GOslim Counts - A.pulchra Expressed Genes and BLASTing to GOslim for deep-dive-expression Project

2025
Acropora pulchra
E5
FastQC
MultiQC
RNAseq
deep-dive-expression
Author

Sam White

Published

June 17, 2025

INTRO

This notebook ran annotated expressed genes with gene ontologies, assigned them to GOslims, and generated corresponding counts of GOs in each GOslim category, as part of urol-e5/deep-dive-expression (GitHub repo).

Note

The contents below are from markdown knitted from 30.00-Apul-transcriptome-GOslims.md (commit 69ad137).

1 BACKGROUND

This notebook will perform annotation of expressed genes, as previously determined by 07-Apul-Hisat.qmd (GitHub).

Briefly, the notebook will perform the following tasks:

  1. Extract all genes from the genome, as GFF and FastA.

  2. Create a subset of only expressed genes, based on gene count matrix.

  3. BLASTx expressed genes against SwissProt database.

  4. Get gene ontology.

  5. Map gene ontology to GOslims and get counts.

Important

Expressed genes were defined as those genes having at least one count across all samples.

1.1 INPUTS

  • Gene count matrix

  • Genome FastA

  • Genome GFF

1.2 OUTPUTS

  • Genes BED

  • Genes FastA

  • Expressed genes FastA

  • Expressed genes SwissProt IDs only file.

  • Expressed genes to SwissProt IDs mapping file.

  • Expressed genes to SwissProtIDs and GO mapping file.

  • Counts file of expressed genes GOslims.

1.3 SOFTWARE

  • DIAMOND BLAST (Buchfink, Reuter, and Drost 2021)

  • bedtools (Quinlan and Hall 2010)

  • samtools (Danecek et al. 2021)

  • Biostrings (H. Pagès 2017) (Bioconductor R package)

  • GO.db (Carlson 2017) (Bioconductor R package)

  • GSEABase (Martin Morgan 2017) (Bioconductor R package)

2 VARIABLES

# PROGRAMS
blastdbs_dir <- file.path("..", "..", "M-multi-species", "data", "blastdbs")
programs_dir <- file.path("", "home", "shared")
bedtools_dir <- file.path(programs_dir, "bedtools-v2.30.0", "bin")
blast_dir <- file.path(programs_dir, "ncbi-blast-2.15.0+", "bin")
diamond <- file.path(programs_dir, "diamond-2.1.8")
fastaFromBed <- file.path(bedtools_dir, "fastaFromBed")
samtools_dir <- file.path(programs_dir, "samtools-1.12")
samtools <- file.path(samtools_dir, "samtools")



# FILES
count_matrix <- "../output/07-Apul-Hisat/Apul-gene_count_matrix.csv"
diamond_db <- "20250618-diamond"
diamond_output <- file.path("..", "output", "30.00-Apul-transcriptome-GOslims", "Apulchra-expressed-genes.blastx.outfmt6")
genome_fasta <- file.path("..", "data", "Apulcra-genome.fa")
genes_fasta <- file.path("..", "output", "30.00-Apul-transcriptome-GOslims", "Apulchra-genes.fasta")
genes_fasta_index <- file.path("..", "output", "30.00-Apul-transcriptome-GOslims", "Apulchra-genes.fasta.fai")
genes_subset_fasta <- file.path("..", "output", "30.00-Apul-transcriptome-GOslims", "Apulchra-subset-genes.fasta")
genes_subset_fasta_index <- file.path("..", "output", "30.00-Apul-transcriptome-GOslims", "Apulchra-subset-genes.fasta.fai")
genes_bed <- file.path("..", "output", "30.00-Apul-transcriptome-GOslims", "Apulchra-genes.bed")
og_genome_gff <- file.path("..", "data", "Apulcra-genome.gff")

# THREADS
threads <- "40"

##### Official GO info - no need to change #####
goslims_obo <- "goslim_generic.obo"
goslims_url <- "http://current.geneontology.org/ontology/subsets/goslim_generic.obo"

# FORMATTING
line <- "-----------------------------------------------"

# Export these as environment variables for bash chunks.
Sys.setenv(
  blastdbs_dir = blastdbs_dir,
  count_matrix = count_matrix,
  diamond = diamond,
  diamond_db = diamond_db,
  diamond_output = diamond_output,
  fastaFromBed = fastaFromBed,
  genes_bed = genes_bed,
  genes_fasta = genes_fasta,
  genes_fasta_index = genes_fasta_index,
  genes_subset_fasta = genes_subset_fasta,
  genes_subset_fasta_index = genes_subset_fasta_index,
  genome_fasta = genome_fasta,
  og_genome_gff = og_genome_gff,
  line = line,
  samtools = samtools,
  threads = threads
)

3 Extract genes as FastA

3.1 Extract genes from GFF

# Read GFF, skipping comment lines
genome_gff <- readr::read_tsv(
  og_genome_gff,
  comment = "#",
  col_names = c("seqid", "source", "type", "start", "end", "score", "strand", "phase", "attributes")
)

# Filter for gene features
genes <- as.data.frame(genome_gff) %>%
  filter(type == "gene") %>%
  mutate(
    chrom = seqid,
    start = start - 1,  # BED is 0-based
    end = end,
    gene_id = sub("ID=([^;]+);?.*", "\\1", attributes)
  ) %>%
  dplyr::select(chrom, start, end, gene_id)

str(genes)
'data.frame':   44371 obs. of  4 variables:
 $ chrom  : chr  "ntLink_0" "ntLink_0" "ntLink_0" "ntLink_0" ...
 $ start  : num  1104 10214 32056 34823 45952 ...
 $ end    : num  7056 15286 33275 42794 51024 ...
 $ gene_id: chr  "FUN_000001" "FUN_000002" "FUN_000003" "FUN_000004" ...

3.1.1 Write genes to BED

# Write to BED
write_tsv(genes, genes_bed, col_names = FALSE)

3.2 Create genes FastA


"${fastaFromBed}" -fi "${genome_fasta}" -bed "${genes_bed}" -nameOnly > "${genes_fasta}"

# Create FastA index
"${samtools}" faidx "${genes_fasta}"

head "${genes_fasta}"

echo ""
echo ""

