Characterization of intestinal gene expression profiles in Crohnʼs disease by genome-wide microarray analysis

2010 ◽  
Vol 16 (10) ◽  
pp. 1717-1728 ◽  
Author(s):  
Colin L. Noble ◽  
Alexander R. Abbas ◽  
Charles W. Lees ◽  
Jennine Cornelius ◽  
Karen Toy ◽  
...  
Keyword(s):  
Gene Expression ◽  
Microarray Analysis ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Genome Wide

scholarly journals Identifying Transcriptomic Correlates of Histology using Deep Learning

2020 ◽  
Author(s):  
Liviu Badea ◽  
Emil Stănescu
Keyword(s):  
Gene Expression ◽  
Deep Learning ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Gene Expression Profiles ◽  
Specific Gene ◽  
Genome Wide ◽  
Gene Perturbations ◽  
Whole Slide Images

AbstractLinking phenotypes to specific gene expression profiles is an extremely important problem in biology, which has been approached mainly by correlation methods or, more fundamentally, by studying the effects of gene perturbations. However, genome-wide perturbations involve extensive experimental efforts, which may be prohibitive for certain organisms. On the other hand, the characterization of the various phenotypes frequently requires an expert’s subjective interpretation, such as a histopathologist’s description of tissue slide images in terms of complex visual features (e.g. ‘acinar structures’). In this paper, we use Deep Learning to eliminate the inherent subjective nature of these visual histological features and link them to genomic data, thus establishing a more precisely quantifiable correlation between transcriptomes and phenotypes. Using a dataset of whole slide images with matching gene expression data from 39 normal tissue types, we first developed a Deep Learning tissue classifier with an accuracy of 94%. Then we searched for genes whose expression correlates with features inferred by the classifier and demonstrate that Deep Learning can automatically derive visual (phenotypical) features that are well correlated with the transcriptome and therefore biologically interpretable. As we are particularly concerned with interpretability and explainability of the inferred histological models, we also develop visualizations of the inferred features and compare them with gene expression patterns determined by immunohistochemistry. This can be viewed as a first step toward bridging the gap between the level of genes and the cellular organization of tissues.

scholarly journals Identifying transcriptomic correlates of histology using deep learning

PLoS ONE ◽  
2020 ◽  
Vol 15 (11) ◽  
pp. e0242858
Author(s):  
Liviu Badea ◽  
Emil Stănescu
Keyword(s):  
Gene Expression ◽  
Deep Learning ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Gene Expression Profiles ◽  
Specific Gene ◽  
Genome Wide ◽  
Gene Perturbations ◽  
Whole Slide Images

Linking phenotypes to specific gene expression profiles is an extremely important problem in biology, which has been approached mainly by correlation methods or, more fundamentally, by studying the effects of gene perturbations. However, genome-wide perturbations involve extensive experimental efforts, which may be prohibitive for certain organisms. On the other hand, the characterization of the various phenotypes frequently requires an expert’s subjective interpretation, such as a histopathologist’s description of tissue slide images in terms of complex visual features (e.g. ‘acinar structures’). In this paper, we use Deep Learning to eliminate the inherent subjective nature of these visual histological features and link them to genomic data, thus establishing a more precisely quantifiable correlation between transcriptomes and phenotypes. Using a dataset of whole slide images with matching gene expression data from 39 normal tissue types, we first developed a Deep Learning tissue classifier with an accuracy of 94%. Then we searched for genes whose expression correlates with features inferred by the classifier and demonstrate that Deep Learning can automatically derive visual (phenotypical) features that are well correlated with the transcriptome and therefore biologically interpretable. As we are particularly concerned with interpretability and explainability of the inferred histological models, we also develop visualizations of the inferred features and compare them with gene expression patterns determined by immunohistochemistry. This can be viewed as a first step toward bridging the gap between the level of genes and the cellular organization of tissues.

scholarly journals Genome-Wide Identification of Soybean ABC Transporters Relate to Aluminum Toxicity

2021 ◽  
Vol 22 (12) ◽  
pp. 6556
Author(s):  
Junjun Huang ◽  
Xiaoyu Li ◽  
Xin Chen ◽  
Yaru Guo ◽  
Weihong Liang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Abc Transporters ◽  
Developmental Stages ◽  
Aluminum Toxicity ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Purifying Selection ◽  
Biotic Stresses ◽  
Genome Wide ◽  
New Germplasm

ATP-binding cassette (ABC) transporter proteins are a gene super-family in plants and play vital roles in growth, development, and response to abiotic and biotic stresses. The ABC transporters have been identified in crop plants such as rice and buckwheat, but little is known about them in soybean. Soybean is an important oil crop and is one of the five major crops in the world. In this study, 255 ABC genes that putatively encode ABC transporters were identified from soybean through bioinformatics and then categorized into eight subfamilies, including 7 ABCAs, 52 ABCBs, 48 ABCCs, 5 ABCDs, 1 ABCEs, 10 ABCFs, 111 ABCGs, and 21 ABCIs. Their phylogenetic relationships, gene structure, and gene expression profiles were characterized. Segmental duplication was the main reason for the expansion of the GmABC genes. Ka/Ks analysis suggested that intense purifying selection was accompanied by the evolution of GmABC genes. The genome-wide collinearity of soybean with other species showed that GmABCs were relatively conserved and that collinear ABCs between species may have originated from the same ancestor. Gene expression analysis of GmABCs revealed the distinct expression pattern in different tissues and diverse developmental stages. The candidate genes GmABCB23, GmABCB25, GmABCB48, GmABCB52, GmABCI1, GmABCI5, and GmABCI13 were responsive to Al toxicity. This work on the GmABC gene family provides useful information for future studies on ABC transporters in soybean and potential targets for the cultivation of new germplasm resources of aluminum-tolerant soybean.

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