BIO Assignment Week 6

Assignment for Week 6
Function

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Assignment 7 >

Note! This assignment is currently inactive. Major and minor unannounced changes may be made at any time.

 
 

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

 

Introduction

 

In this assignment we will perform a few computations with coordinate data in PDB files, 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 and collaborate to share data for our plots. But first we need to go through a few more R concepts.

 

Stereo vision

Task:

Continue with your stereo practice.

Practice at least ...

• two times daily,
• for 3-5 minutes each session.

• Measure your interocular distance and your fusion distance as explained here on the Student Wiki and add it to the table.

Keep up your practice throughout the course. Once again: do not go through your practice sessions mechanically. If you are not making constant progress in your practice sessions, contact me so we can help you on the right track.

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 a few 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 consonant/vowel 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 position (index) of where to put the consonant ...
        pass[idx] <- ch                 # ...  and put it in the right spot

        # same thing for the vowel, but coded with fewer intermediate assignments
        # 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>
}

In this statement, the function is assigned to the name - any valid name in R. Once it is assigned, it the function can be invoked with name(). The parameter list (the values we write into the parentheses followin the function name) 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 available inside the function, and can be used for computations. 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.

The PDB

Search for GO and EC numbers at PDB...

The search options in the PDB structure database are as sophisticated as those at the NCBI. For now, we will try a simple keyword search to get us started.

Task:

1. Visit the RCSB PDB website at http://www.pdb.org/
2. Briefly orient yourself regarding the database contents and its information offerings and services.
3. Enter Mbp1 into the search field.
4. In your journal, note down the PDB IDs for the three Saccharomyces cerevisiae Mbp1 transcription factor structures your search has retrieved.
5. Click on one of the entries and explore the information and services linked from that page.

 

CDD domain annotation

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. Click the lick to access SMART in the normal mode.
3. Paste the YFO Mbp1 UniProtKB Accession number into the Sequence ID or ACC field.
4. 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)
5. 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 actual FASTA sequence instead of the accession number.)

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. The distribution of segments that are annotated as being "low-complexity" or "disordered is particulalry interesting: these are functional features of the amino acid sequence that are often not associated with sequence similarity.

In the following code tutorial, we create a plot similar to the CDD and SMART displays. It is based on the SMART domain annotations of the six-fungal reference species for the course.

Task:
Copy the code to an R script, study and execute it.

# plotDomains
# tutorial and functions to plot a colored rectangle from a list of domain annotations


# First task: create a list structure for the annotations: this is a list of lists
# As you see below, we need to mix strings, numbers and vectors of numbers. In R
# such mixed data types must go into a list.

Mbp1Domains <- list()   # start with an empty list

# For each species annotation, compile the SMART domain annotations in a list.
Mbp1Domains <- rbind(Mbp1Domains, list(  # rbind() appends the list to the existing
    species = "Saccharomyces cerevisiae",
    code    = "SACCE",
    ACC     = "P39678",
    length  = 833,
    KilAN   = c(18,102),  # Note: Vector of (start, end) pairs
    AThook  = NULL,       # Note: NULL, because this annotation was not observed in this sequence
    Seg     = c(108,122,236,241,279,307,700,717),
    DisEMBL = NULL,
    Ankyrin = c(394,423,427,463,512,541),   # Note: Merge overlapping domains, if present
    Coils   = c(633, 655)
    ))

Mbp1Domains <- rbind(Mbp1Domains, list(
    species = "Emericella nidulans",
    code    = "ASPNI",
    ACC     = "Q5B8H6",
    length  = 695,
    KilAN   = c(23,94),
    AThook  = NULL,
    Seg     = c(529,543),
    DisEMBL = NULL,
    Ankyrin = c(260,289,381,413),
    Coils   = c(509,572)
    ))

Mbp1Domains <- rbind(Mbp1Domains, list(
    species = "Candida albicans",
    code    = "CANAL",
    ACC     = "Q5ANP5",
    length  = 852,
    KilAN   = c(19,102),
    AThook  = NULL,
    Seg     = c(351,365,678,692),
    DisEMBL = NULL,
    Ankyrin = c(376,408,412,448,516,545),
    Coils   = c(665,692)
    ))

Mbp1Domains <- rbind(Mbp1Domains, list(
    species = "Neurospora crassa",
    code    = "NEUCR",
    ACC     = "Q7RW59",
    length  = 833,
    KilAN   = c(31,110),
    AThook  = NULL,
    Seg     = c(130,141,253,266,514,525,554,564,601,618,620,629,636,652,658,672,725,735,752,771),
    DisEMBL = NULL,
    Ankyrin = c(268,297,390,419),
    Coils   = c(500,550)
    ))

