bluuebunny/crossref_metadata_2025_split huggingface dataset:
A computational approach to discovering the functions of bacterial phytochromes by analysis of homolog distributions
Authors: Lamparter Tilman 
Published: April 2006

Abstract

Background
Phytochromes are photoreceptors, discovered in plants, that control a wide variety of developmental processes. They have also been found in bacteria and fungi, but for many species their biological role remains obscure. This work concentrates on the phytochrome system of Agrobacterium tumefaciens, a non-photosynthetic soil bacterium with two phytochromes. To identify proteins that might share common functions with phytochromes, a co-distribution analysis was performed on the basis of protein sequences from 138 bacteria.

Results
A database of protein sequences from 138 bacteria was generated. Each sequence was BLASTed against the entire database. The homolog distribution of each query protein was then compared with the homolog distribution of every other protein (target protein) of the same species, and the target proteins were sorted according to their probability of co-distribution under random conditions. As query proteins, phytochromes from Agrobacterium tumefaciens, Pseudomonas aeruginosa, Deinococcus radiodurans and Synechocystis PCC 6803 were chosen along with several phytochrome-related proteins from A. tumefaciens. The Synechocystis photosynthesis protein D1 was selected as a control. In the D1 analyses, the ratio between photosynthesis-related proteins and those not related to photosynthesis among the top 150 in the co-distribution tables was > 3:1, showing that the method is appropriate for finding partner proteins with common functions. The co-distribution of phytochromes with other histidine kinases was remarkably high, although most co-distributed histidine kinases were not direct BLAST homologs of the query protein. This finding implies that phytochromes and other histidine kinases share common functions as parts of signalling networks. All phytochromes tested, with one exception, also revealed a remarkably high co-distribution with glutamate synthase and methionine synthase. This result implies a general role of bacterial phytochromes in ammonium assimilation and amino acid metabolism.

Conclusion
It was possible to identify several proteins that might share common functions with bacterial phytochromes by the co-distribution approach. This computational approach might also be helpful in other cases.
Score-based prediction of genomic islands in prokaryotic genomes using hidden Markov models
Authors: Waack Stephan , Keller Oliver , Asper Roman , Brodag Thomas , Damm Carsten , Fricke Wolfgang Florian , Surovcik Katharina , Meinicke Peter , Merkl Rainer 
Published: April 2006

Abstract

Background
Horizontal gene transfer (HGT) is considered a strong evolutionary force shaping the content of microbial genomes in a substantial manner. It is the difference in speed enabling the rapid adaptation to changing environmental demands that distinguishes HGT from gene genesis, duplications or mutations. For a precise characterization, algorithms are needed that identify transfer events with high reliability. Frequently, the transferred pieces of DNA have a considerable length, comprise several genes and are called genomic islands (GIs) or more specifically pathogenicity or symbiotic islands.

Results
We have implemented the program SIGI-HMM that predicts GIs and the putative donor of each individual alien gene. It is based on the analysis of codon usage (CU) of each individual gene of a genome under study. CU of each gene is compared against a carefully selected set of CU tables representing microbial donors or highly expressed genes. Multiple tests are used to identify putatively alien genes, to predict putative donors and to mask putatively highly expressed genes. Thus, we determine the states and emission probabilities of an inhomogeneous hidden Markov model working on gene level. For the transition probabilities, we draw upon classical test theory with the intention of integrating a sensitivity controller in a consistent manner. SIGI-HMM was written in JAVA and is publicly available. It accepts as input any file created according to the EMBL-format.
It generates output in the common GFF format readable for genome browsers. Benchmark tests showed that the output of SIGI-HMM is in agreement with known findings. Its predictions were both consistent with annotated GIs and with predictions generated by different methods.

Conclusion
SIGI-HMM is a sensitive tool for the identification of GIs in microbial genomes. It allows to interactively analyze genomes in detail and to generate or to test hypotheses about the origin of acquired genes.
INTEGRATOR: interactive graphical search of large protein interactomes over the Web
Authors: Chang Aaron N , McDermott Jason , Frazier Zachary , Guerquin Michal , Samudrala Ram 
Published: April 2006

Abstract

Background
The rapid growth of protein interactome data has elevated the necessity and importance of network analysis tools. However, unlike pure text data, network search spaces are of exponential complexity. This poses special challenges for storing, searching, and navigating this data efficiently. Moreover, development of effective web interfaces has been difficult.

Results
We present Integrator, a web-integrated graphical search tool for protein-protein interaction networks across 50+ genomes.

