MyoMiner: explore gene co-expression in normal and pathological muscle.
Adolescent
Adult
Animals
Child
Child, Preschool
Computational Biology
/ methods
Female
Gene Expression Regulation
Gene Regulatory Networks
Humans
Infant
Infant, Newborn
Male
Mice
Middle Aged
Muscle Proteins
/ genetics
Muscles
/ cytology
Muscular Diseases
/ genetics
Software
Transcriptome
Young Adult
Correlation
Differential correlation
Functional genomics
Gene co-expression
Gene co-expression networks
Transcriptomics
Journal
BMC medical genomics
ISSN: 1755-8794
Titre abrégé: BMC Med Genomics
Pays: England
ID NLM: 101319628
Informations de publication
Date de publication:
11 05 2020
11 05 2020
Historique:
received:
18
10
2019
accepted:
13
04
2020
entrez:
13
5
2020
pubmed:
13
5
2020
medline:
11
5
2021
Statut:
epublish
Résumé
High-throughput transcriptomics measures mRNA levels for thousands of genes in a biological sample. Most gene expression studies aim to identify genes that are differentially expressed between different biological conditions, such as between healthy and diseased states. However, these data can also be used to identify genes that are co-expressed within a biological condition. Gene co-expression is used in a guilt-by-association approach to prioritize candidate genes that could be involved in disease, and to gain insights into the functions of genes, protein relations, and signaling pathways. Most existing gene co-expression databases are generic, amalgamating data for a given organism regardless of tissue-type. To study muscle-specific gene co-expression in both normal and pathological states, publicly available gene expression data were acquired for 2376 mouse and 2228 human striated muscle samples, and separated into 142 categories based on species (human or mouse), tissue origin, age, gender, anatomic part, and experimental condition. Co-expression values were calculated for each category to create the MyoMiner database. Within each category, users can select a gene of interest, and the MyoMiner web interface will return all correlated genes. For each co-expressed gene pair, adjusted p-value and confidence intervals are provided as measures of expression correlation strength. A standardized expression-level scatterplot is available for every gene pair r-value. MyoMiner has two extra functions: (a) a network interface for creating a 2-shell correlation network, based either on the most highly correlated genes or from a list of genes provided by the user with the option to include linked genes from the database and (b) a comparison tool from which the users can test whether any two correlation coefficients from different conditions are significantly different. These co-expression analyses will help investigators to delineate the tissue-, cell-, and pathology-specific elements of muscle protein interactions, cell signaling and gene regulation. Changes in co-expression between pathologic and healthy tissue may suggest new disease mechanisms and help define novel therapeutic targets. Thus, MyoMiner is a powerful muscle-specific database for the discovery of genes that are associated with related functions based on their co-expression. MyoMiner is freely available at https://www.sys-myo.com/myominer.
Sections du résumé
BACKGROUND
High-throughput transcriptomics measures mRNA levels for thousands of genes in a biological sample. Most gene expression studies aim to identify genes that are differentially expressed between different biological conditions, such as between healthy and diseased states. However, these data can also be used to identify genes that are co-expressed within a biological condition. Gene co-expression is used in a guilt-by-association approach to prioritize candidate genes that could be involved in disease, and to gain insights into the functions of genes, protein relations, and signaling pathways. Most existing gene co-expression databases are generic, amalgamating data for a given organism regardless of tissue-type.
METHODS
To study muscle-specific gene co-expression in both normal and pathological states, publicly available gene expression data were acquired for 2376 mouse and 2228 human striated muscle samples, and separated into 142 categories based on species (human or mouse), tissue origin, age, gender, anatomic part, and experimental condition. Co-expression values were calculated for each category to create the MyoMiner database.
RESULTS
Within each category, users can select a gene of interest, and the MyoMiner web interface will return all correlated genes. For each co-expressed gene pair, adjusted p-value and confidence intervals are provided as measures of expression correlation strength. A standardized expression-level scatterplot is available for every gene pair r-value. MyoMiner has two extra functions: (a) a network interface for creating a 2-shell correlation network, based either on the most highly correlated genes or from a list of genes provided by the user with the option to include linked genes from the database and (b) a comparison tool from which the users can test whether any two correlation coefficients from different conditions are significantly different.
CONCLUSIONS
These co-expression analyses will help investigators to delineate the tissue-, cell-, and pathology-specific elements of muscle protein interactions, cell signaling and gene regulation. Changes in co-expression between pathologic and healthy tissue may suggest new disease mechanisms and help define novel therapeutic targets. Thus, MyoMiner is a powerful muscle-specific database for the discovery of genes that are associated with related functions based on their co-expression. MyoMiner is freely available at https://www.sys-myo.com/myominer.
