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Mark Gerstein

Publications and source records attributed to Mark Gerstein.

At least 91 records · Page 5Linked to original sources
PubMed2004

Fast optimal genome tiling with applications to microarray design and homology search.

In this paper, we consider several variations of the following basic tiling problem: given a sequence of real numbers with two size-bound parameters, we want to find a set of tiles of maximum total weight such that each tiles satisfies the size bounds. A solution to this problem is important to a number of computational biology applications such as selecting genomic DNA fragments for PCR-based amplicon microarrays and performing homology searches with long sequence queries. Our goal is to design efficient algorithms with linear or near-linear time and space in the normal range of parameter values for these problems. For this purpose, we first discuss the solution to a basic online interval maximum problem via a sliding-window approach and show how to use this solution in a nontrivial manner for many of the tiling problems introduced. We also discuss NP-hardness results and approximation algorithms for generalizing our basic tiling problem to higher dimensions. Finally, computational results from applying our tiling algorithms to genomic sequences of five model eukaryotes are reported.

Algorithms↗
PubMed2004

Annotation transfer between genomes: protein-protein interologs and protein-DNA regulogs.

Proteins function mainly through interactions, especially with DNA and other proteins. While some large-scale interaction networks are now available for a number of model organisms, their experimental generation remains difficult. Consequently, interolog mapping--the transfer of interaction annotation from one organism to another using comparative genomics--is of significant value. Here we quantitatively assess the degree to which interologs can be reliably transferred between species as a function of the sequence similarity of the corresponding interacting proteins. Using interaction information from Saccharomyces cerevisiae, Caenorhabditis elegans, Drosophila melanogaster, and Helicobacter pylori, we find that protein-protein interactions can be transferred when a pair of proteins has a joint sequence identity >80% or a joint E-value <10(-70). (These "joint" quantities are the geometric means of the identities or E-values for the two pairs of interacting proteins.) We generalize our interolog analysis to protein-DNA binding, finding such interactions are conserved at specific thresholds between 30% and 60% sequence identity depending on the protein family. Furthermore, we introduce the concept of a "regulog"--a conserved regulatory relationship between proteins across different species. We map interologs and regulogs from yeast to a number of genomes with limited experimental annotation (e.g., Arabidopsis thaliana) and make these available through an online database at http://interolog.gersteinlab.org. Specifically, we are able to transfer approximately 90,000 potential protein-protein interactions to the worm. We test a number of these in two-hybrid experiments and are able to verify 45 overlaps, which we show to be statistically significant.

Amino Acid Sequence↗
PubMed2004

CREB binds to multiple loci on human chromosome 22.

The cyclic AMP-responsive element-binding protein (CREB) is an important transcription factor that can be activated by hormonal stimulation and regulates neuronal function and development. An unbiased, global analysis of where CREB binds has not been performed. We have mapped for the first time the binding distribution of CREB along an entire human chromosome. Chromatin immunoprecipitation of CREB-associated DNA and subsequent hybridization of the associated DNA to a genomic DNA microarray containing all of the nonrepetitive DNA of human chromosome 22 revealed 215 binding sites corresponding to 192 different loci and 100 annotated potential gene targets. We found binding near or within many genes involved in signal transduction and neuronal function. We also found that only a small fraction of CREB binding sites lay near well-defined 5' ends of genes; the majority of sites were found elsewhere, including introns and unannotated regions. Several of the latter lay near novel unannotated transcriptionally active regions. Few CREB targets were found near full-length cyclic AMP response element sites; the majority contained shorter versions or close matches to this sequence. Several of the CREB targets were altered in their expression by treatment with forskolin; interestingly, both induced and repressed genes were found. Our results provide novel molecular insights into how CREB mediates its functions in humans.

Binding Sites↗
PubMed2004

Analyzing cellular biochemistry in terms of molecular networks.

