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We are working with a novel plant transcriptome (no reference genome). After annotating the assembly, will removing irrelevant contigs effect read mapping statistics, in a good way, when we perform RNA-Seq analysis? Many of our contigs annotate as retrotransposons, bacterial/viral contaminants (metagenomic leftovers), or have no match to any annotated proteins in public databases. I assume contaminants should be removed, but what about genes of no interest to our research (e.g. transposable elements)? Will our results be more accurate if the our transcriptome only contains known and relevant protein coding contigs?
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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.07-01-2026, 11:43 AM
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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...