Mbp1Domains <- rbind(Mbp1Domains, list(
    species = "Schizosaccharomyces pombe",
    code    = "SCHPO",
    ACC     = "P41412",
    length  = 657,
    KilAN   = c(21,97),
    AThook  = NULL,
    Seg     = c(111,125,136,145,176,191,422,447),
    DisEMBL = NULL,
    Ankyrin = c(247,276,368,397),
    Coils   = c(457,538)
    ))

Mbp1Domains <- rbind(Mbp1Domains, list(
    species = "Ustilago maydis",
    code    = "USTMA",
    ACC     = "Q4P117",
    length  = 956,
    KilAN   = c(21,98),
    AThook  = NULL,
    Seg     = c(106,116,161,183,657,672,776,796),
    DisEMBL = NULL,
    Ankyrin = c(245,274,355,384),
    Coils   = c(581,609)
    ))


# Working with data in lists and dataframes can be awkward, since the result
# of filters and slices are themselves lists, not vectors.
# Therefore we need to use the unlist() function a lot. When in doubt: unlist()

#### Boxes #####
# Define a function to draw colored boxes, given input of a vector with
# (start,end) pairs, a color, and the height where the box should go.
drawBoxes <- function(v, c, h) {   # vector of xleft, xright pairs; color; height
    if (is.null(v)) { return() }
    for (i in seq(1,length(v),by=2)) {
        rect(v[i], h-0.1, v[i+1], h+0.1, border="black", col=c)
    }
}

#### Annotations ####
# Define a function to write the species code, draw a grey
# horizontal line and call drawBoxes() for every annotation type
# in the list
drawGene <- function(rIndex) {
    # define colors:
    kil <- "#32344F"
    ank <- "#691A2C"
    seg <- "#598C9E"
    coi <- "#8B9998"
    xxx <- "#EDF7F7"
    
    text (-30, rIndex, adj=1, labels=unlist(Mbp1Domains[rIndex,"code"]), cex=0.75 )
    lines(c(0, unlist(Mbp1Domains[rIndex,"length"])), c(rIndex, rIndex), lwd=3, col="#999999")

    drawBoxes(unlist(Mbp1Domains[rIndex,"KilAN"]),   kil, rIndex)
    drawBoxes(unlist(Mbp1Domains[rIndex,"AThook"]),  xxx, rIndex)
    drawBoxes(unlist(Mbp1Domains[rIndex,"Seg"]),     seg, rIndex)
    drawBoxes(unlist(Mbp1Domains[rIndex,"DisEMBL"]), xxx, rIndex)
    drawBoxes(unlist(Mbp1Domains[rIndex,"Ankyrin"]), ank, rIndex)
    drawBoxes(unlist(Mbp1Domains[rIndex,"Coils"]),   coi, rIndex)
}

#### Plot ####
# define the size of the plot-frame
yMax <- length(Mbp1Domains[,1])   # number of domains in list
xMax <- max(unlist(Mbp1Domains[,"length"]))  # largest sequence length

# plot an empty frame
plot(1,1, xlim=c(-100,xMax), ylim=c(0, yMax) , type="n", yaxt="n", bty="n", xlab="sequence number", ylab="")

# Finally, iterate over all species and call drawGene()
for (i in 1:length(Mbp1Domains[,1])) {
    drawGene(i)
}

# end

When you execute the code, your plot should look similar to this one:

SMART domain annotations for Mbp1 proteins from six fungal species.

Task:
On the Student Wiki, add the annotations for YFO to the plot:

1. Copy one of the list definitions for Mbp1 domains and edit it with the appropriate values for your own annotations.
2. Test that you can add the YFO annotation to the plot.
3. Submit your validated code block to the Student Wiki here. The goal is to compile an overview of all species we are studying in class.
4. If your working annotation block is in the Wiki before noontime on Wednesday, you will be awarded a 10% bonus on the quiz.

EC

GO

Introduction

Harris (2008) Developing an ontology. Methods Mol Biol 452:111-24. (pmid: 18563371)

[ PubMed ] [ DOI ] Abstract In recent years, biological ontologies have emerged as a means of representing and organizing biological concepts, enabling biologists, bioinformaticians, and others to derive meaning from large datasets.This chapter provides an overview of formal principles and practical considerations of ontology construction and application. Ontology development concepts are illustrated using examples drawn from the Gene Ontology (GO) and other OBO ontologies.

Hackenberg & Matthiesen (2010) Algorithms and methods for correlating experimental results with annotation databases. Methods Mol Biol 593:315-40. (pmid: 19957156)

[ PubMed ] [ DOI ] Abstract An important procedure in biomedical research is the detection of genes that are differentially expressed under pathologic conditions. These genes, or at least a subset of them, are key biomarkers and are thought to be important to describe and understand the analyzed biological system (the pathology) at a molecular level. To obtain this understanding, it is indispensable to link those genes to biological knowledge stored in databases. Ontological analysis is nowadays a standard procedure to analyze large gene lists. By detecting enriched and depleted gene properties and functions, important insights on the biological system can be obtained. In this chapter, we will give a brief survey of the general layout of the methods used in an ontological analysis and of the most important tools that have been developed.

