Difference between revisions of "MuscleDBs"

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(Created page with "===Introduction=== Skeletal muscles have indispensable functions in human body and also possess prominent regenerative ability. The rapid emergence of Next Generation Sequenci...")
 
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====[http://muscledb.org MuscleDB]====
 
====[http://muscledb.org MuscleDB]====
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<p align=justify> Analysis of the model suggested that metabolic activation and recruitment of muscle fibers are closely related, but the degree of metabolic activation inferred from metabolite changes may differ from that of the fiber recruitment. Simulations with a mechanistic, mathematical model demonstrated that the activation as measured by metabolic response in single fibers is distinct from fiber recruitment that is characterized by the number (or mass) of each fiber type involved during a specific exercise. The results from this study underline the need for critical experiments that measure fiber recruitment and metabolism in order to simulate and quantify the contributions of type I and II fibers to the regulation of energy metabolism. Such experimental techniques could be used in combination with the computational model to investigate the relationships between the extents of metabolic activation, number of fibers recruited, and muscle groups engaged at different intensity exercise.</p>
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<p align=justify> The [http://openwetware.org/wiki/HughesLab Hughes] (UMSL) and [http://www.uky.edu/~kaesse2/Recent_Publications.html/ Esser] (Univ. of Kentucky School of Medicine) labs are assembling a database of muscle tissue gene expression in mice and rat. They profiled global gene expression using RNA-sequencing from different muscle tissues, including 8 unique skeletal muscle tissues. In this repository, authors are developing a web-based platform to explore, visualize, and share these data build on a [http://shiny.rstudio.com/ Shiny dashboard]. This data set, MuscleDB, reveals extensive transcriptional diversity, with greater than 50% of transcripts differentially expressed among skeletal muscle tissues. Developers detected mRNA expression of hundreds of putative myokines that may underlie the endocrine functions of skeletal muscle. Authors were able to identify candidate genes that may drive tissue specialization, including ''Smarca4'',''Vegfa'', and ''Myostatin'' (Terry et al., 2018 <cite>2</cite>). This resource allow investigators to perform analyses such as generating muscle-specific ''Cre''-recombinase mouse strains for genetically manipulating specific muscle groups. Most importantly, these data provides the foundation for computational modeling of transcription factor networks, a method authors believe will uncover the genetic mechanisms that establish and maintain muscle specialization.</p>
  
 
====[https://genexx.shinyapps.io/genexx/ GeneXX]====
 
====[https://genexx.shinyapps.io/genexx/ GeneXX]====
 +
<p align=justify> GeneXX has been developed as a new web-based resource to facilitate exploration of skeletal muscle gene responses to exercise (Reibe et al., 2018 <cite>3</cite>). Users can enter any human gene of interest, (e.g., PPARGC1A) and immediately observe log-fold change values, adjusted P values (q value), and the time point post exercise at which the transcript was measured, with color- and shape-coded symbols to indicate statistical significance and sex of participants, respectively. Also included are PubMed scores and a short summary about the gene of interest from the NCBI gene site. The main feature of geneXX is that it provides an accessible and instant insight into the response of a particular gene of interest to exercise in human skeletal muscle.  To demonstrate its utility, authors carried out a meta-analysis on the included data sets and show transcript changes in skeletal muscle that persist regardless of sex, exercise mode, and duration, some of which have had minimal attention in the context of exercise.</p>
  
 
====[http://sunlab.lihs.cuhk.edu.hk/SKmDB/ SKmDB]====
 
====[http://sunlab.lihs.cuhk.edu.hk/SKmDB/ SKmDB]====
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===Summarized table of the databases===
 
===Summarized table of the databases===
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{| class="wikitable"
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{| class="mw-datatable"
−
! Database
+
! style="width:5%; | Database
−
! Short description
+
! style="width:20%; | Short description
−
! Data type
+
! style="width:8%; | Data type
−
! Functionality
+
! style="width:34%; | Functionality
−
! Statistics
+
! style="width:20%; | Statistics
−
! Current status
+
! style="width:8%; | Current status
−
! Reference
+
! style="width:5%; | Reference
 
