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59 results for “Coexpression”
Gene coexpression data in ATTED-II (RDF data)
<p>These are the gene coexpression data in Notation3 format, also provided in Virtuoso in ATTED-II. Details of the data can be found on the download page in ATTED-II. https://atted.jp/download/</p> <p>Example:</p> <p>@prefix m2r: <http://med2rdf.org/ontology/med2rdf#> .<br> @prefix rdfs: <http://www.w3.org/2000/01/rdf-schema#> .<br> @prefix dcterms: <http://purl.org/dc/terms/> .<br> @prefix obo: <http://purl.obolibrary.org/obo/> .<br> @prefix ncbigene: <http://identifiers.org/ncbigene/> .<br> @prefix sio: <http://semanticscience.org/resource/> .<br> @prefix atted: <https://atted.jp/dataset/> .<br> @prefix attedo: <https://atted.jp/ontology/> .<br> @prefix xsd: <http://www.w3.org/2001/XMLSchema#> .</p> <p>atted:Ath-u.c3-0<br> dcterms:identifier "Ath-u.c3-0" ;<br> dcterms:issued "2022-04-07"^^xsd:date .</p> <p>[]<br> attedo:gene ncbigene:836761 ;<br> attedo:gene ncbigene:2745723 ;<br> attedo:lsmr_score 5.6464 ;<br> attedo:dataset atted:Ath-u.c3-0 ;<br> a attedo:CoExpressedGenePair .</p> <p>[]<br> attedo:gene ncbigene:836827 ;<br> attedo:gene ncbigene:2745723 ;<br> attedo:lsmr_score 5.2684 ;<br> attedo:dataset atted:Ath-u.c3-0 ;<br> a attedo:CoExpressedGenePair .</p>
Gene coexpression data in COXPRESdb (RDF data)
<p>These are the gene coexpression data in Notation3 format, also provided in Virtuoso in COXPRESdb. Details of the data can be found on the download page in COXPRESdb. https://coxpresdb.jp/download/</p> <p>Example:</p> <p>@prefix m2r: <http://med2rdf.org/ontology/med2rdf#> .<br> @prefix rdfs: <http://www.w3.org/2000/01/rdf-schema#> .<br> @prefix dcterms: <http://purl.org/dc/terms/> .<br> @prefix obo: <http://purl.obolibrary.org/obo/> .<br> @prefix ncbigene: <http://identifiers.org/ncbigene/> .<br> @prefix sio: <http://semanticscience.org/resource/> .<br> @prefix coxpresdb: <https://coxpresdb.jp/dataset/> .<br> @prefix coxpresdbo: <https://coxpresdb.jp/ontology/> .<br> @prefix xsd: <http://www.w3.org/2001/XMLSchema#> .</p> <p>coxpresdb:Hsa-u.c4-0<br> dcterms:identifier "Hsa-u.c4-0" ;<br> dcterms:issued "2022-06-08"^^xsd:date .</p> <p>[]<br> coxpresdbo:gene ncbigene:9 ;<br> coxpresdbo:gene ncbigene:10 ;<br> coxpresdbo:lsmr_score 6.33 ;<br> coxpresdbo:dataset coxpresdb:Hsa-u.c4-0 ;<br> a coxpresdbo:CoExpressedGenePair .</p> <p>[]<br> coxpresdbo:gene ncbigene:41 ;<br> coxpresdbo:gene ncbigene:100 ;<br> coxpresdbo:lsmr_score 2.49 ;<br> coxpresdbo:dataset coxpresdb:Hsa-u.c4-0 ;<br> a coxpresdbo:CoExpressedGenePair .</p>
Condition-specific gene coexpression in ATTED-II (LS index)
<p>The coexpression index has changed in recent versions of ATTED-II.</p> <ul> <li>ATTED-II ver 11: z-score of LS index (larger value indicates stronger coexpression)</li> <li>ATTED-II ver 10: LS index (larger value indicates stronger coexpression)</li> <li>ATTED-II ver 4 to ver 9: MR index (smaller value indicates stronger coexpression)</li> </ul> <p>To continue providing the condition-specific gene coexpression data, they were converted to z-score of the LS index. </p> <ul> <li>ATTED-II ver 6.0 (September 2010): MR index. AGI (Arabidopsis Genome Initiative) code.</li> <li>ATTED-II ver 11.0 (February 2021): z-score of the LS index. EGI (Entrez Gene ID)code.