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goodmans kruskal gamma  (Goodmans Industries)
90
Goodmans Industries goodmans kruskal gamma
CHD1 density within lincRNAs is higher in active intron rich genes. Here, for each gene, we consider the number of CHD1 peaks (as specified by ENCODE) per unit base pair of each gene and compare this with the number of introns per unit base pair of gene length (in both cases we employ the length of the unspliced gene). We consider those lincRNAs that are transcriptionally active or inactive in each cell type separately. As can be seen, active genes have higher CHD1 density the more introns they have. For H1 active, rho = 0.23, P < 2.2 × 10 −16 , for <t>K562</t> rho = 0.16, P < 2.2 × 10 −16 . For the inactives, the inverse is seen the effect being greatly owing to the great number of intron rich genes without any CHD1: For H1 inactive, rho = −0.11, P < 5.2 × 10 −15 , for K562 rho = −0.19, P < 2.2 × 10 −16 . Concerned that there were many tied values we examined the latter result using the Goodmans Kruskall <t>gamma</t> test, this being more robust to tied values. Results are unaffected (for H1 active, gamma = 0.2048, H1 inactive gamma = −0.0863, K562 active gamma = 0.1353, and K562 inactive gamma = −0.1382; all P ’s < 0.001 from 1,000 simulations). Note that the genes considered active or inactive in the two cells are specific to each cell and the CHD1 measure is similarly specific to each cell type. Thus, the two cell types are independent tests of the same hypothesis. Considering CHD1 coverage (i.e., proportion of gene covered by at least one CHD1 span) does not affect conclusions: H1 active, rho = 0.1, P < 10 −12 , K562 active rho = 0.08, P < 10 −8 , inactives: H1 rho = −0.15, P < 2.2 × 10 −16 , K562 rho = −0.23, P < 2.2 × 10 −16 . Results are again robust to application of Goodmans <t>Kruskal</t> gamma (H1 active gamma = 0.0865, K562 active gamma = 0.0654 and H1 inactive gamma = −0.142 and K562 inactive gamma = −0.1834 and all P < 0.001, from 1,000 simulations).
Goodmans Kruskal Gamma, supplied by Goodmans Industries, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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matlab's robust simulation environment  (MathWorks Inc)
90
MathWorks Inc matlab's robust simulation environment
CHD1 density within lincRNAs is higher in active intron rich genes. Here, for each gene, we consider the number of CHD1 peaks (as specified by ENCODE) per unit base pair of each gene and compare this with the number of introns per unit base pair of gene length (in both cases we employ the length of the unspliced gene). We consider those lincRNAs that are transcriptionally active or inactive in each cell type separately. As can be seen, active genes have higher CHD1 density the more introns they have. For H1 active, rho = 0.23, P < 2.2 × 10 −16 , for <t>K562</t> rho = 0.16, P < 2.2 × 10 −16 . For the inactives, the inverse is seen the effect being greatly owing to the great number of intron rich genes without any CHD1: For H1 inactive, rho = −0.11, P < 5.2 × 10 −15 , for K562 rho = −0.19, P < 2.2 × 10 −16 . Concerned that there were many tied values we examined the latter result using the Goodmans Kruskall <t>gamma</t> test, this being more robust to tied values. Results are unaffected (for H1 active, gamma = 0.2048, H1 inactive gamma = −0.0863, K562 active gamma = 0.1353, and K562 inactive gamma = −0.1382; all P ’s < 0.001 from 1,000 simulations). Note that the genes considered active or inactive in the two cells are specific to each cell and the CHD1 measure is similarly specific to each cell type. Thus, the two cell types are independent tests of the same hypothesis. Considering CHD1 coverage (i.e., proportion of gene covered by at least one CHD1 span) does not affect conclusions: H1 active, rho = 0.1, P < 10 −12 , K562 active rho = 0.08, P < 10 −8 , inactives: H1 rho = −0.15, P < 2.2 × 10 −16 , K562 rho = −0.23, P < 2.2 × 10 −16 . Results are again robust to application of Goodmans <t>Kruskal</t> gamma (H1 active gamma = 0.0865, K562 active gamma = 0.0654 and H1 inactive gamma = −0.142 and K562 inactive gamma = −0.1834 and all P < 0.001, from 1,000 simulations).
