bch441-work-abc-units/RPR-FASTA.R

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# tocID <- "RPR-FASTA.R"
#
# ---------------------------------------------------------------------------- #
# PATIENCE ... #
# Do not yet work wih this code. Updates in progress. Thank you. #
# boris.steipe@utoronto.ca #
# ---------------------------------------------------------------------------- #
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#
# Purpose: A Bioinformatics Course:
# R code accompanying the RPR-FASTA unit.
#
# Version: 1.0
#
# Date: 2017 10 14
# Author: Boris Steipe (boris.steipe@utoronto.ca)
#
# Versions:
# 1.0 New unit.
#
#
# TODO: Make a simple solution first, then extend it to error checking, and
# to handle .mfa files.
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#
#
# == DO NOT SIMPLY source() THIS FILE! =======================================
#
# If there are portions you don't understand, use R's help system, Google for an
# answer, or ask your instructor. Don't continue if you don't understand what's
# going on. That's not how it works ...
#
# ==============================================================================
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#TOC> ==========================================================================
#TOC>
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#TOC> Section Title Line
#TOC> -------------------------------------
#TOC> 1 Reading FASTA 39
#TOC> 2 Interpreting FASTA 227
#TOC> 3 Writing FASTA 248
#TOC>
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#TOC> ==========================================================================
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# = 1 Reading FASTA =======================================================
# FASTA is a text based format, structured in lines that are separated by
# line-feed or paragraph-break characters. Which one of these is used, depends
# on your operating system. But Rs readLines() function knows how to handle
# these correctly, accross platforms. Don't try to read such files "by hand".
# Here is the yeast Mbp1 gene, via SGD.
file.show("./data/S288C_YDL056W_MBP1_coding.fsa")
myFASTA <- readLines("./data/S288C_YDL056W_MBP1_coding.fsa")
# The warning is generated because the programmer who implemented the code to
# write this FASTA file neglected to place a line-break character after the last
# sequence character.
head(myFASTA)
# Note that there are NO line-break characters ("\n") at the end of these
# strings, readLines() has "consumed" them while reading.
tail(myFASTA)
# Also note that the last line has fewer characters - this means readLines()
# imported the whole line, despite it not being terminated.
# It's very straightforward to work with such data, for example by collapsing
# everything after the first line into a single string ...
f <- c(myFASTA[1], paste(myFASTA[-1], sep = "", collapse = ""))
f[1]
nchar(f[2])
# ... but this is making assumptions that everything in line 2 until the end IS
# sequence, the whole sequence and nothing but sequence. That assumption can
# break down in many ways:
#
# - there could be more than one header line. The specification says otherwise,
# but some older files use multiple, consecutive header lines. You don't
# want that to end up in your sequence.
# - this could be not a FASTA file at all. It could be raw sequence, a
# different sequence file format, or a wholly different file altogether.
# If you look at the file, you can immediately tell, but if you are
# reading the file in a complex workflow, your could easily import wrong
# data into your analysis.
# - there could be more than one sequence in the file. Such Multi-FASTA files
# occur commonly, as downloads of ORFs from genome regions or other
# sets of genes or proteins, or as the input / output for multiple
# sequence alignment programs.
#
# Data "from the wild" can (and usually does) have the most unexpected
# variations and it is really, really important to be clear about the
# assumptions that you are making. Here is the structure of a FASTA file,
# specified with as few assumptions as possible.
#
# (1) it contains characters;
# (2) there might be lines that begin with characters other than
# ">", these should be discarded;
# (3) it contains one or more consecutive lines that are sequence blocks;
# (4) each sequence block has one or more header lines;
# (5) header lines start with ">";
# (6) no actual sequence data begins with a ">";
# (7) header lines can contain any character;
# (8) sequence lines only contain letters, "-" (gap characters), or "*" (stop).
#
# This suggests to parse as follows:
# - drop all lines that don't begin with ">" or a letter
# - identify consecutive lines that begin ">" and consecutive lines
# that do not begin ">"
# - collapse each set of consecutive lines in-place
# - drop all remaining lines. In this result the odd-indexed elements
# are headers, and the even-indexed elements are sequences.
# Let's code this as a function. We need some tool that identifies consecutive
# lines of something. The rle() (run-length encoding) function does this. It
# returns a vector of the length of "runs" in its input:
myPets <- c("ant", "bat", "bat", "bat", "cat", "ant", "ant")
(runs <- rle(myPets))
# The cumsum() (cumulative sum) function turns these numbers into indices
# on our original vector.
(idx <- cumsum(runs$lengths))
myPets[idx] # note that this is NOT unique ... "ant" appears twice, because
# there were two separate runs of ants in our input.
# So far so good. But our FASTA file's lines are ALL different, so all the runs
# will only have length 1 ...
rle(myFASTA)$lengths
# How do we deal with that? Obviously we need to actually analyze the strings we
# are working with. grepl(<pattern>, <x>) is exactly what we need here. It
# produces a vector of booleans, of the same length as the input vector <x>,
# which is TRUE if the element matches the <pattern>, FALSE if not.
grepl("^>", myFASTA) # "^>" is a regular expression that means: ">" at the
# beginning ("^") of the line.
(runs <- rle(grepl("^>", myFASTA)))
# Translating that into start positions of blocks takes a bit of bookkeeping:
# the first start has index 1, the following starts can be calculated from
# cumsum()'s and $length's.
(starts <- c(1, (cumsum(runs$lengths)[-length(runs$lengths)] + 1)))
