• Blat vs. Blast
• Blat use restrictions
• Downloading Blat source and documentation
• Replicating web-based Blat parameters in command-line version
• Using the -ooc flag
• Percent identity score calculation

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Question:
"What are the differences between Blat and Blast?"

Response:
Blat is an alignment tool like BLAST, but it is structured differently. On DNA, Blat works by keeping an index of an entire genome in memory. Thus, the target database of BLAT is not a set of GenBank sequences, but instead an index derived from the assembly of the entire genome. The index -- which uses around a megabyte of RAM -- consists of all non-overlapping 11-mers. This smaller size means that Blat is far more easily mirrored. Blat of DNA is designed to quickly find sequences of 95% and greater similarity of length 40 bases or more. It may miss more divergent or short sequence alignments.

On proteins, Blat uses 4-mers rather than 11-mers, finding protein sequences of 80% and greater similarity to the query of length 20+ amino acids. The protein index requires slightly more than 2 gigabytes of RAM. In practice -- due to sequence divergence rates over evolutionary time -- DNA Blat works well within humans and primates, while protein Blat continues to find good matches within terrestrial vertebrates and even earlier organisms for conserved proteins. Within humans, protein Blat gives a much better picture of gene families (paralogs) than DNA Blat. However, BLAST and psi-BLAST at NCBI can find much more remote matches.

From a practical standpoint, Blat has several advantages over BLAST:

• speed (no queues, response in seconds) at the price of lesser homology depth
• the ability to submit a long list of simultaneous queries in fasta format
• five convenient output sort options
• a direct link into the UCSC browser
• alignment block details in natural genomic order
• an option to launch the alignment later as part of a custom track

Question:
"I received a high-volume traffic warning from your Blat server informing me that I had exceeded the server use limitations. Can you give me information on the UCSC Blat server use parameters?"

Response:
Due to the high demand on our Blat servers, we restrict service for users who programatically query Blat or do large batch queries. Program-driven use of Blat is limited to a maximum of one hit every 15 seconds and no more than 5,000 hits per day. Please limit batch queries to 25 sequences or less.

For users with high-volume Blat demands, we recommend downloading Blat for local use. For more information, see Downloading Blat source and documentation.

Question:
"Is the Blat source available for download? Is there documentation available?"

Response:
Blat source and executables are freely available for academic, nonprofit and personal use. Commercial licensing information is available on the Genome Blat website.

Blat source may be downloaded from http://www.soe.ucsc.edu/~kent (look for the blatSrc* zip file with the most recent date). For Blat executables, go to http://www.soe.ucsc.edu/~kent/exe/; binaries are sorted by platform.

Documentation on Blat program specifications is available here.

Question:
"I'm setting up my own Blat server and would like to use the same parameter values that the UCSC web-based Blat server uses."

Response:
Use the following settings to replicate the search results of the UCSC Blat server. Note that you may still observe some slight differences between command line results and web-based results, depending on the seach being performed. gfServer:

• untranslated server:
     gfServer start blatX portX -log=untrans.log *.2bit
• translated server:
     gfServer start blatX portY -trans -mask -log=trans.log *.2bit
• untranslated server plus PCR server:
     gfServer start blatX portX -stepSize=5 -log=untrans.log *.2bit
• For enabling DNA/DNA and DNA/RNA matches, only the host, port and nib files are needed. The same port may be used for both untranslated blat and isPcr. You'll need a separate server on a separate port to enable translated (protein-based) matches.

gfClient:

• Set -minScore=0 and -minIdentity=0. This will result in some low-scoring, generally spurious hits, but for interactive use it's sufficiently easy to ignore them (because results are sorted by score) and sometimes the low-scoring hits come in handy. The -ooc parameter is not set for gfClient.

For more information on the parameters available for blat, gfServer, and gfClient, see the blat specifications.

Question:
"What does the -ooc flag do?"

Response:
Using any -ooc option in blat, such as -ooc=11.ooc, simply serves to speed up searches similar to repeat-masking sequence. The 11.ooc file contains sequences determined to be over-represented in the genome sequence. To speed up searches, these sequences are not used when seeding an alignment against the genome. For reasonably-sized sequences, this will not create a problem and will significantly reduce processing time.

By not using the 11.ooc file, you will increase alignment time, but will also slightly increase sensitivity. This may be important if you are aligning shorter sequences or sequences of poor quality. For example, if a particular sequence consists primarily of sequences in the 11.ooc file, it will never be seeded correctly for an alignment if the -ooc flag is used.

In summary, if you are not finding certain sequences and can afford the extra processing time, you may want to run blat without the 11.ooc file if your particular situation warrants its use.

Question:
"How is the percent identity score calculated?"

Response:
The percent identity score is calculated like this:

100.0 - pslCalcMilliBad(psl, TRUE) * 0.1

Here is the source for pslCalcMilliBad:

int pslCalcMilliBad(struct psl *psl, boolean isMrna)
/* Calculate badness in parts per thousand. */
{
int sizeMul = pslIsProtein(psl) ? 3 : 1;
int qAliSize, tAliSize, aliSize;
int milliBad;
int sizeDif;
int insertFactor;

qAliSize = sizeMul * (psl->qEnd - psl->qStart);
tAliSize = psl->tEnd - psl->tStart;
aliSize = min(qAliSize, tAliSize);
if (aliSize <= 0)
    return 0;
sizeDif = qAliSize - tAliSize;
if (sizeDif < 0)
    {
    if (isMrna)
        sizeDif = 0;
    else
        sizeDif = -sizeDif;
    }
insertFactor = psl->qNumInsert;
if (!isMrna)
    insertFactor += psl->tNumInsert;

milliBad = (1000 * (psl->misMatch*sizeMul + insertFactor +
round(3*log(1+sizeDif)))) / (sizeMul * (psl->match + psl->repMatch +
psl->misMatch));
return milliBad;
}

The complexity in milliBad arises primarily from how it handles inserts. Ignoring the inserts, the calculation is simply mismatches expressed as parts per thousand. However, the algorithm factors in insertion penalties as well, which are relatively weak compared to say blasts but still present. When huge inserts are allowed (which is necessary to accommodate introns), it is typically necessary to resort to logarithms like this calculation does.