MuscleDBs - Revision history

Akberdinir@gmail.com at 09:17, 5 December 2019

2019-12-05T09:17:51Z

Akberdinir@gmail.com: /* Summarized table of the databases */

2019-07-19T12:37:58Z

‎Summarized table of the databases

Akberdinir@gmail.com: /* TiGER */

2019-07-19T12:34:26Z

‎TiGER

Akberdinir@gmail.com: /* TiGER */

2019-07-19T12:33:41Z

‎TiGER

Akberdinir@gmail.com at 12:32, 19 July 2019

2019-07-19T12:32:18Z

Akberdinir@gmail.com: /* SkeletalVis */

2019-07-19T10:48:23Z

‎SkeletalVis

Akberdinir@gmail.com: /* SkeletalVis */

2019-07-19T10:48:08Z

‎SkeletalVis

Akberdinir@gmail.com: /* SkeletalVis */

2019-07-19T10:45:21Z

‎SkeletalVis

Akberdinir@gmail.com: /* SkeletalVis */

2019-07-19T10:44:26Z

‎SkeletalVis

Akberdinir@gmail.com: /* SkeletalVis */

2019-07-19T10:26:57Z

‎SkeletalVis

@@ Line 109: / Line 109: @@
 |}
 <span style="font-size: 90%"> '''Table1'''. Summarized table of the databases with transcriptomics data generated for skeletal muscle in different species.</span>
 ===References===
@@ Line 120: / Line 211: @@
 #7 pmid=18541026	<!-- Liu et al 2008-->
 #8 pmid=18577208	<!-- Jelier et al 2008-->
+#9 pmid=31647849	<!-- Rundqvist et al 2019-->
 </biblio>

@@ Line 97: / Line 97: @@
 | <p align=justify><span style="font-size: 90%"> SkeletalVis provides users with a platform to explore the wealth of available expression data, develop consensus signatures and the ability to compare gene signatures from new experiments to the analysed data to facilitate meta-analysis. </span></p>
 | <p align=justify><span style="font-size: 90%"> It currently hosts 300 analysed experiments with 779 perturbation responses. </span></p>
-| <p align=justify><span style="font-size: 90%"> The most recent version released on 30th Nov 2018 is alive.</span></p>
+| <p align=justify><span style="font-size: 90%"> The most recent version released on 30th Nov 2018 is alive. </span></p>
 | <p align=justify><span style="font-size: 90%"> Soul et al., 2019 <cite>6</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>

@@ Line 40: / Line 40: @@
 ====[http://bioinfo.wilmer.jhu.edu/tiger/ TiGER]====
 <p align=justify> The TiGER (Tissue-specific Gene Expression and Regulation) is a publicly available a database, which summarizes and provides large scale data sets for tissue-specific gene expression and regulation in a variety of human tissues. The database contains three types of data including tissue-specific gene expression profiles, combinatorial gene regulations, and cis-regulatory module (CRM) detections (Liu et al., 2008 <cite>7</cite>). At present the database contains expression profiles for 19,526 UniGene genes, combinatorial regulations for 7,341 transcription factor pairs and 6,232 putative CRMs for 2,130 RefSeq genes. The TiGER database includes information about [http://bioinfo.wilmer.jhu.edu/tiger/db_tissue/est/muscle-index.html 325 RefSeq Genes] which are preferentially expressed in muscle and contains [http://bioinfo.wilmer.jhu.edu/tiger/db_tissue/tfpair/muscle-index.html 730 transcription factor pairs] co-regulating in muscle.</p>
 ===Summarized table of the databases===

@@ Line 39: / Line 39: @@
 ====[http://bioinfo.wilmer.jhu.edu/tiger/ TiGER]====
-<p align=justify> The TiGER (Tissue-specific Gene Expression and Regulation) is a publicly available a database, which summarizes and provides large scale data sets for tissue-specific gene expression and regulation in a variety of human tissues. The database contains three types of data including tissue-specific gene expression profiles, combinatorial gene regulations, and cis-regulatory module (CRM) detections (Liu et al., 2008 <cite>7</cite>). At present the database contains expression profiles for 19,526 UniGene genes, combinatorial regulations for 7,341 transcription factor pairs and 6,232 putative CRMs for 2,130 RefSeq genes. The TiGER database includes information about 325 RefSeq Genes which are preferentially expressed in muscle and contains 730 transcription factor pairs co-regulating in muscle.</p>
+<p align=justify> The TiGER (Tissue-specific Gene Expression and Regulation) is a publicly available a database, which summarizes and provides large scale data sets for tissue-specific gene expression and regulation in a variety of human tissues. The database contains three types of data including tissue-specific gene expression profiles, combinatorial gene regulations, and cis-regulatory module (CRM) detections (Liu et al., 2008 <cite>7</cite>). At present the database contains expression profiles for 19,526 UniGene genes, combinatorial regulations for 7,341 transcription factor pairs and 6,232 putative CRMs for 2,130 RefSeq genes. The TiGER database includes information about [http://bioinfo.wilmer.jhu.edu/tiger/db_tissue/est/muscle-index.html 325 RefSeq Genes] which are preferentially expressed in muscle and contains [http://bioinfo.wilmer.jhu.edu/tiger/db_tissue/tfpair/muscle-index.html 730 transcription factor pairs] co-regulating in muscle.</p>
 ===Summarized table of the databases===

@@ Line 37: / Line 37: @@
 <div style="text-align: center"> <span style="font-size:90%; center"> '''Figure 3 from (Soul et al., 2019)''' <cite>6</cite>. Schematic diagram of the SkeletalVis transcriptomics pipeline.</span></div>
 ===Summarized table of the databases===
@@ Line 76: / Line 80: @@
 | <p align=justify><span style="font-size: 90%"> Expression profiling by microarray (Affymetrix). </span></p>
 | <p align=justify><span style="font-size: 90%"> Its major intended use is in enrichment testing for functional genomics analysis, for which purpose it has been made accessible through three commonly used analytical tools (GSEA, EnrichR, and WebGestalt). </span></p>
-| <p align=justify><span style="font-size: 90%"> The current download is comprised of 1,517 Gene Sets. Of these, 1,156 were derived from authors recent analysis of 302 studies of muscle physiology and disease published from 2005-present, including 4305 separate samples. A further 122 were derived from published ''in vitro'' muscle microarray studies carried out from 2005-present, as used in their previous work, and 185 were derived from a previous meta-analysis carried out by Jelier et al., 2008 <cite>7</cite>. The remaining 54 are from muscle-related gene ontology terms, but also several other muscle-relevant entries in the [http://software.broadinstitute.org/gsea/msigdb/ MSigDB database] (mostly comprising muscle-related pathways from Reactome or Biocarta databases). </span></p>
+| <p align=justify><span style="font-size: 90%"> The current download is comprised of 1,517 Gene Sets. Of these, 1,156 were derived from authors recent analysis of 302 studies of muscle physiology and disease published from 2005-present, including 4305 separate samples. A further 122 were derived from published ''in vitro'' muscle microarray studies carried out from 2005-present, as used in their previous work, and 185 were derived from a previous meta-analysis carried out by Jelier et al., 2008 <cite>8</cite>. The remaining 54 are from muscle-related gene ontology terms, but also several other muscle-relevant entries in the [http://software.broadinstitute.org/gsea/msigdb/ MSigDB database] (mostly comprising muscle-related pathways from Reactome or Biocarta databases). </span></p>
 | <p align=justify><span style="font-size: 90%"> The current version 3 is released March 2019.</span></p>
 | <p align=justify><span style="font-size: 90%"> Malatras et al., 2019 <cite>4</cite> </span></p>
@@ Line 106: / Line 110: @@
 #5 pmid=30684219	<!-- Baig et al 2019-->
 #6 pmid=30481257	<!-- Soul et al 2019-->
-#7 pmid=18577208	<!-- Jelier et al 2008-->
+#7 pmid=18541026	<!-- Liu et al 2008-->

@@ Line 31: / Line 31: @@
 ====[http://phenome.manchester.ac.uk/ SkeletalVis]====
-<p align=justify> The SkeletalVis data portal provides an exploration and comparison platform for analysed skeletal transcriptomics data (Soul et al., 2019 <cite>6</cite>). SkeletalVis collates 287 cross-species skeletal transcriptomic experiments and is an intuitive data-portal to allow exploration and meta-analysis. Analysis of the raw data through the transcriptomics pipeline generated 739 expression response profiles with quality control and PCA plots, differential expression and comprehensive downstream analysis comprising of pathway, active sub-network, GO Term, drug, transcription factor enrichment ((Figure 3)). Transcriptomic signatures from gene perturbation experiments were examined to find the top pairwise similar expression responses to highlight examples of association identification enabled by this re-analysis of the transcriptomic datasets. To identify compounds potentially capable of mimicking or reversing the observed differential expression in the identified perturbation groups, authors of the database generated rank-product consensus signatures and performed drug enrichment analysis using the [https://amp.pharm.mssm.edu/L1000CDS2/#/index LINCS L1000 drug signatures]. Code for the pipeline and post-processing of the data can be found [https://github.com/soulj/SkeletalVis-Pipeline here].</p>
+<p align=justify> The SkeletalVis data portal provides an exploration and comparison platform for analysed skeletal transcriptomics data (Soul et al., 2019 <cite>6</cite>). SkeletalVis collates 287 cross-species skeletal transcriptomic experiments and is an intuitive data-portal to allow exploration and meta-analysis. Analysis of the raw data through the transcriptomics pipeline generated 739 expression response profiles with quality control and PCA plots, differential expression and comprehensive downstream analysis comprising of pathway, active sub-network, GO Term, drug, transcription factor enrichment (Figure 3). Transcriptomic signatures from gene perturbation experiments were examined to find the top pairwise similar expression responses to highlight examples of association identification enabled by this re-analysis of the transcriptomic datasets. To identify compounds potentially capable of mimicking or reversing the observed differential expression in the identified perturbation groups, authors of the database generated rank-product consensus signatures and performed drug enrichment analysis using the [https://amp.pharm.mssm.edu/L1000CDS2/#/index LINCS L1000 drug signatures]. Code for the pipeline and post-processing of the data can be found [https://github.com/soulj/SkeletalVis-Pipeline here].</p>
 [[File:SkeletalViz.png|center]]

← Older revision		Revision as of 10:26, 19 July 2019
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	<p align=justify> The SkeletalVis data portal provides an exploration and comparison platform for analysed skeletal transcriptomics data (Soul et al., 2019 <cite>6</cite>). SkeletalVis collates 287 cross-species skeletal transcriptomic experiments and is an intuitive data-portal to allow exploration and meta-analysis. </p>		<p align=justify> The SkeletalVis data portal provides an exploration and comparison platform for analysed skeletal transcriptomics data (Soul et al., 2019 <cite>6</cite>). SkeletalVis collates 287 cross-species skeletal transcriptomic experiments and is an intuitive data-portal to allow exploration and meta-analysis. </p>

−	[File:SkeletalViz.png\|center]]	+	[[File:SkeletalViz.png\|center]]