Lecture 04

 

 

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Sequence Analysis

What you should take home from this part of the course

• Understand the ideas of analysis by composition and analysis by signal;
• Know what deterministic pattern matching is;
• Recognize and understand the term regular expression;
• Be familiar with common sequence signals in DNA,RNA and proteins;
• Be familiar with the Prosite database and the Prosite scan server;
• Kow where to find EMBOSS tools and how to use them;
• Know about the offerings on the ExPASy tools collection page;
• Work on an understanding how biological facts can be translated into hypotheses and how hypotheses can be translated into computational procedures for analysis.

 

Links summary

• Nature news
• Science news
• ReBase
• PlasMapper
• prosite
• the ExPASy tools collection

 

Exercises

• Retrieve and read the Prosite documentation entry for the Leucine Zipper.
• Download entry 1NWQ from the PDB, visualize the Leucine Zipper with VMD and study its architecture (stereo vision!).

   

Lecture Slides

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From the Science News, Sept. 14. As far as systems biology complexities go, this one must be near the top: intimate interactions between human's most- and second-most complex systems. The key method here is a bioinformatics approach to classifying genes: pattern searches in the promoter regions. (NB. Not studying in isolation but forming study groups is an excellent idea!). Are you more lonely than average ? Check with the UCLA loneliness scale.

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To generate this collection of sequences, the feature "Gal4-binding-site" was searched in the [Saccharomyces Genome Database SGD]; in the resulting overview page binding site annotations recorded by Harbison et al. (2004) were shown for all occurrences; the actual sequences were retrieved by specifying the genome coordinates in the appropriate search form of the database. I have added ten bases upstream and downstream of the core binding region. This procedure could be done by hand in about the same time it took me to write the small screen-scraping program to fetch the sequences. Depending on your programming proficiency, you will find that some tasks can efficiently be done manually, for some tasks it is more efficient to spend the time to search for a better way to achieve them on the Web and only for a comparatively small number of tasks it is worthwhile (or mandatory) to write your own code.

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A consensus sequence simply lists the most frequent amino acid or nucleotide at each position, or a random one if there is more than one with the highest frequency. The consensus sequence is the one that you would synthesize to make an idealized representative of the set. It is likely to bind more tightly or to be more stable than each of the individual sequences in the alignment.

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Introducing nucleotide ambiguity codes to represent situations in which more than one nucleotide has the highest frequency improves the situation a bit, but there is also ambiguity. Consider the situation after the conserved CCG pattern: 9 As and 3Gs: should we report the consensusAA, or ist it more interesting to report that the only observed alternative is another purine base and write Y instead?

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Sequence logo of Gal4 binding sites with 10 nucleotides flanking bases. Created with WebLogo. A Sequence Logo is a graphical representation of aligned sequences where at each position the height of a column is proportional to the (Shannon) information of that position and the relative size of each character is proportional to its frequency within the column. Sequence Logos were pioneered by Tom Schneider who maintains an informative Website about their use and theoretical foundations. Note that there is considerable additional information in the flanking sequences that are not included in the published description of the core binding pattern; it is advantageous if you are able to rerun such analyses, rather than rely on someone else's opinion.

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Since log(0) is not defined, we have to introduce an arbitrary correction for unobserved characters. In this example I have simply added 0.1 to each character frequency before calculating log odds.

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In this informal example, I have simply counted matches with the consensus sequence (excluding "N"). We can slide the PSSM over the entire chromosome, and calculate scores for each position. Only the middle sequence is an annotated binding site. Whatever method we use for probabilistic pattern matching, we will always get a score. It is then our problem to decide what the score means.

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This first order Markov model depends only on the current state. Higher-order models take increasing lengths of "history" into account, how the system arrived in its current state. Note that the exit probabilities fo a state always have to sum to 1.0. The so called "stationary probability" over a long period of time for p(rain) is 0.167 - this is determined by the combined effects of all individual transition probabilities. The stationary probabilities for two- or three consecutive rainy days are 4.2% and 2.1%, respectively.

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Hidden Markov Model: on Wikipedia.

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Signal peptide example for recognition of sequence features with HMMs or NNs: common features in gram-negative signal-peptide sequences are shown in a Sequence Logo. Sequences were aligned on the signal-peptidase cleavage site. Their common features include a positively charged N-terminus, a hydrophobic helical stretch and a small residue that precedes the actual cleavage site.

