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