Our project relates to processing RNA-seq data in order to highlight differentially expressed genes (DEGs).The aim is to investigate whether new bioinformatics methods that do not require the time-consuming step of mapping to a reference genome could be a trustworthy alternative to the current RNA-seq data analysis pipeline.
The biological model used is the dendritic cell in normal medium or exposed to either H1N1 virus or poly I:C (double stranded RNA). The latter two are considered immunostimulants in that they generate an inflammatory response. The medical angle of the experiment is to consider the gene expression perturbation upon H1N1 exposure as a proxy for over-inflammatory cell response, separate the DEGs' signature from other immunostimulants (such as poly I:C) and decipher drugs to suppress it.
The raw data consist of paired end 2 x 125bp Illumina HiSeq reads, with approximately 30M reads per sample. Initially, we will use the standard tools for finding DEGs -STAR, HTSeq count and DESeq2- and subsequently the mapping-independent algorithms, Kallisto and Sailfish (Salmon).
To compare and contrast our results, we will report the number of overlapping genes across the different methods as well as the presence or not of well-known inflammatory genes that are exprected to change in the experiment.
The biological model used is the dendritic cell in normal medium or exposed to either H1N1 virus or poly I:C (double stranded RNA). The latter two are considered immunostimulants in that they generate an inflammatory response. The medical angle of the experiment is to consider the gene expression perturbation upon H1N1 exposure as a proxy for over-inflammatory cell response, separate the DEGs' signature from other immunostimulants (such as poly I:C) and decipher drugs to suppress it.
The raw data consist of paired end 2 x 125bp Illumina HiSeq reads, with approximately 30M reads per sample. Initially, we will use the standard tools for finding DEGs -STAR, HTSeq count and DESeq2- and subsequently the mapping-independent algorithms, Kallisto and Sailfish (Salmon).
To compare and contrast our results, we will report the number of overlapping genes across the different methods as well as the presence or not of well-known inflammatory genes that are exprected to change in the experiment.
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