Impediments to database interoperation: legal issues and security concerns.
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Computational Biology↗
Biomedical subjects
Mark Gerstein
Publications and source records attributed to Mark Gerstein.
Applications of DNA tiling arrays to experimental genome annotation and regulatory pathway discovery.
Microarrays have become a popular and important technology for surveying global patterns in gene expression and regulation. A number of innovative experiments have extended microarray applications beyond the measurement of mRNA expression levels, in order to uncover aspects of large-scale chromosome function and dynamics. This has been made possible due to the recent development of tiling arrays, where all non-repetitive DNA comprising a chromosome or locus is represented at various sequence resolutions. Since tiling arrays are designed to contain the entire DNA sequence without prior consultation of existing gene annotation, they enable the discovery of novel transcribed sequences and regulatory elements through the unbiased interrogation of genomic loci. The implementation of such methods for the global analysis of large eukaryotic genomes presents significant technical challenges. Nonetheless, tiling arrays are expected to become instrumental for the genome-wide identification and characterization of functional elements. Combined with computational methods to relate these data and map the complex interactions of transcriptional regulators, tiling array experiments can provide insight toward a more comprehensive understanding of fundamental molecular and cellular processes.
Gene Expression Profiling↗
A pilot study of transcription unit analysis in rice using oligonucleotide tiling-path microarray.
As the international efforts to sequence the rice genome are completed, an immediate challenge and opportunity is to comprehensively and accurately define all transcription units in the rice genome. Here we describe a strategy of using high-density oligonucleotide tiling-path microarrays to map transcription of the japonica rice genome. In a pilot experiment to test this approach, one array representing the reverse strand of the last 11.2 Mb sequence of chromosome 10 was analyzed in detail based on a mathematical model developed in this study. Analysis of the array data detected 77% of the reference gene models in a mixture of four RNA populations. Moreover, significant transcriptional activities were found in many of the previously annotated intergenic regions. These preliminary results demonstrate the utility of genome tiling microarrays in evaluating annotated rice gene models and in identifying novel transcription units that will facilitate rice genome annotation.
Carbocyanines↗
The temporal patterning microRNA let-7 regulates several transcription factors at the larval to adult transition in C. elegans.
The let-7 microRNA is phylogenetically conserved and temporally expressed in many animals. C. elegans let-7 controls terminal differentiation in a stem cell-like lineage in the hypodermis, while human let-7 has been implicated in lung cancer. To elucidate let-7's role in temporal control of nematode development, we used sequence analysis and reverse genetics to identify candidate let-7 target genes. We show that the nuclear hormone receptor daf-12 is a let-7 target in seam cells, while the forkhead transcription factor pha-4 is a target in the intestine. Additional likely targets are the zinc finger protein die-1 and the putative chromatin remodeling factor lss-4. Together with the previous identification of the hunchback ortholog hbl-1 as a let-7 target in the ventral nerve cord, our findings show that let-7 acts in at least three tissues to regulate different transcription factors, raising the possibility of let-7 as a master temporal regulator.
Animals↗
Issues in the analysis of oligonucleotide tiling microarrays for transcript mapping.
Traditional microarrays use probes complementary to known genes to quantitate the differential gene expression between two or more conditions. Genomic tiling microarray experiments differ in that probes that span a genomic region at regular intervals are used to detect the presence or absence of transcription. This difference means the same sets of biases and the methods for addressing them are unlikely to be relevant to both types of experiment. We introduce the informatics challenges arising in the analysis of tiling microarray experiments as open problems to the scientific community and present initial approaches for the analysis of this nascent technology.
Algorithms↗
Robotic cloning and Protein Production Platform of the Northeast Structural Genomics Consortium.
