mRNN is an implementation of a Gated Recurrent Unit (GRU) network for classification of transcripts as either coding or noncoding.
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mRNN - mRNA RNN (mRNA recurrent neural network)

mRNN is a package for distinguishing coding transcripts from noncoding using gated recurrent neural networks (GRNNs).

How to get mRNN

You can obtain mRNN through github:

git clone

mRNN: Included Files

  1. Testing and Training - training mRNN model based on input training sequences.

    Example: $ python mRNN/ -e 128 -r 32 -d 0.4 -o mRNN.16K.da1.1 -s 3 -E 4 mRNAs.train16K.da1.fa lncRNAs.train16K.da1.fa mRNAs.valid500.fa lncRNAs.valid500.fa > epochs.mRNN.16K.da1.1.txt - test the accuracy of a known set of mRNAs and lncRNAs.

    Example: $ python mRNN/ -w w14u3.pkl -f test mRNAs.test500.fasta lncRNAs.test500.fasta - Ensemble testing with mRNN models. Models are provided as a comma-separated list.

    Example: $ python mRNN/ -w w10u5.pkl,w14u3.pkl,w16u5.pkl,w18u1b.pkl,w23u2.pkl -f test mRNAs.test500.fasta lncRNAs.test500.fasta - Basic prediction of coding probability for a set of input sequences. - Basic prediction of coding probability for a set of input sequences using a set of models.

  2. Further Analysis - For the provided input sequence, perform a mutation analysis involving every possible point-mutation on the input sequence, and computing its corresponding score change. - For a provided input sequence, computer all possible pairs of mutation i,a,j,b where position i is changed to a and position j is changed to b. - For the provided input sequences, shuffle each of them and report the scores of the shuffled sequence to the unaltered sequence. - Coding score trajectory. Compute the mRNN coding probability for all truncations of the input sequence from position 1 to i.

  3. Core mRNN modules:

These modules aren't called directly, but involved in basic function of mRNN - input reading FASTA files - building the mRNN model - Computing accuracy, batch testing - Utilities for various preprocessing

How to get data from mRNN project

The data used to train mRNN and produce the weights provided in the weights directory, you can download them from the following location:


243K lncRNAs.CHALLENGE500.fa


977K lncRNAs.TEST.fa

527K lncRNAs.TEST500.fa

28M lncRNAs.TRAIN.fa

11M lncRNAs.train16K.fa


708K mRNAs.CHALLENGE500.fa


5.0M mRNAs.TEST.fa

1.2M mRNAs.TEST500.fa

191M mRNAs.TRAIN.fa

12M mRNAs.train16K.fa