head "${genes_fasta_index}"
>FUN_000001
ATGATGCCACAAGGGTACAAAAACGCCCTTCCAGGCTATAAAGACCTCTATCTTAGCCAAGCAATCACCGAGGAGGTTCACAATGTAAGTTTGTCCTTTTAGCTTGTGCCTGTTtttcctagtttgtttgcttgtttctttctttctttctttctttttagttctattttgtttcattTGTGATTCTTCAAGATATTTGGTGGTTACTTGTTCAGTCTAGCGTAGCCAAACTATTTGAACACTACAAGATAATTCGCAAGAAACAGGTTGAAGAAAATTCCATTTCAAGCGAAATCTTATTTCATTTTTTTACATTCTCTGTAGCTAATATACTGGCAAGTAATTTTATCGCCAACTGAAGGCAAGCACCAAGGACGTTTACATGGTAAGTTTGTCCTTTTGTTACACAAATTGGGTAGTCTGTTTCCTGGTGTTTTTTTTTTTTTTAATGTTTTAATTCACGATTATTCAGGATATCTAGGGATGAATTATCTGTTTATTACTCCAACATAATTAGTAAGAAACCAATTGGGAAGAATTCCAATAGAAGCAAAATCTTAATCTGTCTTTAAACTACTATGTAGTTAATACTTTAGCGAGTGATCTCTTCTCGGTGGCTATTACCATTTGTTGTGAATGCTATGAACGAAGAGCACCTTATTTTACTACCCGACATTATTGTAGCCCTGAGTTCCCTTTAGATCTGACGCTGCCATTTCTTACTTTAAGGCCTCTTCAATTTCTTGGTATTCGTAGATGTTTTCCACTGGCATTGACTGTGGTGTAAGTTCTTTCAAGCACGGGCCGTCGTTGTCTCTTCTAGCACTAGACAAAAAGGTGAGTGAGTTAAAACATGTTGGCTGTTAGTTCTTTCTACTATTTTCAGTTTTGCACTTGAATTCGTTGGAAGCCTCTGTGACAAGGCTTGATTTAATACTCGCTTTTTGGCATTTCAATTTCGCTTTCACCAAGAGAAACTCGATATTTCGTTGACTTCGCAATAAGTTAGTAGTTTTTCTTTCATATTGAGAGCTTCGCATTAAGGGTTCACATCGATGCTCTTGACTAGGCAAAGAAACGAGTCATTGTGCGAATTGCAGCATCTGTTTCCTACAATCACATATCTACACATATTTCTGGTGGTTTTAATTATAGTATCAATCAATCATCTTGGTTGAATATTGGTTAGGCAAAAGAGCATCCCAAAAAAACGTTTCGATCGAGCGTGTGACCAAACGTGTGGTATGACGGGATTCCTGGTATTGCAATGACCACTGATTTAACACATGAAATGGATACCTTAGAAATATTTCCTGTGATAGTGGTTTTTAAGACATTGTGCTAAATGCCACCCTGGATACTTTATCAACAGCAATTTTGGCCTCAGTAGGCACGTTTAGTAACCTCGAATTAACGCCACTTAATTTTTTTGGCAGCTCTGTGTTATCATATTCAAGTTATTACTCCAAAGCCACCTTCCCCTGTTTTGTCTGCGATCGTTGAACACAACGGTGAGTGAGAACTGAATATCAATATTACTATTAATATATCTTGCTTATAGCAGTAATTGAATTTGCTGCGGATGTACAACAATACTTATATTGTTGTTTTGCACCTCAATTAAGCTCAATTTAACGTCACTTAACTTTCTTGGCAGCTCTGTGTCATCGCATTGATCGAGTCATTATTCCAAGTACGTGGCCTTCCCTTCTGCGCACGATCGCTGAACAAAACGGTGAGTGAGAAAACTGAAGCGTTACATGGAAACTAACTGTAGTCAAGTCGCATCAAGGTTTCACCCTTCTAGAACGACTCAGTTAGCTGTCGTTTTGCTTCAAGTTTTTTTCCGGCAGTACTTAGTAGCATAATTTTGACCATTGACCGCTAATTATTGTGCTCAAAATCTCTCCGCAGATTTCTTTGGTGCGTTTGGCTGGGGCATTTCACCGTAAACACGAGCCCCCTTTGACTCCGTGTCCTCGAACGTCGCAAGAAACAGTGAGTAAAACACTTTGTCTGTATGATTCAACTCCATCAGCTGCATTTGCATGTCAGAAAAGCGTTAATTATCGTTTCATTTCCCTTTTATCTACTTTCAATAACTTGTTTCTTTAGAAAAGAAATTTTATATTCGATAGGCTATCTCACTATTTAGAAAAGAGACAACTTACCTTTGGCAAGCAATTTGATTTTCACTCCTATCATTCAATTGATCATGCCATGTTAAGTATTATTCATAAAATCAATGACCGCTAATTATTATGCTCAAAATCTCCCCGCAGATTTCTTTAGTGCATTTGGACAGGGCGCCGTTTCACCGTAAACACGAGCCCCCTTTGATTCCGTGTCCTCGAACGTCGCAAGAAACGGTGAGTAAAACACTTTGTCTATATGATTCAACTCCATCAGTTGCATTAAATATGTCAGAAAAGCGTTAATTATCGTTTCATTTCCCTTTTATCTTTTTTCAATAACTTGTTACTTTAGAAAAGAAATTTGCATTCGATAGGCTGTCTGACTATTTAGAAAAGAGACAACTTACCTTTGGCAAGCAATTTGATTTTCACTCCTATCATTCAATTGATCATGCCATGTTAAGTATTATTCATAAAATCAATGACCGCTAATTATTATGCTCAAATTCTTCCCGCAGATTTCTTTAGTGCATTTGGACAGGGCGCCGTTTCACCGTAAACACGAGCCCCCTTTGACTCCGTGTCCTCGAACGTCGCAAGAAACGGTGAGTAAAACACTTTGTCTATATGATTTAACTCCATCAGCTGCATTTATATGTTAGAAAAGCTTTAATTATCGTTTCATTTTCCCTTTTATCTATTTTCAATAAGTTGTTGCTGTAGAAAAAAAAAAACTTGTATTCAATAGGCTATCTGACTATTTAGAAAAGAGTCAAGTTACCTTTAGTAAGCAATTTGATTTTTGCACCCATGCAACATTCATCTGATCATGCCAAGTTAAGTATTGGTGATCAACTGCAAAAGGCAATTGCTGGAGATTTTTTAGGCTTGAGCAAAGCTTATTTACCTAGAAAGAAGTCACGTTCTTCCTCACACTGAAAACATGCCAACTTTCTTTCTTTTTCTGACAAGAACGAACGCGTTTTTGACCAGAACAAAAAAAAAAGAGAAGCCTTTTTTGACCGCCTATTGTCGCTTGAAACATTGTTTTGAATGGTTGGTAATAAACTTCACTTTCCTCTCGGTAGATTTCGTTTGCTGGCTTTGACTCAGACGATTCCCTTAGACCATCTTGATTTTCGCACAAAAACAAGATTTCCGCTCCCATGTGCGCCATATTGAACATGGTGACAATGCTGCCTTAATTCACAAGGAACTCTTCTTTTTTTTTCTGCTTTCAGCTGATATAACGAAAAGCTCGCGTTACGATTTCTGACTGCCTGACGCATCTTATATTTTGGTCCTCGCCTGGTTAACAAACACTGACTACCAAATGGAGCGGATTCTCCAAGCTGTTCAAGAAATGTAAAACACCTATTTTCATTTATGTACGTAGTGATCATCGTAATTAGGATAAATTAGCTATCATTTCATTAGGCTTTTACCTGCTTTTCCGTCTTTTTAAGTTCTGCATTAAGCGACAAAAATGGCCTCTTTACTATTTGGCGATTTGTGTTCAACTAGGCGGGGAAAGGAATTGTTTTTCTGTTTTATGCAAGCAAAGGAAATTTTATTCTCTAGTTATAGTGACCAACATATCTGCAAGGCATTATCTTATTTAAGTGTGAACCTCGTTCCGTGTACGGATCCAGAACTCGATTATTTTCAGCAAAAACTTTCATCCCGTTTCTACCCCTTCGGTTTAATTAGTGAACACAATTTGCTCTACCTGAGGACTTTTCTGCAAGCGAATCGTGGCATTCAATTATCCATCCCAAACATTGACAACCAGGCTTATTTACAGGCTCTTTCGACGCAATCTAGTTTGATGTCGCCGCATTATCTTCCATTACAAAGCAATCAGCTCGGCCAAGTCAAACCAACTTTTCAGCTTTCGGAACCAGGTCGCTCTGATGAAAATTTCAGCGACACTGACCCAAAATTCATTTGCAAGCATCCGAGAATTCACGTTCCAACATCAGTCGGTGTTGTACAGCCTAGTGTAAGACGTCGGACTAGCGACAATCCTGTTAGTCCAACTGAAAGTCGATCTGAATCACCTCTATTTTCACTACTACACGAGCCTGAGACAAGTGTCGCTACAACCACTCTAGGTGATCCGACCAATCAATTAGTTTCGCGAACTCTGGCCAAAAGCAATGTCAACCAACTCTCCGCGCAAGATATTCCTAACGATTCCTCCGTTCAGCAAAACAGCCTCGAAAGTCACCTTACCCCTTTGAACCAACCTGTTGATCCTGATCCGCTATTACCCGAATCCATTGATGGTACCAGCAGCATTGAGATCGATTCTTCTAAAGAAAGTACGAAAAGCAGTAGAACTGTGACTCTTTCCGAATCGGAGATGTCACCCCAGTTGCGTTTAGATTTGGAGGAAATACGAAAATTTTATTCTCTTCCAATTAACCTCAATCGTGACGGAGGTGTTCTGCAAGATGTCTCGATAGGGAAAATGTTGGAAAGGATAAAAGGGTTCTTGTGGTTTTTAAAGAAGGTAAAAGGCGTCGAGCCTGCTTTGACTTATTGTATCAATCCGGAAGTCTTACAACAGTTTGTCGAATTTATGATGAAAAATCGTGGTATCAAAGCCATTACTTGTAGCCGGTATGTGACGTCCTTAATAAGTGCCTGCAAAGTGCCACTCGCGTGCACACAAGATGAACAAAAAGAAGAGTCTCTTGAAAAAATTAGGGCCATTCAGAGGCAACTTGAGCGATTGTCCAGACAGGAAAAAATTGATTCCGACAGTCTTAATCCTCAGACAGACAAAGTAGTTTACTCTGAATTGCTAGAATTATGCAGAGAATTCAAATGGGAGGTTTCGGAAAAAACAGGTGCTGATCGTGCACGAAGTTGTATGAATTTGTGCTTGCTTCTCATGTACTGTGCGGTTAACCCGGGCCGAGTCAAAGAATACATCTCACTGAGAATTTATAAAGATCAAAGCGGCGACCAATTGAAAGATCAAAATTTTATCTGGTTCAAGGAGGACGGTGGCATAGTATTGTTGGAAAATAATTACAAGACCAGAAATACTTACGGCCTAAACACCACTGACGTGAGCTCAGTCACATACTTGAATTACTATCTGCAACTATACAAGTCTAAGATGAGATCACTTTTGCTACACGGCAATGACCACGACTTTTTTTTCGTTGCTCCGAGGGGAAATCGTTTCTCGCATGCCTCTTACAACTATTATATATCCGGACTATTCGAAAAGTACTTATCTCGGAGATTGACAACGGTTGACCTTCGAAAAATTGTTGTTAATTACTTTTTGTCGCTTCCAGAAAGTGGCGATTATTCCTTAAGGGAATCGTTTGCGACTCTCATGAAACATTCTATCAGAGCGCAACAAAAATATTACGATGAACGTCCGTTAACCCAAAAAAAAGATAGAGCGCTCGATTTGTTAACCTCTGTGGCTAGACGAAGTCTAGACGAAGATGAACCTGAGATTGTAAGTGATGAAGACCAGGAAGGATATCTCGACTGCTTACCGGTCCCGGGAGATTTTGTGGCCTTGGTCGCAGCCAATTCTACCGAAAAGGTTCCGGAAGTTTTTGTGGCTAAGGTACTGAGACTTTCCGAGGACAAAAAAACTGCTTATCTTGCCGATTTTGCGGAAGAAGAGCCAGGAAGATTTAAATCGAAAGCGGGAAAAAGTTATAAAGAAAATACAAATTCTCTAATTTTCCCAATTGACATCGTCTTTTCGCATTCGGACGGTCTATATGAATTGAGAACGCCAAAAATTGACCTTCATCTTGTGACAGTTCAAAAGAAAAGTTAA
>FUN_000002