In this chapter we describe the core Protein Production Platform of the Northeast Structural Genomics Consortium (NESG) and outline the strategies used for producing high-quality protein samples using Escherichia coli host vectors. The platform is centered on 6X-His affinity-tagged protein constructs, allowing for a similar purification procedure for most targets, and the implementation of high-throughput parallel methods. In most cases, these affinity-purified proteins are sufficiently homogeneous that a single subsequent gel filtration chromatography step is adequate to produce protein preparations that are greater than 98% pure. Using this platform, over 1000 different proteins have been cloned, expressed, and purified in tens of milligram quantities over the last 36-month period (see Summary Statistics for All Targets, ). Our experience using a hierarchical multiplex expression and purification strategy, also described in this chapter, has allowed us to achieve success in producing not only protein samples but also many three-dimensional structures. As of December 2004, the NESG Consortium has deposited over 145 new protein structures to the Protein Data Bank (PDB); about two-thirds of these protein samples were produced by the NESG Protein Production Facility described here. The methods described here have proven effective in producing quality samples of both eukaryotic and prokaryotic proteins. These improved robotic and?or parallel cloning, expression, protein production, and biophysical screening technologies will be of broad value to the structural biology, functional proteomics, and structural genomics communities.
Chromatography, Gel↗
YeastHub: a semantic web use case for integrating data in the life sciences domain.
MOTIVATION: As the semantic web technology is maturing and the need for life sciences data integration over the web is growing, it is important to explore how data integration needs can be addressed by the semantic web. The main problem that we face in data integration is a lack of widely-accepted standards for expressing the syntax and semantics of the data. We address this problem by exploring the use of semantic web technologies-including resource description framework (RDF), RDF site summary (RSS), relational-database-to-RDF mapping (D2RQ) and native RDF data repository-to represent, store and query both metadata and data across life sciences datasets. RESULTS: As many biological datasets are presently available in tabular format, we introduce an RDF structure into which they can be converted. Also, we develop a prototype web-based application called YeastHub that demonstrates how a life sciences data warehouse can be built using a native RDF data store (Sesame). This data warehouse allows integration of different types of yeast genome data provided by different resources in different formats including the tabular and RDF formats. Once the data are loaded into the data warehouse, RDF-based queries can be formulated to retrieve and query the data in an integrated fashion. AVAILABILITY: The YeastHub website is accessible via the following URL: http://yeasthub.gersteinlab.org.
Biology↗
Multi-species microarrays reveal the effect of sequence divergence on gene expression profiles.
Interspecies comparisons of gene expression levels will increase our understanding of the evolution of transcriptional mechanisms and help to identify targets of natural selection. This approach holds particular promise for apes, as many human-specific adaptations are thought to result from differences in gene expression rather than in coding sequence. To date, however, all studies directly comparing interspecies gene expression have been performed on single-species arrays, so that it has been impossible to distinguish differential hybridization due to sequence mismatches from underlying expression differences. To evaluate the severity of this potential problem, we constructed a new multiprimate cDNA array using probes from human, chimpanzee, orangutan, and rhesus. We find a large effect of sequence divergence on hybridization signal, even in the closest pair of species, human and chimpanzee. By comparing single-species array analyses with results from multispecies arrays, we examine how estimates of differential gene expression are affected by sequence divergence. Our results indicate that naive use of single-species arrays in direct interspecies comparisons can yield spurious results.
Animals↗
Assessing the limits of genomic data integration for predicting protein networks.
