Biology subjects

Ruminy, P.

Publications and source records attributed to Ruminy, P..

3 recordsLinked to original sources

bioRxiv2023

Disequilibrium between BRCA1 and BRCA2 circular and messenger RNAs plays a role in breast cancer

AO_SCPLOWBSTRACTC_SCPLOWBreast cancer is a frequent disease for which the discovery of markers for early detection or prognostic assessment remains challenging. Circular RNAs (circRNAs) are single-stranded structures in closed loops, produced by backsplicing. CircRNA and messenger RNA (mRNA) are generated cotranscriptionally and backsplicing and linear splicing compete against each other. As mRNAs are key players in tumorigenesis, we hypothesize that a balance disruption between circRNAs and mRNAs could promote breast cancer. Hence, we developed an assay for a simultaneous study of circRNAs and mRNAs, called Splice and Expression Analyses by exon Ligation and High Throughput Sequencing (SEALigTHS). Following SEALigHTS validation for BRCA1 and BRCA2, our hypothesis was tested using an independent research set of 95 pairs of tumour and adjacent normal breast tissues. On this research set, ratios of BRCA1 and BRCA2 circRNAs/mRNAs were significantly lower in tumour breast tissue compared to normal tissue (p=1.6e-09 and p=4.4e-05 for BRCA1 and BRCA2, respectively). Overall, we developed an innovative method to study linear and backsplicing, described the repertoire of BRCA1 and BRCA2 circRNAs, including 10 novel ones, and showed for the first time that a disequilibrium between BRCA1 and BRCA2 circRNAs and mRNAs plays a role in breast cancer.

cancer biology↗

bioRxiv2020

UMI-Gen: a UMI-based reads simulator for variant calling evaluation in paired-end sequencing NGS libraries

MotivationWith Next Generation Sequencing becoming more affordable every year, NGS technologies asserted themselves as the fastest and most reliable way to detect Single Nucleotide Variants (SNV) and Copy Number Variations (CNV) in cancer patients. These technologies can be used to sequence DNA at very high depths thus allowing to detect abnormalities in tumor cells with very low frequencies. A lot of different variant callers are publicly available and usually do a good job at calling out variants. However, when frequencies begin to drop under 1%, the specificity of these tools suffers greatly as true variants at very low frequencies can be easily confused with sequencing or PCR artifacts. The recent use of Unique Molecular Identifiers (UMI) in NGS experiments offered a way to accurately separate true variants from artifacts. UMI-based variant callers are slowly replacing raw-reads based variant callers as the standard method for an accurate detection of variants at very low frequencies. However, benchmarking done in the tools publication are usually realized on real biological data in which real variants are not known, making it difficult to assess their accuracy. ResultsWe present UMI-Gen, a UMI-based reads simulator for targeted sequencing paired-end data. UMI-Gen generates reference reads covering the targeted regions at a user customizable depth. After that, using a number of control files, it estimates the background error rate at each position and then modifies the generated reads to mimic real biological data. Finally, it will insert real variants in the reads from a list provided by the user. AvailabilityThe entire pipeline is available at https://gitlab.com/vincent-sater/umigen-master under MIT license. Contactvincent.sater@gmail.com

bioinformatics↗

bioRxiv2019

UMI-VarCal: a new UMI-based variant caller that efficiently improves low-frequency variant detection in paired-end sequencing NGS libraries

1DisclaimerThe author has withdrawn version 2 of this manuscript due to a duplicate posting of manuscript number 027532. Therefore, the author does not wish this work to be cited as reference for the project. 775817v1 is the appropriate submission and the published version of the appropriate preprint can be found in the published article link. If you have any questions, please contact the corresponding author.

bioinformatics↗