CiteULike: mariakmejia's library [54 articles]

GeneCAT--novel webtools that combine BLAST and co-expression analyses.

Marek Mutwil — 2008-05-18T21:56:00-00:00

Nucleic acids research (14 May 2008)

The gene co-expression analysis toolbox (GeneCAT) introduces several novel microarray data analyzing tools. First, the multigene co-expression analysis, combined with co-expressed gene networks, provides a more powerful data mining technique than standard, single-gene co-expression analysis. Second, the high-throughput Map-O-Matic tool matches co-expression pattern of multiple query genes to genes present in user-defined subdatabases, and can therefore be used for gene mapping in forward genetic screens. Third, Rosetta combines co-expression analysis with BLAST and can be used to find 'true' gene orthologs in the plant model organisms Arabidopsis thaliana and Hordeum vulgare (Barley). GeneCAT is equipped with expression data for the model plant A. thaliana, and first to introduce co-expression mining tools for the monocot Barley. GeneCAT is available at http://genecat.mpg.de.

SerbGO: searching for the best GO tool.

J L Mosquera — 2008-05-18T21:47:51-00:00

Nucleic acids research (14 May 2008)

In recent years, the scientific community has provided many tools to assist with pathway analysis. Some of these programs can be used to manage functional annotation of gene products, others are oriented to exploring and analyzing data sets and many allow both possibilities. Potential users of these tools are faced with the necessity to decide which of the existing programs are the most appropriate for their needs. SerbGO is a user-friendly web tool created to facilitate this task. It can be used (i) to search for specific functionalities and determine which applications provide them and (ii) to compare several applications on the basis of different types of functionalities. Iterating and combining both functionalities can easily lead to selecting an appropriate tool. Data required by SerbGO is either the desired capabilities within a defined Standard Functionalities Set or the list of the tools to be compared. The analysis performed carries out a cross-classification that produces an easily readable output with the list of tools that implement the capabilities demanded or a table with the categorization of the GO tools that one wishes to compare. SerbGO is freely available and does not require a login. It can be accessed either directly at our server (http://estbioinfo.stat.ub.es/apli/serbgo) or at the GO Consortium website (http://www.geneontology.org/GO.tools.microarray.shtml#serbgo).

Plant biology: In their neighbour's shadow

Ji[rbreve]í Friml — 2008-05-15T23:29:38-00:00

Nature, Vol. 453, No. 7193. (14 May 2008), pp. 298-299.

Widespread Translational Inhibition by Plant miRNAs and siRNAs

Peter Brodersen — 2008-05-16T09:08:52-00:00

Science (15 May 2008), 1159151.

High complementarity between plant miRNAs and their mRNA targets is thought to cause silencing prevalently by endonucleolytic cleavage. We have isolated Arabidopsis mutants defective in miRNA action. Their analysis provides evidence that plant miRNA-guided silencing has a widespread translational inhibitory component that is genetically separable from endonucleolytic cleavage. We further show that the same is true of silencing mediated by short interfering (si)RNA populations. Translational repression is effected in part by the ARGONAUTE proteins AGO1 and AGO10. It also requires the activity of the microtubule-severing enzyme katanin, implicating cytoskeleton dynamics in miRNA action as recently suggested from animal studies. Also as in animals, the decapping component VCS/Ge-1 is required for translational repression by miRNAs, suggesting that the underlying mechanisms in the two kingdoms are related. 10.1126/science.1159151

Use and misuse of the gene ontology annotations.

Seung Yon Rhee — 2008-05-15T05:58:28-00:00

Nature reviews. Genetics (13 May 2008)

The Gene Ontology (GO) project is a collaboration among model organism databases to describe gene products from all organisms using a consistent and computable language. GO produces sets of explicitly defined, structured vocabularies that describe biological processes, molecular functions and cellular components of gene products in both a computer- and human-readable manner. Here we describe key aspects of GO, which, when overlooked, can cause erroneous results, and address how these pitfalls can be avoided.

Genetic approaches to crop improvement: responding to environmental and population changes.

Shin Takeda — 2008-05-16T15:16:17-00:00

Nature reviews. Genetics (13 May 2008)

Crop production is threatened by global climate change, and recent demands for crops to produce bio-fuels have started to affect the worldwide supply of some of the most important foods. How can we support a growing human population in such circumstances? One potential solution is the improvement of crops to increase yield from both irrigated and non-irrigated lands, and to create novel varieties that are more tolerant to environmental stresses. Recent progress has been made in the isolation and functional analyses of genes controlling yield and tolerance to abiotic stresses. In addition, promising new methods are being developed for identifying additional genes and variants of interest and putting these to practical use in crop improvement.

How Athila retrotransposons survive in the Arabidopsis genome

Antonio Marco — 2008-05-15T01:18:55-00:00

BMC Genomics, Vol. 9, No. 1. (2008)

BACKGROUND:Transposable elements are selfish genetic sequences which only occasionally provide useful functions to their host species. In addition, models of mobile element evolution assume a second type of selfishness: elements of different familes do not cooperate, but they independently fight for their survival in the host genome.RESULTS:We show that recombination events among distantly related Athila retrotransposons have led to the generation of new Athila lineages. Their pattern of diversification suggests that Athila elements survive in Arabidopsis by a combination of selfish replication and of amplification of highly diverged copies with coding potential. Many Athila elements are non-autonomous but still conserve intact open reading frames which are under the effect of negative, purifying natural selection. CONCLUSIONS:The evolution of these mobile elements is far more complex than hitherto assumed. Strict selfish replication does not explain all the patterns observed.

