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Identification and annotation of milk associated genes from milk somatic cells using expression and RNA-seq data
It is of interest to identify and annotate milk associated genes using expression profiling and RNA-Seq data from milk somatic cells. RNA-Seq data was pre-processed and mapping was done to identify differentially expressed genes (DEG). The functional insights about the up and down regulated genes we...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Biomedical Informatics
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10266364/ https://www.ncbi.nlm.nih.gov/pubmed/37323558 http://dx.doi.org/10.6026/97320630018703 |
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author | Krishna, Neelam Vishwakarma, Shraddha Katara, Pramod |
author_facet | Krishna, Neelam Vishwakarma, Shraddha Katara, Pramod |
author_sort | Krishna, Neelam |
collection | PubMed |
description | It is of interest to identify and annotate milk associated genes using expression profiling and RNA-Seq data from milk somatic cells. RNA-Seq data was pre-processed and mapping was done to identify differentially expressed genes (DEG). The functional insights about the up and down regulated genes were gleaned using the protein-protein interaction Network in the STRING database followed by CytoHubba analysis in Cytoscope. Gene ontology, annotation and pathway enrichment was completed using ShinyGO, David tool and QTL analysis. These analysis shows that 21 genes are linked with the secretion of milk. |
format | Online Article Text |
id | pubmed-10266364 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Biomedical Informatics |
record_format | MEDLINE/PubMed |
spelling | pubmed-102663642023-06-15 Identification and annotation of milk associated genes from milk somatic cells using expression and RNA-seq data Krishna, Neelam Vishwakarma, Shraddha Katara, Pramod Bioinformation Research Article It is of interest to identify and annotate milk associated genes using expression profiling and RNA-Seq data from milk somatic cells. RNA-Seq data was pre-processed and mapping was done to identify differentially expressed genes (DEG). The functional insights about the up and down regulated genes were gleaned using the protein-protein interaction Network in the STRING database followed by CytoHubba analysis in Cytoscope. Gene ontology, annotation and pathway enrichment was completed using ShinyGO, David tool and QTL analysis. These analysis shows that 21 genes are linked with the secretion of milk. Biomedical Informatics 2022-08-31 /pmc/articles/PMC10266364/ /pubmed/37323558 http://dx.doi.org/10.6026/97320630018703 Text en © 2022 Biomedical Informatics https://creativecommons.org/licenses/by/3.0/This is an Open Access article which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. This is distributed under the terms of the Creative Commons Attribution License. |
spellingShingle | Research Article Krishna, Neelam Vishwakarma, Shraddha Katara, Pramod Identification and annotation of milk associated genes from milk somatic cells using expression and RNA-seq data |
title | Identification and annotation of milk associated genes from milk somatic cells using expression and RNA-seq data |
title_full | Identification and annotation of milk associated genes from milk somatic cells using expression and RNA-seq data |
title_fullStr | Identification and annotation of milk associated genes from milk somatic cells using expression and RNA-seq data |
title_full_unstemmed | Identification and annotation of milk associated genes from milk somatic cells using expression and RNA-seq data |
title_short | Identification and annotation of milk associated genes from milk somatic cells using expression and RNA-seq data |
title_sort | identification and annotation of milk associated genes from milk somatic cells using expression and rna-seq data |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10266364/ https://www.ncbi.nlm.nih.gov/pubmed/37323558 http://dx.doi.org/10.6026/97320630018703 |
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