Number Of Gene Ontology Terms Research Articles

Identification of Phase-Separation-Protein-Related Function Based on Gene Ontology by Using Machine Learning Methods.

Phase-separation proteins (PSPs) are a class of proteins that play a role in the process of liquid-liquid phase separation, which is a mechanism that mediates the formation of membranelle compartments in cells. Identifying phase separation proteins and their associated function could provide insights into cellular biology and the development of diseases, such as neurodegenerative diseases and cancer. Here, PSPs and non-PSPs that have been experimentally validated in earlier studies were gathered as positive and negative samples. Each protein's corresponding Gene Ontology (GO) terms were extracted and used to create a 24,907-dimensional binary vector. The purpose was to extract essential GO terms that can describe essential functions of PSPs and build efficient classifiers to identify PSPs with these GO terms at the same time. To this end, the incremental feature selection computational framework and an integrated feature analysis scheme, containing categorical boosting, least absolute shrinkage and selection operator, light gradient-boosting machine, extreme gradient boosting, and permutation feature importance, were used to build efficient classifiers and identify GO terms with classification-related importance. A set of random forest (RF) classifiers with F1 scores over 0.960 were established to distinguish PSPs from non-PSPs. A number of GO terms that are crucial for distinguishing between PSPs and non-PSPs were found, including GO:0003723, which is related to a biological process involving RNA binding; GO:0016020, which is related to membrane formation; and GO:0045202, which is related to the function of synapses. This study offered recommendations for future research aimed at determining the functional roles of PSPs in cellular processes by developing efficient RF classifiers and identifying the representative GO terms related to PSPs.

A human coronavirus OC43-derived polypeptide causes neuropathic pain.

Human coronaviruses have been recently implicated in neurological sequelae by insufficiently understood mechanisms. We here identify an amino acid sequence within the HCoV‐OC43 p65‐like protein homologous to the evolutionarily conserved motif of myelin basic protein (MBP). Because MBP‐derived peptide exposure in the sciatic nerve produces pronociceptive activity in female rodents, we examined whether a synthetic peptide derived from the homologous region of HCoV‐OC43 (OC43p) acts by molecular mimicry to promote neuropathic pain. OC43p, but not scrambled peptides, induces mechanical hypersensitivity in rats following intrasciatic injections. Transcriptome analyses of the corresponding spinal cords reveal upregulation of genes and signaling pathways with known nociception‐, immune‐, and cellular energy‐related activities. Affinity capture shows the association of OC43p with an Na+/K+‐transporting ATPase, providing a potential direct target and mechanistic insight into virus‐induced effects on energy homeostasis and the sensory neuraxis. We propose that HCoV‐OC43 polypeptides released during infection dysregulate normal nervous system functions through molecular mimicry of MBP, leading to mechanical hypersensitivity. Our findings might provide a new paradigm for virus‐induced neuropathic pain.

Survey of metaproteomics software tools for functional microbiome analysis.

To gain a thorough appreciation of microbiome dynamics, researchers characterize the functional relevance of expressed microbial genes or proteins. This can be accomplished through metaproteomics, which characterizes the protein expression of microbiomes. Several software tools exist for analyzing microbiomes at the functional level by measuring their combined proteome-level response to environmental perturbations. In this survey, we explore the performance of six available tools, to enable researchers to make informed decisions regarding software choice based on their research goals. Tandem mass spectrometry-based proteomic data obtained from dental caries plaque samples grown with and without sucrose in paired biofilm reactors were used as representative data for this evaluation. Microbial peptides from one sample pair were identified by the X! tandem search algorithm via SearchGUI and subjected to functional analysis using software tools including eggNOG-mapper, MEGAN5, MetaGOmics, MetaProteomeAnalyzer (MPA), ProPHAnE, and Unipept to generate functional annotation through Gene Ontology (GO) terms. Among these software tools, notable differences in functional annotation were detected after comparing differentially expressed protein functional groups. Based on the generated GO terms of these tools we performed a peptide-level comparison to evaluate the quality of their functional annotations. A BLAST analysis against the NCBI non-redundant database revealed that the sensitivity and specificity of functional annotation varied between tools. For example, eggNOG-mapper mapped to the most number of GO terms, while Unipept generated more accurate GO terms. Based on our evaluation, metaproteomics researchers can choose the software according to their analytical needs and developers can use the resulting feedback to further optimize their algorithms. To make more of these tools accessible via scalable metaproteomics workflows, eggNOG-mapper and Unipept 4.0 were incorporated into the Galaxy platform.

