On the Representability of Complete Genomes by Multiple Competing Finite-Context (Markov) Models

A finite-context (Markov) model of order yields the probability distribution of the next symbol in a sequence of symbols, given the recent past up to depth . Markov modeling has long been applied to DNA sequences, for example to find gene-coding regions. With the first studies came the discovery that DNA sequences are non-stationary: distinct regions require distinct model orders. Since then, Markov and hidden Markov models have been extensively used to describe the gene structure of prokaryotes and eukaryotes. However, to our knowledge, a comprehensive study about the potential of Markov models to describe complete genomes is still lacking. We address this gap in this paper. Our approach relies on (i) multiple competing Markov models of different orders (ii) careful programming techniques that allow orders as large as sixteen (iii) adequate inverted repeat handling (iv) probability estimates suited to the wide range of context depths used. To measure how well a model fits the data at a particular position in the sequence we use the negative logarithm of the probability estimate at that position. The measure yields information profiles of the sequence, which are of independent interest. The average over the entire sequence, which amounts to the average number of bits per base needed to describe the sequence, is used as a global performance measure. Our main conclusion is that, from the probabilistic or information theoretic point of view and according to this performance measure, multiple competing Markov models explain entire genomes almost as well or even better than state-of-the-art DNA compression methods, such as XM, which rely on very different statistical models. This is surprising, because Markov models are local (short-range), contrasting with the statistical models underlying other methods, where the extensive data repetitions in DNA sequences is explored, and therefore have a non-local character

Disease proteomics

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/62680/1/nature01514.pd

Proteome Databases

The awareness that protein and DNA sequence data are essential to the understanding of biological systems is now well established in the life science community. This community is progressively becoming conscious that this is also true of additional information about protein expression, post-translational modifications, tertiary structure and, of course, function. All of this knowledge needs to be encapsulated in various databases. The goal of this chapter is to describe the data resources that are available to researchers working in the field of proteome studies. We will not attempt here to survey all the different databases that are relevant to this field. Such an exercise would be tedious due to the large number of relevant databases and would only be valid for a very short period of time due to the extreme speed with which new databases are appearing and/or disappearing. It is also for this reason that you will find a table at the end of this chapter (Table 5.l) listing the World-Wide Web (WWW) addresses of the databases described in the following sections. The most important component of this table is the Internet address that allows you to download an upto- date version of the table! We will successively describe the type of information found in the following types of databases: protein sequence, nucleotide sequence, pattern/profile, 2-D PAGE, 3-D structure, post-translational modification, genomic and metabolic. The last section of this chapter will try to predict future trends in the evolution of protein information resources