Bioinformatics. Группа авторов

Figure 3.22 Hit list for the protein–protein FASTA search described in Figure 3.21. Only the first 18 hits are shown. For each hit, the accession number and partial definition line for the hit is provided. The column marked opt gives the raw similarity score, the column marked bits gives a normalized bit score (a measure of similarity between the two sequences), and the column marked E gives the expectation value. The percentage columns indicate percent identity and percent similarity, respectively. The alen column gives the total aligned length for each hit. The +- characters shown at the beginning of some lines indicate that more than one alignment was found between the query and subject; in the case of the first hit (Q7Z5P9), four alignments were returned. The align link at the end of each row takes the user to the alignment for that hit (not shown).

      Several studies have attempted to answer the “which method is better” question by performing systematic analyses with test datasets (Pearson 1995; Agarawal and States 1998; Chen 2003). In one such study, Brenner et al. (1998) performed tests using a dataset derived from already known homologies documented in the Structural Classification of Proteins database (SCOP; Chapter 12). They found that FASTA performed better than BLAST in finding relationships between proteins having >30% sequence identity, and that the performance of all methods declines below 30%. Importantly, while the statistical values reported by BLAST slightly underestimated the true extent of errors when looking for known relationships, they found that BLAST and FASTA (with ktup = 2) were both able to detect most known relationships, calling them both “appropriate for rapid initial searches.”

Summary

The ability to perform pairwise sequence alignments and interpret the results from such analyses has become commonplace for nearly all biologists, no longer being a technique employed solely by bioinformaticians. With time, these methods have undergone a continual evolution, keeping pace with the types and scale of data that are being generated both in individual laboratories and by systematic, organismal sequencing projects. As with all computational techniques, the reader should have a firm grasp of the underlying algorithm, always keeping in mind the algorithm's capabilities and limitations. Intelligent use of the tools presented in this chapter can lead to powerful and interesting biological discoveries, but there have also been many cases documented where improper use of the tools has led to incorrect biological conclusions. By understanding the methods, users can optimally use them and end up with a better set of results than if these methods were treated simply as a “black box.” As biology is increasingly undertaken in a sequence-based fashion, using sequence data to underpin the design and interpretation of experiments, it becomes increasingly important that computational results, such as those generated using BLAST and FASTA, are cross-checked in the laboratory, against the literature, and with additional computational analyses to ensure that any conclusions drawn not only make biological sense but also are actually correct.

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Further Reading