Arabidopsis mRNA polyadenylation machinery: comprehensive analysis of protein-protein interactions and gene expression profiling

Arthur Hunt — 2008-05-15T01:18:18-00:00

BMC Genomics, Vol. 9, No. 1. (2008)

BACKGROUND:The polyadenylation of mRNA is one of the critical processing steps during expression of almost all eukaryotic genes. It is tightly integrated with transcription, particularly its termination, as well as other RNA processing events, i.e. capping and splicing. The poly(A) tail protects the mRNA from unregulated degradation, and it is required for nuclear export and translation initiation. In recent years, it has been demonstrated that the polyadenylation process is also involved in the regulation of gene expression. The polyadenylation process requires two components, the cis-elements on the mRNA and a group of protein factors that recognize the cis-elements and produce the poly(A) tail. Here we report a comprehensive pairwise protein-protein interaction mapping and gene expression profiling of the mRNA polyadenylation protein machinery in Arabidopsis. RESULTS:By protein sequence homology search using human and yeast polyadenylation factors, we identified 28 proteins that may be components of Arabidopsis polyadenylation machinery. To elucidate the protein network and their functions, we first tested their protein-protein interaction profiles. Out of 320 pair-wise protein-protein interaction assays done using the yeast two-hybrid system, 56 (~17%) showed positive interactions. 15 of these interactions were further tested, and all were confirmed by co-immunoprecipitation and/or in vitro co-purification. These interactions organize into three distinct hubs involving the Arabidopsis polyadenylation factors. These hubs are centered around AtCPSF100, AtCLPS, and AtFIPS. The first two are similar to complexes seen in mammals, while the third one stands out as unique to plants. When comparing the gene expression profiles extracted from publicly available microarray datasets, some of the polyadenylation related genes showed tissue-specific expression, suggestive of potential different polyadenylation complex configurations. CONCLUSIONS:An extensive protein network was revealed for plant polyadenylation machinery, in which all predicted proteins were found to be connecting to the complex. The gene expression profiles are indicative that specialized sub-complexes may be formed to carry out targeted processing of mRNA in different developmental stages and tissue types. These results offer a roadmap for further functional characterizations of the protein factors, and for building models when testing the genetic contributions of these genes in plant growth and development.

U12 intron positions are more strongly conserved between animals and plants than U2 intron positions

Malay Basu — 2008-05-15T01:16:52-00:00

Biology Direct, Vol. 3, No. 1. (2008)

We report that the positions of minor, U12 introns are conserved in orthologous genes from human and Arabidopsis to an even greater extent than the positions of the major, U2 introns. The U12 introns, especially, conserved ones are concentrated in 5'-portions of plant and animal genes, where the U12 to U2 conversions occurs preferentially in the 3'-portions of genes. These results are compatible with the hypothesis that the high level of conservation of U12 intron positions and their persistence in genomes despite the unidirectional U12 to U2 conversion are explained by the role of the slowly excised U12 introns in down-regulation of gene expression. Reviewers: This article was reviewed by John Logsdon and Manyuan Long. For the full reviews, please go to the Reviewers' Reports section.

CpG island density and its correlations with genomic features in mammalian genomes

Leng Han — 2008-05-14T12:22:14-00:00

Genome Biology, Vol. 9 (13 May 2008), R79.

Physiology and metabolism 'Tear down this wall'

Markus Pauly — 2008-05-15T01:06:34-00:00

Current opinion in plant biology (7 May 2008)

Regulatory change in YABBY-like transcription factor led to evolution of extreme fruit size during tomato domestication.

Bin Cong — 2008-05-14T22:50:24-00:00

Nature genetics (11 May 2008)

Plant domestication represents an accelerated form of evolution, resulting in exaggerated changes in the tissues and organs of greatest interest to humans (for example, seeds, roots and tubers). One of the most extreme cases has been the evolution of tomato fruit. Cultivated tomato plants produce fruit as much as 1,000 times larger than those of their wild progenitors. Quantitative trait mapping studies have shown that a relatively small number of genes were involved in this dramatic transition, and these genes control two processes: cell cycle and organ number determination. The key gene in the first process has been isolated and corresponds to fw2.2, a negative regulator of cell division. However, until now, nothing was known about the molecular basis of the second process. Here, we show that the second major step in the evolution of extreme fruit size was the result of a regulatory change of a YABBY-like transcription factor (fasciated) that controls carpel number during flower and/or fruit development.

Pre-mRNA splicing: a complex picture in higher definition.

Matthew J Schellenberg — 2008-05-15T01:02:17-00:00

Trends in biochemical sciences (8 May 2008)

Intron excision from pre-mRNAs of higher eukaryotes requires a transition from splice-site recognition across short exons to organization of the spliceosome across long introns. Recently, insight into this transition has been provided and, in addition, it has been shown that an alternative splicing factor, the polypyrimidine-tract-binding protein, can exert its control on splice-site choice by blocking this key step in the assembly of the splicing machinery.

Structural evolution of multisubunit RNA polymerases.

Finn Werner — 2008-05-15T01:01:44-00:00

Trends in microbiology (9 May 2008)

Evolutionarily related multisubunit RNA polymerases (RNAPs) facilitate gene transcription throughout the three domains of life. During the past seven years an increasing number of bacterial and eukaryotic RNAP structures have been solved; however, the archaeal enzyme remained elusive. Two reports from the Murakami and Cramer laboratories have now filled this gap in our knowledge and enable us to hypothesize about the evolution of the structure and function of RNAPs.

Molecular evolution of the RNA polymerase II CTD.

Rob D Chapman — 2008-05-15T01:01:02-00:00

Trends in genetics : TIG (8 May 2008)

In higher eukaryotes, an unusual C-terminal domain (CTD) is crucial to the function of RNA polymerase II in transcription. The CTD consists of multiple heptapeptide repeats; differences in the number of repeats between organisms and their degree of conservation have intrigued researchers for two decades. Here, we review the evolution of the CTD at the molecular level. Several primitive motifs have been integrated into compound heptads that can be readily amplified. The selection of phosphorylatable residues in the heptad repeat provided the opportunity for advanced gene regulation in eukaryotes. Current findings suggest that the CTD should be considered as a collection of continuous overlapping motifs as opposed to a specific functional unit defined by a heptad.