</li> </ul> <p><strong>Reference of condition-specific gene coexpression data in ATTED-II</strong><br> Obayashi et al. (2011) ATTED-II updates: condition-specific gene coexpression to extend coexpression analyses and applications to a broad range of flowering plants. Plant Cell Physiol. 52. 213-9.<br> https://pubmed.ncbi.nlm.nih.gov/21217125/</p> <p><strong>Detail of the condition-specific gene coexpression data</strong><br> The condition-specific gene coexpression data are based on the following AtGenExpress expression data.<br> https://www.arabidopsis.org/portals/expression/microarray/ATGenExpress.jsp</p> <ul> <li>bio: AtGenExpress biotic treatment <ul> <li>ME00341 (Carine Denoux , Fred Ausubel , Julia Dewdney , Simone Ferrari)</li> <li>ME00353 (Xinnian Dong)</li> <li>ME00331 (Birgit Kemmerling , Thorsten Nürnberger)</li> <li>ME00332 (Frederic Brunner , Thorsten Nürnberger)</li> <li>ME00342 (Dierk Scheel , Frederic Brunner , Lore Westphal)</li> <li>ME00354 (Fred Ausubel , Julia Dewdney)</li> </ul> </li> <li>hor: AtGenExpress hormone treatment <ul> <li>ME00333 (Hideki Goda , Shigeo Yoshida , Yukihisa Shimada)</li> <li>ME00334 (Hideki Goda , Shigeo Yoshida , Yukihisa Shimada)</li> <li>ME00335 (Hideki Goda , Shigeo Yoshida , Yukihisa Shimada)</li> <li>ME00336 (Hideki Goda , Shigeo Yoshida , Yukihisa Shimada)</li> <li>ME00337 (Hideki Goda , Shigeo Yoshida , Yukihisa Shimada)</li> <li>ME00343 (Hideki Goda , Shigeo Yoshida , Yukihisa Shimada)</li> <li>ME00344 (Hideki Goda , Shigeo Yoshida , Yukihisa Shimada)</li> <li>ME00352 (Hideki Goda , Yukihisa Shimada)</li> <li>ME00356 (Takatoshi Kiba , Takeshi Mizuno)</li> <li>ME00357 (Hideki Goda , Shigeo Yoshida , Tadao Asami , Yukihisa Shimada)</li> <li>ME00358 (Hideki Goda , Shigeo Yoshida , Tadao Asami , Yukihisa Shimada)</li> <li>ME00359 (Hideki Goda , Shigeo Yoshida , Tadao Asami , Yukihisa Shimada)</li> <li>ME00360 (Hideki Goda , Shigeo Yoshida , Tadao Asami , Yukihisa Shimada)</li> </ul> </li> <li>lig: AtGenExpress light treatment <ul> <li>ME00345 (Thomas Kretsch)</li> </ul> </li> <li>str: AtGenExpress stress series <ul> <li>ME00325 (Cecilia D`Angelo , Joachim Kilian , Joerg Kudla , Oliver Batistic , Stefan Weinl)</li> <li>ME00326 (Holger Puchta , I-Peng Chen)</li> <li>ME00327 (Cecilia D`Angelo , Joachim Kilian , Joerg Kudla , Oliver Batistic , Stefan Weinl)</li> <li>ME00328 (Cecilia D`Angelo , Joachim Kilian , Joerg Kudla , Oliver Batistic , Stefan Weinl)</li> <li>ME00329 (Jakub Horak , Klaus Harter)</li> <li>ME00330 (Dierk Wanke , Klaus Harter)</li> <li>ME00338 (Cecilia D`Angelo , Joachim Kilian , Joerg Kudla , Oliver Batistic , Stefan Weinl)</li> <li>ME00339 (Lutz Nover , Pascal von Koskull-Döring)</li> <li>ME00340 (Andrea Ditzer , Dorothea Bartels , Hans-Hubert Kirch)</li> </ul> </li> <li>tis: AtGenExpress development <ul> <li>ME00319 (Detlef Weigel , Jan Lohmann , Markus Schmid)</li> </ul> </li> </ul>
Coexpression data in ATTED-II ver 11.1 (Xxx-u)
<p>Union coexpression data (Xxx-u) in ATTED-II ver 11.1.</p> <p>The coexpression value is provided as a z-score (larger value indicates stronger coexpression).<br> Details of the data can be found on the download page in ATTED-II. https://atted.jp/download/</p> <ul> <li>Ath-u.c3-0 Ath-u.v22-04.G18957-S27427.combat_pca.subagging.ls.d.zip</li> <li>Gma-u.c3-0 Gma-u.v22-04.G33331-S2104.combat_pca.subagging.ls.d.zip</li> <li>Mtr-u.c3-0 Mtr-u.v22-04.G22252-S1090.combat_pca.subagging.ls.d.zip</li> <li>Osa-u.c3-0 Osa-u.v22-04.G22698-S2553.combat_pca.subagging.ls.d.zip</li> <li>Ppo-u.c3-0 Ppo-u.v22-04.G21727-S1205.combat_pca.subagging.ls.d.zip</li> <li>Sly-u.c3-0 Sly-u.v22-04.G20073-S1141.combat_pca.subagging.ls.d.zip</li> <li>Vvi-u.c3-0 Vvi-u.v22-04.G17304-S1493.combat_pca.subagging.ls.d.zip</li> <li>Zma-u.c3-0 Zma-u.v22-04.G26131-S5017.combat_pca.subagging.ls.d.zip</li> </ul> <p> </p>
