Articles de revues : « Bioinformatic package »

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Theil, Sebastien, et Etienne Rifa. « rANOMALY : AmplicoN wOrkflow for Microbial community AnaLYsis ». F1000Research 10 (7 janvier 2021) : 7. http://dx.doi.org/10.12688/f1000research.27268.1.

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Bioinformatic tools for marker gene sequencing data analysis are continuously and rapidly evolving, thus integrating most recent techniques and tools is challenging. We present an R package for data analysis of 16S and ITS amplicons based sequencing. This workflow is based on several R functions and performs automatic treatments from fastq sequence files to diversity and differential analysis with statistical validation. The main purpose of this package is to automate bioinformatic analysis, ensure reproducibility between projects, and to be flexible enough to quickly integrate new bioinformatic tools or statistical methods. rANOMALY is an easy to install and customizable R package, that uses amplicon sequence variants (ASV) level for microbial community characterization. It integrates all assets of the latest bioinformatics methods, such as better sequence tracking, decontamination from control samples, use of multiple reference databases for taxonomic annotation, all main ecological analysis for which we propose advanced statistical tests, and a cross-validated differential analysis by four different methods. Our package produces ready to publish figures, and all of its outputs are made to be integrated in Rmarkdown code to produce automated reports.

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Love, Michael I., Charlotte Soneson et Rob Patro. « Swimming downstream : statistical analysis of differential transcript usage following Salmon quantification ». F1000Research 7 (27 juin 2018) : 952. http://dx.doi.org/10.12688/f1000research.15398.1.

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Detection of differential transcript usage (DTU) from RNA-seq data is an important bioinformatic analysis that complements differential gene expression analysis. Here we present a simple workflow using a set of existing R/Bioconductor packages for analysis of DTU. We show how these packages can be used downstream of RNA-seq quantification using the Salmon software package. The entire pipeline is fast, benefiting from inference steps by Salmon to quantify expression at the transcript level. The workflow includes live, runnable code chunks for analysis using DRIMSeq and DEXSeq, as well as for performing two-stage testing of DTU using the stageR package, a statistical framework to screen at the gene level and then confirm which transcripts within the significant genes show evidence of DTU. We evaluate these packages and other related packages on a simulated dataset with parameters estimated from real data.

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Love, Michael I., Charlotte Soneson et Rob Patro. « Swimming downstream : statistical analysis of differential transcript usage following Salmon quantification ». F1000Research 7 (14 septembre 2018) : 952. http://dx.doi.org/10.12688/f1000research.15398.2.

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Detection of differential transcript usage (DTU) from RNA-seq data is an important bioinformatic analysis that complements differential gene expression analysis. Here we present a simple workflow using a set of existing R/Bioconductor packages for analysis of DTU. We show how these packages can be used downstream of RNA-seq quantification using the Salmon software package. The entire pipeline is fast, benefiting from inference steps by Salmon to quantify expression at the transcript level. The workflow includes live, runnable code chunks for analysis using DRIMSeq and DEXSeq, as well as for performing two-stage testing of DTU using the stageR package, a statistical framework to screen at the gene level and then confirm which transcripts within the significant genes show evidence of DTU. We evaluate these packages and other related packages on a simulated dataset with parameters estimated from real data.

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Love, Michael I., Charlotte Soneson et Rob Patro. « Swimming downstream : statistical analysis of differential transcript usage following Salmon quantification ». F1000Research 7 (1 octobre 2018) : 952. http://dx.doi.org/10.12688/f1000research.15398.3.

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Detection of differential transcript usage (DTU) from RNA-seq data is an important bioinformatic analysis that complements differential gene expression analysis. Here we present a simple workflow using a set of existing R/Bioconductor packages for analysis of DTU. We show how these packages can be used downstream of RNA-seq quantification using the Salmon software package. The entire pipeline is fast, benefiting from inference steps by Salmon to quantify expression at the transcript level. The workflow includes live, runnable code chunks for analysis using DRIMSeq and DEXSeq, as well as for performing two-stage testing of DTU using the stageR package, a statistical framework to screen at the gene level and then confirm which transcripts within the significant genes show evidence of DTU. We evaluate these packages and other related packages on a simulated dataset with parameters estimated from real data.

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Li, Xiaoying, Xin Lin, Huiling Ren et Jinjing Guo. « Ontological Organization and Bioinformatic Analysis of Adverse Drug Reactions From Package Inserts : Development and Usability Study ». Journal of Medical Internet Research 22, n^o 7 (20 juillet 2020) : e20443. http://dx.doi.org/10.2196/20443.

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Background Licensed drugs may cause unexpected adverse reactions in patients, resulting in morbidity, risk of mortality, therapy disruptions, and prolonged hospital stays. Officially approved drug package inserts list the adverse reactions identified from randomized controlled clinical trials with high evidence levels and worldwide postmarketing surveillance. Formal representation of the adverse drug reaction (ADR) enclosed in semistructured package inserts will enable deep recognition of side effects and rational drug use, substantially reduce morbidity, and decrease societal costs. Objective This paper aims to present an ontological organization of traceable ADR information extracted from licensed package inserts. In addition, it will provide machine-understandable knowledge for bioinformatics analysis, semantic retrieval, and intelligent clinical applications. Methods Based on the essential content of package inserts, a generic ADR ontology model is proposed from two dimensions (and nine subdimensions), covering the ADR information and medication instructions. This is followed by a customized natural language processing method programmed with Python to retrieve the relevant information enclosed in package inserts. After the biocuration and identification of retrieved data from the package insert, an ADR ontology is automatically built for further bioinformatic analysis. Results We collected 165 package inserts of quinolone drugs from the National Medical Products Administration and other drug databases in China, and built a specialized ADR ontology containing 2879 classes and 15,711 semantic relations. For each quinolone drug, the reported ADR information and medication instructions have been logically represented and formally organized in an ADR ontology. To demonstrate its usage, the source data were further bioinformatically analyzed. For example, the number of drug-ADR triples and major ADRs associated with each active ingredient were recorded. The 10 ADRs most frequently observed among quinolones were identified and categorized based on the 18 categories defined in the proposal. The occurrence frequency, severity, and ADR mitigation method explicitly stated in package inserts were also analyzed, as well as the top 5 specific populations with contraindications for quinolone drugs. Conclusions Ontological representation and organization using officially approved information from drug package inserts enables the identification and bioinformatic analysis of adverse reactions caused by a specific drug with regard to predefined ADR ontology classes and semantic relations. The resulting ontology-based ADR knowledge source classifies drug-specific adverse reactions, and supports a better understanding of ADRs and safer prescription of medications.

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Zhbannikov, Ilya Y., Konstantin Arbeev, Svetlana Ukraintseva et Anatoliy I. Yashin. « haploR : an R package for querying web-based annotation tools ». F1000Research 6 (15 mai 2017) : 97. http://dx.doi.org/10.12688/f1000research.10742.2.

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We developed haploR, an R package for querying web based genome annotation tools HaploReg and RegulomeDB. haploR gathers information in a data frame which is suitable for downstream bioinformatic analyses. This will facilitate post-genome wide association studies streamline analysis for rapid discovery and interpretation of genetic associations.

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Zhbannikov, Ilya Y., Konstantin Arbeev et Anatoliy I. Yashin. « haploR : an R-package for querying web-based annotation tools ». F1000Research 6 (1 février 2017) : 97. http://dx.doi.org/10.12688/f1000research.10742.1.

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There exists a set of web-based tools for integration and exploring information linked to annotated genetic variants. We developed haploR, an R-package for querying such web-based genome annotation tools (currently implementing on HaploReg and RegulomeDB) and gathering information in a format suitable for downstream bioinformatic analyses. This will facilitate post-genome wide association studies streamline analysis for rapid discovery and interpretation of genetic associations.

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Rinchai, Darawan, Jessica Roelands, Mohammed Toufiq, Wouter Hendrickx, Matthew C. Altman, Davide Bedognetti et Damien Chaussabel. « BloodGen3Module : blood transcriptional module repertoire analysis and visualization using R ». Bioinformatics 37, n^o 16 (24 février 2021) : 2382–89. http://dx.doi.org/10.1093/bioinformatics/btab121.

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Abstract Motivation We previously described the construction and characterization of fixed reusable blood transcriptional module repertoires. More recently we released a third iteration (‘BloodGen3’ module repertoire) that comprises 382 functionally annotated modules and encompasses 14 168 transcripts. Custom bioinformatic tools are needed to support downstream analysis, visualization and interpretation relying on such fixed module repertoires. Results We have developed and describe here an R package, BloodGen3Module. The functions of our package permit group comparison analyses to be performed at the module-level, and to display the results as annotated fingerprint grid plots. A parallel workflow for computing module repertoire changes for individual samples rather than groups of samples is also available; these results are displayed as fingerprint heatmaps. An illustrative case is used to demonstrate the steps involved in generating blood transcriptome repertoire fingerprints of septic patients. Taken together, this resource could facilitate the analysis and interpretation of changes in blood transcript abundance observed across a wide range of pathological and physiological states. Availability and implementation The BloodGen3Module package and documentation are freely available from Github: https://github.com/Drinchai/BloodGen3Module. Supplementary information Supplementary data are available at Bioinformatics online.

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Fasterius, Erik, et Cristina Al-Khalili Szigyarto. « seqCAT : a Bioconductor R-package for variant analysis of high throughput sequencing data ». F1000Research 7 (12 août 2019) : 1466. http://dx.doi.org/10.12688/f1000research.16083.2.

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High throughput sequencing technologies are flourishing in the biological sciences, enabling unprecedented insights into e.g. genetic variation, but require extensive bioinformatic expertise for the analysis. There is thus a need for simple yet effective software that can analyse both existing and novel data, providing interpretable biological results with little bioinformatic prowess. We present seqCAT, a Bioconductor toolkit for analysing genetic variation in high throughput sequencing data. It is a highly accessible, easy-to-use and well-documented R-package that enables a wide range of researchers to analyse their own and publicly available data, providing biologically relevant conclusions and publication-ready figures. SeqCAT can provide information regarding genetic similarities between an arbitrary number of samples, validate specific variants as well as define functionally similar variant groups for further downstream analyses. Its ease of use, installation, complete data-to-conclusions functionality and the inherent flexibility of the R programming language make seqCAT a powerful tool for variant analyses compared to already existing solutions. A publicly available dataset of liver cancer-derived organoids is analysed herein using the seqCAT package, corroborating the original authors' conclusions that the organoids are genetically stable. A previously known liver cancer-related mutation is additionally shown to be present in a sample though it was not listed in the original publication. Differences between DNA- and RNA-based variant calls in this dataset are also analysed revealing a high median concordance of 97.5%. SeqCAT is an open source software under a MIT licence available at https://bioconductor.org/packages/release/bioc/html/seqCAT.html.