Circadian clock function in Arabidopsis thaliana: time beyond transcription.

Paloma Más — 2008-05-15T00:59:08-00:00

Trends in cell biology (7 May 2008)

The past decade has seen a remarkable advance in our understanding of the plant circadian system, mostly in Arabidopsis thaliana. It is now well established that Arabidopsis clock genes and their protein products operate through autoregulatory feedback loops that promote rhythmic oscillations in cellular, metabolic and physiological activities. This article reviews recent studies that have provided evidence for new mechanisms of clock organization and function. These mechanisms include protein-protein interactions and the regulation of protein stability, which, together, directly connect light signalling to the Arabidopsis circadian system. Evidence of rhythmic changes in chromatin structure has also opened new and exciting ways for regulation of clock gene expression. All of these mechanisms ensure an appropriate synchronization with the environment, which is crucial for successful plant growth and development.

Measuring global credibility with application to local sequence alignment.

BJ Webb-Robertson — 2008-05-11T16:25:06-00:00

PLoS computational biology, Vol. 4, No. 5. (May 2008)

Computational biology is replete with high-dimensional (high-D) discrete prediction and inference problems, including sequence alignment, RNA structure prediction, phylogenetic inference, motif finding, prediction of pathways, and model selection problems in statistical genetics. Even though prediction and inference in these settings are uncertain, little attention has been focused on the development of global measures of uncertainty. Regardless of the procedure employed to produce a prediction, when a procedure delivers a single answer, that answer is a point estimate selected from the solution ensemble, the set of all possible solutions. For high-D discrete space, these ensembles are immense, and thus there is considerable uncertainty. We recommend the use of Bayesian credibility limits to describe this uncertainty, where a (1-alpha)%, 0</=alpha</=1, credibility limit is the minimum Hamming distance radius of a hyper-sphere containing (1-alpha)% of the posterior distribution. Because sequence alignment is arguably the most extensively used procedure in computational biology, we employ it here to make these general concepts more concrete. The maximum similarity estimator (i.e., the alignment that maximizes the likelihood) and the centroid estimator (i.e., the alignment that minimizes the mean Hamming distance from the posterior weighted ensemble of alignments) are used to demonstrate the application of Bayesian credibility limits to alignment estimators. Application of Bayesian credibility limits to the alignment of 20 human/rodent orthologous sequence pairs and 125 orthologous sequence pairs from six Shewanella species shows that credibility limits of the alignments of promoter sequences of these species vary widely, and that centroid alignments dependably have tighter credibility limits than traditional maximum similarity alignments.

Exploiting and integrating rich features for biological literature classification.

H Wang — 2008-05-11T16:24:44-00:00

BMC bioinformatics, Vol. 9 Suppl 3 (2008)

BACKGROUND: Efficient features play an important role in automated text classification, which definitely facilitates the access of large-scale data. In the bioscience field, biological structures and terminologies are described by a large number of features; domain dependent features would significantly improve the classification performance. How to effectively select and integrate different types of features to improve the biological literature classification performance is the major issue studied in this paper. RESULTS: To efficiently classify the biological literatures, we propose a novel feature value schema TF*ML, features covering from lower level domain independent "string feature" to higher level domain dependent "semantic template feature", and proper integrations among the features. Compared to our previous approaches, the performance is improved in terms of AUC and F-Score by 11.5% and 8.8% respectively, and outperforms the best performance achieved in BioCreAtIvE 2006. CONCLUSIONS: Different types of features possess different discriminative capabilities in literature classification; proper integration of domain independent and dependent features would significantly improve the performance and overcome the over-fitting on data distribution.

Identification of transcription factor contexts in literature using machine learning approaches.

H Yang — 2008-05-11T16:24:32-00:00

BMC bioinformatics, Vol. 9 Suppl 3 (2008)

BACKGROUND: Availability of information about transcription factors (TFs) is crucial for genome biology, as TFs play a central role in the regulation of gene expression. While manual literature curation is expensive and labour intensive, the development of semi-automated text mining support is hindered by unavailability of training data. There have been no studies on how existing data sources (e.g. TF-related data from the MeSH thesaurus and GO ontology) or potentially noisy example data (e.g. protein-protein interaction, PPI) could be used to provide training data for identification of TF-contexts in literature. RESULTS: In this paper we describe a text-classification system designed to automatically recognise contexts related to transcription factors in literature. A learning model is based on a set of biological features (e.g. protein and gene names, interaction words, other biological terms) that are deemed relevant for the task. We have exploited background knowledge from existing biological resources (MeSH and GO) to engineer such features. Weak and noisy training datasets have been collected from descriptions of TF-related concepts in MeSH and GO, PPI data and data representing non-protein-function descriptions. Three machine-learning methods are investigated, along with a vote-based merging of individual approaches and/or different training datasets. The system achieved highly encouraging results, with most classifiers achieving an F-measure above 90%. CONCLUSIONS: The experimental results have shown that the proposed model can be used for identification of TF-related contexts (i.e. sentences) with high accuracy, with a significantly reduced set of features when compared to traditional bag-of-words approach. The results of considering existing PPI data suggest that there is not as high similarity between TF and PPI contexts as we have expected. We have also shown that existing knowledge sources are useful both for feature engineering and for obtaining noisy positive training data.

New challenges for text mining: mapping between text and manually curated pathways.

K Oda — 2008-05-11T16:24:10-00:00

BMC bioinformatics, Vol. 9 Suppl 3 (2008)

BACKGROUND: Associating literature with pathways poses new challenges to the Text Mining (TM) community. There are three main challenges to this task: (1) the identification of the mapping position of a specific entity or reaction in a given pathway, (2) the recognition of the causal relationships among multiple reactions, and (3) the formulation and implementation of required inferences based on biological domain knowledge. RESULTS: To address these challenges, we constructed new resources to link the text with a model pathway; they are: the GENIA pathway corpus with event annotation and NF-kB pathway. Through their detailed analysis, we address the untapped resource, 'bio-inference,' as well as the differences between text and pathway representation. Here, we show the precise comparisons of their representations and the nine classes of 'bio-inference' schemes observed in the pathway corpus. CONCLUSIONS: We believe that the creation of such rich resources and their detailed analysis is the significant first step for accelerating the research of the automatic construction of pathway from text.

