Read processing and filtering, de novo transcriptome assembly (rnaSPAdes) of Orciraptor agilis RNA-seq data

1. Run assembly.sh to assemble the transcriptome from processed reads of all libraries. Output is de novo transcriptome assembly of Orciraptor agilis as a fasta.
2. Filter transcriptome for contigs larger than 200 nt with seqkit_length.sh
3. Run blastn search with this transcriptome (nt database v5 updated on 2021-03-10): blastn.sh

• Checked contigs with > 95% identity over a length of minimum 100 nt, saved contig identifiers of all bacterial, viral, ribosomal and algal contigs in contaminants.txt
• Remove these sequences from transcriptome with seqkit.sh

4. ORF prediction with transdecoder.sh
5. Run blastp search of Orciraptor ORFs against Mougeotia predicted ORFs: blastp.sh

• Remove ORFs with a > 95% identity over a length of 150 aa from Orciraptor predicted proteome: seqkit_NA.sh

6. Rename ORFs to pattern "gx_iy.pz" (gene, isoform, peptide) with rename_transdecoder.py. Usage in folder Module_3/transdecoder: Output is "orciraptor_transdecoder.pep_renamed.fasta".

python rename_transdecoder.py orciraptor_200_filtered2.fasta.transdecoder.pep

1. Generate gene_trans_map file for Lace: gene_trans_map.R
2. Mapping the processed reads back to the newly generated transcriptome with bowtie2 and counting with salmon in alignment-mode (bowtie2.sh).

1. Number of number + length statistics of contigs was calculated with TrinityStats.pl script from Trinity toolkit
2. Number, completeness and orientation of ORFs is summarised with transdecoder_count.sh
3. ExN50 statistic is calculated with ExN50.sh

1. Run Lace to generate supertranscript fasta: lace.sh
2. Generate genome index of supertranscriptome for STAR mapping (star_genome.sh), perform STAR mapping of processed reads (star_mapping.sh), index the bam files (index.sh)
3. Run stringtie and merge the gtfs (stringtie.sh)
4. Convert gtf to fasta and predict ORFs (stringtie_fasta.sh and stringtie_transdecoder.sh)