Graph-based exploitation of gene ontology using GOxploreR for scrutinizing biological significance

Gene ontology (GO) is an eminent knowledge base frequently used for providing biological interpretations for the analysis of genes or gene sets from biological, medical and clinical problems. Unfortunately, the interpretation of such results is challenging due to the large number of GO terms, their hierarchical and connected organization as directed acyclic graphs (DAGs) and the lack of tools allowing to exploit this structural information explicitly. For this reason, we developed the R package GOxploreR. The main features of GOxploreR are (I) easy and direct access to structural features of GO, (II) structure-based ranking of GO-terms, (III) mapping to reduced GO-DAGs including visualization capabilities and (IV) prioritizing of GO-terms. The underlying idea of GOxploreR is to exploit a graph-theoretical perspective of GO as manifested by its DAG-structure and the containing hierarchy levels for cumulating semantic information. That means all these features enhance the utilization of structural information of GO and complement existing analysis tools. Overall, GOxploreR provides exploratory as well as confirmatory tools for complementing any kind of analysis resulting in a list of GO-terms, e.g., from differentially expressed genes or gene sets, GWAS or biomarkers. Our R package GOxploreR is freely available from CRAN.

GOMCL: a toolkit to cluster, evaluate, and extract non-redundant associations of Gene Ontology-based functions

BackgroundFunctional enrichment of genes and pathways based on Gene Ontology (GO) has been widely used to describe the results of various -omics analyses. GO terms statistically overrepresented within a set of a large number of genes are typically used to describe the main functional attributes of the gene set. However, these lists of overrepresented GO terms are often too large and contains redundant overlapping GO terms hindering informative functional interpretations.ResultsWe developed GOMCL to reduce redundancy and summarize lists of GO terms effectively and informatively. This lightweight python toolkit efficiently identifies clusters within a list of GO terms using the Markov Clustering (MCL) algorithm, based on the overlap of gene members between GO terms. GOMCL facilitates biological interpretation of a large number of GO terms by condensing them into GO clusters representing non-overlapping functional themes. It enables visualizing GO clusters as a heatmap, networks based on either overlap of members or hierarchy among GO terms, and tables with depth and cluster information for each GO term. Each GO cluster generated by GOMCL can be evaluated and further divided into non-overlapping sub-clusters using the GOMCL-sub module. The outputs from both GOMCL and GOMCL-sub can be imported to Cytoscape for additional visualization effects.ConclusionsGOMCL is a convenient toolkit to cluster, evaluate, and extract non-redundant associations of Gene Ontology-based functions. GOMCL helps researchers to reduce time spent on manual curation of large lists of GO terms, minimize biases introduced by redundant GO terms in data interpretation, and batch processing of multiple GO enrichment datasets. A user guide, a test dataset, and the source code of GOMCL are available at https://github.com/Guannan-Wang/GOMCL and www.lsugenomics.org.