GO

The Gene Ontology project is the most influential contributor to the definition of function in computational biology and the use of GO terms and GO annotations is ubiquitous.

GO: the Gene Ontology project [ link ] [ page ] Expand... Ontologies are important tools to organize and compute with non-standardized information, such as gene annotations. The Gene Ontology project (GO) constructs ontologies for gene and gene product attributes across numerous species. Three major ontologies are being developed: molecular process, biological function and cellular location. Each includes terms, their definition, and their relationships. In addition, genes and gene products are being been annotated with their GO terms and the type of evidence that underlies the annotation. A number of tools such as the AmiGO browser are available to analyse relationships, construct ontologies and curate annotations. Data can be freely downloaded in formats that are convenient for computation.

du Plessis et al. (2011) The what, where, how and why of gene ontology--a primer for bioinformaticians. Brief Bioinformatics 12:723-35. (pmid: 21330331)

[ PubMed ] [ DOI ] Abstract With high-throughput technologies providing vast amounts of data, it has become more important to provide systematic, quality annotations. The Gene Ontology (GO) project is the largest resource for cataloguing gene function. Nonetheless, its use is not yet ubiquitous and is still fraught with pitfalls. In this review, we provide a short primer to the GO for bioinformaticians. We summarize important aspects of the structure of the ontology, describe sources and types of functional annotations, survey measures of GO annotation similarity, review typical uses of GO and discuss other important considerations pertaining to the use of GO in bioinformatics applications.

The GO actually comprises three separate ontologies:

Molecular function

...

Biological Process

...

Cellular component

...

GO terms

GO terms comprise the core of the information in the ontology: a carefully crafted definition of a term in any of GO's separate ontologies.

GO relationships

The nature of the relationships is as much a part of the ontology as the terms themselves. GO uses three categories of relationships:

• is a
• part of
• regulates

GO annotations

The GO terms are conceptual in nature, and while they represent our interpretation of biological phenomena, they do not intrinsically represent biological objects, such a specific genes or proteins. In order to link molecules with these concepts, the ontology is used to annotate genes. The annotation project is referred to as GOA.

Dimmer et al. (2007) Methods for gene ontology annotation. Methods Mol Biol 406:495-520. (pmid: 18287709)

[ PubMed ] [ DOI ] Abstract The Gene Ontology (GO) is an established dynamic and structured vocabulary that has been successfully used in gene and protein annotation. Designed by biologists to improve data integration, GO attempts to replace the multiple nomenclatures used by specialised and large biological knowledgebases. This chapter describes the methods used by groups to create new GO annotations and how users can apply publicly available GO annotations to enhance their datasets.

GO evidence codes

Annotations can be made according to literature data or computational inference and it is important to note how an annotation has been justified by the curator to evaluate the level of trust we should have in the annotation. GO uses evidence codes to make this process transparent. When computing with the ontology, we may want to filter (exclude) particular terms in order to avoid tautologies: for example if we were to infer functional relationships between homologous genes, we should exclude annotations that have been based on the same inference or similar, and compute only with the actual experimental data.

The following evidence codes are in current use; if you want to exclude inferred anotations you would restrict the codes you use to the ones shown in bold: EXP, IDA, IPI, IMP, IEP, and perhaps IGI, although the interpretation of genetic interactions can require assumptions.

Automatically-assigned Evidence Codes

• IEA: Inferred from Electronic Annotation

Curator-assigned Evidence Codes

• Experimental Evidence Codes
  • EXP: Inferred from Experiment
  • IDA: Inferred from Direct Assay
  • IPI: Inferred from Physical Interaction
  • IMP: Inferred from Mutant Phenotype
  • IGI: Inferred from Genetic Interaction
  • IEP: Inferred from Expression Pattern
• Computational Analysis Evidence Codes
  • ISS: Inferred from Sequence or Structural Similarity
  • ISO: Inferred from Sequence Orthology
  • ISA: Inferred from Sequence Alignment
  • ISM: Inferred from Sequence Model
  • IGC: Inferred from Genomic Context
  • IBA: Inferred from Biological aspect of Ancestor
  • IBD: Inferred from Biological aspect of Descendant
  • IKR: Inferred from Key Residues
  • IRD: Inferred from Rapid Divergence
  • RCA: inferred from Reviewed Computational Analysis
• Author Statement Evidence Codes
  • TAS: Traceable Author Statement
  • NAS: Non-traceable Author Statement
• Curator Statement Evidence Codes
  • IC: Inferred by Curator
  • ND: No biological Data available

For further details, see the Guide to GO Evidence Codes and the GO Evidence Code Decision Tree.

 

GO tools

For many projects, the simplest approach will be to download the GO ontology itself. It is a well constructed, easily parseable file that is well suited for computation. For details, see Computing with GO on this wiki.

