Jan 24, 2015
Customer asks about data analysis options for RNA-seq data from control and a knock-out mouse model for a new candidate tumor suppressor gene.
Sat, Jan 24, 2015 at 5:52 PM
AccuraScience LB: Routine RNA-seq data processing/analysis includes the following steps: (1) Sequencing data quality control, (2) Mapping of all reads to the mouse reference genome (allowing identification of exon-exon junctions), and obtain quantification of gene expressions, (3) differential expression analysis - which will produce lists of up- and down-regulated genes each labelled with a p-value of false discovery rate (FDR). Other potential analysis options include: (4) Differential transcript isoform analysis, which will produce a list of differential transcript (or splicing) isoforms between wildtype and knock-out, (5) Pathway analysis based on differential expression analysis, which will produce lists of biological pathways (denoted in Gene Ontology terms) significantly enriched among up- and down-regulated genes, (6) Gene-fusion analysis, which identifies gene-fusion events frequently occurring in tumor samples, (7) For more highly expressed genes, we could try to call single-nucleotide variations (SNVs) and small insertion and deletion variants (Indels) - this is an advantage sequencing-based experiments offers - this might offer mechanistic insight on how the candidate tumor suppressor gene works. Among the SNVs and Indels identified, we could also evaluate their "deleteriousness" or the extent to which they disrupt their host genes' function, and (8) Other options might be possible, if we get to know more about your project, this particular gene, or if other types of data might be used to analyze in an integrated manner.
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Note: LB stands for Lead Bioinformatician. An AccuraScience LB is a senior bioinformatics expert and leader of an AccuraScience data analysis team.
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