Proinflammatory status-stratified blood transcriptome profiling of civilian women with PTSD

Etiology of posttraumatic stress disorder (PTSD) remains largely unknown. Studies have shown that a significant subset of patients with PTSD exhibit increased inflammation, suggesting that the understanding of this disorder could be facilitated by classifying these patients by inflammatory status. Here we performed a microarray-based blood transcriptome analysis on proinflammatory status-stratified Japanese civilian women with PTSD most of whom developed the disorder after experiencing interpersonal violence. By utilizing our previously identified cut-off serum interleukin-6 (IL-6) level that approximately corresponded to the median IL-6 level of our PTSD patients, we classified patients into those with high IL-6 levels and those with normal IL-6 levels (n = 16 for each). Transcriptome profiles of these 2 groups were compared with the profile of 16 age-matched healthy control women. Differentially expressed genes between high IL-6 patients and controls showed significant enrichment in a number of gene ontology terms and pathways primarily involved in immune/inflammatory responses, and their protein-protein interaction network was significantly enriched. In contrast, differentially expressed genes between normal IL-6 patients and controls showed significant enrichment in several gene ontology terms related to ion transport and neural function. The microarray data were confirmed by reverse transcription quantitative PCR. These findings illustrate the heterogeneous molecular mechanisms of PTSD within this relatively homogeneous sample in terms of sex, trauma type, and ethnicity, suggesting that peripheral proinflammatory status such as IL-6 levels could be a useful subtyping marker for this disorder. With further research, it is hoped that our findings will be translated into personalized medicine.

Screening of key miRNAs and evaluation of their diagnostic and prognostic values in nasopharyngeal carcinoma.

Increasing evidence has revealed the importance of microRNA (miRNA/miR) in cancer genesis and progression. The aim of the current study was to identify the key miRNAs involved in the onset and development of nasopharyngeal carcinoma (NPC) and to further evaluate their diagnostic and prognostic values. Microarray data were obtained and analyzed to screen differentially expressed miRNAs (DEMs) between patients with NPC and healthy controls. The target genes of the DEMs were predicted and their possible functions were evaluated. The diagnostic and prognostic values of the DEMs were subsequently investigated. A total of 4 DEMs, including miR-18a, miR-135b, miR-204 and miR-497, were identified. Gene Ontology (GO) and pathway enrichment analysis revealed that the target genes were enriched in a number of GO terms and signaling pathways. The results demonstrated that the selected DEMs may present potential diagnostic factors for NPC. In addition, miR-18a [Hazard ratio (HR), 3.405; 95% confidence interval (CI), 1.334-8.693] and miR-135b (HR, 2.482; 95% CI, 1.014-6.076) may serve prognostic roles for patients with NPC. In summary, the present study identified 4 miRNAs that may be involved in the genesis and development of NPC. In addition, miR-18a and miR-135b may present useful prognostic markers for patients with NPC. Future in vitro and in vivo investigations are warranted to substantiate the results obtained in the current study.

GOGO: An improved algorithm to measure the semantic similarity between gene ontology terms

Measuring the semantic similarity between Gene Ontology (GO) terms is an essential step in functional bioinformatics research. We implemented a software named GOGO for calculating the semantic similarity between GO terms. GOGO has the advantages of both information-content-based and hybrid methods, such as Resnik’s and Wang’s methods. Moreover, GOGO is relatively fast and does not need to calculate information content (IC) from a large gene annotation corpus but still has the advantage of using IC. This is achieved by considering the number of children nodes in the GO directed acyclic graphs when calculating the semantic contribution of an ancestor node giving to its descendent nodes. GOGO can calculate functional similarities between genes and then cluster genes based on their functional similarities. Evaluations performed on multiple pathways retrieved from the saccharomyces genome database (SGD) show that GOGO can accurately and robustly cluster genes based on functional similarities. We release GOGO as a web server and also as a stand-alone tool, which allows convenient execution of the tool for a small number of GO terms or integration of the tool into bioinformatics pipelines for large-scale calculations. GOGO can be freely accessed or downloaded from http://dna.cs.miami.edu/GOGO/.

Identification of crucial genes associated with esophageal squamous cell carcinoma by gene expression profile analysis.

