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Originally posted by sdriscoll
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Yes, seems to be a bit of confusion as to whether GSNAP (post 12) or SNAP (post 21) is producing 46% alignment rate, OP has contradictory posts. -
Sorry the SNAP in post 21 was a typo, I meant GSNAP. I haven't run it on GSNAP yet. I used Bowtie2 when I got the 46% alignment.
I am getting my alignment information via the align_summary.txt file.Comment
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Let us know when you try some of the suggestions we've posted otherwise we will get nowhere. Thanks./* Shawn Driscoll, Gene Expression Laboratory, Pfaff
Salk Institute for Biological Studies, La Jolla, CA, USA */Comment
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I'm going to align with Star and I'll dump the alignment information for Bowtie2, Tophat2, and Star on here once that's finished this weekend so you guys can give me some insight.
Thanks again for all your help.Comment
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any update from the OP? I'm have similar problem...Comment
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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...
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