MIND

See overview and documentation:

MIND Prediction

Merge gene predictions of MRAKER (maker-final.gff3) with gene predictions INferred Directly (DI-final.gff3).

Note: See the details to generate these two predictions in make and DirectInf.

• Consolidate all the transcripts from maker-final.gff3 and DI-final.gff3, and predict potential protein coding sequence by Mikado:

  1. Make a configure file and prepare transcripts

     You should prepare a list_MIND.txt as below to include gtf path (1st column), gtf abbrev (2nd column), stranded-specific or not (3rd column):

     maker-final.gff3    mk    False
     DI-final.gff3 DI     False
     

     Then run the script as below:

     ./01_runMikado_round1.sh TAIR10_chr_all.fas junctions.bed list_MIND.txt MIND

     This will generate MIND_prepared.fasta file that will be used for predicting ORFs in the next step.

     Note: junctions.bed is the same file generate from DirectInf step.

  2. Predict potential CDS from transcripts:

     ./02_runTransDecoder.sh MIND_prepared.fasta

     We will use MIND_prepared.fasta.transdecoder.bed in the next step.

     Note: Here we only kept complete CDS for next step. You can revise 02_runTransDecoder.sh to use both incomplete and complete CDS if you need.

  3. Pick best transcripts for each locus and annotate them as gene:

     ./03_runMikado_round2.sh MIND_prepared.fasta.transdecoder.bed MIND
     

     This will generate:

     mikado.metrics.tsv
     mikado.scores.tsv
     MIND.loci.gff3
     

• Optional: Filter out transcripts with redundant CDS:

  ./04_rm_redundance.sh MIND.loci.gff3 TAIR10_chr_all.fas
  

• Optional: Filter out transcripts whose predicted proteins mapped to transposon elements:

  ./05_TEsorter.sh filter.pep.fa MIND.loci.gff3
  

  Note: filter.pep.fa is an output from previous step for removing redundant CDSs. You can also use all protein sequence if you don't want to remove redundant CDSs.