Ringo, Doty, Demeter and Simard, Cerebral Cortex 1994;4:331-343: a proof of the need for the spatial clustering of interneuronal connections to enhance cortical computation.
It has been argued that an important principle driving the organization of the cerebral cortex towards local processing has been the need to decrease time lost to interneuronal conduction delay. In this paper, I show for a simplified model of the cerebral cortex, using analytical means, that if interneuronal conduction time increases proportional to interneuronal distance, then the only way to increase the numbers of synaptic events occurring in a fixed finite time period is to spatially cluster interneuronal connections. (+info)
The Genexpress IMAGE knowledge base of the human brain transcriptome: a prototype integrated resource for functional and computational genomics.
Expression profiles of 5058 human gene transcripts represented by an array of 7451 clones from the first IMAGE Consortium cDNA library from infant brain have been collected by semiquantitative hybridization of the array with complex probes derived by reverse transcription of mRNA from brain and five other human tissues. Twenty-one percent of the clones corresponded to transcripts that could be classified in general categories of low, moderate, or high abundance. These expression profiles were integrated with cDNA clone and sequence clustering and gene mapping information from an upgraded version of the Genexpress Index. For seven gene transcripts found to be transcribed preferentially or specifically in brain, the expression profiles were confirmed by Northern blot analyses of mRNA from eight adult and four fetal tissues, and 15 distinct regions of brain. In four instances, further documentation of the sites of expression was obtained by in situ hybridization of rat-brain tissue sections. A systematic effort was undertaken to further integrate available cytogenetic, genetic, physical, and genic map informations through radiation-hybrid mapping to provide a unique validated map location for each of these genes in relation to the disease map. The resulting Genexpress IMAGE Knowledge Base is illustrated by five examples presented in the printed article with additional data available on a dedicated Web site at the address http://idefix.upr420.vjf.cnrs.fr/EXPR++ +/ welcome.html. (+info)
Genome-wide bioinformatic and molecular analysis of introns in Saccharomyces cerevisiae.
Introns have typically been discovered in an ad hoc fashion: introns are found as a gene is characterized for other reasons. As complete eukaryotic genome sequences become available, better methods for predicting RNA processing signals in raw sequence will be necessary in order to discover genes and predict their expression. Here we present a catalog of 228 yeast introns, arrived at through a combination of bioinformatic and molecular analysis. Introns annotated in the Saccharomyces Genome Database (SGD) were evaluated, questionable introns were removed after failing a test for splicing in vivo, and known introns absent from the SGD annotation were added. A novel branchpoint sequence, AAUUAAC, was identified within an annotated intron that lacks a six-of-seven match to the highly conserved branchpoint consensus UACUAAC. Analysis of the database corroborates many conclusions about pre-mRNA substrate requirements for splicing derived from experimental studies, but indicates that splicing in yeast may not be as rigidly determined by splice-site conservation as had previously been thought. Using this database and a molecular technique that directly displays the lariat intron products of spliced transcripts (intron display), we suggest that the current set of 228 introns is still not complete, and that additional intron-containing genes remain to be discovered in yeast. The database can be accessed at http://www.cse.ucsc.edu/research/compbi o/yeast_introns.html. (+info)
Bayesian inference on biopolymer models.
MOTIVATION: Most existing bioinformatics methods are limited to making point estimates of one variable, e.g. the optimal alignment, with fixed input values for all other variables, e.g. gap penalties and scoring matrices. While the requirement to specify parameters remains one of the more vexing issues in bioinformatics, it is a reflection of a larger issue: the need to broaden the view on statistical inference in bioinformatics. RESULTS: The assignment of probabilities for all possible values of all unknown variables in a problem in the form of a posterior distribution is the goal of Bayesian inference. Here we show how this goal can be achieved for most bioinformatics methods that use dynamic programming. Specifically, a tutorial style description of a Bayesian inference procedure for segmentation of a sequence based on the heterogeneity in its composition is given. In addition, full Bayesian inference algorithms for sequence alignment are described. AVAILABILITY: Software and a set of transparencies for a tutorial describing these ideas are available at http://www.wadsworth.org/res&res/bioinfo/ (+info)
DNA sequence chromatogram browsing using JAVA and CORBA.
DNA sequence chromatograms (traces) are the primary data source for all large-scale genomic and expressed sequence tags (ESTs) sequencing projects. Access to the sequencing trace assists many later analyses, for example contig assembly and polymorphism detection, but obtaining and using traces is problematic. Traces are not collected and published centrally, they are much larger than the base calls derived from them, and viewing them requires the interactivity of a local graphical client with local data. To provide efficient global access to DNA traces, we developed a client/server system based on flexible Java components integrated into other applications including an applet for use in a WWW browser and a stand-alone trace viewer. Client/server interaction is facilitated by CORBA middleware which provides a well-defined interface, a naming service, and location independence. [The software is packaged as a Jar file available from the following URL: http://www.ebi.ac.uk/jparsons. Links to working examples of the trace viewers can be found at http://corba.ebi.ac.uk/EST. All the Washington University mouse EST traces are available for browsing at the same URL.] (+info)
Detecting selective expression of genes and proteins.
Selective expression of a gene product (mRNA or protein) is a pattern in which the expression is markedly high, or markedly low, in one particular tissue compared with its level in other tissues or sources. We present a computational method for the identification of such patterns. The method combines assessments of the reliability of expression quantitation with a statistical test of expression distribution patterns. The method is applicable to small studies or to data mining of abundance data from expression databases, whether mRNA or protein. Though the method was developed originally for gene-expression analyses, the computational method is, in fact, rather general. It is well suited for the identification of exceptional values in many sorts of intensity data, even noisy data, for which assessments of confidences in the sources of the intensities are available. Moreover, the method is indifferent as to whether the intensities are experimentally or computationally derived. We show details of the general method and examples of computational results on gene abundance data. (+info)
Protein subcellular location prediction.
The function of a protein is closely correlated with its subcellular location. With the rapid increase in new protein sequences entering into data banks, we are confronted with a challenge: is it possible to utilize a bioinformatic approach to help expedite the determination of protein subcellular locations? To explore this problem, proteins were classified, according to their subcellular locations, into the following 12 groups: (1) chloroplast, (2) cytoplasm, (3) cytoskeleton, (4) endoplasmic reticulum, (5) extracell, (6) Golgi apparatus, (7) lysosome, (8) mitochondria, (9) nucleus, (10) peroxisome, (11) plasma membrane and (12) vacuole. Based on the classification scheme that has covered almost all the organelles and subcellular compartments in an animal or plant cell, a covariant discriminant algorithm was proposed to predict the subcellular location of a query protein according to its amino acid composition. Results obtained through self-consistency, jackknife and independent dataset tests indicated that the rates of correct prediction by the current algorithm are significantly higher than those by the existing methods. It is anticipated that the classification scheme and concept and also the prediction algorithm can expedite the functionality determination of new proteins, which can also be of use in the prioritization of genes and proteins identified by genomic efforts as potential molecular targets for drug design. (+info)
A two-dimensional model of brightness perception based on spatial filtering consistent with retinal processing.
We have applied a multiple scale, 2-D model of brightness perception to a broad range of brightness phenomena. The filters encapsulate only processing that is well established to occur in retinal ganglion cells. Their outputs are then combined in the simplest way compatible with the earliest levels of cortical processing. Not only essential features of a number of the phenomena but also more subtle shading effects are reproduced. Because of the retinal nature of this model, these results would appear to support previous speculation that much of the ground work for brightness perception is performed at the retinal level. (+info)