One way to understand cells and circumscribe the function of proteins is through molecular networks. These networks take a variety of forms including webs of protein-protein interactions, regulatory circuits linking transcription factors and targets, and complex pathways of metabolic reactions. We first survey experimental techniques for mapping networks (e.g., the yeast two-hybrid screens). We then turn our attention to computational approaches for predicting networks from individual protein features, such as correlating gene expression levels or analyzing sequence coevolution. All the experimental techniques and individual predictions suffer from noise and systematic biases. These problems can be overcome to some degree through statistical integration of different experimental datasets and predictive features (e.g., within a Bayesian formalism). Next, we discuss approaches for characterizing the topology of networks, such as finding hubs and analyzing subnetworks in terms of common motifs. Finally, we close with perspectives on how network analysis represents a preliminary step toward a systems approach for modeling cells.

Biochemical Phenomena↗
PubMed2003

Relationship between gene co-expression and probe localization on microarray slides.

BACKGROUND: Microarray technology allows simultaneous measurement of thousands of genes in a single experiment. This is a potentially useful tool for evaluating co-expression of genes and extraction of useful functional and chromosomal structural information about genes. RESULTS: In this work we studied the association between the co-expression of genes, their location on the chromosome and their location on the microarray slides by analyzing a number of eukaryotic expression datasets, derived from the S. cerevisiae, C. elegans, and D. melanogaster. We find that in several different yeast microarray experiments the distribution of the number of gene pairs with correlated expression profiles as a function of chromosomal spacing is peaked at short separations and has two superimposed periodicities. The longer periodicity has a spacing of 22 genes (approximately 42 Kb), and the shorter periodicity is 2 genes (approximately 4 Kb). CONCLUSION: The relative positioning of DNA probes on microarray slides and source plates introduces subtle but significant correlations between pairs of genes. Careful consideration of this spatial artifact is important for analysis of microarray expression data. It is particularly relevant to recent microarray analyses that suggest that co-expressed genes cluster along chromosomes or are spaced by multiples of a fixed number of genes along the chromosome.

Animals↗
PubMed2003

A "polyORFomic" analysis of prokaryote genomes using disabled-homology filtering reveals conserved but undiscovered short ORFs.

Prokaryote gene annotation is complicated by large numbers of short open reading frames (ORFs) that arise naturally from genetic code design. Historically, many hypothetical ORFs have been annotated as genes in microbes, usually with an arbitrary length threshold (e.g. greater than 100 codons). Given the use of such thresholds, what is the extent of genuine undiscovered short genes in the current sampling of prokaryote genomes? To assess rigorously the potential under-annotation of short ORFs with homology, we exhaustively compared the polyORFome--all possible ORFs in 64 prokaryotes (53 bacteria and 11 archaea) plus budding yeast--to itself and to all known proteins. The novelty of our analysis is that, firstly, sequence comparisons to/between both annotated and un-annotated ORFs are considered, and secondly a two-step disabled-homology filter is applied to set aside putative pseudogenes and spurious ORFs. We find that un-annotated homologous short ORFs (uhORFs) correspond to a small but non-negligible fraction of the annotated prokaryote proteomes (0.5-3.8%, depending on selection criteria). Moreover, the disabled-homology filter indicates that about a third of uhORFs correspond to putative pseudogenes or spurious ORFs. Our analysis shows that the use of annotation length thresholds is unnecessary, as there are manageable numbers of short ORF homologies conserved (without disablements) across microbial genomes. Data on uhORFs are available from http://pseudogene.org/polyo

Conserved Sequence↗
PubMed2003

A genome-wide analysis of blue-light regulation of Arabidopsis transcription factor gene expression during seedling development.

A microarray based on PCR amplicons of 1864 confirmed and predicted Arabidopsis transcription factor genes was produced and used to profile the global expression pattern in seedlings, specifically their light regulation. We detected expression of 1371 and 1241 genes in white-light- and dark-grown 6-d-old seedlings, respectively. Together they account for 84% of the transcription factor genes examined. This array was further used to study the kinetics of transcription factor gene expression change of dark-grown seedlings in response to blue light and the role of specific photoreceptors in this blue-light regulation. The expression of about 20% of those transcription factor genes are responsive to blue-light exposure, with 249 and 115 genes up or down-regulated, respectively. A large portion of blue-light-responsive transcription factor genes exhibited very rapid expression changes in response to blue light, earlier than the bulk of blue-light-regulated genes. This result suggests the involvement of transcription cascades in blue-light control of genome expression. Comparative analysis of the expression profiles of wild type and various photoreceptor mutants demonstrated that during early seedling development cryptochromes are the major photoreceptors for blue-light control of transcription factor gene expression, whereas phytochrome A and phototropins play rather limited roles.

Arabidopsis↗
PubMed2003

A Bayesian networks approach for predicting protein-protein interactions from genomic data.

We have developed an approach using Bayesian networks to predict protein-protein interactions genome-wide in yeast. Our method naturally weights and combines into reliable predictions genomic features only weakly associated with interaction (e.g., messenger RNAcoexpression, coessentiality, and colocalization). In addition to de novo predictions, it can integrate often noisy, experimental interaction data sets. We observe that at given levels of sensitivity, our predictions are more accurate than the existing high-throughput experimental data sets. We validate our predictions with TAP (tandem affinity purification) tagging experiments. Our analysis, which gives a comprehensive view of yeast interactions, is available at genecensus.org/intint.

Bayes Theorem↗
PubMed2003

Prediction of regulatory networks: genome-wide identification of transcription factor targets from gene expression data.

MOTIVATION: Defining regulatory networks, linking transcription factors (TFs) to their targets, is a central problem in post-genomic biology. One might imagine one could readily determine these networks through inspection of gene expression data. However, the relationship between the expression timecourse of a transcription factor and its target is not obvious (e.g. simple correlation over the timecourse), and current analysis methods, such as hierarchical clustering, have not been very successful in deciphering them. RESULTS: Here we introduce an approach based on support vector machines (SVMs) to predict the targets of a transcription factor by identifying subtle relationships between their expression profiles. In particular, we used SVMs to predict the regulatory targets for 36 transcription factors in the Saccharomyces cerevisiae genome based on the microarray expression data from many different physiological conditions. We trained and tested our SVM on a data set constructed to include a significant number of both positive and negative examples, directly addressing data imbalance issues. This was non-trivial given that most of the known experimental information is only for positives. Overall, we found that 63% of our TF-target relationships were confirmed through cross-validation. We further assessed the performance of our regulatory network identifications by comparing them with the results from two recent genome-wide ChIP-chip experiments. Overall, we find the agreement between our results and these experiments is comparable to the agreement (albeit low) between the two experiments. We find that this network has a delocalized structure with respect to chromosomal positioning, with a given transcription factor having targets spread fairly uniformly across the genome. AVAILABILITY: The overall network of the relationships is available on the web at http://bioinfo.mbb.yale.edu/expression/echipchip

Algorithms↗
PubMed2003

Distribution of NF-kappaB-binding sites across human chromosome 22.

We have mapped the chromosomal binding site distribution of a transcription factor in human cells. The NF-kappaB family of transcription factors plays an essential role in regulating the induction of genes involved in several physiological processes, including apoptosis, immunity, and inflammation. The binding sites of the NF-kappaB family member p65 were determined by using chromatin immunoprecipitation and a genomic microarray of human chromosome 22 DNA. Sites of binding were observed along the entire chromosome in both coding and noncoding regions, with an enrichment at the 5' end of genes. Strikingly, a significant proportion of binding was seen in intronic regions, demonstrating that transcription factor binding is not restricted to promoter regions. NF-kappaB binding was also found at genes whose expression was regulated by tumor necrosis factor alpha, a known inducer of NF-kappaB-dependent gene expression, as well as adjacent to genes whose expression is not affected by tumor necrosis factor alpha. Many of these latter genes are either known to be activated by NF-kappaB under other conditions or are consistent with NF-kappaB's role in the immune and apoptotic responses. Our results suggest that binding is not restricted to promoter regions and that NF-kappaB binding occurs at a significant number of genes whose expression is not altered, thereby suggesting that binding alone is not sufficient for gene activation.