To uncover the genes associated with the development of esophageal squamous cell carcinoma (ESCC), an ESCC microarray dataset was used to identify genes differentially expressed between ESCC and normal control tissues. The dataset GSE17351 was downloaded from the Gene Expression Omnibus, containing 5 tumor esophageal mucosa samples and 5 adjacent normal esophageal mucosa samples from 5 male patients with ESCC. The differentially expressed genes (DEGs) were identified using the Linear Models for Microarray Data R package. Then, a co-expression network was constructed using the Weighted Correlation Network Analysis (WGCNA) package, and co-expression network modules were obtained with a hierarchical clustering algorithm. Additionally, functional enrichment analyses for DEGs in the top 2 modules with the highest significance were respectively conducted using the WGCNA package and the cluster Profiler package. In total, 487 upregulated and 468 downregulated DEGs were identified. A total of 24 modules were obtained from the co-expression network, and the top 2 modules with the highest significance, designated as ‘blue4’ and ‘magenta’, were further analyzed. In the module blue4, DEGs were significantly enriched in a number of Gene Ontology terms, including ‘spindle organization’ [e.g., ubiquitin conjugating enzyme E2 C (UBE2C) and SAC3 domain containing 1] and ‘cell cycle process’ [e.g., UBE2C, minichromosome maintenance complex component 6 (MCM6) and cell division cycle 20 (CDC20)]. Furthermore, a number of DEGs (e.g., UBE2C, CDC20 and MCM6) were enriched in the ‘cell cycle’ and ‘ubiquitin mediated proteolysis’ pathways. In the module ‘magenta’, a number of DEGs [e.g., transferrin receptor (TFRC) and TEA domain transcription factor 4 (TEAD4)] were enriched in the primary metabolic process and intracellular membrane-bounded organelle. Additionally, 308 upregulated genes and 215 downregulated genes were differentially expressed in the same pattern in another dataset, GSE20347, including UBE2C, CDC20, MCM6, TFRC, TEAD4, protein phosphatase 1 regulatory subunit 3C and MAL, T-cell differentiation protein. These DEGs may function in the development of ESCC.

Exploring drought stress-regulated genes in senna (Cassia angustifolia Vahl.): a transcriptomic approach.

De novo assembly of reads produced by next-generation sequencing (NGS) technologies offers a rapid approach to obtain expressed gene sequences for non-model organisms. Senna (Cassia angustifolia Vahl.) is a drought-tolerant annual undershrub of Caesalpiniaceae, a subfamily of Fabaceae. There are insufficient transcriptomic and genomic data in public databases for understanding the molecular mechanism underlying the drought tolerance of senna. Therefore, the main purpose of this study was to know the transcriptome profile of senna, with special reference to drought stress. RNA from two different stages of leaf development was extracted and sequenced separately using the Illumina technology. A total of 200 million reads were generated, and a de novo assembly of processed reads in the pooled transcriptome using Trinity yielded 43,413 transcripts which were further annotated using NCBI BLAST with "green plant database (txid 33090)," Swiss Prot, Kyoto Encyclopedia of Genes and Genomes (KEGG), Clusters of Orthologous Groups (COG), and Gene Ontology (GO). Out of the total transcripts, 42,280 (95.0%) were annotated by BLASTX against the green plant database of NCBI. Senna transcriptome showed the highest similarity to Glycine max (41%), followed by Phaseolus vulgaris (16 %), Cicer arietinum (15 %), and Medicago trancatula (5 %). The highest number of GO terms were enriched for the molecular functions category; of these "catalytic activity" (GO: 0003824) (25.10 %) and "binding activity" (GO: 0005488) (20.10 %) were most abundantly represented. We used InterProscan to see protein similarity at domain level; a total of 33,256 transcripts were annotated against the Pfam domains. The transcripts were assigned with various KEGG pathways. Coding DNA sequences (CDS) encoding various drought stress-regulated pathways such as signaling factors, protein-modifying/degrading enzymes, biosynthesis of phytohormone, phytohormone signaling, osmotically active compounds, free radical scavengers, chlorophyll metabolism, leaf cuticular wax, polyamines, and protective proteins were identified through BLASTX search. The lucine-rich repeat kinase family was the most abundantly found group of protein kinases. Orphan, bHLH, and bZIP family TFs were the most abundantly found in senna. Six genes encoding MYC2 transcription factor, 9-cis-epoxycarotenoid dioxygenase (NCED), l -ascorbate peroxidase (APX), aminocyclopropane carboxylate oxidase (ACO), abscisic acid 8'-hydroxylase (ABA), and WRKY transcription factor were confirmed through reverse transcriptase-PCR (RT-PCR) and Sanger sequencing for the first time in senna. The potential drought stress-related transcripts identified in this study provide a good start for further investigation into the drought adaptation in senna. Additionally, our transcriptome sequences are the valuable resource for accelerated genomics-assisted genetic improvement programs and facilitate manipulation of biochemical pathways for developing drought-tolerant genotypes of crop plants.