Matlab's Robust Simulation Environment, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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robust simulation and test platform  (MathWorks Inc)
90
MathWorks Inc robust simulation and test platform
CHD1 density within lincRNAs is higher in active intron rich genes. Here, for each gene, we consider the number of CHD1 peaks (as specified by ENCODE) per unit base pair of each gene and compare this with the number of introns per unit base pair of gene length (in both cases we employ the length of the unspliced gene). We consider those lincRNAs that are transcriptionally active or inactive in each cell type separately. As can be seen, active genes have higher CHD1 density the more introns they have. For H1 active, rho = 0.23, P < 2.2 × 10 −16 , for <t>K562</t> rho = 0.16, P < 2.2 × 10 −16 . For the inactives, the inverse is seen the effect being greatly owing to the great number of intron rich genes without any CHD1: For H1 inactive, rho = −0.11, P < 5.2 × 10 −15 , for K562 rho = −0.19, P < 2.2 × 10 −16 . Concerned that there were many tied values we examined the latter result using the Goodmans Kruskall <t>gamma</t> test, this being more robust to tied values. Results are unaffected (for H1 active, gamma = 0.2048, H1 inactive gamma = −0.0863, K562 active gamma = 0.1353, and K562 inactive gamma = −0.1382; all P ’s < 0.001 from 1,000 simulations). Note that the genes considered active or inactive in the two cells are specific to each cell and the CHD1 measure is similarly specific to each cell type. Thus, the two cell types are independent tests of the same hypothesis. Considering CHD1 coverage (i.e., proportion of gene covered by at least one CHD1 span) does not affect conclusions: H1 active, rho = 0.1, P < 10 −12 , K562 active rho = 0.08, P < 10 −8 , inactives: H1 rho = −0.15, P < 2.2 × 10 −16 , K562 rho = −0.23, P < 2.2 × 10 −16 . Results are again robust to application of Goodmans <t>Kruskal</t> gamma (H1 active gamma = 0.0865, K562 active gamma = 0.0654 and H1 inactive gamma = −0.142 and K562 inactive gamma = −0.1834 and all P < 0.001, from 1,000 simulations).
Robust Simulation And Test Platform, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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ab initio molecular dynamics (aimd) simulations  (Molecular Dynamics Inc)
90
Molecular Dynamics Inc ab initio molecular dynamics (aimd) simulations
CHD1 density within lincRNAs is higher in active intron rich genes. Here, for each gene, we consider the number of CHD1 peaks (as specified by ENCODE) per unit base pair of each gene and compare this with the number of introns per unit base pair of gene length (in both cases we employ the length of the unspliced gene). We consider those lincRNAs that are transcriptionally active or inactive in each cell type separately. As can be seen, active genes have higher CHD1 density the more introns they have. For H1 active, rho = 0.23, P < 2.2 × 10 −16 , for <t>K562</t> rho = 0.16, P < 2.2 × 10 −16 . For the inactives, the inverse is seen the effect being greatly owing to the great number of intron rich genes without any CHD1: For H1 inactive, rho = −0.11, P < 5.2 × 10 −15 , for K562 rho = −0.19, P < 2.2 × 10 −16 . Concerned that there were many tied values we examined the latter result using the Goodmans Kruskall <t>gamma</t> test, this being more robust to tied values. Results are unaffected (for H1 active, gamma = 0.2048, H1 inactive gamma = −0.0863, K562 active gamma = 0.1353, and K562 inactive gamma = −0.1382; all P ’s < 0.001 from 1,000 simulations). Note that the genes considered active or inactive in the two cells are specific to each cell and