GWENA: Gene Co-expression Networks Analysis and Extended Modules Characterization in a Single Bioconductor Package

Abstract Background: Network-based analysis of gene expression through co-expression networks can be used to investigate modular interactions occurring between genes toward diﬀerent biological functions. An extended description of the network modules is therefore a critical step to understand the underlying processes contributing to a disease or a phenotype. Biological integration, topology study and conditions comparison (e.g. wild vs mutant) are the main methods to do so, but to date no tool combines them all into a single pipeline.Results: Here we present GWENA, a new R package that integrates gene co-expression network construction and a whole characterization of the detected modules through gene set enrichment, phenotypic association, hub genes detection, topological metric computation, and diﬀerential co-expression. To demonstrate its performances, we applied GWENA on two skeletal muscle datasets from young and old patients of GTEx study. We successfully prioritized a gene whose involvement was unknown in the muscle development and growth. We also gave new insight about the variations in patterns of co-expression as the already known age-dependent loss of connectivity was found coupled to a genes interactions reorganization leading to the expression of other functions involved in aging. Conclusion: GWENA is an R package available through Bioconductor (https://bioconductor.org/packages/release/bioc/html/GWENA.html) developed to perform extended analysis of gene co-expression networks. Thanks to biological and topological information as well as conditions comparison, it eases the understanding of genes interactions involved in diseases or phenotypes. Going beyond actual packages to perform co-expression analysis, GWENA includes new tools to fully characterize modules, such as diﬀerential co-expression, additional enrichment databases, and network visualization.

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GWENA: gene co-expression networks analysis and extended modules characterization in a single Bioconductor package

BMC Bioinformatics ◽

10.1186/s12859-021-04179-4 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Gwenaëlle G. Lemoine ◽

Marie-Pier Scott-Boyer ◽

Bathilde Ambroise ◽

Olivier Périn ◽

Arnaud Droit

Keyword(s):

Muscle Development ◽

R Package ◽

Topological Information ◽

Old Patients ◽

Network Modules ◽

Extended Analysis ◽

Networks Analysis ◽

Underlying Processes

Abstract Background Network-based analysis of gene expression through co-expression networks can be used to investigate modular relationships occurring between genes performing different biological functions. An extended description of each of the network modules is therefore a critical step to understand the underlying processes contributing to a disease or a phenotype. Biological integration, topology study and conditions comparison (e.g. wild vs mutant) are the main methods to do so, but to date no tool combines them all into a single pipeline. Results Here we present GWENA, a new R package that integrates gene co-expression network construction and whole characterization of the detected modules through gene set enrichment, phenotypic association, hub genes detection, topological metric computation, and differential co-expression. To demonstrate its performance, we applied GWENA on two skeletal muscle datasets from young and old patients of GTEx study. Remarkably, we prioritized a gene whose involvement was unknown in the muscle development and growth. Moreover, new insights on the variations in patterns of co-expression were identified. The known phenomena of connectivity loss associated with aging was found coupled to a global reorganization of the relationships leading to expression of known aging related functions. Conclusion GWENA is an R package available through Bioconductor (https://bioconductor.org/packages/release/bioc/html/GWENA.html) that has been developed to perform extended analysis of gene co-expression networks. Thanks to biological and topological information as well as differential co-expression, the package helps to dissect the role of genes relationships in diseases conditions or targeted phenotypes. GWENA goes beyond existing packages that perform co-expression analysis by including new tools to fully characterize modules, such as differential co-expression, additional enrichment databases, and network visualization.

