STAble: a novel approach to de novo assembly of RNA-seq data and its application in a metabolic model network based metatranscriptomic workflow.

BACKGROUND: De novo assembly of RNA-seq data allows the study of transcriptome in absence of a reference genome either if data is obtained from a single organism or from a mixed sample as in metatranscriptomics studies. Given the high number of sequences obtained from NGS approaches, a critical step in any analysis workflow is the assembly of reads to reconstruct transcripts thus reducing the complexity of the analysis. Despite many available tools show a good sensitivity, there is a high percentage of false positives due to the high number of assemblies considered and it is likely that the high frequency of false positive is underestimated by currently used benchmarks. The reconstruction of not existing transcripts may false the biological interpretation of results as - for example - may overestimate the identification of "novel" transcripts. Moreover, benchmarks performed are usually based on RNA-seq data from annotated genomes and assembled transcripts are compared to annotations and genomes to identify putative good and wrong reconstructions, but these tests alone may lead to accept a particular type of false positive as true, as better described below. RESULTS: Here we present a novel methodology of de novo assembly, implemented in a software named STAble (Short-reads Transcriptome Assembler). The novel concept of this assembler is that the whole reads are used to determine possible alignments instead of using smaller k-mers, with the aim of reducing the number of chimeras produced. Furthermore, we applied a new set of benchmarks based on simulated data to better define the performance of assembly method and carefully identifying true reconstructions. STAble was also used to build a prototype workflow to analyse metatranscriptomics data in connection to a steady state metabolic modelling algorithm. This algorithm was used to produce high quality metabolic interpretations of small gene expression sets obtained from already published RNA-seq data that we assembled with STAble. CONCLUSIONS: The presented results, albeit preliminary, clearly suggest that with this approach is possible to identify informative reactions not directly revealed by raw transcriptomic data.

Clinical impact on ovarian cancer patients of massive parallel sequencing for BRCA mutation detection: the experience at Gemelli hospital and a literature review.

OBJECTIVE: Massive parallel sequencing (MPS) is the new frontier for molecular diagnostics. Twenty-four papers regarding BRCA analysis were considered for reviewing all pipelines evaluated in this field. METHODS: Proposed here is an integrated MPS workflow able to successfully identify BRCA1/2 mutational status on 212 Italian ovarian cancer patients. The review of literature data is reported. RESULT: The pipeline can be routinely used as robust molecular diagnostic strategy, being highly sensitive and specific. CONCLUSION: Literature data report that efforts are being made in order to fully translate MPS-based BRCA1/2 gene assay into routine clinical diagnostics. However, this study highlights the need of an integrated MPS BRCA1/2 molecular workflow fulfilling the standardized requirements needed in the routine clinical laboratory practice.

NGS-Trex: an automatic analysis workflow for RNA-Seq data.

RNA-Seq technology allows the rapid analysis of whole transcriptomes taking advantage of next-generation sequencing platforms. Moreover with the constant decrease of the cost of NGS analysis RNA-Seq is becoming very popular and widespread. Unfortunately data analysis is quite demanding in terms of bioinformatic skills and infrastructures required, thus limiting the potential users of this method. Here we describe the complete analysis of sample data from raw sequences to data mining of results by using NGS-Trex platform, a low user interaction, fully automatic analysis workflow. Used through a web interface, NGS-Trex processes data and profiles the transcriptome of the samples identifying expressed genes, transcripts, and new and known splice variants. It also detects differentially expressed genes and transcripts across different experiments.