Genomic data integration--the process of statistically combining diverse sources of information from functional genomics experiments to make large-scale predictions--is becoming increasingly prevalent. One might expect that this process should become progressively more powerful with the integration of more evidence. Here, we explore the limits of genomic data integration, assessing the degree to which predictive power increases with the addition of more features. We focus on a predictive context that has been extensively investigated and benchmarked in the past-the prediction of protein-protein interactions in yeast. We start by using a simple Naive Bayes classifier for integrating diverse sources of genomic evidence, ranging from coexpression relationships to similar phylogenetic profiles. We expand the number of features considered for prediction to 16, significantly more than previous studies. Overall, we observe a small, but measurable improvement in prediction performance over previous benchmarks, based on four strong features. This allows us to identify new yeast interactions with high confidence. It also allows us to quantitatively assess the inter-relations amongst different genomic features. It is known that subtle correlations and dependencies between features can confound the strength of interaction predictions. We investigate this issue in detail through calculating mutual information. To our surprise, we find no appreciable statistical dependence between the many possible pairs of features. We further explore feature dependencies by comparing the performance of our simple Naive Bayes classifier with a boosted version of the same classifier, which is fairly resistant to feature dependence. We find that boosting does not improve performance, indicating that, at least for prediction purposes, our genomic features are essentially independent. In summary, by integrating a few (i.e., four) good features, we approach the maximal predictive power of current genomic data integration; moreover, this limitation does not reflect (potentially removable) inter-relationships between the features.
Algorithms↗
Normal modes for predicting protein motions: a comprehensive database assessment and associated Web tool.
We carry out an extensive statistical study of the applicability of normal modes to the prediction of mobile regions in proteins. In particular, we assess the degree to which the observed motions found in a comprehensive data set of 377 nonredundant motions can be modeled by a single normal-mode vibration. We describe each motion in our data set by vectors connecting corresponding atoms in two crystallographically known conformations. We then measure the geometric overlap of these motion vectors with the displacement vectors of the lowest-frequency mode, for one of the conformations. Our study suggests that the lowest mode contains useful information about the parts of a protein that move most (i.e., have the largest amplitudes) and about the direction of this movement. Based on our findings, we developed a Web tool for motion prediction (available from http://molmovdb.org/nma) and apply it here to four representative motions--from bacteriorhodopsin, calmodulin, insulin, and T7 RNA polymerase.
Databases, Protein↗
Use of thioredoxin as a reporter to identify a subset of Escherichia coli signal sequences that promote signal recognition particle-dependent translocation.
We have previously reported that the DsbA signal sequence promotes efficient, cotranslational translocation of the cytoplasmic protein thioredoxin-1 via the bacterial signal recognition particle (SRP) pathway. However, two commonly used signal sequences, those of PhoA and MalE, which promote export by a posttranslational mechanism, do not export thioredoxin. We proposed that this difference in efficiency of export was due to the rapid folding of thioredoxin in the cytoplasm; cotranslational export by the DsbA signal sequence avoids the problem of cytoplasmic folding (C. F. Schierle, M. Berkmen, D. Huber, C. Kumamoto, D. Boyd, and J. Beckwith, J. Bacteriol. 185:5706-5713, 2003). Here, we use thioredoxin as a reporter to distinguish SRP-dependent from non-SRP-dependent cleavable signal sequences. We screened signal sequences exhibiting a range of hydrophobicity values based on a method that estimates hydrophobicity. Successive iterations of screening and refining the method defined a threshold hydrophobicity required for SRP recognition. While all of the SRP-dependent signal sequences identified were above this threshold, there were also a few signal sequences above the threshold that did not utilize the SRP pathway. These results suggest that a simple measure of the hydrophobicity of a signal sequence is an important but not a sufficient indicator for SRP recognition. In addition, by fusing a number of both classes of signal sequences to DsbA, we found that DsbA utilizes an SRP-dependent signal sequence to achieve efficient export to the periplasm. Our results suggest that those proteins found to be exported by SRP-dependent signal sequences may require this mode of export because of their tendency to fold rapidly in the cytoplasm.
Blotting, Western↗
DNA replication-timing analysis of human chromosome 22 at high resolution and different developmental states.
