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1.0.0 | ||
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- update network model (add middle-scale layers) | ||
- improve preprocessing code (numpy implementation; HDF5 related code) | ||
- improve optimization code ('--weight_decay_auto'; '--restart_lr') | ||
- improve documentation | ||
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0.9.1 | ||
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- clean unnecessary code | ||
- update weight initialization for bias | ||
- add more detailed documentation | ||
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0.9.0 (2021-09-12) | ||
------------------ | ||
0.9.0 | ||
----- | ||
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- First public version |
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@@ -68,7 +68,7 @@ If the installation is complete, the following commands are available from the c | |
`mural_train` trains MuRaL models with training and validation mutation data, and exports training results under the "./ray_results/" folder. | ||
* Input data \ | ||
Input data files include the reference sequence file (FASTA format, required), a training data file (required) and a validation data file (optional). If the validation data file isn't provided, a fraction of the sites sampled from the training data file are used as validation data. \ | ||
Input training and validation data files are in BED format (more info about BED at https://genome.ucsc.edu/FAQ/FAQformat.html#format1). Some example lines of an input BED file are shown below. | ||
Input training and validation data files are in BED format (more info about BED format [here](https://genome.ucsc.edu/FAQ/FAQformat.html#format1)). Some example lines of an input BED file are shown below. | ||
``` | ||
chr1 2333436 2333437 . 0 + | ||
chr1 2333446 2333447 . 2 - | ||
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``` | ||
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### 4. Scale MuRaL-predicted mutation rates to per base per generation rates <a name="Scaling"> | ||
The raw MuRaL-predicted mutation rates are not mutation rates per bp per generation. To obtain a mutation rate per bp per generation for each nucleotide, one can scale the MuRaL-predicted rates using reported genome-wide DNM mutation rate and spectrum per generation. First, use the tool `calc_mu_scaling_factor` to calculate scaling factors for specific groups of sites (e.g. A/T sites, C/G sites). Then use the scaling factors to scale mutation rates in prediction files via the tool `scale_mu`. | ||
The raw MuRaL-predicted mutation rates are not mutation rates per bp per generation. To obtain a mutation rate per bp per generation for each nucleotide, one can scale the MuRaL-predicted rates using reported genome-wide DNM mutation rate and spectrum per generation. First, use the command `calc_mu_scaling_factor` to calculate scaling factors for specific groups of sites (e.g. A/T sites, C/G sites). Then use the scaling factors to scale mutation rates in prediction files via the command `scale_mu`. | ||
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Note that we cannot compare, nor add up raw predicted rates from different MuRaL models (e.g. A/T model and C/G model), but we can do that with scaled mutation rates. | ||
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Predicted single-nucleotide mutation rate profiles for these genomes are available at [ScienceDB](https://www.doi.org/10.11922/sciencedb.01173). | ||
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### 6. Citation <a name="Citation"></a> | ||
Fang Y, Deng S, Li C. 2021. A deep learning-based framework for estimating fine-scale germline mutation rates. bioRxiv [doi:10.1101/2021.10.25.465689](https://doi.org/10.1101/2021.10.25.465689) | ||
Fang Y, Deng S, Li C. 2021. A generalizable deep learning framework for inferring fine-scale germline mutation rate maps. bioRxiv [doi:10.1101/2021.10.25.465689](https://doi.org/10.1101/2021.10.25.465689) | ||
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### 7. Contact <a name="Contact"></a> | ||
For reporting issues or requests related to the package, please write to [email protected]. |