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Dai, Fangfang, Jinglin Wu, Zhimin Deng, Hengxing Li, Wei Tan, Mengqin Yuan, Dongyong Yang et al. « Integrated Bioinformatic Analysis of DNA Methylation and Immune Infiltration in Endometrial Cancer ». BioMed Research International 2022 (20 juin 2022) : 1–13. http://dx.doi.org/10.1155/2022/5119411.

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Background. Endometrial cancer greatly threatens the health of female. Emerging evidences have demonstrated that DNA methylation and immune infiltration are involved in the occurrence and development of endometrial cancer. However, the mechanism and prognostic biomarkers of endometrial cancer are still unclear. In this study, we assess DNA methylation and immune infiltration via bioinformatic analysis. Methods. The latest RNA-Seq, DNA methylation data, and clinical data related to endometrial cancer were downloaded from the UCSC Xena dataset. The methylation-driven genes were selected, and then the risk score was obtained using “MethylMix” and “corrplot” R packages. The connection between methylated genes and the expression of screened driven genes were explored using “survminer” and “beeswarm” packages, respectively. Finally, the role of VTCN1in immune infiltration was analyzed using “CIBERSORT” package. Results. In this study, 179 upregulated genes, and 311 downregulated genes were identified and found to be related to extracellular matrix organization, cell–cell junctions, and cell adhesion molecular binding. The methylation-driven gene VTCN1 was selected, and patients classified to the hypomethylation and high expression group displayed poor prognosis. The VTCN1 gene exhibited highest correlation coefficient between methylation and expression. More importantly, the hypomethylation of promoter of VTCN1 led to its high expression, thereby induce tumor development by inhibiting CD8+ T cell infiltration. Conclusions. Overall, our study was the first to reveal the mechanism of endometrial cancer by assessing DNA methylation and immune infiltration via integrated bioinformatic analysis. In addition, we found a pivotal prognostic biomarker for the disease. Our study provides potential targets for the diagnosis and prognosis of endometrial cancer in the future.

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Hillje, Roman, Pier Giuseppe Pelicci et Lucilla Luzi. « Cerebro : interactive visualization of scRNA-seq data ». Bioinformatics 36, n^o 7 (25 novembre 2019) : 2311–13. http://dx.doi.org/10.1093/bioinformatics/btz877.

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Abstract Despite the growing availability of sophisticated bioinformatic methods for the analysis of single-cell RNA-seq data, few tools exist that allow biologists without extensive bioinformatic expertise to directly visualize and interact with their own data and results. Here, we present Cerebro (cell report browser), a Shiny- and Electron-based standalone desktop application for macOS and Windows which allows investigation and inspection of pre-processed single-cell transcriptomics data without requiring bioinformatic experience of the user. Through an interactive and intuitive graphical interface, users can (i) explore similarities and heterogeneity between samples and cell clusters in two-dimensional or three-dimensional projections such as t-SNE or UMAP, (ii) display the expression level of single genes or gene sets of interest, (iii) browse tables of most expressed genes and marker genes for each sample and cluster and (iv) display trajectories calculated with Monocle 2. We provide three examples prepared from publicly available datasets to show how Cerebro can be used and which are its capabilities. Through a focus on flexibility and direct access to data and results, we think Cerebro offers a collaborative framework for bioinformaticians and experimental biologists that facilitates effective interaction to shorten the gap between analysis and interpretation of the data. Availability and implementation The Cerebro application, additional documentation, and example datasets are available at https://github.com/romanhaa/Cerebro. Similarly, the cerebroApp R package is available at https://github.com/romanhaa/cerebroApp. All components are released under the MIT License. Supplementary information Supplementary data are available at Bioinformatics online.

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Gruenstaeudl, Michael. « annonex2embl : automatic preparation of annotated DNA sequences for bulk submissions to ENA ». Bioinformatics 36, n^o 12 (30 mars 2020) : 3841–48. http://dx.doi.org/10.1093/bioinformatics/btaa209.

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Abstract Motivation The submission of annotated sequence data to public sequence databases constitutes a central pillar in biological research. The surge of novel DNA sequences awaiting database submission due to the application of next-generation sequencing has increased the need for software tools that facilitate bulk submissions. This need has yet to be met with the concurrent development of tools to automate the preparatory work preceding such submissions. Results The author introduce annonex2embl, a Python package that automates the preparation of complete sequence flatfiles for large-scale sequence submissions to the European Nucleotide Archive. The tool enables the conversion of DNA sequence alignments that are co-supplied with sequence annotations and metadata to submission-ready flatfiles. Among other features, the software automatically accounts for length differences among the input sequences while maintaining correct annotations, automatically interlaces metadata to each record and displays a design suitable for easy integration into bioinformatic workflows. As proof of its utility, annonex2embl is employed in preparing a dataset of more than 1500 fungal DNA sequences for database submission. Availability and implementation annonex2embl is freely available via the Python package index at http://pypi.python.org/pypi/annonex2embl. Supplementary information Supplementary data are available at Bioinformatics online.

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Fasterius, Erik, et Cristina Al-Khalili Szigyarto. « seqCAT : a Bioconductor R-package for variant analysis of high throughput sequencing data ». F1000Research 7 (14 septembre 2018) : 1466. http://dx.doi.org/10.12688/f1000research.16083.1.

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High throughput sequencing technologies are flourishing in the biological sciences, enabling unprecedented insights into e.g. genetic variation, but require extensive bioinformatic expertise for the analysis. There is thus a need for simple yet effective software that can analyse both existing and novel data, providing interpretable biological results with little bioinformatic prowess. We present seqCAT, a Bioconductor toolkit for analysing genetic variation in high throughput sequencing data. It is a highly accessible, easy-to-use and well-documented R-package that enables a wide range of researchers to analyse their own and publicly available data, providing biologically relevant conclusions and publication-ready figures. SeqCAT can provide information regarding genetic similarities between an arbitrary number of samples, validate specific variants as well as define functionally similar variant groups for further downstream analyses. Its ease of use, installation, complete data-to-conclusions functionality and the inherent flexibility of the R programming language make seqCAT a powerful tool for variant analyses compared to already existing solutions. A publicly available dataset of liver cancer-derived organoids is analysed herein using the seqCAT package, demonstrating that the organoids are genetically stable. A previously known liver cancer-related mutation is additionally shown to be present in a sample though it was not listed in the original publication. Differences between DNA- and RNA-based variant calls in this dataset are also analysed revealing a high median concordance of 97.5%.

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Jiang, Zhen-yu, Yi Zhou, Lu Zhou, Shao-wei Li et Bang-mao Wang. « Identification of Key Genes and Immune Infiltrate in Nonalcoholic Steatohepatitis : A Bioinformatic Analysis ». BioMed Research International 2021 (11 septembre 2021) : 1–15. http://dx.doi.org/10.1155/2021/7561645.

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Background. Nonalcoholic steatohepatitis (NASH) can progress to cirrhosis and hepatic carcinoma and is closely associated with changes in the neurological environment. The discovery of new biomarkers would aid in the treatment of NASH. Methods. Data GSE89632 were downloaded from the Gene Expression Omnibus (GEO) database, and R package “limma” was used to identify differentially expressed genes (DEGs) for NASH vs. normal tissues. The STRING database was used to construct a protein-protein interaction (PPI) network, and the Cytoscape software program (Version 3.80) was used to visualize the PPI network and identify key genes. The immune infiltration of NASH was determined using the R package “CIBERSORT”. Results. We screened 41 DEGs. GO and KEGG enrichment analyses of the DEGs revealed the enrichment of pathways related to NAFLD steatosis and inflammation. A PPI network analysis was also performed on the DEGs, and seven genes (MYC, CXCL8, FOS, SOCS1, SOCS3, IL6, and PTGS2) were identified as hub genes. An immune infiltration assessment revealed that macrophages M2, memory resting CD4+ T cells, and γΔ T cells play important roles in the immune microenvironment of NASH, which may be mediated by the seven identified hub genes.

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Stubbs, Benjamin J., Shweta Gopaulakrishnan, Kimberly Glass, Nathalie Pochet, Celine Everaert, Benjamin Raby et Vincent Carey. « TFutils : Data structures for transcription factor bioinformatics ». F1000Research 8 (5 février 2019) : 152. http://dx.doi.org/10.12688/f1000research.17976.1.

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DNA transcription is intrinsically complex. Bioinformatic work with transcription factors (TFs) is complicated by a multiplicity of data resources and annotations. The Bioconductor package TFutils includes data structures and functions to enhance the precision and utility of integrative analyses that have components involving TFs. TFutils provides catalogs of human TFs from three reference sources (CISBP, HOCOMOCO, and GO), a catalog of TF targets derived from MSigDb, and multiple approaches to enumerating TF binding sites. Aspects of integration of TF binding patterns and genome-wide association study results are explored in examples.

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Stubbs, Benjamin J., Shweta Gopaulakrishnan, Kimberly Glass, Nathalie Pochet, Celine Everaert, Benjamin Raby et Vincent Carey. « TFutils : Data structures for transcription factor bioinformatics ». F1000Research 8 (17 mai 2019) : 152. http://dx.doi.org/10.12688/f1000research.17976.2.

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DNA transcription is intrinsically complex. Bioinformatic work with transcription factors (TFs) is complicated by a multiplicity of data resources and annotations. The Bioconductor package TFutils includes data structures and functions to enhance the precision and utility of integrative analyses that have components involving TFs. TFutils provides catalogs of human TFs from three reference sources (CISBP, HOCOMOCO, and GO), a catalog of TF targets derived from MSigDb, and multiple approaches to enumerating TF binding sites, including an interface to results of 690 ENCODE experiments. Aspects of integration of TF binding patterns and genome-wide association study results are explored in examples.

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Fancello, Laura, Alessandro Guida, Gianmaria Frige, Arnaud Gerard Michel Ceol, Gabriele Babini, Giovanni Luca Scaglione, Mario Zanfardino et al. « TMBleR : a bioinformatic tool to optimize TMB estimation and predictive power ». Bioinformatics 38, n^o 6 (20 décembre 2021) : 1724–26. http://dx.doi.org/10.1093/bioinformatics/btab836.