Coexpression and purification of HTT domains with HAP40 - 2018/12/03
<p><strong>Project: </strong>High resolution structural analysis of purified HTT proteins</p> <p><strong>Experiment: </strong>Coexpression and purification of HTT domains with HAP40</p> <p><strong>Date completed:­ </strong>2018/12/03</p> <p><strong>Rationale: </strong>HTT domain fragments are challenging to express and purify. However, the full-length protein sample was shown to have superior biophysical characteristics when coexpressed and purified with HAP40. As HAP40 has an extensive interaction surface with HTT, it could be possible to copurify domain fragments of HTT with HAP40, stabilizing the individual HEAT domains. </p>
Coexpression and purification of HTT and HTT-HAP40 with NEDD4L and HAP1 2019/05/02
<p><strong>Project: </strong>Biophysical investigation of purified HTT protein and protein complex samples</p> <p><strong>Experiment: </strong>Coexpression and purification of HTT and HTT-HAP40 with NEDD4L and HAP1</p> <p><strong>Date completed:­ </strong>2019/05/02</p> <p><strong>Rationale: </strong>HAP1 and NEDD4L have been suggested as high probability interaction partners of HTT although it is unclear if they interact with HTT or HTT-HAP40. Coexpression of the constructs was conducted in baculoviral expression system (Sf9 cells) to determine if they copurify with HTT or HTT-HAP40. </p>
Data from: Gene coexpression networks reveal key drivers of phenotypic divergence in lake whitefish
BACKGROUND: A functional understanding of processes involved in adaptive divergence is one of the awaiting opportunities afforded by high throughput transcriptomic technologies. Functional analysis of co-expressed genes has succeeded in the biomedical field in identifying key drivers of disease pathways. However, in ecology and evolutionary biology, functional interpretation of transcriptomic data is still limited. RESULTS: Here we used Weighted Gene Co-Expression Network Analysis (WGCNA) to identify modules of co-expressed genes in muscle and brain tissue of a lake whitefish backcross progeny. Modules were connected to gradients of known adaptive traits involved in the ecological speciation process between benthic and limnetic ecotypes. Key drivers, i.e. hub genes of functional modules related to reproduction, growth, and behavior were identified, and module preservation was assessed in natural populations. Using this approach, we identified modules of co-expressed genes involved in phenotypic divergence and their key drivers, and further identified a module part specifically rewired in the backcross progeny. CONCLUSIONS: Functional analysis of transcriptomic data can significantly contribute to the understanding of the mechanisms underlying ecological speciation. Our findings point to BMP and Calcium signaling as common pathways involved in coordinated evolution of trophic behavior, trophic morphology (gill rakers), and reproduction. Results also point to pathways implicating hemoglobins and constitutive stress response (HSP70) governing growth in lake whitefish.
Gene coexpression data in ALCOdb
<h2>Coexpression data provided in ALCOdb.</h2><p>Details of the data can be found on the download page in ALCOdb. http://alcodb.jp/coexpression/download</p><ul><li>Coexpression data<ul><li>Cre.v15-08.G15519-S172.quantile.mrgeo.d.zip</li><li>Cre.v15-02.G15540-S180.qnorm.mrgeo.d.zip</li><li>Cre.v13-10.G18773-S137.qnorm.mrgeo.d.zip</li><li>Cme1.v14-06.G4586-S75.d.zip</li><li>Cme2.v14-06.G6506-S48.d.zip</li></ul></li><li>Gene expression table to calculate the coexpression data<ul><li>Cre-R1-15-08_GeneExpressionTable.tsv.gz</li><li>Cme1_GeneExpressionTable.tsv.zip</li><li>Cme2_GeneExpressionTable.tsv.zip </li></ul></li><li>Codon usage similarity for evaluation<ul><li>Cre_v55.CodonUsage.G17741.zip</li></ul></li></ul><h4>Data Format</h4><p>Gene coexpression data folder contains a number of files, and file names correspond to the query gene IDs. Each file includes the coexpressed gene list as in the following format (Example about Cre01g001400 in cre.v15-08.G15519-S172.quantile.mrgeo.d).</p><ul><li>File Name: Cre01g001400</li><li>Line1: Cre03g197300 1.00 0.8352</li><li>Line2: Cre13g605200 6.63 0.6938</li><li>Line3: Cre09g411751 7.35 0.7082</li><li>Line4: Cre15g638551 9.38 0.7128</li><li>Line5: Cre01g037600 10.25 0.6761</li></ul><p> </p><ul><li>Columns are tab-deliminated.</li><li>Lines are sorted by column2.</li><li>Column1: ID of coexpressed gene.</li><li>Column2: Matual Rank (MR). [smaller MR indicates stronger coexpression]</li><li>Column3: Pearson's Correlation Coefficient (PCC). [larger PCC indicates stronger coexpression]</li></ul>
Gene coexpression data in ALCOdbCyano
<h2>Coexpression data provided in ALCOdbCyano.</h2><p>Details of the data can be found on the download page in ALCOdbCyano. http://alcodb.jp/cyano/download</p><ul><li>Coexpression data<ul><li>PCC6803.v15-11.G3660-S18.quantile.mrgeo.d.zip</li><li>PCC7120.v16-10.G5330-S116.d.zip</li><li>PCC7942.v15-11.G2714-S34.quantile.mrgeo.d.zip</li></ul></li></ul><h4>Data Format</h4><p>Gene coexpression data folder contains a number of files, and file names correspond to the query gene IDs. Each file includes the coexpressed gene list as in the following format (Example about Cre01g001400 in cre.v15-08.G15519-S172.quantile.mrgeo.d).</p><ul><li>File Name: Cre01g001400</li><li>Line1: Cre03g197300 1.00 0.8352</li><li>Line2: Cre13g605200 6.63 0.6938</li><li>Line3: Cre09g411751 7.35 0.7082</li><li>Line4: Cre15g638551 9.38 0.7128</li><li>Line5: Cre01g037600 10.25 0.6761</li></ul><p> </p><ul><li>Columns are tab-deliminated.</li><li>Lines are sorted by column2.</li><li>Column1: ID of coexpressed gene.</li><li>Column2: Matual Rank (MR). [smaller MR indicates stronger coexpression]</li><li>Column3: Pearson's Correlation Coefficient (PCC). [larger PCC indicates stronger coexpression]</li></ul>
Gene coexpression data in ALCOdbMarpo
<h2>Coexpression data provided in ALCOdbMarpo.</h2><p>Details of the data can be found on the download page in ALCOdbMarpo. http://alcodb.jp/marpo/download</p><ul><li>Coexpression data<ul><li>Mpo.v17-12.G10913-S266.quantile.mrgeo.d.geneid.zip</li></ul></li></ul><h4>Data Format</h4><p>Gene coexpression data folder contains a number of files, and file names correspond to the query gene IDs. Each file includes the coexpressed gene list as in the following format (Example about Mapoly0001s0025 in Mpo.v17-12.G10913-S266.quantile.mrgeo.d.geneid).</p><ul><li>File Name: Mapoly0001s0025</li><li>Line1: Mapoly0102s0025 1.00 0.9677</li><li>Line2: Mapoly0058s0006 1.73 0.9601</li><li>Line3: Mapoly0066s0050 2.45 0.9643</li><li>Line4: Mapoly0083s0003 4.47 0.9598</li><li>Line5: Mapoly0006s0261 5.00 0.9563</li></ul><p> </p><ul><li>Columns are tab-deliminated.</li><li>Lines are sorted by column2.</li><li>Column1: ID of coexpressed gene.</li><li>Column2: Matual Rank (MR). [smaller MR indicates stronger coexpression]</li><li>Column3: Pearson's Correlation Coefficient (PCC). [larger PCC indicates stronger coexpression]</li></ul>
Coexpression data in COXPRESdb ver 8.1
<p>Coexpression data provided in COXPRESdb ver 8.1. Details of the data can be found on the download page in COXPRESdb. https://coxpresdb.jp/download/</p> <ul> <li>Cel-m.c5-0 Cel-m.v21-06.G17256-S1357.combat_pca.subagging.ls.d.zip</li> <li>Cel-r.c3-0 Cel-r.v21-10.G14532-S5785.combat_pca.subagging.ls.d.zip</li> <li>Cel-u.c3-1 Cel-u.v22-05.G14532-S7142.combat_pca.subagging.z.d.zip</li> <li>Cfa-m.c4-0 Cfa-m.v21-06.G16214-S619.combat_pca.subagging.ls.d.zip</li> <li>Cfa-r.c3-0 Cfa-r.v21-10.G15365-S1361.combat_pca.subagging.ls.d.zip</li> <li>Cfa-u.c3-1 Cfa-u.v22-05.G15365-S1980.combat_pca.subagging.z.d.zip</li> <li>Dme-m.c5-0 Dme-m.v21-06.G12626-S3401.combat_pca.subagging.ls.d.zip</li> <li>Dme-r.c4-0 Dme-r.v21-10.G12209-S13514.combat_pca.subagging.ls.d.zip</li> <li>Dme-u.c3-1 Dme-u.v22-05.G12209-S15610.combat_pca.subagging.z.d.zip</li> <li>Dre-m.c5-0 Dre-m.v21-06.G10112-S1321.combat_pca.subagging.ls.d.zip</li> <li>Dre-r.c3-0 Dre-r.v21-10.G20344-S10037.combat_pca.subagging.ls.d.zip</li> <li>Dre-u.c3-1 Dre-u.v22-05.G20344-S11358.combat_pca.subagging.z.d.zip</li> <li>Fca-r.c1-0 Fca-r.v21-11.G19735-S267.combat_pca.subagging.z.d.zip</li> <li>Gga-m.c5-0 Gga-m.v21-06.G13757-S1155.combat_pca.subagging.ls.d.zip</li> <li>Gga-r.c3-0 Gga-r.v21-10.G15998-S3333.combat_pca.subagging.ls.d.zip</li> <li>Gga-u.c3-1 Gga-u.v22-05.G15998-S4488.combat_pca.subagging.z.d.zip</li> <li>Hsa-m.c7-0 Hsa-m.v21-06.G20283-S25362.combat_pca.subagging.ls.d.zip</li> <li>Hsa-m2.c4-0 Hsa2-m.v21-06.G20199-S10511.combat_pca.subagging.ls.d.zip</li> <li>Hsa-r.c6-0 Hsa-r.v22-05.G16651-S235187.combat_pca.subagging.z.d.zip</li> <li>Hsa-u.c4-0 Hsa-u.v22-05.G16651-S245698.combat_pca.subagging.z.d.zip</li> <li>Mcc-m.c4-0 Mcc-m.v21-06.G15782-S590.combat_pca.subagging.ls.d.zip</li> <li>Mcc-r.c3-0 Mcc-r.v21-10.G16312-S5665.combat_pca.subagging.ls.d.zip</li> <li>Mcc-u.c3-1 Mcc-u.v22-05.G16312-S6255.combat_pca.subagging.z.d.zip</li> <li>Mmu-m.c5-0 Mmu-m.v21-06.G20962-S25087.combat_pca.subagging.ls.d.zip</li> <li>Mmu-r.c6-0 Mmu-r.v22-05.G17008-S214753.combat_pca.subagging.z.d.zip</li> <li>Mmu-u.c4-0 Mmu-u.v22-05.G17008-S239840.combat_pca.subagging.z.d.zip</li> <li>Rno-m.c5-0 Rno-m.v21-06.G13751-S7872.combat_pca.subagging.ls.d.zip</li> <li>Rno-r.c3-0 Rno-r.v21-10.G16521-S13267.combat_pca.subagging.ls.d.zip</li> <li>Rno-u.c3-1 Rno-u.v22-05.G16521-S21139.combat_pca.subagging.z.d.zip</li> <li>Sce-m.c4-0 Sce-m.v21-06.G4461-S3071.combat_pca.subagging.ls.d.zip</li> <li>Sce-r.c3-0 Sce-r.v21-11.G5718-S6225.combat_pca.subagging.ls.d.zip</li> <li>Sce-u.c3-1 Sce-u.v22-05.G5718-S8789.combat_pca.subagging.z.d.zip</li> <li>Spo-m.c4-0 Spo-m.v21-06.G4881-S166.combat_pca.subagging.ls.d.zip</li> <li>Spo-r.c3-0 Spo-r.v21-10.G5512-S556.combat_pca.subagging.ls.d.zip</li> <li>Spo-u.c3-1 Spo-u.v22-05.G5512-S722.combat_pca.subagging.z.d.zip</li> </ul> <p> </p>
Coexpression data in COXPRESdb ver 7.1 (Xxx-u)
<p>Union coexpression data in COXPRESdb ver 7.1. The coexpression index is MR (smaller value indicates stronger coexpression).<br> Details of the data can be found on the download page in COXPRESdb. https://coxpresdb.jp/download/</p> <ul> <li>Cel-u.c1-0 Cel-u.v18-10.G18947-S3326.combat_pca_subagging.mrgeo.d.zip</li> <li>Cfa-u.c1-0 Cfa-u.v18-10.G19530-S1030.combat_pca_subagging.mrgeo.d.zip</li> <li>Dme-u.c1-0 Dme-u.v18-10.G13856-S8805.combat_pca_subagging.mrgeo.d.zip</li> <li>Dre-u.c1-0 Dre-u.v18-10.G20643-S4472.combat_pca_subagging.mrgeo.d.zip</li> <li>Gga-u.c1-0 Gga-u.v18-10.G17621-S2200.combat_pca_subagging.mrgeo.d.zip</li> <li>Hsa-u.c1-0 Hsa-u.v18-10.G22004-S56567.combat_pca_subagging.mrgeo.d.zip</li> <li>Mcc-u.c1-0 Mcc-u.v18-10.G20198-S2211.combat_pca_subagging.mrgeo.d.zip</li> <li>Mmu-u.c1-0 Mmu-u.v18-10.G22278-S63643.combat_pca_subagging.mrgeo.d.zip</li> <li>Rno-u.c1-0 Rno-u.v18-10.G17352-S16119.combat_pca_subagging.mrgeo.d.zip</li> <li>Sce-u.c1-0 Sce-u.v18-10.G5711-S4798.combat_pca_subagging.mrgeo.d.zip</li> <li>Spo-u.c1-0 Spo-u.v18-10.G5713-S309.combat_pca_subagging.mrgeo.d.zip</li> </ul> <p>Union coexpression is calculated by the average of the logit-transformed MR values of RNAseq and microarray coexpression; for gene pairs with only RNAseq coexpression, RNAseq coexpression values were converted to union values by linear regression.</p> <p>Please also check our related publication for a plant coexpression database, ATTED-II:<br> Obayashi T, Hibara H, Kagaya Y, Aoki Y, Kinoshita K. (2022) ATTED-II v11: a plant gene coexpression database using a sample balancing technique by subagging of principal components. Plant Cell Physiology, 63: 869-881. </p>
Coexpression data in COXPRESdb ver 7.1 (Xxx-r)
<p>RNAseq Coexpression data in COXPRESdb ver 7.1.</p> <p>The coexpression index is MR (smaller value indicates stronger coexpression).<br> Details of the data can be found on the download page in COXPRESdb. https://coxpresdb.jp/download/</p> <ul> <li>Cel-r.c1-0 Cel-r.v18-09.G13690-S1546.combat_pca_subagging.mrgeo.d.zip</li> <li>Cfa-r.c1-0 Cfa-r.v18-09.G15303-S253.combat_pca_subagging.mrgeo.d.zip</li> <li>Dme-r.c2-0 Dme-r.v18-09.G11937-S4596.combat_pca_subagging.mrgeo.d.zip</li> <li>Dre-r.c1-0 Dre-r.v18-09.G18446-S3049.combat_pca_subagging.mrgeo.d.zip</li> <li>Gga-r.c1-0 Gga-r.v18-09.G15554-S698.combat_pca_subagging.mrgeo.d.zip</li> <li>Hsa-r.c3-0 Hsa-r.v18-10.G16732-S36368.combat_pca_subagging.mrgeo.d.zip</li> <li>Mcc-r.c1-0 Mcc-r.v18-09.G15050-S1205.combat_pca_subagging.mrgeo.d.zip</li> <li>Mmu-r.c3-0 