This repository contains different python scripts to predict translation initiation efficiency from transcript sequences using TranslateLSTM, an artificial neural network architecture as presented in "Current limitations in predicting mRNA translation with deep learning models".
This repository contains different python scripts to predict various measures of translation output from transcript sequences using TranslateLSTM, an artificial neural network architecture as presented in "Current limitations in predicting mRNA translation with deep learning models".
There are deep learning scripts for essentially three different usecases:
(1) training a model on synthetic MPRA data in the directory translateLSTM_MPRA/
(2) training a model on endogenous TE data in the directory translateLSTM_endogenous/
(3) do transfer learning from (1) to (2) in the directory tl_TranslateLSTM_endogenous/
[1] training a model on synthetic MPRA data in the directory translateLSTM_MPRA/
[2] training a model on endogenous TE data in the directory translateLSTM_endogenous/
[3] do transfer learning from (1) to (2) in the directory tl_TranslateLSTM_endogenous/
Example training data (MPRA from Sample et.al. (2019), endogenous data based on Alexaki et.al. (2020), and clinvar variations based on Landrum et. al. (2020)) are provided in the directory HEK293_training_data/.
Scripts to turn the output of RNAseq and ribosome profiling data into an endogenous data set, appending non-sequential features to a given data set, and constructing a data set based on a vcf file can be found in the directory training_data_preprocessing/.
