Long non-coding RNA transcriptome of uncharacterized samples can be accurately imputed using protein-coding genes.
Brief Bioinform
; 21(2): 637-648, 2020 03 23.
Article
en En
| MEDLINE
| ID: mdl-30657858
ABSTRACT
Long non-coding RNAs (lncRNAs) play an important role in gene regulation and are increasingly being recognized as crucial mediators of disease pathogenesis. However, the vast majority of published transcriptome datasets lack high-quality lncRNA profiles compared to protein-coding genes (PCGs). Here we propose a framework to harnesses the correlative expression patterns between lncRNA and PCGs to impute unknown lncRNA profiles. The lncRNA expression imputation (LEXI) framework enables characterization of lncRNA transcriptome of samples lacking any lncRNA data using only their PCG profiles. We compare various machine learning and missing value imputation algorithms to implement LEXI and demonstrate the feasibility of this approach to impute lncRNA transcriptome of normal and cancer tissues. Additionally, we determine the factors that influence imputation accuracy and provide guidelines for implementing this approach.
Palabras clave
Texto completo:
1
Banco de datos:
MEDLINE
Asunto principal:
Proteínas
/
Perfilación de la Expresión Génica
/
Transcriptoma
/
ARN Largo no Codificante
Límite:
Humans
Idioma:
En
Revista:
Brief Bioinform
Asunto de la revista:
BIOLOGIA
/
INFORMATICA MEDICA
Año:
2020
Tipo del documento:
Article
País de afiliación:
Estados Unidos