(Newbie question)Different run results with analysis example

Hi, I’d just like to start off by saying I’m a complete newbie, not just to mothur, but to bioinformatics in general. And I don’t really have a bug to report, but I’m not sure where else to post my question.

Anyway, I’ve been running the Costello and Sogin analysis examples and have got one big question. It seems that the results from my runs are different than the results that’s posted in the step-by-step tutorial.

For example, I started noticing differences between my runs and the posted results in the Costello example after the first unique.seqs command. The posted result says there’s 20343 unique seqs, while my run said there’s 20530. Similarly, for the Sogin example, the diversity estimates I got at the end were different than the ones posted.

I know that mothur has been updated a number of time since the examples were posted, so I just wanted to check that the difference that I saw was due to differences in mothur version and not because there was something wrong with my computer that gives me the wrong output. (Like I said, I’m a total newb to all this!)

Thank you so much!

Hi,

It’s likely due to a couple of things. First, there may be some randomness at play in the differences since some of the algorithms use a random number generator. Second, we found that running the same code with the same data but on different OSs will result in subtle differences. Finally, as we make updates to mothur we find bugs that we fix. The two examples you are using have not been updated in more than 2 or 3 years. I’d encourage you to use the Schloss SOP to get a better handle of the current features and how to use them.

Pat

Thank you for your reply and thank you for the great work!

I also observed such a different output in unique.seqs and classify.seqs by running the exactly same thing twice!

getting repeatable results are important to us. when using randomness, can you offer a input paramenter “random seed” so that I can get the same results each time.

thanks
Chen