Apostolico, Alberto

Giancarlo, Raffaele

Elsevier - Publisher Connector 

Theoretical Computer Science 335 (2005) 1–2
www.elsevier.com/locate/tcs
Foreword
One of the ultimate goals of Genomic Research is to extract “structure” and “meaning”
from biological sequences. Knowledge of “structure” and “meaning” may lead to better
understanding of genetic diseases and to better methods for the design of proteins. Pattern
Discovery has therefore assumed a fundamental role in this context. It deals with the devel-
opment of automatedmethods for the inference of structure that presents itself in the form of
regularities, subaggregates and associations thereof in Biosequences. The PROSITE Data
Base is a good example. This is a database of protein families, where each family is de-
scribed by a regular expression (the common “motif” or “pattern”) highlighting important
domains of “consensus” among the sequences of the given protein family. In PROSITE,
motifs have been extracted semi automatically through the analysis of the alignment of
multiple sequences: such a procedure is no longer adequate, given the amount of biological
data being produced worldwide.
In general, the deﬁnition of motif/pattern emerges from the biological context in which
these terms are used. Satellites and tandem repeats are repetitive structures, important to
identify genetic markers and for their association to some genetic diseases; patterns such as
subsequences common to a set of sequences indicate conservation, which in turn suggests
that the pattern is implicated in an important (sometimes, still unknown) function; promoter
sequences and regulatory regions have a speciﬁc “high level” structure which is believed to
be essential to the machinery of cells; and so on.
Exploration of most of these issues has only begun: as websites and data banks accumu-
late known proteins, DNA sequences, and 3D structures in growing numbers, increasingly
fast and sophisticatedmethods are sought. Pattern andMotif Discovery is perhaps the single
most pervasive tool of Bioinformatics, ubiquitous to specialized (Prosite, Splash, Meme,
Verbumculus, Teiresias, etc.) as well as more general, classical ones such as Blast, Bioaccel-
erator, etc. Antagonist trends represented by data explosion on the one hand, and a growing
need for integration and cross-correlation on the other, make advances in motif and rule dis-
covery a foremost need in this domain. Searches for relationships in the growing collection
of data are carried out currently by manual annotation and experimentation. Capabilities
akin to automatically cluster, classify, and annotate data across the traditional boundaries
of individual databases is posing an increasing need for novel models and algorithms for
the generation, analysis and cross-annotation of scattered biological data.
Pattern Discovery predates, on the one hand, over the rich arsenal of techniques and tools
set up in the course of the development of Pattern Matching and, on the other, on some
sophisticated probabilistic insights that accompanied the process. It differs from Pattern
0304-3975/$ - see front matter © 2005 Elsevier B.V. All rights reserved.
doi:10.1016/j.tcs.2004.12.011
2 Foreword / Theoretical Computer Science 335 (2005) 1 --2
Matching in so far as the latter searches for ﬁnely prescribed patterns, whereas the former
tries to unearth patterns that are loosely speciﬁed at best. The papers presented in this special
issue offer a snapshot of the state of progress of this fascinating and challenging subject as
applied to one of its most natural testbeds, the analysis of Biosequences. Because modelling
is such an integral part of these studies, and often the only relevant validation for them, the
papers feature as a rule a non-negligible experimental component.
The manuscripts were submitted in response to the Call for Papers for this Special Issue,
and have been refereed according to the standards and procedures that TCS applies to its
normal submissions.
Alberto Apostolico
Purdue University and Università di Padova
E-mail address: axa@cs.purdue.edu
Raffaele Giancarlo
Università di Palermo
E-mail address: raffaele@math.unipa.it


Foreword 

file:///data/core-remote/dit/data/elsevier/pdf/c89/aHR0cDovL2FwaS5lbHNldmllci5jb20vY29udGVudC9hcnRpY2xlL3BpaS9zMDMwNDM5NzUwNDAwODA0Nw==.pdf

Foreword

Abstract

Similar works

Full text

Available Versions

Elsevier - Publisher Connector