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Biology 478/595 – Introduction to Bioinformatics (graduate section)
The graduate section of Introduction to Bioinformatics focuses on reading original papers from
the scientific literature. These papers are a mix of “classics” and more recent papers that relate
to the weekly content of the lecture course. The goal of the graduate section is to gain
experience in critically reading the scientific literature, and to provide survey of background
literature in Computational Biology as a steppingstone to advance studies.
Course requirements
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
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Each week read all papers, and be prepared to discuss them. In NIH terms this means you
are a tertiary reviewer. Everyone is a tertiary reviewer of every paper.
Write a brief (approximately one page) report on your assigned paper focusing on:
o very brief synopsis of paper - not a detailed description of every point; focus on
the logical argument and the following questions: What data? What analysis?
What is their argument and does it support their conclusions?
o strong points of the paper
o weak points of the paper
o validity of conclusions
In NIH terms this is a secondary reviewer. You will be assigned one written review per
week.
One person each week will additionally be assigned as primary reviewer. The primary
reviewer, in addition to writing a review, presents a brief oral overview of the paper as an
introduction to open discussion.
Grading is based on participation in discussion, reviews and presentations
NIH Review panels
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NIH panels discuss proposals in a fairly narrow subject area, e.g., microbial cell biology,
biological data management, cancer, etc. Usually, the proposals considered in a panel are
funded by a single NIH institute.
Each review typically covers 50-100 proposals (3 times a year). Panelists receive proposals
in advance, three reviewers are assigned to each; each reviewer generally gets 5-10
assignments.
 Primary reviewer
 reads the proposal in complete detail
 Makes initial oral summary to panel
 Write review summary to be sent to applicant
 Secondary reviewer
 reads proposal completely
 provides written review (sent to applicant)
 orally adds to primary reviewers review
 Tertiary reviewer
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
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 reads proposal completely
 provides written review
 contributes to discussion
At the review meeting: 10-20 panelists : 50-100 proposals : 2-3 days
Before discussions, panelists provide a list of their “bottom half” of all of the proposals. A
consensus from these lists are not discussed – they are “not recommended for funding”,
popularly referred to being “nerfed” or “triaged”. This is what happened if you get your
proposal back “unscored”. Any proposal that any panelist thinks should be discussed (as
well as new investigator, post-doc and student fellowships, and a few others) are pulled
back from being nerfed.
All panelists, whether they read the proposal or not, score on 1 (excellent) – 5 (horrible)
scale. The overall score is the average over all panelists; 1.00 is perfect. By comparing
scores across many panels and programs and review periods, NIH also provides a percentile
ranking.
595 students
ni
Nick Ianotti
js
Jiayin Shen
aj
Alisha Johnson
bh
Bingjie Hu
mo
Mary-Jane Orr
cp
Craig Pederson
[email protected]
[email protected]
[email protected]
[email protected]
[email protected]
[email protected]
ty
Tairan Yuwen
[email protected]
qw
Qiong Wu
[email protected]
595 Paper schedule
1⁰ = primary reviewer
To be arranged
Week
1 Sep - 5 Sep
8 Sep - 12 Sep
15 Sep - 19 Sep
Lecture
Topic
Scoring &
Alignment
Alignment
& Gene
Finding
Assign
2⁰ = secondary reviewer 3⁰ = tertiary reviewer
TBA
Papers
1⁰
2⁰
3⁰
mo
ni
all
1⁰
2⁰
3⁰
qw
ty
all
Organization
 Dayhoff, MO, Schwarz, RM, Orcutt BC, A model of
evolutionary change in proteins, in Atlas of Protein
Sequence and Structure, Vol5, Supp. 3, 1978, MO Dayhoff,
ed., National Biomedical Research Foundation,
Georgetown University Medical Center, Washington DC.
 Henikoff S, Henikoff JG., Amino acid substitution matrices
from protein blocks. Proc Natl Acad Sci U S A. 1992, 89,
10915-10919.
