Different approaches in microRNA analysis
Аутори
Jenko Bizjan, BarbaraStančič, Bine
Sabolić, Iva
Štalekar, Maja
Prosenc Zmrzljak, Uršula
Остала ауторства
Morić, IvanaĐorđević, Valentina
Конференцијски прилог (Објављена верзија)
,
© 2023 Institute of Molecular Genetics and Genetic Engineering, University of Belgrade
Метаподаци
Приказ свих података о документуАпстракт
MicroRNA might serve as a predictive biomarker for treatment response in stem cell
treatment in knee osteoarthritis. Different sample types are going to be collected to
enlighten the true biological role. MicroRNA analysis necessitates diverse approaches
based on the sample type. In this study, we examined microRNA profiles in plasma
samples, synovial fluid, and adipose-derived fat tissue. We conducted a comparative
analysis of different microRNA analysis methods to assess the data.
The first approach involved a series of steps, including adapter trimming, quality filtering,
size filtering, and mapping of all reads to the human reference genome (GRCh38.p12).
Subsequently, genome-mapped reads were aligned to known miRNA sequences from
miRBase. Reads that did not match miRNAs were subjected to further classification using
additional databases, such as RNAcentral. The second pipeline also encompassed adapter
trimming, quality filtering, and size filtering. Additionally, it invo...lved collapsing individual
reads into repeat sequences, followed by alignment to the mature index of miRBase.
Unaligned reads were classified as isomiRs based on their alignment to the hairpin index
of miRBase.
We processed sequences from three plasma samples, three adipose fat tissue samples,
and three synovial fluid samples. Although there were slight variations in microRNA
read counts, the average ratio between counts was 0.92 (SD=0.29). Notably, the second
pipeline yielded higher read counts compared to the first pipeline.
The results obtained from both microRNA bioinformatic pipelines demonstrated similar
outcomes, suggesting that the choice of pipeline is unlikely to have a significant impact on
the derived biological insights.
Кључне речи:
bioinformatics / microRNA / sequencingИзвор:
4th Belgrade Bioinformatics Conference, 2023, 4, 94-94Издавач:
- Belgrade : Institute of molecular genetics and genetic engineering
Финансирање / пројекти:
- This work was performed as a part of project: “Development of advanced prediction tool for successful and optimized treatment course in pathological joint changes based on quantification of inflammatory biomarkers - PredicTest” which is co-funded by EUREKA member countries and the European Union Horizon 2020 Framework Programme
Напомена:
- Book of abstract: 4th Belgrade Bioinformatics Conference, June 19-23, 2023
Колекције
Институција/група
Institut za molekularnu genetiku i genetičko inženjerstvoTY - CONF AU - Jenko Bizjan, Barbara AU - Stančič, Bine AU - Sabolić, Iva AU - Štalekar, Maja AU - Prosenc Zmrzljak, Uršula PY - 2023 UR - https://belbi.bg.ac.rs/ UR - https://imagine.imgge.bg.ac.rs/handle/123456789/2039 AB - MicroRNA might serve as a predictive biomarker for treatment response in stem cell treatment in knee osteoarthritis. Different sample types are going to be collected to enlighten the true biological role. MicroRNA analysis necessitates diverse approaches based on the sample type. In this study, we examined microRNA profiles in plasma samples, synovial fluid, and adipose-derived fat tissue. We conducted a comparative analysis of different microRNA analysis methods to assess the data. The first approach involved a series of steps, including adapter trimming, quality filtering, size filtering, and mapping of all reads to the human reference genome (GRCh38.p12). Subsequently, genome-mapped reads were aligned to known miRNA sequences from miRBase. Reads that did not match miRNAs were subjected to further classification using additional databases, such as RNAcentral. The second pipeline also encompassed adapter trimming, quality filtering, and size filtering. Additionally, it involved collapsing individual reads into repeat sequences, followed by alignment to the mature index of miRBase. Unaligned reads were classified as isomiRs based on their alignment to the hairpin index of miRBase. We processed sequences from three plasma samples, three adipose fat tissue samples, and three synovial fluid samples. Although there were slight variations in microRNA read counts, the average ratio between counts was 0.92 (SD=0.29). Notably, the second pipeline yielded higher read counts compared to the first pipeline. The results obtained from both microRNA bioinformatic pipelines demonstrated similar outcomes, suggesting that the choice of pipeline is unlikely to have a significant impact on the derived biological insights. PB - Belgrade : Institute of molecular genetics and genetic engineering C3 - 4th Belgrade Bioinformatics Conference T1 - Different approaches in microRNA analysis EP - 94 SP - 94 VL - 4 UR - https://hdl.handle.net/21.15107/rcub_imagine_2039 ER -
@conference{ author = "Jenko Bizjan, Barbara and Stančič, Bine and Sabolić, Iva and Štalekar, Maja and Prosenc Zmrzljak, Uršula", year = "2023", abstract = "MicroRNA might serve as a predictive biomarker for treatment response in stem cell treatment in knee osteoarthritis. Different sample types are going to be collected to enlighten the true biological role. MicroRNA analysis necessitates diverse approaches based on the sample type. In this study, we examined microRNA profiles in plasma samples, synovial fluid, and adipose-derived fat tissue. We conducted a comparative analysis of different microRNA analysis methods to assess the data. The first approach involved a series of steps, including adapter trimming, quality filtering, size filtering, and mapping of all reads to the human reference genome (GRCh38.p12). Subsequently, genome-mapped reads were aligned to known miRNA sequences from miRBase. Reads that did not match miRNAs were subjected to further classification using additional databases, such as RNAcentral. The second pipeline also encompassed adapter trimming, quality filtering, and size filtering. Additionally, it involved collapsing individual reads into repeat sequences, followed by alignment to the mature index of miRBase. Unaligned reads were classified as isomiRs based on their alignment to the hairpin index of miRBase. We processed sequences from three plasma samples, three adipose fat tissue samples, and three synovial fluid samples. Although there were slight variations in microRNA read counts, the average ratio between counts was 0.92 (SD=0.29). Notably, the second pipeline yielded higher read counts compared to the first pipeline. The results obtained from both microRNA bioinformatic pipelines demonstrated similar outcomes, suggesting that the choice of pipeline is unlikely to have a significant impact on the derived biological insights.", publisher = "Belgrade : Institute of molecular genetics and genetic engineering", journal = "4th Belgrade Bioinformatics Conference", title = "Different approaches in microRNA analysis", pages = "94-94", volume = "4", url = "https://hdl.handle.net/21.15107/rcub_imagine_2039" }
Jenko Bizjan, B., Stančič, B., Sabolić, I., Štalekar, M.,& Prosenc Zmrzljak, U.. (2023). Different approaches in microRNA analysis. in 4th Belgrade Bioinformatics Conference Belgrade : Institute of molecular genetics and genetic engineering., 4, 94-94. https://hdl.handle.net/21.15107/rcub_imagine_2039
Jenko Bizjan B, Stančič B, Sabolić I, Štalekar M, Prosenc Zmrzljak U. Different approaches in microRNA analysis. in 4th Belgrade Bioinformatics Conference. 2023;4:94-94. https://hdl.handle.net/21.15107/rcub_imagine_2039 .
Jenko Bizjan, Barbara, Stančič, Bine, Sabolić, Iva, Štalekar, Maja, Prosenc Zmrzljak, Uršula, "Different approaches in microRNA analysis" in 4th Belgrade Bioinformatics Conference, 4 (2023):94-94, https://hdl.handle.net/21.15107/rcub_imagine_2039 .