Excerpts

Hence, the identification of 410 proteins is highly significant ( p = 2.15 × 10 − 13 ), and as such, is defined as a common interactome.

The overlap between our data and all cancer types in TCGA was highly significant, both for miRNAs within a fusion transcript in at least one tumor (odds ratio 2.93, 95% CI [2.15–4.03], p = 3.29 × 10 −13 , Fisher’s exact test), for miRNAs recurrent in our data and with at least one fusion in TCGA (odds ratio 2.99, 95% CI [2.29–3.93], p < 2.20 × 10 −16 , Fisher’s exact test), as well as for miRNAs recurrent in both data sets (odds ratio 3.03, 95% CI [1.75–5.39], p = 2.62 × 10 −5 , Fisher’s exact test).

All R values shown in the scatter plots were highly significant for SC and BP choices (SC&BP-graded regions in Fig. 5 c: R = 0.17, p = 4.3 × 10 −13 and R = −0.15, p = 7.8 × 10 −12 ; SC/BP-graded regions in Fig. 5 d: R = 0.26, p = 3.3 × 10 −28 and R = −0.19, p = 2.0 × 10 −21 ; SC&BP-graded parietal region in Fig. 5 e: R = 0.17, p = 2.9 × 10 −6 and R = −0.16, p = 1.3 × 10 −6 ; two-tailed Spearman’s correlation).

A pooled dataset, consisting of 19 studies with long-term clinical follow-up, also demonstrated a highly significant (p= 5.80 × 10 −13 ) inverse correlation between DICER and hypoxia.

Notably, a highly significant enrichment of RGAC motifs at NKAP-binding footprints (CIMS or CITS along with a 5-nucleotide flanking sequence) was identified by iCLIP-sequencing ( P < 10 −12 , Wilcoxon rank-sum test, Fig. 5g ), suggesting that NKAP preferentially binds the m 6 A core motif RGAC.

In our HSA14 simulations, the presence of heterochromatin slightly reduces the absolute value of the correlation, which however remains highly significant (Spearman correlation r ≃ −0.83, p < 10 −12 ).

Comparison of effect size for the replicated loci showed a significant trend for higher effect sizes in QGP compared to BBJ (regression slope = 1.21; 95% CI = 1.01–1.42; P = 3.2 × 10 −12 ; Fig. 4 ).

Removing gaze content resulted in a large and highly significant degradation of model fit (Δ−2 log L = 58.36, df = 4, P = 6.4 × 10−12).

Tukey’s HSD revealed that the difference between WT Dox (+) and G175S Dox (+) was statistically significant ( p = 3.36e-5), and there was a highly significant difference between G175S Dox (-) and G175S Dox (+) ( p = 6.635e-12).

This difference was slightly greater and still highly significant 15 weeks post-tumor initiation (Fig. 1F and Supplementary Fig. 3C ; P < 7 × 10 −12 ).

The PWM score changes are highly significant between activating and repressing variants (one-sided Wilcoxon Rank Sum test, p -value < 10 −11 ).

In agreement with this hypothesis, we found a significant trend toward a higher standard error of the mean for growth rates measured for BioBrick plasmids that had higher burden ( p = 2.0 × 10 −11 , two-tailed t -test for a non-zero slope) (Supplementary Fig. 6 ).

This difference was highly significant ( T -test: t = -8.1516, DF = 58, p -value = 3.344e–11) (Fig. 5d ).

Our most significant DMR is located on the PM20D1 gene, where highly significant promoter hypomethylation was observed in AD patients ( P -value = 5.16 × 10 −11 , Sidak adjusted P- value = 3.12 × 10 −7 ).

Focusing on the subset of 66 genes with nominally significant PolyStrat scores, NRXN1 had the largest difference between cases and ExAC ( χ 2 = 82.3, df = 16, uncorrected p = 6.37 × 10 −11 ; corrected p = 1.27 × 10 −6 ) and no difference between controls and ExAC ( χ 2 = 10.5, df = 16, uncorrected p = 0.84) (Fig. 4d ).

We computed estimated axon conduction velocities from diameters of myelinated and unmyelinated fibers 28 along the proximal to distal axis and observed a strong and highly significant correlation (Fig. 2g , r 2 = 0.83, p < 10 − 10 ).

This revealed four regions with highly significant differentiation ( P < 10 −10 after Bonferroni correction, per SNP χ 2 test (d.f. = 1); Supplementary Fig. 1 ).

Both fast and slow timescales of cross-correlations increased with the RF-center distance in both monkeys, but the increase in the fast timescale did not reach statistical significance in monkey N ( τ 2 : p < 10 −10 , τ 1 : p G < 10 −10 , p N = 0.36, two-sided Wilcoxon rank-sum test), possibly due to narrower range of RF-center distances in monkey N compared to monkey G (median d RF,N = 0.77, d RF,G = 2.08 dva).

5C ), and this difference was highly significant within an animal across within-sequence repetitions (Fig. 5D , p < 10 −10 , Wilcoxon Rank Sum test, n 1 = 162, n 2 = 1428) and across animals (Fig. 5E , p = 0.0079, Wilcoxon Rank Sum test, n = 5), as well as in single cells recorded in the ORR in 2p in the same animals (Fig. 5F , p < 10 −100 , Wilcoxon Rank Sum test, n = 765).

A highly significant ( P <2.664e −10 , two-sample test for equality of proportions) negative correlation ( R =−0.87, Pearson's product–momentum correlation) between Esrp2 KO and overexpression results indicates that developmentally regulated splicing of a subset of pre-mRNAs is extremely sensitive to ESRP2 levels ( Fig. 6f ).

This model was found to be highly significant (ANOVA CV p = 3.1 E -10 ) and no regional influence could be discerned.

We perform whole-exome or genome sequencing of 146 kindreds with sporadic (n = 138) or familial (n = 8) CFM, identifying a highly significant burden of loss of function variants in SF3B2 (P = 3.8 × 10 −10 ), a component of the U2 small nuclear ribonucleoprotein complex, in probands.

As a result, and despite the limited number of points in the regression, we obtained a highly significant negative correlation ( P = 4.1 × 10 − 10 and R = −0.99), as in the example in Fig. 2d –f.

Only a small number of patients were identified to carry CREBBP mutations, therefore to more holistically link CREBBP-activity to ferroptotic susceptibility, we correlated gene sets involved in ferroptosis with CREBBP expression, demonstrating a highly significant negative correlation ( R = –0.42, p = 6.6e –10 ) (Supplementary Data Table 4 , Fig. 5f ).

The step wise reduction in the SVR rate associated with the increase in the number of TOPs present at baseline was highly significant (logistic regression P = 6.6 × 10 −10 ).