User:Deathscythe86

Background
Professor Steven Henikoff who had made very significant contribution to the bioinformatics community. These contributions comes in the form of specialize tools used to align protein sequences to search tools for protein sequence. One of these tools is the BLOSUM matrix.

Below is the history of Professor Henikoff’s education taken from his curriculum vitae [1].

1964 to 1968	University of Chicago, Chicago, Illinois. BS in Chemistry. Research on optical properties of biopolymers, Dr. G. Holzwarth, advisor

1971 to 1977	Harvard University, Cambridge, Massachusetts. PhD in Biochemistry and Molecular Biology. Dr. M. Meselson, advisor. Thesis: RNA from heat induced puff sites in Drosophila

1977 to 1980	University of Washington, Seattle, Washington. Postdoctoral fellow in Zoology. Research on position-effect variegation in Drosophila, Dr. C. Laird, advisor, Leukemia Society of America fellow

And this is Professor Henikoff’s professional history also taken from his curriculum vitae [1].

1981 to 1985	Fred Hutchinson Cancer Research Centre, Seattle, Washington. Assistant Member in Basic Sciences

1981to present	University of Washington, Seattle. Affiliate Assistant, Associate and Full Professor of Genetics/Genome Sciences

1985 to 1988	Fred Hutchinson Cancer Research Centre, Seattle, Washington. Associate Member in Basic Sciences

1988 to present	Fred Hutchinson Cancer Research Centre, Seattle, Washington. Member in Basic Sciences

1990 to present	Investigator, Howard Hughes Medical Institute

One of the many significant contribution that Professor Henikoff made to the scientific and bioinformatics community that would be covered in detail is the BLOSUM matrix [2]. This matrix was developed in 1992 with his wife Joria G. Henikoff, details of this contribution will be covered in the preceding sections of this paper. The development of this matrix allowed for better alignment of proteins as compared to the commonly used Dayhoff model matrix [3] or PAM matrices.

To assess the significance of his research, one method would be the use of number research paper published and the number of citation. Using this, it is possible to derive the average number of citations per year and the H-index can then be determined. Average citation is determined by the total number of citations divided by the number of results that was found [4]. H-index was developed by J. E. Hirsch [5]; it is used to determine the impact and productivity of a published work. It reflects the number of publication made by the researcher and the number of times the publication had been cited. The higher the value of the H-index reflects the recognition that the academic community has for the works of that particular researcher. In this case the H-index is 80, from this value, it is possible to infer that his work have been recognised by the scientific community.

Contributions to Bioinformatics
Top 5 Bioinformatics related papers

1.	Amino acid substitution matrices from protein blocks [2] 2.	Predicting deleterious amino acid substitutions [6] 3.	Automated assembly of protein blocks for database searching [7] 4.	Protein family classification based on searching a database of blocks [8] 5.	Performance evaluation of amino acid substitution matrices [9]

Major Contribution to bioinformatics

BLOSUM

BLOSUM matrices are currently used as the default for BLAST. Before the BLOSUM matrix was developed by Professor Henikoff, BLAST or Basic Local Alignment Search Tool uses the Dayhoff model of matrices known as PAM. PAM or Point Accepted Mutation are matrices that use substitution rates derived from alignment of protein sequences of at least 85% sequence identity. This method has a disadvantage in that as the distance between the protein sequences increases, the lesser the accuracy of the alignment is. Professor Henikoff proposed another method to create a new kind of matrix that uses the protein blocks instead of the PAM model. This new matrix, BLOSUM, is created by first deriving a frequency table based on a database of protein blocks, followed by the Log Odd score.

The log odd score is determined by Sij Sij = (1/λ) log(qij/(pi*pj))

Sij is the observed frequency and is used with the scaling factor 1/λ to produce the matrix qij is the probability of occurrence of pair i and j pi is the probability of occurrence of the ith amino acid in pair i and j

The BLOSUM matrix comes in different forms such as BLOSUM 62, BLOSUM 45 and BLOSUM 80, the number shows the percentage of identity two sequences are to each other. This matrix allows for a higher accuracy in alignment of proteins and has also replaced PAM as the default setting when using the protein BLAST program. This gave the bioinformatics community a tool that is more accurate and reliable when protein sequences alignments are required.