|-
 
|-
−
| <p align=justify><span style="font-size: 90%"> [https://www.ebi.ac.uk/biomodels-main/BIOMD0000000248 BIOMD0000000248]</span></p>
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| <p align=justify><span style="font-size: 90%"> [http://muscledb.org MuscleDB]</span></p>
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| <p align=justify><span style="font-size: 90%"> Lai2007_O2_Transport_Metabolism.</span></p>
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| <p align=justify><span style="font-size: 90%"> MuscleDB is a project that uses unbiased RNA sequencing (RNA-seq) to profile global mRNA expression in a wide array of smooth, cardiac, and skeletal muscle tissues from mice and rats.</span></p>
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| <p align=justify><span style="font-size: 90%"> The mathematical model simulates oxygen transport and metabolism in skeletal muscle in response to a step change from a warm-up steady state to a higher work rate corresponding to exercise at different levels of intensity: moderate (M), heavy (H) and very heavy (VH). </span></p>
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| <p align=justify><span style="font-size: 90%"> Expression profiling by high throughput sequencing. </span></p>
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| <p align=justify><span style="font-size: 90%"> Lai et al., 2007 <cite>28</cite> </span></p>
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| <p align=justify><span style="font-size: 90%"> User can filter the database search by:<br> 1. gene symbol (like ‘Per1’); <br> 2. gene ontology (like ‘GTPase activity’); <br> 3. muscle tissue type; <br> 4. expression level; <br> 5. p-value (statistically significant difference between tissues (based on a two-way ANOVA)); <br> 6. change in expression, relative to another tissue type.<br> <br> User can also select which muscle tissues are interest of. By default, all tissues are checked. At the bottom of the plot options, just below ‘advanced filtering’, are the different ways to display the data. User can choose to show:<br> 1. plot (default): a bar graph of the expression levels in the tissues (in FPKM, Fragments Per Kilobase per Million reads) for each transcript, and options to save the plots; <br> 2. table: numeric table with the gene symbols, transcript names, expression levels in the tissues (in FPKM, Fragments Per Kilobase per Million reads), and the q-value (difference between tissues from a two-way ANOVA); <br> 3. volcano plot: volcano plot comparing two muscles, showing the logarithm of q-value versus the logarithm of the fold-change in expression; <br> 4. heat map: a dynamic heat map comparing the expression level of each transcript for each tissue; <br> 5. compare genes: a series of scatter plots comparing the expression levels to a particular reference tissue.</span></p>
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| <p align=justify><span style="font-size: 90%"> '''Relevant'''</span></p>
+
| <p align=justify><span style="font-size: 90%"> 126 samples, 17 mouse tissues (all from males), 2 female rat tissues, 2 male rat tissues. Six replicates for each tissue; each replicate is 3 individual samples pooled. For mouse tissues, 3 are smooth muscle, 3 are cardiac muscle and 11 are skeletal muscle. For male and female rat samples, both tissues are skeletal. </span></p>
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| <p align=justify><span style="font-size: 90%"> '''Relevant'''</span></p>
+
| <p align=justify><span style="font-size: 90%"> The beta-version is alive. The [https://github.com/flaneuse/muscleDB last update] is 2 years ago.</span></p>
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| <p align=justify><span style="font-size: 90%"> '''Relevant'''</span></p>
+
| <p align=justify><span style="font-size: 90%"> Terry et al., 2018 <cite>2</cite> </span></p>
 +
|-
 +
| <p align=justify><span style="font-size: 90%"> [https://genexx.shinyapps.io/genexx/ GeneXX]</span></p>
 +
| <p align=justify><span style="font-size: 90%"> GeneXX is an online tool for the exploration of transcript changes in skeletal muscle associated with exercise.</span></p>
 +
| <p align=justify><span style="font-size: 90%"> Expression profiling by microarray (Illumina, Affymetrix, or Agilent) and high throughput sequencing (Illumina HiSeq 2000). </span></p>
 +
| <p align=justify><span style="font-size: 90%"> </span></p>
 +
| <p align=justify><span style="font-size: 90%">  </span></p>
 +
| <p align=justify><span style="font-size: 90%"> </span></p>
 +
| <p align=justify><span style="font-size: 90%"> Reibe et al., 2018 <cite>3</cite> </span></p>
 
|}
 
|}
 
<span style="font-size: 90%"> '''Table1'''. Summarized table of the databases with transcriptomics data generated for skeletal muscle in different species.</span>
 
<span style="font-size: 90%"> '''Table1'''. Summarized table of the databases with transcriptomics data generated for skeletal muscle in different species.</span>
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<biblio>
 
<biblio>
 
#1 pmid=30165538 <!-- Yuan et al 2019 -->
 
#1 pmid=30165538 <!-- Yuan et al 2019 -->
−
#2 pmid=21188163 <!-- Baker et al 2010 -->
+
#2 pmid=29809149 <!-- Terry et al 2018 -->
−
#3 pmid=8789418        <!-- Schonekess et al 1995 -->
+
#3 pmid=29547064 <!-- Reibe et al 2018 -->
−
#4 pmid=6780344 <!-- Cohen 1980 -->
 
−
#5 pmid=21117169 <!-- Sola-Penna et al 2010 -->
 
−
#6 pmid=17566629 <!-- Korzeniewski 2007 -->
 
−
#7 pmid=23547908 <!-- Glancy et al 2013 -->
 
−
#8 pmid=10666039 <!-- Territo et al 2000 -->
 
−
#9 pmid=20401754 <!-- Olesen et al 2010 -->
 
−
#10 pmid=25927001 <!-- Popov et al 2015 -->
 
−
#11 pmid=18511502 <!-- Miura et al 2008 -->
 
−
#12 pmid=19233136 <!-- Yoshioka et al 2009 -->
 
−
#13 pmid=19773550 <!-- Zhang et al 2009 -->
 
−
#14 pmid=26254272 <!-- Thom et al 2014 -->
 
−
#15 pmid=22282499 <!-- Chang et al 2012 -->
 
−
#16 pmid=24843073 <!-- Zhang et al 2014 -->
 
−
#17 pmid=21862727 <!-- Norrbom et al 2011 -->
 
−
#18 pmid=24400142 <!-- Ydfors et al 2013 -->
 
−
#19 pmid=24907054 <!-- Popov et al 2014 -->
 
−
#20 pmid=19966219 <!-- Chinsomboon et al 2009 -->
 
−
#21 pmid=21098736 <!-- Tadaishi et al 2011 -->
 
−
#22 pmid=25136584 <!-- Wen et al 2014 -->
 
−
#23 pmid=26293291 <!-- Popov et al 2015 -->
 
−
#24 pmid=18380005 <!-- Röckl et al 2008 -->
 
−
#25 https://link.springer.com/content/pdf/10.1007/978-3-540-69389-5_14.pdf <!-- Cui and Kaandorp 2008 -->
 
−
#26 pmid=17046757 <!-- Shin et al 2006 -->
 
−
#27 pmid=16169970 <!-- Shannon et al 2005 -->
 
−
#28 pmid=18689454 <!-- Saucerman and Bers 2008 -->
 
−
#29 pmid=19045836 <!-- Groenendaal et al 2008 -->
 
−
#30 https://e-space.mmu.ac.uk/315671/1/SENSORY%20PATHWAYS%20OF%20MUSCLE%20PHENOTYPIC%20PLASTICITY_FINAL_DEC2012.pdf <!--Eilers et al 2012 -->
 
−
#31 pmid=25054156 <!-- Eilers et al 2014 -->
 
−
#32 pmid=12928489 <!-- Bradshaw et al 2003 -->
 
−
#33 pmid=14722083 <!-- Gaertner et al 2004 -->
 
  
  

Revision as of 19:22, 17 July 2019

Introduction

Skeletal muscles have indispensable functions in human body and also possess prominent regenerative ability. The rapid emergence of Next Generation Sequencing (NGS) data in recent years offers us an unprecedented perspective to understand gene regulatory networks governing skeletal muscle development and regeneration. However, the data from public NGS database are often in raw data format or processed with different procedures, causing obstacles to make full use of them (Yuan et al., 2019) [1]. Herein, we have integrated all information about current databases developed to represent disparate and heterogeneous omics data (with a focus on transcriptomics data) generated for skeletal muscle in different species.


Databases

MuscleDB

The Hughes (UMSL) and Esser (Univ. of Kentucky School of Medicine) labs are assembling a database of muscle tissue gene expression in mice and rat. They profiled global gene expression using RNA-sequencing from different muscle tissues, including 8 unique skeletal muscle tissues. In this repository, authors are developing a web-based platform to explore, visualize, and share these data build on a Shiny dashboard. This data set, MuscleDB, reveals extensive transcriptional diversity, with greater than 50% of transcripts differentially expressed among skeletal muscle tissues. Developers detected mRNA expression of hundreds of putative myokines that may underlie the endocrine functions of skeletal muscle. Authors were able to identify candidate genes that may drive tissue specialization, including Smarca4,Vegfa, and Myostatin (Terry et al., 2018 [2]). This resource allow investigators to perform analyses such as generating muscle-specific Cre-recombinase mouse strains for genetically manipulating specific muscle groups. Most importantly, these data provides the foundation for computational modeling of transcription factor networks, a method authors believe will uncover the genetic mechanisms that establish and maintain muscle specialization.

GeneXX

GeneXX has been developed as a new web-based resource to facilitate exploration of skeletal muscle gene responses to exercise (Reibe et al., 2018 [3]). Users can enter any human gene of interest, (e.g., PPARGC1A) and immediately observe log-fold change values, adjusted P values (q value), and the time point post exercise at which the transcript was measured, with color- and shape-coded symbols to indicate statistical significance and sex of participants, respectively. Also included are PubMed scores and a short summary about the gene of interest from the NCBI gene site. The main feature of geneXX is that it provides an accessible and instant insight into the response of a particular gene of interest to exercise in human skeletal muscle. To demonstrate its utility, authors carried out a meta-analysis on the included data sets and show transcript changes in skeletal muscle that persist regardless of sex, exercise mode, and duration, some of which have had minimal attention in the context of exercise.

SKmDB

MGS resource

NeuroMuscleDB

[Human Skeletal Muscle Proteome Project]

SkeletalVis

Summarized table of the databases

Database Short description Data type Functionality Statistics Current status Reference

MuscleDB

MuscleDB is a project that uses unbiased RNA sequencing (RNA-seq) to profile global mRNA expression in a wide array of smooth, cardiac, and skeletal muscle tissues from mice and rats.

Expression profiling by high throughput sequencing.

User can filter the database search by:
1. gene symbol (like ‘Per1’);
2. gene ontology (like ‘GTPase activity’);
3. muscle tissue type;
4. expression level;
5. p-value (statistically significant difference between tissues (based on a two-way ANOVA));
6. change in expression, relative to another tissue type.

User can also select which muscle tissues are interest of. By default, all tissues are checked. At the bottom of the plot options, just below ‘advanced filtering’, are the different ways to display the data. User can choose to show:
1. plot (default): a bar graph of the expression levels in the tissues (in FPKM, Fragments Per Kilobase per Million reads) for each transcript, and options to save the plots;
2. table: numeric table with the gene symbols, transcript names, expression levels in the tissues (in FPKM, Fragments Per Kilobase per Million reads), and the q-value (difference between tissues from a two-way ANOVA);
3. volcano plot: volcano plot comparing two muscles, showing the logarithm of q-value versus the logarithm of the fold-change in expression;
4. heat map: a dynamic heat map comparing the expression level of each transcript for each tissue;
5. compare genes: a series of scatter plots comparing the expression levels to a particular reference tissue.

126 samples, 17 mouse tissues (all from males), 2 female rat tissues, 2 male rat tissues. Six replicates for each tissue; each replicate is 3 individual samples pooled. For mouse tissues, 3 are smooth muscle, 3 are cardiac muscle and 11 are skeletal muscle. For male and female rat samples, both tissues are skeletal.

The beta-version is alive. The last update is 2 years ago.

Terry et al., 2018 [2]

GeneXX

GeneXX is an online tool for the exploration of transcript changes in skeletal muscle associated with exercise.

Expression profiling by microarray (Illumina, Affymetrix, or Agilent) and high throughput sequencing (Illumina HiSeq 2000).

Reibe et al., 2018 [3]

Table1. Summarized table of the databases with transcriptomics data generated for skeletal muscle in different species.

References

  1. Yuan J, Zhou J, Wang H, and Sun H. SKmDB: an integrated database of next generation sequencing information in skeletal muscle. Bioinformatics. 2019 Mar 1;35(5):847-855. DOI:10.1093/bioinformatics/bty705 | PubMed ID:30165538 | HubMed [1]
  2. Terry EE, Zhang X, Hoffmann C, Hughes LD, Lewis SA, Li J, Wallace MJ, Riley LA, Douglas CM, Gutierrez-Monreal MA, Lahens NF, Gong MC, Andrade F, Esser KA, and Hughes ME. Transcriptional profiling reveals extraordinary diversity among skeletal muscle tissues. Elife. 2018 May 29;7. DOI:10.7554/eLife.34613 | PubMed ID:29809149 | HubMed [2]
  3. Reibe S, Hjorth M, Febbraio MA, and Whitham M. GeneXX: an online tool for the exploration of transcript changes in skeletal muscle associated with exercise. Physiol Genomics. 2018 May 1;50(5):376-384. DOI:10.1152/physiolgenomics.00127.2017 | PubMed ID:29547064 | HubMed [3]
All Medline abstracts: PubMed | HubMed
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