{"id":720,"date":"2023-03-15T14:30:04","date_gmt":"2023-03-15T07:30:04","guid":{"rendered":"https:\/\/conf.icgbio.ru\/bgrs98\/?page_id=720"},"modified":"2023-04-11T14:35:25","modified_gmt":"2023-04-11T07:35:25","slug":"056_identifying-dna-and-protein-patterns-with-statistically-significant-alignment-matrices","status":"publish","type":"page","link":"https:\/\/conf.icgbio.ru\/bgrs98\/abstracts\/abstract-list\/056_identifying-dna-and-protein-patterns-with-statistically-significant-alignment-matrices\/","title":{"rendered":"IDENTIFYING DNA AND PROTEIN PATTERNS WITH STATISTICALLY SIGNIFICANT ALIGNMENT MATRICES"},"content":{"rendered":"

HERTZ GERALD Z.<\/a>+<\/sup>,\u00a0STORMO GARY D.<\/a><\/p>\n

Department of Molecular, Cellular, and Developmental Biology; University of Colorado; Campus Box 347; Boulder, CO 80309; USA; hertz or e-mail:\u00a0stormo@colorado.edu<\/a><\/p>\n

+<\/sup>Corresponding author<\/p>\n

Keywords<\/a>: patterns, statistically significant alignment matrix, information context<\/p>\n

 <\/p>\n

We have been developing computational methods for identifying consensus patterns in sets of DNA or protein sequences that are functionally related according to previous experiments. For example, functionally related DNA sequences can be coordinately regulated promoters, or DNA sequences that are bound by a common protein. Our methods align the functionally related sequences to identify the consensus pattern. Once a pattern is identified, it can be used to predict related domains in other sequences.<\/p>\n

We describe consensus patterns with alignment matrices that summarize a sequence alignment. We use various matrix models that range from simple ungapped patterns to complex patterns containing gaps (i.e. insertions and deletions) and adjacent correlations. Alignment matrices are initially compared using a log-likelihood score we call information content. However, the ultimate statistical significance of an alignment is the product of the p-value of its information content and the number of alignments that can be created from the data. Previously, we have been able to determine good estimates of the number of possible alignments due to the data. Here, we introduce a method for accurately estimating the p-value of the information content of an alignment matrix.<\/p>\n

1. Describing sequence alignments with matrices<\/b><\/p>\n

 <\/p>\n

\"\"<\/a><\/p>\n

Figure 1 An example of a matrix summarizing a DNA sequence alignment not containing gaps. On the top is an alignment of four 6-mers. Below is a matrix containing the number of times that the indicated letter is observed at the indicated position of this alignment.<\/p>\n

 <\/p>\n

Functionally related DNA and protein sequences are generally expected to share some common sequence elements. For example, a DNA-binding protein is expected to bind related DNA sequences. The pattern shared by a set of functionally related sequences is commonly identified during the process of aligning the sequences to maximize sequence conservation. The oldest method for describing a sequence alignment is the consensus sequence, which contains the most highly conserved letter (i.e. base for DNA or amino acid for protein) at each position of the alignment. However, most alignments are not limited to just a single letter at each position. At some positions of an alignment any letter may be permissible, although some letters may be much more frequent than others. A more complete description of an alignment is the alignment matrix, which, in its simplest form, lists the occurrence of each letter at each position of the alignment (Figure 1).1<\/sup>\u00a0However, alignment matrices can also describe more complex patterns that contain gaps (i.e. sequences contain insertions and deletions relative to each other) or in which different positions are correlated with each other (Figure 2).2<\/sup><\/p>\n

 <\/p>\n

\"\"<\/a><\/p>\n

Figure 2. An example of a matrix summarizing a DNA sequence alignment that contains gaps. On the top is an alignment of four sequences: the first and last are 6-mers, and the middle two are 4-mers. Below is a matrix whose first five rows represent our simplest matrix for alignments containing gaps. The first four rows contain the number of times that the indicated letter is observed at the indicated position of this alignment, and the fifth row contains the number of times that a gap is observed at the indicated position. A more complex matrix representation includes gap-letter correlations and contains the four lower rows showing the number of times that a letter (\"\"<\/a>) or a gap (- ) is preceded by a letter or a gap.<\/p>\n

2. Measuring sequence conservation with information content.<\/b><\/p>\n

2.1 Information content of a simple alignment<\/i><\/b><\/p>\n

We score the significance of a sequence alignment by what we call the information content of the alignment matrix.1,2<\/sup> The higher the information content, the rarer the pattern described by the alignment. The following is the formula for determining the information content \"\"<\/a>\u00a0of a simple alignment matrix (Figure 1):<\/p>\n\n\n\n
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\"\"<\/a>, where<\/p>\n<\/td>\n

(1)<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n