Optimizing of pre-processing analysis for Illumina RNA-Seq data in Arabidopsis thaliana

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00843989%3A_____%2F24%3AE0111542" target="_blank" >RIV/00843989:_____/24:E0111542 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/00216305:26220/24:PU154766

Výsledek na webu

<a href="https://www.eeict.cz/eeict_download/archiv/sborniky/EEICT_2024_sbornik_2.pdf" target="_blank" >https://www.eeict.cz/eeict_download/archiv/sborniky/EEICT_2024_sbornik_2.pdf</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Optimizing of pre-processing analysis for Illumina RNA-Seq data in Arabidopsis thaliana

Popis výsledku v původním jazyce

Gene expression analysis through RNA sequencing (RNA-Seq) has revolutionized molecular biology, providing profound insights into the intricate transcriptional landscapes of organisms. Arabidopsis thaliana, a widely studied model plant, serves as a cornerstone for investigating fundamental biological and ecology processes. However, accurate interpretation of RNASeq data hinges on meticulous pre-processing methods to ensure data integrity and trustworthiness, especially in the context of Illumina sequencing. In this research, we present a comprehensive framework for optimizing pre-processing analysis tailored specifically for Arabidopsis thaliana RNA-Seq datasets generated through Illumina sequencing. Our approach encompasses rigorous quality control, precise read alignment, transcript quantification, and normalization procedures crucial for subsequent differential expression analysis. Additionally, we address unique considerations and challenges inherent to Arabidopsis thaliana datasets, providing valuable insights for researchers in the field.

Název v anglickém jazyce

Optimizing of pre-processing analysis for Illumina RNA-Seq data in Arabidopsis thaliana

Popis výsledku anglicky

Gene expression analysis through RNA sequencing (RNA-Seq) has revolutionized molecular biology, providing profound insights into the intricate transcriptional landscapes of organisms. Arabidopsis thaliana, a widely studied model plant, serves as a cornerstone for investigating fundamental biological and ecology processes. However, accurate interpretation of RNASeq data hinges on meticulous pre-processing methods to ensure data integrity and trustworthiness, especially in the context of Illumina sequencing. In this research, we present a comprehensive framework for optimizing pre-processing analysis tailored specifically for Arabidopsis thaliana RNA-Seq datasets generated through Illumina sequencing. Our approach encompasses rigorous quality control, precise read alignment, transcript quantification, and normalization procedures crucial for subsequent differential expression analysis. Additionally, we address unique considerations and challenges inherent to Arabidopsis thaliana datasets, providing valuable insights for researchers in the field.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

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Návaznosti

V - Vyzkumna aktivita podporovana z jinych verejnych zdroju

Rok uplatnění

2024

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Proceedings II of the 30th Conference STUDENT EEICT 2024: Selected papers

ISBN

978-80-214-6230-4

ISSN

2788-1334

e-ISSN

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Počet stran výsledku

4

Strana od-do

142-145

Název nakladatele

Vysoké učení technické,

Místo vydání

Brno : Vysoké učení technické, 2024

Místo konání akce

Brno, Czech Republic

Datum konání akce

23. 4. 2024

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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