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Abstract 344: Finding the Achilles’ heel of Muscle Giant---TALEN-mediated Gene-editing in Zebrafish Titin

Circulation Research ◽

10.1161/res.117.suppl_1.344 ◽

2015 ◽

Vol 117 (suppl_1) ◽

Author(s):

Yu-Huan Shih ◽

Xiaolei Xu

Keyword(s):

Alternative Splicing ◽

Gene Editing ◽

Muscle Development ◽

Exon Skipping ◽

Transcription Activator ◽

Detailed Characterization ◽

A Domains ◽

Underlying Mechanisms ◽

Sarcomere Assembly

Background: TITIN (TTN) has more than 300 exons and encodes a gigantic protein that is crucial for heart and muscle development. Mutations in TTN caused a variety of human diseases including cardiomyopathy and muscular dystrophy. Recently, dilated cardiomyopathy-associated mutations on TTN have been found more frequently in exons encoding A-band domains but less in exons encoding the N-terminal Z-disc domains, suggesting that mutations in different exons of TTN cause distinct consequences. To elucidate the underlying mechanisms, we leveraged the Transcription Activator-Like Effects Nuclease (TALEN) technology in zebrafish to introduce truncating mutations in different exons of ttn, and then study their effects on heart and somites. Results: We generated truncational mutations in different exons of zebrafish titins encoding Z-disc, N2B, Novex-3, and A domains, respectively. Because zebrafish contains two titin homologues, ttna and ttnb, we introduced mutations in both genes at the corresponding loci. While both Z-disc and A band mutations on ttna disrupted sarcomere assembly in heart and somites, Z-disc or A band mutations on ttnb only affect somites without affecting the heart. Interestingly, a Z-disc mutation on ttna resulted in milder phenotypes than an A-band mutation, while a Z-disc mutation on ttnb generated severer phenotypes than an A-band mutation. No phenotype was observed in the homozygous fish in either ttna-novex-3 or ttnb-N2B mutant fish. Conclusions: A spectrum of truncational mutations in ttna and ttnb has been generated in zebrafish using the TALEN technology. Mutations in different exons result in different phenotypes. Detailed characterization of these mutants and double mutants will be presented, which shall elicit distinct contribution of alternative splicing and exon skipping as two candidate mechanisms during pathogenesis of Titinopathies.

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Functional characterization of Cynoglossus semilaevis R-spondin2 and its role in muscle development during embryogenesis

Genes & Genetic Systems ◽

10.1266/ggs.18-00009 ◽

2018 ◽

Vol 93 (5) ◽

pp. 181-190 ◽

Cited By ~ 1

Author(s):

Bo Wang ◽

Fan Yang ◽

Rui Li ◽

Xuemei Li ◽

Xiaolong Wu ◽

...

Keyword(s):

Muscle Development ◽

Functional Characterization ◽

Cynoglossus Semilaevis

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Enhancing gene set enrichment using networks

F1000Research ◽

10.12688/f1000research.17824.2 ◽

2019 ◽

Vol 8 ◽

pp. 129 ◽

Cited By ~ 1

Author(s):

Michael Prummer

Keyword(s):

Biological Function ◽

Automated Analysis ◽

Gene Set Analysis ◽

Molecular Pathways ◽

Human Intervention ◽

Gene Set Enrichment ◽

Topological Information ◽

Gene Set ◽

Gene Sets ◽

Differential Gene

Differential gene expression (DGE) studies often suffer from poor interpretability of their primary results, i.e., thousands of differentially expressed genes. This has led to the introduction of gene set analysis (GSA) methods that aim at identifying interpretable global effects by grouping genes into sets of common context, such as, molecular pathways, biological function or tissue localization. In practice, GSA often results in hundreds of differentially regulated gene sets. Similar to the genes they contain, gene sets are often regulated in a correlative fashion because they share many of their genes or they describe related processes. Using these kind of neighborhood information to construct networks of gene sets allows to identify highly connected sub-networks as well as poorly connected islands or singletons. We show here how topological information and other network features can be used to filter and prioritize gene sets in routine DGE studies. Community detection in combination with automatic labeling and the network representation of gene set clusters further constitute an appealing and intuitive visualization of GSA results. The RICHNET workflow described here does not require human intervention and can thus be conveniently incorporated in automated analysis pipelines.