Gene Ontology density estimation and discourse analysis for automatic GeneRiF extraction.

J Gobeill — 2008-05-11T16:23:57-00:00

BMC bioinformatics, Vol. 9 Suppl 3 (2008)

BACKGROUND: This paper describes and evaluates a sentence selection engine that extracts a GeneRiF (Gene Reference into Functions) as defined in ENTREZ-Gene based on a MEDLINE record. Inputs for this task include both a gene and a pointer to a MEDLINE reference. In the suggested approach we merge two independent sentence extraction strategies. The first proposed strategy (LASt) uses argumentative features, inspired by discourse-analysis models. The second extraction scheme (GOEx) uses an automatic text categorizer to estimate the density of Gene Ontology categories in every sentence; thus providing a full ranking of all possible candidate GeneRiFs. A combination of the two approaches is proposed, which also aims at reducing the size of the selected segment by filtering out non-content bearing rhetorical phrases. RESULTS: Based on the TREC-2003 Genomics collection for GeneRiF identification, the LASt extraction strategy is already competitive (52.78%). When used in a combined approach, the extraction task clearly shows improvement, achieving a Dice score of over 57% (+10%). CONCLUSIONS: Argumentative representation levels and conceptual density estimation using Gene Ontology contents appear complementary for functional annotation in proteomics.

RDFScape: Semantic Web meets systems biology.

A Splendiani — 2008-05-11T16:22:53-00:00

BMC bioinformatics, Vol. 9 Suppl 4 (2008)

BACKGROUND: The recent availability of high-throughput data in molecular biology has increased the need for a formal representation of this knowledge domain. New ontologies are being developed to formalize knowledge, e.g. about the functions of proteins. As the Semantic Web is being introduced into the Life Sciences, the basis for a distributed knowledge-base that can foster biological data analysis is laid. However, there still is a dichotomy, in tools and methodologies, between the use of ontologies in biological investigation, that is, in relation to experimental observations, and their use as a knowledge-base. RESULTS: RDFScape is a plugin that has been developed to extend a software oriented to biological analysis with support for reasoning on ontologies in the semantic web framework. We show with this plugin how the use of ontological knowledge in biological analysis can be extended through the use of inference. In particular, we present two examples relative to ontologies representing biological pathways: we demonstrate how these can be abstracted and visualized as interaction networks, and how reasoning on causal dependencies within elements of pathways can be implemented. CONCLUSIONS: The use of ontologies for the interpretation of high-throughput biological data can be improved through the use of inference. This allows the use of ontologies not only as annotations, but as a knowledge-base from which new information relevant for specific analysis can be derived.

Bioinformatic analyses of mammalian 5'-UTR sequence properties of mRNAs predicts alternative translation initiation sites

Jill Wegrzyn — 2008-05-10T00:57:17-00:00

BMC Bioinformatics, Vol. 9 (08 May 2008), 232.

Metrics for GO based protein semantic similarity: a systematic evaluation.

C Pesquita — 2008-05-11T16:22:05-00:00

BMC bioinformatics, Vol. 9 Suppl 5 (2008)

BACKGROUND: Several semantic similarity measures have been applied to gene products annotated with Gene Ontology terms, providing a basis for their functional comparison. However, it is still unclear which is the best approach to semantic similarity in this context, since there is no conclusive evaluation of the various measures. Another issue, is whether electronic annotations should or not be used in semantic similarity calculations. RESULTS: We conducted a systematic evaluation of GO-based semantic similarity measures using the relationship with sequence similarity as a means to quantify their performance, and assessed the influence of electronic annotations by testing the measures in the presence and absence of these annotations. We verified that the relationship between semantic and sequence similarity is not linear, but can be well approximated by a rescaled Normal cumulative distribution function. Given that the majority of the semantic similarity measures capture an identical behaviour, but differ in resolution, we used the latter as the main criterion of evaluation. CONCLUSIONS: This work has provided a basis for the comparison of several semantic similarity measures, and can aid researchers in choosing the most adequate measure for their work. We have found that the hybrid simGIC was the measure with the best overall performance, followed by Resnik's measure using a best-match average combination approach. We have also found that the average and maximum combination approaches are problematic since both are inherently influenced by the number of terms being combined. We suspect that there may be a direct influence of data circularity in the behaviour of the results including electronic annotations, as a result of functional inference from sequence similarity.

Gene Ontology annotations: what they mean and where they come from.

DP Hill — 2008-05-11T16:21:46-00:00

BMC bioinformatics, Vol. 9 Suppl 5 (2008)

To address the challenges of information integration and retrieval, the computational genomics community increasingly has come to rely on the methodology of creating annotations of scientific literature using terms from controlled structured vocabularies such as the Gene Ontology (GO). Here we address the question of what such annotations signify and of how they are created by working biologists. Our goal is to promote a better understanding of how the results of experiments are captured in annotations, in the hope that this will lead both to better representations of biological reality through annotation and ontology development and to more informed use of GO resources by experimental scientists.

Ontology-guided data preparation for discovering genotype-phenotype relationships

Adrien Coulet — 2008-04-30T15:14:25-00:00

BMC Bioinformatics, Vol. 9, No. Suppl 4. (2008)

BACKGROUND:Complexity and amount of post-genomic data constitute two major factors limiting the application of Knowledge Discovery in Databases (KDD) methods in life sciences. Bio-ontologies may nowadays play key roles in knowledge discovery in life science providing semantics to data and to extracted units, by taking advantage of the progress of Semantic Web technologies concerning the understanding and availability of tools for knowledge representation, extraction, and reasoning.RESULTS:This paper presents a method that exploits bio-ontologies for guiding data selection within the preparation step of the KDD process. We propose three scenarios in which domain knowledge and ontology elements such as subsumption, properties, class descriptions, are taken into account for data selection, before the data mining step. Each of these scenarios is illustrated within a case-study relative to the search of genotype-phenotype relationships in a familial hypercholesterolemia dataset. The guiding of data selection based on domain knowledge is analysed and shows a direct influence on the volume and significance of the data mining results.CONCLUSIONS:The method proposed in this paper is an efficient alternative to numerical methods for data selection based on domain knowledge. In turn, the results of this study may be reused in ontology modelling and data integration.