ATGGCTTTCGTTCGCTTGCCGAGGCCATTACTGGACGAATTCATAACGCTGTGGGACCAAGTTCAATCCATTTCGCCGACGCTTCCCGAACAGGCTAGGCATCTTATTCATCGTGTAGATGACAAGGTCGCCGAAATTAGGAGCCAAGCAAGCGACACGCCTACGACAGGAGGTCACGGTGCAGAATCCCAAAGTACAGGTGGGTGAACTAAGCAATGTTAGGTTGACTTATTAAGCAGAACATATGAGGAAATTCATCACTATTATGATCACATCCCTAGAATTTCATACTTCTTTAATGACTTCTAATATTCAGCATTCAATTGTCTTCAGTCATTAATTATAATCTTCTCTCCTTTGAAATGTCTTAATCATTGCACATGCTTTTAATGTTTTTAGCAGCTGACCAGCCACCACAGATCCCTTGCTTGGCCATGCAAGAGGAAACAGCCTCCTCAAGTGGTAAGAGCAGCTGTCTTCCCTGATAAACTATGAGCACTCAGTTCGGTTAACTGTCAACTAAACTCTGACAATAGGCATAGTATACAATAGTTTACTGCCATAGTATGCAATGGGGGAATGTACTGAGAGGAGAGCTGTGAACATCTAAAACATATATCTATATACACCACTTGGAACCAACAACTGACATAAATGTGtaaaaaaaaaatcataataatactaataaataattatGTTGGCATGGAAACTACTGACATGCAGGTGTTTCCATTCAAAAAAATGTCTCAAGATCCAGTCAAGTAAATCAATCTATTGATGAAGAAATTGATATGGTTTAAGAGTTTTCCTGTATTGTACTGATGGGAATGAGACATAACAGCTATCCATCCTCTTCATATAATTTTTTTCTCAAAAATAACCATAGATTCCAACCTTTTGGAAGGTTACATCCTCATAAAGGGATTACTGTGAGGACAGCAAGCATTTGTAAAAAAAATATTTATATATTTACCTTTCTTGGAGCCAAATCAGCTATCATAAATCTCCACTCTTTATGTTGGCAAGTAAACAACTGACATTAAGGTGTTACTTTCAGTACATGTCTCAAGATCCAGTCAAGAGGAGCATGATGATGAAATTGGTATGGTTTAAGAGTTTTCCTGCATAATTTGTACTGATTGAATGTGTTAAGGATGACAGCCATTGTATCATTAAATTTTGTCTTTAAATGTTATTTTCTTCTGAAAAGGTCTTTTATGTAACAGCAAGAAATGCTGTAATGAAAGAAGTGCTGAATCTTAGCAAACATCACTGATATCATAACTTTTAATCCCTCCATCAGATGACCAGCCAGCAACTCCACTGGTTAGGAAGCGCAAGAGGAGAGTTGCTCAGAAAATAACAAGCTCTGCTCAGGTAATTGAAAGAAATTTTACTTCAATAATACTATTATTGCTGGTGCGACCCTGTACACACAGATTTATATTGAAGAGAACAAATAACCTGGGCCTCCATCCCCCAATTAACACAATATCTACCCCACCACTTCCCCTGTAGAACCACAGTTCACTGGAAAATAGATCAATAAAAGAATCAAGATTGAATGATTTGCCATGATTTATTCTCCAGTAAACAACAAAAACAGCAACAAAAGCAAAATAATATTATTATTCTGACTCCTGGATTTCATTGAGACTTCAAGTAACTGGCCATCATTTACTTATCTATTTTTTTAAGTGTCTTAGTGTAGTCCACCGTAATAATAATGTTTGCTGCAACTGCAAGACAAAATTTCTCTTCCAAGAAATATATCTTCAAAATGCCTTACTGAAGAAACTAAGATACTTCTTTCCCATAGTCAATCAATTAAGCAAATAAAAAAGTAACATTTTGTTCATGGTAAAATTGCTGTCATTTAACTCCTGACTATGCTAGTGAAGTTTATTTGTATTGTCCGCATTTCCTGAGGATAAGAGCCTTTCATGCAAATTTACTACATAGTTCTGAAGACGTTAAGCAGACAGAAATGAGCCAAAATTAATTTTCTTTCCTTTTAAACTTATAGGGCATCCAGTTGCCAGCACGGCCACCCGACATCGTGTCAGAGCCCACCCCTGACATAAACTTTGAAAGCATGGAGATCAAGTATCTCAAGCAAGCCTTAGCCTCTGTGGTACAACCATCAACCAAGGCAGAGGTTTACTTGAAATATCTTGCAAAATGTGACTTCAATACTACATTGGAGCCACACTCCATTATCAAGTTTAATCAGGATGATGTTCGGAGAATGGTTGGAATTGGGCAAAATCCCAATGGTGAAGAAGAAGTAAGACAATTTTTAAATAAAAAGCAACACAAATGCAGTGCAAAGTACCTTTGAAGGAATTTCAGTCGAGAATAAGTTCAATCTTTCAAAATTAGCGAGGGGCTCTGATGGTGCTAATCACTGCTTTTCATGGGAATGACCTCTGCATGATAGTTTGCACTGCATTAGCCAATAAAAAAAATTAATACAAACTCATTGCCTGAGTTGTAAAATTATTTTCGAAATAACTTATACCCTATTCACACCAACTAAACATGTCTAATTAAACTCGCTTTAACAAAGAAAATCAGTCCAAAAAAAGTATGGGACTGGTTTTTACAAAAGACCCTGATAAGTTTTAATTTATATGCATTGATTGATATGTACTAAATTTGAAGTTGACAAAACATCATTGCACACGATTTGATGTGAAGACAACTTAAAACCATGTTTAACTAAACAGCTTTTAAACATGTTAGAGCTTTGGTGTGAATAAAGCTTTAGTCACAGACAGTCATGAGTTCATGAAGTATATTCCACAAAATAAAATTGTCTCTTTCTTTTCTCACTGTTTCCTATCCATTGATTGTTAGGTTGTTGGAAAATTACACCAAGCCCTCCAACAGAGATTGCAGCTGGCTGTCCAACAGGCATTTTCAATAGGAGAATTCATGTCGACGTGCATCAAGGAGCATGGATCAACTTTGCCAGATGAGGACCATCAGCGACGCCGTGGAAGACCTCGAGTTTCATCATTATTAGACACGCTGGATTCCATCGAGTCACTTGGAGCTTCATCATCGTGTCTACAGGACCAAGTTTTGATTTTTCAAGCTATTTCAAATTTTCCATTATTGAAATATGTTAATGAGCCTATGACTCACTTTGTGTGTAATGACGTCAGGTTAGCGATTAGGCATCTACCAGCAGCATTGCATCACAAGTGTGATGTACCAGAAAACATCCAAGATGCCCATATCAACTTGAGACCGTTTAGGTCTTTGGTGTCAGATCAAGAAAGTCCACCGCTTTCGGGTGACATGAGTTACTCCAATGTAACTCCATGTGATGGTGATGGTCTCGTTGTTGGTATTAATCTTGACCACCGAGAACCACTGGAAAATGCTGAATATACACGCTTTTACGGTATCGATGCCCCTGAGTTGTCATCTGTTCATTTTATTAAAACTAATGACTTCCAGCATGTTTTCTGTAAGCAGGTTGGGCACATAAGCCTCTGTGCGGTACATTTGTTTTTACAGATGTTCTTGTTGAGTGGATCTGCAAAGCTATGCGAGGAACTCCCCCGGGAAGCGGCTCCACAGCCAAGGGACATTTACAACAGAGCACTCAAGGAGTATTGGTTCAAGTTCATCACACCTCCGTCTCAACATCTGGAGAAAGTATTCCTGCAGTCTCTTGAAGAGCTAGTCCCTCCAACATCTGAATCCCGAAAGAGGCTCATGTCGCCTTTTCCAGCAACCATGGCAACTGCTGCCAATCCATTCTTGTTGTCACTAAATGCTCTTCTTGTTGTTTCAGGTTTTTGTCACGTCTTCACGAAGTATTGCCAAGATGGATTTCTTTTAGGGCTGCAGGCAATTGCACGGGACAACAAGCTAGGGCCAATTTGGTGTGGTGCTTCTCGGAAGTTCATTTTTGGGTGTACTTCTGGAAACAACACTGATTTTTTTTTGAAGCACTTTACACCAGAAACAACATCTCACTTGGCCAGGGCTGGTTTTCCCTTCAAACACTCAAATGCTTTTCTCCCGTGGCACGAGAGACAAATGCTGAAACAGCTGTGCTCACAGGAGACTACAAGAACCGCAGCAAGAAATCACTTGGCACAGCATCTCCCAGGAATGGAACCCCAGTTTGGCATGTATATTGACATCCAGAGGTTGGTGATTATTGTTGAATAATAACTACTACCCTATTCACACCAACTAAACATGTTCAATTAAATTCGGTTTAACAAAAGAAAATCAATCCAAAAAAGCAAAAGACCCTAGTAAGTTTTAAATTACATGCATTGATATGTACTAAATTTGAAATTGACAAAAATCATTGCACACGATTTGATTTGAAGACAACTTGAAACCATGTTTAACTACAAACAGCTTTTAAACATGTTTAAGTTTTGGTGTGAATAGGGCTTATAATTCTATTATTTTTTCATTGTTACCTAATTGTGACCTCATGATAACATCATCACATTTTTGTCAATAAAATTTGTAGATCTAACCAAGGAGAAGGATATCAAACAGTGAGGACTGGTGAGGCTTACCTTAAAGTTATGAATAGCCAGGTAGTTGGGATAGGAGGTAATTCTACAGGACTGGGTTTGTTCACCCTTAAAAAGATTCCTAGAGGCACTCTTGTATGTGCCTATGCTCCAACTGCAACAATTTGGGAAGGTAAACTAAATGGTGATTATGTTTTAGAAACTTCTTTTAATAACAAGGTAATTTCTGTCAATGGGAAGGAAAATCTTTTTGAATTGGGGTTGGGAATTTATTGTAATGATGGAAGCTTTCCATTTTCCTTAGCTAGGGCTAGGTTTTCAAATGTTATTTCACATAGAGTAAATTGTGAATACTGTAAATGTGGGGATGGCATATGGCTAAAGACCGTCAGAGATGTTTCCGCAGGGGAAGAGCTGCTGATGTGCTATTCTCAAGATGGAAGCTACTGGGCTACTATCTTCAGTAGGGAACAACTTAACCAGATCACAGCTGCCTTAAACTCATGTGGCCCATCACTTCAGGATGCCGAGCGCTGCATTAGACTACTTCAAGTCTAA
>FUN_000003
ATGTCCAACTTCAGTATAAAGGTTGGTTGTATAGAAGGAAAGTGTTCGCTTTCATTTCATATTTATATTTGAAAATATTTTTATTGTCATAGTTTTTTTTCATTGCTCATTCCTGGTTTAGATATATCAGTATGCTCCGGCGGAAAAGAGAAGGTTTTCTGCCTGCGTGCGCATTTTGCGTCGGTAACGAAGAAATCAATTGACTGTGCTGTCCGGCACATCTTGGGTGTGAGAAGAAGATCTTGAACCTCTCTCTCCATATTTGCTTTGCTTCTGTAGTCTTTTTATTGGTAATTCCTGATCAGTTGGCCGTTTTGTATAGGTAAAAACGGTTGCTATTGAAAGAATATTTTAGGAGCATTTGTTTTTACAGATGTTCTTGTTGAGTGGATCTGCAAAGTTATGTTTGTTTCACGAAAAGCGAGAAGAATCCTAATTCTTTGAGGTTGATTTTTGGTTTTTGACTTGTGGGACAGCTGCTATCGCAACTGTGGTAGTGCGCGCATTTTGCGCAAATCAGTTGGCCGTACTTTCGTATGATGAAAACCTGTTCACATTCGTTCATAAACATGTCTCCAATGGGACCCGGGCTTATCTCATTGATCCAGCTTTCATTTCAGAGATTCGGAGTGTCTTAGTAATGGTCGCCATTTTTACCTGGAAAATTCGGCCTACTATGTTACGGAAGAACAGAGCAGTGTCCAATGCAATTTCAAAACAGGCGATTGATACCTTCTTGACGGTATTCTAGGCTCGGGCCTTGATGTAGTCATACAGCTTGCTAAGAAAACTTTAAATTGAGCCGATTGGAGCGTCCTCTTAGCGTGCAAATTCtttggtttttgccgttctgtttttgctttttgttttaatttttAAAAATTTCTCCGTGCTGAGTAATAGAGGCGTCATGTTCCGATGGCAATGGATGAATTCTCGTTGGCCGTTACTGCTTGCAATGCAGTTCTCCTTGCTCCTCTTGTCTTCATTGTTTGTTGGGTTGTGGGAGAACGAATGACACCCCGCTTTGTTACATGCTTAACTGTATTAATTTTTCATCCCACAGTCAGAAGATGAGCCGGCAAGCCCTTTGGTCAGAACTGATAAGAATGAAGAGTTACCCACAATACCACAAGCTTTCAGGAAGCTAAAGGAGTGTTACTGCAACTGTTCTTGGGGAGATTCTTTACATGCAGATCCTCAGTTAACTGGAGAGTAA
>FUN_000004
