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springScape: visualisation of microarray and contextual bioinformatic data using spring embedding and an 'information landscape'.
The interpretation of microarray and other high-throughput data is highly dependent on the biological context of experiments. However, standard analysis packages are poor at simultaneously presenting both the array and related bioinformatic data. We have addressed this challenge by developing a system springScape based on 'spring embedding' and an 'information landscape' allowing several related data sources to be dynamically combined while highlighting one particular feature. Each data source is represented as a network of nodes connected by weighted edges. The networks are combined and embedded in the 2-D plane by spring embedding such that nodes with a high similarity are drawn close together. Complex relationships can be discovered by varying the weight of each data source and observing the dynamic response of the spring network. By modifying Procrustes analysis, we find that the visualizations have an acceptable degree of reproducibility. The 'information landscape' highlights one particular data source, displaying it as a smooth surface whose height is proportional to both the information being viewed and the density of nodes. The algorithm is demonstrated using several microarray data sets in combination with protein-protein interaction data and GO annotations. Among the features revealed are the spatio-temporal profile of gene expression and the identification of GO terms correlated with gene expression and protein interactions. The power of this combined display lies in its interactive feedback and exploitation of human visual pattern recognition. Overall, springScape shows promise as a tool for the interpretation of microarray data in the context of relevant bioinformatic information.
Algorithms↗
The Unified Medical Language System (UMLS): integrating biomedical terminology.
The Unified Medical Language System (http://umlsks.nlm.nih.gov) is a repository of biomedical vocabularies developed by the US National Library of Medicine. The UMLS integrates over 2 million names for some 900,000 concepts from more than 60 families of biomedical vocabularies, as well as 12 million relations among these concepts. Vocabularies integrated in the UMLS Metathesaurus include the NCBI taxonomy, Gene Ontology, the Medical Subject Headings (MeSH), OMIM and the Digital Anatomist Symbolic Knowledge Base. UMLS concepts are not only inter-related, but may also be linked to external resources such as GenBank. In addition to data, the UMLS includes tools for customizing the Metathesaurus (MetamorphoSys), for generating lexical variants of concept names (lvg) and for extracting UMLS concepts from text (MetaMap). The UMLS knowledge sources are updated quarterly. All vocabularies are available at no fee for research purposes within an institution, but UMLS users are required to sign a license agreement. The UMLS knowledge sources are distributed on CD-ROM and by FTP.
Animals↗
Functional annotation prediction: all for one and one for all.
In an era of rapid genome sequencing and high-throughput technology, automatic function prediction for a novel sequence is of utter importance in bioinformatics. While automatic annotation methods based on local alignment searches can be simple and straightforward, they suffer from several drawbacks, including relatively low sensitivity and assignment of incorrect annotations that are not associated with the region of similarity. ProtoNet is a hierarchical organization of the protein sequences in the UniProt database. Although the hierarchy is constructed in an unsupervised automatic manner, it has been shown to be coherent with several biological data sources. We extend the ProtoNet system in order to assign functional annotations automatically. By leveraging on the scaffold of the hierarchical classification, the method is able to overcome some frequent annotation pitfalls.
Algorithms↗
Discovering protein-protein interactions.
The ongoing genomics and proteomics efforts have helped identify many new genes and proteins in living organisms. However, simply knowing the existence of genes and proteins does not tell us much about the biological processes in which they participate. Many major biological processes are controlled by protein interaction networks. A comprehensive description of protein-protein interactions is therefore necessary to understand the genetic program of life. In this tutorial, we provide an overview of the various current high-throughput methods for discovering protein-protein interactions, covering both the conventional experimental methods and new computational approaches.
Artificial Gene Fusion↗
An empirical analysis of training protocols for probabilistic gene finders.
BACKGROUND: Generalized hidden Markov models (GHMMs) appear to be approaching acceptance as a de facto standard for state-of-the-art ab initio gene finding, as evidenced by the recent proliferation of GHMM implementations. While prevailing methods for modeling and parsing genes using GHMMs have been described in the literature, little attention has been paid as of yet to their proper training. The few hints available in the literature together with anecdotal observations suggest that most practitioners perform maximum likelihood parameter estimation only at the local submodel level, and then attend to the optimization of global parameter structure using some form of ad hoc manual tuning of individual parameters. RESULTS: We decided to investigate the utility of applying a more systematic optimization approach to the tuning of global parameter structure by implementing a global discriminative training procedure for our GHMM-based gene finder. Our results show that significant improvement in prediction accuracy can be achieved by this method. CONCLUSIONS: We conclude that training of GHMM-based gene finders is best performed using some form of discriminative training rather than simple maximum likelihood estimation at the submodel level, and that generalized gradient ascent methods are suitable for this task. We also conclude that partitioning of training data for the twin purposes of maximum likelihood initialization and gradient ascent optimization appears to be unnecessary, but that strict segregation of test data must be enforced during final gene finder evaluation to avoid artificially inflated accuracy measurements.