Binding Sites↗
PubMed2003

Patterns of nucleotide substitution, insertion and deletion in the human genome inferred from pseudogenes.

Nucleotide substitution, insertion and deletion (indel) events are the major driving forces that have shaped genomes. Using the recently identified human ribosomal protein (RP) pseudogene sequences, we have thoroughly studied DNA mutation patterns in the human genome. We analyzed a total of 1726 processed RP pseudogene sequences, comprising more than 700 000 bases. To be sure to differentiate the sequence changes occurring in the functional genes during evolution from those occurring in pseudogenes after they were fixed in the genome, we used only pseudogene sequences originating from parts of RP genes that are identical in human and mouse. Overall, we found that nucleotide transitions are more common than transversions, by roughly a factor of two. Moreover, the substitution rates amongst the 12 possible nucleotide pairs are not homogeneous as they are affected by the type of immediately neighboring nucleotides and the overall local G+C content. Finally, our dataset is large enough that it has many indels, thus allowing for the first time statistically robust analysis of these events. Overall, we found that deletions are about three times more common than insertions (3740 versus 1291). The frequencies of both these events follow characteristic power-law behavior associated with the size of the indel. However, unexpectedly, the frequency of 3 bp deletions (in contrast to 3 bp insertions) violates this trend, being considerably higher than that of 2 bp deletions. The possible biological implications of such a 3 bp bias are discussed.

Algorithms↗
PubMed2003

Comparing protein abundance and mRNA expression levels on a genomic scale.

Attempts to correlate protein abundance with mRNA expression levels have had variable success. We review the results of these comparisons, focusing on yeast. In the process, we survey experimental techniques for determining protein abundance, principally two-dimensional gel electrophoresis and mass-spectrometry. We also merge many of the available yeast protein-abundance datasets, using the resulting larger 'meta-dataset' to find correlations between protein and mRNA expression, both globally and within smaller categories.

Animals↗
PubMed2003

The human genome has 49 cytochrome c pseudogenes, including a relic of a primordial gene that still functions in mouse.

Using a computational approach, we have identified 49 cytochrome c (cyc) pseudogenes in the human genome. Analysis of these provides a detailed description of the molecular evolution of the cyc gene. Almost all of the pseudogenes are full-length, and we have concluded that they mostly originated from independent retrotransposition events (i.e. they are processed). Based on phylogenetic analysis and detailed sequence comparison, we have further divided these pseudogenes into two groups. The first, consisting of four young pseudogenes that were dated to be between 27 and 34 Myr old, originated from a gene almost identical to the modern human cyc gene. The second group of pseudogenes is much older and appears to have descended from ancient genes similar to modern rodent cyc genes. Thus, our results support the observation that accelerated evolution in cyc sequence had occurred in the primate lineage. The oldest pseudogene in the second group, dated to be over 80 Myr old, resembles the testis-specific cyc gene in modern rodents. It is likely that the mammalian ancestor had both the somatic and the testis-specific cyc genes. While the testis-specific gene is still functional in modern rodents, the human has lost it, retaining only a pseudogene in its place. Thus, our study may have identified a pseudogene that is a dead relic of a gene that has completely died off in the human lineage.

Amino Acid Sequence↗
PubMed2003

ExpressYourself: A modular platform for processing and visualizing microarray data.