AmiGO

practical work with GO: at first via the AmiGO browser AmiGO is a GO browser developed by the Gene Ontology consortium and hosted on their website.

AmiGO - Gene products

Task:

1. Navigate to the GO homepage.
2. Enter SOX2 into the search box to initiate a search for the human SOX2 transcription factor (WP, HUGO) (as gene or protein name).
3. There are a number of hits in various organisms: sulfhydryl oxidases and (sex determining region Y)-box genes. Check to see the various ways by which you could filter and restrict the results.
4. Select Homo sapiens as the species filter and set the filter. Note that this still does not give you a unique hit, but ...
5. ... you can identify the Transcription factor SOX-2 and follow its gene product information link. Study the information on that page.
6. Later, we will need Entrez Gene IDs. The GOA information page provides these as GeneID in the External references section. Note it down. With the same approach, find and record the Gene IDs (a) of the functionally related Oct4 (POU5F1) protein, (b) the human cell-cycle transcription factor E2F1, (c) the human bone morphogenetic protein-4 transforming growth factor BMP4, (d) the human UDP glucuronosyltransferase 1 family protein 1, an enzyme that is differentially expressed in some cancers, UGT1A1, and (d) as a positive control, SOX2's interaction partner NANOG, which we would expect to be annotated as functionally similar to both Oct4 and SOX2.

AmiGO - Associations

GO annotations for a protein are called associations.

Task:

1. Open the associations information page for the human SOX2 protein via the link in the right column in a separate tab. Study the information on that page.
2. Note that you can filter the associations by ontology and evidence code. You have read about the three GO ontologies in your previous assignment, but you should also be familiar with the evidence codes. Click on any of the evidence links to access the Evidence code definition page and study the definitions of the codes. Make sure you understand which codes point to experimental observation, and which codes denote computational inference, or say that the evidence is someone's opinion (TAS, IC etc.). Note: it is good practice - but regrettably not universally implemented standard - to clearly document database semantics and keep definitions associated with database entries easily accessible, as GO is doing here. You won't find this everywhere, but as a user please feel encouraged to complain to the database providers if you come across a database where the semantics are not clear. Seriously: opaque semantics make database annotations useless.
3. There are many associations (around 60) and a good way to select which ones to pursue is to follow the most specific ones. Set IDA as a filter and among the returned terms select GO:0035019 – somatic stem cell maintenance in the Biological Process ontology. Follow that link.
4. Study the information available on that page and through the tabs on the page, especially the graph view.
5. In the Inferred Tree View tab, find the genes annotated to this go term for homo sapiens. There should be about 55. Click on the number behind the term. The resulting page will give you all human proteins that have been annotated with this particular term. Note that the great majority of these is via the IEA evidence code.

Semantic similarity

A good, recent overview of ontology based functional annotation is found in the following article. This is not a formal reading assignment, but do familiarize yourself with section 3: Derivation of Semantic Similarity between Terms in an Ontology as an introduction to the code-based annotations below.

Gan et al. (2013) From ontology to semantic similarity: calculation of ontology-based semantic similarity. ScientificWorldJournal 2013:793091. (pmid: 23533360)

[ PubMed ] [ DOI ] Abstract Advances in high-throughput experimental techniques in the past decade have enabled the explosive increase of omics data, while effective organization, interpretation, and exchange of these data require standard and controlled vocabularies in the domain of biological and biomedical studies. Ontologies, as abstract description systems for domain-specific knowledge composition, hence receive more and more attention in computational biology and bioinformatics. Particularly, many applications relying on domain ontologies require quantitative measures of relationships between terms in the ontologies, making it indispensable to develop computational methods for the derivation of ontology-based semantic similarity between terms. Nevertheless, with a variety of methods available, how to choose a suitable method for a specific application becomes a problem. With this understanding, we review a majority of existing methods that rely on ontologies to calculate semantic similarity between terms. We classify existing methods into five categories: methods based on semantic distance, methods based on information content, methods based on properties of terms, methods based on ontology hierarchy, and hybrid methods. We summarize characteristics of each category, with emphasis on basic notions, advantages and disadvantages of these methods. Further, we extend our review to software tools implementing these methods and applications using these methods.

Practical work with GO: bioconductor.

The bioconductor project hosts the GOSemSim package for semantic similarity.

Task:

1. Work through the following R-code. If you have problems, discuss them on the mailing list. Don't go through the code mechanically but make sure you are clear about what it does.

