RNA-Seq data from Ovarian Tumours
The data presented in this item contain sensitive information that cannot be shared openly. Work on depositing the data in FEGA Sweden has been initiated. FEGA Sweden is a national node of the Federated European Genome-phenome Archive (FEGA), which allows data to be shared under controlled access. The datasets in FEGA Sweden are findable through the European Genome-phenome Archive web portal (https://ega-archive.org).
This dataset refers to the raw data used in the preprint "Deep plasma proteomics identifies and validates an eight-protein biomarker panel that separate benign from malignant tumors in ovarian cancer" available on MedRxiv (DOI: 10.1101/2024.10.10.24315232v1)
Data Set Description
Samples: 81 fresh frozen tumor tissue samples, either benign or malignant, from women with suspected ovarian cancer were used for analysis of mRNA expression.
Library Preparation and Sequencing: Sequencing libraries were prepared from 122-194 ng (three samples), 200 ng (seven samples) or 500 ng (71 samples) total RNA using the TruSeq stranded mRNA library preparation kit (cat# 20020595, Illumina Inc.) including polyA-selection. The libraries were then sequenced on a NovaSeq 6000 system (Illumina Inc.) on S4 flowcells with version 1.5 sequencing chemistry on three lanes. Paired-end sequencing of with read lengths of 150 bp was used.
Library preparation and sequencing was performed by the SNP&SEQ Technology platform, SciLifeLab, National Genomics Infrastructure Uppsala, Sweden.
Funding
Multi-omics characterization of tumour-tissue, plasma and cervicovaginal fluid for early detection of ovarian cancer
Swedish Research Council
Find out more...The Swedish Cancer Foundation 220604FE
The Swedish Cancer Foundation 232874PJ
The Swedish Cancer Foundation 190008PJ
The Swedish Cancer Foundation CAN211848
The Swedish state under the agreement between the Swedish government and the county council, the ALF-agreement
History
Publisher
Uppsala UniversityAccess request email
stefan.enroth@igp.uu.seContact email
stefan.enroth@igp.uu.seSciLifeLab acknowledgement
- Bioinformatics platform (NBIS)
- National Genomics Infrastructure unit