Terminologies for text-mining; an experiment in the lipoprotein metabolism domain.

D Alexopoulou — 2008-05-11T16:18:25-00:00

BMC bioinformatics, Vol. 9 Suppl 4 (2008)

BACKGROUND: The engineering of ontologies, especially with a view to a text-mining use, is still a new research field. There does not yet exist a well-defined theory and technology for ontology construction. Many of the ontology design steps remain manual and are based on personal experience and intuition. However, there exist a few efforts on automatic construction of ontologies in the form of extracted lists of terms and relations between them. RESULTS: We share experience acquired during the manual development of a lipoprotein metabolism ontology (LMO) to be used for text-mining. We compare the manually created ontology terms with the automatically derived terminology from four different automatic term recognition (ATR) methods. The top 50 predicted terms contain up to 89% relevant terms. For the top 1000 terms the best method still generates 51% relevant terms. In a corpus of 3066 documents 53% of LMO terms are contained and 38% can be generated with one of the methods. CONCLUSIONS: Given high precision, automatic methods can help decrease development time and provide significant support for the identification of domain-specific vocabulary. The coverage of the domain vocabulary depends strongly on the underlying documents. Ontology development for text mining should be performed in a semi-automatic way; taking ATR results as input and following the guidelines we described. AVAILABILITY: The TFIDF term recognition is available as Web Service, described at http://gopubmed4.biotec.tu-dresden.de/IdavollWebService/services/CandidateTermGeneratorService?wsdl.

A Semantic Web for bioinformatics: goals, tools, systems, applications.

N Cannata — 2008-05-11T16:16:58-00:00

BMC bioinformatics, Vol. 9 Suppl 4 (2008)

Novel motifs in amino acid permease genes from Leishmania

Martin Akerman — 2007-12-05T15:49:09-00:00

Biochemical and Biophysical Research Communications, Vol. 325, No. 1. (3 December 2004), pp. 353-366.

Eight amino acid permease genes from the protozoan parasite Leishmania donovani (AAPLDs) were cloned, sequenced, and shown to be expressed in promastigotes. Seven of these belong to the amino acid transporter-1 and one to the amino acid polyamino-choline superfamilies. Using these sequences as well as known and characterized amino acid permease genes from all kingdoms, a training set was established and used to search for motifs, using the MEME motif discovery tool. This study revealed two motifs that are specific to the genus Leishmania, four to the family trypanosomatidae, and a single motif that is common between trypanosomatidae and mammalian systems A1 and N. Interestingly, most of these motifs are clustered in two regions of 50-60 amino acids. Blast search analyses indicated a close relationship between the L. donovani and Trypanosoma brucei amino acid permeases. The results of this work describe the cloning of the first amino acid permease genes in parasitic protozoa and contribute to the understanding of amino acid permease evolution in these organisms. Furthermore, the identification of genus-specific motifs in these proteins might be useful to better understand parasite physiology within its hosts.

Transcriptional regulatory code of a eukaryotic genome

Christopher Harbison — 2006-01-16T14:50:46-00:00

Nature, Vol. 431, No. 7004. (2004), pp. 99-104.

DBD: a transcription factor prediction database.

SK Kummerfeld — 2006-01-05T12:53:50-00:00

Nucleic Acids Res, Vol. 34, No. Database issue. (1 January 2006)

Regulation of gene expression influences almost all biological processes in an organism; sequence-specific DNA-binding transcription factors are critical to this control. For most genomes, the repertoire of transcription factors is only partially known. Hitherto transcription factor identification has been largely based on genome annotation pipelines that use pairwise sequence comparisons, which detect only those factors similar to known genes, or on functional classification schemes that amalgamate many types of proteins into the category of 'transcription factor'. Using a novel transcription factor identification method, the DBD transcription factor database fills this void, providing genome-wide transcription factor predictions for organisms from across the tree of life. The prediction method behind DBD identifies sequence-specific DNA-binding transcription factors through homology using profile hidden Markov models (HMMs) of domains. Thus, it is limited to factors that are homologus to those HMMs. The collection of HMMs is taken from two existing databases (Pfam and SUPERFAMILY), and is limited to models that exclusively detect transcription factors that specifically recognize DNA sequences. It does not include basal transcription factors or chromatin-associated proteins, for instance. Based on comparison with experimentally verified annotation, the prediction procedure is between 95% and 99% accurate. Between one quarter and one-half of our genome-wide predicted transcription factors represent previously uncharacterized proteins. The DBD (www.transcriptionfactor.org) consists of predicted transcription factor repertoires for 150 completely sequenced genomes, their domain assignments and the hand curated list of DNA-binding domain HMMs. Users can browse, search or download the predictions by genome, domain family or sequence identifier, view families of transcription factors based on domain architecture and receive predictions for a protein sequence.

Systematic discovery of regulatory motifs in human promoters and 3[prime] UTRs by comparison of several mammals

Xiaohui Xie — 2005-02-28T05:59:22-00:00

Nature, Vol. aop, No. current. (27 February 2005)

An ORFeome-based analysis of human transcription factor genes and the construction of a microarray to interrogate their expression.

DN Messina — 2006-06-26T15:41:16-00:00

Genome Res, Vol. 14, No. 10B. (October 2004), pp. 2041-2047.

