Orciraptor_agilis_2021_Trinity

Read processing and filtering, de novo transcriptome assembly (Trinity), differential gene expression analysis and functional annotation of Orciraptor agilis RNA-seq.

Module 1: Read processing and de novo transcriptome assembly of prey organism Mougeotia sp.

1. Run readprocessing_and_assembly.sh, output is de novo transcriptome assembly of Mougeotia sp.
2. Predict ORFs to use later for decontamination: transdecoder.sh
3. Filter transcriptome for contigs smaller than 200 nt: seqkit_length.sh (only for upload)

Module 2: Read processing of Orciraptor agilis

1. Run symlinks.sh
2. Run readprocessing.sh. Output are quality filtered and adapter trimmed reads.
3. Run mapping.sh. Output are reads that do not map to sequences from rRNA and/or Mougeotia sp.

Module 3: De novo transcriptome assembly, decontamination, ORF prediction

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. 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

3. ORF prediction with transdecoder.sh
4. Use Change_Seqname_TransDecoder.py to change names of the transcdecoder output
5. Perform a diamond blastp search with diamond.sh comparing all ORFs against each other
6. Run ParseORFsVSORFsblastp.py on the diamond output and the renamed transcdecoder file to obtain a non-redundant .pep file, rename output to "Orciraptor_non-redundant.faa"
7. Use Change_Seqname_TransDecoder.py to change the names of the corresponding coding sequences
8. Call Extract_seq_using_FASTA.py to obtain the coding sequences of the non-redundant .pep file

Module 4: Functional annotation

1. Run eggnog-mapper in diamond and hmm mode: eggnog.sh.
2. Run InterProScan using interproscan.sh
3. Run a Diamond blastp search vs Swiss-Prot database (UniProt Release 2021_01): diamond.sh
4. Annotation of carbohydrate-active enzymes (CAZymes) with with dbcan2 in HMM mode (database dbCAN-HMMdb-V9): cazy.sh

Module 5: Differential gene expression analysis

1. Mapping the processed reads back to the newly generated transcriptome with bowtie2 and counting with salmon in alignment-mode (bowtie2.sh).
2. Run DESeq2 analysis and generate figures (DESeq2/DESeq2.R)

Module 6: Assembly summary statistics

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

Module 8: GH5_5 phylogeny

1. Run AssignRandomSeqnames.py on input sequences to generate random names
2. Simplify tip labels and generate annotation file for FigTree with Renaming_and_annotation.R
3. Run alignment and trimming: align_and_trim.sh
4. Find best model with IQtree_modelfind.sh
5. Generate tree with IQtree.sh
6. Run RenameTrees.py to get tip labels