Abstract
Housekeeping genes (HKGs), are essential for gene expression based studies performed through Reverse Transcriptase-polymerase Chain Reaction (RT-qPCR). These genes are related with the basic cellular processes that are essential for cell maintenance, survival and function. Thus, HKGs should be expressed in all cells of an organism regardless of the tissue type, cell state or cell condition. High-throughput technologies, including RNA sequencing (RNA-seq), are used to study and identify these types of genes. RNA-seq is a high-throughput method that allows the measurement of gene expression profiles in a target tissue or an isolated cell. Moreover, machine learning methods are routinely applied in different genomics related areas to enable the interpretation of large datasets, including those related to gene expression. This study reports a new machine learning based approach to identify candidate HKGs in silico from RNA-seq gene expression data. The approach enabled the identification of stable HKGs candidates in RNA-seq data from Corynebacterium pseudotuberculosis. These genes showed stable expression under different stress conditions as well as low variation index and fold changes. Furthermore, some of these genes were already reported in the literature as HKGs or HKGs candidates for the same or other bacterial organisms, which reinforced the accuracy of the proposed method. We present a novel approach based on K-means algorithm, internal metrics and machine learning methods that can identify stable housekeeping genes from gene expression data with high accuracy and efficiency.
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Franco, E.F. et al. (2020). A Clustering Approach to Identify Candidates to Housekeeping Genes Based on RNA-seq Data. In: Kowada, L., de Oliveira, D. (eds) Advances in Bioinformatics and Computational Biology. BSB 2019. Lecture Notes in Computer Science(), vol 11347. Springer, Cham. https://doi.org/10.1007/978-3-030-46417-2_8
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