Transcription factors (TFs) are essential regulators of gene expression, and mutated TF genes have been shown to cause numerous human genetic diseases. Yet to date, no single, comprehensive database of human TFs exists. In this work, we describe the collection of an essentially complete set of TF genes from one depiction of the human ORFeome, and the design of a microarray to interrogate their expression. Taking 1468 known TFs from TRANSFAC, InterPro, and FlyBase, we used this seed set to search the ScriptSure human transcriptome database for additional genes. ScriptSure's genome-anchored transcript clusters allowed us to work with a nonredundant high-quality representation of the human transcriptome. We used a high-stringency similarity search by using BLASTN, and a protein motif search of the human ORFeome by using hidden Markov models of DNA-binding domains known to occur exclusively or primarily in TFs. Four hundred ninety-four additional TF genes were identified in the overlap between the two searches, bringing our estimate of the total number of human TFs to 1962. Zinc finger genes are by far the most abundant family (762 members), followed by homeobox (199 members) and basic helix-loop-helix genes (117 members). We designed a microarray of 50-mer oligonucleotide probes targeted to a unique region of the coding sequence of each gene. We have successfully used this microarray to interrogate TF gene expression in species as diverse as chickens and mice, as well as in humans.

Defining transcriptional networks through integrative modeling of mRNA expression and transcription factor binding data.

F Gao — 2006-01-27T18:07:26-00:00

BMC Bioinformatics, Vol. 5 (18 March 2004)

BACKGROUND: Functional genomics studies are yielding information about regulatory processes in the cell at an unprecedented scale. In the yeast S. cerevisiae, DNA microarrays have not only been used to measure the mRNA abundance for all genes under a variety of conditions but also to determine the occupancy of all promoter regions by a large number of transcription factors. The challenge is to extract useful information about the global regulatory network from these data. RESULTS: We present MA-Networker, an algorithm that combines microarray data for mRNA expression and transcription factor occupancy to define the regulatory network of the cell. Multivariate regression analysis is used to infer the activity of each transcription factor, and the correlation across different conditions between this activity and the mRNA expression of a gene is interpreted as regulatory coupling strength. Applying our method to S. cerevisiae, we find that, on average, 58% of the genes whose promoter region is bound by a transcription factor are true regulatory targets. These results are validated by an analysis of enrichment for functional annotation, response for transcription factor deletion, and over-representation of cis-regulatory motifs. We are able to assign directionality to transcription factors that control divergently transcribed genes sharing the same promoter region. Finally, we identify an intrinsic limitation of transcription factor deletion experiments related to the combinatorial nature of transcriptional control, to which our approach provides an alternative. CONCLUSION: Our reliable classification of ChIP positives into functional and non-functional TF targets based on their expression pattern across a wide range of conditions provides a starting point for identifying the unknown sequence features in non-coding DNA that directly or indirectly determine the context dependence of transcription factor action. Complete analysis results are available for browsing or download at http://bussemaker.bio.columbia.edu/papers/MA-Networker/.

Integrated assessment and prediction of transcription factor binding.

A Beyer — 2006-06-30T09:45:02-00:00

PLoS Comput Biol, Vol. 2, No. 6. (16 June 2006)

Systematic chromatin immunoprecipitation (chIP-chip) experiments have become a central technique for mapping transcriptional interactions in model organisms and humans. However, measurement of chromatin binding does not necessarily imply regulation, and binding may be difficult to detect if it is condition or cofactor dependent. To address these challenges, we present an approach for reliably assigning transcription factors (TFs) to target genes that integrates many lines of direct and indirect evidence into a single probabilistic model. Using this approach, we analyze publicly available chIP-chip binding profiles measured for yeast TFs in standard conditions, showing that our model interprets these data with significantly higher accuracy than previous methods. Pooling the high-confidence interactions reveals a large network containing 363 significant sets of factors (TF modules) that cooperate to regulate common target genes. In addition, the method predicts 980 novel binding interactions with high confidence that are likely to occur in so-far untested conditions. Indeed, using new chIP-chip experiments we show that predicted interactions for the factors Rpn4p and Pdr1p are observed only after treatment of cells with methyl-methanesulfonate, a DNA-damaging agent. We outline the first approach for consistently integrating all available evidences for TF-target interactions and we comprehensively identify the resulting TF module hierarchy. Prioritizing experimental conditions for each factor will be especially important as increasing numbers of chIP-chip assays are performed in complex organisms such as humans, for which "standard conditions" are ill defined.

REDUCE: An online tool for inferring cis-regulatory elements and transcriptional module activities from microarray data.

C Roven — 2006-06-29T13:53:41-00:00

Nucleic Acids Res, Vol. 31, No. 13. (1 July 2003), pp. 3487-3490.

REDUCE is a motif-based regression method for microarray analysis. The only required inputs are (i) a single genome-wide set of absolute or relative mRNA abundances and (ii) the DNA sequence of the regulatory region associated with each gene that is probed. Currently supported organisms are yeast, worm and fly; it is an open question whether in its current incarnation our approach can be used for mouse or human. REDUCE uses unbiased statistics to identify oligonucleotide motifs whose occurrence in the regulatory region of a gene correlates with the level of mRNA expression. Regression analysis is used to infer the activity of the transcriptional module associated with each motif. REDUCE is available online at http://bussemaker.bio.columbia.edu/reduce/. This web site provides functionality for the upload and management of microarray data. REDUCE analysis results can be viewed and downloaded, and optionally be shared with other users or made publicly accessible.

Close sequence comparisons are sufficient to identify human cis-regulatory elements.

