MorphDB : prioritizing genes for specialized metabolism pathways and gene ontology categories in plants

Recent times have seen an enormous growth of "omics" data, of which high-throughput gene expression data are arguably the most important from a functional perspective. Despite huge improvements in computational techniques for the functional classification of gene sequences, common similarity-based methods often fall short of providing full and reliable functional information. Recently, the combination of comparative genomics with approaches in functional genomics has received considerable interest for gene function analysis, leveraging both gene expression based guilt-by-association methods and annotation efforts in closely related model organisms. Besides the identification of missing genes in pathways, these methods also typically enable the discovery of biological regulators (i.e., transcription factors or signaling genes). A previously built guilt-by-association method is MORPH, which was proven to be an efficient algorithm that performs particularly well in identifying and prioritizing missing genes in plant metabolic pathways. Here, we present MorphDB, a resource where MORPH-based candidate genes for large-scale functional annotations (Gene Ontology, MapMan bins) are integrated across multiple plant species. Besides a gene centric query utility, we present a comparative network approach that enables researchers to efficiently browse MORPH predictions across functional gene sets and species, facilitating efficient gene discovery and candidate gene prioritization. MorphDB is available at http://bioinformatics.psb.ugent.be/webtools/morphdb/morphDB/index/. We also provide a toolkit, named "MORPH bulk" (https://github.com/arzwa/morph-bulk), for running MORPH in bulk mode on novel data sets, enabling researchers to apply MORPH to their own species of interest

A machine learning pipeline for discriminant pathways identification

Motivation: Identifying the molecular pathways more prone to disruption during a pathological process is a key task in network medicine and, more in general, in systems biology. Results: In this work we propose a pipeline that couples a machine learning solution for molecular profiling with a recent network comparison method. The pipeline can identify changes occurring between specific sub-modules of networks built in a case-control biomarker study, discriminating key groups of genes whose interactions are modified by an underlying condition. The proposal is independent from the classification algorithm used. Three applications on genomewide data are presented regarding children susceptibility to air pollution and two neurodegenerative diseases: Parkinson's and Alzheimer's. Availability: Details about the software used for the experiments discussed in this paper are provided in the Appendix

Inferring gene ontologies from pairwise similarity data.

MotivationWhile the manually curated Gene Ontology (GO) is widely used, inferring a GO directly from -omics data is a compelling new problem. Recognizing that ontologies are a directed acyclic graph (DAG) of terms and hierarchical relations, algorithms are needed that: analyze a full matrix of gene-gene pairwise similarities from -omics data; infer true hierarchical structure in these data rather than enforcing hierarchy as a computational artifact; and respect biological pleiotropy, by which a term in the hierarchy can relate to multiple higher level terms. Methods addressing these requirements are just beginning to emerge-none has been evaluated for GO inference.MethodsWe consider two algorithms [Clique Extracted Ontology (CliXO), LocalFitness] that uniquely satisfy these requirements, compared with methods including standard clustering. CliXO is a new approach that finds maximal cliques in a network induced by progressive thresholding of a similarity matrix. We evaluate each method's ability to reconstruct the GO biological process ontology from a similarity matrix based on (a) semantic similarities for GO itself or (b) three -omics datasets for yeast.ResultsFor task (a) using semantic similarity, CliXO accurately reconstructs GO (>99% precision, recall) and outperforms other approaches (<20% precision, <20% recall). For task (b) using -omics data, CliXO outperforms other methods using two -omics datasets and achieves ∼30% precision and recall using YeastNet v3, similar to an earlier approach (Network Extracted Ontology) and better than LocalFitness or standard clustering (20-25% precision, recall).ConclusionThis study provides algorithmic foundation for building gene ontologies by capturing hierarchical and pleiotropic structure embedded in biomolecular data

Discovering transcriptional modules by Bayesian data integration

Motivation: We present a method for directly inferring transcriptional modules (TMs) by integrating gene expression and transcription factor binding (ChIP-chip) data. Our model extends a hierarchical Dirichlet process mixture model to allow data fusion on a gene-by-gene basis. This encodes the intuition that co-expression and co-regulation are not necessarily equivalent and hence we do not expect all genes to group similarly in both datasets. In particular, it allows us to identify the subset of genes that share the same structure of transcriptional modules in both datasets. Results: We find that by working on a gene-by-gene basis, our model is able to extract clusters with greater functional coherence than existing methods. By combining gene expression and transcription factor binding (ChIP-chip) data in this way, we are better able to determine the groups of genes that are most likely to represent underlying TMs