Conclusion
Integrator provides single and multiple protein searches of the Bioverse database containing experimentally-derived and predicted protein-protein interactions. The interface provides animated local network views, rapid subgraph manipulation, and cross-referencing of functional annotations. Integrator is available at http://bioverse.compbio.washington.edu/integrator.
Ensemble attribute profile clustering: discovering and characterizing groups of genes with similar patterns of biological features
Authors: Semeiks JR , Rizki A , Bissell MJ , Mian IS 
Published: April 2006

Abstract

Background
Ensemble attribute profile clustering is a novel, text-based strategy for analyzing a user-defined list of genes and/or proteins. The strategy exploits annotation data present in gene-centered corpora and utilizes ideas from statistical information retrieval to discover and characterize properties shared by subsets of the list. The practical utility of this method is demonstrated by employing it in a retrospective study of two non-overlapping sets of genes defined by a published investigation as markers for normal human breast luminal epithelial cells and myoepithelial cells.

Results
Each genetic locus was characterized using a finite set of biological properties and represented as a vector of features indicating attributes associated with the locus (a gene attribute profile). In this study, the vector space models for a pre-defined list of genes were constructed from the Gene Ontology (GO) terms and the Conserved Domain Database (CDD) protein domain terms assigned to the loci by the gene-centered corpus LocusLink. This data set of GO- and CDD-based gene attribute profiles, vectors of binary random variables, was used to estimate multiple finite mixture models and each ensuing model utilized to partition the profiles into clusters. The resultant partitionings were combined using a unanimous voting scheme to produce consensus clusters, sets of profiles that co-occured consistently in the same cluster. Attributes that were important in defining the genes assigned to a consensus cluster were identified. The clusters and their attributes were inspected to ascertain the GO and CDD terms most associated with subsets of genes and in conjunction with external knowledge such as chromosomal location, used to gain functional insights into human breast biology. The 52 luminal epithelial cell markers and 89 myoepithelial cell markers are disjoint sets of genes. Ensemble attribute profile clustering-based analysis indicated that both lists contained groups of genes with the functional properties of membrane receptor biology/signal transduction and nucleic acid binding/transcription. A subset of the luminal markers was associated with metabolic and oxidoreductase activities, whereas a subset of myoepithelial markers was associated with protein hydrolase activity.

Conclusion
Given a set of genes and/or proteins associated with a phenomenon, process or system of interest, ensemble attribute profile clustering provides a simple method for collating and sythesizing the annotation data pertaining to them that are present in text-based, gene-centered corpora. The results provide information about properties common and unique to subsets of the list and hence insights into the biology of the problem under investigation.
CARMA: A platform for analyzing microarray datasets that incorporate replicate measures
Authors: Greer Kevin A , McReynolds Matthew R , Brooks Heddwen L , Hoying James B 
Published: April 2006

AbstractBackgroundThe incorporation of statistical models that account for experimental variability provides a necessary framework for the interpretation of microarray data. A robust experimental design coupled with an analysis of variance (ANOVA) incorporating a model that accounts for known sources of experimental variability can significantly improve the determination of differences in gene expression and estimations of their significance.ResultsTo realize the full benefits of performing analysis of variance on microarray data we have developed CARMA, a microarray analysis platform that reads data files generated by most microarray image processing software packages, performs ANOVA using a user-defined linear model, and produces easily interpretable graphical and numeric results. No pre-processing of the data is required and user-specified parameters control most aspects of the analysis including statistical significance criterion. The software also performs location and intensity dependent lowess normalization, automatic outlier detection and removal, and accommodates missing data.ConclusionCARMA provides a clear quantitative and statistical characterization of each measured gene that can be used to assess marginally acceptable measures and improve confidence in the interpretation of microarray results. Overall, applying CARMA to microarray datasets incorporating repeated measures effectively reduces the number of gene incorrectly identified as differentially expressed and results in a more robust and reliable analysis.
GOurmet: A tool for quantitative comparison and visualization of gene expression profiles based on gene ontology (GO) distributions
Authors: Doherty Jason M , Carmichael Lynn K , Mills Jason C 
Published: April 2006

Abstract

Background
The ever-expanding population of gene expression profiles (EPs) from specified cells and tissues under a variety of experimental conditions is an important but difficult resource for investigators to utilize effectively. Software tools have been recently developed to use the distribution of gene ontology (GO) terms associated with the genes in an EP to identify specific biological functions or processes that are over- or under-represented in that EP relative to other EPs. Additionally, it is possible to use the distribution of GO terms inherent to each EP to relate that EP as a whole to other EPs. Because GO term annotation is organized in a tree-like cascade of variable granularity, this approach allows the user to relate (e.g., by hierarchical clustering) EPs of varying length and from different platforms (e.g., GeneChip, SAGE, EST library).