ATGCTCATTTTCAATCGAGGTATTGTCTATGTTGTCATGAACAGTGTGTGTTTAATAGGAGTTTGAAAAAATAAGAGCCCCATTGAACACTCGTACATATCTATTGTTGGCTTAAAGAGAACTCTTTGGCGTTTATATAAACGAATCGTAGATCATGAGAAGATGGCGCTAATCCCTCTATTACGTTCGCAGACCCTCATGATAAATGTGTCAGCCAGACAAAATCGAAATTTTTGGCAACGGAATGCCAGATAGCcagggctcagttgttaaaatccggattaagctaatcatggattagaggaaattttagttgttatttatttaccgttaaagggggctttttcataggattatggcttaaggaaaagaaatttgtaatttataaccttattgggtcccaatgttgtggcaaaacctccttttagccgtaaataaatagcaattaaaataacctaggattagcttaatcgagcttcgaacaactgggccctggtctcttgagagAACACGGCGATCCTGGGTACGAACGATACAATGCAAATGACTTATTTTTCGACCAATACGAGATGACTCCACGTGACTTGAATTCTTTCCGTTGAGGCGGCGTGGCTTCAACGGTCTTTCTTCAATCGTATTTACACATGACTTTGATGTACATCATGCAACAAGAAAGTGACAATCAAGGCAAACCGACTAACAGCAAATTGCAGTTATTGCAAACTCATGCAAGAGGTGTCCATGTGTAACACTCAGTGGATTGCCAAAAGTTGTCTTTCAGAACACTCACGACAATGAGAAGATTTAGATTTAACCATTTCATGCACACATTATGAATCTTGCAGACCTTACCTTCGACACATCTTCAAATCAGAGATAAACATTTACGAACATTTCAACATGGTATTAAAGACCCTTCTCTCCCCTGCCAAGGGAATTCAATTGTTACGATttgttgttgttgtcgttgttTAAATATTTTATAGTTACTTTTCGAGTTAAACATTGACTTCAAAAACTCTTAAAAACCTGGAATTTCAAGTGTTTAGATTTTGAAAAAGAGCCTGATATATCACAAAAAATTTTCCAGTTATAATTAATTAGTAACATTTCCATATCGTCCAAAACTGAGACTCAAGTCAACTAACAACTAACAACTAATTCTTTTCTCTTGTTAACTGATGAAATATAATTATGTAACACATGTTCTGTTGACCTTTGGATAATAAAATGTGTTGTTTGTTATCTTCGCCATAATCAATATGTTAACCGTTATCATCAAAAATAGAAGCCTGAAATTTTATGTCAAGCCCGGAAATAACTCATTTCGCACTCTTTACTCTAGGATCGCCCTTAATTGAAAGCACGTACGTGCGGCTTGTGGACATATTTCCGGTTCTGGCAGTTCATTGCCCTCTGTGGGGCATTAACCTAATCAACAACAAATGCAAAGGTGACATGCTCTGATCGGAAGGAACTCTGACACGCTTTAAAGCTACTCTGCTGAATATGCTCCGACCTTTTTAAAATTTATTTAAGTCAGAGAGTGTCCTGACTCAAAAGCTTTTGAAAGGAAACGTGAAAGGTTGTCTACTGTTAATCGCGCAACGGAGATGTCTCGTAAAGTTTCAATGGCGGTGATTTAATTAAAGGCCAGAAATAAACATGTAGGTACCTCGTAAGGTGCTAGCGGTGGTGTTTAATGTCTCTCTAGTTGTGAAACTGGTTAAAAAAGGCCATAATAATAGGTTTCCTTTCTACTCCTTCGCGGAACTGAGCGGGTTTCTCTGTGTTAGTTTCTACTTCTCCCTTTTTGGTCTGTTTTCGAATAATGTGGCTGTGACAGGGAGGATGCAAAACCACCATCAGTGCTATGGTATTCCTTGACTTATATCAACGTTGACCTTTTACCTGTTAGTCATTGCGCACGAGCTACAATAAATTCATTATGATTTTTGTCTGCTTTTCCTGTTTGCCGCAGGGGAGCTTCTGTTTTTACCAATCAGGAACGTTCTCTGCATGATGCCACAAGGGTACAAAAACGCCCTTCCAGGCTATAAAGACCTCTATCTTAGCCAAGCAATCACCGAGGAGGTTCACAATGTAAGTTTGTCCTTTTAGCTTGTGCCTGTTtttcctagtttgtttgcttgtttctttctttctttctttctttttagttctattttgtttcattTGTGATTCTTCAAGATATTTGGTGGTTACTTGTTCAGTCTAGCGTAGCCAAACTATTTGAACACTACAAGATAATTCGCAAGAAACAGGTTGAAGAAAATTCCATTTCAAGCGAAATCTTATTTCATTTTTTTACATTCTCTGTAGCTAATATACTGGCAAGTAATTTTATCGCCAACTGAAGGCAAGCACCAAGGACGTTTACATGGTAAGTTTGTCCTTTTGTTACACAAATTGGGTAGTCTGTTTCCTGGTGTTTTTTTTTTTTTTAATGTTTTAATTCACGATTATTCAGGATATCTAGGGATGAATTATCTGTTTATTACTCCAACATAATTAGTAAGAAACCAATTGGGAAGAATTCCAATAGAAGCAAAATCTTAATCTGTCTTTAAACTACTATGTAGTTAATACTTTAGCGAGTGATCTCTTCTCGGTGGCTATTACCATTTGTTGTGAATGCTATGAACGAAGAGCACCTTATTTTACTACCCGACATTATTGTAGCCCTGAGTTCCCTTTAGATCTGACGCTGCCATTTCTTACTTTAAGGCCTCTTCAATTTCTTGGTATTCGTAGATGTTTTCCACTGGCATTGACTGTGGTGTAAGTTCTTTCAAGCACGGGCCGTCGTTGTCTCTTCTAGCACTAGACAAAAAGGTGAGTGAGTTAAAACATGTTGGCTGTTAGTTCTTTCTACTATTTTCAGTTTTGCACTTGAATTCGTTGGAAGCCTCTGTGACAAGGCTTGATTTAATACTCGCTTTTTGGCATTTCAATTTCGCTTTCACCAAGAGAAACTCGATATTTCGTTGACTTCGCAATAAGTTAGTAGTTTTTCTTTCATATTGAGAGCTTCGCATTAAGGGTTCACATCGATGCTCTTGACTAGGCAAAGAAACGAGTCATTGTGCGAATTGCAGCATCTGTTTCCTACAATCACATATCTACACATATTTCTGGTGGTTTTAATTATAGTATCAATCAATCATCTTGGTTGAATATTGGTTAGGCAAAAGAGCATCCCAAAAAAACGTTTCGATCGAGCGTGTGACCAAACGTGTGGTATGACGGGATTCCTGGTATTGCAATGACCACTGATTTAACACATGAAATGGATACCTTAGAAATATTTCCTGTGATAGTGGTTTTTAAGACATTGTGCTAAATGCCACCCTGGATACTTTATCAACAGCAATTTTGGCCTCAGTAGGCACGTTTAGTAACCTCGAATTAACGCCACTTAATTTTTTTGGCAGCTCTGTGTTATCATATTCAAGTTATTACTCCAAAGCCACCTTCCCCTGTTTTGTCTGCGATCGTTGAACACAACGGTGAGTGAGAACTGAATATCAATATTACTATTAATATATCTTGCTTATAGCAGTAATTGAATTTGCTGCGGATGTACAACAATACTTATATTGTTGTTTTGCACCTCAATTAAGCTCAATTTAACGTCACTTAACTTTCTTGGCAGCTCTGTGTCATCGCATTGATCGAGTCATTATTCCAAGTACGTGGCCTTCCCTTCTGCGCACGATCGCTGAACAAAACGGTGAGTGAGAAAACTGAAGCGTTACATGGAAACTAACTGTAGTCAAGTCGCATCAAGGTTTCACCCTTCTAGAACGACTCAGTTAGCTGTCGTTTTGCTTCAAGTTTTTTTCCGGCAGTACTTAGTAGCATAATTTTGACCATTGACCGCTAATTATTGTGCTCAAAATCTCTCCGCAGATTTCTTTGGTGCGTTTGGCTGGGGCATTTCACCGTAAACACGAGCCCCCTTTGACTCCGTGTCCTCGAACGTCGCAAGAAACAGTGAGTAAAACACTTTGTCTGTATGATTCAACTCCATCAGCTGCATTTGCATGTCAGAAAAGCGTTAATTATCGTTTCATTTCCCTTTTATCTACTTTCAATAACTTGTTTCTTTAGAAAAGAAATTTTATATTCGATAGGCTATCTCACTATTTAGAAAAGAGACAACTTACCTTTGGCAAGCAATTTGATTTTCACTCCTATCATTCAATTGATCATGCCATGTTAAGTATTATTCATAAAATCAATGACCGCTAATTATTATGCTCAAAATCTCCCCGCAGATTTCTTTAGTGCATTTGGACAGGGCGCCGTTTCACCGTAAACACGAGCCCCCTTTGATTCCGTGTCCTCGAACGTCGCAAGAAACGGTGAGTAAAACACTTTGTCTATATGATTCAACTCCATCAGTTGCATTAAATATGTCAGAAAAGCGTTAATTATCGTTTCATTTCCCTTTTATCTTTTTTCAATAACTTGTTACTTTAGAAAAGAAATTTGCATTCGATAGGCTGTCTGACTATTTAGAAAAGAGACAACTTACCTTTGGCAAGCAATTTGATTTTCACTCCTATCATTCAATTGATCATGCCATGTTAAGTATTATTCATAAAATCAATGACCGCTAATTATTATGCTCAAATTCTTCCCGCAGATTTCTTTAGTGCATTTGGACAGGGCGCCGTTTCACCGTAAACACGAGCCCCCTTTGACTCCGTGTCCTCGAACGTCGCAAGAAACGGTGAGTAAAACACTTTGTCTATATGATTTAACTCCATCAGCTGCATTTATATGTTAGAAAAGCTTTAATTATCGTTTCATTTTCCCTTTTATCTATTTTCAATAAGTTGTTGCTGTAGAAAAAAAAAAACTTGTATTCAATAGGCTATCTGACTATTTAGAAAAGAGTCAAGTTACCTTTAGTAAGCAATTTGATTTTTGCACCCATGCAACATTCATCTGATCATGCCAAGTTAAGTATTGGTGATCAACTGCAAAAGGCAATTGCTGGAGATTTTTTAGGCTTGAGCAAAGCTTATTTACCTAGAAAGAAGTCACGTTCTTCCTCACACTGAAAACATGCCAACTTTCTTTCTTTTTCTGACAAGAACGAACGCGTTTTTGACCAGAACAAAAAAAAAAGAGAAGCCTTTTTTGACCGCCTATTGTCGCTTGAAACATTGTTTTGAATGGTTGGTAATAAACTTCACTTTCCTCTCGGTAGATTTCGTTTGCTGGCTTTGACTCAGACGATTCCCTTAGACCATCTTGATTTTCGCACAAAAACAAGATTTCCGCTCCCATGTGCGCCATATTGAACATGGTGACAATGCTGCCTTAATTCACAAGGAACTCTTCTTTTTTTTTCTGCTTTCAGCTGATATAACGAAAAGCTCGCGTTACGATTTCTGACTGCCTGACGCATCTTATATTTTGGTCCTCGCCTGGTTAACAAACACTGACTACCAAATGGAGCGGATTCTCCAAGCTGTTCAAGAAATGTAAAACACCTATTTTCATTTATGTACGTAGTGATCATCGTAATTAGGATAAATTAGCTATCATTTCATTAGGCTTTTACCTGCTTTTCCGTCTTTTTAAGTTCTGCATTAAGCGACAAAAATGGCCTCTTTACTATTTGGCGATTTGTGTTCAACTAGGCGGGGAAAGGAATTGTTTTTCTGTTTTATGCAAGCAAAGGAAATTTTATTCTCTAGTTATAGTGACCAACATATCTGCAAGGCATTATCTTATTTAAGTGTGAACCTCGTTCCGTGTACGGATCCAGAACTCGATTATTTTCAGCAAAAACTTTCATCCCGTTTCTACCCCTTCGGTTTAATTAGTGAACACAATTTGCTCTACCTGAGGACTTTTCTGCAAGCGAATCGTGGCATTCAATTATCCATCCCAAACATTGACAACCAGGCTTATTTACAGGCTCTTTCGACGCAATCTAGTTTGATGTCGCCGCATTATCTTCCATTACAAAGCAATCAGCTCGGCCAAGTCAAACCAACTTTTCAGCTTTCGGAACCAGGTCGCTCTGATGAAAATTTCAGCGACACTGACCCAAAATTCATTTGCAAGCATCCGAGAATTCACGTTCCAACATCAGTCGGTGTTGTACAGCCTAGTGTAAGACGTCGGACTAGCGACAATCCTGTTAGTCCAACTGAAAGTCGATCTGAATCACCTCTATTTTCACTACTACACGAGCCTGAGACAAGTGTCGCTACAACCACTCTAGGTGATCCGACCAATCAATTAGTTTCGCGAACTCTGGCCAAAAGCAATGTCAACCAACTCTCCGCGCAAGATATTCCTAACGATTCCTCCGTTCAGCAAAACAGCCTCGAAAGTCACCTTACCCCTTTGAACCAACCTGTTGATCCTGATCCGCTATTACCCGAATCCATTGATGGTACCAGCAGCATTGAGATCGATTCTTCTAAAGAAAGTACGAAAAGCAGTAGAACTGTGACTCTTTCCGAATCGGAGATGTCACCCCAGTTGCGTTTAGATTTGGAGGAAATACGAAAATTTTATTCTCTTCCAATTAACCTCAATCGTGACGGAGGTGTTCTGCAAGATGTCTCGATAGGGAAAATGTTGGAAAGGATAAAAGGGTTCTTGTGGTTTTTAAAGAAGGTAAAAGGCGTCGAGCCTGCTTTGACTTATTGTATCAATCCGGAAGTCTTACAACAGTTTGTCGAATTTATGATGAAAAATCGTGGTATCAAAGCCATTACTTGTAGCCGGTATGTGACGTCCTTAATAAGTGCCTGCAAAGTGCCACTCGCGTGCACACAAGATGAACAAAAAGAAGAGTCTCTTGAAAAAATTAGGGCCATTCAGAGGCAACTTGAGCGATTGTCCAGACAGGAAAAAATTGATTCCGACAGTCTTAATCCTCAGACAGACAAAGTAGTTTACTCTGAATTGCTAGAATTATGCAGAGAATTCAAATGGGAGGTTTCGGAAAAAACAGGTGCTGATCGTGCACGAAGTTGTATGAATTTGTGCTTGCTTCTCATGTACTGTGCGGTTAACCCGGGCCGAGTCAAAGAATACATCTCACTGAGAATTTATAAAGATCAAAGCGGCGACCAATTGAAAGATCAAAATTTTATCTGGTTCAAGGAGGACGGTGGCATAGTATTGTTGGAAAATAATTACAAGACCAGAAATACTTACGGCCTAAACACCACTGACGTGAGCTCAGTCACATACTTGAATTACTATCTGCAACTATACAAGTCTAAGATGAGATCACTTTTGCTACACGGCAATGACCACGACTTTTTTTTCGTTGCTCCGAGGGGAAATCGTTTCTCGCATGCCTCTTACAACTATTATATATCCGGACTATTCGAAAAGTACTTATCTCGGAGATTGACAACGGTTGACCTTCGAAAAATTGTTGTTAATTACTTTTTGTCGCTTCCAGAAAGTGGCGATTATTCCTTAAGGGAATCGTTTGCGACTCTCATGAAACATTCTATCAGAGCGCAACAAAAATATTACGATGAACGTCCGTTAACCCAAAAAAAAGATAGAGCGCTCGATTTGTTAACCTCTGTGGCTAGACGAAGTCTAGACGAAGATGAACCTGAGATTGTAAGTGATGAAGACCAGGAAGGATATCTCGACTGCTTACCGGTCCCGGGAGATTTTGTGGCCTTGGTCGCAGCCAATTCTACCGAAAAGGTTCCGGAAGTTTTTGTGGCTAAGGTACTGAGACTTTCCGAGGACAAAAAAACTGCTTATCTTGCCGATTTTGCGGAAGAAGAGCCAGGAAGATTTAAATCGAAAGCGGGAAAAAGTTATAAAGAAAATACAAATTCTCTAATTTTCCCAATTGACATCGTCTTTTCGCATTCGGACGGTCTATATGAATTGAGAACGCCAAAAATTGACCTTCATCTTGTGACAGTTCAAAAGAAAAGTTAA
>FUN_000005
ATGGCTTTCGTTCGCTTGCCGAGGCCATTACTGGACGAATTCATAACGCTGTGGGACCAAGTTCAATCCATTTCGCCGACGCTTCCCGAACAGGCTAGGCATCTTATTCATCGTGTAGATGACAAGGTCGCCGAAATTAGGAGCCAAGCAAGCGACACGCCTACGACAGGAGGTCACGGTGCAGAATCCCAAAGTACAGGTGGGTGAACTAAGCAATGTTAGGTTGACTTATTAAGCAGAACATATGAGGAAATTCATCACTATTATGATCACATCCCTAGAATTTCATACTTCTTTAATGACTTCTAATATTCAGCATTCAATTGTCTTCAGTCATTAATTATAATCTTCTCTCCTTTGAAATGTCTTAATCATTGCACATGCTTTTAATGTTTTTAGCAGCTGACCAGCCACCACAGATCCCTTGCTTGGCCATGCAAGAGGAAACAGCCTCCTCAAGTGGTAAGAGCAGCTGTCTTCCCTGATAAACTATGAGCACTCAGTTCGGTTAACTGTCAACTAAACTCTGACAATAGGCATAGTATACAATAGTTTACTGCCATAGTATGCAATGGGGGAATGTACTGAGAGGAGAGCTGTGAACATCTAAAACATATATCTATATACACCACTTGGAACCAACAACTGACATAAATGTGtaaaaaaaaaatcataataatactaataaataattatGTTGGCATGGAAACTACTGACATGCAGGTGTTTCCATTCAAAAAAATGTCTCAAGATCCAGTCAAGTAAATCAATCTATTGATGAAGAAATTGATATGGTTTAAGAGTTTTCCTGTATTGTACTGATGGGAATGAGACATAACAGCTATCCATCCTCTTCATATAATTTTTTTCTCAAAAATAACCATAGATTCCAACCTTTTGGAAGGTTACATCCTCATAAAGGGATTACTGTGAGGACAGCAAGCATTTGTAAAAAAAATATTTATATATTTACCTTTCTTGGAGCCAAATCAGCTATCATAAATCTCCACTCTTTATGTTGGCAAGTAAACAACTGACATTAAGGTGTTACTTTCAGTACATGTCTCAAGATCCAGTCAAGAGGAGCATGATGATGAAATTGGTATGGTTTAAGAGTTTTCCTGCATAATTTGTACTGATTGAATGTGTTAAGGATGACAGCCATTGTATCATTAAATTTTGTCTTTAAATGTTATTTTCTTCTGAAAAGGTCTTTTATGTAACAGCAAGAAATGCTGTAATGAAAGAAGTGCTGAATCTTAGCAAACATCACTGATATCATAACTTTTAATCCCTCCATCAGATGACCAGCCAGCAACTCCACTGGTTAGGAAGCGCAAGAGGAGAGTTGCTCAGAAAATAACAAGCTCTGCTCAGGTAATTGAAAGAAATTTTACTTCAATAATACTATTATTGCTGGTGCGACCCTGTACACACAGATTTATATTGAAGAGAACAAATAACCTGGGCCTCCATCCCCCAATTAACACAATATCTACCCCACCACTTCCCCTGTAGAACCACAGTTCACTGGAAAATAGATCAATAAATGAATCAAGATTGAATGATTTGCCATGATTTATTCTCCAGTAAACAACAAAAACAGCAACAAAAGCAAAATAATATTATTATTCTGACTCCTGGATTTCATTGAGACTTCAAGTAACTGGCCATCATTTACTTATCTATTTTTTTAAGTGTCTTAGTGTAGTCCACCGTAATAATAATGTTTGCTGCAACTGCAAGACAAAATTTCTCTTCCAAGAAATATATCTTCAAAATGCCTTACTGAAGAAACTAAGATACTTCTTTCCCATAGTCAATCAATTAAGCAAATAAAAAAGTAACATTTTGTTCATGGTAAAATTGCTGTCATTTAACTCCTGACTATGCTAGTGAAGTTTATTTGTATTGTCCGCATTTCCTGAGGATAAGAGCCTTTCATGCAAATTTACTACATAGTTCTGAAGACGTTAAGCAGACAGAAATGAGCCAAAATTAATTTTCTTTCCTTTTAAACTTATAGGGCATCCAGTTGCCAGCACGGCCACCCGACATCGTGTCAGAGCCCACCCCTGACATAAACTTTGAAAGCATGGAGATCAAGTATCTCAAGCAAGCCTTAGCCTCTGTGGTACAACCATCAACCAAGGCAGAGGTTTACTTGAAATATCTTGCAAAATGTGACTTCAATACTACATTGGAGCCACACTCCATTATCAAGTTTAACCAGGATGATGTTCGGAGAATGGTTGGAATTGGGCAAAATCCCAATGGTGAAGAAGAAGTAAGACAATTTTTAAATAAAAAGCAACACAAATGCAGTGCAAAGTACCTTTGAAGGAATTCCAGTCGAGAATAAGTTCAATCTTTCAAAATTAGCGAGGGGCTCTGATGGTGCTAATCACTGCTTTTCATGGGAATGACCTCTGCATGATAGTTTGCACTGCATTAGCCAATAAAAAAAATTAATACAAACTCATTGCCTGAGTTGTAAAATTATTTTCGAAATAACTTATACCCTATTCACACCAACTAAACATGTCTAATTAAACTCGCTTTAACAAAGAAAATCAGTCCAAAAAAAGTATGGGACTGGTTTTTACAAAAGACCCTGGTAAGTTTTAATTTATATGCATTGATTGATATGTACTAAATTTGAAGTTGACAAAACATCATTGCACACGATTTGATGTGAAGACAACTTAAAACCATGTTTAACTAAACAGCTTTTAAACATGTTAGAGCTTTGGTGTGAATAAAGCTTTAGTCACAGACAGTCATGAGTTCATGAAGTATATTCCACAAAATAAAATTGTCTCTTTCTTTTCTCACTGTTTCCTATCCATTGATTGTTAGGTTGTTGGAAAATTACACCAAGCCCTCCAACAGAGATTGCAGCTGGCTGTCCAACAGGCATTTTCAATAGGAGAATTCATGTCGACGTGCATCAAGGAGCATGGATCAACTTTGCCAGATGAGGACCATCAGCGACGCCGTGGAAGACCTCGAGTTTCATCATTATTAGACACGCTGGATTCCATCGAGTCACTTGGAGCTTCATCATCGTGTCTACAGGACCAAGTTTTGATTTTTCAAGCTATTTCAAATTTTCCATTATTGAAATATGTTAATGAGCCTATGACTCACTTTGTGTGTAATGACGTCAGGTTAGCGATTAGGCATCTACCAGCAGCATTGCATCACAAGTGTGATGTACCAGAAAACATCCAAGATGCCCATATCAACTTGAGACCGTTTAGGTCTTTGGTGTCAGATCAAGAAAGTCCACCGCTTTCGGGTGACATGAGTTACTCCAATGTAACTCCATGTGATGGTGATGGTCTCGTTGTTGGTATTAATCTTGACCACCGAGAACCACTGGAAAATGCTGAATATACACGCTTTTACGGTATCGATGCCCCTGAGTTGTCATCTGTTCATTTTATTAAAACTAATGACTTCCAGCATGTTTTCTGTAAGCAGGTTGGGCACATAAGCCTCTGCGCAGTACATTTGTTTTTACAGATGTTCTTGTTGAGTGGATCTGCAAAGCTATGCGAGGAACTCCCCTGGGAAGCGGCTCCACAGCCAAGGGACATTTACAACAGAGCACTCAAGGAGTATTGGTTCAAGTTCATCACACCTCCGTCTCAACATCTGGAGAAAGTATTCCTGCAGTCTCTTGAAGAGCTAGTCCCTCCAACATCTGAATCCCGAAAGAGGCTCATGTCGCCTTTTCCAGCAACCATGGCAACTGCTGCCAATCCATTCTTGTTGTCACTAAATGCTCTTCTTGTTGTTTCAGGTTTTTGTCACGTCTTCACGAAGTATTGCCAAGATGGATTTCTTTTAGGGCTGCAGGCAATTGCACGGGACAACAAACTAGGGCCAATTTGGTGTGGTGCTTCTCGGAAGTTCATTTTTGGGTGTACTTCTGGAAACAACACTGATTTTTTTTTGAAGCACTTTACACCAGAAACAACATCTCACTTGGCCAGGGCTGGTTTTCCCTTCAAACACTCAAATGCTTTTCTCCCGTGGCACGAGAGACAAATGCTGAAACAGCTGTGCTCACAGGAGACTACAAGAACCGCAGCAAGAAATCACTTGGCACAGCATCTCCCAGGAATGGAACCCCAGTTTGGCATGTATATTGACATCCAGAGGTTGGTGATTATTGTTGAATAATAACTACTACCCTATTCACACCAACTAAACATGTTCAATTAAATTCGGTTTAACAAAAGAAAATCAATCCAAAAAAGCAAAAGACCCTAGTAAGTTTTAAATTACATGCATTGATATGTACTAAATTTGAAATTGACAAAAATCATTGCACACGATTTGATTTGAAGACAACTTGAAACCATGTTTAACTACAAACAGCTTTTAAACATGTTTAAGTTTTGGTGTGAATAGGGCTTATAATTCTATTATTTTTTCATTGTTACCTAATTGTGACCTCATGATAACATCATCACATTTTTGTCAATAAAATTTGTAGATCTAACCAAGGAGAAGGATATCAAACAGTGAGGACTGGTGAGGCTTACCTTAAAGTTATGAATAGCCAGGTAGTTGGGATAGGAGGTAATTCTACAGGACTGGGTTTGTTCACCCTTAAAAAGATTCCTAGAGGCACTCTTGTATGTGCCTATGCTCCAACTGCAACAATTTGGGAAGGTAAACTAAATGGTGATTATGTTTTAGAAACTTCTTTTAATAACAAGGTAATTTCTGTCAATGGGAAGGAAAATCTTTTTGAATTGGGGTTGGGAATTTATTGTAATGATGGAAGCTTTCCATTTTCCTTAGCTAGGGCTAGGTTTTCAAATGTTATTTCACATAGAGTAAATTGTGAATACTGTAAATGTGGGGATGGCATATGGCTAAAGACCGTCAGAGATGTTTCCGCAGGGGAAGAGCTGCTGATGTGCTATTCTCAAGATGGAAGCTACTGGGCTACTATCTTCAGTAGGGAACAACTTAACCAGATCACAGCTGCCTTAAACTCATGTGGCCCATCACTTCAGGATGCCGAGCGCTGCATTAGACTACTTCAAGTCTAA


