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Augmenting RNA-seq data analysis with Machine Learning.


Identification/detection of differentially expressed genes (DEGs) to investigate gene function and underlying molecular mechanisms are pivotal to our understanding of biological processes specially diseases. Real-time reverse transcription PCR (qRT-PCR), cDNA microarray analysis, whole genome tiling array and RNA sequencing (RNA-seq) are used to analyse DEGs. Owing to the rapid advancement of sequencing technology and cost of adoption getting cheaper day by day, RNA-seq has become the most preferred method for DEGs analysis. However, identification of DEGs by RNA-seq is riddled by biases mainly false-positives and -negatives. These biases are produced due to the process followed in designing the experiments and subsequent data analysis. This affects RNA-seq results both on reproducibility as well as reliability. Data processing workflows for DEGs analysis consists of several crucial steps such as reads alignment, transcript quantification and normalization followed by statistical analysis. Each of these steps can produce false-positives and -negatives and thus influence the accuracy and sensitivity of the analysis.

Can we eliminate some of these issues in traditional RNA-seq data analysis by applying machine learning (ML) based-methods to further improve the reproducibility and reliability. ML-based methods are increasingly becoming popular for biological studies, including biological image analysis, cancer study, robust phenotyping, gene discovery and so on.

Previously, ML–based differential network analysis has been used for the prediction of genes through learning the expression patterns characteristics of already known genes.

Thus, augmentation of traditional RNA-seq data analysis with ML would significantly improve the sensitivity of the DEGs identification.

At iOligos, we help our partners choose right strategies and platforms for their genomic data analysis. Our bioinformatics team brings together expertise in next generation sequencing, validated data processing, statistics, bioinformatics, genetics, and genomics. We discuss and understand your need to design the work flows using ML-based advance data science tools for RNA-seq analysis and predictions. We help our partners save critical time and resources.

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