the CHD1 measure is similarly specific to each cell type. Thus, the two cell types are independent tests of the same hypothesis. Considering CHD1 coverage (i.e., proportion of gene covered by at least one CHD1 span) does not affect conclusions: H1 active, rho = 0.1, P < 10 −12 , K562 active rho = 0.08, P < 10 −8 , inactives: H1 rho = −0.15, P < 2.2 × 10 −16 , K562 rho = −0.23, P < 2.2 × 10 −16 . Results are again robust to application of Goodmans <t>Kruskal</t> gamma (H1 active gamma = 0.0865, K562 active gamma = 0.0654 and H1 inactive gamma = −0.142 and K562 inactive gamma = −0.1834 and all P < 0.001, from 1,000 simulations).
Ab Initio Molecular Dynamics (Aimd) Simulations, supplied by Molecular Dynamics Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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matlab/simulink environment  (MathWorks Inc)
90
MathWorks Inc matlab/simulink environment
CHD1 density within lincRNAs is higher in active intron rich genes. Here, for each gene, we consider the number of CHD1 peaks (as specified by ENCODE) per unit base pair of each gene and compare this with the number of introns per unit base pair of gene length (in both cases we employ the length of the unspliced gene). We consider those lincRNAs that are transcriptionally active or inactive in each cell type separately. As can be seen, active genes have higher CHD1 density the more introns they have. For H1 active, rho = 0.23, P < 2.2 × 10 −16 , for <t>K562</t> rho = 0.16, P < 2.2 × 10 −16 . For the inactives, the inverse is seen the effect being greatly owing to the great number of intron rich genes without any CHD1: For H1 inactive, rho = −0.11, P < 5.2 × 10 −15 , for K562 rho = −0.19, P < 2.2 × 10 −16 . Concerned that there were many tied values we examined the latter result using the Goodmans Kruskall <t>gamma</t> test, this being more robust to tied values. Results are unaffected (for H1 active, gamma = 0.2048, H1 inactive gamma = −0.0863, K562 active gamma = 0.1353, and K562 inactive gamma = −0.1382; all P ’s < 0.001 from 1,000 simulations). Note that the genes considered active or inactive in the two cells are specific to each cell and the CHD1 measure is similarly specific to each cell type. Thus, the two cell types are independent tests of the same hypothesis. Considering CHD1 coverage (i.e., proportion of gene covered by at least one CHD1 span) does not affect conclusions: H1 active, rho = 0.1, P < 10 −12 , K562 active rho = 0.08, P < 10 −8 , inactives: H1 rho = −0.15, P < 2.2 × 10 −16 , K562 rho = −0.23, P < 2.2 × 10 −16 . Results are again robust to application of Goodmans <t>Kruskal</t> gamma (H1 active gamma = 0.0865, K562 active gamma = 0.0654 and H1 inactive gamma = −0.142 and K562 inactive gamma = −0.1834 and all P < 0.001, from 1,000 simulations).
Matlab/Simulink Environment, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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capture–mark–recapture–resight data  (Yukon Kuskokwim Health)
90
Yukon Kuskokwim Health capture–mark–recapture–resight data
CHD1 density within lincRNAs is higher in active intron rich genes. Here, for each gene, we consider the number of CHD1 peaks (as specified by ENCODE) per unit base pair of each gene and compare this with the number of introns per unit base pair of gene length (in both cases we employ the length of the unspliced gene). We consider those lincRNAs that are transcriptionally active or inactive in each cell type separately. As can be seen, active genes have higher CHD1 density the more introns they have. For H1 active, rho = 0.23, P < 2.2 × 10 −16 , for <t>K562</t> rho = 0.16, P < 2.2 × 10 −16 . For the inactives, the inverse is seen the effect being greatly owing to the great number of intron rich genes without any CHD1: For H1 inactive, rho = −0.11, P < 5.2 × 10 −15 , for K562 rho = −0.19, P < 2.2 × 10 −16 . Concerned that there were many tied values we examined the latter result using