metabolism.
2022-02-16 | PXD023584 | Pride
Project description:Genomic and proteomic data were integrated into the proteogenomic workflow to identify coding genomic variants of Human Embryonic Kidney 293 (HEK-293) standard cell line at the proteome level. Shotgun proteome data published by Geiger et al (2012) and obtained in this work for HEK-293 were searched against the customized genomic databased generated using exome data published by Lin et al (2014). 54 unique variants out of ~1,200 coding variants annotated in the exome were found at the proteome level. 27 of them were validated by two search engines, X!Tandem and Andromeda. 16 (60%) of those validated variants were confidently identified in both own and published proteome datasets. Some of the variants found belonged to widely known genomic polymorphisms originated from the germline, while others are more likely to result from somatic mutations. Notably, the peptide subsets identified by only one, or the other search engine were enriched by the sequences with miscleavages. This can be due to the large presence of false-positive hits in these subsets that is especially true for the subset of variant peptides. High-resolution mass-spectra of HEK-293 cell line were deposited to ProteomeXchange repository, project accession PXD002613.
2016-07-22 | PXD002613 | Pride
Project description:Transcriptome studies in patients with rare genetic diseases can potentially aid in the interpretation of likely causal genetic variation through identification of altered transcript abundance and/or structure. RNA-Seq is the most sensitive assay for both investigating transcript structure and abundance. The primary aim of this pilot project is to investigate to what degree integrating exome-Seq and RNA-Seq data on the same individual can accelerate the identification of causal alleles for rare genetic diseases. There are two main strands to this: (i) identifying which variants discovered in exome-seq appear to be having a functional impact on transcripts, and (ii)identifying transcript outliers, especially among known causal genes, that may not necessarily have a causal variant identified from exome sequencing. The latter may identify the presence of causal variants that lie far from coding regions (e.g. the formation of cryptic splice sites deep within introns, or loss of long range regulatory elements), which can be confirmed with further targeted genetic assays. Just over 50% of all disease-causing variants recorded in theHuman Gene Mutation Database (HGMD) affect transcript structure and abundance (e.g.nonsense SNVs, essential splice site SNVs, frame shifting indels, CNVs).This pilot project will study RNA from lymphoblastoid cell-lines from 12 patients with primordial dwarfism syndromes, for 10 of these samples we have previously generate exome data as part of our collaboration with the group of Prof Andrew Jackson. The two remaining samples are positive controls where the causal mutation is known, and is known to affect transcript structure and/or abundance. Primordial dwarfism is a prime candidate for these RNA-seq studies because all known causal mutations to date have key roles in DNA replication and thus, unsurprisingly, the products of the causal genes are typically ubiquitously expressed. Each RNA will be sequenced, with two technical replicates (independent RT-PCR and libraries) per sample, and each replicate run in 1/2 of a HiSeq lane using 100bp paired reads. Samples preparation was as follows :The cells were grown to confluency, then pellets frozen at -80. RNA samples were prepared using the Qiagen RNeasy kit, then nanodropped and analyzed using the bioanalyzer to determine concentration and purity. This data is part of a pre-publication release. For information on the proper use of pre-publication data shared by the Wellcome Trust Sanger Institute (including details of any publication moratoria), please see http://www.sanger.ac.uk/datasharing/
2013-08-07 | E-ERAD-74 | biostudies-arrayexpress
Project description:We collected blood samples of two non-obstructive azoospermia patients, and performed whole exome sequencing to explore the causal mutations for male infertility.