Identifiants
pubmed: 32393257
doi: 10.1186/s12920-020-0712-3
pii: 10.1186/s12920-020-0712-3
pmc: PMC7216615
doi:
Substances chimiques
Muscle Proteins
0
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
67Références
Bioinformatics. 2012 Mar 15;28(6):882-3
pubmed: 22257669
Nat Genet. 2015 Jun;47(6):569-76
pubmed: 25915600
Elife. 2016 Mar 03;5:
pubmed: 26939790
Biostatistics. 2003 Apr;4(2):249-64
pubmed: 12925520
Genome Biol. 2019 Nov 14;20(1):236
pubmed: 31727119
Nucleic Acids Res. 2015 Jan;43(Database issue):D1113-6
pubmed: 25361974
Genome Res. 2007 Nov;17(11):1614-25
pubmed: 17921353
Proc Natl Acad Sci U S A. 2013 Oct 29;110(44):17778-83
pubmed: 24128763
Nucleic Acids Res. 2015 Jan;43(Database issue):D82-6
pubmed: 25392420
Plant Cell Physiol. 2016 Jan;57(1):e5
pubmed: 26546318
Database (Oxford). 2011 Jul 23;2011:bar030
pubmed: 21785142
Curr Protoc Mol Biol. 2013 Jan;Chapter 22:Unit 22.1.
pubmed: 23288464
Database (Oxford). 2010 Aug 05;2010:baq020
pubmed: 20689021
Biostatistics. 2007 Jan;8(1):118-27
pubmed: 16632515
Genomics. 2012 Dec;100(6):337-44
pubmed: 22959562
Bioinformatics. 2007 Jul 1;23(13):i282-8
pubmed: 17646307
Nucleic Acids Res. 2019 Jan 8;47(D1):D786-D792
pubmed: 30304474
Nucleic Acids Res. 2007;35(12):4154-63
pubmed: 17567617
Nucleic Acids Res. 2005 Nov 10;33(20):e175
pubmed: 16284200
Nucleic Acids Res. 2015 Jan;43(Database issue):D1133-9
pubmed: 25326331
Nat Rev Microbiol. 2010 Oct;8(10):717-29
pubmed: 20805835
Nucleic Acids Res. 2003 Apr 1;31(7):1962-8
pubmed: 12655013
Nucleic Acids Res. 2014 Jan;42(Database issue):D358-63
pubmed: 24234451
Bioinformatics. 2012 Feb 15;28(4):573-80
pubmed: 22247279
Nucleic Acids Res. 2011 Jan;39(Database issue):D52-7
pubmed: 21115458
Genome Biol. 2003;4(10):R66
pubmed: 14519201
Proc Natl Acad Sci U S A. 2002 Dec 24;99(26):16875-80
pubmed: 12486219
Nucleic Acids Res. 2014 Dec 1;42(21):
pubmed: 25294822
BMC Bioinformatics. 2007 Feb 08;8:48
pubmed: 17288599
Plant Cell Environ. 2009 Dec;32(12):1633-51
pubmed: 19712066
Curr Opin Cell Biol. 2007 Aug;19(4):409-16
pubmed: 17662592
Nucleic Acids Res. 2017 Jan 4;45(D1):D158-D169
pubmed: 27899622
Science. 1995 Oct 20;270(5235):467-70
pubmed: 7569999
Oncol Rep. 2014 May;31(5):2157-64
pubmed: 24626613
Nucleic Acids Res. 2013 Jan;41(Database issue):D991-5
pubmed: 23193258
Nucleic Acids Res. 2017 Jan 4;45(D1):D353-D361
pubmed: 27899662
Nucleic Acids Res. 2006 Jul 1;34(Web Server issue):W504-9
pubmed: 16845059
Nat Methods. 2013 Aug;10(8):690-1
pubmed: 23900247
Proc Natl Acad Sci U S A. 2016 Apr 26;113(17):E2393-402
pubmed: 27078110
Biostatistics. 2016 Jan;17(1):29-39
pubmed: 26272994
IEEE Trans Vis Comput Graph. 2011 Dec;17(12):2301-9
pubmed: 22034350
BMC Res Notes. 2012 Jun 06;5:265
pubmed: 22672625
Proc Natl Acad Sci U S A. 2014 Mar 4;111(9):3538-43
pubmed: 24550449
Database (Oxford). 2016 Jun 23;2016:
pubmed: 27337980
Nat Methods. 2005 May;2(5):345-50
pubmed: 15846361
PLoS Comput Biol. 2009 May;5(5):e1000382
pubmed: 19412532
Nat Methods. 2019 Sep;16(9):843-852
pubmed: 31471613
Nat Methods. 2012 Jul 15;9(8):796-804
pubmed: 22796662
Adv Bioinformatics. 2008;2008:420747
pubmed: 19956698
Plant J. 2006 Apr;46(2):336-48
pubmed: 16623895
Nat Genet. 2001 Dec;29(4):365-71
pubmed: 11726920
Trends Genet. 2010 Jul;26(7):326-33
pubmed: 20570387
BMC Bioinformatics. 2009 Oct 14;10:332
pubmed: 19828039
Nat Biotechnol. 1996 Dec;14(13):1675-80
pubmed: 9634850
BMC Bioinformatics. 2011 May 07;12:137
pubmed: 21548974
Methods Mol Biol. 2018;1754:155-165
pubmed: 29536442
Bioinformatics. 2004 Aug 4;20 Suppl 1:i194-9
pubmed: 15262799
Eur J Hum Genet. 2011 Nov;19(11):1173-80
pubmed: 21654723
Genome Biol. 2002;3(1):RESEARCH0005
pubmed: 11806828
Science. 2015 May 8;348(6235):648-60
pubmed: 25954001
BMC Genomics. 2006 Dec 27;7:325
pubmed: 17192196
Tumour Biol. 2014 Jun;35(6):5159-65
pubmed: 24481662
Nat Rev Genet. 2010 Oct;11(10):733-9
pubmed: 20838408
Nat Methods. 2015 Mar;12(3):211-4, 3 p following 214
pubmed: 25581801
Skelet Muscle. 2019 May 3;9(1):10
pubmed: 31053169