HYNŠT, Jakub, Karla PLEVOVÁ, Lenka RADOVÁ, Vojtěch BYSTRÝ, Karol PÁL a Šárka POSPÍŠILOVÁ. Bioinformatic pipelines for whole transcriptome sequencing data exploitation in leukemia patients with complex structural variants. PeerJ. London: PEERJ INC, 2019, roč. 7, JUN, s. 7071-7086. ISSN 2167-8359. Dostupné z: https://dx.doi.org/10.7717/peerj.7071.
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Základní údaje
Originální název Bioinformatic pipelines for whole transcriptome sequencing data exploitation in leukemia patients with complex structural variants
Autoři HYNŠT, Jakub (203 Česká republika, domácí), Karla PLEVOVÁ (203 Česká republika, domácí), Lenka RADOVÁ (203 Česká republika, domácí), Vojtěch BYSTRÝ (203 Česká republika, domácí), Karol PÁL (703 Slovensko, domácí) a Šárka POSPÍŠILOVÁ (203 Česká republika, garant, domácí).
Vydání PeerJ, London, PEERJ INC, 2019, 2167-8359.
Další údaje
Originální jazyk angličtina
Typ výsledku Článek v odborném periodiku
Obor 30204 Oncology
Stát vydavatele Velká Británie a Severní Irsko
Utajení není předmětem státního či obchodního tajemství
WWW URL
Impakt faktor Impact factor: 2.379
Kód RIV RIV/00216224:14740/19:00108521
Organizační jednotka Středoevropský technologický institut
Doi http://dx.doi.org/10.7717/peerj.7071
UT WoS 000471213700009
Klíčová slova anglicky Chromothripsis; Complex structural variants; Fusion gene; Gene expression; Bioinformatic pipeline; Next-generation sequencing; Leukemia; Transcriptomics; Chronic lymphocytic leukemia; Statistics
Štítky 14110212, podil, rivok
Příznaky Mezinárodní význam, Recenzováno
Změnil Změnila: Mgr. Pavla Foltynová, Ph.D., učo 106624. Změněno: 31. 3. 2020 21:24.
Anotace
Background. Extensive genome rearrangements, known as chromothripsis, have been recently identified in several cancer types. Chromothripsis leads to complex structural variants (cSVs) causing aberrant gene expression and the formation of de novo fusion genes, which can trigger cancer development, or worsen its clinical course. The functional impact of cSVs can be studied at the RNA level using whole transcriptome sequencing (total RNA-Seq). It represents a powerful tool for discovering, profiling, and quantifying changes of gene expression in the overall genomic context. However, bioinformatic analysis of transcriptomic data, especially in cases with cSVs, is a complex and challenging task, and the development of proper bioinformatic tools for transcriptome studies is necessary. Methods. We designed a bioinformatic workflow for the analysis of total RNA-Seq data consisting of two separate parts (pipelines): The first pipeline incorporates a statistical solution for differential gene expression analysis in a biologically heterogeneous sample set. We utilized results from transcriptomic arrays which were carried out in parallel to increase the precision of the analysis. The second pipeline is used for the identification of de novo fusion genes. Special attention was given to the filtering of false positives (FPs), which was achieved through consensus fusion calling with several fusion gene callers. We applied the workflow to the data obtained from ten patients with chronic lymphocytic leukemia (CLL) to describe the consequences of their cSVs in detail. The fusion genes identified by our pipeline were correlated with genomic break-points detected by genomic arrays. Results. We set up a novel solution for differential gene expression analysis of individual samples and de novo fusion gene detection from total RNA-Seq data. The results of the differential gene expression analysis were concordant with results obtained by transcriptomic arrays, which demonstrates the analytical capabilities of our method. We also showed that the consensus fusion gene detection approach was able to identify true positives (TPs) efficiently. Detected coordinates of fusion gene junctions were in concordance with genomic breakpoints assessed using genomic arrays. Discussion. By applying our methods to real clinical samples, we proved that our approach for total RNA-Seq data analysis generates results consistent with other genomic analytical techniques. The data obtained by our analyses provided clues for the study of the biological consequences of cSVs with far-reaching implications for clinical outcome and management of cancer patients. The bioinformatic workflow is also widely applicable for addressing other research questions in different contexts, for which transcriptomic data are generated.
Návaznosti
MUNI/A/1105/2018, interní kód MUNázev: Nové přístupy ve výzkumu, diagnostice a terapii hematologických malignit VI (Akronym: VýDiTeHeMA VI)
Investor: Masarykova univerzita, Nové přístupy ve výzkumu, diagnostice a terapii hematologických malignit VI, DO R. 2020_Kategorie A - Specifický výzkum - Studentské výzkumné projekty
NV15-31834A, projekt VaVNázev: Vliv selekce genomických poškození na průběh chronické lymfocytární leukémie
VytisknoutZobrazeno: 27. 4. 2024 00:20