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Novel Protein Sequence Comparison Method Based on Transition Probability Graph and Information Entropy.
Qi, Zhaohui; Wen, Xinlong.
  • Qi Z; College of Information Science and Engineering Hunan Normal University, Changsha 410081,China.
  • Wen X; College of Information Science and Engineering Hunan Normal University, Changsha 410081,China.
Comb Chem High Throughput Screen ; 25(3): 392-400, 2022.
Article in English | MEDLINE | ID: covidwho-740472
ABSTRACT
AIM AND

OBJECTIVE:

Aim and

Objective:

Sequence analysis is one of the foundations in bioinformatics. It is widely used to find out the feature metrics hidden in the sequence. Otherwise, the graphical representation of the biologic sequence is an important tool for sequencing analysis. This study is undertaken to find out a new graphical representation of biosequences. MATERIALS AND

METHODS:

The transition probability is used to describe amino acid combinations of protein sequences. The combinations are composed of amino acids directly adjacent to each other or separated by multiple amino acids. The transition probability graph is built up by the transition probabilities of amino acid combinations. Next, a map is defined as a representation from the transition probability graph to transition probability vector by the k-order transition probability graph. Transition entropy vectors are developed by the transition probability vector and information entropy. Finally, the proposed method is applied to two separate applications, 499 HA genes of H1N1, and 95 coronaviruses.

RESULTS:

By constructing a phylogenetic tree, it was found that the results of each application are consistent with other studies.

CONCLUSION:

The graphical representation proposed in this article is a practical and correct method.
Subject(s)
Keywords

Full text: Available Collection: International databases Database: MEDLINE Main subject: Influenza A Virus, H1N1 Subtype Type of study: Randomized controlled trials Language: English Journal: Comb Chem High Throughput Screen Journal subject: Molecular Biology / Chemistry Year: 2022 Document Type: Article Affiliation country: 1386207323666200901103001

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Influenza A Virus, H1N1 Subtype Type of study: Randomized controlled trials Language: English Journal: Comb Chem High Throughput Screen Journal subject: Molecular Biology / Chemistry Year: 2022 Document Type: Article Affiliation country: 1386207323666200901103001