Duplication of the genome during the S phase of the cell cycle does not occur simultaneously; rather, different sequences are replicated at different times. The replication timing of specific sequences can change during development; however, the determinants of this dynamic process are poorly understood. To gain insights into the contribution of developmental state, genomic sequence, and transcriptional activity to replication timing, we investigated the timing of DNA replication at high resolution along an entire human chromosome (chromosome 22) in two different cell types. The pattern of replication timing was correlated with respect to annotated genes, gene expression, novel transcribed regions of unknown function, sequence composition, and cytological features. We observed that chromosome 22 contains regions of early- and late-replicating domains of 100 kb to 2 Mb, many (but not all) of which are associated with previously described chromosomal bands. In both cell types, expressed sequences are replicated earlier than nontranscribed regions. However, several highly transcribed regions replicate late. Overall, the DNA replication-timing profiles of the two different cell types are remarkably similar, with only nine regions of difference observed. In one case, this difference reflects the differential expression of an annotated gene that resides in this region. Novel transcribed regions with low coding potential exhibit a strong propensity for early DNA replication. Although the cellular function of such transcripts is poorly understood, our results suggest that their activity is linked to the replication-timing program.
Cell Differentiation↗
Global identification of human transcribed sequences with genome tiling arrays.
Elucidating the transcribed regions of the genome constitutes a fundamental aspect of human biology, yet this remains an outstanding problem. To comprehensively identify coding sequences, we constructed a series of high-density oligonucleotide tiling arrays representing sense and antisense strands of the entire nonrepetitive sequence of the human genome. Transcribed sequences were located across the genome via hybridization to complementary DNA samples, reverse-transcribed from polyadenylated RNA obtained from human liver tissue. In addition to identifying many known and predicted genes, we found 10,595 transcribed sequences not detected by other methods. A large fraction of these are located in intergenic regions distal from previously annotated genes and exhibit significant homology to other mammalian proteins.
Animals↗
Information assessment on predicting protein-protein interactions.
BACKGROUND: Identifying protein-protein interactions is fundamental for understanding the molecular machinery of the cell. Proteome-wide studies of protein-protein interactions are of significant value, but the high-throughput experimental technologies suffer from high rates of both false positive and false negative predictions. In addition to high-throughput experimental data, many diverse types of genomic data can help predict protein-protein interactions, such as mRNA expression, localization, essentiality, and functional annotation. Evaluations of the information contributions from different evidences help to establish more parsimonious models with comparable or better prediction accuracy, and to obtain biological insights of the relationships between protein-protein interactions and other genomic information. RESULTS: Our assessment is based on the genomic features used in a Bayesian network approach to predict protein-protein interactions genome-wide in yeast. In the special case, when one does not have any missing information about any of the features, our analysis shows that there is a larger information contribution from the functional-classification than from expression correlations or essentiality. We also show that in this case alternative models, such as logistic regression and random forest, may be more effective than Bayesian networks for predicting interactions. CONCLUSIONS: In the restricted problem posed by the complete-information subset, we identified that the MIPS and Gene Ontology (GO) functional similarity datasets as the dominating information contributors for predicting the protein-protein interactions under the framework proposed by Jansen et al. Random forests based on the MIPS and GO information alone can give highly accurate classifications. In this particular subset of complete information, adding other genomic data does little for improving predictions. We also found that the data discretizations used in the Bayesian methods decreased classification performance.
Artificial Intelligence↗
A high productivity/low maintenance approach to high-performance computation for biomedicine: four case studies.
The rapid advances in high-throughput biotechnologies such as DNA microarrays and mass spectrometry have generated vast amounts of data ranging from gene expression to proteomics data. The large size and complexity involved in analyzing such data demand a significant amount of computing power. High-performance computation (HPC) is an attractive and increasingly affordable approach to help meet this challenge. There is a spectrum of techniques that can be used to achieve computational speedup with varying degrees of impact in terms of how drastic a change is required to allow the software to run on an HPC platform. This paper describes a high- productivity/low-maintenance (HP/LM) approach to HPC that is based on establishing a collaborative relationship between the bioinformaticist and HPC expert that respects the former's codes and minimizes the latter's efforts. The goal of this approach is to make it easy for bioinformatics researchers to continue to make iterative refinements to their programs, while still being able to take advantage of HPC. The paper describes our experience applying these HP/LM techniques in four bioinformatics case studies: (1) genome-wide sequence comparison using Blast, (2) identification of biomarkers based on statistical analysis of large mass spectrometry data sets, (3) complex genetic analysis involving ordinal phenotypes, (4) large-scale assessment of the effect of possible errors in analyzing microarray data. The case studies illustrate how the HP/LM approach can be applied to a range of representative bioinformatics applications and how the approach can lead to significant speedup of computationally intensive bioinformatics applications, while making only modest modifications to the programs themselves.