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Abstract Motivation Tumor mutational burden (TMB) has been proposed as a predictive biomarker for immunotherapy response in cancer patients, as it is thought to enrich for tumors with high neoantigen load. TMB assessed by whole-exome sequencing is considered the gold standard but remains confined to research settings. In the clinical setting, targeted gene panels sampling various genomic sizes along with diverse strategies to estimate TMB were proposed and no real standard has emerged yet. Results We provide the community with TMBleR, a tool to measure the clinical impact of various strategies of panel-based TMB measurement. Availability and implementation R package and docker container (GPL-3 Open Source license): https://acc-bioinfo.github.io/TMBleR/. Graphical-user interface website: https://bioserver.ieo.it/shiny/app/tmbler. Supplementary information Supplementary data are available at Bioinformatics online.

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Rosolowski, M., H. Berger, C. Schwaenen, S. Wessendorf, M. Loeffler, D. Hasenclever et M. Kreuz. « Development and Implementation of an Analysis Tool for Array-based Comparative Genomic Hybridization ». Methods of Information in Medicine 46, n^o 05 (2007) : 608–13. http://dx.doi.org/10.1160/me9064.

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Summary Objectives: Array-comparative genomic hybridization (aCGH) is a high-throughput method to detect and map copy number aberrations in the genome. Multi-step analysis of high-dimensional data requires an integrated suite of bioinformatic tools. In this paperwe detail an analysis pipeline for array CGH data. Methods: We developed an analysis tool for array CGH data which supports single and multi-chip analyses as well as combined analyses with paired mRNA gene expression data. The functions supporting relevant steps of analysis were implemented using the open source software R and combined as package aCGHPipeline. Analysis methods were illustrated using 189 CGH arrays of aggressive B-cell lymphomas. Results: The package covers data input, quality control, normalization, segmentation and classification. For multi-chip analysis aCGHPipeline offers an algorithm for automatic delineation of recurrent regions. This task was performed manuallyup to now. The package also supports combined analysis with mRNA gene expression data. Outputs consist of HTML documents to facilitate communication with clinical partners. Conclusions: The R package aCGHPipeline supports basic tasks of single and multi-chip analysis of array CGH data.

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Eriksson, Pontus, Nour-al-dain Marzouka, Gottfrid Sjödahl, Carina Bernardo, Fredrik Liedberg et Mattias Höglund. « A comparison of rule-based and centroid single-sample multiclass predictors for transcriptomic classification ». Bioinformatics 38, n^o 4 (12 novembre 2021) : 1022–29. http://dx.doi.org/10.1093/bioinformatics/btab763.

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Abstract Motivation Gene expression-based multiclass prediction, such as tumor subtyping, is a non-trivial bioinformatic problem. Most classifier methods operate by comparing expression levels relative to other samples. Methods that base predictions on the expression pattern within a sample have been proposed as an alternative. As these methods are invariant to the cohort composition and can be applied to a sample in isolation, they can collectively be termed single sample predictors (SSP). Such predictors could potentially be used for preprocessing-free classification of new samples and be built to function across different expression platforms where proper batch and dataset normalization is challenging. Here, we evaluate the behavior of several multiclass SSPs based on binary gene-pair rules (k-Top Scoring Pairs, Absolute Intrinsic Molecular Subtyping and a new Random Forest approach) and compare them to centroids built with centered or raw expression values, with the criteria that an optimal predictor should have high accuracy, overcome differences in tumor purity, be robust across expression platforms and provide an informative prediction output score. Results We found that gene-pair-based SSPs showed excellent performance on many expression-based classification tasks. The three methods differed in prediction score output, handling of tied scores and behavior in low purity samples. The k-Top Scoring Pairs and Random Forest approach both achieved high classification accuracy while providing an informative prediction score. Although gene-pair-based SSPs have been touted as being cross-platform compatible (through training on mixed platform data), out-of-the-box compatibility with a new dataset remains a potential issue that warrants cohort-to-cohort verification. Availability and implementation Our R package ‘multiclassPairs’ (https://cran.r-project.org/package=multiclassPairs) (https://doi.org/10.1093/bioinformatics/btab088) is freely available and enables easy training, prediction, and visualization using the gene-pair rule-based Random Forest SSP method and provides additional multiclass functionalities to the switchBox k-Top-Scoring Pairs package. Supplementary information Supplementary data are available at Bioinformatics online.

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Mora-Márquez, Fernando, José Luis Vázquez-Poletti et Unai López de Heredia. « NGScloud2 : optimized bioinformatic analysis using Amazon Web Services ». PeerJ 9 (16 avril 2021) : e11237. http://dx.doi.org/10.7717/peerj.11237.

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Background NGScloud was a bioinformatic system developed to perform de novo RNAseq analysis of non-model species by exploiting the cloud computing capabilities of Amazon Web Services. The rapid changes undergone in the way this cloud computing service operates, along with the continuous release of novel bioinformatic applications to analyze next generation sequencing data, have made the software obsolete. NGScloud2 is an enhanced and expanded version of NGScloud that permits the access to ad hoc cloud computing infrastructure, scaled according to the complexity of each experiment. Methods NGScloud2 presents major technical improvements, such as the possibility of running spot instances and the most updated AWS instances types, that can lead to significant cost savings. As compared to its initial implementation, this improved version updates and includes common applications for de novo RNAseq analysis, and incorporates tools to operate workflows of bioinformatic analysis of reference-based RNAseq, RADseq and functional annotation. NGScloud2 optimizes the access to Amazon’s large computing infrastructures to easily run popular bioinformatic software applications, otherwise inaccessible to non-specialized users lacking suitable hardware infrastructures. Results The correct performance of the pipelines for de novo RNAseq, reference-based RNAseq, RADseq and functional annotation was tested with real experimental data, providing workflow performance estimates and tips to make optimal use of NGScloud2. Further, we provide a qualitative comparison of NGScloud2 vs. the Galaxy framework. NGScloud2 code, instructions for software installation and use are available at https://github.com/GGFHF/NGScloud2. NGScloud2 includes a companion package, NGShelper that contains Python utilities to post-process the output of the pipelines for downstream analysis at https://github.com/GGFHF/NGShelper.

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Wang, Yi, Huan Luo, Jing Cao et Chao Ma. « Bioinformatic Identification of Neuroblastoma Microenvironment-Associated Biomarkers with Prognostic Value ». Journal of Oncology 2020 (10 septembre 2020) : 1–13. http://dx.doi.org/10.1155/2020/5943014.

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The microenvironment plays a vital role in the tumor recurrence of neuroblastoma. This research aimed at exploring prognostic genes that are involved in neuroblastoma microenvironment. We used “estimate” R package to calculate the immune/stromal/ESTIMATE scores of each sample of ArrayExpress dataset E-MTAB-8248 based on the ESTIMATE algorithm. Then we found that immune/stromal/ESTIMATE scores were not correlated with age/chromosome 11q, but tumor stage, MYCN gene amplifications, and chromosome 1p. Samples were then divided into high- and low-score groups, and 280 common differentially expressed genes (DEGs) were identified. 64 potential prognostic genes were harvested through overall survival analysis from the common DEGs. 14 prognostic genes (ABCA6, SEPP1, SLAMF8, GPR171, ABCA9, ARHGAP15, IL7R, HLA-DPB1, GZMA, GPR183, CCL19, ITK, FGL2, and CD1C) were obtained after screening in two independent cohorts. GO and KEGG analysis discovered that common DEGs and 64 potential prognostic genes are mainly involved in T-cell activation, lymphocyte activation regulation, leukocyte migration, and the interaction of cytokines and cytokine receptors. Correlation analysis showed that all prognostic genes were negatively correlated with MYCN amplification. Cox analysis identified 5 independent prognostic genes (ARHGAP15, ABCA9, CCL19, SLAMF8, and CD1C).

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Macrander, Jason, Jyothirmayi Panda, Daniel Janies, Marymegan Daly et Adam M. Reitzel. « Venomix : a simple bioinformatic pipeline for identifying and characterizing toxin gene candidates from transcriptomic data ». PeerJ 6 (31 juillet 2018) : e5361. http://dx.doi.org/10.7717/peerj.5361.

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The advent of next-generation sequencing has resulted in transcriptome-based approaches to investigate functionally significant biological components in a variety of non-model organism. This has resulted in the area of “venomics”: a rapidly growing field using combined transcriptomic and proteomic datasets to characterize toxin diversity in a variety of venomous taxa. Ultimately, the transcriptomic portion of these analyses follows very similar pathways after transcriptome assembly often including candidate toxin identification using BLAST, expression level screening, protein sequence alignment, gene tree reconstruction, and characterization of potential toxin function. Here we describe the Python package Venomix, which streamlines these processes using common bioinformatic tools along with ToxProt, a publicly available annotated database comprised of characterized venom proteins. In this study, we use the Venomix pipeline to characterize candidate venom diversity in four phylogenetically distinct organisms, a cone snail (Conidae; Conus sponsalis), a snake (Viperidae; Echis coloratus), an ant (Formicidae; Tetramorium bicarinatum), and a scorpion (Scorpionidae; Urodacus yaschenkoi). Data on these organisms were sampled from public databases, with each original analysis using different approaches for transcriptome assembly, toxin identification, or gene expression quantification. Venomix recovered numerically more candidate toxin transcripts for three of the four transcriptomes than the original analyses and identified new toxin candidates. In summary, we show that the Venomix package is a useful tool to identify and characterize the diversity of toxin-like transcripts derived from transcriptomic datasets. Venomix is available at: https://bitbucket.org/JasonMacrander/Venomix/.

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Zhu, Zhengya, Zhongyuan He, Tao Tang, Fuan Wang, Hongkun Chen, Baoliang Li, Guoliang Chen et al. « Integrative Bioinformatics Analysis Revealed Mitochondrial Dysfunction-Related Genes Underlying Intervertebral Disc Degeneration ». Oxidative Medicine and Cellular Longevity 2022 (11 octobre 2022) : 1–35. http://dx.doi.org/10.1155/2022/1372483.

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Objective. Mitochondrial dysfunction plays an important role in intervertebral disc degeneration (IDD). We aim to explore the pathways and key genes that cause mitochondrial dysfunction during IDD and to further reveal the pathogenesis of IDD based on bioinformatic analyses. Methods. Datasets GSE70362 and GSE124272 were downloaded from the Gene Expression Omnibus. Differentially expressed genes (DEGs) of mitochondrial dysfunction between IDD patients and healthy controls were screened by package limma package. Critical genes were identified by adopting gene ontology (GO), Kyoto encyclopedia of genes and genomes (KEGG) pathways, and protein-protein interaction (PPI) networks. We collected both degenerated and normal disc tissues obtained surgically, and we performed western blot and qPCR to verify the key DEGs identified in intervertebral disc tissues. Results. In total, 40 cases of IDD and 24 healthy controls were included. We identified 152 DEGs, including 67 upregulated genes and 85 downregulated genes. Four genes related to mitochondrial dysfunction (SOX9, FLVCR1, NR5A1 and UCHL1) were screened out. Of them, SOX9, FLVCR1, and UCHL1 were down-regulated in peripheral blood and intervertebral disc tissues of IDD patients, while NR5A1 was up-regulated. The analysis of immune infiltration showed the concentrations of mast cells activated were significantly the highest in IDD patients. Compared with the control group, the level of T cells CD4 memory resting was the lowest in the patients. In addition, 24 cases of IDD tissues and 12 cases of normal disc tissues were obtained to verify the results of bioinformatics analysis. Both western blot and qPCR results were consistent with the results of bioinformatics analysis. Conclusion. We identified four genes (SOX9, FLVCR1, NR5A1 and UCHL1) associated with mitochondrial dysfunction that play an important role in the progress of disc degeneration. The identification of these differential genes may provide new insights for the diagnosis and treatment of IDD.

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Kotlyarov, Stanislav, et Anna Kotlyarova. « Bioinformatic Analysis of ABCA1 Gene Expression in Smoking and Chronic Obstructive Pulmonary Disease ». Membranes 11, n^o 9 (31 août 2021) : 674. http://dx.doi.org/10.3390/membranes11090674.

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Smoking is a key modifiable risk factor for developing the chronic obstructive pulmonary disease (COPD). When smoking, many processes, including the reverse transport of cholesterol mediated by the ATP binding cassette transporter A1 (ABCA1) protein are disrupted in the lungs. Changes in the cholesterol content in the lipid rafts of plasma membranes can modulate the function of transmembrane proteins localized in them. It is believed that this mechanism participates in increasing the inflammation in COPD. Methods: Bioinformatic analysis of datasets from Gene Expression Omnibus (GEO) was carried out. Gene expression data from datasets of alveolar macrophages and the epithelium of the respiratory tract in smokers and COPD patients compared with non-smokers were used for the analysis. To evaluate differentially expressed genes, bioinformatic analysis was performed in comparison groups using the limma package in R (v. 4.0.2), and the GEO2R and Phantasus tools (v. 1.11.0). Results: The conducted bioinformatic analysis showed changes in the expression of the ABCA1 gene associated with smoking. In the alveolar macrophages of smokers, the expression levels of ABCA1 were lower than in non-smokers. At the same time, in most of the airway epithelial datasets, gene expression did not show any difference between the groups of smokers and non-smokers. In addition, it was shown that the expression of ABCA1 in the epithelial cells of the trachea and large bronchi is higher than in small bronchi. Conclusions: The conducted bioinformatic analysis showed that smoking can influence the expression of the ABCA1 gene, thereby modulating lipid transport processes in macrophages, which are part of the mechanisms of inflammation development.

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Wang, C., S. X. Zhang, S. Song, J. Qiao, R. Zhao, M. J. Chang, Y. Zhang, G. Y. Liu, P. F. He et X. Li. « POS0743 GENE EXPRESSION MICROARRAY IN LUPUS NEPHRITIS BY BIOINFORMATIC ANALYSIS ». Annals of the Rheumatic Diseases 80, Suppl 1 (19 mai 2021) : 623.1–623. http://dx.doi.org/10.1136/annrheumdis-2021-eular.2062.

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Background:Nephritis is one of the predominant causes of morbidity and mortality in patients with lupus1 2.The lack of understanding regarding the molecular mechanisms of lupus nephritis(LN) hinders the development of specific targeted therapy for this progressive disease3.Objectives:In this study, we use bioinformatics method to analyze the genes involved in regulating the potential pathogenesis of LN.Methods:The expression profile of LN(GSE104948 and GSE32591) was obtained from the GEO database.GSE104948 was a memory chip, which included 32 LN glomerular biopsy tissues and 3 glomerular tissues from living donors.GSE32591 dataset included 32 LN glomerular biopsy tissues and 15 glomerular tissues from living donors. The Oligo package was used to process the data to obtain the expression matrix files of all the related genes.P<0.05 and |log2(FC)|>2 were setted as cut-off criteria for the DEGs.Ggplot2, heatmap packages were used to DEGs visualization. Metascape online tool was used to annotating DEGs for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis performed.We used STRING online database to construct protein-protein interaction (PPI) network. Hub genes were identified by Cytoscape.Results:In differential expression analysis,357 DEGs were identified,including 248 up-regulated genes and 109 down-regulated genes (Figure 1A,B).GO enrichment showed that these DEGs were primarily enriched in biological pathways, cell localization and molecular function and revealed that LN-related genes mainly involved in immune response.KEGG pathway annotation enrichment analysis revealed these DEGs were closely associated with Staphylococcus aureus infection,Complement and coagulation cascades (Figure 1D). Fourteen hub genes(IFT3,IRF7,OAS3,GBP2,RSAD2,MX1,IFIT2,IFI6,MX2,ISF15,IFIT1,QAS2,OASL,OAS1) were identified from PPI network (Figure 1C,E).Conclusion:Illuminating the molecular mechanisms of LN was help for deep understanding of LN.References:[1]Song J, Zhao L, Li Y. Comprehensive bioinformatics analysis of mRNA expression profiles and identification of a miRNA-mRNA network associated with lupus nephritis. Lupus 2020;29(8):854-61. doi: 10.1177/0961203320925155 [published Online First: 2020/05/22].[2]Yao F, Sun L, Fang W, et al. HsamiR3715p inhibits human mesangial cell proliferation and promotes apoptosis in lupus nephritis by directly targeting hypoxiainducible factor 1alpha. Mol Med Rep 2016;14(6):5693-98. doi: 10.3892/mmr.2016.5939 [published Online First: 2016/11/24].[3]Dall’Era M. Treatment of lupus nephritis: current paradigms and emerging strategies. Curr Opin Rheumatol 2017;29(3):241-47. doi: 10.1097/BOR.0000000000000381 [published Online First: 2017/02/17].Acknowledgements:This project was supported by National Science Foundation of China (82001740), Open Fund from the Key Laboratory of Cellular Physiology (Shanxi Medical University) (KLCP2019) and Innovation Plan for Postgraduate Education in Shanxi Province (2020BY078).Disclosure of Interests:None declared

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Li, Yan-zhen, Hao-jie Xu, Jia-min Hu, Shi-zhu Lin, Na Zhang, Hong-da Cai, Kai Zeng et al. « Bioinformatic Analysis of Gene Expression Profile in Plasma of Hypertensive Patients ». American Journal of Hypertension 33, n^o 6 (21 mai 2020) : 581. http://dx.doi.org/10.1093/ajh/hpaa040.

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Abstract Background To analyze expression profiles of long noncoding RNA (lncRNA) and messenger RNA (mRNA) in patients with essential hypertension (EH) and normotensive adults. Methods The gene chip dataset GSE76845, which was generated from 5 plasma samples from patients with EH and 5 normotensives, was downloaded from the National Biotechnology Information Center Public Data Platform. Each sample (total RNA) was pooled from the total RNA of 3 age- and gender-matched subjects (EH patients or healthy controls). A ClusterProfiler package including gene set enrichment analysis (GSEA) was used to identify differentially expressed genes. The target microRNA and mRNA were predicted by microcode, microDB, microTarBase, and TargetScan databases. Finally, a competing endogenous RNAs (ceRNA) regulatory network was constructed. Results Compared with the healthy control adults, 191 differential lncRNAs (90 upregulated and 101 downregulated) and 1,187 differential mRNAs (533 upregulated and 654 downregulated) were identified in EH patients. GSEA analysis showed that 17 pathways, including ubiquinone and terpenoid-quinone biosynthesis, parathyroid hormone synthesis secretion and action, fatty acid metabolism, and steroid hormone biosynthesis are involved in hypertension. A ceRNA network consisting of 150 nodes and 488 interactive pairs was constructed. Conclusions lncRNA and mRNA profile analysis provides new insight into molecular mechanisms of EH pathogenesis and potential targets for therapeutic interventions.

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Xie, Liangzhen, GuanShen Huang, Mingjian Gao, Jianming Huang, Hai Li, Hao Xia, Xiuting Xiang, Saizhu Wu et Yunjun Ruan. « Identification of Atrial Fibrillation-Related lncRNA Based on Bioinformatic Analysis ». Disease Markers 2022 (4 février 2022) : 1–11. http://dx.doi.org/10.1155/2022/8307975.

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Background. Atrial fibrillation (AF) is the most common arrhythmia in the world. Long noncoding RNA (lncRNA) has been found to play an important role in cardiovascular diseases including heart failure, myocardial infarction, and atherosclerosis. However, the role of lncRNA in AF has rarely been studied. The purpose of this study is to identify the expression profile of lncRNA in AF patients, explore the function of lncRNA in AF, and provide a potential scientific basis for the treatment of AF in the future. Methods. The lncRNA and mRNA expression profiles were obtained from the atrial appendage samples of GSE31821, GSE411774, GSE79768, and GSE115574 in the Gene Expression Omnibus (GEO) database. Functional analysis was performed via Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Variation Analysis (GSVA). The “CIBERSORT” R kit was used to analyze 22 immune cell infiltrates in AF and sinus rhythm (SR) patients. The “CORRPLOT” R package was used to analyze the immune correlation between lncRNA and immune cells. Results. A total of 6 differentially expressed lncRNAs and 45 differentially expressed mRNAs were identified in the AF and SR groups. GO, KEGG, and GSVA results showed that abnormally expressed lncRNAs were involved in signaling pathways related to the atrium, including the Toll-like receptor signaling pathway and calcium signaling pathway. Immune cell infiltration analysis revealed that native B cells, follicular helper T cells, and resting dendritic cells may be involved in the AF process. In addition, LINC00844 was negatively correlated with resting dendritic cells. Conclusion. The expression profile of lncRNA in AF patients was different from that in normal controls. The physiological functions of these differentially expressed lncRNAs may be related to the pathogenesis of AF, which provide a scientific basis for the prognosis and treatment of patients with AF.

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Song, Chao, Shixiong Wei, Yunlong Fan et Shengli Jiang. « Bioinformatic-based Identification of Genes Associated with Aortic Valve Stenosis ». Heart Surgery Forum 25, n^o 1 (24 janvier 2022) : E069—E078. http://dx.doi.org/10.1532/hsf.4263.

