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J W Fickett

Publications and source records attributed to J W Fickett.

16 recordsLinked to original sources
PubMed1996

Coordinate positioning of MEF2 and myogenin binding sites.

The MEF2 and MyoD families of transcriptional regulatory factors both play central roles in the terminal differentiation of skeletal muscle. Further, binding sites for the two families often occur nearby, and there have been a number of indications that members of the two families may bind coordinately. The present study provides evidence that known binding sites for the two occur with precise geometric restrictions related to the DNA helical repeat unit, that pairs of putative sites following these restrictions are indicative of skeletal muscle-specific transcriptional regulatory regions, and that the geometric relationship can help provide a consistent interpretation for data that has until now been difficult to explain.

Animals↗
PubMed1996

Finding genes by computer: the state of the art.

Discovering new genes, and their functions, can be aided not only by special purpose gene (and coding region) finding software, but also by searches in key databases, and by programs for finding particular sites relevant to gene expression, such as promoters and splice sites. No one software package includes all the necessary tools. I describe here the main kinds of tools; their working principles, strengths and limitations; and how combined evidence from multiple tools can aid in optimum gene identification.

Amino Acid Sequence↗
PubMed1996

Quantitative discrimination of MEF2 sites.

Myocyte-specific enhancer factor 2 (MEF2) is a family of closely related transcription factors that play a key role in the differentiation of muscle tissues and are important in the muscle-specific expression of a number of genes. Given the centrality of MEF2 in muscle differentiation, regulatory regions newly determined to be muscle specific are often studied for potential MEF2 binding sites. Possible sites are often located by comparison to a homologous gene or by matching to the consensus MEF2 sequence. Enough data have accumulated that a richer description of the MEF2 binding site, a position weight matrix, can be reliably constructed and its usefulness can be assessed. It was shown that scores from such a matrix approximate MEF2 binding energy and enable recognition of naturally occurring MEF2 sites with high sensitivity and specificity. Regulation of genes via MEF2-like sites is complicated by the fact that a number of transcription factors are involved. Not only is MEF2 itself a family of proteins, but several other, nonhomologous, transcription factors overlap MEF2 in DNA-binding specificity. Thus, more quantitative methods for recognizing potential sites may help with the lengthy process of disentangling the complex regulatory circuits of muscle-specific expression.

Amino Acid Sequence↗
PubMed1995

Distinctive sequence features in protein coding genic non-coding, and intergenic human DNA.

We have studied the behavior of a number of sequence statistics, mostly indicative of protein coding function, in a large set of human clone sequences randomly selected in the course of genome mapping (randomly selected clone sequences), and compared this with the behavior in known sequences containing genes (which we term genic sequences). As expected, given the higher coding density of the genic sequences, the sequence statistics studied behave in a substantially different manner in the randomly selected clone sequences (mostly intergenic DNA) and in the genic sequences. Strong differences in behavior of a number of such statistics are also observed, however when the randomly selected clone sequences are compared with only the non-coding fraction of the genic sequences, suggesting that intergenic and genic non-coding DNA constitute two different classes of non-coding DNA. By studying the behavior of the sequence statistics in simulated DNA of different C+G content, we have observed that a number of them are strongly dependent on C+G content. Thus, most differences between intergenic and genic non-coding DNA can be explained by differences in C+G content. A+T-rich intergenic DNA appears to be at the compositional equilibrium expected under random mutation, while C+G richer non-coding genic DNA is far from this equilibrium. The results obtained in simulated DNA indicate, on the other hand, that a very large fraction of the variation in the coding statistics that underlie gene identification algorithms is due simply to C+G content, and is not directly related to protein coding function. It appears, thus, that the performance of gene-finding algorithms should be improved by carefully distinguishing the effects of protein coding function from those of mere base compositional variation on such coding statistics.

Algorithms↗
PubMed1995

ORFs and genes: how strong a connection?

The length of an open reading frame (ORF) is one important piece of evidence often used in locating new genes, particularly in organisms where splicing is rare. However, there have been no systematic studies quantifying the degree of correlation between length of ORF, on the one hand, and likelihood of gene function, on the other. In this paper, techniques are derived to estimate the conditional probability of gene function, given ORF length, based on evidence both from the databases and from simulation. Several complete chromosomes of Saccharomyces cerevisiae have now been sequenced, and considerable effort is being expended on locating and characterizing the genes in these sequences. Thus, we illustrate the techniques for this organism.

Amino Acid Sequence↗
PubMed1994

Inferring genes from open reading frames.

One expects that in DNA without protein coding function, stop codons (which constitute three of the 64 possible codons) should occur frequently in all reading frames, and that a long open reading frame (ORF) can be interpreted as a sign for the existence of a gene. We make a beginning on introducing quantitative measures of confidence into this inference--taking Saccharomyces cerevisiae as a sample case--and show that some common assumptions can reasonably be questioned. In particular we show that statistical support for the biological function of shorter ORFs listed as putative genes in recent papers is in fact very weak. This is an issue of practical as well as theoretical interest, since researching the function of a putative gene is difficult and expensive.

