Transcriptogramer: an R/Bioconductor package for transcriptional analysis based on protein–protein interaction

2019 ◽  
Vol 35 (16) ◽  
pp. 2875-2876 ◽  
Author(s):  
Diego A A Morais ◽  
Rita M C Almeida ◽  
Rodrigo J S Dalmolin
Keyword(s):  
Differential Expression ◽  
Protein Interaction ◽  
Topological Analysis ◽  
Gene List ◽  
Enrichment Analysis ◽  
R Package ◽  
Transcriptional Analysis ◽  
Supplementary Information ◽  
Protein Protein Interaction ◽  
Data Platform

Abstract Motivation Several freely available tools perform analysis using algorithms developed to identify significant variation of gene expression individually. The transcriptogramer R package uses protein–protein interaction to perform differential expression of functionally associated genes. The software assesses expression profile of entire genetic systems and reveals which biological systems are significantly altered in case-control designed transcriptome experiments. Results R/Bioconductor transcriptogramer package projects expression values on an ordered gene list to perform topological analysis, differential expression and gene ontology enrichment analysis, independently of data platform or operating system. Availability and implementation http://bioconductor.org/packages/transcriptogramer. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Decoding the molecular mechanism of parthenocarpy in Musa spp. through protein–protein interaction network

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Suthanthiram Backiyarani ◽  
Rajendran Sasikala ◽  
Simeon Sharmiladevi ◽  
Subbaraya Uma
Keyword(s):  
Candidate Genes ◽  
Protein Interaction ◽  
Target Genes ◽  
Interaction Network ◽  
Enrichment Analysis ◽  
Auxin Signaling ◽  
Pathway Enrichment Analysis ◽  
Ppi Network ◽  
Protein Protein Interaction ◽  
The Difference

AbstractBanana, one of the most important staple fruit among global consumers is highly sterile owing to natural parthenocarpy. Identification of genetic factors responsible for parthenocarpy would facilitate the conventional breeders to improve the seeded accessions. We have constructed Protein–protein interaction (PPI) network through mining differentially expressed genes and the genes used for transgenic studies with respect to parthenocarpy. Based on the topological and pathway enrichment analysis of proteins in PPI network, 12 candidate genes were shortlisted. By further validating these candidate genes in seeded and seedless accession of Musa spp. we put forward MaAGL8, MaMADS16, MaGH3.8, MaMADS29, MaRGA1, MaEXPA1, MaGID1C, MaHK2 and MaBAM1 as possible target genes in the study of natural parthenocarpy. In contrary, expression profile of MaACLB-2 and MaZEP is anticipated to highlight the difference in artificially induced and natural parthenocarpy. By exploring the PPI of validated genes from the network, we postulated a putative pathway that bring insights into the significance of cytokinin mediated CLAVATA(CLV)–WUSHEL(WUS) signaling pathway in addition to gibberellin mediated auxin signaling in parthenocarpy. Our analysis is the first attempt to identify candidate genes and to hypothesize a putative mechanism that bridges the gaps in understanding natural parthenocarpy through PPI network.

DISTANCE-WISE PATHWAY DISCOVERY FROM PROTEIN–PROTEIN INTERACTION NETWORKS WEIGHTED BY SEMANTIC SIMILARITY

2014 ◽  
Vol 12 (01) ◽  
pp. 1450004 ◽  
Author(s):  
SLAVKA JAROMERSKA ◽  
PETR PRAUS ◽  
YOUNG-RAE CHO
Keyword(s):  
Signaling Pathways ◽  
Semantic Similarity ◽  
Protein Interaction ◽  
Protein Pair ◽  
Semantic Analysis ◽  
Topological Analysis ◽  
Cellular Mechanisms ◽  
Semantic Similarity Measure ◽  
Protein Protein Interaction ◽  
Ppi Networks

Reconstruction of signaling pathways is crucial for understanding cellular mechanisms. A pathway is represented as a path of a signaling cascade involving a series of proteins to perform a particular function. Since a protein pair involved in signaling and response have a strong interaction, putative pathways can be detected from protein–protein interaction (PPI) networks. However, predicting directed pathways from the undirected genome-wide PPI networks has been challenging. We present a novel computational algorithm to efficiently predict signaling pathways from PPI networks given a starting protein and an ending protein. Our approach integrates topological analysis of PPI networks and semantic analysis of PPIs using Gene Ontology data. An advanced semantic similarity measure is used for weighting each interacting protein pair. Our distance-wise algorithm iteratively selects an adjacent protein from a PPI network to build a pathway based on a distance condition. On each iteration, the strength of a hypothetical path passing through a candidate edge is estimated by a local heuristic. We evaluate the performance by comparing the resultant paths to known signaling pathways on yeast. The results show that our approach has higher accuracy and efficiency than previous methods.