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Background: Aortic valve stenosis (AS) disease is the most common valvular disease in developed countries. The pathology of AS is complex, and its main processes include calcification of the valve stroma and involve genetic factors, lipoprotein deposition and oxidation, chronic inflammation, osteogenic transition of cardiac valve interstitial cells, and active valve calcification. The aim of this study was to identify potential genes associated with AS. Methods: Three original gene expression profiles (GSE153555, GSE12644, and GSE51472) were downloaded from the Gene Expression Omnibus (GEO) database and analyzed by GEO2R tool or ‘limma’ in R to identify differentially expressed genes (DEGs). Functional enrichment was analyzed using the ClusterProfiler package in R Bioconductor. STRING was utilized for the Protein–Protein Interaction (PPI) Network construct, and tissue-specific gene expression were identified using BioGPS database. The hub genes were screened out using the Cytoscape software. Related miRNAs were predicted in Targetscan, miWalk, miRDB, Hoctar, and TarBase. Results: A total of 58 upregulated genes and 20 downregulated genes were screened out, which were mostly enriched in matrix remodeling and the immune system process. A module was thus clustered into by PPI network analysis, which mainly involved in Fc gamma R-mediated phagocytosis, Osteoclast differentiation. Ten genes (IBSP, NCAM1, MMP9, FCGR3B, COL4A3, FCGR1A, THY1, RUNX2, ITGA4, and COL10A1) with the highest degree scores were subsequently identified as the hub genes for AS by applying the CytoHubba plugin. And hsa-miR-1276 was finally identified as potential miRNA and miRNA-gene regulatory network was constructed using NetworkAnalyst. Conclusions: Our analysis suggested that IBSP, NCAM1, MMP9, FCGR3B, COL4A3, FCGR1A, THY1, RUNX2, ITGA4, and COL10A1 might be hub genes associated with AS, and hsa-miR-1276 was potential miRNA. This result could provide novel insight into pathology and therapy of AS in the future.

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Kotlyarov, Stanislav, et Anna Kotlyarova. « Analysis of ABC Transporter Gene Expression in Atherosclerosis ». Cardiogenetics 11, n^o 4 (4 novembre 2021) : 204–20. http://dx.doi.org/10.3390/cardiogenetics11040021.

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ABC transporters are a large family of membrane proteins that transport chemically diverse substrates across the cell membrane. Disruption of transport mechanisms mediated by ABC transporters causes the development of various diseases, including atherosclerosis. Methods: A bioinformatic analysis of a dataset from Gene Expression Omnibus (GEO) was performed. A GEO dataset containing data on gene expression levels in samples of atherosclerotic lesions and control arteries without atherosclerotic lesions from carotid, femoral, and infrapopliteal arteries was used for analysis. To evaluate differentially expressed genes, a bioinformatic analysis was performed in comparison groups using the limma package in R (v. 4.0.2) and the GEO2R and Phantasus tools (v. 1.11.0). Results: The obtained data indicate the differential expression of many ABC transporters belonging to different subfamilies. The differential expressions of ABC transporter genes involved in lipid transport, mechanisms of multidrug resistance, and mechanisms of ion exchange are shown. Differences in the expression of transporters in tissue samples from different arteries are established. Conclusions: The expression of ABC transporter genes demonstrates differences in atherosclerotic samples and normal arteries, which may indicate the involvement of transporters in the pathogenesis of atherosclerosis.

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Urrutia, Eugene, Li Chen, Haibo Zhou et Yuchao Jiang. « Destin : toolkit for single-cell analysis of chromatin accessibility ». Bioinformatics 35, n^o 19 (1 mars 2019) : 3818–20. http://dx.doi.org/10.1093/bioinformatics/btz141.

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Abstract Summary Single-cell assay of transposase-accessible chromatin followed by sequencing (scATAC-seq) is an emerging new technology for the study of gene regulation with single-cell resolution. The data from scATAC-seq are unique—sparse, binary and highly variable even within the same cell type. As such, neither methods developed for bulk ATAC-seq nor single-cell RNA-seq data are appropriate. Here, we present Destin, a bioinformatic and statistical framework for comprehensive scATAC-seq data analysis. Destin performs cell-type clustering via weighted principle component analysis, weighting accessible chromatin regions by existing genomic annotations and publicly available regulomic datasets. The weights and additional tuning parameters are determined via model-based likelihood. We evaluated the performance of Destin using downsampled bulk ATAC-seq data of purified samples and scATAC-seq data from seven diverse experiments. Compared to existing methods, Destin was shown to outperform across all datasets and platforms. For demonstration, we further applied Destin to 2088 adult mouse forebrain cells and identified cell-type-specific association of previously reported schizophrenia GWAS loci. Availability and implementation Destin toolkit is freely available as an R package at https://github.com/urrutiag/destin. Supplementary information Supplementary data are available at Bioinformatics online.

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Shan, Qingqing, Yifan Zhang, Xu Zhang, Wei Wang et Zongan Liang. « The Effect of Coumestrol on Hub Genes in Lung Squamous Cell Carcinoma Based on Bioinformatic Strategy ». Natural Product Communications 17, n^o 10 (octobre 2022) : 1934578X2211279. http://dx.doi.org/10.1177/1934578x221127960.

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Purpose There is limited treatment for lung squamous cell carcinoma (LUSC), so there is an urgent need to find new antitumor drugs. Materials and Methods We downloaded datasets from the Gene Expression Omnibus (GEO) and the Cancer Genome Atlas databases. We used GEO2R and the “limma” package to screen differentially expressed genes. We used the Cytoscape software to screen out hub genes. We screened herbs that act on hub genes on the Chinese medicine website. We then studied the effect of coumestrol (CM) on the hub genes in the H226 cell line. Results Seven hub genes were screened, namely CCNB2, CENPF, KIF11, MELK, nucleolar and spindle-associated protein 1 (NUSAP1), PBK, and RRM2. We observed that CM had a tumor-inhibiting effect on H226 cells by inhibiting the expression of CCNB2, KIF11, and NUSAP1. Conclusion CM, screened by bioinformatics and network pharmacology, can inhibit H226 cells by downregulating CCNB2, KIF11, and NUSAP1.

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Jia, Yanfei, Wentao Wu, Youchao Xiao, Kefan Cai, Songbai Gui, Qiang Li et Tian Li. « Integrin α6 Indicates a Poor Prognosis of Craniopharyngioma through Bioinformatic Analysis and Experimental Validation ». Journal of Oncology 2022 (11 octobre 2022) : 1–11. http://dx.doi.org/10.1155/2022/6891655.

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Background. Craniopharyngioma (CP) is a benign slow-growing tumor. It tends to affect children, and the number of patients is on rise. Considering the high morbidity and mortality of CP, it is urgent and pivotal to identify new biomarkers to uncover the etiology and pathogenesis of CP. Methods. The “limma” package was utilized to calculate the data from the Gene Expression Omnibus (GEO) database. Based on differentially expressed genes (DEGs), gene ontology and pathway analysis were deduced from the DAVID web tool. Further, we constructed a protein-protein interaction (PPI) network. Weighted correlation network analysis (WGCNA) was utilized to build a coexpression network. Finally, Western blotting and survival analysis were performed to examine the expression level of important metabolism-related genes. Results. Three hundred and eighty-four DEGs were identified between normal tissues and CPs from the GSE94349 and GSE26966 datasets. The Venn diagram for DEGs and hub genes in the ‘turquoise’ module revealed four key genes. Finally, the outcome of the survival analysis suggested that Integrin α6 (ITGA6) significantly affected the overall survival time of the patients with CP. Conclusion. IGTA6, as a metabolism-related molecule, was found to be substantially related to the overall survival of patients with CP.

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Kancherla, Jayaram, Alexander Zhang, Brian Gottfried et Hector Corrada Bravo. « Epiviz Web Components : reusable and extensible component library to visualize functional genomic datasets ». F1000Research 7 (17 juillet 2018) : 1096. http://dx.doi.org/10.12688/f1000research.15433.1.

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Interactive and integrative data visualization tools and libraries are integral to exploration and analysis of genomic data. Web based genome browsers allow integrative data exploration of a large number of data sets for a specific region in the genome. Currently available web-based genome browsers are developed for specific use cases and datasets, therefore integration and extensibility of the visualizations and the underlying libraries from these tools is a challenging task. Genomic data visualization and software libraries that enable bioinformatic researchers and developers to implement customized genomic data viewers and data analyses for their application are much needed. Using recent advances in core web platform APIs and technologies including Web Components, we developed the Epiviz Component Library, a reusable and extensible data visualization library and application framework for genomic data. Epiviz Components can be integrated with most JavaScript libraries and frameworks designed for HTML. To demonstrate the ease of integration with other frameworks, we developed an R/Bioconductor epivizrChart package, that provides interactive, shareable and reproducible visualizations of genomic data objects in R, Shiny and also create standalone HTML documents. The component library is modular by design, reusable and natively extensible and therefore simplifies the process of managing and developing bioinformatic applications.

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Wei, Rui-Qi, Wen-Mei Zhang, Zhe Liang, Chunmei Piao et Guangfa Zhu. « Identification of Signal Pathways and Hub Genes of Pulmonary Arterial Hypertension by Bioinformatic Analysis ». Canadian Respiratory Journal 2022 (29 août 2022) : 1–12. http://dx.doi.org/10.1155/2022/1394088.

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Pulmonary arterial hypertension (PAH) is a progressive and complex pulmonary vascular disease with poor prognosis. The aim of this study was to provide a new understanding of the pathogenesis of disease and potential treatment targets for patients with PAH based on multiple-microarray analysis.Two microarray datasets (GSE53408 and GSE113439) downloaded from the Gene Expression Omnibus (GEO) database were analysed. All the raw data were processed by R, and differentially expressed genes (DEGs) were screened out by the “limma” package. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed and visualized by R and Cytoscape software. Protein-protein interactions (PPI) of DEGs were analysed based on the NetworkAnalyst online tool. A total of 442 upregulated DEGs and 84 downregulated DEGs were identified. GO enrichment analysis showed that these DEGs were mainly enriched in mitotic nuclear division, organelle fission, chromosome segregation, nuclear division, and sister chromatid segregation. Significant KEGG pathway enrichment included ribosome biogenesis in eukaryotes, RNA transport, proteoglycans in cancer, dilated cardiomyopathy, rheumatoid arthritis, vascular smooth muscle contraction, focal adhesion, regulation of the actin cytoskeleton, and hypertrophic cardiomyopathy. The PPI network identified 10 hub genes including HSP90AA1, CDC5L, MDM2, LRRK2, CFTR, IQGAP1, CAND1, TOP2A, DDX21, and HIF1A. We elucidated potential biomarkers and therapeutic targets for PAH by bioinformatic analysis, which provides a theoretical basis for future study.

