BIO Assignment Week 4

Assignment for Week 4
Domain annotations

Note! This assignment is currently active. All significant changes will be announced on the mailing list.

 
 

Concepts and activities (and reading, if applicable) for this assignment will be topics on next week's quiz.

 

Introduction

 

In this assignment we will look more in depth at domain annotations, and compare them across different proteins related to yeast Mbp1. We will write a function in R to help us plot a graphic of the comparison. But first we need to go through a few more R concepts.

 

Programming R code

First, we will cover essentials of R programming: the fundamental statements that are needed to write programs–conditional expressions and loops, and how to define functions that allow us to use programming code. But let's start with two more data types of R so we can use the concepts later on: matrices, lists and data frames.

Task:

Please begin by working through the short R - tutorial: matrices section and the following sections on "Lists" and Data frames.

Note that what we have done here is just the bare minimum on vectors, matrices and lists. The concepts are very generally useful, and there are many useful ways to extract subsets of values. We'll come across these in various parts of R sample code. But unless you read the provided code examples line by line, make sure you understand every single statement and ask if you are not clear about the syntax, this will all be quickly forgotten. Use it or lose it!

R is a full featured programming language and in order to be that, it needs the ability to manipulate Control Flow, i.e. the order in which instructions are executed. By far the most important control flow statement is a conditional branch i.e. the instruction to execute code at alternative points of the program, depending on the presence or absence of a condition. Using such conditional branch instructions, two main types of programming constructs can be realized: conditional expressions and loops.

Conditional expressions

The template for conditional expression in R is:

if( <expression 1> ) {
  <statement 1>
} 
else if ( <expresssion 2> ) {
  <statement 2>
} 
else {
  <statement 3>
}

...where both the else if (...) { ... } and the else (...) block are optional. We have encountered this construct previously, when we assigned the appropriate colors for amino acids in the frequency plot:

if      (names(logRatio[i]) == "F") { barColors[i] <- hydrophobic }
else if (names(logRatio[i]) == "G") { barColors[i] <- plain }
   [... etc ...]
else                                { barColors[i] <- plain }

Logical expressions

We have to consider the <expression> in a bit more detail: anything that is, or produces, or can be interpreted as, a Boolean TRUE or FALSE value can serve as an expression in a conditional statement.

Task:
Here are some examples. Copy the code to an R script, predict what will happen on in each line and try it out:

# A boolean constant is interpreted as is:
if (TRUE)    {print("true")} else {print("false")}
if (FALSE)   {print("true")} else {print("false")}

# Strings of "true" and "false" are coerced to their 
# Boolean equivalent, but contrary to other programming languages
# arbitrary, non-empty or empty strings are not interpreted.
if ("true") {print("true")} else {print("false")}
if ("false") {print("true")} else {print("false")}
if ("widdershins")    {print("true")} else {print("false")}
if ("") {print("true")} else {print("false")}

# All non-zero, defined numbers are TRUE
if (1)      {print("true")} else {print("false")}
if (0)      {print("true")} else {print("false")}
if (-1)     {print("true")} else {print("false")}
if (pi)     {print("true")} else {print("false")}
if (NULL)   {print("true")} else {print("false")}
if (NA)     {print("true")} else {print("false")}
if (NaN)    {print("true")} else {print("false")}
if (Inf)    {print("true")} else {print("false")}

# functions can return Boolean values
affirm <- function() { return(TRUE) }
deny <- function() { return(FALSE) }
if (affirm())    {print("true")} else {print("false")}
if (deny())    {print("true")} else {print("false")}

# N.B. coercion of Booleans into numbers can be done as well
and is sometimes useful: consider ...
a <- c(TRUE, TRUE, FALSE, TRUE, FALSE)
a
as.numeric(a)
sum(a)

# ... or coercing the other way ...
as.logical(-1:1)

Logical operators

To actually write a conditional statement, we have to be able to test a condition and this is what logical operators do. Is something equal to something else? Is it less? Does something exist? Is it a number?

Task:
Here are some examples. Again, predict what will happen ...

TRUE             # Just a statement.

#   unary operator
! TRUE           # NOT ...

# binary operators
FALSE > TRUE     # GREATER THAN ...
FALSE < TRUE     # ... these are coerced to numbers
FALSE < -1        
0 == FALSE       # Careful! == compares, = assigns!!!

"x" == "u"       # using lexical sort order ...
"x" >= "u"
"x" <= "u"
"x" != "u"
"aa" > "u"       # ... not just length, if different.
"abc" < "u"  

TRUE | FALSE    # OR: TRUE if either is true
TRUE & FALSE    # AND: TRUE if both are TRUE

# equality and identity
?identical
a <- c(TRUE)
b <- c(TRUE)
a; b
a == b
identical(a, b)

b <- 1
a; b
a == b
identical(a, b)   # Aha: equal, but not identical


# some other useful tests for conditional expressions
?all
?any
?duplicated
?exists
?is.character
?is.factor
?is.integer
?is.null
?is.numeric
?is.unsorted
?is.vector

Loops

Loops allow you to repeat tasks many times over. The template is:

for (<name> in <vector>) {
   <statement>
}

Task:
Consider the following: Again, copy the code to a script, study it, predict what will happen and then run it.

# simple for loop
for (i in 1:10) {
	print(c(i, i^2, i^3))
}

# Compare excution times: one million square roots from a vector ...
n <- 1000000
x <- 1:n
y <- sqrt(x)

# ... or done explicitly in a for-loop
for (i in 1:n) {
  y[i] <- sqrt (x[i])
}

If you can achieve your result with an R vector expression, it will be faster than using a loop. But sometimes you need to do things explicitly, for example if you need to access intermediate results.

Here is an example to play with loops: a password generator. Passwords are a pain. We need them everywhere, they are frustrating to type, to remember and since the cracking programs are getting smarter they become more and more likely to be broken. Here is a simple password generator that creates random strings with consonant/vowel alterations. These are melodic and easy to memorize, but actually as strong as an 8-character, fully random password that uses all characters of the keyboard such as )He.{2jJ or #h$bB2X^ (which is pretty much unmemorizable). The former is taken from 20⁷ * 7⁷ 10¹⁵ possibilities, the latter is from 94⁸ ~ 6*10¹⁵ possibilities. HIgh-end GPU supported password crackers can test about 10⁹ passwords a second, the passwords generated by this little algorithm would thus take on the order of 10⁶ seconds or eleven days to crack. This is probably good enough to deter a casual attack.

Task:
Copy, study and run ...

# Suggest memorizable passwords
# Below we use the functions: 
?nchar
?sample
?substr
?paste
?print

#define a string of  consonants ...
con <- "bcdfghjklmnpqrstvwxz"
# ... and a string of of vowels
vow <- "aeiouy"

for (i in 1:10) {  # ten sample passwords to choose from ...
    pass = rep("", 14)  # make an empty character vector
    for (j in 1:7) {  # seven cononant/vbowel pairs to be created ...
        k   <- sample(1:nchar(con), 1) # pick a random index for consonants ...
        ch  <- substr(con,k,k) #  ... get the corresponding character ...
        idx <- (2*j)-1  # ... compute the index where to put the consonant ...
        pass[idx] <- ch # ...  and put it in the right spot

        # same thing for the vowel, but written with less intermediate assignment
        # of results to variables
        k <- sample(1:nchar(vow), 1)
        pass[(2*j)] <- substr(vow,k,k) 
    }
    print( paste(pass, collapse="") )  # collapse the vector in to a string and print
}

Functions

Finally: functions. Functions look very much like the statements we have seen above. the template looks like:

<name> <- function (<parameters>) {
   <statements>
}

The name is any valid name in R. The function is then invoked with name(). The parameter list can be empty, or hold a list of variable names. If variable names are present, you need to enter the corresponding parameters when you execute the function. These assigned variables are then available inside the function to compute with. This is called "passing the variable into the function".

You have encountered a function to choose YFO names. In this function, your Student ID was the parameter. Here is another example to play with: a function that calculates how old you are. In days. This is neat - you can celebrate your 10,000 birthday - or so.

Task:
Copy, explore and run ...

Define the function ...

# A lifedays calculator function

myLifeDays <- function(date = NULL) { # give "date" a default value so we can test whether it has been set
    if (is.null(date)) {
        print ("Enter your birthday as a string in \"YYYY-MM-DD\" format.")
        return()
    }
    x <- strptime(date, "%Y-%m-%d") # convert string to time
    y <- format(Sys.time(), "%Y-%m-%d") # convert "now" to time
    diff <- round(as.numeric(difftime(y, x, unit="days")))
    print(paste("This date was ", diff, " days ago."))
}

Use the function (example)

   myLifeDays("1932-09-25")  # Glenn Gould's birthday

Here is a good opportunity to play and practice programming: modify this function to accept a second argument. When a second argument is present (e.g. 10000) the function should print the calendar date on which the input date will be that number of days ago. Then you could use it to know when to celebrate your 10,000^th lifeDay, or your 777^th anniversary day or whatever.

Enjoy.

CDD domain annotation

Back to molecular biology. In the last assignment, you followed a link to CDD Search Results from the RefSeq record for yeast Mbp1 and briefly looked at the information offered by the NCBI's Conserved Domain Database, a database of Position Specific Scoring Matrices that embody domain definitions. Rather than access precomputed results, you can also search CDD with sequences: assuming you have saved the YFO Mbp1 sequence in FASTA format, this is straightforward. If you did not save this sequence, return to Assignment 3 and retrieve it again.

Task:

1. Access the CDD database at http://www.ncbi.nlm.nih.gov/Structure/cdd/cdd.shtml
2. Read the information. CDD is a superset of various other database domain annotations as well as NCBI-curated domain definitions.
3. Copy the YFO Mbp1 FASTA sequence, paste it into the search form and click Submit.
   1. On the result page, clik on View full result
   2. Note that there are a number of partially overlapping ankyrin domain modules. We will study ankyrin domains in a later assignment.
   3. Also note that there may be blocks of sequence colored cyan in the sequence bar. Hover your mouse over the blocks to see what these blocks signify.
   4. Open the link to Search for similar domain architecture in a separate window and study it. This is the CDART database. Think about what these results may be useful for.
   5. Click on one of the ANK superfamily graphics and see what the associated information looks like: there is a summary of structure and function, links to specific literature and a tree of the relationship of related sequences.

SMART domain annotation

The SMART database at the EMBL in Heidelberg offers an alternative view on domain architectures. I personally find it more useful for annotations because it integrates a number of additional features. You can search by sequence, or by accession number and that raises the question of how to retrieve a database cross-reference from an NCBI sequence identifier to a UniProt sequence ID:

ID mapping

Task:

1. Access the UniProt ID mapping service at http://www.uniprot.org/mapping/
2. Paste the RefSeq identifier for YFO Mbp1 into the search field.
3. Use the menu to choose From RefSeq Protein and To UniprotKB AC–the UniProt Knowledge Base Accession number.
4. Click on Map to execute the search.
5. Note the ID - it probably starts with a letter, followed by numbers and letters. Here are some examples for fungal Mbp1-like proteins: P39678 Q5B8H6 Q5ANP5 P41412 etc.
6. Click on the link, and explore how the UniProt sequence page is similar or different from the RefSeq page.

SMART search

Task:

1. Access the SMART database at http://smart.embl-heidelberg.de/
2. Paste the YFO Mbp1 UniProtKB Accession number into the Sequence ID or ACC field.
3. Check the boxes for:
   1. PFAM domains (domains defined by sequence similarity in the PFAM database)
   2. signal peptides (using the Gunnar von Heijne's SignalP 4.0 server at the Technical University in Lyngby, Denmark)
   3. internal repeats (using the programs ariadne and prospero at the Wellcome Trust Centre for Human Genetics at Oxford University, England)
   4. intrinsic protein disorder (using Rune Linding's DisEMBL program at the EMBL in Heidelberg, Germany)
4. Click on Sequence SMART to run the search and annotation. (In case you get an error like: "Sorry, your entry seems to have no SMART domain ...", let me know and repeat the search with the FASTA sequence instead.)

Study the results. Specifically, have a look at the proteins with similar domain ORGANISATION and COMPOSITION. This is similar to the NCBI's CDART.

Visual comparison of domain annotations in R

The versatile plotting functions of R allow us to compare domain annotations. I am particularly interested in the distribution of segments that are annotated as being "low-complexity" or "disordered. These are functional features of the amino acid sequence, that are often not associated with sequence similarity.

To create a plot similar to the CDD and SMART displays, we need to store our data in a a data structure that can accommodate vectors and strings. In R this is a "list". Here is an example of how to construct a single list item that stores annotation information for Saccharomyces cerevisiae Mbp1.

[TBC ...]

 

Links and resources

 

Footnotes and references

 

Ask, if things don't work for you!

If anything about the assignment is not clear to you, please ask on the mailing list. You can be certain that others will have had similar problems. Success comes from joining the conversation.

• Do consider how to ask your questions so that a meaningful answer is possible:
  • How to create a Minimal, Complete, and Verifiable example on stackoverflow and ...
  • How to make a great R reproducible example are required reading.