Biomedical subjects

Pierre Baldi

Publications and source records attributed to Pierre Baldi.

9 recordsLinked to original sources

PubMed2025

Cilia in the brain display region-dependent oscillations of length and orientation.

In this study, we conducted high-throughput spatiotemporal analysis of primary cilia length and orientation across 22 mouse brain regions. We developed automated image analysis algorithms, which enabled us to examine over 10 million individual cilia, generating the largest spatiotemporal atlas of cilia. We found that cilia length and orientation display substantial variations across different brain regions and exhibit fluctuations over a 24-h period, with region-specific peaks during light-dark phases. Our analysis revealed unique orientation patterns of cilia, suggesting that cilia orientation within the brain is not random but follows specific patterns. Using BioCycle, we identified rhythmic fluctuations in cilia length across five brain regions: the nucleus accumbens core, somatosensory cortex, and the dorsomedial, ventromedial, and arcuate hypothalamic nuclei. Our findings present novel insights into the brain cilia dynamics, and highlight the need for further investigation into cilia's role in the brain's response to environmental changes and regulation of oscillatory physiological processes.

Animals↗

PubMed2003

Global gene expression profiling in Escherichia coli K12. The effects of oxygen availability and FNR.

The work presented here is a first step toward a long term goal of systems biology, the complete elucidation of the gene regulatory networks of a living organism. To this end, we have employed DNA microarray technology to identify genes involved in the regulatory networks that facilitate the transition of Escherichia coli cells from an aerobic to an anaerobic growth state. We also report the identification of a subset of these genes that are regulated by a global regulatory protein for anaerobic metabolism, FNR. Analysis of these data demonstrated that the expression of over one-third of the genes expressed during growth under aerobic conditions are altered when E. coli cells transition to an anaerobic growth state, and that the expression of 712 (49%) of these genes are either directly or indirectly modulated by FNR. The results presented here also suggest interactions between the FNR and the leucine-responsive regulatory protein (Lrp) regulatory networks. Because computational methods to analyze and interpret high dimensional DNA microarray data are still at an early stage, and because basic issues of data analysis are still being sorted out, much of the emphasis of this work is directed toward the development of methods to identify differentially expressed genes with a high level of confidence. In particular, we describe an approach for identifying gene expression patterns (clusters) obtained from multiple perturbation experiments based on a subset of genes that exhibit high probability for differential expression values.

Cell Division↗

PubMed2003

LineUp: statistical detection of chromosomal homology with application to plant comparative genomics.

The identification of homologous regions between chromosomes forms the basis for studies of genome organization, comparative genomics, and evolutionary genomics. Identification of these regions can be based on either synteny or colinearity, but there are few methods to test statistically for significant evidence of homology. In the present study, we improve a preexisting method that used colinearity as the basis for statistical tests. Improvements include computational efficiency and a relaxation of the colinearity assumption. Two algorithms perform the method: FullPermutation, which searches exhaustively for runs of markers, and FastRuns, which trades faster run times for exhaustive searches. The algorithms described here are available in the LineUp package (http://www.igb.uci.edu/ approximately baldig/lineup). We explore the performance of both algorithms on simulated data and also on genetic map data from maize (Zea mays ssp. mays). The method has reasonable power to detect a homologous region; for example, in >90% of simulations, both algorithms detect a homologous region of 10 markers buried in a random background, even when the homologous regions have diverged by numerous inversion events. The methods were applied to four maize molecular maps. All maps indicate that the maize genome contains extensive regions of genomic duplication and multiplication. Nonetheless, maps differ substantially in the location of homologous regions, probably reflecting the incomplete nature of genetic map data. The variation among maps has important implications for evolutionary inference from genetic map data.

Chromosome Mapping↗

PubMed2003

Differential analysis of DNA microarray gene expression data.

Here, we review briefly the sources of experimental and biological variance that affect the interpretation of high-dimensional DNA microarray experiments. We discuss methods using a regularized t-test based on a Bayesian statistical framework that allow the identification of differentially regulated genes with a higher level of confidence than a simple t-test when only a few experimental replicates are available. We also describe a computational method for calculating the global false-positive and false-negative levels inherent in a DNA microarray data set. This method provides a probability of differential expression for each gene based on experiment-wide false-positive and -negative levels driven by experimental error and biological variance.

Analysis of Variance↗

PubMed2002

Global gene expression profiling in Escherichia coli K12. The effects of leucine-responsive regulatory protein.

Leucine-responsive regulatory protein (Lrp) is a global regulatory protein that affects the expression of multiple genes and operons in bacteria. Although the physiological purpose of Lrp-mediated gene regulation remains unclear, it has been suggested that it functions to coordinate cellular metabolism with the nutritional state of the environment. The results of gene expression profiles between otherwise isogenic lrp(+) and lrp(-) strains of Escherichia coli support this suggestion. The newly discovered Lrp-regulated genes reported here are involved either in small molecule or macromolecule synthesis or degradation, or in small molecule transport and environmental stress responses. Although many of these regulatory effects are direct, others are indirect consequences of Lrp-mediated changes in the expression levels of other global regulatory proteins. Because computational methods to analyze and interpret high dimensional DNA microarray data are still an early stage, much of the emphasis of this work is directed toward the development of methods to identify differentially expressed genes with a high level of confidence. In particular, we describe a Bayesian statistical framework for a posterior estimate of the standard deviation of gene measurements based on a limited number of replications. We also describe an algorithm to compute a posterior estimate of differential expression for each gene based on the experiment-wide global false positive and false negative level for a DNA microarray data set. This allows the experimenter to compute posterior probabilities of differential expression for each individual differential gene expression measurement.