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Liu, Zhendong, Ruotian Zhang, Zhenying Sun, Jiawei Yao, Penglei Yao, Xin Chen, Xinzhuang Wang et al. « Identification of hub genes and small-molecule compounds in medulloblastoma by integrated bioinformatic analyses ». PeerJ 8 (14 avril 2020) : e8670. http://dx.doi.org/10.7717/peerj.8670.

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Background Medulloblastoma (MB) is the most common intracranial malignant tumor in children. The genes and pathways involved in the pathogenesis of MB are relatively unknown. We aimed to identify potential biomarkers and small-molecule drugs for MB. Methods Gene expression profile data sets were obtained from the Gene Expression Omnibus (GEO) database and the differentially expressed genes (DEGs) were identified using the Limma package in R. Functional annotation, and cell signaling pathway analysis of DEGs was carried out using DAVID and Kobas. A protein-protein interaction network was generated using STRING. Potential small-molecule drugs were identified using CMap. Result We identified 104 DEGs (29 upregulated; 75 downregulated). Gene ontology analysis showed enrichment in the mitotic cell cycle, cell cycle, spindle, and DNA binding. Cell signaling pathway analysis identified cell cycle, HIF-1 signaling pathway, and phospholipase D signaling pathway as key pathways. SYN1, CNTN2, FAIM2, MT3, and SH3GL2 were the prominent hub genes and their expression level were verified by RT-qPCR. Vorinostat, resveratrol, trichostatin A, pyrvinium, and prochlorperazine were identified as potential drugs for MB. The five hub genes may be targets for diagnosis and treatment of MB, and the small-molecule compounds are promising drugs for effective treatment of MB. Conclusion In this study we obtained five hub genes of MB, SYN1, CNTN2, FAIM2, MT3, and SH3GL2 were confirmed as hub genes. Meanwhile, Vorinostat, resveratrol, trichostatin A, pyrvinium, and prochlorperazine were identified as potential drugs for MB.

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Dong, Shu, Zhimin Ding, Hao Zhang et Qiwen Chen. « Identification of Prognostic Biomarkers and Drugs Targeting Them in Colon Adenocarcinoma : A Bioinformatic Analysis ». Integrative Cancer Therapies 18 (janvier 2019) : 153473541986443. http://dx.doi.org/10.1177/1534735419864434.

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Objective: To identify prognostic biomarkers and drugs that target them in colon adenocarcinoma (COAD) based on the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus databases. Methods: The TCGA dataset was used to identify the top 50 upregulated differentially expressed genes (DEGs), and Gene Expression Omnibus profiles were used for validation. Survival analyses were conducted with the TCGA dataset using the RTCGAToolbox package in the R software environment. Drugs targeting the candidate prognostic biomarkers were searched in the DrugBank and herbal databases. Results: Among the top 50 upregulated DEGs in patients with COAD in the TCGA dataset, the Wnt signaling pathway and cytokine-cytokine receptor interactions and pathways in cancer Kyoto Encyclopedia of Genes and Genomes pathway analysis were enriched in DEGs. Tissue development and regulation of cell proliferation were the main Gene Ontology biological processes associated with upregulated DEGs. MYC and KLK6 were overexpressed in tumors validated in the TCGA, GSE41328, and GSE113513 databases (all P < .001) and were significantly associated with overall survival in patients with COAD ( P = .021 and P = .047). Nadroparin and benzamidine were identified as inhibitors of MYC and KLK6 in DrugBank, and 8 herbs targeting MYC, including Da Huang ( Radix Rhei Et Rhizome), Hu Zhang ( Polygoni Cuspidati Rhizoma Et Radix), Huang Lian ( Coptidis Rhizoma), Ban Xia ( Arum Ternatum Thunb), Tu Fu Ling ( Smilacis Glabrae Rhixoma), Lei Gong Teng ( Tripterygii Radix), Er Cha ( Catechu), and Guang Zao ( Choerospondiatis Fructus), were identified. Conclusion: MYC and KLK6 may serve as candidate prognostic predictors and therapeutic targets in patients with COAD.

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Wen, Zhifeng, Fangxi Liu, Xinyu Lin, Shanshan Zhong, Xiuchun Zhang, Zhike Zhou, Jukka Jolkkonen, Chang Liu et Chuansheng Zhao. « Prognostic Signature for Human Umbilical Cord Mesenchymal Stem Cell Treatment of Ischemic Cerebral Infarction by Integrated Bioinformatic Analysis ». BioMed Research International 2022 (13 décembre 2022) : 1–11. http://dx.doi.org/10.1155/2022/9973232.

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In recent studies, stem cell-based therapy is a potential new approach in the treatment of stroke. The mechanism of human umbilical cord mesenchymal stem cell (hUMSC) transplantation as one of the new approaches in the treatment of ischemic stroke is still unclear. The aim of this study was to determine the traits of immune responses during stroke progression after treatment with human umbilical cord blood MSCs by bioinformatics, to predict potential prognostic biomarkers that could lead to sex differences, and to reveal potential therapeutic targets. The microarray dataset GSE78731 (mRNA profile) of middle cerebral artery occlusion (MCAO) rats was obtained from the Gene Expression Omnibus (GEO) database. First, two potentially expressed genes (DEGs) were screened using the Bioconductor R package. Ultimately, 30 specific DEGs were obtained (22 upregulated and 353 downregulated). Next, bioinformatic analysis was performed on these specific DEGs. We performed a comparison for the differentially expressed genes screened from between the hUMSC and MCAO groups. Gene Ontology enrichment and pathway enrichment analyses were then performed for annotation and visualization. Gene Ontology (GO) functional annotation analysis shows that DEGs are mainly enriched in leukocyte migration, neutrophil activation, neutrophil degranulation, the external side of plasma membrane, cytokine receptor binding, and carbohydrate binding. KEGG pathway enrichment analysis showed that the first 5 enrichment pathways were cytokine-cytokine receptor interaction, chemokine signal pathway, viral protein interaction with cytokine and cytokine receptor, cell adhesion molecules (CAMs), and phagosome. The top 10 key genes of the constructed PPI network were screened, including Cybb, Ccl2, Cd68, Ptprc, C5ar1, Il-1b, Tlr2, Itgb2, Itgax, and Cd44. In summary, hUMSC is likely to be a promising means of treating IS by immunomodulation.

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Li, Wei Vivian, et Jingyi Jessica Li. « A statistical simulator scDesign for rational scRNA-seq experimental design ». Bioinformatics 35, n^o 14 (juillet 2019) : i41—i50. http://dx.doi.org/10.1093/bioinformatics/btz321.

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Abstract Motivation Single-cell RNA sequencing (scRNA-seq) has revolutionized biological sciences by revealing genome-wide gene expression levels within individual cells. However, a critical challenge faced by researchers is how to optimize the choices of sequencing platforms, sequencing depths and cell numbers in designing scRNA-seq experiments, so as to balance the exploration of the depth and breadth of transcriptome information. Results Here we present a flexible and robust simulator, scDesign, the first statistical framework for researchers to quantitatively assess practical scRNA-seq experimental design in the context of differential gene expression analysis. In addition to experimental design, scDesign also assists computational method development by generating high-quality synthetic scRNA-seq datasets under customized experimental settings. In an evaluation based on 17 cell types and 6 different protocols, scDesign outperformed four state-of-the-art scRNA-seq simulation methods and led to rational experimental design. In addition, scDesign demonstrates reproducibility across biological replicates and independent studies. We also discuss the performance of multiple differential expression and dimension reduction methods based on the protocol-dependent scRNA-seq data generated by scDesign. scDesign is expected to be an effective bioinformatic tool that assists rational scRNA-seq experimental design and comparison of scRNA–seq computational methods based on specific research goals. Availability and implementation We have implemented our method in the R package scDesign, which is freely available at https://github.com/Vivianstats/scDesign. Supplementary information Supplementary data are available at Bioinformatics online.

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Xiang, Mengmeng, Qian Chen, Yang Feng, Yilun Wang, Jie Wang, Jun Liang et Jinhua Xu. « Bioinformatic analysis of key biomarkers and immune filtration of skin biopsy in discoid lupus erythematosus ». Lupus 30, n^o 5 (2 février 2021) : 807–17. http://dx.doi.org/10.1177/0961203321992434.

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Objective Discoid lupus erythematosus (DLE) is the most common category of chronic cutaneous lupus erythematosus, where the pathological process is proved to be closely associated with immunity. This bioinformatic analysis sought to identify key biomarkers and to perform immune infiltration analysis in the skin biopsy samples of DLE. Methods GSE120809, GSE100093, GSE72535, GSE81071 were used as the data source of gene expression profiles, altogether containing 79 DLE samples and 47 normal controls (NC). Limma package was applied to identify differentially expressed genes (DEGs) and additional Gene Ontology (GO) together with The Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were done. Protein-protein interaction network (PPI) was constructed using STRING and Cytoscape. Hub genes were selected by CytoHubba. Finally, immune filtration analysis was finished by the CIBERSORT algorithm, and comparisons between the two groups were accomplished. Results A total of 391 DEGs were identified, which were composed of 57 up-regulated genes and 334 down-regulated genes. GO and KEGG enrichment analyses revealed that DEGs were closely related with different steps in the immune response. Top 10 hub genes included GBP2, HLA-F, IFIT2, RSAD2, ISG15, IFIT1, IFIT3, MX1, XAF1 and IFI6. Immune filtration analysis from CIBERSORT had found that compared with NC, DLE samples had higher percentages of CD8+ T cells, T cells CD4 memory activated, T cells gamma delta, macrophages M1 and lower percentages of T cells regulatory, macrophages M2, dendritic cells resting, mast cells resting, mast cells activated. Conclusion This bioinformatic study selected key biomarkers from the contrast between DLE and NC skin samples and is the first research to analyze immune cell filtration in DLE.

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Chen, Dongdong, Zhijun Feng, Mingzhen Zhou, Zhijian Ren, Fan Zhang et Yumin Li. « Bioinformatic Evidence Reveals that Cell Cycle Correlated Genes Drive the Communication between Tumor Cells and the Tumor Microenvironment and Impact the Outcomes of Hepatocellular Carcinoma ». BioMed Research International 2021 (26 octobre 2021) : 1–25. http://dx.doi.org/10.1155/2021/4092635.