Mmu-r.v18-10.G16474-S42681.combat_pca_subagging.mrgeo.d.zip</li> <li>Rno-r.c1-0 Rno-r.v18-09.G15410-S2368.combat_pca_subagging.mrgeo.d.zip</li> <li>Sce-r.c1-0 Sce-r.v18-09.G5674-S1205.combat_pca_subagging.mrgeo.d.zip</li> <li>Spo-r.c1-0 Spo-r.v18-09.G5310-S143.combat_pca_subagging.mrgeo.d.zip</li> </ul> <p><strong>Reference</strong><br> Obayashi T, Kagaya Y, Aoki Y, Tadaka S, Kinoshita K. (2019) COXPRESdb v7: a gene coexpression database for 11 animal species supported by 23 coexpression platforms for technical evaluation and evolutionary inference. Nucleic Acids Res. 47: D55-D62</p> <p> </p>
Coexpression networks of 31 GTEx and 256 SRA RNA-Seq datasets
<p>This data repository contains coexpression networks from publicly-available RNA-Seq datasets (obtained from the recount2 database) that were generated using the best workflows identified in the benchmarking study: <a href="https://doi.org/10.1101/2020.09.22.308577">Johnson KA, Krishnan A (2020) Robust normalization and transformation techniques for constructing gene coexpression networks from RNA-seq data. bioRxiv 10.1101/2020.09.22.308577</a>.</p> <p><strong>GTEx coexpression networks</strong><br> There are 62 coexpression networks built from 31 GTEx datasets (each dataset corresponding to one GTEx tissue) reconstructed using two different network-building workflows: i) CTF_CLR: Counts adjusted using TMM Factors followed by CLR transformation of the Pearson correlation coefficients; ii) CTF: Counts adjusted using TMM Factors (without any further transformation).</p> <p><strong>SRA coexpression networks</strong><br> There are 256 coexpression networks built from 256 SRA datasets. Each dataset corresponds to a set of samples generated as part of the same transcriptome experiment from the same tissue. These networks are reconstructed using the top-performing workflow: CTF, Counts adjusted using TMM Factors.</p> <p>Refer to the preprint for more details on the workflows and the steps used for obtaining the original datasets.</p>
Data from: Cooperation and coexpression: how coexpression networks shift in response to multiple mutualists
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Data from: Gene coexpression networks reveal key drivers of phenotypic divergence in lake whitefish
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Data from: Variable light environments induce plastic spectral tuning by regional opsin coexpression in the African cichlid fish, Metriaclima zebra
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Data from: Comparison of gene coexpression profiles and construction of conserved gene networks to find functional modules
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Coexpression of MYCN and ALK Induces Neuroblastoma-Like Tumors From Human iPS Cell-Derived Cranial Neural Crest Cells [RNA-seq]
GEO Series GSE294865. Homo sapiens. 6 samples. Type: Expression profiling by high throughput sequencing.
Linking cell dynamics with coexpression networks to characterize key events in chronic virus infections
GEO Series GSE123134. Mus musculus. 8 samples. Type: Expression profiling by high throughput sequencing.
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