Dataset Information

Automated analysis of immunosequencing datasets reveals novel immunoglobulin D genes across diverse species.

ABSTRACT: Immunoglobulin genes are formed through V(D)J recombination, which joins the variable (V), diversity (D), and joining (J) germline genes. Since variations in germline genes have been linked to various diseases, personalized immunogenomics focuses on finding alleles of germline genes across various patients. Although reconstruction of V and J genes is a well-studied problem, the more challenging task of reconstructing D genes remained open until the IgScout algorithm was developed in 2019. In this work, we address limitations of IgScout by developing a probabilistic MINING-D algorithm for D gene reconstruction, apply it to hundreds of immunosequencing datasets from multiple species, and validate the newly inferred D genes by analyzing diverse whole genome sequencing datasets and haplotyping heterozygous V genes.

SUBMITTER: Bhardwaj V

PROVIDER: S-EPMC7295240 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Automated analysis of immunosequencing datasets reveals novel immunoglobulin D genes across diverse species.

Bhardwaj Vinnu V Franceschetti Massimo M Rao Ramesh R Pevzner Pavel A PA Safonova Yana Y

PLoS computational biology 20200427 4

Immunoglobulin genes are formed through V(D)J recombination, which joins the variable (V), diversity (D), and joining (J) germline genes. Since variations in germline genes have been linked to various diseases, personalized immunogenomics focuses on finding alleles of germline genes across various patients. Although reconstruction of V and J genes is a well-studied problem, the more challenging task of reconstructing D genes remained open until the IgScout algorithm was developed in 2019. In thi ...[more]

PMID: 32339161

Similar Datasets

Project description:Immunoglobulin A (IgA) nephropathy (IgAN) is the most common glomerular disease. The major pathological changes associated with it affect cell proliferation, fibrosis, apoptosis, inflammation and extracellular matrix (ECM) organization. However, the molecular events underlying IgAN remain to be fully elucidated. In the present study, an integrated bioinformatics analysis was applied to further explore novel potential gene targets for IgAN. The mRNA expression profile datasets GSE93798 and GSE37460 were downloaded from the Gene Expression Omnibus database. After data preprocessing, differentially expressed genes (DEGs) were identified. Gene Ontology (GO) enrichment analysis of DEGs was performed. Protein-protein interaction (PPI) networks of the DEGs were built with the STRING online search tool and visualized by using Cytoscape, and hub genes were identified through the degree of connectivity in the PPI. The hub genes were subjected to Kyoto Encyclopedia of Genes and Genomes pathway analysis, and co-expression analysis was performed. A total of 298 DEGs between IgAN and control groups were identified, and 148 and 150 of these DEGs were upregulated and downregulated, respectively. The DEGs were enriched in distinct GO terms for Biological Process, including cell growth, epithelial cell proliferation, ERK1 and ERK2 cascades, regulation of apoptotic signaling pathway and ECM organization. The top 10 hub genes were then screened from the PPI network by Cytoscape. As novel hub genes, Fos proto-oncogene, AP-1 transcription factor subunit and early growth response 1 were determined to be closely associated with apoptosis and cell proliferation in IgAN. Tumor protein 53, integrin subunit β2 and fibronectin 1 may also be involved in the occurrence and development of IgAN. Co-expression analysis suggested that these hub genes were closely linked with each other. In conclusion, the present integrated bioinformatics analysis provided novel insight into the molecular events and novel candidate gene targets of IgAN.

Project description:The scale and complexity of genomic data lend themselves to analysis using sophisticated mathematical techniques to yield information that can generate new hypotheses and so guide further experimental investigations. An ensemble clustering method has the ability to perform consensus clustering over the same set of genes from different microarray datasets by combining results from different clustering methods into a single consensus result.In this paper we have performed comprehensive analysis of forty yeast microarray datasets. One recently described Bi-CoPaM method can analyse expressions of the same set of genes from various microarray datasets while using different clustering methods, and then combine these results into a single consensus result whose clusters' tightness is tunable from tight, specific clusters to wide, overlapping clusters. This has been adopted in a novel way over genome-wide data from forty yeast microarray datasets to discover two clusters of genes that are consistently co-expressed over all of these datasets from different biological contexts and various experimental conditions. Most strikingly, average expression profiles of those clusters are consistently negatively correlated in all of the forty datasets while neither profile leads or lags the other.The first cluster is enriched with ribosomal biogenesis genes. The biological processes of most of the genes in the second cluster are either unknown or apparently unrelated although they show high connectivity in protein-protein and genetic interaction networks. Therefore, it is possible that this mostly uncharacterised cluster and the ribosomal biogenesis cluster are transcriptionally oppositely regulated by some common machinery. Moreover, we anticipate that the genes included in this previously unknown cluster participate in generic, in contrast to specific, stress response processes. These novel findings illuminate coordinated gene expression in yeast and suggest several hypotheses for future experimental functional work. Additionally, we have demonstrated the usefulness of the Bi-CoPaM-based approach, which may be helpful for the analysis of other groups of (microarray) datasets from other species and systems for the exploration of global genetic co-expression.

Dataset Information

Automated analysis of immunosequencing datasets reveals novel immunoglobulin D genes across diverse species.

Publications

Automated analysis of immunosequencing datasets reveals novel immunoglobulin D genes across diverse species.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets