DirectInf

See overview and documentation:

Evidence (direct inference) gene models.

If you have difficulty to install any software for Direct inference prediction, we suggest you to download the singularity container for this step at here. This container includes all tools required to run the pipeline.

To use the container, add singularity run --cleanenv evidence.sif before the tools in each script. For example:

singularity run --cleanenv evidence.sif hisat2

• Do RNA-Seq alignment by Hisat2:

  while read line; do
      ./01_runHisat2.sh ${line} TAIR10_chr_all.fas;
  done < SRR_Acc_List.txt

  Note: Cufflinks and Class2 may takes a long time to process BAM file with deep depth region. Better to generate single bam file for each RNA-Seq data for next step.

• Run Transcriptome assemblies using Class2, Cufflinks and Stringtie:

  while read line; do
      ./02_runClass2.sh ${line}_sorted.bam;
      ./03_runCufflinks.sh ${line}_sorted.bam;
      ./04_runStringtie.sh ${line}_sorted.bam;
  done < SRR_Acc_List.txt

  Note: Class2, Cufflinks and StringTie generates a gtf format transcripts for each SRR sample. You can use more transcriptome assembler as you need.

• Generate high confidence splice sites that are useful for picking correct transcritps:

  ./05_runPortCullis.sh TAIR10_rnaseq.bam

  Note: The merged bam file (TAIR10_rnaseq.bam) obtained from the process of braker. You can also provide multiple single bam files got from 01_runHisat2.sh, but it takes some time to merge bam files. It's faster to provide merged bam if you already run braker.

  Here generate bed and tab file in portcullis_out/3-filt, we only use bed file.

• Consolidate all the transcripts, and predict potential protein coding sequence by Mikado:

  1. Make a configure file and prepare transcripts

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

     SRRID1_class.gtf    cs_SRRID1    False
     SRRID1_cufflinks.gtf cl_SRRID1     False
     SRRID1_stringtie.gtf st_SRRID1    False
     SRRID2_class.gtf    cs_SRRID2    False
     SRRID2_cufflinks.gtf cl_SRRID2     False
     SRRID2_stringtie.gtf st_SRRID2    False
     ...
     

     Then run the script as below:

     ./06_runMikado_round1.sh TAIR10_chr_all.fas junctions.bed list.txt DI

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

  2. Predict potential CDS from transcripts:

     ./07_runTransDecoder.sh DI_prepared.fasta

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

     Note: Here we only kept complete CDS for next step. You can revise 07_runTransDecoder.sh to use both incomplete and complete CDS if you need.

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

     ./08_runMikado_round2.sh DI_prepared.fasta.transdecoder.bed DI
     

     This will generate:

     mikado.metrics.tsv
     mikado.scores.tsv
     DI.loci.gff3
     

• Optional: Filter out transcripts with redundant CDS:

  ./09_rm_redundance.sh DI.loci.gff3 TAIR10_chr_all.fas
  

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

  ./10_TEsorter.sh filter.pep.fa DI.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.