Not sure how I should deal with the forecast results and the results of the existing database about tetools HOT 5 CLOSED

Hi,

That is a good question. But, this does not necessarily sound like a problem. RepeatModeler processes samples of a particular genome assembly, so it may miss some specific TEs due to "bad luck" or due to limitations in the sequencing or assembly process that make them difficult to recognize. Dfam and RepBase include TE families from ancestral species, which are known from prior research but are too fragmented or mutated to meet RepeatModeler's thresholds. Due to the different limitations of the two approaches, it can be more informative to combine the newly discovered elements into one library as you did.

Does this seem likely to explain the differences in your results?

from tetools.

Hello!
Very happy to receive your reply!
You said that we used the combined collection as the input file. We tried it, and the result was very confusing to me.
We use the combined (cat ) result "the prediction result of RepeatModeler, dfam and repbase" and the result is 44.71% as l said before.
If we first use the prediction result of RepeatModeler to shield the repetitive sequence., and then the shielded sequence again run the set of dfam and repbase the result is 43.64%. We first use the set of dfam and repbase and then run the the prediction result of RepeatModeler, the result is 43.91%.
I would like to ask which method should we use?
The combine is a very confusing place for us. What causes such a deviation?
Looking forward to hearing from you!

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The combine is a very confusing place for us. What causes such a deviation?

Running RepeatMasker twice with two different libraries will likely produce different results from running it once with a combined library. For example, the two repeat libraries could include similar but not identical families or fragments of families. In this situation, the first RepeatMasker run might mask most of an element. Then, the second RepeatMasker run might not recognize the leftover part because it is too short. So, each RepeatMasker run can affect the other run depending on the order. If RepeatMasker starts with a combined library instead, it can more effectively discover the elements from both libraries at once.

I would like to ask which method should we use?

The most appropriate method will depend on your goal and how well the RepeatModeler libraries came out for your species. For example, one method might mask more sequence, while another could produce a cleaner annotation with fewer fragments or more well-known names.

I hope this explanation helps you to decide what method to use!

from tetools.

Thank you very much for your valuable advice and patient help.
Your help is really of great significance to our work.
We decided to "combine" it with the method you said.
Maybe this is a more appropriate method.
Sincerely yours

from tetools.

Glad to hear! It seems like this question has been answered, but please re-open this or a new issue if you have more.

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