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debar, a sequence-by-sequence denoiser for COI-5P DNA barcode data

10.1101/2021.01.04.425285 ◽

2021 ◽

Author(s):

Cameron M. Nugent ◽

Tyler A. Elliott ◽

Sujeevan Ratnasingham ◽

Paul D. N. Hebert ◽

Sarah J. Adamowicz

Keyword(s):

High Throughput Sequencing ◽

Dna Barcode ◽

R Package ◽

Error Rates ◽

Real World Data ◽

Species Discovery ◽

Consensus Sequences ◽

In Silico Studies ◽

Coi Sequences

AbstractDNA barcoding and metabarcoding are now widely used to advance species discovery and biodiversity assessments. High-throughput sequencing (HTS) has expanded the volume and scope of these analyses, but elevated error rates introduce noise into sequence records that can inflate estimates of biodiversity. Denoising —the separation of biological signal from instrument (technical) noise—of barcode and metabarcode data currently employs abundance-based methods which do not capitalize on the highly conserved structure of the cytochrome c oxidase subunit I (COI) region employed as the animal barcode. This manuscript introduces debar, an R package that utilizes a profile hidden Markov model to denoise indel errors in COI sequences introduced by instrument error. In silico studies demonstrated that debar recognized 95% of artificially introduced indels in COI sequences. When applied to real-world data, debar reduced indel errors in circular consensus sequences obtained with the Sequel platform by 75%, and those generated on the Ion Torrent S5 by 94%. The false correction rate was less than 0.1%, indicating that debar is receptive to the majority of true COI variation in the animal kingdom. In conclusion, the debar package improves DNA barcode and metabarcode workflows by aiding the generation of more accurate sequences aiding the characterization of species diversity.

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Pedometric tools for classification of southwestern Amazonian soils: A quali-quantitative interpretation incorporating visible-near infrared spectroscopy

Journal of Near Infrared Spectroscopy ◽

10.1177/09670335211061854 ◽

2022 ◽

pp. 096703352110618

Author(s):

Orlando CH Tavares ◽

Tiago R Tavares ◽

Carlos R Pinheiro Junior ◽

Luciélio M da Silva ◽

Paulo GS Wadt ◽

...

Keyword(s):

Near Infrared ◽

Environmental Variability ◽

Diffuse Reflectance Spectroscopy ◽

Soil Classification ◽

R Package ◽

Chemical Components ◽

Soil Profiles ◽

Wide Range

The southwestern region of the Amazon has great environmental variability, presents a great complexity of pedoenvironments due to its rich variability of geological and geomorphological environments, as well as for being a transition region with other two Brazilian biomes. In this study, the use of pedometric tools (the Algorithms for Quantitative Pedology (AQP) R package and diffuse reflectance spectroscopy) was evaluated for the characterization of 15 soil profiles in southwestern Amazon. The AQP statistical package—which evaluates the soil in-depth based on slicing functions—indicated a wide range of variation in soil attributes, especially in the superficial horizons. In addition, the results obtained in the similarity analysis corroborated with the description of physical, chemical components and oxide contents in-depth, aiding the classification of soil profiles. The in-depth characterization of visible-near infrared spectra allowed inference of the pedogenetic processes of some profiles, setting precedents for future work aiming to establish analytical strategies for soil classification in southwestern Amazon based on spectral data.

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Characterization of an MLP Homologue from Haemaphysalis longicornis (Acari: Ixodidae) Ticks

Pathogens ◽

10.3390/pathogens9040284 ◽

2020 ◽

Vol 9 (4) ◽

pp. 284

Author(s):

Jin Luo ◽

Hui Shen ◽

Qiaoyun Ren ◽

Guiquan Guan ◽

Bo Zhao ◽

...

