Evaluation and methylation prediction

Evaluation types

• single-read level

• genomic-loci (group-reads) level

Evaluation metrics

• ROC-AUC

• PR-AUC

Methylation prediction

We provided independent trained models on each 5mC and 6mA datasets of different motifs and methyltransferases in the ./trained_model fold.:

MODEL="BERT_plus"
MODEL_SAVE_PATH=<model saved path>
REF=<reference genome fasta file>
FAST5_FOLD=<fast5 files to be analyzed>
OUTPUT=<output file>

time python detect.py --model ${MODEL} --model_dir ${MODEL_SAVE_PATH} \
--gpu cuda:0  --fast5_fold ${FAST5_FOLD} --num_worker 12 \
--motif ${MOTIF} --m_shift ${NUCLEOTIDE_LOC_IN_MOTIF} --evalMode test_mode --w_len ${W_LEN} --ref_genome ${REF} --output_file ${OUTPUT}

We generate the same output format as the deepSignal (https://github.com/bioinfomaticsCSU/deepsignal).:

# output example
NC_000913.3     4581829 +       4581829 43ea7b03-8d2b-4df3-b395-536b41872137    t       1.0     3.0398369e-06   0       TGCGGGTCTTCGCCATACACG
NC_000913.3     4581838 +       4581838 43ea7b03-8d2b-4df3-b395-536b41872137    t       0.9999996       0.00013372302   0       TCGCCATACACGCGCTCAAAC
NC_000913.3     4581840 +       4581840 43ea7b03-8d2b-4df3-b395-536b41872137    t       1.0     0.0     0       GCCATACACGCGCTCAAACGG
NC_000913.3     4581848 +       4581848 43ea7b03-8d2b-4df3-b395-536b41872137    t       1.0     0.0     0       CGCGCTCAAACGGCTGCAAAT
NC_000913.3     4581862 +       4581862 43ea7b03-8d2b-4df3-b395-536b41872137    t       1.0     0.0     0       TGCAAATGCTCGTCGGTAAAC