Sparse regressions for predicting and interpreting subcellular localization of multi-label proteins.

BackgroundPredicting protein subcellular localization is indispensable for inferring protein functions. Recent studies have been focusing on predicting not only single-location proteins, but also multi-location proteins. Almost all of the high performing predictors proposed recently use gene ontology (GO) terms to construct feature vectors for classification. Despite their high performance, their prediction decisions are difficult to interpret because of the large number of GO terms involved.ResultsThis paper proposes using sparse regressions to exploit GO information for both predicting and interpreting subcellular localization of single- and multi-location proteins. Specifically, we compared two multi-label sparse regression algorithms, namely multi-label LASSO (mLASSO) and multi-label elastic net (mEN), for large-scale predictions of protein subcellular localization. Both algorithms can yield sparse and interpretable solutions. By using the one-vs-rest strategy, mLASSO and mEN identified 87 and 429 out of more than 8,000 GO terms, respectively, which play essential roles in determining subcellular localization. More interestingly, many of the GO terms selected by mEN are from the biological process and molecular function categories, suggesting that the GO terms of these categories also play vital roles in the prediction. With these essential GO terms, not only where a protein locates can be decided, but also why it resides there can be revealed.ConclusionsExperimental results show that the output of both mEN and mLASSO are interpretable and they perform significantly better than existing state-of-the-art predictors. Moreover, mEN selects more features and performs better than mLASSO on a stringent human benchmark dataset. For readers’ convenience, an online server called SpaPredictor for both mLASSO and mEN is available at http://bioinfo.eie.polyu.edu.hk/SpaPredictorServer/.

Predicting the Pro-Longevity or Anti-Longevity Effect of Model Organism Genes with New Hierarchical Feature Selection Methods.

Ageing is a highly complex biological process that is still poorly understood. With the growing amount of ageing-related data available on the web, in particular concerning the genetics of ageing, it is timely to apply data mining methods to that data, in order to try to discover novel patterns that may assist ageing research. In this work, we introduce new hierarchical feature selection methods for the classification task of data mining and apply them to ageing-related data from four model organisms: Caenorhabditis elegans (worm), Saccharomyces cerevisiae (yeast), Drosophila melanogaster (fly), and Mus musculus (mouse). The main novel aspect of the proposed feature selection methods is that they exploit hierarchical relationships in the set of features (Gene Ontology terms) in order to improve the predictive accuracy of the Naïve Bayes and 1-Nearest Neighbour (1-NN) classifiers, which are used to classify model organisms' genes into pro-longevity or anti-longevity genes. The results show that our hierarchical feature selection methods, when used together with Naïve Bayes and 1-NN classifiers, obtain higher predictive accuracy than the standard (without feature selection) Naïve Bayes and 1-NN classifiers, respectively. We also discuss the biological relevance of a number of Gene Ontology terms very frequently selected by our algorithms in our datasets.

