RNA-Sequencing and de novo assembly have enabled the analysis of species with non-available reference transcriptomes, although intrinsic features (biological and technical) induce errors in the reconstruction. A strategy to resolve these errors consists of varying assembling process parameters to generate multiple reconstructions. However, the best assembly selection remains a challenge. Quantitative metrics for quality assessment have been inconsistent when compared with pertinent references. In this paper, a criterion for supporting assembly selection based on mapping DNA microarray hybridized probes to assembly sets is proposed. Mouse and fruit fly RNA-Seq datasets were assembled with standard de novo procedures. Quality assessment was estimated using quantitative metrics and the proposed criterion. The assembly that best mapped to the available reference transcriptomes of these model species provided the highest quality assembly. The hybridized probes identified the best assemblies, whereas quantitative metrics remained inconsistent. For example, subtle probe mapping difference of 0.25 percent, but statistically significant (ANOVA, p < 0.05), enabled the assembly selection that led to identify 3,719 more contigs and led to 1,049 further mapped contigs to the mouse reference transcriptome. The microarray data availability for non-model species makes the proposed criterion suitable for quality assessment of multiple de novo assembly strategies.

中文翻译：

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基于芯片的质量评估作为从头转录组装配选择的支持标准。

尽管固有​​特征（生物学和技术上的差异）会导致构建过程中的错误，但RNA测序和从头组装已使分析具有不可用参考转录组的物种成为可能。解决这些错误的策略包括更改组装过程参数以生成多个重构。但是，最佳组装选择仍然是一个挑战。与相关参考文献相比，用于质量评估的定量指标不一致。在本文中，提出了一种基于将DNA微阵列杂交探针映射到装配集来支持装配选择的标准。小鼠和果蝇RNA-Seq数据集使用标准的从头程序组装。使用定量指标和拟议标准对质量评估进行了估算。最佳映射到这些模型物种的可用参考转录组的程序集提供了最高质量的程序集。杂交探针确定了最佳组装，而定量指标仍然不一致。例如，细微的探针定位差异为0.25％，但具有统计学意义（ANOVA，p <0.05），可进行组装选择，从而确定了3,719个以上的重叠群，并进一步将1,049个重叠群映射到了小鼠参考转录组。非模型物种的微阵列数据可用性使所提出的标准适用于多种从头组装策略的质量评估。例如，细微的探针定位差异为0.25％，但具有统计学意义（ANOVA，p <0.05），可进行组装选择，从而确定了3,719个以上的重叠群，并进一步将1,049个重叠群映射到了小鼠参考转录组。非模型物种的微阵列数据可用性使所提出的标准适用于多种从头组装策略的质量评估。例如，细微的探针定位差异为0.25％，但具有统计学意义（ANOVA，p <0.05），可进行组装选择，从而确定了3,719个以上的重叠群，并进一步将1,049个重叠群映射到了小鼠参考转录组。非模型物种的微阵列数据可用性使所提出的标准适用于多种从头组装策略的质量评估。