DNA microarrays are widely used in biological research; by analyzing differential hybridization on a single microarray slide, one can detect changes in mRNA expression levels, increases in DNA copy numbers and the location of transcription factor binding sites on a genomic scale. Having performed the experiments, the major challenge is to process large, noisy datasets in order to identify the specific array elements that are significantly differentially hybridized. This normally requires aggregating different, often incompatible programs into a multi-step pipeline. Here we present ExpressYourself, a fully integrated platform for processing microarray data. In completely automated fashion, it will correct the background array signal, normalize the Cy5 and Cy3 signals, score levels of differential hybridization, combine the results of replicate experiments, filter problematic regions of the array and assess the quality of individual and replicate experiments. ExpressYourself is designed with a highly modular architecture so various types of microarray analysis algorithms can readily be incorporated as they are developed; for example, the system currently implements several normalization methods, including those that simultaneously consider signal intensity and slide location. The processed data are presented using a web-based graphical interface to facilitate comparison with the original images of the array slides. In particular, Express Yourself is able to regenerate images of the original microarray after applying various steps of processing, which greatly facilities identification of position-specific artifacts. The program is freely available for use at http://bioinfo.mbb.yale.edu/expressyourself.

Algorithms↗
PubMed2003

Data mining crystallization databases: knowledge-based approaches to optimize protein crystal screens.

Protein crystallization is a major bottleneck in protein X-ray crystallography, the workhorse of most structural proteomics projects. Because the principles that govern protein crystallization are too poorly understood to allow them to be used in a strongly predictive sense, the most common crystallization strategy entails screening a wide variety of solution conditions to identify the small subset that will support crystal nucleation and growth. We tested the hypothesis that more efficient crystallization strategies could be formulated by extracting useful patterns and correlations from the large data sets of crystallization trials created in structural proteomics projects. A database of crystallization conditions was constructed for 755 different proteins purified and crystallized under uniform conditions. Forty-five percent of the proteins formed crystals. Data mining identified the conditions that crystallize the most proteins, revealed that many conditions are highly correlated in their behavior, and showed that the crystallization success rate is markedly dependent on the organism from which proteins derive. Of the proteins that crystallized in a 48-condition experiment, 60% could be crystallized in as few as 6 conditions and 94% in 24 conditions. Consideration of the full range of information coming from crystal screening trials allows one to design screens that are maximally productive while consuming minimal resources, and also suggests further useful conditions for extending existing screens.

Archaeal Proteins↗
PubMed2003

SPINE 2: a system for collaborative structural proteomics within a federated database framework.

We present version 2 of the SPINE system for structural proteomics. SPINE is available over the web at http://nesg.org. It serves as the central hub for the Northeast Structural Genomics Consortium, allowing collaborative structural proteomics to be carried out in a distributed fashion. The core of SPINE is a laboratory information management system (LIMS) for key bits of information related to the progress of the consortium in cloning, expressing and purifying proteins and then solving their structures by NMR or X-ray crystallography. Originally, SPINE focused on tracking constructs, but, in its current form, it is able to track target sample tubes and store detailed sample histories. The core database comprises a set of standard relational tables and a data dictionary that form an initial ontology for proteomic properties and provide a framework for large-scale data mining. Moreover, SPINE sits at the center of a federation of interoperable information resources. These can be divided into (i) local resources closely coupled with SPINE that enable it to handle less standardized information (e.g. integrated mailing and publication lists), (ii) other information resources in the NESG consortium that are inter-linked with SPINE (e.g. crystallization LIMS local to particular laboratories) and (iii) international archival resources that SPINE links to and passes on information to (e.g. TargetDB at the PDB).

Cooperative Behavior↗
PubMed2003

A method to assess compositional bias in biological sequences and its application to prion-like glutamine/asparagine-rich domains in eukaryotic proteomes.

We have derived a novel method to assess compositional biases in biological sequences, which is based on finding the lowest-probability subsequences for a given residue-type set. As a case study, the distribution of prion-like glutamine/asparagine-rich ((Q+N)-rich) domains (which are linked to amyloidogenesis) was assessed for budding and fission yeasts and four other eukaryotes. We find more than 170 prion-like (Q+N)-rich regions in budding yeast, and, strikingly, many fewer in fission yeast. Also, some residues, such as tryptophan or isoleucine, are unlikely to form biased regions in any eukaryotic proteome.

Amino Acid Motifs↗