Algorithms↗
The use of edge-betweenness clustering to investigate biological function in protein interaction networks.
BACKGROUND: This paper describes an automated method for finding clusters of interconnected proteins in protein interaction networks and retrieving protein annotations associated with these clusters. RESULTS: Protein interaction graphs were separated into subgraphs of interconnected proteins, using the JUNG implementation of Girvan and Newman's Edge-Betweenness algorithm. Functions were sought for these subgraphs by detecting significant correlations with the distribution of Gene Ontology terms which had been used to annotate the proteins within each cluster. The method was implemented using freely available software (JUNG and the R statistical package). Protein clusters with significant correlations to functional annotations could be identified and included groups of proteins know to cooperate in cell metabolism. The method appears to be resilient against the presence of false positive interactions. CONCLUSION: This method provides a useful tool for rapid screening of small to medium size protein interaction datasets.
Algorithms↗
The odorant receptor repertoire of teleost fish.
BACKGROUND: Vertebrate odorant receptors comprise three types of G protein-coupled receptors: the OR, V1R and V2R receptors. The OR superfamily contains over 1,000 genes in some mammalian species, representing the largest gene superfamily in the mammalian genome. RESULTS: To facilitate an informed analysis of OR gene phylogeny, we identified the complete set of 143 OR genes in the zebrafish genome, as well as the OR repertoires in two pufferfish species, fugu (44 genes) and tetraodon (42 genes). Although the genomes analyzed here contain fewer genes than in mammalian species, the teleost OR genes can be grouped into a larger number of major clades, representing greater overall OR diversity in the fish. CONCLUSION: Based on the phylogeny of fish and mammalian repertoires, we propose a model for OR gene evolution in which different ancestral OR genes or gene families were selectively lost or expanded in different vertebrate lineages. In addition, our calculations of the ratios of non-synonymous to synonymous codon substitutions among more recently expanding OR subgroups in zebrafish implicate residues that may be involved in odorant binding.
Algorithms↗
Protein family expansions and biological complexity.
During the course of evolution, new proteins are produced very largely as the result of gene duplication, divergence and, in many cases, combination. This means that proteins or protein domains belong to families or, in cases where their relationships can only be recognised on the basis of structure, superfamilies whose members descended from a common ancestor. The size of superfamilies can vary greatly. Also, during the course of evolution organisms of increasing complexity have arisen. In this paper we determine the identity of those superfamilies whose relative sizes in different organisms are highly correlated to the complexity of the organisms. As a measure of the complexity of 38 uni- and multicellular eukaryotes we took the number of different cell types of which they are composed. Of 1,219 superfamilies, there are 194 whose sizes in the 38 organisms are strongly correlated with the number of cell types in the organisms. We give outline descriptions of these superfamilies. Half are involved in extracellular processes or regulation and smaller proportions in other types of activity. Half of all superfamilies have no significant correlation with complexity. We also determined whether the expansions of large superfamilies correlate with each other. We found three large clusters of correlated expansions: one involves expansions in both vertebrates and plants, one just in vertebrates, and one just in plants. Our work identifies important protein families and provides one explanation of the discrepancy between the total number of genes and the apparent physiological complexity of eukaryotic organisms.
Animals↗
Systematic analysis of head-to-head gene organization: evolutionary conservation and potential biological relevance.
Several "head-to-head" (or "bidirectional") gene pairs have been studied in individual experiments, but genome-wide analysis of this gene organization, especially in terms of transcriptional correlation and functional association, is still insufficient. We conducted a systematic investigation of head-to-head gene organization focusing on structural features, evolutionary conservation, expression correlation and functional association. Of the present 1,262, 1,071, and 491 head-to-head pairs identified in human, mouse, and rat genomes, respectively, pairs with 1- to 400-base pair distance between transcription start sites form the majority (62.36%, 64.15%, and 55.19% for human, mouse, and rat,respectively) of each dataset, and the largest group is always the one with a transcription start site distance of 101 to 200 base pairs. The phylogenetic analysis among Fugu, chicken, and human indicates a negative selection on the separation of head-to-head genes across vertebrate evolution, and thus the ancestral existence of this gene organization. The expression analysis shows that most of the human head-to-head genes are significantly correlated,and the correlation could be positive, negative, or alternative depending on the experimental conditions. Finally, head to-head genes statistically tend to perform similar functions, and gene pairs associated with the significant cofunctions seem to have stronger expression correlations. The findings indicate that the head-to-head gene organization is ancient and conserved, which subjects functionally related genes to correlated transcriptional regulation and thus provides an exquisite mechanism of transcriptional regulation based on gene organization. These results have significantly expanded the knowledge about head-to-head gene organization. Supplementary materials for this study are available at http://www.scbit.org/h2h.
Animals↗
The biology of the post-genomic era: the proteomics.