# GOsemanticSimilarity.R
# GO semantic similarity example
# B. Steipe for BCB420, January 2014

setwd("~/your-R-project-directory")

# GOSemSim is an R-package in the bioconductor project. It is not installed via
# the usual install.packages() comand (via CRAN) but via an installation script
# that is run from the bioconductor Website.

source("http://bioconductor.org/biocLite.R")
biocLite("GOSemSim")

library(GOSemSim)

# This loads the library and starts the Bioconductor environment.
# You can get an overview of functions by executing ...
browseVignettes()
# ... which will open a listing in your Web browser. Open the 
# introduction to GOSemSim PDF. As the introduction suggests,
# now is a good time to execute ...
help(GOSemSim)

# The simplest function is to measure the semantic similarity of two GO
# terms. For example, SOX2 was annotated with GO:0035019 (somatic stem cell
# maintenance), QSOX2 was annotated with GO:0045454 (cell redox homeostasis),
# and Oct4 (POU5F1) with GO:0009786 (regulation of asymmetric cell division),
# among other associations. Lets calculate these similarities.
goSim("GO:0035019", "GO:0009786", ont="BP", measure="Wang")
goSim("GO:0035019", "GO:0045454", ont="BP", measure="Wang")

# Fair enough. Two numbers. Clearly we would appreciate an idea of the values
# that high similarity and low similarity can take. But in any case - 
# we are really less interested in the similarity of GO terms - these
# are a function of how the Ontology was constructed. We are more
# interested in the functional similarity of our genes, and these
# have a number of GO terms associated with them.

# GOSemSim provides the functions ...
?geneSim()
?mgeneSim()
# ... to compute these values. Refer to the vignette for details, in
# particular, consider how multiple GO terms are combined, and how to
# keep/drop evidence codes.
# Here is a pairwise similarity example: the gene IDs are the ones you
# have recorded previously. Note that this will download a package
# of GO annotations - you might not want to do this on a low-bandwidth
# connection.
geneSim("6657", "5460", ont = "BP", measure="Wang", combine = "BMA")
# Another number. And the list of GO terms that were considered.

# Your task: use the mgeneSim() function to calculate the similarities
# between all six proteins for which you have recorded the GeneIDs
# previously (SOX2, POU5F1, E2F1, BMP4, UGT1A1 and NANOG) in the 
# biological process ontology. 

# This will run for some time. On my machine, half an hour or so. 

# Do the results correspond to your expectations?

GO reading and resources

General

OBO Foundry (Open Biological and Biomedical Ontologies)

Sauro & Bergmann (2008) Standards and ontologies in computational systems biology. Essays Biochem 45:211-22. (pmid: 18793134)

[ PubMed ] [ DOI ] Abstract With the growing importance of computational models in systems biology there has been much interest in recent years to develop standard model interchange languages that permit biologists to easily exchange models between different software tools. In the present chapter two chief model exchange standards, SBML (Systems Biology Markup Language) and CellML are described. In addition, other related features including visual layout initiatives, ontologies and best practices for model annotation are discussed. Software tools such as developer libraries and basic editing tools are also introduced, together with a discussion on the future of modelling languages and visualization tools in systems biology.

Phenotype etc. Ontologies

Human Phenotype Ontology
See also:

Köhler et al. (2014) The Human Phenotype Ontology project: linking molecular biology and disease through phenotype data. Nucleic Acids Res 42:D966-74. (pmid: 24217912)

[ PubMed ] [ DOI ] Abstract The Human Phenotype Ontology (HPO) project, available at http://www.human-phenotype-ontology.org, provides a structured, comprehensive and well-defined set of 10,088 classes (terms) describing human phenotypic abnormalities and 13,326 subclass relations between the HPO classes. In addition we have developed logical definitions for 46% of all HPO classes using terms from ontologies for anatomy, cell types, function, embryology, pathology and other domains. This allows interoperability with several resources, especially those containing phenotype information on model organisms such as mouse and zebrafish. Here we describe the updated HPO database, which provides annotations of 7,278 human hereditary syndromes listed in OMIM, Orphanet and DECIPHER to classes of the HPO. Various meta-attributes such as frequency, references and negations are associated with each annotation. Several large-scale projects worldwide utilize the HPO for describing phenotype information in their datasets. We have therefore generated equivalence mappings to other phenotype vocabularies such as LDDB, Orphanet, MedDRA, UMLS and phenoDB, allowing integration of existing datasets and interoperability with multiple biomedical resources. We have created various ways to access the HPO database content using flat files, a MySQL database, and Web-based tools. All data and documentation on the HPO project can be found online.

