Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

An exome sequencing approach to defining the genetic risk factors for Achilles tendinopathy

ABSTRACT: Musculoskeletal soft tissue injuries are complex phenotypes. Case control association studies using the candidate gene approach have predominantly been used to identify risk loci for these injuries. However, the ability to identify all risk conferring variants using this approach alone is unlikely. Therefore, this study aimed to define the genetic profile of these injuries using whole exome sequencing and a customized analysis pipeline. The exomes of ten exemplar asymptomatic controls and ten exemplar cases with Achilles tendinopathy were individually sequenced using a platform that included the coverage of the untranslated regions and miRBase miRNA genes. These results provide a proof of concept for the use of a customized pipeline for the exploration of a larger genomic dataset. This approach, using previous research to guide a targeted analysis of the data has generated new genetic signatures in the biology of musculoskeletal soft tissue injuries.

PROVIDER: EGAS00001003234 | EGA |

REPOSITORIES: EGA

ACCESS DATA

Json Xml

Similar Datasets

RNA expression data from primary neuroblastoma tumors

Project description:RNA expression profiles of 105 primary neuroblastomas derived from customized 4 x 44k oligonucleotide microarrays (Agilent Technologies). These profiles are part of an integrative study combining genomewide epigenetic profiles with transcriptome data of the same neuroblastoma cohort. Tumors were derived from 40 low-risk, 9 intermediate-risk and 56 high-risk patients. Transcription profiles of 105 primary neuroblastomas derived from customized 4 x 44k oligonucleotide microarrays (Agilent Technologies)

2016-09-08 | E-GEOD-73517 | biostudies-arrayexpress

Genomic profiling of TP53 and non-TP53 Li-Fraumeni syndrome tumors reveals hits in the p53 and overlapping pathways

Project description:Li-Fraumeni syndrome (LFS) is a rare, clinically and genetically heterogeneous inherited cancer syndrome. Most cases are due to mutations in TP53. CHK2 is a minor predisposing locus; we recently mapped a third locus to 1q23. In both TP53 and non-TP53 LFS, there is evidence for risk heterogeneity within and between kindreds, suggesting additional risk modifiers. Using BAC- and SNP-based microarrays, we performed genomic profiling of primary soft tissue sarcomas, osteosarcomas and matching constitutive samples of 10 LFS patients (6 with and 4 without TP53 mutations), to identify genome-wide patterns of copy number changes and loss-of-heterozygosity (LOH). Our complementing global approaches revealed several interesting patterns for TP53 and non-TP53 LFS tumors, including positive (1q/7, 3p/15, 4q/9q, 8q/19p, 9p/10q, 13/14 and 15q/18q) and negative (2q/9q, 3q/14q and 4q/6q) associations between chromosomal regions. The region containing the oncogene TWIST1 (7p21.1) was the most common gain independent of TP53 status and tumor type, while LOH of 8q11.2 with the tumor suppressor ST18 was the only region exclusively associated with non-TP53 soft tissue sarcomas. We resequenced known mutations in BRAF, KRAS and NRAS and identified somatic NRAS mutations in 2 of 10 tumors. TP53 and non-TP53 LFS tumors shared multiple hits in genes of the p53 and overlapping pathways. Although common dogma in cancer genetics holds that multiple hits in the same pathway are redundant and thus unlikely, we show that different combinations of genetic alterations in both TP53 and non-TP53 LFS tumors appear to act together in the p53 network in LFS tumorigenesis Keywords: Comparative genomic hybridization Using the Spectral Genomics dye-swap BAC arrays we studied 10 Li Fraumeni syndrome tumor cases

2008-07-16 | E-GEOD-8798 | biostudies-arrayexpress

Trancription profiling of human soft tissue sarcomas: characterization & reclassification of malignant fibrous histiocytoma

Project description:In soft tissue sarcomas, diagnosis of malignant fibrous histiocytoma (MFH) has been a very controversial issue, and MFH is now considered to be reclassified into pleomorphic subtypes of other sarcomas. To characterize and reclassify MFH genetically, we analyzed gene expression in 105 samples from ten types of soft tissue tumors. Experiment Overall Design: Gene expression of 105 soft tissue tumor samples consisting of synovial sarcoma (n=16), myxoid liposarcoma (n=19), lipoma (n=3), well-differentiated liposarcoma (n=3), dedifferentiated liposarcoma (n=15), myxofibrosarcoma (n=15), leiomyosarcoma (n=6), malignant peripheral nerve sheath tumor (n=3), fibrosarcoma (n=4) and malignant fibrous histiocytoma (n=21) were analyzed using an Affymetrix HG-U133A array.

2008-11-22 | E-GEOD-6481 | biostudies-arrayexpress

Using RNA-Seq to create sample-specific proteomic databases that enable mass spectrometric discovery of splice junction peptides

Project description:Many new alternative splice forms have been detected at the transcript level using next generation sequencing (NGS) methods, especially RNA-Seq, but it is not known how many of these transcripts are being translated. Leveraging the unprecedented capabilities of NGS, we collected RNA-Seq and proteomics data from the same cell population (Jurkat cells) and created a bioinformatics pipeline that builds customized databases for the discovery of novel splice-junction peptides. Results: Eighty million paired-end Illumina reads and ~500,000 tandem mass spectra were used to identify 12,873 transcripts (19,320 including isoforms) and 6,810 proteins. We developed a bioinformatics workflow to retrieve high-confidence, novel splice junction sequences from the RNA data, translate these sequences into the analogous polypeptide sequence, and create a customized splice junction database for MS searching.

2013-05-17 | GSE45428 | GEO

Gene Expression Analysis of Soft Tissue Sarcomas: Characterization & Reclassification of Malignant Fibrous Histiocytoma

2007-04-01 | GSE6481 | GEO

nextPP for chromosome-based proteomic research using neXtProt and GENCODE databases

Project description:In this study, we introduce a streamlined pipeline using customized and concatenated GENCODE and neXtProt databases with controlled false discovery rate (FDR) for finding GENCODE ASVs and missing proteins while mapping onto single amino acid variants (SAAVs) from Hippocampal dataset.

2017-07-25 | MSV000081385 | MassIVE

Transcription profiling of soft tissue tumors 10 cases of DTF (blue) and 13 cases of SFT (orange)

Project description:Soft Tissue Tumor Gene Expression of Ten Cases of DTF (blue) and Thirteen Cases of SFT (orange), Based on Expression Profiling on 42,000 Element cDNA Microarrays.

2005-11-11 | E-SMDB-3271 | biostudies-arrayexpress

RNA expression data from primary neuroblastoma tumors

2016-09-08 | GSE73517 | GEO

NBdata_20130716

Project description:Revised risk estimation and treatment stratification of low- and intermediate-risk neuroblastoma patients by integrating clinical and molecular prognostic markers. To optimize neuroblastoma treatment stratification, we aimed at developing a novel risk estimation system by integrating gene expression-based classification and established prognostic markers. Gene expression profiles were generated from 709 neuroblastoma specimens using customized 4x44K microarrays. Classification models were built using 75 tumors with contrasting courses of disease. Validation was performed in an independent test set (n=634) by Kaplan-Meier estimates and Cox regression analyses. Combination of gene expression-based classification and established prognostic markers improves risk estimation of LR/IR neuroblastoma patients. We propose to implement our revised treatment stratification system in a prospective clinical trial.

2014-10-09 | E-MTAB-1781 | biostudies-arrayexpress

Using RNA-Seq to create sample-specific proteomic databases that enable mass spectrometric discovery of splice junction peptides

2013-05-17 | E-GEOD-45428 | biostudies-arrayexpress