Shyam Prabhakar — 2006-06-16T19:46:25-00:00

Genome Res (12 June 2006)

Cross-species DNA sequence comparison is the primary method used to identify functional noncoding elements in human and other large genomes. However, little is known about the relative merits of evolutionarily close and distant sequence comparisons. To address this problem, we identified evolutionarily conserved noncoding regions in primate, mammalian, and more distant comparisons using a uniform approach (Gumby) that facilitates unbiased assessment of the impact of evolutionary distance on predictive power. We benchmarked computational predictions against previously identified cis-regulatory elements at diverse genomic loci and also tested numerous extremely conserved human-rodent sequences for transcriptional enhancer activity using an in vivo enhancer assay in transgenic mice. Human regulatory elements were identified with acceptable sensitivity (53%-80%) and true-positive rate (27%-67%) by comparison with one to five other eutherian mammals or six other simian primates. More distant comparisons (marsupial, avian, amphibian, and fish) failed to identify many of the empirically defined functional noncoding elements. Our results highlight the practical utility of close sequence comparisons, and the loss of sensitivity entailed by more distant comparisons. We derived an intuitive relationship between ancient and recent noncoding sequence conservation from whole-genome comparative analysis that explains most of the observations from empirical benchmarking. Lastly, we determined that, in addition to strength of conservation, genomic location and/or density of surrounding conserved elements must also be considered in selecting candidate enhancers for in vivo testing at embryonic time points.

Functional analysis of human and chimpanzee promoters.

F Heissig — 2006-07-24T16:58:32-00:00

Genome Biol, Vol. 6, No. 7. (2005)

BACKGROUND: It has long been argued that changes in gene expression may provide an additional and crucial perspective on the evolutionary differences between humans and chimpanzees. To investigate how often expression differences seen in tissues are caused by sequence differences in the proximal promoters, we tested the expression activity in cultured cells of human and chimpanzee promoters from genes that differ in mRNA expression between human and chimpanzee tissues. RESULTS: Twelve promoters for which the corresponding gene had been shown to be differentially expressed between humans and chimpanzees in liver or brain were tested. Seven showed a significant difference in activity between the human promoter and the orthologous chimpanzee promoter in at least one of the two cell lines used. However, only three of them showed a difference in the same direction as in the tissues. CONCLUSION: Differences in proximal promoter activity are likely to be common between humans and chimpanzees, but are not linked in a simple fashion to gene-expression levels in tissues. This suggests that several genetic differences between humans and chimpanzees might be responsible for a single expression difference and thus that relevant expression differences between humans and chimpanzees will be difficult to predict from cell culture experiments or DNA sequences.

Multiple-laboratory comparison of microarray platforms

Rafael Irizarry — 2005-04-22T06:16:46-00:00

Nature Methods, Vol. 2, No. 5. (21 April 2005), pp. 345-350.

Applied bioinformatics for the identification of regulatory elements.

WW Wasserman — 2006-06-05T16:25:43-00:00

Nat Rev Genet, Vol. 5, No. 4. (April 2004), pp. 276-287.

Transcriptome network component analysis with limited microarray data.

Simon J Galbraith — 2006-06-19T12:17:58-00:00

Bioinformatics (9 June 2006)

Network component analysis (NCA) is a method to deduce transcription factor (TF) activities and TF-gene regulation control strengths from gene expression data and a TF-gene binding connectivity network. Previously, this method could analyze a maximum number of regulators equal to the total sample size because of the identifiability limit in data decomposition. As such, the total number of source signal components was limited to the total number of experiments rather than the total number of biological regulators. However, networks that have less transcriptome data points than the number of regulators are of interest. Thus it is imperative to develop a theoretical basis that allows realistic source signal extraction based on relatively few data points. On the other hand, such methods would inherently increase numerical challenges leading to multiple solutions. Therefore, solutions to both problems are needed. RESULTS: We have improved NCA for transcription factor activity (TFA) estimation, based the observation that most genes are regulated by only a few TFs. This observation leads to the derivation of a new identifiability criterion which is tested during numerical iteration that allows us to decompose data when the number of TFs is greater than the number of experiments. To show that our method works with real microarray data and has biological utility, we analyze Saccharomyces cerevisiae cell cycle microarray data (73 experiments) using a TF-gene connectivity network (96 TFs) derived from ChIP-chip binding data. We compare the results of NCA analysis to results obtained from ChIP-chip regression methods, and we show that NCA and regression produce TFAs that are qualitatively similar, but the NCA TFAs outperform regression in statistical tests. We also show that NCA can extract subtle TFA signals that correlate with known cell cycle TF function and cell cycle phase. Overall we determined that 31 TFs have statistically periodic TFAs in one or more experiments, 75% of which are known cell cycle regulators. In addition we find that the 12 TFAs that are periodic in two or more experiments correspond to well known cell cycle regulators. We also investigated TFA sensitivity to the choice of connectivity network we constructed two networks using different ChIP-chip p-value cut-offs. AVAILABILITY: The NCA Toolbox for MATLAB is available at http://www.seas.ucla.edu/~liaoj/download.htm.

Stochastic model of transcription factor-regulated gene expression.

R Karmakar — 2006-10-27T14:31:28-00:00

Phys Biol, Vol. 3, No. 3. (September 2006), pp. 200-208.

We consider a stochastic model of transcription factor (TF)-regulated gene expression. The model describes two genes, gene A and gene B, which synthesize the TFs and the target gene proteins, respectively. We show through analytic calculations that the TF fluctuations have a significant effect on the distribution of the target gene protein levels when the mean TF level falls in the highest sensitive region of the dose-response curve. We further study the effect of reducing the copy number of gene A from two to one. The enhanced TF fluctuations yield results different from those in the deterministic case. The probability that the target gene protein level exceeds a threshold value is calculated with the knowledge of the probability density functions associated with the TF and target gene protein levels. Numerical simulation results for a more detailed stochastic model are shown to be in agreement with those obtained through analytic calculations. The relevance of these results in the context of the genetic disorder haploinsufficiency is pointed out. Some experimental observations on the haploinsufficiency of the tumour suppressor gene, Nkx 3.1, are explained with the help of the stochastic model of TF-regulated gene expression.

The genome of the kinetoplastid parasite, Leishmania major.

AC Ivens — 2007-05-09T11:29:53-00:00

Science, Vol. 309, No. 5733. (15 July 2005), pp. 436-442.