Base Sequence↗

PubMed2002

Prediction of coordination number and relative solvent accessibility in proteins.

Knowing the coordination number and relative solvent accessibility of all the residues in a protein is crucial for deriving constraints useful in modeling protein folding and protein structure and in scoring remote homology searches. We develop ensembles of bidirectional recurrent neural network architectures to improve the state of the art in both contact and accessibility prediction, leveraging a large corpus of curated data together with evolutionary information. The ensembles are used to discriminate between two different states of residue contacts or relative solvent accessibility, higher or lower than a threshold determined by the average value of the residue distribution or the accessibility cutoff. For coordination numbers, the ensemble achieves performances ranging within 70.6-73.9% depending on the radius adopted to discriminate contacts (6A-12A). These performances represent gains of 16-20% over the baseline statistical predictor, always assigning an amino acid to the largest class, and are 4-7% better than any previous method. A combination of different radius predictors further improves performance. For accessibility thresholds in the relevant 15-30% range, the ensemble consistently achieves a performance above 77%, which is 10-16% above the baseline prediction and better than other existing predictors, by up to several percentage points. For both problems, we quantify the improvement due to evolutionary information in the form of PSI-BLAST-generated profiles over BLAST profiles. The prediction programs are implemented in the form of two web servers, CONpro and ACCpro, available at http://promoter.ics.uci.edu/BRNN-PRED/.

Amino Acids↗

PubMed2002

Improving the prediction of protein secondary structure in three and eight classes using recurrent neural networks and profiles.

Secondary structure predictions are increasingly becoming the workhorse for several methods aiming at predicting protein structure and function. Here we use ensembles of bidirectional recurrent neural network architectures, PSI-BLAST-derived profiles, and a large nonredundant training set to derive two new predictors: (a) the second version of the SSpro program for secondary structure classification into three categories and (b) the first version of the SSpro8 program for secondary structure classification into the eight classes produced by the DSSP program. We describe the results of three different test sets on which SSpro achieved a sustained performance of about 78% correct prediction. We report confusion matrices, compare PSI-BLAST to BLAST-derived profiles, and assess the corresponding performance improvements. SSpro and SSpro8 are implemented as web servers, available together with other structural feature predictors at: http://promoter.ics.uci.edu/BRNN-PRED/.

Algorithms↗

PubMed2002

Distribution patterns of over-represented k-mers in non-coding yeast DNA.

MOTIVATION: Over-represented k-mers in genomic DNA regions are often of particular biological interest. For example, over-represented k-mers in co-regulated families of genes are associated with the DNA binding sites of transcription factors. To measure over-representation, we introduce a statistical background model based on single-mismatches, and apply it to the pooled 500 bp ORF Upstream Regions (USRs) of yeast. More importantly, we investigate the context and spatial distribution of over-represented k-mers in yeast USRs. RESULTS: Single and double-stranded spatial distributions of most over-represented k-mers are highly non-random, and predominantly cluster into a small number of classes that are robust with respect to over-representation measures. Specifically, we show that the three most common distribution patterns can be related to DNA structure, function, and evolution and correspond to: (a) homologous ORF clusters associated with sharply localized distributions; (b) regulatory elements associated with a symmetric broad hill-shaped distribution in the 50-200 bp USR; and (c) runs of As, Ts, and ATs associated with a broad hill-shaped distribution also in the 50-200 bp USR, with extreme structural properties. Analysis of over-representation, homology, localization, and DNA structure are essential components of a general data-mining approach to finding biologically important k-mers in raw genomic DNA and understanding the 'lexicon' of regulatory regions.

Amino Acid Motifs↗

PubMed2002

Why are complementary DNA strands symmetric?

MOTIVATION: Over sufficiently long windows, complementary strands of DNA tend to have the same base composition. A few reports have indicated that this first-order parity rule extends at higher orders to oligonucleotide composition, at least in some organisms or taxa. However, the scientific literature falls short of providing a comprehensive study of reverse-complement symmetry at multiple orders and across the kingdom of life. It also lacks a characterization of this symmetry and a convincing explanation or clarification of its origin. RESULTS: We develop methods to measure and characterize symmetry at multiple orders, and analyze a wide set of genomes, encompassing single- and double-stranded RNA and DNA viruses, bacteria, archae, mitochondria, and eukaryota. We quantify symmetry at orders 1 to 9 for contiguous sequences and pools of coding and non-coding upstream regions, compare the observed symmetry levels to those predicted by simple statistical models, and factor out the effect of lower-order distributions. We establish the universality and variability range of first-order strand symmetry, as well as of its higher-order extensions, and demonstrate the existence of genuine high-order symmetric constraints. We show that ubiquitous reverse-complement symmetry does not result from a single cause, such as point mutation or recombination, but rather emerges from the combined effects of a wide spectrum of mechanisms operating at multiple orders and length scales.

Base Composition↗