Schriml et al. (2012) Disease Ontology: a backbone for disease semantic integration. Nucleic Acids Res 40:D940-6. (pmid: 22080554)

[ PubMed ] [ DOI ] Abstract The Disease Ontology (DO) database (http://disease-ontology.org) represents a comprehensive knowledge base of 8043 inherited, developmental and acquired human diseases (DO version 3, revision 2510). The DO web browser has been designed for speed, efficiency and robustness through the use of a graph database. Full-text contextual searching functionality using Lucene allows the querying of name, synonym, definition, DOID and cross-reference (xrefs) with complex Boolean search strings. The DO semantically integrates disease and medical vocabularies through extensive cross mapping and integration of MeSH, ICD, NCI's thesaurus, SNOMED CT and OMIM disease-specific terms and identifiers. The DO is utilized for disease annotation by major biomedical databases (e.g. Array Express, NIF, IEDB), as a standard representation of human disease in biomedical ontologies (e.g. IDO, Cell line ontology, NIFSTD ontology, Experimental Factor Ontology, Influenza Ontology), and as an ontological cross mappings resource between DO, MeSH and OMIM (e.g. GeneWiki). The DO project (http://diseaseontology.sf.net) has been incorporated into open source tools (e.g. Gene Answers, FunDO) to connect gene and disease biomedical data through the lens of human disease. The next iteration of the DO web browser will integrate DO's extended relations and logical definition representation along with these biomedical resource cross-mappings.

Evelo et al. (2011) Answering biological questions: querying a systems biology database for nutrigenomics. Genes Nutr 6:81-7. (pmid: 21437033)

[ PubMed ] [ DOI ] Abstract The requirement of systems biology for connecting different levels of biological research leads directly to a need for integrating vast amounts of diverse information in general and of omics data in particular. The nutritional phenotype database addresses this challenge for nutrigenomics. A particularly urgent objective in coping with the data avalanche is making biologically meaningful information accessible to the researcher. This contribution describes how we intend to meet this objective with the nutritional phenotype database. We outline relevant parts of the system architecture, describe the kinds of data managed by it, and show how the system can support retrieval of biologically meaningful information by means of ontologies, full-text queries, and structured queries. Our contribution points out critical points, describes several technical hurdles. It demonstrates how pathway analysis can improve queries and comparisons for nutrition studies. Finally, three directions for future research are given.

Oti et al. (2009) The biological coherence of human phenome databases. Am J Hum Genet 85:801-8. (pmid: 20004759)

[ PubMed ] [ DOI ] Abstract Disease networks are increasingly explored as a complement to networks centered around interactions between genes and proteins. The quality of disease networks is heavily dependent on the amount and quality of phenotype information in phenotype databases of human genetic diseases. We explored which aspects of phenotype database architecture and content best reflect the underlying biology of disease. We used the OMIM-based HPO, Orphanet, and POSSUM phenotype databases for this purpose and devised a biological coherence score based on the sharing of gene ontology annotation to investigate the degree to which phenotype similarity in these databases reflects related pathobiology. Our analyses support the notion that a fine-grained phenotype ontology enhances the accuracy of phenome representation. In addition, we find that the OMIM database that is most used by the human genetics community is heavily underannotated. We show that this problem can easily be overcome by simply adding data available in the POSSUM database to improve OMIM phenotype representations in the HPO. Also, we find that the use of feature frequency estimates--currently implemented only in the Orphanet database--significantly improves the quality of the phenome representation. Our data suggest that there is much to be gained by improving human phenome databases and that some of the measures needed to achieve this are relatively easy to implement. More generally, we propose that curation and more systematic annotation of human phenome databases can greatly improve the power of the phenotype for genetic disease analysis.

Groth et al. (2007) PhenomicDB: a new cross-species genotype/phenotype resource. Nucleic Acids Res 35:D696-9. (pmid: 16982638)

[ PubMed ] [ DOI ] Abstract Phenotypes are an important subject of biomedical research for which many repositories have already been created. Most of these databases are either dedicated to a single species or to a single disease of interest. With the advent of technologies to generate phenotypes in a high-throughput manner, not only is the volume of phenotype data growing fast but also the need to organize these data in more useful ways. We have created PhenomicDB (freely available at http://www.phenomicdb.de), a multi-species genotype/phenotype database, which shows phenotypes associated with their corresponding genes and grouped by gene orthologies across a variety of species. We have enhanced PhenomicDB recently by additionally incorporating quantitative and descriptive RNA interference (RNAi) screening data, by enabling the usage of phenotype ontology terms and by providing information on assays and cell lines. We envision that integration of classical phenotypes with high-throughput data will bring new momentum and insights to our understanding. Modern analysis tools under development may help exploiting this wealth of information to transform it into knowledge and, eventually, into novel therapeutic approaches.

Semantic similarity

Wu et al. (2013) Improving the measurement of semantic similarity between gene ontology terms and gene products: insights from an edge- and IC-based hybrid method. PLoS ONE 8:e66745. (pmid: 23741529)

