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Gene Ontology enrichment
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A bottleneck in high-throughput functional genomics experiments is identifying the most important genes and their relevant functions from a list of gene hits. Gene Ontology (GO) enrichment methods provide insight at the gene set level.
35p
viarchimedes
26-01-2022
6
0
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Trait ontology (TO) analysis is a powerful system for functional annotation and enrichment analysis of genes. However, given the complexity of the molecular mechanisms underlying phenomes, only a few hundred gene-to-TO relationships in plants have been elucidated to date, limiting the pace of research in this “big data” era.
13p
visilicon2711
20-08-2021
5
1
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At sexual maturity, the liver of laying hens undergoes many metabolic changes to support vitellogenesis. In published transcriptomic approaches, hundreds of genes were reported to be overexpressed in laying hens and functional gene annotation using gene ontology tools have essentially revealed an enrichment in lipid and protein metabolisms.
16p
viansan2711
30-07-2021
13
1
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Salmonella enterica subsp. enterica is a public health issue related to food safety, and its adaptation to animal sources remains poorly described at the pangenome scale. Firstly, serovars presenting potential mono- and multi-animal sources were selected from a curated and synthetized subset of Enterobase.
21p
viansan2711
30-07-2021
20
1
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Gene Ontology (GO) enrichment analysis remains one of the most common methods for hypothesis generation from high throughput datasets. However, we believe that researchers strive to test other hypotheses that fall outside of GO. Here, we developed and evaluated a tool for hypothesis generation from gene or protein lists using ontological concepts present in manually curated text that describes those genes and proteins.
10p
viwyoming2711
16-12-2020
16
1
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Reliability and Reproducibility of differentially expressed genes (DEGs) are essential for the biological interpretation of microarray data. The microarray quality control (MAQC) project launched by US Food and Drug Administration (FDA) elucidated that the lists of DEGs generated by intra- and inter-platform comparisons can reach a high level of concordance, which mainly depended on the statistical criteria used for ranking and selecting DEGs.
10p
viwyoming2711
16-12-2020
12
1
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Significance analysis plays a major role in identifying and ranking genes, transcription factor binding sites, DNA methylation regions, and other high-throughput features associated with illness. We propose a new approach, called gene set bagging, for measuring the probability that a gene set replicates in future studies.
8p
viwyoming2711
16-12-2020
11
1
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Despite the widespread use of high throughput expression platforms and the availability of a desktop implementation of Gene Set Enrichment Analysis (GSEA) that enables non-experts to perform gene set based analyses, the availability of the necessary precompiled gene sets is rare for species other than human.
6p
vikentucky2711
26-11-2020
10
3
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Over the last ten years, there has been explosive development in methods for measuring gene expression. These methods can identify thousands of genes altered between conditions, but understanding these datasets and forming hypotheses based on them remains challenging.
10p
vioklahoma2711
19-11-2020
8
1
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High-throughput technologies such as ChIP-sequencing, RNA-sequencing, DNA sequencing and quantitative metabolomics generate a huge volume of data. Researchers often rely on functional enrichment tools to interpret the biological significance of the affected genes from these high-throughput studies.
10p
vioklahoma2711
19-11-2020
14
1
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The number of genomics and proteomics experiments is growing rapidly, producing an ever-increasing amount of data that are awaiting functional interpretation. A number of function prediction algorithms were developed and improved to enable fast and automatic function annotation.
13p
vioklahoma2711
19-11-2020
13
0
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Biological interpretation of gene/protein lists resulting from -omics experiments can be a complex task. A common approach consists of reviewing Gene Ontology (GO) annotations for entries in such lists and searching for enrichment patterns.
8p
vicoachella2711
27-10-2020
9
1
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RNA-seq, wherein RNA transcripts expressed in a sample are sequenced and quantified, has become a widely used technique to study disease and development. With RNA-seq, transcription abundance can be measured, differential expression genes between groups and functional enrichment of those genes can be computed.
6p
vicoachella2711
27-10-2020
6
1
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In the era of precision oncology and publicly available datasets, the amount of information available for each patient case has dramatically increased. From clinical variables and PET-CT radiomics measures to DNAvariant and RNA expression profiles, such a wide variety of data presents a multitude of challenges.
9p
vijisoo2711
27-10-2020
15
1
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Gene Ontology enrichment analysis provides an effective way to extract meaningful information from complex biological datasets. By identifying terms that are significantly overrepresented in a gene set, researchers can uncover biological features shared by genes. In addition to extracting enriched terms, it is also important to visualize the results in a way that is conducive to biological interpretation.
8p
vijisoo2711
27-10-2020
12
1
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Functional enrichment of genes and pathways based on Gene Ontology (GO) has been widely used to describe the results of various -omics analyses. GO terms statistically overrepresented within a set of a large number of genes are typically used to describe the main functional attributes of the gene set.
9p
vicolorado2711
22-10-2020
12
0
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