acvill

tri2sym <- 
  function(m, triangle) {
    if (!is.numeric(m)) {
      message("matrix must be numeric")
      stop()
    }
    if (dim(m)[1] != dim(m)[2]) {
      message("matrix must be square")
      stop()
    }

acvill

entrez2dss <-
  function(id_list) {
    
    require(rentrez)    # v1.2.3
    require(Biostrings) # v2.66.0
    require(stringr)    # v1.5.0
    
    # fetch all sequences, trim empty elements
    raw_ <-
      entrez_fetch(db = "nucleotide",

acvill

make_unambiguous <- function(dna) {
  require(tidyverse)
  require(S4Vectors)
  iupac <- tibble(code = c("A", "C", "G", "T",
                           "R", "Y", "S", "W",
                           "K", "M", "B", "D",
                           "H", "V", "N"),
                  base = c("A", "C", "G", "T",
                           "AG", "CT", "GC", "AT",
                           "GT", "AC", "CGT", "AGT",

acvill

# Given vcf and gff, plot position and allele freq. of SNPs and indels over ranges
# tested with VCFv4.1 and gff version 3
# expects certain vcf and gff attributes, may not work with all files

plot_vcf <- function(vcf, gff, ranges) {

  require(tidyverse) # v2.0.0
  require(gggenes) # v0.5.0
  require(patchwork) # v1.1.2
  

acvill

# assumes fixed number of lines in header and footer
# might change with different versions of MEME suite

read_PWMs <- function(file) {
  pset <- head(readLines(con = file, n = -10)[-c(1:29)], -10)
  pset <- subset(pset, !grepl(pattern = "letter", pset))
  i1 <- !nzchar(pset)
  pset <- unname(split(pset[!i1], cumsum(i1)[!i1]))
  names(pset) <- gsub(pattern = " ",
                      replacement = "_",

acvill

# requires the native pipe from R >= 4.1
## maps each base to a particular color
## colors can be named or hex values
## outputs a format recognized by the forna webapp's custom color editor
## http://rna.tbi.univie.ac.at/forna/forna.html

forna_colors <- function(sequence, colormap) {
  inseq <- gsub(x = strsplit(toupper(as.character(sequence)),split="")[[1]],
                pattern="T",
                replacement="U")

acvill

msa_motifs <- function(msa, motifs) {
  require(tibble)
  require(Biostrings)
  require(dplyr)
  # read in fasta as Biostrings object
  seqs <- Biostrings::readAAStringSet(filepath = msa, format = "fasta")
  maxseqlen <- max(width(seqs))
  # initiate results tibble
  results <- tibble::tibble(position = seq(1:maxseqlen))
  # readAAMultipleAlignment requires equal length of sequences

acvill

#!/bin/bash

# FUNCTION
## this script recursively searches a directory for fasta files matching a pattern
## found files are concatenated and sorted by descending sequence length

# INPUT
## first positional parameter is the directory to search
## second is the pattern to match in fasta filenames
## third is the output filename

acvill

# tested with R v4.2.0 and tidyverse v2.0.0
read_gtf <- function(file) {
  
  require(tidyverse)
  cnames <- c("seqname","source","feature","start","end","score","strand","frame","attribute")
  
  # read in raw gtf as tsv and remove comment rows
  messy <- read_tsv(file, col_names = cnames, comment = "#")
  
  # get the unique attribute types

acvill

require(tibble)
require(dplyr)
require(stats)

# make mock data, genes as columns
control <- tibble(subject = paste0("control", sprintf('%0.2d', 1:20)),
                  gene1 = rnorm(20, 10, 2),
                  gene2 = rnorm(20, 10, 2),
                  gene3 = rnorm(20, 10, 2))