# ... and with that, we can parse our FASTA data. We take the specification
# above and translate it into code. That's how we develop code: write up step by
# instructions as comments, then implement them one by one.
# Here is an example
FA <- c(">head1 part a", ">head1 part b", "abcdef", "ghi", # two headers
"", # empty line
">head2", "jkl", # one header
">head3", "mno", "pqrs") # two sequence lines
# - drop all lines that don't begin with ">" or a letter, "-", or "*"
FA <- FA[grepl("^[A-Za-z>*-]", FA)]
# - identify consecutive lines that begin ">" and consecutive lines
# that do not begin ">"
runs <- rle(grepl("^>", FA))
starts <- c(1, (cumsum(runs$lengths)[-length(runs$lengths)] + 1))
# - collapse each set of consecutive lines in-place
for (i in seq_along(starts)) {
FA[starts[i]] <- paste(FA[starts[i]:(starts[i] + runs$lengths[i] - 1)],
sep ="",
collapse = "")
}
# - drop all remaining lines.
FA <- FA[starts]
# In this resulting vector the odd-indexed elements
# are headers, and the even-indexed elements are sequences.
# As a function:
readFASTA <- function(IN) {
# Read a FASTA formatted file from IN, remove all non-header, non-sequence
# element, return collapsed sequences.
# Parameters:
# IN chr Input file name (or connection)
# Value:
# chr vector in which the odd-indexed elements are headers, and the
# even-indexed elements are sequences.
FA <- readLines(IN)
FA <- FA[grepl("^[A-Za-z>*-]", FA)]
runs <- rle(grepl("^>", FA))
starts <- c(1, (cumsum(runs$lengths)[-length(runs$lengths)] + 1))
for (i in seq_along(starts)) { # collapse runs in-place
FA[starts[i]] <- paste(FA[starts[i]:(starts[i] + runs$lengths[i] - 1)],
sep ="",
collapse = "")
}
# return collapsed lines
return(FA[starts])
}
# Try this: Let's try to use only the first 3 elements of myFASTA ... it's a
# lengthy sequence. But how? We don't have a file with that contents and the
# function expects to read from a file. Do we need to write myFASTA[1:3] to a
# temporary file and then read it? We could - but wherever a file is expected we
# can also pass in a "text connection" from an object in memory, with the
# textConnection() function, like so:
readFASTA(textConnection(myFASTA[1:3]))
# Here is a "real" example - a multi FASTA file of aligned APSES domain
# sequences:
(refAPSES <- readFASTA("./data/refAPSES.mfa"))
# Subset all headers:
refAPSES[seq(1, length(refAPSES), by = 2)]
# Subset the sequence with "P39678" in the header
refAPSES[grep("P39678", refAPSES) + 1] # grep() the string and add 1
# = 2 Interpreting FASTA ==================================================
# FASTA files are straightforward to interpret - just one thing may be of note:
# when working with strings, we can use substr(<string>, <start>, <stop>) to
# extract substrings, but more often we expand the string into a vector of
# single characters with strsplit(<string>, ""). strsplit() returns a list,
# to accommodate that <string> could be a vector of many elements, therefore
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# we usually unlist() the result if we use it only on a single string.
# Example: How many positive charged residues in "MBP1_SACCE"?
s <- unlist(strsplit(refAPSES[grep("MBP1_SACCE", refAPSES) + 1], ""))
head(s)
sum(grepl("[HKR]", s)) # 20 (+) charged residues. grepl() returns TRUE and FALSE
# for the characters, sum() coerces to 1 and 0
# respectively, and that gives us the result.
100 * sum(grepl("[HKR]", s)) / length(s) # in percent: 20.2 %
# = 3 Writing FASTA =======================================================
# Writing FASTA files mostly just the revrese reverse of reading, with one
# twist: we need to break the long sequence string into chunks of the desired
# width. The FASTA specification calls for a maximum of 120 characters per line,
# but writing out much less than that is common since it allows to comfortably
# view lines on the console, or printing them on a sheet of paper (do we still
# do that actually?). How do we break a string into chunks? A combination of
# seq(<from>, <to>, <by>) with substring(<string>, <start>, <stop>) will work
# nicely. (Note that substring() is vectorized, whereas substr() is not!) As we
# loop through our FASTA object in memory, we can build the output by c()'ing
# blocks of header + sequence to each other. For VERY large objects this might
# be slow - in that case, we might want to precalculate the size of the output
# object. But that's more of a hypothetical consideration.
s <- refAPSES[2]
nchar(s)
w <- 30 # width of chunk
(starts <- seq(1, nchar(s), by = w)) # starting index of chunk
(ends <- c((starts - 1)[-1], nchar(s))) # ending index of chunk
# Task: Is this safe? What happens if nchar(s) is shorter than w?
# What happens if nchar(s) is an exact multiple of w?
substring(s, starts, ends)
# Here's the function ...
writeFASTA <- function(s, OUT = stdout(), width = 60) {
# Write an object "s" that contains one or more header/sequence pairs to file.
# Parameters:
# s chr Vector with a FASTA header string in odd elements,
# sequence in one-letter code in even elements.
# OUT chr connection to be written to; defaults to stdout() i.e.
# output is written console.
# width int max number of sequence characters per line of output.
# Value:
# NA Invoked for side effect of writing data to file
txt <- character()
idx <- seq(1, length(s), by = 2)
for (i in idx) {
txt <- c(txt, s[i]) # add header line to txt
starts <- seq(1, nchar(s[i + 1]), by = width) # starting indices of chunks
ends <- c((starts - 1)[-1], nchar(s[i + 1])) # ending indices of chunks
txt <- c(txt, substring(s[i + 1], starts, ends)) # add chunks to txt
}
writeLines(txt, OUT)
}
# Let's try this. If we don't specify OUT, the result is written to the console
# by default. Default width for sequence is 60 characters
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writeFASTA(refAPSES)
# [END]