FUN_000001  5952    12  5952    5953
FUN_000002  5072    5977    5072    5073
FUN_000003  1219    11062   1219    1220
FUN_000004  7971    12294   7971    7972
FUN_000005  5072    20278   5072    5073
FUN_000006  16585   25363   16585   16586
FUN_000007  1032    41961   1032    1033
FUN_000008  638 43006   638 639
FUN_000009  9245    43657   9245    9246
FUN_000010  1750    52915   1750    1751

4 Subset Expressed Genes

Only those with at least one read in each sample

4.0.1 Peek at count matrix

head "${count_matrix}" | column -t -s","

echo ""
echo ""

wc -l "${count_matrix}"
gene_id     RNA-ACR-140  RNA-ACR-145  RNA-ACR-150  RNA-ACR-173  RNA-ACR-178
FUN_035039  553          340          256          485          510
FUN_035038  2486         775          743          1250         1092
FUN_035031  46           6            25           41           29
FUN_035030  183          252          48           78           73
FUN_035033  1519         311          555          990          370
FUN_035032  1764         1297         1035         1763         1360
FUN_035035  601          256          231          521          126
FUN_035034  3644         3770         3137         3225         3322
FUN_035037  5546         4928         3061         4720         3018


44372 ../output/07-Apul-Hisat/Apul-gene_count_matrix.csv

4.1 Import count matrix

# Read the data into a data frame
count_matrix_df <- read.csv(count_matrix, header = TRUE)

str(count_matrix_df)
'data.frame':   44371 obs. of  6 variables:
 $ gene_id    : chr  "FUN_035039" "FUN_035038" "FUN_035031" "FUN_035030" ...
 $ RNA.ACR.140: int  553 2486 46 183 1519 1764 601 3644 5546 1224 ...
 $ RNA.ACR.145: int  340 775 6 252 311 1297 256 3770 4928 301 ...
 $ RNA.ACR.150: int  256 743 25 48 555 1035 231 3137 3061 511 ...
 $ RNA.ACR.173: int  485 1250 41 78 990 1763 521 3225 4720 677 ...
 $ RNA.ACR.178: int  510 1092 29 73 370 1360 126 3322 3018 841 ...

4.2 Only genes with at least one read per sample

# Filter rows where all values are greater than 0
filtered_count_matrix_df <- count_matrix_df[apply(count_matrix_df > 0, 1, all), ]


str(filtered_count_matrix_df)
'data.frame':   19789 obs. of  6 variables:
 $ gene_id    : chr  "FUN_035039" "FUN_035038" "FUN_035031" "FUN_035030" ...
 $ RNA.ACR.140: int  553 2486 46 183 1519 1764 601 3644 5546 1224 ...
 $ RNA.ACR.145: int  340 775 6 252 311 1297 256 3770 4928 301 ...
 $ RNA.ACR.150: int  256 743 25 48 555 1035 231 3137 3061 511 ...
 $ RNA.ACR.173: int  485 1250 41 78 990 1763 521 3225 4720 677 ...
 $ RNA.ACR.178: int  510 1092 29 73 370 1360 126 3322 3018 841 ...

