quantitative expression study in twins

Sambucas

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Join Date: Feb 2012

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quantitative expression study in twins

02-03-2012, 08:08 PM

Hi all,

I am about to undertake a study investigating the gene expression differences between human twins using tissue from the pregnancy. I aim to quantify expression differences and hopefully allele-specific differences and splice variants between the individuals.

The abundance of transcripts differentially expressed is largely unknown and I was hoping for some input on calculating what sequencing depth I should be aiming for.

From trolling the literature I figure 8 gigabases of data per sample from 100bp paired-end Illumina Hi-seq run should give me more than enough data.

I also plan on running a 35bp single end library in addition to this for each sample for miRNA, etc.

Any thoughts, comments etc on how deep I should aim with the human transcriptome?
Tags: depth of coverage, rna-seq

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Reply to Nine Things a Sample Prep Scientist Thinks About Before Sequencing

by GATTACAT

Love this - good data definitely starts from good input, and poor input can only give relatively poor data. I particularly like the mention of Nanodrop/absorbance based methods for quantification. It's such a toss up if you'll get an accurate reading or what amounts to a randomly generated number, and a lot of library/sequencing related issues can be traced back to poor quant.
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I’m not a sequencing expert. I’m a purification scientist who uses NGS to evaluate workflows my group develops. With this perspective, we think about the sample first and the NGS workflow second. The sequencer is an exceptionally honest reporter, but it can only report on what you give it, so whether you get clean, interpretable data from an NGS workflow is largely determined before you begin.

Here are nine questions we think about, in roughly the order they matter, before...
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From Collection to Sequencing: Why Sample Preparation and Preservation Define Sequencing Data

by SEQadmin2

Data variability is still an issue in sequencing technologies despite the advances in reproducibility and accuracy of these platforms. But the problem does not originate in the sequencing itself, but in the previous steps, before the sample reaches the sequencer.

The first step is collection, followed by preservation and sample preparation for analysis. Most scientists overlook those steps, but not being careful might just be skewing the experiment’s results.
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