Keyword(s):

Muscle Development ◽

Developmental Stages ◽

Rabbit Antiserum ◽

Haemaphysalis Longicornis ◽

Mrna Levels ◽

Muscle Lim Protein ◽

Rapid Amplification ◽

Differentiation And Proliferation ◽

Phosphate Buffered Saline

Members of the cysteine-rich protein (CRP) family are known to participate in muscle development in vertebrates. Muscle LIM protein (MLP) belongs to the CRP family and has an important function in the differentiation and proliferation of muscle cells. In this study, the full-length cDNA encoding MLP from Haemaphysalis longicornis (H. longicornis; HLMLP) ticks was obtained by 5′ rapid amplification of cDNA ends (RACE). To verify the transcriptional status of MLP in ticks, HLMLP gene expression was assessed during various developmental stages by real-time PCR (RT-PCR). Interestingly, HLMLP expression in the integument was significantly (P < 0.01) higher than that observed in other tested tissues of engorged adult ticks. In addition, HLMLP mRNA levels were significantly downregulated in response to thermal stress at 4 °C for 48 h. Furthermore, recombinant HLMLP was expressed in Escherichia coli, and Western blot analysis showed that rabbit antiserum against H. longicornis adults recognized HLMLP and MLPs from different ticks. Ten 3-month-old rabbits that had never been exposed to ticks were used for the immunization and challenge experiments. The rabbits were divided into two groups of five rabbits each, where rabbits in the first group were immunized with HLMLP, while those in the second group were immunized with phosphate-buffered saline (PBS) diluent as controls. The vaccination of rabbits with the recombinant HLMLP conferred partial protective immunity against ticks, resulting in 20.00% mortality and a 17.44% reduction in the engorgement weight of adult ticks. These results suggest that HLMLP is not ideal as a candidate for use in anti-tick vaccines. However, the results of this study generated novel information on the MLP gene in H. longicornis and provide a basis for further investigation of the function of this gene that could potentially lead to a better understanding of the mechanism of myofiber determination and transformation

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Fold-Change-Specific Enrichment Analysis (FSEA): Quantification of Transcriptional Response Magnitude for Functional Gene Groups

Genes ◽

10.3390/genes11040434 ◽

2020 ◽

Vol 11 (4) ◽

pp. 434 ◽

Cited By ~ 1

Author(s):

Daniil S. Wiebe ◽

Nadezhda A. Omelyanchuk ◽

Aleksei M. Mukhin ◽

Ivo Grosse ◽

Sergey A. Lashin ◽

...

Keyword(s):

Abiotic Factors ◽

Transcriptional Response ◽

Enrichment Analysis ◽

R Package ◽

Fold Change ◽

Functional Gene ◽

Gene Set Enrichment Analysis ◽

Gene Set Enrichment ◽

Go Terms ◽

Singular Enrichment Analysis

Gene expression profiling data contains more information than is routinely extracted with standard approaches. Here we present Fold-Change-Specific Enrichment Analysis (FSEA), a new method for functional annotation of differentially expressed genes from transcriptome data with respect to their fold changes. FSEA identifies Gene Ontology (GO) terms, which are shared by the group of genes with a similar magnitude of response, and assesses these changes. GO terms found by FSEA are fold-change-specifically (e.g., weakly, moderately, or strongly) affected by a stimulus under investigation. We demonstrate that many responses to abiotic factors, mutations, treatments, and diseases occur in a fold-change-specific manner. FSEA analyses suggest that there are two prevailing responses of functionally-related gene groups, either weak or strong. Notably, some of the fold-change-specific GO terms are invisible by classical algorithms for functional gene enrichment, Singular Enrichment Analysis (SEA), and Gene Set Enrichment Analysis (GSEA). These are GO terms not enriched compared to the genome background but strictly regulated by a factor within specific fold-change intervals. FSEA analysis of a cancer-related transcriptome suggested that the gene groups with a tightly coordinated response can be the valuable source to search for possible regulators, markers, and therapeutic targets in oncogenic processes. Availability and Implementation: FSEA is implemented as the FoldGO Bioconductor R package and a web-server.

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