Results
Here we present GOurmet, a software package that calculates the distribution of GO terms represented by the genes in an individual expression profile (EP), clusters multiple EPs based on these integrated GO term distributions, and provides users several tools to visualize and compare EPs. GOurmet is particularly useful in meta-analysis to examine EPs of specified cell types (e.g., tissue-specific stem cells) that are obtained through different experimental procedures. GOurmet also introduces a new tool, the Targetoid plot, which allows users to dynamically render the multi-dimensional relationships among individual elements in any clustering analysis. The Targetoid plotting tool allows users to select any element as the center of the plot, and the program will then represent all other elements in the cluster as a function of similarity to the selected central element.

Conclusion
GOurmet is a user-friendly, GUI-based software package that greatly facilitates analysis of results generated by multiple EPs. The clustering analysis features a dynamic targetoid plot that is generalizable for use with any clustering application.
Domain-based small molecule binding site annotation
Authors: Snyder Kevin A , Feldman Howard J , Dumontier Michel , Salama John J , Hogue Christopher WV 
Published: April 2006

Abstract

Background
Accurate small molecule binding site information for a protein can facilitate studies in drug docking, drug discovery and function prediction, but small molecule binding site protein sequence annotation is sparse. The Small Molecule Interaction Database (SMID), a database of protein domain-small molecule interactions, was created using structural data from the Protein Data Bank (PDB). More importantly it provides a means to predict small molecule binding sites on proteins with a known or unknown structure and unlike prior approaches, removes large numbers of false positive hits arising from transitive alignment errors, non-biologically significant small molecules and crystallographic conditions that overpredict ion binding sites.

Description
Using a set of co-crystallized protein-small molecule structures as a starting point, SMID interactions were generated by identifying protein domains that bind to small molecules, using NCBI's Reverse Position Specific BLAST (RPS-BLAST) algorithm. SMID records are available for viewing at http://smid.blueprint.org. The SMID-BLAST tool provides accurate transitive annotation of small-molecule binding sites for proteins not found in the PDB. Given a protein sequence, SMID-BLAST identifies domains using RPS-BLAST and then lists potential small molecule ligands based on SMID records, as well as their aligned binding sites. A heuristic ligand score is calculated based on E-value, ligand residue identity and domain entropy to assign a level of confidence to hits found. SMID-BLAST predictions were validated against a set of 793 experimental small molecule interactions from the PDB, of which 472 (60%) of predicted interactions identically matched the experimental small molecule and of these, 344 had greater than 80% of the binding site residues correctly identified. Further, we estimate that 45% of predictions which were not observed in the PDB validation set may be true positives.

Conclusion
By focusing on protein domain-small molecule interactions, SMID is able to cluster similar interactions and detect subtle binding patterns that would not otherwise be obvious. Using SMID-BLAST, small molecule targets can be predicted for any protein sequence, with the only limitation being that the small molecule must exist in the PDB. Validation results and specific examples within illustrate that SMID-BLAST has a high degree of accuracy in terms of predicting both the small molecule ligand and binding site residue positions for a query protein.
DNPTrapper: an assembly editing tool for finishing and analysis of complex repeat regions
Authors: Arner Erik , Tammi Martti T , Tran Anh-Nhi , Kindlund Ellen , Andersson Bjorn 
Published: April 2006

Abstract

Background
Many genome projects are left unfinished due to complex, repeated regions. Finishing is the most time consuming step in sequencing and current finishing tools are not designed with particular attention to the repeat problem.

Results
We have developed DNPTrapper, a shotgun sequence finishing tool, specifically designed to address the problems posed by the presence of repeated regions in the target sequence. The program detects and visualizes single base differences between nearly identical repeat copies, and offers the overview and flexibility needed to rapidly resolve complex regions within a working session. The use of a database allows large amounts of data to be stored and handled, and allows viewing of mammalian size genomes. The program is available under an Open Source license.

Conclusion
With DNPTrapper, it is possible to separate repeated regions that previously were considered impossible to resolve, and finishing tasks that previously took days or weeks can be resolved within hours or even minutes.
Modeling Sage data with a truncated gamma-Poisson model
Authors: Thygesen Helene H , Zwinderman Aeilko H 
Published: April 2006

Abstract

Background
Serial Analysis of Gene Expressions (SAGE) produces gene expression measurements on a discrete scale, due to the finite number of molecules in the sample. This means that part of the variance in SAGE data should be understood as the sampling error in a binomial or Poisson distribution, whereas other variance sources, in particular biological variance, should be modeled using a continuous distribution function, i.e. a prior on the intensity of the Poisson distribution. One challenge is that such a model predicts a large number of genes with zero counts, which cannot be observed.

Results
We present a hierarchical Poisson model with a gamma prior and three different algorithms for estimating the parameters in the model. It turns out that the rate parameter in the gamma distribution can be estimated on the basis of a single SAGE library, whereas the estimate of the shape parameter becomes unstable. This means that the number of zero counts cannot be estimated reliably. When a bivariate model is applied to two SAGE libraries, however, the number of predicted zero counts becomes more stable and in approximate agreement with the number of transcripts observed across a large number of experiments. In all the libraries we analyzed there was a small population of very highly expressed tags, typically 1% of the tags, that could not be accounted for by the model. To handle those tags we chose to augment our model with a non-parametric component. We also show some results based on a log-normal distribution instead of the gamma distribution.

