Mikado models to Gold standard models

Mikado models to Gold standard models for Augustus training

The input and output files for the below commands already exist in the directory below

cd /home/train/Annotation_workshop/Augustus/Training/Inputs

1. Setup the PATH

source activate_reat

2. Extract CDS fasta from the Mikado GFF3 file using the reference genome

cat Mikado_all.loci.Chr3.gff3 | gffread --stream -S -g Athaliana_447_TAIR10.Chr3.fa -x proteins.fna

3. Convert CDS fasta to protein fasta using appropriate translate tables/genetic codes.

cat proteins.fna | seqkit translate -T 1 | strip_stop_codon > proteins.faa

4. Download curated database (Uniprot, in this case) and create diamond database for blastp of our query protein fasta

diamond makedb -d uniprot_sp_tr.fasta.dmnd --in uniprot_sp_tr.fasta

5. Run blastp of our query protein fasta against the curated database using diamond

/usr/bin/time -v diamond blastp -p 30 -d uniprot_sp_tr.fasta.dmnd -q proteins.faa -f6 qseqid sseqid qlen slen pident length mismatch gapopen qstart qend sstart send evalue
 bitscore ppos btop > diamond.hits.tsv

6. Format Mikado GFF3 into clusters using gffread for downstream classify_transcripts command.

gffread -g Athaliana_447_TAIR10.Chr3.fa --cluster-only --keep-genes -P Mikado_all.loci.Chr3.gff3 > all_models.clustered.gff

7. Run the classify_transcripts command to create the Gold standard models gold.gff set for Augustus training. [2 mins]

/usr/bin/time -v classify_transcripts --evalue_filter 1.0E-6 --min_pct_cds_fraction 0.5 --max_tp_utr_complete 1 --max_tp_utr 2 --min_tp_utr 1 --max_fp_utr_complete 2 --max
_fp_utr 3 --min_fp_utr 1 --query_start_hard_filter_distance 10 --query_start_score 5 --query_start_scoring_distance 30 --query_end_hard_filter_distance 10 --query_end_score 5 -
-query_end_scoring_distance 30 --target_start_hard_filter_distance 10 --target_start_score 5 --target_start_scoring_distance 30 --target_end_hard_filter_distance 10 --target_en
d_score 5 --target_end_scoring_distance 30 --min_query_coverage_hard_filter 90 --min_query_coverage_score 5 --min_query_coverage_scoring_percentage 30 --min_target_coverage_har
d_filter 90 --min_target_coverage_score 5 --min_target_coverage_scoring_percentage 30 --max_single_gap_hard_filter 20 --max_single_gap_score 5 --max_single_gap_scoring_length 3
0 -b diamond.hits.tsv -t all_models.clustered.gff