Christian Burks

The GenBank genetic sequence databank

The GenBank Genetic Sequence Data Bank contains over 5700 entries for DNA and RNA sequences that have been reported since 1967. This paper briefly describes the contents of the database, the forms in which the database is distributed, and the services we offer to scientists who use the GenBank database.

Identifying potential tRNA genes in genomic DNA sequences

We have developed an algorithm that automatically and reproducibly identifies potential tRNA genes in genomic DNA sequences, and we present a general strategy for testing the sensitivity of such algorithms. This algorithm is useful for the flagging and characterization of long genomic sequences that have not been experimentally analyzed for identification of functional regions, and for the scanning of nucleotide sequence databases for errors in the sequences and the functional assignments associated with them. In an exhaustive scan of the GenBank database, 97.5% of the 744 known tRNA genes were correctly identified (true-positives), and 42 previously unidentified sequences were predicted to be tRNAs. A detailed analysis of these latter predictions reveals that 16 of the 42 are very similar to known tRNA genes, and we predict that they do, in fact, code for tRNA, yielding a false-positive rate for the algorithm of 0.003%. The new algorithm and testing strategy are a considerable improvement over any previously described strategies for recognizing tRNA genes, and they allow detections of genes (including introns) embedded in long genomic sequences.

Genetic Algorithms, Operators, and DNA Fragment Assembly

We study different genetic algorithm operators for one permutation problem associated with the Human Genome Project—the assembly of DNA sequence fragments from a parent clone whose sequence is unknown into a consensus sequence corresponding to the parent sequence. The sorted-order representation, which does not require specialized operators, is compared with a more traditional permutation representation, which does require specialized operators. The two representations and their associated operators are compared on problems ranging from 2K to 34K base pairs (KB). Edge-recombination crossover used in conjunction with several specialized operators is found to perform best in these experiments; these operators solved a 10KB sequence, consisting of 177 fragments, with no manual intervention. Natural building blocks in the problem are exploited at progressively higher levels through “macro-operators.” This significantly improves performance.

CABIOS REVIEW The GenBank nucleic acid sequence database

The GenBank nucleic acid sequence database is a computer-based collection of all published DNA and RNA sequences; it contains over five million bases in close to six thousand sequence entries drawn from four thousand five hundred published articles. Each sequence is accompanied by relevant biological annotation. The database is available either on magnetic tape, on floppy diskettes, on-line or in hardcopy form. We discuss the structure of the database, the extent of the data and the implications of the database for research on nucleic acids.

TOWARDS MODELING DNA SEQUENCES AS AUTOMATA

Abstract We seek to describe a starting point for modeling the evolution and role of DNA sequences within the framework of cellular automata by discussing the current understanding of genetic information storage in DNA sequences. This includes alternately viewing the role of DNA in living organisms as a simple scheme and as a complex scheme; a brief review of strategies for identifying and classifying patterns in DNA sequences; and finally, notes towards establishing DNA-like automata models, including a discussion of the extent of experimentally determined DNA sequence data present in the database at Los Alamos.

Access to molecular biology databases

The LiMB database was created to catalogue and begin coordinating access to the rapidly proliferating databases relevant to molecular biology. LiMB contains information about these databases and is currently implemented in a relational database management system that allows for complex, multiconditional queries. We present an overview of LiMB, a description of its current implementation, and a discussion of the role of LiMB in developing strategies for automated access to distributed, heterogeneous databases.

Calculating shared fragments for the single digest problem

A partial restriction map can be computed by using single digest fragments from a set of clones that are known to be in a contig. If there is no error or uncertainty in the data, so that the set of unique restriction fragments can be calculated exactly, then the restriction map can be assembled using a polynomial-time algorithm based on the consecutive 1s property. However, there are typically both error and uncertainty in the data. The data being used are from the chromosome 16 mapping project at Los Alamos National Laboratory. The authors have developed an algorithm that consists of two steps: (1) calculate the set of unique restriction fragments and (2) assemble the fragments such that the set of fragments for each clone is contiguous. The problem of solving the first step when there are error and uncertainty in the data is addressed.<<ETX>>

GRAM and genfragII: solving and testing the single-digest, partially ordered restriction map problem

GRAM (Genomic Restriction map AsseMbly) takes as input single-digest restriction fragments for a set of overlapping clones and outputs one or more plausible partially ordered restriction maps. For each restriction map, GRAM shows the corresponding alignment of the input clone fragments. Due to the error and uncertainty in experimental data, this problem is computationally difficult to solve; therefore, the principle objective in the design of GRAM is to facilitate man-machine collaborative problem solving. GRAM quickly approximates a solution, as follows. (i) A clustering algorithm determines a probable set of restriction fragments. (ii) An assembly algorithm permutes the set of restriction fragments such that the maximal number of clone fragments are contiguous. The output of the GRAM algorithm is displayed for the user to query and edit. This paper describes the stochastic assembly algorithm and shows how it works with the interactive graphics to support man-machine problem solving. In order to test and verify the performance of GRAM, we have developed a program called genfragII to simulate the digestion of clones and fragments; this program is described and results are presented. GRAM is also being used for a number of genome mapping projects.

DNA Structural Patterns and Nucleosome Positioning

There is no clear picture to date of the mechanisms determining nucleosome positioning. Generally, local DNA sequence signals (sequence-dependent positioning) or non-local signals (e.g. boundary effects) are possible. We have analyzed the DNA sequences of a series of positioned and mapped nucleosome cores in a systematic search for local sequence signals. The data set consists of 113 mapped nucleosome cores, mapped in vivo, in situ, or in reconstituted chromatin. The analysis focuses on the periodic distribution of sequence elements implied by each of six different published DNA structural models. We have also investigated the periodic distribution of all mono-, di-, and trinucleotides. An identical analysis was performed on a set of isolated chicken nucleosome cores (nucleosome data from the literature) that are presumably positioned due to local sequence signals. The results show that the sequences of the isolated nucleosome cores have a number of characteristic features that distinguish them clearly from randomly chosen reference DNA. This confirms that the positioning of these nucleosomes is mainly sequence-dependent (i.e., dependent on local octamer-DNA interactions) and that our algorithms are able to detect these patterns. Using the same algorithms, the sequences of the mapped nucleosome cores, however, are on average very similar to randomly chosen reference DNA. This suggests that the position of the majority of these nucleosomes can not be attributed to the sequence patterns implemented in our algorithms. The arrangement of positioned nucleosomes seems to be the result of a dynamic interplay of octamer-DNA interactions, nucleosome-nucleosome interactions and other positioning signals with varying relative contributions along the DNA.

A Quantitative Measure of DNA Curvature Enabling the Comparison of Predicted Structures

A growing body of data indicates that the equilibrium structures of some DNA fragments are curved and that curvature is sequence-directed. We describe a quantitative measure of DNA curvature that can be used for evaluating and comparing current proposed models for the molecular basis of DNA curvature. We demonstrate that this measure, in conjunction with any given prediction model, enables both the comparison of experimental data to predictions and the scanning of nucleotide sequence databases for potential curved regions.