Conclusion
By modeling SAGE data with a hierarchical Poisson model it is possible to separate the sampling variance from the variance in gene expression. If expression levels are reported at the gene level rather than at the tag level, genes mapped to multiple tags must be kept separate, since their expression levels show a different statistical behavior. A log-normal prior provided a better fit to our data than the gamma prior, but except for a small subpopulation of tags with very high counts, the two priors are similar.
Amplification of the Gene Ontology annotation of Affymetrix probe sets
Authors: Muro Enrique M , Perez-Iratxeta Carolina , Andrade-Navarro Miguel A 
Published: April 2006

Abstract

Background
The annotations of Affymetrix DNA microarray probe sets with Gene Ontology terms are carefully selected for correctness. This results in very accurate but incomplete annotations which is not always desirable for microarray experiment evaluation.

Results
Here we present a protocol to amplify the set of Gene Ontology annotations associated to Affymetrix DNA microarray probe sets using information from related databases.

Conclusion
Predicted novel annotations and the evidence producing them can be accessed at Probe2GO: http://www.ogic.ca/p2g. Scripts are available on demand.
An application of statistics to comparative metagenomics
Authors: Rodriguez-Brito Beltran , Rohwer Forest , Edwards Robert A 
Published: April 2006

Abstract

Background
Metagenomics, sequence analyses of genomic DNA isolated directly from the environments, can be used to identify organisms and model community dynamics of a particular ecosystem. Metagenomics also has the potential to identify significantly different metabolic potential in different environments.

Results
Here we use a statistical method to compare curated subsystems, to predict the physiology, metabolism, and ecology from metagenomes. This approach can be used to identify those subsystems that are significantly different between metagenome sequences. Subsystems that were overrepresented in the Sargasso Sea and Acid Mine Drainage metagenome when compared to non-redundant databases were identified.

Conclusion
The methodology described herein applies statistics to the comparisons of metabolic potential in metagenomes. This analysis reveals those subsystems that are more, or less, represented in the different environments that are compared. These differences in metabolic potential lead to several testable hypotheses about physiology and metabolism of microbes from these ecosystems.
Optic cup and facial patterning defects in ocular ectoderm β-catenin gain-of-function mice
Authors: Miller Leigh-Anne D , Smith April N , Taketo M Mark , Lang Richard A 
Published: April 2006

Abstract

Background
The canonical Wnt signaling pathway has a number of critical functions during embryonic development and, when activated aberrantly, in the genesis of cancer. Current evidence suggests that during eye development, regulation of Wnt signaling is critical for patterning the surface ectoderm that will contribute to multiple components of the eye. Wnt signaling loss-of-function experiments show that a region of periocular ectoderm will form ectopic lentoid bodies unless the Wnt pathway modifies its fate towards other structures. Consistent with this, Wnt signaling gain of function in the ocular region ectoderm results in a suppression of lens fate.

Results
Here we demonstrate that ectoderm-specific Wnt signaling gain-of-function embryos exhibit additional defects besides those noted in the lens. There are profound facial defects including a foreshortened snout, malformation of the nasal region, and clefting of the epidermis along the ocular-nasal axis. Furthermore, despite the restriction of Wnt pathway gain-of-function to the surface ectoderm, the optic cup is inappropriately patterned and ultimately forms a highly convoluted, disorganized array of epithelium with the characteristics of retina and retinal pigmented epithelium.

Conclusion
We suggest that activation of the Wnt pathway in surface ectoderm may disrupt the normal exchange of signals between the presumptive lens and retina that coordinate development of a functional eye.
Long-term maintenance of in vitro cultured honeybee (Apis mellifera) embryonic cells
Authors: Bergem Monica , Norberg Kari , Aamodt Randi M 
Published: April 2006

AbstractBackgroundIn vitrocultivation of cells allows novel investigation ofin vivo-mechanisms and is a helpful tool in developmental biology, biochemistry and functional genomics. Numerous cell lines of insect species, e.g., silkworm and mosquito, have been reported. However, this is not the case for successful long-term cultivation of cells in honeybees.ResultsMethods for cultivation of honeybee embryonic cells are discussed here. Pre-gastrula stage embryos were used to initiate cultures, and cells were reared on 96-wells microplates with Grace insect medium, supplemented with Fetal Bovine Serum. Cells proliferated in clusters, and maintained viable and mitotic for more than three months.ConclusionWe report here, for the first time, long-term cultivation of honeybee cells. Results represent a highly usefulin vitro-system for studying a model organism of increasing importance in areas such as aging, sociality and neurobiology.