We therefore investigated the performance of two recently developed user-independent software algorithms (24, 25) . The degradometer software provided a reliable evaluation of RNA integrity based on the identification of additional 'degradation peak signals' and their integration in a mathematical calculation together with the ribosomal peak heights. It allowed characterization of the integrity of 84% of the samples tested, one-third with an alert flag, which was first found to be fairly informative, as it strongly reduces the complexity of the metrics by introducing three distinct classes labeled Yellow, Orange and Red, and can be used as a first straightforward simple filtering step. However, degradation factors (DegFact) metrics yield precise measures with less than 32% CV and are much more valuable than flag alerts for the purpose of standardization. The same is true for the RNA Integrity Number 'RIN' software which allowed the characterization of the integrity of 91% of the RNA samples tested, with a RIN value for 363 RNA sample profiles with less than 12% CV. In general, there was a good agreement between the human classification, the degradation factor and the RIN (see Figure 4B ). This provided a cross-validation of the user-independent qualification systems tested. Both resulted in the refinement of human interpretations, validating four statistically relevant classes of samples, namely good (HC-level 1), regular/ moderate (HC-level 2 and 3), poor (HC-level 4) and degraded (HC-level 5). Moreover, the 5% RNA samples previously flagged by the operators as displaying an atypical temperature-sensitive shape were unambiguously assigned to one or the other category of samples [RIN = 7.3 (IC 6.8-7.8); DegFact = 11.9 (IC 9.5-14.2); data not shown]. Altogether, we found the degradometer and RIN algorithms to be highly reliable user-independent methods for automated assessment of RNA degradation and integrity. The RIN system is a slightly more informative tool, able to compute assessment metrics for 91% of the RNA profiles, compared to 84% with the degradometer software; the remaining being flagged respectively as N/A or Black alert. For samples available below a low limit of 20 ng (N = 80) the RIN system provided Figure 6 . Workflow of operational procedure for RNA quality assessment. Integrity of the RNA, once extracted and purified from cell lines, clinical or biological tissues samples, is controlled from the widely used bioanalyzer electrophoretic traces. As standard part of the Agilent analysis software (25), a RIN metrics is first calculated, scoring each RNA sample into 10 numerically predefined categories of integrity (RIN, from 1 to 10; N is a threshold value). As an independent control, a degradation factor metrics (DegFact, from 1 to ¥; N 0 is a threshold value) may optionally be allocated to each RNA sample using the bioanalyzer-independent degradometer software (24) . In a standard operating procedure, RIN and/or DegFact metrics will first be used as a standard exchange language to document RNA integrity and degradation, second to classify the RNA in homogeneous groups, and finally to select samples of comparable RNA integrity to improve the scheme of meaningful downstream experiments. The standard operating procedure will benefit from feedback information that will help users to define threshold integrity metrics values based on the results of RNA-based analyses. metric values for 85% of them, compared to only 46% with the degradometer software. Similarly, the RIN system was able to provide metric values for 81% of poor quality samples (including low quality and degraded samples; N = 96), whereas the degradometer software could classify only 44% of them. Another advantage with the RIN classifier is that, if there are critical anomalies detected (including genomic DNA contamination, wavy baseline, etc.), threshold settings may be changed and a reliable RIN value computed. This was the case for 25 of the 363 RNA sample profiles successfully classified by the system.