2021-06-22 | E-MTAB-9287 | biostudies-arrayexpress
Project description:Genomic and proteomic data were integrated into the proteogenomic workflow to identify coding genomic variants of Human Embryonic Kidney 293 (HEK-293) standard cell line at the proteome level. Shotgun proteome data published by Geiger et al (2012) and obtained in this work for HEK-293 were searched against the customized genomic databased generated using exome data published by Lin et al (2014). 54 unique variants out of ~1,200 coding variants annotated in the exome were found at the proteome level. 27 of them were validated by two search engines, X!Tandem and Andromeda. 16 (60%) of those validated variants were confidently identified in both own and published proteome datasets. Some of the variants found belonged to widely known genomic polymorphisms originated from the germline, while others are more likely to result from somatic mutations. Notably, the peptide subsets identified by only one, or the other search engine were enriched by the sequences with miscleavages. This can be due to the large presence of false-positive hits in these subsets that is especially true for the subset of variant peptides. High-resolution mass-spectra of HEK-293 cell line were deposited to ProteomeXchange repository, project accession PXD002613.
2017-03-28 | MSV000080712 | MassIVE
Project description:To efficiently identify genetic susceptibility variants for gastric cancer, including rare coding variants, we performed an exome chip-based array study. We found that a linkage disequilibrium (LD) block containing 2 significant variants in PSCA gene increased the risk and two blocks that included 15 suggested variants including TRIM31, TRIM 40, TRIM 10, and TRIM26 regions, and included one suggested variant and OR2H2 gene showed protective associations with gastric cancer susceptibility. In addition, the PLEC region (rs200893203), FBLN2 region (rs201192415), and EPHA2 region (rs3754334) were associated with increased susceptibility We performed an exome chip-based array study in 329 gastric cancer cases and 683 controls.
2014-06-11 | E-GEOD-58356 | biostudies-arrayexpress
Project description:Purpose: The goal of this study was to compare exome sequences of Ackr4-deficient mice with C57BL/6 wildtype sequences to understand phenotypic differences which can not be explained by Ackr4-deficiency. Methods: Tail DNA of one wild-type (WT, C57BL/6) and atypical chemokine receptor 4 knockout (Ackr4−/−) mice was sequenced by exome sequencing using an Illumina NovaSeq 6000 sequencing system (2x 100bp). After demultiplexing (Illumina bcl2fastq 2.19) and adapter trimming (Skewer, 0.2.2), the trimmed reads were aligned with to the murine genome (mm10) with Burrows-Wheeler Aligner (BWA-mem 0.7.2-cegat). Reads aligned to multiple sides with the same mapping score were discarded. Further, duplicated reads were removed with SAMtool 0.1.18. Genetic variants were identified with Verscan 2.4.2-cegat and variants with a frequency of ≥ 2% were annotated with SnpEff (version 4.2 with GRCm38.75). Results: 100,000 to 140,000 million sequence reads per mouse were mapped to the mouse genome (build mm10). Each mouse had approx. 20,000 variants (insertions, deletions, SNPs) compared to the reference genome. In Ackr4-/- mice, 3,367 variants were present in both mice analysed, yet not detected in the C57BL/6 mouse used as control. 1781 of these 'shared' variants were present on the chromosome with the targeted Ackr4 allele (Chr9) and predominantly in close proximity to the Ackr4 locus, indicating that these variants originate from the 129-genetic background which was used for targeted mutagenesis during the generation of these mice. Conclusions: Our study shows that Ackr4-/- mice in generation F12 after backcrossing to the C57BL/6 background are still congenic for 129-derived DNA around the targeted Ackr4 locus, indicating that passenger mutations can influence the phenotype of these mice.
2019-12-21 | GSE138124 | GEO
Project description:Whole exome sequencing of 5 HCLc tumor-germline pairs. Genomic DNA from HCLc tumor cells and T-cells for germline was used. Whole exome enrichment was performed with either Agilent SureSelect (50Mb, samples S3G/T, S5G/T, S9G/T) or Roche Nimblegen (44.1Mb, samples S4G/T and S6G/T). The resulting exome libraries were sequenced on the Illumina HiSeq platform with paired-end 100bp reads to an average depth of 120-134x. Bam files were generated using NovoalignMPI (v3.0) to align the raw fastq files to the reference genome sequence (hg19) and picard tools (v1.34) to flag duplicate reads (optical or pcr), unmapped reads, reads mapping to more than one location, and reads failing vendor QC.
2016-02-01 | E-MTAB-4313 | biostudies-arrayexpress