 Needleman SB, Wunsch CD., A general method applicable
to the search for similarities in the amino acid sequence of
two proteins., J Mol Biol. 1970 48:443-453
 Altschul SF, Madden TL, Schaeffer AA, Zhang J, Zhang Z,
Miller W, Lipman DJ, Gapped BLAST and PSI-BLAST: a new
generation of protein database search programs, 1997,
Nucleic Acids Res. 25:3389-3402.
595 Paper schedule
1⁰ = primary reviewer
To be arranged
Week
22 Sep - 25 Sep
29 Sep - 3 Oct
Lecture
Topic
Sequence
Motifs
Evolution &
Phylogeny
6 Oct - 10 Oct
Phylogenet
ics
13 Oct - 17 Oct
Comparativ
e Genomics
20 Oct - 24 Oct
Gene
expression
27 Oct - 31 Oct
Systems
Biology
Protein
Families
& Evolution
HMM
3 Nov - 7 Nov
10 Nov - 14 Nov
Assign
2⁰ = secondary reviewer 3⁰ = tertiary reviewer
TBA
Papers
 TBA
 Julie D.Thompson, Desmond G.Higgins and Toby J.Gibson,
CLUSTAL W: improving the sensitivity of progressive
multiple sequence alignment through sequence weighting,
position-specific gap penalties and weight matrix choice,
Nucleic Acids Research, 1994, Vol. 22, 4673-4680,
 David J. Lipman, Stephen F. Altschul, And John D.
Kececioglu, A tool for multiple sequence alignment, Proc.
Nail. Acad. Sci. USA, 86, pp. 4412-4415, 1989
 L. Vigilant, M. Stoneking, H. Harpending, K.
 Hawkes, A. C. Wilson, Science 253, 1503 (1991).
 Templeton AR, Human origins and analysis of
mitochondrial DNA sequences, Science 255, 737 (1992)
 Hedges SB, Kumar, S, Tamura K, Science 255, 737-739
(1992)
 Ke Y, Su B, Song X, Lu D, Chen L, Li H, Qi C, Marzuki S, Deka
R, Underhill P, Xiao C, Shriver M, Lell J, Wallace D, Wells RS,
Seielstad M, Oefner P, Zhu D, Jin J, Huang W, Chakraborty
R, Chen Z, Jin L. African origin of modern humans in East
Asia: a tale of 12,000 Y chromosomes. Science 292, 11511153 (2001).
 R Overbeek, M Fonstein, M D’souza, G D. Pusch, and N
Maltsev, The use of gene clusters to infer functional
coupling, Proc. Natl. Acad. Sci. USA, 96, 2896–2901, 1999

 Tamames, J, Evolution of gene order conservation in
prokaryotes, Genome Biology 2001, 2(6):research0020.1–
0020.11
 Cluster analysis and display of genome-wide expression
patterns. MB Eisen, PT Spellman, PO Brown, and D
Botstein, PNAS, 95;14863-14868 (1998)
 Molecular Classification of Cancer: Class Discovery and
Class Prediction by Gene Expression Monitoring, T. R.
Golub et al., Science 286, 531-537(1999)
 TBA
 C Chothia and A M Lesk, The relation between the
divergence of sequence and structure in proteins. EMBO J.
1986 April; 5(4): 823–826.
 Gribskov, M., McLachlan, A.D., and Eisenberg, D., “Profile
analysis: detection of distantly related proteins”,
Proceedings of the National Academy of Sciences 84, 43554358, 1987.
 Using Dirichlet mixture priors to derive hidden Markov
models for protein families - M Brown, R Hughey, A Krogh,
595 Paper schedule
1⁰ = primary reviewer
To be arranged
Week
Lecture
Topic
Assign
2⁰ = secondary reviewer 3⁰ = tertiary reviewer
TBA
Papers
IS Mian, K Sjolander, Proceedings of the Third International
Conference on Intelligent Systems for Molecular Biology,
pp , 1993.
17 Nov - 21 Nov
24Nov - 25 Nov
1 Dec – 5 Dec
8 Dec - 12 Dec
Protein
Structure

 Tatusov RL, Koonin EV,Lipman DJ, A Genomic Perspective
on Protein Families, Science 278, 631-637 (1997)
Thanksgiving holiday
Molecular
Dynamics
Homology
Modeling
 TBA
 TBA