<div dir="ltr">Hmmm, the false positive rate was 0.015. Here are the load-into-counting parameters:<div><br></div><div><div>PARAMETERS:</div><div> - kmer size = 20 (-k)</div><div> - n tables = 4 (-N)</div>
<div> - min tablesize = 3.7e+10 (-x)</div></div><div><br></div><div>Any ideas for diagnosing if normalize-by-median is keeping many highly erroneous reads? Would that be apparent from the kmer histogram?</div><div><br>
</div><div>The final discard rate for filter-below-abundance with a cutoff of 225 was 16% (reads normalized to C=20). Does this seem high given your experience?</div><div><br></div><div>-Chuck</div></div><div class="gmail_extra">
<br><br><div class="gmail_quote">On Tue, Jun 10, 2014 at 11:26 AM, C. Titus Brown <span dir="ltr"><<a href="mailto:ctb@msu.edu" target="_blank">ctb@msu.edu</a>></span> wrote:<br><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex">
<div class="HOEnZb"><div class="h5">On Mon, Jun 09, 2014 at 08:33:45PM -0400, Chuck wrote:<br>
> I'm curious about typical values that people are seeing with<br>
> filter-below-abundance. With the default cutoff (50) I was discarding ~50%<br>
> of bp (after normalizing with C=20). If I increase the cutoff to 225 the<br>
> discard rate drops to 25%. I thought I was rigorously adapter trimming my<br>
> reads (I generally use scythe with default parameters and I monitor the<br>
> output fairly closely). Is this way outside the developers' experience?<br>
><br>
> Also, at a cutoff of 235, I discard 0%. Not sure how to interpret this. I<br>
> realize that you don't count kmers above 255 by default with<br>
> load-into-counting. It seems that I don't have any kmers at the ends of<br>
> reads at a depth >=235 but I trim much more data with what seems like a<br>
> small change in the cutoff value from 235 to 225. Also, 235 < 255 :) .<br>
<br>
</div></div>That's tremendously weird.<br>
<br>
I have no other useful comment :)<br>
<br>
I can come up with some wild hypotheses about what might be going on,<br>
but have never seen this before.<br>
<br>
If, for example, your data was high coverage but each read had a lot of errors,<br>
then normalize-by-median might be keeping a lot of the highly erroneous<br>
reads while filter-below-abund trimmed of the legitimate sequence.<br>
<br>
I have no idea how to interpret the 225-to-235 numbers! Fascinating.<br>
<br>
Hmm, what table size are you using and what false positive rate is being<br>
reported?<br>
<br>
cheers,<br>
--titus<br>
<span class="HOEnZb"><font color="#888888">--<br>
C. Titus Brown, <a href="mailto:ctb@msu.edu">ctb@msu.edu</a><br>
</font></span></blockquote></div><br></div>