HPC-T-Annotator: an HPC tool for de novo transcriptome assembly annotation.

BACKGROUND: The availability of transcriptomic data for species without a reference genome enables the construction of de novo transcriptome assemblies as alternative reference resources from RNA-Seq data. A transcriptome provides direct information about a species' protein-coding genes under specific experimental conditions. The de novo assembly process produces a unigenes file in FASTA format, subsequently targeted for the annotation. Homology-based annotation, a method to infer the function of sequences by estimating similarity with other sequences in a reference database, is a computationally demanding procedure. RESULTS: To mitigate the computational burden, we introduce HPC-T-Annotator, a tool for de novo transcriptome homology annotation on high performance computing (HPC) infrastructures, designed for straightforward configuration via a Web interface. Once the configuration data are given, the entire parallel computing software for annotation is automatically generated and can be launched on a supercomputer using a simple command line. The output data can then be easily viewed using post-processing utilities in the form of Python notebooks integrated in the proposed software. CONCLUSIONS: HPC-T-Annotator expedites homology-based annotation in de novo transcriptome assemblies. Its efficient parallelization strategy on HPC infrastructures significantly reduces computational load and execution times, enabling large-scale transcriptome analysis and comparison projects, while its intuitive graphical interface extends accessibility to users without IT skills.

Risk of recurrence after nephrectomy: Comparison of predictive ability of validated risk models.

OBJECTIVE: To assess accuracy of University of California Los Angeles Integrated Staging System (UISS), Stage, Size, Grade and Necrosis (SSIGN) score, Leibovich score and GRade, Age, Nodes and Tumor (GRANT) score, the ASSURE (Adjuvant Sunitinib or Sorafenib vs. placebo in resected Unfavorable REnal cell carcinoma) score models and seventh American Joint Committee on Cancer (AJCC)/TNM staging system in predicting recurrence-free survival (RFS) in surgically-treated non-metastatic clear cell renal cell carcinoma (ccRCC) patients. MATERIALS AND METHODS: Kaplan-Meier curves and the log-rank test tested RFS according to risk groups among the UISS, SSIGN, Leibovich and GRANT models and the AJCC/TNM system. The Heagerty's C-index for survival tested for discrimination of each model at different time points after nephrectomy. RESULTS: Three hundred and fifty-eight M0 ccRCC patients were included. RFS significantly differed among each risk category for all models (P < 0.001). SSIGN showed the highest c-index over time (from 0.89 at 6-month to 0.82 at 60-month), followed by Leibovich (from 0.89-0.82), AJCC/TNM stage (from 0.82-0.77), ASSURE (from 0.81 to 0.76), GRANT (from 0.83-0.73) and UISS (from 0.76-0.72). For all models, peak discriminatory ability was reached before 12 months. The most prominent decline occurred within 24 months and reaches the lowest discriminatory ability at 60 months. CONCLUSIONS: Predictive models, with preference for SSIGN and Leibovich scores, are reliable to predict recurrence after nephrectomy and should be recommended to tailor postoperative surveillance protocols.