The complete identification of coding sequences in a number of species has led to announce the beginning of the post-genomic era, new tools have become available to study complex phenomena in biological systems. Rapid advances in genomic sequencing and bioinformatics have established the field of genomics to investigate thousands genes' activity through mRNA display. However, recent studies have demonstrated a lack of correlation between the transcriptional profiles and the actual protein levels in cells, so investigation of the expressed part of the genome is also required to link genomic data to biological function. It is possible that evolutional development occured by increasing complexity of regulation processes at the level of RNA and protein molecules instead of simple increase in gene number, so investigation of proteins and protein complexes became important fields of our post-genomic era. High-resolution two-dimensional gels combined with sensitive mass spectrometry can reveal virtually all proteins present in cells opening new insights into functions of cells, tissues and whole organisms.
Animals↗
Challenges and opportunities for biological language modelling in biomedical high-throughput genomic and proteomic informatics.
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Biomedical Engineering↗
[Why biology needs to study what biology knows?].
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Biology↗
Learning a predictive model for growth inhibition from the NCI DTP human tumor cell line screening data: does gene expression make a difference?
We address the problem of learning a predictive model for growth inhibition from the NCI DTP human tumor cell line screening data. Extending the classical Quantitative Structure Activity Relationship paradigm, we investigate whether including gene expression data leads to a statistically significant improvement of prediction quality. Our analysis shows that the straightforward approach of including individual gene expression as features does not necessarily improve, but on the contrary, may degrade performance significantly. When gene expression information is aggregated, for instance by features representing the correlation with reference cell lines, performance can be improved significantly. Further improvements may be expected if the learning task is structured by grouping features and instances.
Cell Line, Tumor↗
[Integrated database (2): Data oriented approach to genome network informatics].
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Animals↗
Mendel-GFDb and Mendel-ESTS: databases of plant gene families and ESTs annotated with gene family numbers and gene family names.
There is no control over the information provided with sequences when they are deposited in the sequence databases. Consequently mistakes can seed the incorrect annotation of other sequences. Grouping genes into families and applying controlled annotation overcomes the problems of incorrect annotation associated with individual sequences. Two databases (http://www.mendel.ac.uk) were created to apply controlled annotation to plant genes and plant ESTs: Mendel-GFDb is a database of plant protein (gene) families based on gapped-BLAST analysis of all sequences in the SWISS-PROT family of databases. Sequences are aligned (ClustalW) and identical and similar residues shaded. The families are visually curated to ensure that one or more criteria, for example overall relatedness and/or domain similarity relate all sequences within a family. Sequence families are assigned a 'Gene Family Number' and a unified description is developed which best describes the family and its members. If authority exists the gene family is assigned a 'Gene Family Name'. This information is placed in Mendel-GFDb. Mendel-ESTS is primarily a database of plant ESTs, which have been compared to Mendel-GFDb, completely sequenced genomes and domain databases. This approach associated ESTs with individual sequences and the controlled annotation of gene families and protein domains; the information being placed in Mendel-ESTS. The controlled annotation applied to genes and ESTs provides a basis from which a plant transcription database can be developed.
Computational Biology↗
A bioinformatician's view of the metabolome.
The study of a collection of metabolites as a whole (metabolome), as opposed to isolated small molecules, is a fast-growing field promising to take us one step further towards understanding cell biology, and relating the genetic capabilities of an organism to its observed phenotype. The new sciences of metabolomics and metabonomics can exploit a variety of existing experimental and computational methods, but they also require new technology that can deal with both the amount and the diversity of the data relating to the rich world of metabolites. More specifically, the collaboration between bioinformaticians and chemoinformaticians promises to advance our view of cognate molecules, by shedding light on their atomic structure and properties. Modelling of the interactions of metabolites with other entities in the cell, and eventually complete modelling of reaction pathways will be essential for analysis of the experimental data, and prediction of an organism's response to environmental challenges.
Chromatography↗
In silico determination of intracellular glycosylation and phosphorylation sites in human selectins: implications for biological function.
Post-translational modifications provide the proteins with the possibility to perform functions in addition to those determined by their primary sequence. However, analysis of multifunctional protein structures in the environment of cells and body fluids is made especially difficult by the presence of other interacting proteins. Bioinformatics tools are therefore helpful to predict protein multifunctionality through the identification of serine and threonine residues wherein the hydroxyl group is likely to become modified by phosphorylation or glycosylation. Moreover, serines and threonines where both modifications are likely to occur can also be predicted (YinYang sites), to suggest further functional versatility. Structural modifications of hydroxyl groups of P-, E-, and L-selectins have been predicted and possible functions resulting from such modifications are proposed. Functional changes of the three selectins are based on the assumption that transitory and reversible protein modifications by phosphate and O-GlcNAc cause specific conformational changes and generate binding sites for other proteins. The computer-assisted prediction of glycosylation and phosphorylation sites in selectins should be helpful to assess the contribution of dynamic protein modifications in selectin-mediated inflammatory responses and cell-cell adhesion processes that are difficult to determine experimentally.
Algorithms↗