Leishmania species cause a spectrum of human diseases in tropical and subtropical regions of the world. We have sequenced the 36 chromosomes of the 32.8-megabase haploid genome of Leishmania major (Friedlin strain) and predict 911 RNA genes, 39 pseudogenes, and 8272 protein-coding genes, of which 36% can be ascribed a putative function. These include genes involved in host-pathogen interactions, such as proteolytic enzymes, and extensive machinery for synthesis of complex surface glycoconjugates. The organization of protein-coding genes into long, strand-specific, polycistronic clusters and lack of general transcription factors in the L. major, Trypanosoma brucei, and Trypanosoma cruzi (Tritryp) genomes suggest that the mechanisms regulating RNA polymerase II-directed transcription are distinct from those operating in other eukaryotes, although the trypanosomatids appear capable of chromatin remodeling. Abundant RNA-binding proteins are encoded in the Tritryp genomes, consistent with active posttranscriptional regulation of gene expression.

How to infer gene networks from expression profiles

Mukesh Bansal — 2007-02-21T21:52:28-00:00

Mol Syst Biol, Vol. 3 (13 February 2007)

Genetic regulation of root hair development in Arabidopsis thaliana: a network model.

L Mendoza — 2005-12-14T09:05:45-00:00

J Theor Biol, Vol. 204, No. 3. (7 June 2000), pp. 311-326.

The root epidermis of Arabidopsis thaliana is formed by alternate files of hair and non-hair cells. Epidermal cells overlying two cortex cells eventually develop a hair, while those overlying only one cortex cell do not. Here we propose a network model that integrates most of the available genetic and molecular data on the regulatory and signaling pathways underlying root epidermal differentiation. The network architecture includes two pathways; one formed by the genes TTG, R homolog, GL2 and CPC, and the other one by the signal transduction proteins ETR1 and CTR1. Both parallel pathways regulate the activity of AXR2 and RHD6, which in turn control the development of root hairs. The regulatory network was simulated as a dynamical system of eight discrete state variables. The distinction between epidermal cells contacting one or two cortical cells was accounted for by fixing the initial states of CPC and ETR1 proteins. The model allows for predictions of mutants and pharmacological effects because it includes the ethylene receptor. The dynamical system reaches one of the six stable states depending upon the initial state of the CPC variable and the ethylene receptor. Two of the stable states describe the activation patterns observed in mature trichoblasts (hair cells) and atrichoblasts (non-hair cells) in the wild-type phenotype and under normal ethylene availability. The other four states correspond to changes in the number of hair cells due to experimentally induced changes in ethylene availability. This model provides a hypothesis on the interactions among genes that encode transcription factors that regulate root hair development and the proteins involved in the ethylene transduction pathway. This is the first effort to use a dynamical system to understand the complex genetic regulatory interactions that rule Arabidopsis primary root development. The advantages of this type of models over static schematic representations are discussed.

Gapped BLAST and PSI-BLAST: a new generation of protein database search programs.

SF Altschul — 2005-06-26T00:48:58-00:00

Nucleic Acids Res, Vol. 25, No. 17. (1 September 1997), pp. 3389-3402.

The BLAST programs are widely used tools for searching protein and DNA databases for sequence similarities. For protein comparisons, a variety of definitional, algorithmic and statistical refinements described here permits the execution time of the BLAST programs to be decreased substantially while enhancing their sensitivity to weak similarities. A new criterion for triggering the extension of word hits, combined with a new heuristic for generating gapped alignments, yields a gapped BLAST program that runs at approximately three times the speed of the original. In addition, a method is introduced for automatically combining statistically significant alignments produced by BLAST into a position-specific score matrix, and searching the database using this matrix. The resulting Position-Specific Iterated BLAST (PSI-BLAST) program runs at approximately the same speed per iteration as gapped BLAST, but in many cases is much more sensitive to weak but biologically relevant sequence similarities. PSI-BLAST is used to uncover several new and interesting members of the BRCT superfamily.

Bioinformatics challenges of new sequencing technology

Mihai Pop — 2008-02-13T12:33:43-00:00

Trends in Genetics, Vol. In Press, Corrected Proof

New DNA sequencing technologies can sequence up to one billion bases in a single day at low cost, putting large-scale sequencing within the reach of many scientists. Many researchers are forging ahead with projects to sequence a range of species using the new technologies. However, these new technologies produce read lengths as short as 35-40 nucleotides, posing challenges for genome assembly and annotation. Here we review the challenges and describe some of the bioinformatics systems that are being proposed to solve them. We specifically address issues arising from using these technologies in assembly projects, both de novo and for resequencing purposes, as well as efforts to improve genome annotation in the fragmented assemblies produced by short read lengths.

CAMERA: A Community Resource for Metagenomics

Rekha Seshadri — 2008-04-04T11:55:30-00:00

PLoS Biology, Vol. 5, No. 3. (1 March 2007), e75.

Comparative genome assembly.

M Pop — 2007-03-07T04:40:34-00:00

Brief Bioinform, Vol. 5, No. 3. (September 2004), pp. 237-248.

One of the most complex and computationally intensive tasks of genome sequence analysis is genome assembly. Even today, few centres have the resources, in both software and hardware, to assemble a genome from the thousands or millions of individual sequences generated in a whole-genome shotgun sequencing project. With the rapid growth in the number of sequenced genomes has come an increase in the number of organisms for which two or more closely related species have been sequenced. This has created the possibility of building a comparative genome assembly algorithm, which can assemble a newly sequenced genome by mapping it onto a reference genome. We describe here a novel algorithm for comparative genome assembly that can accurately assemble a typical bacterial genome in less than four minutes on a standard desktop computer. The software is available as part of the open-source AMOS project.

Pyrobayes: an improved base caller for SNP discovery in pyrosequences

Aaron Quinlan — 2008-01-31T11:58:19-00:00

Nature Methods, Vol. 5, No. 2. (13 January 2008), pp. 179-181.