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CLC Genomic Workbench
Hi guys,
I am new to bioinformatics and have little bioinfo knowledge.
I got treated and control transcriptome data from a cell line from a cat. I want to find differentially express genes between the two. I am using CLC Genomic workbench for analysis. I have several questions I hope you guys can help. For finding differently gene expression I did de novo assembly on control reads and then map my treated reads back to assembled control reads by RNA seq. this way de novo control act as the reference.
Is this the right way to do it..? or should I de novo treated reads also before mapping back to de novo control reads..?
after I did that I create box plot for quality control.the mean line is on the same level.
so, should I conduct normalization..? if yes, the software provide 3 ways to normalize data which are by scaling, quantile and reads per million.
which one should I choose..? I read the reads per million is the suitable one for RNA high throughput sequencing data.
or should I use reference gene like GAPDH or beta-actin expression value for normalization..? if yes, how do I do it using this software..?
Thank you.
I am new to bioinformatics and have little bioinfo knowledge.
I got treated and control transcriptome data from a cell line from a cat. I want to find differentially express genes between the two. I am using CLC Genomic workbench for analysis. I have several questions I hope you guys can help. For finding differently gene expression I did de novo assembly on control reads and then map my treated reads back to assembled control reads by RNA seq. this way de novo control act as the reference.
Is this the right way to do it..? or should I de novo treated reads also before mapping back to de novo control reads..?
after I did that I create box plot for quality control.the mean line is on the same level.
so, should I conduct normalization..? if yes, the software provide 3 ways to normalize data which are by scaling, quantile and reads per million.
which one should I choose..? I read the reads per million is the suitable one for RNA high throughput sequencing data.
or should I use reference gene like GAPDH or beta-actin expression value for normalization..? if yes, how do I do it using this software..?
Thank you.