scholarly journals TFEA.ChIP: a tool kit for transcription factor binding site enrichment analysis capitalizing on ChIP-seq datasets

2019 ◽  
Vol 35 (24) ◽  
pp. 5339-5340 ◽  
Author(s):  
Laura Puente-Santamaria ◽  
Wyeth W Wasserman ◽  
Luis del Peso
Keyword(s):  
Genomic Analysis ◽  
Enrichment Analysis ◽  
R Package ◽  
Supplementary Information ◽  
Web Based ◽  
Factor Binding Site ◽  
Gene Sets ◽  
Transcription Regulators ◽  
Computational Identification ◽  
On Chip

Abstract Summary The computational identification of the transcription factors (TFs) [more generally, transcription regulators, (TR)] responsible for the co-regulation of a specific set of genes is a common problem found in genomic analysis. Herein, we describe TFEA.ChIP, a tool that makes use of ChIP-seq datasets to estimate and visualize TR enrichment in gene lists representing transcriptional profiles. We validated TFEA.ChIP using a wide variety of gene sets representing signatures of genetic and chemical perturbations as input and found that the relevant TR was correctly identified in 126 of a total of 174 analyzed. Comparison with other TR enrichment tools demonstrates that TFEA.ChIP is an highly customizable package with an outstanding performance. Availability and implementation TFEA.ChIP is implemented as an R package available at Bioconductor https://www.bioconductor.org/packages/devel/bioc/html/TFEA.ChIP.html and github https://github.com/LauraPS1/TFEA.ChIP_downloads. A web-based GUI to the package is also available at https://www.iib.uam.es/TFEA.ChIP/ Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals DEsingle for detecting three types of differential expression in single-cell RNA-seq data

2017 ◽  
Author(s):  
Zhun Miao ◽  
Ke Deng ◽  
Xiaowo Wang ◽  
Xuegong Zhang
Keyword(s):  
Single Cell ◽  
Differential Expression ◽  
Negative Binomial ◽  
Single Cells ◽  
R Package ◽  
Supplementary Information ◽  
Binomial Model ◽  
Supplementary Data ◽  
Rna Seq ◽  
Real Zeros

AbstractSummaryThe excessive amount of zeros in single-cell RNA-seq data include “real” zeros due to the on-off nature of gene transcription in single cells and “dropout” zeros due to technical reasons. Existing differential expression (DE) analysis methods cannot distinguish these two types of zeros. We developed an R package DEsingle which employed Zero-Inflated Negative Binomial model to estimate the proportion of real and dropout zeros and to define and detect 3 types of DE genes in single-cell RNA-seq data with higher accuracy.Availability and ImplementationThe R package DEsingle is freely available at https://github.com/miaozhun/DEsingle and is under Bioconductor’s consideration [email protected] informationSupplementary data are available at bioRxiv online.

scholarly journals Identification of Immune-Related Genes in Sepsis due to Community-Acquired Pneumonia

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Yanyan Li ◽  
Jiqin Wang ◽  
Yuzhen Li ◽  
Chunyan Liu ◽  
Xia Gong ◽  
...  
Keyword(s):  
Differentially Expressed Genes ◽  
Protein Interaction ◽  
Enrichment Analysis ◽  
Community Acquired Pneumonia ◽  
Related Research ◽  
Protein Protein Interaction ◽  
Limma Package ◽  
Key Genes ◽  
Immune Related Genes ◽  
Immune Score