4.3 Subset genes FastA

Only expressed genes

# Get the row names (gene_ids) of the filtered data frame
filtered_gene_ids <- filtered_count_matrix_df$gene_id

fasta <- readDNAStringSet(genes_fasta)

subset_fasta <- fasta[names(fasta) %in% filtered_gene_ids]
writeXStringSet(subset_fasta, genes_subset_fasta)

4.3.1 Peek at genes subset FastA

head "${genes_subset_fasta}"

echo ""
grep "^>" --count "${genes_subset_fasta}"
>FUN_000001
ATGATGCCACAAGGGTACAAAAACGCCCTTCCAGGCTATAAAGACCTCTATCTTAGCCAAGCAATCACCGAGGAGGTTCA
CAATGTAAGTTTGTCCTTTTAGCTTGTGCCTGTTTTTCCTAGTTTGTTTGCTTGTTTCTTTCTTTCTTTCTTTCTTTTTA
GTTCTATTTTGTTTCATTTGTGATTCTTCAAGATATTTGGTGGTTACTTGTTCAGTCTAGCGTAGCCAAACTATTTGAAC
ACTACAAGATAATTCGCAAGAAACAGGTTGAAGAAAATTCCATTTCAAGCGAAATCTTATTTCATTTTTTTACATTCTCT
GTAGCTAATATACTGGCAAGTAATTTTATCGCCAACTGAAGGCAAGCACCAAGGACGTTTACATGGTAAGTTTGTCCTTT
TGTTACACAAATTGGGTAGTCTGTTTCCTGGTGTTTTTTTTTTTTTTAATGTTTTAATTCACGATTATTCAGGATATCTA
GGGATGAATTATCTGTTTATTACTCCAACATAATTAGTAAGAAACCAATTGGGAAGAATTCCAATAGAAGCAAAATCTTA
ATCTGTCTTTAAACTACTATGTAGTTAATACTTTAGCGAGTGATCTCTTCTCGGTGGCTATTACCATTTGTTGTGAATGC
TATGAACGAAGAGCACCTTATTTTACTACCCGACATTATTGTAGCCCTGAGTTCCCTTTAGATCTGACGCTGCCATTTCT

19789

5 BLASTx

5.1 Download SwissProt

cd "${blastdbs_dir}"

curl -O https://ftp.uniprot.org/pub/databases/uniprot/current_release/knowledgebase/complete/uniprot_sprot.fasta.gz

mv uniprot_sprot.fasta.gz 20250618-uniprot_sprot.fasta.gz

gunzip --keep 20250618-uniprot_sprot.fasta.gz
  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current
                                 Dload  Upload   Total   Spent    Left  Speed
  0     0    0     0    0     0      0      0 --:--:-- --:--:-- --:--:--     0  0 88.7M    0  7853    0     0  23582      0  1:05:47 --:--:--  1:05:47 23511 16 88.7M   16 14.5M    0     0  11.1M      0  0:00:07  0:00:01  0:00:06 11.1M 55 88.7M   55 49.3M    0     0  21.3M      0  0:00:04  0:00:02  0:00:02 21.3M 97 88.7M   97 86.2M    0     0  25.9M      0  0:00:03  0:00:03 --:--:-- 25.9M100 88.7M  100 88.7M    0     0  26.2M      0  0:00:03  0:00:03 --:--:-- 26.2M

5.2 Create BLASTdb

cd "${blastdbs_dir}"

"${diamond}" makedb \
--in 20250618-uniprot_sprot.fasta.gz \
--db "${diamond_db}" \
--quiet \
--threads "${threads}"

5.3 Run DIAMOND BLASTx

"${diamond}" blastx \
--db "${blastdbs_dir}"/"${diamond_db}.dmnd" \
--query "${genes_subset_fasta}" \
--out "${diamond_output}" \
--outfmt 6  \
--sensitive \
--evalue 1e-10 \
--max-target-seqs 1 \
--block-size 15.0 \
--index-chunks 4 \
--threads "${threads}" \
2> ../output/30.00-Apul-transcriptome-GOslims/diamond-blastx.log

head "${diamond_output}"

echo ""

wc -l "${diamond_output}"
FUN_000067  sp|P84227|H32_BOVIN 99.3    136 1   0   1   408 1   136 2.74e-84    250
FUN_000084  sp|P84227|H32_BOVIN 99.3    136 1   0   1   408 1   136 2.74e-84    250
FUN_000090  sp|P84227|H32_BOVIN 99.3    136 1   0   1   408 1   136 2.74e-84    250
FUN_000094  sp|P84227|H32_BOVIN 99.3    136 1   0   1   408 1   136 2.74e-84    250
FUN_000102  sp|P84227|H32_BOVIN 99.3    136 1   0   1   408 1   136 2.74e-84    250
FUN_000122  sp|P35061|H2A_ACRFO 100 125 0   0   711 1085    1   125 1.97e-74    240
FUN_000123  sp|P84227|H32_BOVIN 99.3    136 1   0   1   408 1   136 2.74e-84    250
FUN_000133  sp|P35059|H4_ACRFO  100 103 0   0   312 4   1   103 4.15e-66    197
FUN_000141  sp|P35067|H2B_ACRFO 99.2    125 1   0   1211    837 1   125 8.07e-74    229
FUN_000149  sp|P35067|H2B_ACRFO 98.4    125 2   0   2442    2068    1   125 1.61e-69    228

9141 ../output/30.00-Apul-transcriptome-GOslims/Apulchra-expressed-genes.blastx.outfmt6

6 GENE ONTOLOGY

6.1 Get gene IDs and SwissProt IDs

awk -F"|" '{print $1"\t"$2}' "${diamond_output}" \
| awk '{print $1"\t"$3}' \
> ../output/30.00-Apul-transcriptome-GOslims/gene-SPIDs.txt

head ../output/30.00-Apul-transcriptome-GOslims/gene-SPIDs.txt

echo ""
echo ""

wc -l ../output/30.00-Apul-transcriptome-GOslims/gene-SPIDs.txt
FUN_000067  P84227
FUN_000084  P84227
FUN_000090  P84227
FUN_000094  P84227
FUN_000102  P84227
FUN_000122  P35061
FUN_000123  P84227
FUN_000133  P35059
FUN_000141  P35067
FUN_000149  P35067


9141 ../output/30.00-Apul-transcriptome-GOslims/gene-SPIDs.txt

6.2 Get SwissProt IDs

awk -F"|" '{print $2}' "${diamond_output}" \
| sort --unique \
> ../output/30.00-Apul-transcriptome-GOslims/SPIDs.txt

head ../output/30.00-Apul-transcriptome-GOslims/SPIDs.txt

echo ""
echo ""

wc -l ../output/30.00-Apul-transcriptome-GOslims/SPIDs.txt
A0A061IR73
A0A072UTP9
A0A075F932
A0A096X8J7
A0A0A2JW91
A0A0C2SRU0
A0A0G2JZ79
A0A0G2K047
A0A0G2K2P5
A0A0G2L7I0


6283 ../output/30.00-Apul-transcriptome-GOslims/SPIDs.txt

6.3 Retrieve UniProt records

A difference in number of records could be due to retrieval from only “reviewed” records, while BLAST may have included both “reviewed” and “unreviewed.” SwissProt records

python3 \
../../M-multi-species/code/uniprot-retrieval.py \
../output/30.00-Apul-transcriptome-GOslims/SPIDs.txt \
../output/30.00-Apul-transcriptome-GOslims/

gunzip ../output/30.00-Apul-transcriptome-GOslims/uniprot-retrieval.tsv.gz

echo ""
echo ""

wc -l ../output/30.00-Apul-transcriptome-GOslims/uniprot-retrieval.tsv
500 / 500
500 / 500
500 / 500
500 / 500
500 / 500
500 / 500
500 / 500
500 / 500
500 / 500
500 / 500
500 / 500
500 / 500
283 / 283
gzip: ../output/30.00-Apul-transcriptome-GOslims/uniprot-retrieval.tsv already exists;  not overwritten


6295 ../output/30.00-Apul-transcriptome-GOslims/uniprot-retrieval.tsv

6.4 Map GO to Genes

# Read gene-SPIDs.txt
gene_spids <- read.delim("../output/30.00-Apul-transcriptome-GOslims/gene-SPIDs.txt", header = FALSE, stringsAsFactors = FALSE)
colnames(gene_spids) <- c("GeneID", "Entry")

# Read uniprot-retrieval.tsv
uniprot <- read.delim("../output/30.00-Apul-transcriptome-GOslims/uniprot-retrieval.tsv", header = TRUE, stringsAsFactors = FALSE, check.names = FALSE)

# Merge on Entry
gene_SPID_GOID_merged <- merge(gene_spids, uniprot[, c("Entry", "Gene Ontology IDs")], by = "Entry", all.x = TRUE)

# View result
str(gene_SPID_GOID_merged)
'data.frame':   9141 obs. of  3 variables:
 $ Entry            : chr  "A0A061IR73" "A0A072UTP9" "A0A075F932" "A0A096X8J7" ...
 $ GeneID           : chr  "FUN_031929" "FUN_010760" "FUN_005029" "FUN_017765" ...
 $ Gene Ontology IDs: chr  "GO:0001750; GO:0005524; GO:0005778; GO:0005829; GO:0006625; GO:0016558; GO:0016561; GO:0016562; GO:0016887; GO:"| __truncated__ "GO:0004197; GO:0005615; GO:0005764; GO:0006955; GO:0051603; GO:2001235" "GO:0000149; GO:0001786; GO:0005509; GO:0005543; GO:0005544; GO:0005737; GO:0005886; GO:0008021; GO:0017158; GO:"| __truncated__ "GO:0005452; GO:0005886; GO:0008509; GO:0015701; GO:0051453; GO:0055085; GO:0086001" ...

6.4.1 Write merged to file

# Optionally, write to file
write.table(gene_SPID_GOID_merged, "../output/30.00-Apul-transcriptome-GOslims/gene-SPIDs-GOIDs.tsv", sep = "\t", row.names = FALSE, quote = FALSE)

6.5 Clean up merged

full.gene.df <- as.data.frame(gene_SPID_GOID_merged)

# Clean whitespace, filter NA/empty rows, select columns, and split GO terms using column name variables
gene.GO.df <- full.gene.df %>%
  mutate(!!"Gene Ontology IDs" := str_replace_all(.data[["Gene Ontology IDs"]], "\\s*;\\s*", ";")) %>% # Clean up spaces around ";"
  filter(!is.na(.data[["GeneID"]]) & !is.na(.data[["Gene Ontology IDs"]]) & .data[["Gene Ontology IDs"]] != "") %>% 
  dplyr::select(all_of(c("GeneID", "Gene Ontology IDs")))


str(gene.GO.df)
'data.frame':   9011 obs. of  2 variables:
 $ GeneID           : chr  "FUN_031929" "FUN_010760" "FUN_005029" "FUN_017765" ...
 $ Gene Ontology IDs: chr  "GO:0001750;GO:0005524;GO:0005778;GO:0005829;GO:0006625;GO:0016558;GO:0016561;GO:0016562;GO:0016887;GO:0043335;G"| __truncated__ "GO:0004197;GO:0005615;GO:0005764;GO:0006955;GO:0051603;GO:2001235" "GO:0000149;GO:0001786;GO:0005509;GO:0005543;GO:0005544;GO:0005737;GO:0005886;GO:0008021;GO:0017158;GO:0030154;G"| __truncated__ "GO:0005452;GO:0005886;GO:0008509;GO:0015701;GO:0051453;GO:0055085;GO:0086001" ...