[ PubMed ] [ DOI ] Abstract BACKGROUND: Explicit comparisons based on the semantic similarity of Gene Ontology terms provide a quantitative way to measure the functional similarity between gene products and are widely applied in large-scale genomic research via integration with other models. Previously, we presented an edge-based method, Relative Specificity Similarity (RSS), which takes the global position of relevant terms into account. However, edge-based semantic similarity metrics are sensitive to the intrinsic structure of GO and simply consider terms at the same level in the ontology to be equally specific nodes, revealing the weaknesses that could be complemented using information content (IC). RESULTS AND CONCLUSIONS: Here, we used the IC-based nodes to improve RSS and proposed a new method, Hybrid Relative Specificity Similarity (HRSS). HRSS outperformed other methods in distinguishing true protein-protein interactions from false. HRSS values were divided into four different levels of confidence for protein interactions. In addition, HRSS was statistically the best at obtaining the highest average functional similarity among human-mouse orthologs. Both HRSS and the groupwise measure, simGIC, are superior in correlation with sequence and Pfam similarities. Because different measures are best suited for different circumstances, we compared two pairwise strategies, the maximum and the best-match average, in the evaluation. The former was more effective at inferring physical protein-protein interactions, and the latter at estimating the functional conservation of orthologs and analyzing the CESSM datasets. In conclusion, HRSS can be applied to different biological problems by quantifying the functional similarity between gene products. The algorithm HRSS was implemented in the C programming language, which is freely available from http://cmb.bnu.edu.cn/hrss.

Gan et al. (2013) From ontology to semantic similarity: calculation of ontology-based semantic similarity. ScientificWorldJournal 2013:793091. (pmid: 23533360)

[ PubMed ] [ DOI ] Abstract Advances in high-throughput experimental techniques in the past decade have enabled the explosive increase of omics data, while effective organization, interpretation, and exchange of these data require standard and controlled vocabularies in the domain of biological and biomedical studies. Ontologies, as abstract description systems for domain-specific knowledge composition, hence receive more and more attention in computational biology and bioinformatics. Particularly, many applications relying on domain ontologies require quantitative measures of relationships between terms in the ontologies, making it indispensable to develop computational methods for the derivation of ontology-based semantic similarity between terms. Nevertheless, with a variety of methods available, how to choose a suitable method for a specific application becomes a problem. With this understanding, we review a majority of existing methods that rely on ontologies to calculate semantic similarity between terms. We classify existing methods into five categories: methods based on semantic distance, methods based on information content, methods based on properties of terms, methods based on ontology hierarchy, and hybrid methods. We summarize characteristics of each category, with emphasis on basic notions, advantages and disadvantages of these methods. Further, we extend our review to software tools implementing these methods and applications using these methods.

Alvarez & Yan (2011) A graph-based semantic similarity measure for the gene ontology. J Bioinform Comput Biol 9:681-95. (pmid: 22084008)

[ PubMed ] [ DOI ] Abstract Existing methods for calculating semantic similarities between pairs of Gene Ontology (GO) terms and gene products often rely on external databases like Gene Ontology Annotation (GOA) that annotate gene products using the GO terms. This dependency leads to some limitations in real applications. Here, we present a semantic similarity algorithm (SSA), that relies exclusively on the GO. When calculating the semantic similarity between a pair of input GO terms, SSA takes into account the shortest path between them, the depth of their nearest common ancestor, and a novel similarity score calculated between the definitions of the involved GO terms. In our work, we use SSA to calculate semantic similarities between pairs of proteins by combining pairwise semantic similarities between the GO terms that annotate the involved proteins. The reliability of SSA was evaluated by comparing the resulting semantic similarities between proteins with the functional similarities between proteins derived from expert annotations or sequence similarity. Comparisons with existing state-of-the-art methods showed that SSA is highly competitive with the other methods. SSA provides a reliable measure for semantics similarity independent of external databases of functional-annotation observations.

Jain & Bader (2010) An improved method for scoring protein-protein interactions using semantic similarity within the gene ontology. BMC Bioinformatics 11:562. (pmid: 21078182)

[ PubMed ] [ DOI ] Abstract BACKGROUND: Semantic similarity measures are useful to assess the physiological relevance of protein-protein interactions (PPIs). They quantify similarity between proteins based on their function using annotation systems like the Gene Ontology (GO). Proteins that interact in the cell are likely to be in similar locations or involved in similar biological processes compared to proteins that do not interact. Thus the more semantically similar the gene function annotations are among the interacting proteins, more likely the interaction is physiologically relevant. However, most semantic similarity measures used for PPI confidence assessment do not consider the unequal depth of term hierarchies in different classes of cellular location, molecular function, and biological process ontologies of GO and thus may over-or under-estimate similarity. RESULTS: We describe an improved algorithm, Topological Clustering Semantic Similarity (TCSS), to compute semantic similarity between GO terms annotated to proteins in interaction datasets. Our algorithm, considers unequal depth of biological knowledge representation in different branches of the GO graph. The central idea is to divide the GO graph into sub-graphs and score PPIs higher if participating proteins belong to the same sub-graph as compared to if they belong to different sub-graphs. CONCLUSIONS: The TCSS algorithm performs better than other semantic similarity measurement techniques that we evaluated in terms of their performance on distinguishing true from false protein interactions, and correlation with gene expression and protein families. We show an average improvement of 4.6 times the F1 score over Resnik, the next best method, on our Saccharomyces cerevisiae PPI dataset and 2 times on our Homo sapiens PPI dataset using cellular component, biological process and molecular function GO annotations.