the Goodmans Kruskall <t>gamma</t> test, this being more robust to tied values. Results are unaffected (for H1 active, gamma = 0.2048, H1 inactive gamma = −0.0863, K562 active gamma = 0.1353, and K562 inactive gamma = −0.1382; all P ’s < 0.001 from 1,000 simulations). Note that the genes considered active or inactive in the two cells are specific to each cell and the CHD1 measure is similarly specific to each cell type. Thus, the two cell types are independent tests of the same hypothesis. Considering CHD1 coverage (i.e., proportion of gene covered by at least one CHD1 span) does not affect conclusions: H1 active, rho = 0.1, P < 10 −12 , K562 active rho = 0.08, P < 10 −8 , inactives: H1 rho = −0.15, P < 2.2 × 10 −16 , K562 rho = −0.23, P < 2.2 × 10 −16 . Results are again robust to application of Goodmans <t>Kruskal</t> gamma (H1 active gamma = 0.0865, K562 active gamma = 0.0654 and H1 inactive gamma = −0.142 and K562 inactive gamma = −0.1834 and all P < 0.001, from 1,000 simulations).
Capture–Mark–Recapture–Resight Data, supplied by Yukon Kuskokwim Health, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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matlab/simulink  (MathWorks Inc)
90
MathWorks Inc matlab/simulink
CHD1 density within lincRNAs is higher in active intron rich genes. Here, for each gene, we consider the number of CHD1 peaks (as specified by ENCODE) per unit base pair of each gene and compare this with the number of introns per unit base pair of gene length (in both cases we employ the length of the unspliced gene). We consider those lincRNAs that are transcriptionally active or inactive in each cell type separately. As can be seen, active genes have higher CHD1 density the more introns they have. For H1 active, rho = 0.23, P < 2.2 × 10 −16 , for <t>K562</t> rho = 0.16, P < 2.2 × 10 −16 . For the inactives, the inverse is seen the effect being greatly owing to the great number of intron rich genes without any CHD1: For H1 inactive, rho = −0.11, P < 5.2 × 10 −15 , for K562 rho = −0.19, P < 2.2 × 10 −16 . Concerned that there were many tied values we examined the latter result using the Goodmans Kruskall <t>gamma</t> test, this being more robust to tied values. Results are unaffected (for H1 active, gamma = 0.2048, H1 inactive gamma = −0.0863, K562 active gamma = 0.1353, and K562 inactive gamma = −0.1382; all P ’s < 0.001 from 1,000 simulations). Note that the genes considered active or inactive in the two cells are specific to each cell and the CHD1 measure is similarly specific to each cell type. Thus, the two cell types are independent tests of the same hypothesis. Considering CHD1 coverage (i.e., proportion of gene covered by at least one CHD1 span) does not affect conclusions: H1 active, rho = 0.1, P < 10 −12 , K562 active rho = 0.08, P < 10 −8 , inactives: H1 rho = −0.15, P < 2.2 × 10 −16 , K562 rho = −0.23, P < 2.2 × 10 −16 . Results are again robust to application of Goodmans <t>Kruskal</t> gamma (H1 active gamma = 0.0865, K562 active gamma = 0.0654 and H1 inactive gamma = −0.142 and K562 inactive gamma = −0.1834 and all P < 0.001, from 1,000 simulations).
Matlab/Simulink, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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matlab robust control toolbox  (MathWorks Inc)
93
MathWorks Inc matlab robust control toolbox
CHD1 density within lincRNAs is higher in active intron rich genes. Here, for each gene, we consider the number of CHD1 peaks (as specified by ENCODE) per unit base pair of each gene and compare this with the number of introns per unit base pair of gene length (in both cases we employ the length of the unspliced gene). We consider those lincRNAs that are transcriptionally active or inactive in each cell type separately. As can be seen, active genes have higher CHD1 density the more introns they have. For H1 active, rho = 0.23, P < 2.2 × 10 −16 , for <t>K562</t> rho = 0.16, P < 2.2 × 10 −16 . For the inactives, the inverse is seen the effect being greatly owing to the great number of intron rich genes without any CHD1: For H1 inactive, rho = −0.11, P < 5.2 × 10 −15 , for K562 rho = −0.19, P < 2.2 × 10 −16 . Concerned that there were many tied values we examined the latter result using the Goodmans Kruskall <t>gamma</t> test, this being more robust to tied values. Results are unaffected (for H1 active, gamma = 0.2048, H1 inactive gamma = −0.0863, K562 active gamma = 0.1353, and K562 inactive gamma = −0.1382; all P ’s < 0.001 from 1,000 simulations). Note that the genes considered active or inactive in the two cells are specific to each cell and the CHD1 measure is similarly specific to each cell type. Thus, the two cell types are independent tests of the same hypothesis. Considering CHD1 coverage (i.e., proportion of gene covered by at least one CHD1 span) does not affect conclusions: H1 active, rho = 0.1, P < 10 −12 , K562 active rho = 0.08, P < 10 −8 , inactives: H1 rho = −0.15, P < 2.2 × 10 −16 , K562 rho = −0.23, P < 2.2 × 10 −16 . Results are again robust to application of Goodmans <t>Kruskal</t> gamma (H1 active gamma = 0.0865, K562 active gamma = 0.0654 and H1 inactive gamma = −0.142 and K562 inactive gamma = −0.1834 and all P < 0.001, from 1,000 simulations).
Matlab Robust Control Toolbox, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 93/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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simulation software package  (COMSOL Inc)
90
COMSOL Inc simulation software package
CHD1 density within lincRNAs is higher in active intron rich genes. Here, for each gene, we consider the number of CHD1 peaks (as specified by ENCODE) per unit base pair of each gene and compare this with the number of introns per unit base pair of gene length (in both cases we employ the length of the unspliced gene). We consider those lincRNAs that are transcriptionally active or inactive in each cell type separately. As can be seen, active genes have higher CHD1 density the more introns they have. For H1 active, rho = 0.23, P < 2.2 × 10 −16 , for <t>K562</t> rho = 0.16, P < 2.2 × 10 −16 . For the inactives, the inverse is seen the effect being greatly owing to the great number of intron rich genes without any CHD1: For H1 inactive, rho = −0.11, P < 5.2 × 10 −15 , for K562 rho = −0.19, P < 2.2 × 10 −16 . Concerned that there were many tied values we examined the latter result using the Goodmans Kruskall <t>gamma</t> test, this being more robust to tied values. Results are unaffected (for H1 active, gamma = 0.2048, H1 inactive gamma = −0.0863, K562 active gamma = 0.1353, and K562 inactive gamma = −0.1382; all P ’s < 0.001 from 1,000 simulations). Note that the genes considered active or inactive in the two cells are specific to each cell and the CHD1 measure is similarly specific to each cell type. Thus, the two cell types are independent tests of the same hypothesis. Considering CHD1 coverage (i.e., proportion of gene covered by at least one CHD1 span) does not affect conclusions: H1 active, rho = 0.1, P < 10 −12 , K562 active rho = 0.08, P < 10 −8 , inactives: H1 rho = −0.15, P < 2.2 × 10 −16 , K562 rho = −0.23, P < 2.2 × 10 −16 . Results are again robust to application of Goodmans <t>Kruskal</t> gamma (H1 active gamma = 0.0865, K562 active gamma = 0.0654 and H1 inactive gamma = −0.142 and K562 inactive gamma = −0.1834 and all P < 0.001, from 1,000 simulations).
Simulation Software Package, supplied by COMSOL Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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high-resolution atmospheric model simulations  (HighRes Biosolutions)
90
HighRes Biosolutions high-resolution atmospheric model simulations