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SignalP is the premier Web server to detect signal sequences.

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... however: machine learning will find correlations, not causalities. It cannot replace your biological insight to distinguish a statistical anomaly from a biologically meaningful result!

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This is why statistics is cool.

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The distinction is somewhat artificial in practice: parameter estimations from probability theory can be excellent descriptors!

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You should be familiar with these most fundamental descriptors, they come up time- and time again in the literature. Here is a series of highly readable reviews on topics of medical statistics by Jonathan Ball and Coauthors:
 

*Presenting and summarising data
*Samples and populations
*Hypothesis testing and P values
*Sample size calculations
*Comparison of means
*Nonparametric methods
*Correlation and regression
*Qualitative data - tests of association
*One-way analysis of variance
*Further nonparametric methods
*Assessing risk
*Survival analysis
*Receiver operating characteristic curves
*Logistic regression

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Statistical model: on Wikipedia.

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Probability: on Wikipedia and on MathWorld.

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(*) Worst error in the clipart: no two faces of a die have the same number of dots. Three more errors: opposing sides must add to seven. The ones should be at the bottom if the sixes face up.

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Still not convinced? Try the simulation here.

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Probability distribution: on Wikipedia and on MathWorld.

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Uniform distribution: on Wikipedia and on MathWorld.

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Geometric distribution: on Wikipedia and on MathWorld.

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Binomial distribution: on Wikipedia and on MathWorld.

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Poisson distribution: on Wikipedia and on MathWorld.

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Normal distribution: on Wikipedia and on MathWorld.

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Extreme value distribution: on Wikipedia and on MathWorld.

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Don't use: Type I error - say "False positive". Don't use: Type II error - say "False negative".

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Estimator: on Wikipedia and on MathWorld.
Confidence interval: on Wikipedia and on MathWorld.

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Significance is really the key concept of our entire discussion. This is one term you must be completely familiar with. Significance: on Wikipedia and on MathWorld.

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Note that a P-value is not the probability of a particular event occurring - that probability could be arbitrarily small, depending on the resolution of the measurement. It is the probability that an event occurs that is equal to or larger than the observed one.

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Z-score: on Wikipedia and on MathWorld.

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Multiple testing: on Wikipedia

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Hypothesis testing: on Wikipedia and on MathWorld

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Bootstrapping is a type of simulation testing. The resampling procedure simulates possible samples that we could have observed and establishes how sensitive our conclusions are to the precise composition of the sample. More on Wikipedia and on MathWorld

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We can describe a set of observations as a distribution, and we can express this distribution as a vector if we define each element of the vector to represent a particular amino acid. This gives us a convenient and intuitive way to define a metric to compare two distributions - by considering the difference between all components of the two distributions. If we interpret this geometrically, the distribution of n-elements corresponds to a point in an n-dimensional spaceand the difference we are using here is the distance between the two points defined by the two distributions. We could use different metrics, but this one (the vector norm) is intuitive and convenient. The comparison between the frequency distribution of all amino acids in the sequence database (f_exp, the expected distribution for a random sample of amino acids )

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We can apply the same metric to a set of the same number of simulated amino acids, in which the probability of picking an amino acid is given by its expectation value, f_exp. If we do this many times, we will obtain a distribution of d values that tells us how different the relative frequencies of amino acids are, when they are generated by our simulator, relative to what we see in the database. Note that under many simulations we still gat an error every time, simply because the number of amino acids in every single run is small (20, in our example) and thus do what we want, the sample can never exactly reproduce the database distribution. This is important to understand: we are not simulating the distribution, we are simulating the influence of a limited-size sample!

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Once we have simulated the experiment many times, we can compare the observed outcome with the one that would be expected if the amino acids had been randomly picked from a database distribution. In our example, the result deviates considerably from what we would expect, but not as much so that it meet a significance level of 95%.

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. Standard random number generators generate uniformly distributed random numbers. Thus when you check into which interval one has fallen, this designates a choice with the right target probability so in that interval will then pick a

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If we want to simulate events according to a particular probability distribution, we can use the procedure given above. The procedure is not very efficient, since many values will be discarded if the interval is large. For each particular distribution there will be more efficient, specialized ways to generate it. However this procedure is completely general and it is trivial to change the target probability distribution's parameters; all you need is the definition of the distribution.

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