Base Composition↗
PubMed1993

Estimation of protein coding density in a corpus of DNA sequence data.

A number of experimental methods have been reported for estimating the number of genes in a genome, or the closely related coding density of a genome, defined as the fraction of base pairs in codons. Recently, DNA sequence data representative of the genome as a whole have become available for several organisms, making the problem of estimating coding density amenable to sequence analytic methods. Estimates of coding density for a single genome vary widely, so that methods with characterized error bounds have become increasingly desirable. We present a method to estimate the protein coding density in a corpus of DNA sequence data, in which a 'coding statistic' is calculated for a large number of windows of the sequence under study, and the distribution of the statistic is decomposed into two normal distributions, assumed to be the distributions of the coding statistic in the coding and noncoding fractions of the sequence windows. The accuracy of the method is evaluated using known data and application is made to the yeast chromosome III sequence and to C. elegans cosmid sequences. It can also be applied to fragmentary data, for example a collection of short sequences determined in the course of STS mapping.

Animals↗
PubMed1992

Assessment of protein coding measures.

A number of methods for recognizing protein coding genes in DNA sequence have been published over the last 13 years, and new, more comprehensive algorithms, drawing on the repertoire of existing techniques, continue to be developed. To optimize continued development, it is valuable to systematically review and evaluate published techniques. At the core of most gene recognition algorithms is one or more coding measures--functions which produce, given any sample window of sequence, a number or vector intended to measure the degree to which a sample sequence resembles a window of 'typical' exonic DNA. In this paper we review and synthesize the underlying coding measures from published algorithms. A standardized benchmark is described, and each of the measures is evaluated according to this benchmark. Our main conclusion is that a very simple and obvious measure--counting oligomers--is more effective than any of the more sophisticated measures. Different measures contain different information. However there is a great deal of redundancy in the current suite of measures. We show that in future development of gene recognition algorithms, attention can probably be limited to six of the twenty or so measures proposed to date.

Algorithms↗
PubMed1992

Base compositional structure of genomes.

We model the base compositional structure of the human and Escherichia coli genomes. Three particular properties are first quantified: (1) There is a significant tendency for any region of either genome to have a strand-symmetric base composition. (2) The variation in base composition from region to region, within each genome, is very much larger than expected from common homogeneous stochastic models. (3) A given local base composition tends to persist over a scale of at least kilobases (E. coli) or tens of kilobases (human). Multidomain stochastic models from the literature are reviewed and sharpened. In particular, quantitative measurements of the third property lead us to suggest a significant shift in the style of domain models, in which the variation of A+T content with position is modeled by a random walk with frequent small steps rather than with large quantum jumps. As an application, we suggest a way to reduce the amount of computation in the assembly of large sequences from sequences of randomly chosen fragments.

Escherichia coli↗
PubMed1991

Electronic data publishing and GenBank.

GenBank, the national repository for nucleotide sequence data, has implemented a new model of scientific data management, which we term electronic data publishing. In traditional publishing, both scientific conclusions and supporting data are communicated via the printed page, and in electronic journal publishing, both types of information are communicated via electronic media. In electronic data publishing, by contrast, conclusions are published in a journal while data are published via a network-accessible, electronic database.

Base Sequence↗
PubMed1991

A program for computer-assisted scoring of Southern blots.

SCORE, a program for computer-assisted scoring of Southern blots of clone DNA, retains the use of expert human judgment while taking over much of the drudgery of the scoring task. The primary functions of the program are to help make an aligned overlay of the fluorescence gel image and the autoradiogram blot image, to keep track of band and lane locations and to store the resulting data directly into a database. Use of SCORE has resulted in greatly increased efficiency and accuracy.

Autoradiography↗
PubMed1986

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.

Animals↗
PubMed1985

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.

Base Sequence↗
PubMed1984

Fast optimal alignment.

We show how to speed up sequence alignment algorithms of the type introduced by Needleman and Wunsch (and generalized by Sellers and others). Faster alignment algorithms have been introduced, but always at the cost of possibly getting sub-optimal alignments. Our modification results in the optimal alignment still being found, often in 1/10 the usual time. What we do is reorder the computation of the usual alignment matrix so that the optimal alignment is ordinarily found when only a small fraction of the matrix is filled. The number of matrix elements which have to be computed is related to the distance between the sequences being aligned; the better the optimal alignment, the faster the algorithm runs.

Base Sequence↗
PubMed1982

Recognition of protein coding regions in DNA sequences.

We give a test for protein coding regions which is based on simple and universal differences between protein-coding and noncoding DNA. The test is simple enough to use without a computer and is completely objective. The test has been thoroughly proven on 400,000 bases of sequence data: it misclassifies 5% of the regions tested and gives an answer of "No Opinion" one fifth of the time. We predict some new coding and noncoding regions in published sequences.

Computers↗