Figure 3.11 Performing a BLAST 2 Sequences alignment. Clicking the check box at the bottom of the Enter Query Sequence section expands the search page, generating a new Enter Subject Sequence section. Here, sequences for the transcription factor SOX-1 from human and the ctenophore Mnemiopsis leidyi have been used as the query and subject, respectively (Schnitzler et al. 2014). Here, only the BLASTP algorithm is available in the Program Selection section, as a one-to-one alignment has already been specified. The usual set of algorithm parameters is available, allowing the user to fine-tune the alignment as needed.
MegaBLAST
MegaBLAST is a variation of the BLASTN algorithm that has been optimized specifically for use in aligning either long or highly similar (>95%) nucleotide sequences and is a method of choice when looking for exact matches in nucleotide databases. The use of a greedy gapped alignment routine (Zhang et al. 2000) allows MegaBLAST to handle longer nucleotide sequences approximately 10 times faster than BLASTN would. MegaBLAST is particularly well suited to finding whether a sequence is part of a larger contig, detecting potential sequencing errors, and for comparing large, similar datasets against each other. The run speeds that are achieved using MegaBLAST come from changing two aspects of the traditional BLASTN routine. First, longer default word lengths are used; in BLASTN, the default word length is 11, whereas MegaBLAST uses a default word length of 28. Second, MegaBLAST uses a non-affine gap penalty scheme, meaning that there is no penalty for opening the gap; there is only a penalty for extending the gap, with a constant charge for each position in the gap. MegaBLAST is capable of accepting batch queries by simply pasting multiple sequences in FASTA format or a list of accession numbers into the query window.
Figure 3.12 Typical output from a BLAST 2 Sequences alignment, based on the query issued in Figure 3.11. The standard graphical view is shown at the top of the figure, here indicating two high-scoring segment pairs (HSPs) for the alignment of the sequences for the transcription factor SOX-1 from human and the ctenophore Mnemiopsis leidyi. The dot matrix view is an alternative view of the alignment, with the query sequence represented on the horizontal axis and the subject sequence represented by the vertical axis; the diagonal indicates the regions of alignment captured within the two HSPs. The detailed alignments are shown at the bottom of the figure, along with the E values and alignment statistics for each HSP.
There is also a variation of MegaBLAST called discontiguous MegaBLAST. This version has been designed for comparing divergent sequences from different organisms, sequences where one would expect there to be low sequence identity. This method uses a discontiguous word approach that is quite different from those used by the rest of the programs in the BLAST suite. Here, rather than looking for query words of a certain length to seed the search, non-consecutive positions are examined over longer sequence segments (Ma et al. 2002). The approach has been shown to find statistically significant alignments even when the degree of similarity between sequences is very low.
PSI-BLAST
The variation of the BLAST algorithm known as PSI-BLAST (for position-specific iterated BLAST) is particularly well suited for identifying distantly related proteins – proteins that may not have been found using the traditional BLASTP method (Altschul et al. 1997; Altschul and Koonin 1998). PSI-BLAST relies on the use of position-specific scoring matrices (PSSMs), which are also often called hidden Markov models or profiles (Schneider et al. 1986; Gribskov et al. 1987; Staden 1988; Tatusov et al. 1994; Bücher et al. 1996). PSSMs are, quite simply, a numerical representation of a multiple sequence alignment, much like the multiple sequence alignments that will be discussed in Chapter 8. Embedded within a multiple sequence alignment is intrinsic sequence information that represents the common characteristics of that particular collection of sequences, frequently a protein family. By using a PSSM, one is able to use these embedded, common characteristics to find similarities between sequences with little or no absolute sequence identity, allowing for the identification and analysis of distantly related proteins. PSSMs are constructed by taking a multiple sequence alignment representing a protein family and then asking a series of questions, as follows.
What residues are seen at each position of the alignment?
How often does a particular residue appear at each position of the alignment?
Are there positions that show absolute conservation?
Can gaps be introduced anywhere in the alignment?
As soon as those questions are answered, the PSSM is constructed, and the numbers in the table now represent the multiple sequence alignment (Figure 3.13). The numbers within the PSSM reflect the probability of any given amino acid occurring at each position. The PSSM numbers also reflect the effect of a conservative or non-conservative substitution at each position in the alignment, much like the PAM or BLOSUM matrices do. This PSSM now can be used for comparison against single sequences, or in an iterative approach where newly found sequences can be incorporated into the original PSSM to find additional sequences that may be of interest.
The Method
Starting with a query sequence of interest, the PSI-BLAST process operates by taking a query protein sequence and performing a standard BLASTP search, as described above. This