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Hepatocellular carcinoma (HCC) is an aggressive cancer type with poor prognosis; thus, there is especially necessary and urgent to screen potential prognostic biomarkers for early diagnosis and novel therapeutic targets. In this study, we downloaded target data sets from the GEO database, and obtained codifferentially expressed genes using the limma R package and identified key genes through the protein–protein interaction network and molecular modules, and performed GO and KEGG pathway analyses for key genes via the clusterProfiler package and further determined their correlations with clinicopathological features using the Oncomine database. Survival analysis was completed in the GEPIA and the Kaplan–Meier plotter database. Finally, correlations between key genes, cell types infiltrated in the tumor microenvironment (TME), and hypoxic signatures were explored based on the TIMER database. From the results, 11 key genes related to the cell cycle were determined, and high levels of these key genes’ expression were focused on advanced and higher grade status HCC patients, as well as in samples of TP53 mutation and vascular invasion. Besides, the 11 key genes were significantly associated with poor prognosis of HCC and also were positively related to the infiltration level of MDSCs in the TME and the HIF1A and VEGFA of hypoxic signatures, but a negative correlation was found with endothelial cells (ECs) and hematopoietic stem cells. The result determined that 11 key genes (RRM2, NDC80, ECT2, CCNB1, ASPM, CDK1, PRC1, KIF20A, DTL, TOP2A, and PBK) could play a vital role in the pathogenesis of HCC, drive the communication between tumor cells and the TME, and act as probably promising diagnostic, therapeutic, and prognostic biomarkers in HCC patients.

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Yi, Jing, Zhili Wen et Youwen Hu. « Bioinformatic Deconstruction of Differentially Expressed Sequence Tags in Hepatocellular Carcinoma Based on Artificial Neural Network ». Contrast Media & ; Molecular Imaging 2022 (10 octobre 2022) : 1–12. http://dx.doi.org/10.1155/2022/6716324.

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Traditional medical imaging methods for diagnosing hepatocellular carcinoma can only provide information for differential diagnosis in terms of morphology and blood supply of the lesion, and the determination of the nature of the lesion still relies on tissue biopsy. Although ultrasound or CT-guided biopsy has become an effective method for the diagnosis of liver cancer in recent years, the puncture has the possibility of tumor irritation, liver tumor rupture, or needle tract metastasis. In this paper, the use of bioinformatics method is to gradually screen potentially high-risk genes associated with HCC recurrence on a genome-wide scale would help to discover the key target molecules. The ANN method was used to establish a gene prediction model that can predict the recurrence and survival of HCC, so as to construct a tool to identify patients at risk of HCC recurrence. It provided a certain therapeutic basis for future clinical work, thereby improving the prognosis of patients with HCC. Using the “survfit” function of the “survival” package in the R language, the log-rank test (the log-rank test was a common method for comparing two survival curves) was performed on all genes with posthoc recurrence of hepatocellular carcinoma as the outcome event. Then, the BLAST tool (Basic Local Alignment Search Tool) was used to search the similarity of each hepatocellular carcinoma database to find out the genes with similar sequences to each hepatocellular carcinoma, so as to determine the function of each differentially expressed sequence tag. This paper found that the AUC of the ANN model was greater than that of the discriminant analysis model ( P < 0.05 ). This paper promoted the development of new therapeutic measures for hepatocellular carcinoma and provided important theoretical guidance for human beings to fight cancer.

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Zhang, Fengjun, Cheng Yu, Wenchang Xu, Xiao Li, Junchen Feng, Hongshuo Shi, Jingrong Yang et al. « Identification of critical genes and molecular pathways in COVID-19 myocarditis and constructing gene regulatory networks by bioinformatic analysis ». PLOS ONE 17, n^o 6 (24 juin 2022) : e0269386. http://dx.doi.org/10.1371/journal.pone.0269386.

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Background There is growing evidence of a strong relationship between COVID-19 and myocarditis. However, there are few bioinformatics-based analyses of critical genes and the mechanisms related to COVID-19 Myocarditis. This study aimed to identify critical genes related to COVID-19 Myocarditis by bioinformatic methods, explore the biological mechanisms and gene regulatory networks, and probe related drugs. Methods The gene expression data of GSE150392 and GSE167028 were obtained from the Gene Expression Omnibus (GEO), including cardiomyocytes derived from human induced pluripotent stem cells infected with SARS-CoV-2 in vitro and GSE150392 from patients with myocarditis infected with SARS-CoV-2 and the GSE167028 gene expression dataset. Differentially expressed genes (DEGs) (adjusted P-Value <0.01 and |Log2 Fold Change| ≥2) in GSE150392 were assessed by NetworkAnalyst 3.0. Meanwhile, significant modular genes in GSE167028 were identified by weighted gene correlation network analysis (WGCNA) and overlapped with DEGs to obtain common genes. Functional enrichment analyses were performed by using the "clusterProfiler" package in the R software, and protein-protein interaction (PPI) networks were constructed on the STRING website (https://cn.string-db.org/). Critical genes were identified by the CytoHubba plugin of Cytoscape by 5 algorithms. Transcription factor-gene (TF-gene) and Transcription factor-microRibonucleic acid (TF-miRNA) coregulatory networks construction were performed by NetworkAnalyst 3.0 and displayed in Cytoscape. Finally, Drug Signatures Database (DSigDB) was used to probe drugs associated with COVID-19 Myocarditis. Results Totally 850 DEGs (including 449 up-regulated and 401 down-regulated genes) and 159 significant genes in turquoise modules were identified from GSE150392 and GSE167028, respectively. Functional enrichment analysis indicated that common genes were mainly enriched in biological processes such as cell cycle and ubiquitin-protein hydrolysis. 6 genes (CDK1, KIF20A, PBK, KIF2C, CDC20, UBE2C) were identified as critical genes. TF-gene interactions and TF-miRNA coregulatory network were constructed successfully. A total of 10 drugs, (such as Etoposide, Methotrexate, Troglitazone, etc) were considered as target drugs for COVID-19 Myocarditis. Conclusions Through bioinformatics method analysis, this study provides a new perspective to explore the pathogenesis, gene regulatory networks and provide drug compounds as a reference for COVID-19 Myocarditis. It is worth highlighting that critical genes (CDK1, KIF20A, PBK, KIF2C, CDC20, UBE2C) may be potential biomarkers and treatment targets of COVID-19 Myocarditis for future study.

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Salihov, Sergey, Dmitriy Maltsov, Maria Samsonova et Konstantin Kozlov. « Solution of Mixed-Integer Optimization Problems in Bioinformatics with Differential Evolution Method ». Mathematics 9, n^o 24 (20 décembre 2021) : 3329. http://dx.doi.org/10.3390/math9243329.

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The solution of the so-called mixed-integer optimization problem is an important challenge for modern life sciences. A wide range of methods has been developed for its solution, including metaheuristics approaches. Here, a modification is proposed of the differential evolution entirely parallel (DEEP) method introduced recently that was successfully applied to mixed-integer optimization problems. The triangulation recombination rule was implemented and the recombination coefficients were included in the evolution process in order to increase the robustness of the optimization. The deduplication step included in the procedure ensures the uniqueness of individual integer-valued parameters in the solution vectors. The developed algorithms were implemented in the DEEP software package and applied to three bioinformatic problems. The application of the method to the optimization of predictors set in the genomic selection model in wheat resulted in dimensionality reduction such that the phenotype can be predicted with acceptable accuracy using a selected subset of SNP markers. The method was also successfully used to optimize the training set of samples for such a genomic selection model. According to the obtained results, the developed algorithm was capable of constructing a non-linear phenomenological regression model of gene expression in developing a Drosophila eye with almost the same average accuracy but significantly less standard deviation than the linear models obtained earlier.

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Wang, Qi, Xufeng Huang, Shujing Zhou, Yuntao Ding, Huizhi Wang, Weiye Jiang et Min Xu. « IL1RN and PRRX1 as a Prognostic Biomarker Correlated with Immune Infiltrates in Colorectal Cancer : Evidence from Bioinformatic Analysis ». International Journal of Genomics 2022 (29 novembre 2022) : 1–24. http://dx.doi.org/10.1155/2022/2723264.

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The extensive morbidity of colorectal cancer (CRC) and the inferior prognosis of terminal CRC urgently call for reliable prognostic biomarkers. For this, we identified 704 differentially expressed genes (DEGs) by intersecting three datasets, GSE41328, GSE37364, and GSE15960 from Gene Expression Omnibus database, to maximize the accuracy of the results. Preliminary analysis of the DEGs was then performed using online gene analysis datasets, such as DAVID, UCSC Cancer Genome Browser, CBioPortal, STRING, and UCSC Cancer Genome Browser. Cytoscape was utilized to visualize the protein perception interaction network of DEGs, and the bubble map of GO and KEGG enrichment function was demonstrated using the R package. The Molecular Complex Detection (MCODE), Biological Network Gene Oncology (BiNGO) plug-in in Cytoscape, was applied to further screen the DEGs to obtain 15 seed genes, which were IL1RN, GALNT12, ADH6, SCN7A, CXCL1, FGF18, SOX9, ACACB, PRRX1, MZB1, SLC22A3, CNNM4, LY6E, IFITM2, and GDPD3. Among them, IL1RN, ADH6, SCN7A, ACACB, MZB1, and GDPD3 exhibited statistically significant survival differences, whereas limited studies were conducted in CRC. Based on the enrichment results of the “Gene Ontology“(GO) and “Kyoto Encyclopedia of Genes and genomes “(KEGG) as well as documented findings of key genes, we further emphasized the potential of IL1RN and PRRX1 as markers of immune infiltrates in CRC and confirmed our hypothesis by compiling data from the UALCAN, Tumor Immune Estimation Resource, and TISIDB databases for these two genes. The above-mentioned genes might offer a valuable insight into the diagnosis, immunotherapeutic targets, and prognosis of CRC.

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Zhang, Xiuzhi, Chunyan Kang, Ningning Li, Xiaoli Liu, Jinzhong Zhang, Fenglan Gao et Liping Dai. « Identification of special key genes for alcohol-related hepatocellular carcinoma through bioinformatic analysis ». PeerJ 7 (6 février 2019) : e6375. http://dx.doi.org/10.7717/peerj.6375.