CHD1 density within lincRNAs is higher in active intron rich genes. Here, for each gene, we consider the number of CHD1 peaks (as specified by ENCODE) per unit base pair of each gene and compare this with the number of introns per unit base pair of gene length (in both cases we employ the length of the unspliced gene). We consider those lincRNAs that are transcriptionally active or inactive in each cell type separately. As can be seen, active genes have higher CHD1 density the more introns they have. For H1 active, rho = 0.23, P < 2.2 × 10 −16 , for <t>K562</t> rho = 0.16, P < 2.2 × 10 −16 . For the inactives, the inverse is seen the effect being greatly owing to the great number of intron rich genes without any CHD1: For H1 inactive, rho = −0.11, P < 5.2 × 10 −15 , for K562 rho = −0.19, P < 2.2 × 10 −16 . Concerned that there were many tied values we examined the latter result using the Goodmans Kruskall <t>gamma</t> test, this being more robust to tied values. Results are unaffected (for H1 active, gamma = 0.2048, H1 inactive gamma = −0.0863, K562 active gamma = 0.1353, and K562 inactive gamma = −0.1382; all P ’s < 0.001 from 1,000 simulations). Note that the genes considered active or inactive in the two cells are specific to each cell and the CHD1 measure is similarly specific to each cell type. Thus, the two cell types are independent tests of the same hypothesis. Considering CHD1 coverage (i.e., proportion of gene covered by at least one CHD1 span) does not affect conclusions: H1 active, rho = 0.1, P < 10 −12 , K562 active rho = 0.08, P < 10 −8 , inactives: H1 rho = −0.15, P < 2.2 × 10 −16 , K562 rho = −0.23, P < 2.2 × 10 −16 . Results are again robust to application of Goodmans <t>Kruskal</t> gamma (H1 active gamma = 0.0865, K562 active gamma = 0.0654 and H1 inactive gamma = −0.142 and K562 inactive gamma = −0.1834 and all P < 0.001, from 1,000 simulations).
High Resolution Atmospheric Model Simulations, supplied by HighRes Biosolutions, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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biospring simulation engine  (Biospring)
90
Biospring biospring simulation engine
CHD1 density within lincRNAs is higher in active intron rich genes. Here, for each gene, we consider the number of CHD1 peaks (as specified by ENCODE) per unit base pair of each gene and compare this with the number of introns per unit base pair of gene length (in both cases we employ the length of the unspliced gene). We consider those lincRNAs that are transcriptionally active or inactive in each cell type separately. As can be seen, active genes have higher CHD1 density the more introns they have. For H1 active, rho = 0.23, P < 2.2 × 10 −16 , for <t>K562</t> rho = 0.16, P < 2.2 × 10 −16 . For the inactives, the inverse is seen the effect being greatly owing to the great number of intron rich genes without any CHD1: For H1 inactive, rho = −0.11, P < 5.2 × 10 −15 , for K562 rho = −0.19, P < 2.2 × 10 −16 . Concerned that there were many tied values we examined the latter result using the Goodmans Kruskall <t>gamma</t> test, this being more robust to tied values. Results are unaffected (for H1 active, gamma = 0.2048, H1 inactive gamma = −0.0863, K562 active gamma = 0.1353, and K562 inactive gamma = −0.1382; all P ’s < 0.001 from 1,000 simulations). Note that the genes considered active or inactive in the two cells are specific to each cell and the CHD1 measure is similarly specific to each cell type. Thus, the two cell types are independent tests of the same hypothesis. Considering CHD1 coverage (i.e., proportion of gene covered by at least one CHD1 span) does not affect conclusions: H1 active, rho = 0.1, P < 10 −12 , K562 active rho = 0.08, P < 10 −8 , inactives: H1 rho = −0.15, P < 2.2 × 10 −16 , K562 rho = −0.23, P < 2.2 × 10 −16 . Results are again robust to application of Goodmans <t>Kruskal</t> gamma (H1 active gamma = 0.0865, K562 active gamma = 0.0654 and H1 inactive gamma = −0.142 and K562 inactive gamma = −0.1834 and all P < 0.001, from 1,000 simulations).