Background. Immunosuppression has a key function in sepsis pathogenesis, so it is of great significance to find immune-related markers for the treatment of sepsis. Methods. Datasets of community-acquired pneumonia (CAP) with sepsis from the ArrayExpress database were extracted. Differentially expressed genes (DEGs) between the CAP group and normal group by Limma package were performed. After calculation of immune score through the ESTIMATE algorithm, the DEGs were selected between the high immune score group and the low immune score group. Enrichment analysis of the intersected DEGs was conducted. Further, the protein-protein interaction (PPI) of the intersected DEGs was drawn by Metascape tools. Related publications of the key DEGs were searched in NCBI PubMed through Biopython models, and RT-qPCR was used to verify the expression of key genes. Results. 360 intersected DEGs (157 upregulated and 203 downregulated) were obtained between the two groups. Meanwhile, the intersected DEGs were enriched in 157 immune-related terms. The PPI of the DEGs was performed, and 8 models were obtained. In sepsis-related research, eight genes were obtained with degree ≥ 10 , included in the models. Conclusion. CXCR3, CCR7, HLA-DMA, and GPR18 might participate in the mechanism of CAP with sepsis.

scholarly journals Bioinformatics Analysis of the Molecular Mechanism and Potential Treatment Target of Ankylosing Spondylitis

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Fanyan Meng ◽  
Ningna Du ◽  
Daoming Xu ◽  
Li Kuai ◽  
Lanying Liu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Ankylosing Spondylitis ◽  
Molecular Mechanism ◽  
Soft Tissues ◽  
Bioinformatics Analysis ◽  
Enrichment Analysis ◽  
R Package ◽  
Gene Expression Omnibus ◽  
Treatment Targets ◽  
Protein Protein Interaction

Ankylosing spondylitis (AS) is an autoimmune disease that mainly affects the spinal joints, sacroiliac joints, and adjacent soft tissues. We conducted bioinformatics analysis to explore the molecular mechanism related to AS pathogenesis and uncover novel potential molecular targets for the treatment of AS. The profiles of GSE25101, containing gene expression data extracted from the blood of 16 AS patients and 16 matched controls, were acquired from the Gene Expression Omnibus (GEO) database. The background correction and standardization were carried out utilizing the transcript per million (TPM) method. After analysis of AS patients and the normal groups, we identified 199 differentially expressed genes (DEGs) with upregulation and 121 DEGs with downregulation by the limma R package. The results of the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and Gene Ontology (GO) biological process enrichment analysis revealed that the DEGs with upregulation were mainly associated with spliceosome, ribosome, RNA-catabolic process, electron transport chain, etc. And the DEGs with downregulation primarily participated in T cell-associated pathways and processes. After analysis of the protein-protein interaction (PPI) network, our data revealed that the hub genes, comprising MRPL13, MRPL22, LSM3, COX7A2, COX7C, EP300, PTPRC, and CD4, could be the treatment targets in AS. Our data furnish new hints to uncover the features of AS and explore more promising treatment targets towards AS.

scholarly journals NCOR2 Mutations Are Associated with IMiD Resistance in Human Myeloma Cells

Blood ◽  
2020 ◽  
Vol 136 (Supplement 1) ◽  
pp. 4-4
Author(s):  
Tomoaki Mori ◽  
Cristina Panaroni ◽  
Chukwuamaka Onyewadume ◽  
Noopur S. Raje
Keyword(s):  
Cell Lines ◽  
Protein Interaction ◽  
Enrichment Analysis ◽  
Myeloma Cell ◽  
Resistant Cell ◽  
Ppi Network ◽  
Myeloma Cells ◽  
Knock Out ◽  
Protein Protein Interaction ◽  
Bet Inhibitor

The immunomodulatory drug thalidomide, and its analogs, lenalidomide, and pomalidomide (IMiDs) have significantly changed the treatment paradigm of multiple myeloma (MM). Despite this progress, IMiD resistance develops in the majority of patients resulting in the development of refractory disease. Cereblon (CRBN), a direct target, has been implicated in IMiD resistance. However, alternate mechanisms of IMiD resistance independent of CRBN remain largely unknown. To understand and study the mechanisms responsible for the development of IMiD resistance, we created lenalidomide-resistant (Len-R) and pomalidomide-resistant (Pom-R) human myeloma MM.1s cell lines, by continuous culture in the presence of lenalidomide or pomalidomide for 3 months. Whole genome sequencing of these 2 resistant cell lines compared with parental MM.1s revealed 172 genes with exonic mutations in both Len-R and Pom-R myeloma cells. Furthermore, a protein-protein interaction (PPI) network was constructed based on Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. The PPI network demonstrated 8 genes that scored a high degree of protein-protein interaction. Among these genes, we identified NCOR2, a corepressor that negatively regulates gene expression, as a downregulated gene in resistant cell lines. To study this further, we created NCOR2 knock out MM.1s cell lines using CRISPR/cas9 gene modification. Our data demonstrates that depletion of NCOR2 confers IMiD resistance independent to CRBN. Interestingly, Len-R, Pom-R and NCOR2 knock out MM.1s showed increased MYC protein expression, which is essential for myeloma cell survival and proliferation. A BET inhibitor, known to disrupt the binding of BRD4 to chromatin, inhibited the proliferation of Len-R and Pom-R and NCOR2 knock out MM.1s by completely suppressing MYC expression. These results indicate that NCOR2 down regulation in IMiD resistant cells induces MYC upregulation which may in part result in IMiD resistance. Our findings reveal a novel molecular mechanism associated with IMiD resistance, independent of CRBN and suggest that NCOR2-MYC pathway may be a new target for IMiD refractory patients. Disclosures Raje: Celgene: Consultancy.