Amino Acid Sequence↗
Regulation of gene expression by a metabolic enzyme.
Gene expression in eukaryotes is normally believed to be controlled by transcriptional regulators that activate genes encoding structural proteins and enzymes. To identify previously unrecognized DNA binding activities, a yeast proteome microarray was screened with DNA probes; Arg5,6, a well-characterized mitochondrial enzyme involved in arginine biosynthesis, was identified. Chromatin immunoprecipitation experiments revealed that Arg5,6 is associated with specific nuclear and mitochondrial loci in vivo, and Arg5,6 binds to specific fragments in vitro. Deletion of Arg5,6 causes altered transcript levels of both nuclear and mitochondrial target genes. These results indicate that metabolic enzymes can directly regulate eukaryotic gene expression.
Aldehyde Oxidoreductases↗
Genomic analysis of regulatory network dynamics reveals large topological changes.
Network analysis has been applied widely, providing a unifying language to describe disparate systems ranging from social interactions to power grids. It has recently been used in molecular biology, but so far the resulting networks have only been analysed statically. Here we present the dynamics of a biological network on a genomic scale, by integrating transcriptional regulatory information and gene-expression data for multiple conditions in Saccharomyces cerevisiae. We develop an approach for the statistical analysis of network dynamics, called SANDY, combining well-known global topological measures, local motifs and newly derived statistics. We uncover large changes in underlying network architecture that are unexpected given current viewpoints and random simulations. In response to diverse stimuli, transcription factors alter their interactions to varying degrees, thereby rewiring the network. A few transcription factors serve as permanent hubs, but most act transiently only during certain conditions. By studying sub-network structures, we show that environmental responses facilitate fast signal propagation (for example, with short regulatory cascades), whereas the cell cycle and sporulation direct temporal progression through multiple stages (for example, with highly inter-connected transcription factors). Indeed, to drive the latter processes forward, phase-specific transcription factors inter-regulate serially, and ubiquitously active transcription factors layer above them in a two-tiered hierarchy. We anticipate that many of the concepts presented here--particularly the large-scale topological changes and hub transience--will apply to other biological networks, including complex sub-systems in higher eukaryotes.
Algorithms↗
Comprehensive analysis of pseudogenes in prokaryotes: widespread gene decay and failure of putative horizontally transferred genes.
BACKGROUND: Pseudogenes often manifest themselves as disabled copies of known genes. In prokaryotes, it was generally believed (with a few well-known exceptions) that they were rare. RESULTS: We have carried out a comprehensive analysis of the occurrence of pseudogenes in a diverse selection of 64 prokaryote genomes. Overall, we find a total of around 7,000 candidate pseudogenes. Moreover, in all the genomes surveyed, pseudogenes occur in at least 1 to 5% of all gene-like sequences, with some genomes having considerably higher occurrence. Although many large populations of pseudogenes arise from large, diverse protein families (for example, the ABC transporters), notable numbers of pseudogenes are associated with specific families that do not occur that widely. These include the cytochrome P450 and PPE families (PF00067 and PF00823) and others that have a direct role in DNA transposition. CONCLUSIONS: We find suggestive evidence that a large fraction of prokaryote pseudogenes arose from failed horizontal transfer events. In particular, we find that pseudogenes are more than twice as likely as genes to have anomalous codon usage associated with horizontal transfer. Moreover, we found a significant difference in the number of horizontally transferred pseudogenes in pathogenic and non-pathogenic strains of Escherichia coli.
Escherichia coli K12↗