CoCoCoNet: Conserved and Comparative Co-expression Across a Diverse Set of Species

ABSTRACTCo-expression analysis has provided insight into gene function in organisms from Arabidopsis to Zebrafish. Comparison across species has the potential to enrich these results, for example by prioritizing among candidate human disease genes based on their network properties, or by finding alternative model systems where their co-expression is conserved. Here, we present CoCoCoNet as a tool for identifying conserved gene modules and comparing co-expression networks. CoCoCoNet is a resource for both data and methods, providing gold-standard networks and sophisticated tools for on-the-fly comparative analyses across 14 species. We show how CoCoCoNet can be used in two use cases. In the first, we demonstrate deep conservation of a nucleolus gene module across very divergent organisms, and in the second, we show how the heterogeneity of autism mechanisms in humans can be broken down by functional groups, and translated to model organisms. CoCoCoNet is free to use and available to all at https://milton.cshl.edu/CoCoCoNet, with data and R scripts available at ftp://milton.cshl.edu/data.

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CoCoCoNet: conserved and comparative co-expression across a diverse set of species

Nucleic Acids Research ◽

10.1093/nar/gkaa348 ◽

2020 ◽

Vol 48 (W1) ◽

pp. W566-W571 ◽

Cited By ~ 3

Author(s):

John Lee ◽

Manthan Shah ◽

Sara Ballouz ◽

Megan Crow ◽

Jesse Gillis

Keyword(s):

Alternative Model ◽

Model Systems ◽

Model Organisms ◽

Disease Genes ◽

Gene Module ◽

Network Properties ◽

Gene Modules ◽

Conserved Gene ◽

Human Disease Genes ◽

Insight Into

Abstract Co-expression analysis has provided insight into gene function in organisms from Arabidopsis to zebrafish. Comparison across species has the potential to enrich these results, for example by prioritizing among candidate human disease genes based on their network properties or by finding alternative model systems where their co-expression is conserved. Here, we present CoCoCoNet as a tool for identifying conserved gene modules and comparing co-expression networks. CoCoCoNet is a resource for both data and methods, providing gold standard networks and sophisticated tools for on-the-fly comparative analyses across 14 species. We show how CoCoCoNet can be used in two use cases. In the first, we demonstrate deep conservation of a nucleolus gene module across very divergent organisms, and in the second, we show how the heterogeneity of autism mechanisms in humans can be broken down by functional groups and translated to model organisms. CoCoCoNet is free to use and available to all at https://milton.cshl.edu/CoCoCoNet, with data and R scripts available at ftp://milton.cshl.edu/data.

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How much do model organism phenotypes contribute to the computational identification of human disease genes?

10.1101/2021.12.24.474099 ◽

2021 ◽

Author(s):

Sarah M Alghamdi ◽

Paul N Schofield ◽

Robert Hoehndorf

Keyword(s):

Human Gene ◽

Model Organism ◽

Model Organisms ◽

Disease Genes ◽

Loss Of Function ◽

Relative Contribution ◽

Phenotype Data ◽

Disease Associations ◽

Disease Associated Genes ◽

Human Disease Genes

Computing phenotypic similarity has been shown to be useful in identification of new disease genes and for rare disease diagnostic support. Genotype--phenotype data from orthologous genes in model organisms can compensate for lack of human data to greatly increase genome coverage. Work over the past decade has demonstrated the power of cross-species phenotype comparisons, and several cross-species phenotype ontologies have been developed for this purpose. The relative contribution of different model organisms to identifying disease-associated genes using computational approaches is not yet fully explored. We use methods based on phenotype ontologies to semantically relate phenotypes resulting from loss-of-function mutations in different model organisms to disease-associated phenotypes in humans. Semantic machine learning methods are used to measure how much different model organisms contribute to the identification of known human gene--disease associations. We find that only mouse phenotypes can accurately predict human gene--disease associations. Our work has implications for the future development of integrated phenotype ontologies, as well as for the use of model organism phenotypes in human genetic variant interpretation.

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Network properties of human disease genes with pleiotropic effects

BMC Systems Biology ◽

10.1186/1752-0509-4-78 ◽

2010 ◽

Vol 4 (1) ◽

Cited By ~ 57

Author(s):

Sreenivas Chavali ◽

Fredrik Barrenas ◽

Kartiek Kanduri ◽

Mikael Benson

Keyword(s):

Human Disease ◽

Pleiotropic Effects ◽

Disease Genes ◽

Network Properties ◽

Human Disease Genes

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A surprising abundance of human disease genes in a simple “basal” animal, the starlet sea anemone (Nematostella vectensis)

Genome ◽

10.1139/g07-045 ◽

2007 ◽

Vol 50 (7) ◽

pp. 689-692 ◽

Cited By ~ 19

Author(s):

James C. Sullivan ◽

John R. Finnerty

Keyword(s):

Experimental Model ◽

Human Disease ◽

Human Diseases ◽

Model Organisms ◽

Nematostella Vectensis ◽

Disease Genes ◽

Invertebrate Animal ◽

Invertebrate Animals ◽

Invertebrate Model ◽

Human Disease Genes

Invertebrate animals have provided important insights into the mechanisms of, and treatment for, numerous human diseases. A surprisingly high proportion of genes underlying human disease are present in the genome of a simple, evolutionarily basal invertebrate animal, Nematostella vectensis , including some genes that are absent in established invertebrate model organisms. This, together with the laboratory tractability and regenerative capability of N. vectensis, recommends the species as an important new experimental model for the study of genes underlying human disease.

