Broad Bibliography

skirlorangeBiotechnology

Oct 1, 2013 (3 years and 6 months ago)

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Carmen Nigro

September 14, 2009

Topic:
Bioinformatics: Sequence Alignment

Description:
This research examines different algorithms
for determining relationships between
sequences of amino acids
or nucleotides
from DNA, RNA, or proteins.

Motivation:

Sequence alignment can help scientists
hypothesize the function of a particular
sequence of DNA or protein. Similarities in
different
sequences can imply similarities in function
and structure.

References:



D.J. Lipman, S.F. Altschul
, and
J.D. Kececioglu
, “A Tool for Multiple Sequence
Alignment”,
Proc. Nail. Acad. Sci. USA, Vol. 86, pp. 4412
-
4415, June 1989.

[
This article offers an alternative to dynamic programming for multiple sequence
alignment.
Dynamic programming has become impractic
al for multiple sequence
alignment and this article proposes a more efficient algorithm
]



R. Chenna, H. Sugawara, T. Koike, R. Lopez, T.J. Gibson, D.G. Higgins
,
and J.D.
Thompson
, “
Multiple sequence alignment with the Clustal series of programs”,
Oxford
Journals:
Nucleic Acids Research, Vol. 31, pp.
3497
-
3500, 2003.


[
The Clustal series of programs are the most widely used programs for sequence
alignment. This article describes the Clusta
l series and its implementation
]



J.D. Thompson, F. Plewniak,
and
O. Poch
, “A comprehensive comparison of multiple
sequence alignment programs”,
Oxford Journals:
Nucleic Acids Research, Vol.
27, pp.
2682
-
2690, 1999.


[This article compares the most widely used programs for multiple
sequence alignment. The results show that iterative algorithms offer better accuracy, but
take much longer to compute.
]



J.D.

Thompson,

T.J.

Gibson,

F. Plewniak,

F. Jeanmougin,

and

D. G.

Higgins, “
The
CLUSTAL_X windows interface: flexible strategies for multiple sequence alignment
aided by quality analysis tools”
,
Oxford Journals:
Nucleic Acids Research, Vol. 25, pp.
4876
-
4882, 1997.

[This article describes the Clustal_X user interface and

useful new
features. The article also describes the algorithms used to check alignment quality.]



M.
S. Waterman, “Efficient Sequence Alignment Algorithms”,
J. theor. Biol.
,

Vol.

108,
pp.
333
-
337
, 1984.

[This article evaluates sequence alignment algorithms and compares them using big O
notation. The article proposes the use of concave weighting functi
ons in order to increase
efficiency.]



H. Rangwala

and

G. Karypis, “
Incremental window
-
based protein

sequence

alignment

algorithms

,

Oxford Journals: Bioinformatics, Vol. 23, pp. e17
-
e23, 2007.
[This article proposes a new algorithm for sequence alignment
, which is based on short
fixed
-
or
-
variable length high
-
scoring subsequences. The results show that this algorithm
gives comparable results to algorithms already in use.]



I. M. Wallace

,

O. Orla,

and D. G. Higgins, “Evaluation of Iterative Alignment
Algorithms for Multiple Alignment”, Oxford Journals: Bioinformatics, Vol. 21, pp.
1408
-
1414, 2005.



[This article compares different iterative algorithms for multiple alignment. The paper
analyzes the results of several tests that were run on
iterative algorithms.]



L. A. Newberg, “Memory efficient dynamic programming backtrace and pair
wise local
sequence alignment”, Oxford Journals: Bioinformatics, Vol. 24, pp.
1772
-
1778, 2008.

[Because it is insufficient to store all intermediate sequences in a cache, this article
proposes a memory efficient algorithm for calculating these intermediate values as they
are needed. The article describes the results obtained from experiments with th
is check
-
pointing system on pairwise local sequences.]



J. Hérisson,

G. Payen,

and

R. Gherbi, “A 3D pattern matching algorithm for DNA
sequences” ,
Oxford Journals: Bioinformatics, Vol. 23, pp. 680
-
686, 2007
.
[The article pro
poses a 3D model for DNA rather than the traditional textual models. A
3D model would allow scientists to study syntax and other properties of DNA. ]



T. W. Lam,

W. K. Sung,

S. L. Tam,

C. K. Wong,

and

S. M. Yiu, “Compressed indexing
and local alignment of
DNA”, Oxford Journals: Bioinformatics, Vol. 24, pp. 791
-
797,
2008.


[The
article focuses on finding local alignments of DNA sequences through indexing
certain sequences of DNA. This is a faster alternative to dynamic programming;
however, it is a heuristic
-
based approach and may not be as accurate.]