mxfold
Inconsistent prediction result with Web Service
Hello Developers,
I’m trying to use mxfold2 to predict for some RNA sequences. However, I encountered a inconsistent result between my local prediction and the web service at http://www.dna.bio.keio.ac.jp/mxfold2/predict as being shown below:
#my local running
$mxfold2 predict demo5.fa
>KC131142_UTR5
AGTTGTTAGTCTACGTGGACCGACAAAGACAGATTCTTCGAGGAAGCTAAGCTTAACGTAGTTCTAACAGTTTTTTAATTAGAGAGCAGATCTCTG
................................................................................................ (4.8)
>KR920365_UTR5
AGTTGTTAGTCTACGTGGACCGACAAAGACAGATTCTTTGAGGAAGCTAAGCTTAACGTAGTTCTAACAGTTTTTTAATTAGAGAGCAGATCTCTG
................................................................................................ (4.7)
$mxfold2 predict @./mxfold2/models/TrainSetAB.conf demo5.fa
>KC131142_UTR5
AGTTGTTAGTCTACGTGGACCGACAAAGACAGATTCTTCGAGGAAGCTAAGCTTAACGTAGTTCTAACAGTTTTTTAATTAGAGAGCAGATCTCTG
................................................................................................ (4.8)
>KR920365_UTR5
AGTTGTTAGTCTACGTGGACCGACAAAGACAGATTCTTTGAGGAAGCTAAGCTTAACGTAGTTCTAACAGTTTTTTAATTAGAGAGCAGATCTCTG
................................................................................................ (4.7)
The input two sequences in `demo5.fa` has no secondary structure from my local prediction, neither `default` settings , nor the `mxfold2/models/TrainSetAB.conf`.
However, the same sequences were input to `mxfold2` Web Service, and will have two well-formed secondary structures:
Why the same input sequences have a different results? Did I use the wrong local running settings? Could you please help me ?
Hi,
Thank you for using MXfold2. Please convert 'T' to 'U' and try again. The web server does this conversion automatically.
Kengo
After converting T to U, it works.Thank you.
By the way, do you have any suggestions for running mxfold2 locally for short sequences (30~50bp) or longer sequences (5000 ~ 11000bp)? Because I don't find any detailed official guidelines or tutorials, I don't know how to tune those settings of predict (I don’t know anything about deep learning or its parameters). Thank you.
MXfold2 works well for short sequences (30~50bp). However, I don't think it is very accurate for very long sequences (5000 ~ 11000bp), although it can be tried if memory and computation time allow. This is the same situation with other secondary structure prediction methods. Or, you can use LinearFold, which may work relatively well for very long sequences.
Thanks for your reply.
Could you please point out that how long the sequences' secondary structure will be accurately predicted by mxfold2? And should I tune the predict parameters for sequences with varied length, such as --model, --embed-size, ... .etc.？