Biospring Simulation Engine, supplied by Biospring, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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matlab r2014  (MathWorks Inc)
90
MathWorks Inc matlab r2014
CHD1 density within lincRNAs is higher in active intron rich genes. Here, for each gene, we consider the number of CHD1 peaks (as specified by ENCODE) per unit base pair of each gene and compare this with the number of introns per unit base pair of gene length (in both cases we employ the length of the unspliced gene). We consider those lincRNAs that are transcriptionally active or inactive in each cell type separately. As can be seen, active genes have higher CHD1 density the more introns they have. For H1 active, rho = 0.23, P < 2.2 × 10 −16 , for <t>K562</t> rho = 0.16, P < 2.2 × 10 −16 . For the inactives, the inverse is seen the effect being greatly owing to the great number of intron rich genes without any CHD1: For H1 inactive, rho = −0.11, P < 5.2 × 10 −15 , for K562 rho = −0.19, P < 2.2 × 10 −16 . Concerned that there were many tied values we examined the latter result using the Goodmans Kruskall <t>gamma</t> test, this being more robust to tied values. Results are unaffected (for H1 active, gamma = 0.2048, H1 inactive gamma = −0.0863, K562 active gamma = 0.1353, and K562 inactive gamma = −0.1382; all P ’s < 0.001 from 1,000 simulations). Note that the genes considered active or inactive in the two cells are specific to each cell and the CHD1 measure is similarly specific to each cell type. Thus, the two cell types are independent tests of the same hypothesis. Considering CHD1 coverage (i.e., proportion of gene covered by at least one CHD1 span) does not affect conclusions: H1 active, rho = 0.1, P < 10 −12 , K562 active rho = 0.08, P < 10 −8 , inactives: H1 rho = −0.15, P < 2.2 × 10 −16 , K562 rho = −0.23, P < 2.2 × 10 −16 . Results are again robust to application of Goodmans <t>Kruskal</t> gamma (H1 active gamma = 0.0865, K562 active gamma = 0.0654 and H1 inactive gamma = −0.142 and K562 inactive gamma = −0.1834 and all P < 0.001, from 1,000 simulations).
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CHD1 density within lincRNAs is higher in active intron rich genes. Here, for each gene, we consider the number of CHD1 peaks (as specified by ENCODE) per unit base pair of each gene and compare this with the number of introns per unit base pair of gene length (in both cases we employ the length of the unspliced gene). We consider those lincRNAs that are transcriptionally active or inactive in each cell type separately. As can be seen, active genes have higher CHD1 density the more introns they have. For H1 active, rho = 0.23, P < 2.2 × 10 −16 , for K562 rho = 0.16, P < 2.2 × 10 −16 . For the inactives, the inverse is seen the effect being greatly owing to the great number of intron rich genes without any CHD1: For H1 inactive, rho = −0.11, P < 5.2 × 10 −15 , for K562 rho = −0.19, P < 2.2 × 10 −16 . Concerned that there were many tied values we examined the latter result using the Goodmans Kruskall gamma test, this being more robust to tied values. Results are unaffected (for H1 active, gamma = 0.2048, H1 inactive gamma = −0.0863, K562 active gamma = 0.1353, and K562 inactive gamma = −0.1382; all P ’s < 0.001 from 1,000 simulations). Note that the genes considered active or inactive in the two cells are specific to each cell and the CHD1 measure is similarly specific to each cell type. Thus, the two cell types are independent tests of the same hypothesis. Considering CHD1 coverage (i.e., proportion of gene covered by at least one CHD1 span) does not affect conclusions: H1 active, rho = 0.1, P < 10 −12 , K562 active rho = 0.08, P < 10 −8 , inactives: H1 rho = −0.15, P < 2.2 × 10 −16 , K562 rho = −0.23, P < 2.2 × 10 −16 . Results are again robust to application of Goodmans Kruskal gamma (H1 active gamma = 0.0865, K562 active gamma = 0.0654 and H1 inactive gamma = −0.142 and K562 inactive gamma = −0.1834 and all P < 0.001, from 1,000 simulations).

