Sequence Comparison.pdf

8.8 Bibliographic Notes and Further Reading 165
empirical study of Pearson [159] illustrates that scaling similarity scores by the logarithm
of the size of the database can dramatically improve the performance of a
scoring matrix.
8.5
The method discussed in this section is from the paper of Yu and Altschul [212].
An improved method is given in the paper of Altschul et al. [9].
8.6
This section is written based on the paper of States, Gish, and Altschul [184].
The Henikoff and Henikoff method was used to construct scoring matrix for aligning
non-coding genomic sequences in the paper of Chiaromonte, Yap, and Miller
[45] (see also [46]). More methods for nucleotide scoring matrix can be found in
the papers of Müller, Spang, and Vingron [148] and Schwartz et al. [178]. The transition
and transversion rate is given in the paper of Li, Wu, and Luo [126].
8.7
The affine gap cost was first proposed by Smith and Waterman [180]. The generalized
affine gap cost discussed in this section is due to Altschul [1]. When c = 2b,
the generalized affine gap cost reduces to a cost model proposed by Zuker and Somorjal
for protein structural alignment [217]. The empirical study of Zachariah et
al. [213] shows that the generalized affine gap model allows fewer residue pairs
aligned than the affine gap model but achieves significantly higher per-residue accuracy.
The empirical studies on the distribution of insertions/deletions are found in
the papers of Benner, Cohen, and Gonnet [26] and Pascarella and Argos [158].