Knowledge Discovery in Biological Databases for Revealing Candidate Genes Linked to Complex Phenotypes

Genetics and “omics” studies designed to uncover genotype to phenotype relationships often identify large numbers of potential candidate genes, among which the causal genes are hidden. Scientists generally lack the time and technical expertise to review all relevant information available from the literature, from key model species and from a potentially wide range of related biological databases in a variety of data formats with variable quality and coverage. Computational tools are needed for the integration and evaluation of heterogeneous information in order to prioritise candidate genes and components of interaction networks that, if perturbed through potential interventions, have a positive impact on the biological outcome in the whole organism without producing negative side effects. Here we review several bioinformatics tools and databases that play an important role in biological knowledge discovery and candidate gene prioritization. We conclude with several key challenges that need to be addressed in order to facilitate biological knowledge discovery in the future.&nbsp

Semantic Biclustering

Tato disertační práce se zaměřuje na problém hledání interpretovatelných a prediktivních vzorů, které jsou vyjádřeny formou dvojshluků, se specializací na biologická data. Prezentované metody jsou souhrnně označovány jako sémantické dvojshlukování, jedná se o podobor dolování dat. Termín sémantické dvojshlukování je použit z toho důvodu, že zohledňuje proces hledání koherentních podmnožin řádků a sloupců, tedy dvojshluků, v 2-dimensionální binární matici a zárove ň bere také v potaz sémantický význam prvků v těchto dvojshlucích. Ačkoliv byla práce motivována biologicky orientovanými daty, vyvinuté algoritmy jsou obecně aplikovatelné v jakémkoli jiném výzkumném oboru. Je nutné pouze dodržet požadavek na formát vstupních dat. Disertační práce představuje dva originální a v tomto ohledu i základní přístupy pro hledání sémantických dvojshluků, jako je Bicluster enrichment analysis a Rule a tree learning. Jelikož tyto metody nevyužívají vlastní hierarchické uspořádání termů v daných ontologiích, obecně je běh těchto algoritmů dlouhý čin může docházet k indukci hypotéz s redundantními termy. Z toho důvodu byl vytvořen nový operátor zjemnění. Tento operátor byl včleněn do dobře známého algoritmu CN2, kde zavádí dvě redukční procedury: Redundant Generalization a Redundant Non-potential. Obě procedury pomáhají dramaticky prořezat prohledávaný prostor pravidel a tím umožňují urychlit proces indukce pravidel v porovnání s tradičním operátorem zjemnění tak, jak je původně prezentován v CN2. Celý algoritmus spolu s redukčními metodami je publikován ve formě R balííčku, který jsme nazvali sem1R. Abychom ukázali i možnost praktického užití metody sémantického dvojshlukování na reálných biologických problémech, v disertační práci dále popisujeme a specificky upravujeme algoritmus sem1R pro dv+ úlohy. Zaprvé, studujeme praktickou aplikaci algoritmu sem1R v analýze E-3 ubikvitin ligázy v trávicí soustavě s ohledem na potenciál regenerace tkáně. Zadruhé, kromě objevování dvojshluků v dat ech genové exprese, adaptujeme algoritmus sem1R pro hledání potenciálne patogenních genetických variant v kohortě pacientů.This thesis focuses on the problem of finding interpretable and predic tive patterns, which are expressed in the form of biclusters, with an orientation to biological data. The presented methods are collectively called semantic biclustering, as a subfield of data mining. The term semantic biclustering is used here because it reflects both a process of finding coherent subsets of rows and columns in a 2-dimensional binary matrix and simultaneously takes into account a mutual semantic meaning of elements in such biclusters. In spite of focusing on applications of algorithms in biological data, the developed algorithms are generally applicable to any other research field, there are only limitations on the format of the input data. The thesis introduces two novel, and in that context basic, approaches for finding semantic biclusters, as Bicluster enrichment analysis and Rule and tree learning. Since these methods do not exploit the native hierarchical order of terms of input ontologies, the run-time of algorithms is relatively long in general or an induced hypothesis might have terms that are redundant. For this reason, a new refinement operator has been invented. The refinement operator was incorporated into the well-known CN2 algorithm and uses two reduction procedures: Redundant Generalization and Redundant Non-potential, both of which help to dramatically prune the rule space and consequently, speed-up the entire process of rule induction in comparison with the traditional refinement operator as is presented in CN2. The reduction procedures were published as an R package that we called sem1R. To show a possible practical usage of semantic biclustering in real biological problems, the thesis also describes and specifically adapts the algorithm for two real biological problems. Firstly, we studied a practical application of sem1R algorithm in an analysis of E-3 ubiquitin ligase in the gastrointestinal tract with respect to tissue regeneration potential. Secondly, besides discovering biclusters in gene expression data, we adapted the sem1R algorithm for a different task, concretely for finding potentially pathogenic genetic variants in a cohort of patients