scholarly journals gprofiler2 -- an R package for gene list functional enrichment analysis and namespace conversion toolset g:Profiler

F1000Research ◽  
2020 ◽  
Vol 9 ◽  
pp. 709 ◽  
Author(s):  
Liis Kolberg ◽  
Uku Raudvere ◽  
Ivan Kuzmin ◽  
Jaak Vilo ◽  
Hedi Peterson
Keyword(s):  
Gene List ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Automated Analysis ◽  
R Package ◽  
Biological Data ◽  
Functional Enrichment ◽  
Link Type ◽  
Functional Profiling ◽  
Rest Api

g:Profiler (https://biit.cs.ut.ee/gprofiler) is a widely used gene list functional profiling and namespace conversion toolset that has been contributing to reproducible biological data analysis already since 2007. Here we introduce the accompanying R package, gprofiler2, developed to facilitate programmatic access to g:Profiler computations and databases via REST API. The gprofiler2 package provides an easy-to-use functionality that enables researchers to incorporate functional enrichment analysis into automated analysis pipelines written in R. The package also implements interactive visualisation methods to help to interpret the enrichment results and to illustrate them for publications. In addition, gprofiler2 gives access to the versatile gene/protein identifier conversion functionality in g:Profiler enabling to map between hundreds of different identifier types or orthologous species. The gprofiler2 package is freely available at the CRAN repository.

scholarly journals Protein–protein interaction sites prediction by ensemble random forests with synthetic minority oversampling technique

2018 ◽  
Vol 35 (14) ◽  
pp. 2395-2402 ◽  
Author(s):  
Xiaoying Wang ◽  
Bin Yu ◽  
Anjun Ma ◽  
Cheng Chen ◽  
Bingqiang Liu ◽  
...  
Keyword(s):  
Ensemble Learning ◽  
Protein Interaction ◽  
Feature Selection Method ◽  
Feature Space ◽  
Supplementary Information ◽  
Sequence Profile ◽  
Protein Protein Interaction ◽  
Interaction Sites ◽  
Imbalance Problem ◽  
Ppi Networks

Abstract Motivation The prediction of protein–protein interaction (PPI) sites is a key to mutation design, catalytic reaction and the reconstruction of PPI networks. It is a challenging task considering the significant abundant sequences and the imbalance issue in samples. Results A new ensemble learning-based method, Ensemble Learning of synthetic minority oversampling technique (SMOTE) for Unbalancing samples and RF algorithm (EL-SMURF), was proposed for PPI sites prediction in this study. The sequence profile feature and the residue evolution rates were combined for feature extraction of neighboring residues using a sliding window, and the SMOTE was applied to oversample interface residues in the feature space for the imbalance problem. The Multi-dimensional Scaling feature selection method was implemented to reduce feature redundancy and subset selection. Finally, the Random Forest classifiers were applied to build the ensemble learning model, and the optimal feature vectors were inserted into EL-SMURF to predict PPI sites. The performance validation of EL-SMURF on two independent validation datasets showed 77.1% and 77.7% accuracy, which were 6.2–15.7% and 6.1–18.9% higher than the other existing tools, respectively. Availability and implementation The source codes and data used in this study are publicly available at http://github.com/QUST-AIBBDRC/EL-SMURF/. Supplementary information Supplementary data are available at Bioinformatics online.

Sign in / Sign up

Export Citation Format

Share Document