6.6 Flatten gene GOID file

flat.gene.GO.df <- gene.GO.df %>% separate_rows(!!sym("Gene Ontology IDs"), sep = ";")

str(flat.gene.GO.df)
tibble [109,816 × 2] (S3: tbl_df/tbl/data.frame)
 $ GeneID           : chr [1:109816] "FUN_031929" "FUN_031929" "FUN_031929" "FUN_031929" ...
 $ Gene Ontology IDs: chr [1:109816] "GO:0001750" "GO:0005524" "GO:0005778" "GO:0005829" ...

6.7 Group by GOID

grouped.gene.GO.df <- flat.gene.GO.df %>%
  group_by(!!sym("Gene Ontology IDs")) %>%
  summarise(!!"GeneID" := paste(.data[["GeneID"]], collapse = ","))

str(grouped.gene.GO.df)
tibble [12,284 × 2] (S3: tbl_df/tbl/data.frame)
 $ Gene Ontology IDs: chr [1:12284] "GO:0000002" "GO:0000009" "GO:0000012" "GO:0000014" ...
 $ GeneID           : chr [1:12284] "FUN_025331,FUN_001651,FUN_001649,FUN_033608,FUN_009696,FUN_006166,FUN_035449,FUN_006171,FUN_008305,FUN_011990,FUN_016408" "FUN_008599" "FUN_001342,FUN_011804,FUN_008094,FUN_010555" "FUN_027937,FUN_038659,FUN_038683,FUN_038674,FUN_038658,FUN_038643,FUN_038681,FUN_007893,FUN_024354,FUN_041874" ...

6.8 Vectorize GOIDs

# Vector of GO IDs
go_ids <- grouped.gene.GO.df[["Gene Ontology IDs"]]

str(go_ids)
 chr [1:12284] "GO:0000002" "GO:0000009" "GO:0000012" "GO:0000014" ...

6.9 Prepare GOslim OBO

# Find GSEAbase installation location
gseabase_location <- find.package("GSEABase")

# Load path to GOslim OBO file
goslim_obo_destintation <- file.path(gseabase_location, "extdata", goslims_obo, fsep = "/")

# Download the GOslim OBO file
download.file(url = goslims_url, destfile = goslim_obo_destintation)

# Loads package files
gseabase_files <- system.file("extdata", goslims_obo, package="GSEABase")

6.10 GOslims from OBO

# Create GSEAbase GOCollection using `go_ids`
myCollection <- GOCollection(go_ids)

# Retrieve GOslims from GO OBO file set
slim <- getOBOCollection(gseabase_files)

str(slim)
Formal class 'OBOCollection' [package "GSEABase"] with 7 slots
  ..@ .stanza     :'data.frame':    153 obs. of  1 variable:
  .. ..$ value: chr [1:153] "Root" "Term" "Term" "Term" ...
  ..@ .subset     :'data.frame':    22 obs. of  1 variable:
  .. ..$ value: chr [1:22] "Rhea list of ChEBI terms representing the major species at pH 7.3." "Term not to be used for direct annotation" "Terms planned for obsoletion" "AGR slim" ...
  ..@ .kv         :'data.frame':    2110 obs. of  3 variables:
  .. ..$ stanza_id: chr [1:2110] ".__Root__" ".__Root__" ".__Root__" ".__Root__" ...
  .. ..$ key      : chr [1:2110] "format-version" "data-version" "synonymtypedef" "synonymtypedef" ...
  .. ..$ value    : chr [1:2110] "1.2" "go/releases/2025-06-01/subsets/goslim_generic.owl" "syngo_official_label \"label approved by the SynGO project\"" "systematic_synonym \"Systematic synonym\" EXACT" ...
  ..@ evidenceCode: chr [1:26] "EXP" "IDA" "IPI" "IMP" ...
  ..@ ontology    : chr NA
  ..@ ids         : chr [1:141] "GO:0000228" "GO:0000278" "GO:0000910" "GO:0001618" ...
  ..@ type        : chr "OBO"

6.11 Biological Process GOslims

# Retrieve Biological Process (BP) GOslims
slimdf <- goSlim(myCollection, slim, "BP", verbose)

str(slimdf)
'data.frame':   72 obs. of  3 variables:
 $ Count  : int  90 18 20 430 63 75 18 3 90 51 ...
 $ Percent: num  1.11 0.222 0.247 5.305 0.777 ...
 $ Term   : chr  "mitotic cell cycle" "cytokinesis" "cytoplasmic translation" "immune system process" ...

6.12 Map GO to GOslims

# List of GOslims and all GO IDs from `go_ids`
gomap <- as.list(GOBPOFFSPRING[rownames(slimdf)])

# Maps `go_ids` to matching GOslims
mapped <- lapply(gomap, intersect, ids(myCollection))

# Append all mapped GO IDs to `slimdf`
# `sapply` needed to apply paste() to create semi-colon delimited values
slimdf$GO.IDs <- sapply(lapply(gomap, intersect, ids(myCollection)), paste, collapse=";")

# Remove "character(0) string from "GO.IDs" column
slimdf$GO.IDs[slimdf$GO.IDs == "character(0)"] <- ""

# Add self-matching GOIDs to "GO.IDs" column, if not present
for (go_id in go_ids) {
  # Check if the go_id is present in the row names
  if (go_id %in% rownames(slimdf)) {
    # Check if the go_id is not present in the GO.IDs column
    # Also removes white space "trimws()" and converts all to upper case to handle
    # any weird, "invisible" formatting issues.
    if (!go_id %in% trimws(toupper(strsplit(slimdf[go_id, "GO.IDs"], ";")[[1]]))) {
      # Append the go_id to the GO.IDs column with a semi-colon separator
      if (length(slimdf$GO.IDs) > 0 && nchar(slimdf$GO.IDs[nrow(slimdf)]) > 0) {
        slimdf[go_id, "GO.IDs"] <- paste0(slimdf[go_id, "GO.IDs"], "; ", go_id)
      } else {
        slimdf[go_id, "GO.IDs"] <- go_id
      }
    }
  }
}

str(slimdf)
'data.frame':   72 obs. of  4 variables:
 $ Count  : int  90 18 20 430 63 75 18 3 90 51 ...
 $ Percent: num  1.11 0.222 0.247 5.305 0.777 ...
 $ Term   : chr  "mitotic cell cycle" "cytokinesis" "cytoplasmic translation" "immune system process" ...
 $ GO.IDs : chr  "GO:0000022;GO:0000070;GO:0000082;GO:0000086;GO:0000132;GO:0000281;GO:0007052;GO:0007064;GO:0007076;GO:0007079;G"| __truncated__ "GO:0000281;GO:0000915;GO:0007110;GO:0007111;GO:0007112;GO:0032465;GO:0032466;GO:0032467;GO:0036089;GO:0036090;G"| __truncated__ "GO:0001731;GO:0001732;GO:0002182;GO:0002183;GO:0002188;GO:0002191;GO:0017183;GO:0140018;GO:0140708;GO:1900249;G"| __truncated__ "GO:0001771;GO:0001774;GO:0001776;GO:0001779;GO:0001780;GO:0001782;GO:0001787;GO:0001805;GO:0001812;GO:0001867;G"| __truncated__ ...

6.13 Flatten GOslims

# "Flatten" file so each row is single GO ID with corresponding GOslim
# rownames_to_column needed to retain row name info
slimdf_separated <- as.data.frame(slimdf %>%
  rownames_to_column('GOslim') %>%
  separate_rows(GO.IDs, sep = ";"))

# Group by unique GO ID
grouped_slimdf <- slimdf_separated %>%
  filter(!is.na(GO.IDs) & GO.IDs != "") %>%
  group_by(GO.IDs) %>%
  summarize(GOslim = paste(GOslim, collapse = ";"),
            Term = paste(Term, collapse = ";"))


str(grouped_slimdf)
tibble [5,628 × 3] (S3: tbl_df/tbl/data.frame)
 $ GO.IDs: chr [1:5628] " GO:0000278" " GO:0002181" " GO:0002376" " GO:0003014" ...
 $ GOslim: chr [1:5628] "GO:0000278" "GO:0002181" "GO:0002376" "GO:0003014" ...
 $ Term  : chr [1:5628] "mitotic cell cycle" "cytoplasmic translation" "immune system process" "renal system process" ...

6.14 Counts of GOslims

slimdf.sorted <- slimdf %>% arrange(desc(Count))

slim.count.df <- slimdf.sorted %>% 
  dplyr::select(Term, Count, Percent)

str(slim.count.df)
'data.frame':   72 obs. of  3 variables:
 $ Term   : chr  "anatomical structure development" "cell differentiation" "signaling" "immune system process" ...
 $ Count  : int  1768 739 728 430 285 254 251 204 199 182 ...
 $ Percent: num  21.81 9.12 8.98 5.3 3.52 ...

6.14.1 Write GOslims to file

Need to create a column name for GOslimIDs from data frame rownames.

# Create header vector
header <- c("GOslimID", colnames(slim.count.df))

# Write header to file
writeLines(paste(header, collapse = "\t"), "../output/30.00-Apul-transcriptome-GOslims/GOslim-counts.tsv")

# Append data frame contents to existing file, which contains header info
write.table(
  slim.count.df,
  "../output/30.00-Apul-transcriptome-GOslims/GOslim-counts.tsv",
  sep = "\t",
  row.names = TRUE,
  quote = FALSE,
  col.names = FALSE,
  append = TRUE
)

7 REFERENCES

Buchfink, Benjamin, Klaus Reuter, and Hajk-Georg Drost. 2021. “Sensitive Protein Alignments at Tree-of-Life Scale Using DIAMOND.” Nature Methods 18 (4): 366–68. https://doi.org/10.1038/s41592-021-01101-x.
Carlson, Marc. 2017. “GO.db.” https://doi.org/10.18129/B9.BIOC.GO.DB.
Danecek, Petr, James K Bonfield, Jennifer Liddle, John Marshall, Valeriu Ohan, Martin O Pollard, Andrew Whitwham, et al. 2021. “Twelve Years of SAMtools and BCFtools.” GigaScience 10 (2). https://doi.org/10.1093/gigascience/giab008.
H. Pagès, P. Aboyoun. 2017. “Biostrings.” https://doi.org/10.18129/B9.BIOC.BIOSTRINGS.
Martin Morgan, Seth Falcon. 2017. “GSEABase.” https://doi.org/10.18129/B9.BIOC.GSEABASE.
Quinlan, Aaron R., and Ira M. Hall. 2010. “BEDTools: A Flexible Suite of Utilities for Comparing Genomic Features.” Bioinformatics 26 (6): 841–42. https://doi.org/10.1093/bioinformatics/btq033.