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Background Alcohol-related hepatocellular carcinoma (HCC) was reported to be diagnosed at a later stage, but the mechanism was unknown. This study aimed to identify special key genes (SKGs) during alcohol-related HCC development and progression. Methods The mRNA data of 369 HCC patients and the clinical information were downloaded from the Cancer Genome Atlas project (TCGA). The 310 patients with certain HCC-related risk factors were included for analysis and divided into seven groups according to the risk factors. Survival analyses were applied for the HCC patients of different groups. The patients with hepatitis B virus or hepatitis C virus infection only were combined into the HCC-V group for further analysis. The differentially expressed genes (DEGs) between the HCCs with alcohol consumption only (HCC-A) and HCC-V tumors were identified through limma package in R with cutoff criteria│log2 fold change (logFC)|>1.0 and p < 0.05. The DEGs between eight alcohol-related HCCs and their paired normal livers of GSE59259 from the Gene Expression Omnibus (GEO) were identified through GEO2R (a built-in tool in GEO database) with cutoff criteria |logFC|> 2.0 and adj.p < 0.05. The intersection of the two sets of DEGs was considered SKGs which were then investigated for their specificity through comparisons between HCC-A and other four HCC groups. The SKGs were analyzed for their correlations with HCC-A stage and grade and their prognostic power for HCC-A patients. The expressional differences of the SKGs in the HCCs in whole were also investigated through Gene Expression Profiling Interactive Analysis (GEPIA). The SKGs in HCC were validated through Oncomine database analysis. Results Pathological stage is an independent prognostic factor for HCC patients. HCC-A patients were diagnosed later than HCC patients with other risk factors. Ten SKGs were identified and nine of them were confirmed for their differences in paired samples of HCC-A patients. Three (SLC22A10, CD5L, and UROC1) and four (SLC22A10, UROC1, CSAG3, and CSMD1) confirmed genes were correlated with HCC-A stage and grade, respectively. SPP2 had a lower trend in HCC-A tumors and was negatively correlated with HCC-A stage and grade. The SKGs each was differentially expressed between HCC-A and at least one of other HCC groups. CD5L was identified to be favorable prognostic factor for overall survival while CSMD1 unfavorable prognostic factor for disease-free survival for HCC-A patients and HCC patients in whole. Through Oncomine database, the dysregulations of the SKGs in HCC and their clinical significance were confirmed. Conclusion The poor prognosis of HCC-A patients might be due to their later diagnosis. The SKGs, especially the four stage-correlated genes (CD5L, SLC22A10, UROC1, and SPP2) might play important roles in HCC development, especially alcohol-related HCC development and progression. CD5L might be useful for overall survival and CSMD1 for disease-free survival predication in HCC, especially alcohol-related HCC.

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Danger, Richard, Quentin Moiteaux, Yodit Feseha, Estelle Geffard, Gérard Ramstein et Sophie Brouard. « FaDA : A web application for regular laboratory data analyses ». PLOS ONE 16, n^o 12 (20 décembre 2021) : e0261083. http://dx.doi.org/10.1371/journal.pone.0261083.

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Web-based data analysis and visualization tools are mostly designed for specific purposes, such as the analysis of data from whole transcriptome RNA sequencing or single-cell RNA sequencing. However, generic tools designed for the analysis of common laboratory data for noncomputational scientists are also needed. The importance of such web-based tools is emphasized by the continuing increases in the sample capacity of conventional laboratory tools such as quantitative PCR, flow cytometry or ELISA instruments. We present a web-based application FaDA, developed with the R Shiny package that provides users with the ability to perform statistical group comparisons, including parametric and nonparametric tests, with multiple testing corrections suitable for most standard wet-laboratory analyses. FaDA provides data visualizations such as heatmaps, principal component analysis (PCA) plots, correlograms and receiver operating curves (ROCs). Calculations are performed through the R language. The FaDA application provides a free and intuitive interface that allows biologists without bioinformatic skill to easily and quickly perform common laboratory data analyses. The application is freely accessible at https://shiny-bird.univ-nantes.fr/app/Fada.

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Castellano-Escuder, Pol, Raúl González-Domínguez, Francesc Carmona-Pontaque, Cristina Andrés-Lacueva et Alex Sánchez-Pla. « POMAShiny : A user-friendly web-based workflow for metabolomics and proteomics data analysis ». PLOS Computational Biology 17, n^o 7 (1 juillet 2021) : e1009148. http://dx.doi.org/10.1371/journal.pcbi.1009148.

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Metabolomics and proteomics, like other omics domains, usually face a data mining challenge in providing an understandable output to advance in biomarker discovery and precision medicine. Often, statistical analysis is one of the most difficult challenges and it is critical in the subsequent biological interpretation of the results. Because of this, combined with the computational programming skills needed for this type of analysis, several bioinformatic tools aimed at simplifying metabolomics and proteomics data analysis have emerged. However, sometimes the analysis is still limited to a few hidebound statistical methods and to data sets with limited flexibility. POMAShiny is a web-based tool that provides a structured, flexible and user-friendly workflow for the visualization, exploration and statistical analysis of metabolomics and proteomics data. This tool integrates several statistical methods, some of them widely used in other types of omics, and it is based on the POMA R/Bioconductor package, which increases the reproducibility and flexibility of analyses outside the web environment. POMAShiny and POMA are both freely available at https://github.com/nutrimetabolomics/POMAShiny and https://github.com/nutrimetabolomics/POMA, respectively.

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Marchand-Austin, Alex, Raymond S. W. Tsang, Jennifer L. Guthrie, Jennifer H. Ma, Gillian H. Lim, Natasha S. Crowcroft, Shelley L. Deeks, David J. Farrell et Frances B. Jamieson. « Short-Read Whole-Genome Sequencing for Laboratory-Based Surveillance of Bordetella pertussis ». Journal of Clinical Microbiology 55, n^o 5 (22 février 2017) : 1446–53. http://dx.doi.org/10.1128/jcm.02436-16.

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ABSTRACTBordetella pertussisis a Gram-negative bacterium that causes respiratory infections in humans. Ongoing molecular surveillance ofB. pertussisacellular vaccine (aP) antigens is critical for understanding the interaction between evolutionary pressures, disease pathogenesis, and vaccine effectiveness. Methods currently used to characterize aP components are relatively labor-intensive and low throughput. To address this challenge, we sought to derive aP antigen genotypes from minimally processed short-read whole-genome sequencing data generated from 40 clinicalB. pertussisisolates and analyzed using the SRST2 bioinformatic package. SRST2 was able to identify aP antigen genotypes for all antigens with the exception of pertactin, possibly due to low read coverage in GC-rich low-complexity regions of variation. Two main genotypes were observed in addition to a singular third genotype that contained an 84-bp deletion that was identified by SRST2 despite the issues in allele calling. This method has the potential to generate large pools ofB. pertussismolecular data that can be linked to clinical and epidemiological information to facilitate research of vaccine effectiveness and disease severity in the context of emerging vaccine antigen-deficient strains.

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Olson, Nathan D., Nidhi Shah, Jayaram Kancherla, Justin Wagner, Joseph N. Paulson et Hector Corrada Bravo. « metagenomeFeatures : an R package for working with 16S rRNA reference databases and marker-gene survey feature data ». Bioinformatics 35, n^o 19 (1 mars 2019) : 3870–72. http://dx.doi.org/10.1093/bioinformatics/btz136.

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Abstract Summary We developed the metagenomeFeatures R Bioconductor package along with annotation packages for three 16S rRNA databases (Greengenes, RDP and SILVA) to facilitate working with 16S rRNA databases and marker-gene survey feature data. The metagenomeFeatures package defines two classes, MgDb for working with 16S rRNA sequence databases, and mgFeatures for marker-gene survey feature data. The associated annotation packages provide a consistent interface to the different databases facilitating database comparison and exploration. The mgFeatures-class represents a crucial step in the development of a common data structure for working with 16S marker-gene survey data in R. Availability and implementation https://bioconductor.org/packages/release/bioc/html/metagenomeFeatures.html. Supplementary information Supplementary material is available at Bioinformatics online.

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Hasan, Fuad, Armyn Hakim Daulay, Ferdy Saputra et Isyana Khaerunnisa. « Indonesian River Buffalo Molecular Phylogeny Compared to Other Mammals Based on STAT1 Sequence ». Jurnal Agripet 22, n^o 1 (1 avril 2022) : 57–61. http://dx.doi.org/10.17969/agripet.v22i1.20889.

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ABSTRACT. Genes differ in sequence, size, and functional domains among species. According to studies, STAT1 provides information on the rate of evolution that correlates with its function in the immune system. STAT1 is also considered a genetic marker for economic traits in mammals. Studying sequence comparison is an important issue in bioinformatic study and can explain phylogenetic. Therefore, this study aimed to identify the molecular phylogeny of river buffalo (Bubalus bubalis) and other mammals based on STAT1 gene sequences. This study used 7 STAT1 sequences from Ensembl (Bos grunniens, Bos indicus, Bos Mutus, Capra hircus, Cervus hanglu yarkandensis, Moschus moschiferus) and previous studies (Bubalus bubalis). The sequences were analyzed using the MEGA X 10.2.6 software to observe the nucleotide composition and the phylogeny (based on UPGMA). The adegenet package in the R 4.0.0 software is used to observe the STAT1 sequence dimensionally among mammals. The STAT1 sequence has almost similar diversity among the livestock of the same genus. Based on the STAT1 sequence, Bubalus bubalis has closer genetic proximity to the genus Bos than to another genus. In conclusion, we found STAT1 is more dynamic in evolution and more conserved and found in the similar related genus. (Filogeni kerbau Indonesia dibandingkan mamalia lain berdasarkan runutan nukleotida gen STAT1) ABSTRAK. Gen berbeda dalam urutan, ukuran, dan domain fungsional di antara spesies. Menurut penelitian sebelumnya, STAT1 memberikan informasi tentang laju evolusi yang berkorelasi dengan fungsinya dalam sistem kekebalan. STAT1 juga dianggap sebagai penanda genetik untuk sifat bernilai ekonomi pada mamalia. Studi perbandingan urutan merupakan isu penting dalam studi bioinformatika dan dapat menjelaskan filogenetik. Oleh karena itu, penelitian ini bertujuan untuk mengidentifikasi filogeni molekuler kerbau sungai (Bubalus bubalis) dan spesies mamalia lain berdasarkan sekuens gen STAT1. Penelitian ini menggunakan 7 sekuen STAT1 yang diambil dari Ensembl (Bos grunniens, Bos indicus, Bos mutus, Capra hircus, Cervus hanglu yarkandensis, Moschus moschiferus) dan penelitian sebelumnya (Bubalus bubalis). Sekuen dianalisis menggunakan program MEGA X 10.2.6 untuk melihat komposisi nukleotida dan filogeni (berdasarkan UPGMA). Adegenet package dalam program R 4.0.0 digunakan untuk mengamati urutan STAT1 secara dimensional diantara mamalia. Sekuen STAT1 memiliki keragaman yang hampir sama di antara ternak dari genus yang sama. Berdasarkan sekuen STAT1, Bubalus bubalis memiliki jarak genetik yang lebih dekat dengan genus Bos dibandingkan dengan genus lainnya. Sebagai kesimpulan, kami menemukan STAT1 lebih dinamis dalam evolusi dan lebih terkonservasi serta ditemukan dalam genus terkait yang serupa.

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