Journal: Molecular Biology and Evolution

Article Title: Purifying Selection on Splice-Related Motifs, Not Expression Level nor RNA Folding, Explains Nearly All Constraint on Human lincRNAs

doi: 10.1093/molbev/msu249

Figure Lengend Snippet: CHD1 density within lincRNAs is higher in active intron rich genes. Here, for each gene, we consider the number of CHD1 peaks (as specified by ENCODE) per unit base pair of each gene and compare this with the number of introns per unit base pair of gene length (in both cases we employ the length of the unspliced gene). We consider those lincRNAs that are transcriptionally active or inactive in each cell type separately. As can be seen, active genes have higher CHD1 density the more introns they have. For H1 active, rho = 0.23, P < 2.2 × 10 −16 , for K562 rho = 0.16, P < 2.2 × 10 −16 . For the inactives, the inverse is seen the effect being greatly owing to the great number of intron rich genes without any CHD1: For H1 inactive, rho = −0.11, P < 5.2 × 10 −15 , for K562 rho = −0.19, P < 2.2 × 10 −16 . Concerned that there were many tied values we examined the latter result using the Goodmans Kruskall gamma test, this being more robust to tied values. Results are unaffected (for H1 active, gamma = 0.2048, H1 inactive gamma = −0.0863, K562 active gamma = 0.1353, and K562 inactive gamma = −0.1382; all P ’s < 0.001 from 1,000 simulations). Note that the genes considered active or inactive in the two cells are specific to each cell and the CHD1 measure is similarly specific to each cell type. Thus, the two cell types are independent tests of the same hypothesis. Considering CHD1 coverage (i.e., proportion of gene covered by at least one CHD1 span) does not affect conclusions: H1 active, rho = 0.1, P < 10 −12 , K562 active rho = 0.08, P < 10 −8 , inactives: H1 rho = −0.15, P < 2.2 × 10 −16 , K562 rho = −0.23, P < 2.2 × 10 −16 . Results are again robust to application of Goodmans Kruskal gamma (H1 active gamma = 0.0865, K562 active gamma = 0.0654 and H1 inactive gamma = −0.142 and K562 inactive gamma = −0.1834 and all P < 0.001, from 1,000 simulations).

Article Snippet: Results are again robust to application of Goodmans Kruskal gamma (H1 active gamma = 0.0865, K562 active gamma = 0.0654 and H1 inactive gamma = −0.142 and K562 inactive gamma = −0.1834 and all P < 0.001, from 1,000 simulations).

Techniques:

Correlation between Intragenic DHS Density and CHD1 Coverage Density Occupancy within Active Genes.

Journal: Molecular Biology and Evolution

Article Title: Purifying Selection on Splice-Related Motifs, Not Expression Level nor RNA Folding, Explains Nearly All Constraint on Human lincRNAs

doi: 10.1093/molbev/msu249

Figure Lengend Snippet: Correlation between Intragenic DHS Density and CHD1 Coverage Density Occupancy within Active Genes.

Article Snippet: Results are again robust to application of Goodmans Kruskal gamma (H1 active gamma = 0.0865, K562 active gamma = 0.0654 and H1 inactive gamma = −0.142 and K562 inactive gamma = −0.1834 and all P < 0.001, from 1,000 simulations).

Techniques:

Spearman Correlation between the Focal lincRNA Gene’s Intron Count Density per Kilobase and DHS Coverage per Kilobase in ± 50 kb Flanks.

Journal: Molecular Biology and Evolution

Article Title: Purifying Selection on Splice-Related Motifs, Not Expression Level nor RNA Folding, Explains Nearly All Constraint on Human lincRNAs

doi: 10.1093/molbev/msu249

Figure Lengend Snippet: Spearman Correlation between the Focal lincRNA Gene’s Intron Count Density per Kilobase and DHS Coverage per Kilobase in ± 50 kb Flanks.

Article Snippet: Results are again robust to application of Goodmans Kruskal gamma (H1 active gamma = 0.0865, K562 active gamma = 0.0654 and H1 inactive gamma = −0.142 and K562 inactive gamma = −0.1834 and all P < 0.001, from 1,000 simulations).

Techniques:

Spearman Correlation between the Focal lincRNA Gene’s CHD1 Density per Kilobase and DHS Coverage per Kilobase in ±50 kb Flanks.

Journal: Molecular Biology and Evolution

Article Title: Purifying Selection on Splice-Related Motifs, Not Expression Level nor RNA Folding, Explains Nearly All Constraint on Human lincRNAs

doi: 10.1093/molbev/msu249

Figure Lengend Snippet: Spearman Correlation between the Focal lincRNA Gene’s CHD1 Density per Kilobase and DHS Coverage per Kilobase in ±50 kb Flanks.

Article Snippet: Results are again robust to application of Goodmans Kruskal gamma (H1 active gamma = 0.0865, K562 active gamma = 0.0654 and H1 inactive gamma = −0.142 and K562 inactive gamma = −0.1834 and all P < 0.001, from 1,000 simulations).

Techniques: