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Michael Waterman
&
Saul Needleman
Michael S. Waterman
• Born: June 28, 1942
Coquille, Oregon
• B.S. & M.S. - Oregon State University
• Ph.D - statistics and probability
from Michigan State University
• Previously:
Los Alamos National Laboratory
Idaho State University
• Currently:
University of Southern California
Endowed Associates Chair in Biological
Sciences, Mathematics and Computer Science
Contributions
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Founder and leader of computational biology
Apply mscs techniques to molecular biology
Contributed to most widely-used tools
Smith-Waterman algorithm - basis for many sequence
comparison programs.
Landmark paper "Genomic mapping by fingerprinting random
clones: A mathematical analysis"
Cornerstone for DNA mapping and sequencing projects,
especially the Human Genome Project.
Authored one of the earliest textbooks: Introduction to
Computational Biology.
Began the international conference Research in Computational
Biology (RECOMB)
Founding editor of Journal of Computational Biology
Honor and Award
• Fellow of the American Academy of Art and Sciences
since 1995
• Elected to the United States National Academy of
Sciences in 2001.
• Since 2005, he is an elected Academician of the French
Académie des Sciences.
• Guggenheim Fellowship Recipient, 1995-1996
• Gairdner Foundation International Award in 2002
Needleman–Wunsch algorithm
• Global alignment on two sequences
• Align protein or nucleotide sequences in bioinformatics
• Published in 1970 by Saul B. Needleman and Christian D.
Wunsch
• Example of dynamic programming
• First application of dynamic programming to biological
sequence comparison.
Needleman–Wunsch algorithm
A
G
C
T
A
10
-1
-3
-4
G
-1
7
-5
-3
C
-3
-5
9
0
T
-4
-3
0
8
Gap penalty: -5
AGAT
CGA -
-3 + 7 + 10 + (-5) = 9
AGAT
CG - A
-3 + 7 + (-5) + (-4) = 5
A G A T -3 + (-5) + (-1) + (-4) = -13
C - GA
A G A T -5 + (-5) + (-1) + (-4) = -15
- CGA
Needleman–Wunsch algorithm
C
G
A
0
-5
-10
-15
A
-5
-3
-6
0
G
-10
-8
4
-1
A G A T
C G A -
A
-15
-13
-1
14
T
-20
-18
-6
9
C
G
A
B
U
U
U
A
L
D
D
D
G
L
L
D
U
A
L
L
L
D
T
L
L
L
L