NETME: on-the-fly knowledge network construction from biomedical literature

Background: The rapidly increasing biological literature is a key resource to automatically extract and gain knowledge concerning biological elements and their relations. Knowledge Networks are helpful tools in the context of biological knowledge discovery and modeling. Results: We introduce a novel system called NETME, which, starting from a set of full-texts obtained from PubMed, through an easy-to-use web interface, interactively extracts biological elements from ontological databases and then synthesizes a network inferring relations among such elements. The results clearly show that our tool is capable of inferring comprehensive and reliable biological networks

Yeast gene CMR1/YDL156W is consistently co-expressed with genes participating in DNA-metabolic processes in a variety of stringent clustering experiments

© 2013 The Authors. Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/3.0/, which permits unrestricted use, provided the original author and source are credited.The binarization of consensus partition matrices (Bi-CoPaM) method has, among its unique features, the ability to perform ensemble clustering over the same set of genes from multiple microarray datasets by using various clustering methods in order to generate tunable tight clusters. Therefore, we have used the Bi-CoPaM method to the most synchronized 500 cell-cycle-regulated yeast genes from different microarray datasets to produce four tight, specific and exclusive clusters of co-expressed genes. We found 19 genes formed the tightest of the four clusters and this included the gene CMR1/YDL156W, which was an uncharacterized gene at the time of our investigations. Two very recent proteomic and biochemical studies have independently revealed many facets of CMR1 protein, although the precise functions of the protein remain to be elucidated. Our computational results complement these biological results and add more evidence to their recent findings of CMR1 as potentially participating in many of the DNA-metabolism processes such as replication, repair and transcription. Interestingly, our results demonstrate the close co-expressions of CMR1 and the replication protein A (RPA), the cohesion complex and the DNA polymerases α, δ and ɛ, as well as suggest functional relationships between CMR1 and the respective proteins. In addition, the analysis provides further substantial evidence that the expression of the CMR1 gene could be regulated by the MBF complex. In summary, the application of a novel analytic technique in large biological datasets has provided supporting evidence for a gene of previously unknown function, further hypotheses to test, and a more general demonstration of the value of sophisticated methods to explore new large datasets now so readily generated in biological experiments.National Institute for Health Researc

Extending pathways based on gene lists using InterPro domain signatures

<p>Abstract</p> <p>Background</p> <p>High-throughput technologies like functional screens and gene expression analysis produce extended lists of candidate genes. Gene-Set Enrichment Analysis is a commonly used and well established technique to test for the statistically significant over-representation of particular pathways. A shortcoming of this method is however, that most genes that are investigated in the experiments have very sparse functional or pathway annotation and therefore cannot be the target of such an analysis. The approach presented here aims to assign lists of genes with limited annotation to previously described functional gene collections or pathways. This works by comparing InterPro domain signatures of the candidate gene lists with domain signatures of gene sets derived from known classifications, e.g. KEGG pathways.</p> <p>Results</p> <p>In order to validate our approach, we designed a simulation study. Based on all pathways available in the KEGG database, we create test gene lists by randomly selecting pathway genes, removing these genes from the known pathways and adding variable amounts of noise in the form of genes not annotated to the pathway. We show that we can recover pathway memberships based on the simulated gene lists with high accuracy. We further demonstrate the applicability of our approach on a biological example.</p> <p>Conclusion</p> <p>Results based on simulation and data analysis show that domain based pathway enrichment analysis is a very sensitive method to test for enrichment of pathways in sparsely annotated lists of genes. An R based software package <it>domainsignatures</it>, to routinely perform this analysis on the results of high-throughput screening, is available via Bioconductor.</p