I am doing some simple statistics now, and had to review some basic concepts like standard deviation.
As a note to myself and anyone interested, here it stays.

The mean is just a sum of all your numerical observations, divided by the number of observations. E.g., if you have measured how tall your 5 children are, and got the values 1.42, 1.56, 1.05, 1.89, 1.92, the “mean height” of your children will be x = (1.42 + 1.56 + 1.05 + 1.89 + 1.92) / 5, x = 7.84 / 5 = 1.568 (all values in metres).

The mean itself doesn’t tell you much, however. If you had this 1.568 mean available, you wouldn’t know even the range of heights.

The standard deviation helps with this. First of all, it is measured in the same units as initial data – i.e. metres in our example. Second, it gives you an idea of how strongly do the measured values differ in your sample – the bigger the deviation, the longer is the value range measured in the sample.
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As I previously mentioned, in converting PFM to PWM single variable – [prior] background nucleotide frequency – was ambiguous to me. From other articles I noticed that it is usually set to 0.25 (1/4 – because there 4 nucleotides, thus in “perfectly random” sequence they would appear in 25% of cases each). In that post, I also thought of using “real” background frequency of nucleotides, calculated from the sequence, to which the matrix is to be applied.

I wrote a program to search all the 1000-basepair upstream sequences from all human, rat and mouse genes, present in Ensembl database release 40 (assuming those 1kb upstreams to be “promoters” of genes). For each promoter, only the single best score was returned. Then I draw a graph of the distribution of the number of promoters (y-axis) depending on the best match scores (x-axis). I did the search twice – one with p(b) = 0.25, and one with p(b) set to calculated values of A/C/G/T content in each promoter.
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In the course of my current research, I was dealing with the TFBS (Transcription Factor Binding Sites) search. To perfrom the search, one needs position weight matrix (PWM) for each TFBS. When you refer to the TRANSFAC database of transcription factors (and matrices), you will get position frequency matrix (PFM), and will need to convert PFM into PWM.

I did find a couple of conversion formulas, but that was quite an effort to figure out which one is correct – I had seen two different formula variations. Here I will share what I had found.
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Sometimes, writing automatic HTML forms processors, you need to post several values with the same name of the form field, e.g.:
collection_gene = str_chrom_name
collection_gene = gene_stable_id

This is against the RFC on form fields design and submitting, but this approach is used – for example, by Ensembl. I spent some time to figure out how to make HTTP_Client and HTTP_Request submit multiple ‘name-value’ pairs instead of one (the latest defined, which overrides the previous). The solution is extremely simple:
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If you fetch large amounts of data (e.g. over 2MB per request) using HTTP_Client (or HTTP_request), you may get “out of memory” fatal errors, especially if:

1. memory_limit is set to default 8M, and
2. you process multiple pages using single non-reset instance of HTTP_Client object.

This problem can manifest itself by producing fatal error after a couple of cycles of successful page retrieval – but always, if run with the same parameters, after some constant or only slightly variable number of cycles.

In my case the problem was that HTTP_Request (a dependancy of HTTP_Client) was holding in memory all the previously fetched pages of the current session (the ‘history’ feature). To force HTTP_Request to hold only the most recent page, you need to ‘disable’ history after creating the HTTP_Client or HTTP_Request object instance:

Hope this helps you.

I needed to find out common transcription factor (TF) binding sites (TFBS) in the structure of the promoters of interferon-regulated genes. I tried several different approaches – starting from the simplest sequence-sequence comparisons using BLAST, ClustalW alignments, and searching each of the promoters for transcription factor binding sites – to compare and find common.

But the easiest way was to use Genomatix’s “Gene2Promoter” tool. With it, the whole procedure is extremely simple:
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