English

Summary This study provides genome expression analyses from the blackcurrant cultivar ‘Ben Gairn’ after five days of drought stress. RNA Sequencing (RNA-Seq) data was utilized to generate a non-redundant set of 40,225 predicted transcripts used to design a custom Ribes microarray. A set of 2,115 differentially expressed genes were identified during drought treatment; 429 of these genes were up-regulated, with 263 showing homology to unique Arabidopsis thaliana (At) accessions, and 1,686 genes were down-regulated, with 675 unique At numbers. The Arabidopsis homologs were analysed for enrichment of GO (gene ontology) terms using the Term Enrichment Tool. This showed a number of GO terms highly enriched in the drought up-regulated and down-regulated gene lists in GO categories associated with molecular function, biological process and cellular component. The identification of several hormone metabolism, cell wall, cell cycle, and transcription factor genes indicated that they could play an important role in the drought stress tolerance response. The results provide relevant information for focusing future studies with the aim to develop drought tolerant cultivars for sustainable production.

Identification and Analysis of Single- and Multiple-Region Mitotic Protein Complexes by Grouping Gene Ontology Terms

Many mitotic proteins are assembled into protein super complexes in three regions - midbody, centrosome and kinetochore (MCK) - with distinctive roles in modulating the mitosis process. However, more than 16% of the mitotic proteins are in multiple regions. Advance identification of mitotic proteins will be helpful to realize the molecular regulatory mechanisms of this organelle. Few ensemble-classifier methods can solve this problem but these methods often fuse various complementary features. In which, Gene ontology (GO) terms play an important role but the GO-term search space is massive and sparse. This motives this work to present an easily implemented method, namely mMck-GO, by identifying a small number of GO terms with support vector machine (SVM) andk-nearest neighbor (KNN) in predicting single-and multiple-region MCK proteins. The mMck-GO method using a simple grouping scheme based on a SVM classifier assembles the GO terms into several groups according to their numbers of annotated proteins in the training dataset, and then measures which top-grouped GO terms performs the best. A new MCK protein dataset containing 701 (611 single-and 90 multiple-region) is established in this work. None of the MCK proteins has a 25% pair-wise sequence identity with any other proteins in the same region. When performing on this dataset, we find that the GO term with the maximum annotation number annotates 49.2% of the training protein sequences; contrarily, 56.5% of the GO terms annotate single one protein sequence. This shows the sparse character of GO terms and the effectiveness of top-grouped GO terms in distinguishing MCK proteins. Accordingly, a small group of top 134 GO terms is identified and mMck-GO fuses the GO terms with amino acid composition (AAC) as input features to yield and independent-testing accuracies of 71.66% and 69.18%, respectively. Top 30 GO terms contain eight, eight, and 14 GO terms belonging to molecular function, biological process and cellular component branches, respectively. The 14 GO terms in cellular-component ontology in addition to centrosome and kinetochore are reverent to subcellular compartments, microtubule, membrane, and spindle, where GO:0005737 (cytoplasm) is ranked first. The eight GO terms enabling molecular functions comprise GO:0005515 (protein binding), GO:0000166 (nucleotide binding), and GO:0005524 (ATP binding). Most of the eight GO terms in biological-process ontology are reverent to cell cycle, cell division and mitosis but two GO terms, GO:0045449 and GO:0045449, are reverent to regulation of transcription and transport processes, which helps us to clarify the molecular regulatory mechanisms of this organelle. The top-grouped GO terms can be as an indispensable feature set when concerning other feature types to solve multiple-class problems in the investigation of biological functions.

Early postmortem gene expression and its relationship to composition and quality traits in pig Longissimus dorsi muscle1