Yu et al. (2010) GOSemSim: an R package for measuring semantic similarity among GO terms and gene products. Bioinformatics 26:976-8. (pmid: 20179076)

[ PubMed ] [ DOI ] Abstract SUMMARY: The semantic comparisons of Gene Ontology (GO) annotations provide quantitative ways to compute similarities between genes and gene groups, and have became important basis for many bioinformatics analysis approaches. GOSemSim is an R package for semantic similarity computation among GO terms, sets of GO terms, gene products and gene clusters. Four information content (IC)- and a graph-based methods are implemented in the GOSemSim package, multiple species including human, rat, mouse, fly and yeast are also supported. The functions provided by the GOSemSim offer flexibility for applications, and can be easily integrated into high-throughput analysis pipelines. AVAILABILITY: GOSemSim is released under the GNU General Public License within Bioconductor project, and freely available at http://bioconductor.org/packages/2.6/bioc/html/GOSemSim.html.

GO

Gene Ontology Consortium (2012) The Gene Ontology: enhancements for 2011. Nucleic Acids Res 40:D559-64. (pmid: 22102568)

[ PubMed ] [ DOI ] Abstract The Gene Ontology (GO) (http://www.geneontology.org) is a community bioinformatics resource that represents gene product function through the use of structured, controlled vocabularies. The number of GO annotations of gene products has increased due to curation efforts among GO Consortium (GOC) groups, including focused literature-based annotation and ortholog-based functional inference. The GO ontologies continue to expand and improve as a result of targeted ontology development, including the introduction of computable logical definitions and development of new tools for the streamlined addition of terms to the ontology. The GOC continues to support its user community through the use of e-mail lists, social media and web-based resources.

Bastos et al. (2011) Application of gene ontology to gene identification. Methods Mol Biol 760:141-57. (pmid: 21779995)

[ PubMed ] [ DOI ] Abstract Candidate gene identification deals with associating genes to underlying biological phenomena, such as diseases and specific disorders. It has been shown that classes of diseases with similar phenotypes are caused by functionally related genes. Currently, a fair amount of knowledge about the functional characterization can be found across several public databases; however, functional descriptors can be ambiguous, domain specific, and context dependent. In order to cope with these issues, the Gene Ontology (GO) project developed a bio-ontology of broad scope and wide applicability. Thus, the structured and controlled vocabulary of terms provided by the GO project describing the biological roles of gene products can be very helpful in candidate gene identification approaches. The method presented here uses GO annotation data in order to identify the most meaningful functional aspects occurring in a given set of related gene products. The method measures this meaningfulness by calculating an e-value based on the frequency of annotation of each GO term in the set of gene products versus the total frequency of annotation. Then after selecting a GO term related to the underlying biological phenomena being studied, the method uses semantic similarity to rank the given gene products that are annotated to the term. This enables the user to further narrow down the list of gene products and identify those that are more likely of interest.

Gene Ontology Consortium (2010) The Gene Ontology in 2010: extensions and refinements. Nucleic Acids Res 38:D331-5. (pmid: 19920128)

[ PubMed ] [ DOI ] Abstract The Gene Ontology (GO) Consortium (http://www.geneontology.org) (GOC) continues to develop, maintain and use a set of structured, controlled vocabularies for the annotation of genes, gene products and sequences. The GO ontologies are expanding both in content and in structure. Several new relationship types have been introduced and used, along with existing relationships, to create links between and within the GO domains. These improve the representation of biology, facilitate querying, and allow GO developers to systematically check for and correct inconsistencies within the GO. Gene product annotation using GO continues to increase both in the number of total annotations and in species coverage. GO tools, such as OBO-Edit, an ontology-editing tool, and AmiGO, the GOC ontology browser, have seen major improvements in functionality, speed and ease of use.

Carol Goble on the tension between purists and pragmatists in life-science ontology construction. Plenary talk at SOFG2...

Goble & Wroe (2004) The Montagues and the Capulets. Comp Funct Genomics 5:623-32. (pmid: 18629186)

[ PubMed ] [ DOI ] Abstract Two households, both alike in dignity, In fair Genomics, where we lay our scene, (One, comforted by its logic's rigour, Claims ontology for the realm of pure, The other, with blessed scientist's vigour, Acts hastily on models that endure), From ancient grudge break to new mutiny, When 'being' drives a fly-man to blaspheme. From forth the fatal loins of these two foes, Researchers to unlock the book of life; Whole misadventured piteous overthrows, Can with their work bury their clans' strife. The fruitful passage of their GO-mark'd love, And the continuance of their studies sage, Which, united, yield ontologies undreamed-of, Is now the hour's traffic of our stage; The which if you with patient ears attend, What here shall miss, our toil shall strive to mend.

That is all.

 

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.

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