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Network Properties of Complex Human Disease Genes Identified through Genome-Wide Association Studies

PLoS ONE ◽

10.1371/journal.pone.0008090 ◽

2009 ◽

Vol 4 (11) ◽

pp. e8090 ◽

Cited By ~ 88

Author(s):

Fredrik Barrenas ◽

Sreenivas Chavali ◽

Petter Holme ◽

Reza Mobini ◽

Mikael Benson

Keyword(s):

Human Disease ◽

Association Studies ◽

Genome Wide Association ◽

Disease Genes ◽

Genome Wide Association Studies ◽

Complex Human Disease ◽

Genome Wide ◽

Network Properties ◽

Human Disease Genes

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A novel workflow to improve multi-locus genotyping of wildlife species: an experimental set-up with a known model system

10.1101/638288 ◽

2019 ◽

Cited By ~ 2

Author(s):

Mark A.F. Gillingham ◽

B. Karina Montero ◽

Kerstin Wihelm ◽

Kara Grudzus ◽

Simone Sommer ◽

...

Keyword(s):

Sequence Data ◽

Model Organism ◽

Model Systems ◽

Model Organisms ◽

Amplification Efficiency ◽

Amplification Bias ◽

Link Type ◽

Multiple Loci ◽

Number Variation ◽

Set Up

ABSTRACTGenotyping novel complex multigene systems is particularly challenging in non-model organisms. Target primers frequently amplify simultaneously multiple loci leading to high PCR and sequencing artefacts such as chimeras and allele amplification bias. Most next-generation sequencing genotyping pipelines have been validated in non-model systems whereby the real genotype is unknown and the generation of artefacts may be highly repeatable. Further hindering accurate genotyping, the relationship between artefacts and copy number variation (CNV) within a PCR remains poorly described. Here we investigate the latter by experimentally combining multiple known major histocompatibility complex (MHC) haplotypes of a model organism (chicken, Gallus gallus, 43 artificial genotypes with 2-13 alleles per amplicon). In addition to well defined “optimal” primers, we simulated a non-model species situation by designing “naive” primers, with sequence data from closely related Galliform species. We applied a novel open-source genotyping pipeline (ACACIA) to the data, and compared its performance with another, previously published, pipeline. ACACIA yielded very high allele calling accuracy (>98%). Non-chimeric artefacts increased linearly with increasing CNV but chimeric artefacts leveled when amplifying more than 4-6 alleles. As expected, we found heterogeneous amplification efficiency of allelic variants when co-amplifying multiple loci. Using our validated ACACIA pipeline and the example data of this study, we discuss in detail the pitfalls researchers should avoid in order to reliably genotype complex multigene systems. ACACIA and the datasets used in this study are publicly available at GitLab and FigShare (https://gitlab.com/psc_santos/ACACIAandhttps://figshare.com/projects/ACACIA/66485).

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Faculty Opinions recommendation of Systematic screen for human disease genes in yeast.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.1008951.116808 ◽

2002 ◽

Author(s):

Andrew Wilkie

Keyword(s):

Human Disease ◽

Disease Genes ◽

Human Disease Genes

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A Survey on Computational Methods for Essential Proteins and Genes Prediction

Current Bioinformatics ◽

10.2174/1574893613666181112150422 ◽

2019 ◽

Vol 14 (3) ◽

pp. 211-225 ◽

Cited By ~ 2

Author(s):

Ming Fang ◽

Xiujuan Lei ◽

Ling Guo

Keyword(s):

Computational Methods ◽

Drug Targets ◽

Disease Genes ◽

Essential Proteins ◽

Experimental Identification ◽

Cellular Life ◽

Different Types ◽

The Stability ◽

Human Disease Genes ◽

Biology Research

Background: Essential proteins play important roles in the survival or reproduction of an organism and support the stability of the system. Essential proteins are the minimum set of proteins absolutely required to maintain a living cell. The identification of essential proteins is a very important topic not only for a better comprehension of the minimal requirements for cellular life, but also for a more efficient discovery of the human disease genes and drug targets. Traditionally, as the experimental identification of essential proteins is complex, it usually requires great time and expense. With the cumulation of high-throughput experimental data, many computational methods that make useful complements to experimental methods have been proposed to identify essential proteins. In addition, the ability to rapidly and precisely identify essential proteins is of great significance for discovering disease genes and drug design, and has great potential for applications in basic and synthetic biology research. Objective: The aim of this paper is to provide a review on the identification of essential proteins and genes focusing on the current developments of different types of computational methods, point out some progress and limitations of existing methods, and the challenges and directions for further research are discussed.

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