An mRNA expression study has been performed in 20- to 25-min postmortem-obtained samples from Longissimus dorsi muscle of 59 Duroc × Landrace/ Large White (LD/LW) pigs to search for gene sequences related to meat quality (pH(24), pH(45), "Lab" color coordinates, curing yield, and exudation at 3 different times) or meat composition [intramuscular fat, content of several fatty acids (C16:0, C18:0, C18:1, and C18:2), ratio of saturated, monounsaturated, and polyunsaturated fatty acids, and protein and humidity contents] traits to find targets for selection. Gene ontology analysis, biological pathway, and gene network studies all show that many more differentially expressed genes (506 vs. 279) are related to meat quality (Group P or perimortem characters) than to meat composition traits (Group L or whole life traits). The difference between the number of gene ontology terms annotated, biological pathways, and gene networks in groups P and L is notable due to the differences in the complexity of the "generation process" of P-traits and involvement of other tissues or organs in the generation of variability of L-traits. Also, interactions between differentially expressed genes were found in extracellular matrix (ECM)-receptor interaction, tumor growth factor (TGF)-beta signaling pathway, fatty acid elongation in mitochondria, and adipocytokine signaling pathway, indicating that a substantial fraction of the gene networks could be associated with interactions between differentially expressed genes related to traits under study. A large number of the most overexpressed genes are related to muscle development and functionality and repair mechanisms; they could be good candidates for breeding programs in which the main goal is to enhance meat quality.

The representation of heart development in the gene ontology

An understanding of heart development is critical in any systems biology approach to cardiovascular disease. The interpretation of data generated from high-throughput technologies (such as microarray and proteomics) is also essential to this approach. However, characterizing the role of genes in the processes underlying heart development and cardiovascular disease involves the non-trivial task of data analysis and integration of previous knowledge. The Gene Ontology (GO) Consortium provides structured controlled biological vocabularies that are used to summarize previous functional knowledge for gene products across all species. One aspect of GO describes biological processes, such as development and signaling.In order to support high-throughput cardiovascular research, we have initiated an effort to fully describe heart development in GO; expanding the number of GO terms describing heart development from 12 to over 280. This new ontology describes heart morphogenesis, the differentiation of specific cardiac cell types, and the involvement of signaling pathways in heart development. This work also aligns GO with the current views of the heart development research community and its representation in the literature. This extension of GO allows gene product annotators to comprehensively capture the genetic program leading to the developmental progression of the heart. This will enable users to integrate heart development data across species, resulting in the comprehensive retrieval of information about this subject.The revised GO structure, combined with gene product annotations, should improve the interpretation of data from high-throughput methods in a variety of cardiovascular research areas, including heart development, congenital cardiac disease, and cardiac stem cell research. Additionally, we invite the heart development community to contribute to the expansion of this important dataset for the benefit of future research in this area.

Induction and selection of the most interesting Gene Ontology based multiattribute rules for descriptions of gene groups

A rules induction algorithm dedicated to describe groups of genes with similar expression profiles by means of Gene Ontology terms is discussed in the paper. The presented algorithm takes into consideration information contained in the Gene Ontology graph. A huge number of created rules requires defining the rules quality and similarity measures, thus the paper presents such measures and proposes a new method of the most interesting rules selection. Features reduction method based on the rough sets theory is adopted and applied in order to reduce the number of Gene Ontology terms occurring in rules. The paper presents results of performed experiments and describes shortly the internet application RuleGO in which the proposed methods were implemented.

QTL global meta-analysis: are trait determining genes clustered?

BackgroundA key open question in biology is if genes are physically clustered with respect to their known functions or phenotypic effects. This is of particular interest for Quantitative Trait Loci (QTL) where a QTL region could contain a number of genes that contribute to the trait being measured.ResultsWe observed a significant increase in gene density within QTL regions compared to non-QTL regions and/or the entire bovine genome. By grouping QTL from the Bovine QTL Viewer database into 8 categories of non-redundant regions, we have been able to analyze gene density and gene function distribution, based on Gene Ontology (GO) with relation to their location within QTL regions, outside of QTL regions and across the entire bovine genome. We identified a number of GO terms that were significantly over represented within particular QTL categories. Furthermore, select GO terms expected to be associated with the QTL category based on common biological knowledge have also proved to be significantly over represented in QTL regions.ConclusionOur analysis provides evidence of over represented GO terms in QTL regions. This increased GO term density indicates possible clustering of gene functions within QTL regions of the bovine genome. Genes with similar functions may be grouped in specific locales and could be contributing to QTL traits. Moreover, we have identified over-represented GO terminology that from a biological standpoint, makes sense with respect to QTL category type.