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

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

Potential of Core-Collapse Supernova Neutrino Detection at JUNO

JUNO is an underground neutrino observatory under construction in Jiangmen, China. It uses 20kton liquid scintillator as target, which enables it to detect supernova burst neutrinos of a large statistics for the next galactic core-collapse supernova (CCSN) and also pre-supernova neutrinos from the nearby CCSN progenitors. All flavors of supernova burst neutrinos can be detected by JUNO via several interaction channels, including inverse beta decay, elastic scattering on electron and proton, interactions on C12 nuclei, etc. This retains the possibility for JUNO to reconstruct the energy spectra of supernova burst neutrinos of all flavors. The real time monitoring systems based on FPGA and DAQ are under development in JUNO, which allow prompt alert and trigger-less data acquisition of CCSN events. The alert performances of both monitoring systems have been thoroughly studied using simulations. Moreover, once a CCSN is tagged, the system can give fast characterizations, such as directionality and light curve

Detection of the Diffuse Supernova Neutrino Background with JUNO

As an underground multi-purpose neutrino detector with 20 kton liquid scintillator, Jiangmen Underground Neutrino Observatory (JUNO) is competitive with and complementary to the water-Cherenkov detectors on the search for the diffuse supernova neutrino background (DSNB). Typical supernova models predict 2-4 events per year within the optimal observation window in the JUNO detector. The dominant background is from the neutral-current (NC) interaction of atmospheric neutrinos with 12C nuclei, which surpasses the DSNB by more than one order of magnitude. We evaluated the systematic uncertainty of NC background from the spread of a variety of data-driven models and further developed a method to determine NC background within 15\% with {\it{in}} {\it{situ}} measurements after ten years of running. Besides, the NC-like backgrounds can be effectively suppressed by the intrinsic pulse-shape discrimination (PSD) capabilities of liquid scintillators. In this talk, I will present in detail the improvements on NC background uncertainty evaluation, PSD discriminator development, and finally, the potential of DSNB sensitivity in JUNO

Real-time Monitoring for the Next Core-Collapse Supernova in JUNO

Core-collapse supernova (CCSN) is one of the most energetic astrophysical events in the Universe. The early and prompt detection of neutrinos before (pre-SN) and during the SN burst is a unique opportunity to realize the multi-messenger observation of the CCSN events. In this work, we describe the monitoring concept and present the sensitivity of the system to the pre-SN and SN neutrinos at the Jiangmen Underground Neutrino Observatory (JUNO), which is a 20 kton liquid scintillator detector under construction in South China. The real-time monitoring system is designed with both the prompt monitors on the electronic board and online monitors at the data acquisition stage, in order to ensure both the alert speed and alert coverage of progenitor stars. By assuming a false alert rate of 1 per year, this monitoring system can be sensitive to the pre-SN neutrinos up to the distance of about 1.6 (0.9) kpc and SN neutrinos up to about 370 (360) kpc for a progenitor mass of 30$M_{\odot}$ for the case of normal (inverted) mass ordering. The pointing ability of the CCSN is evaluated by using the accumulated event anisotropy of the inverse beta decay interactions from pre-SN or SN neutrinos, which, along with the early alert, can play important roles for the followup multi-messenger observations of the next Galactic or nearby extragalactic CCSN.Comment: 24 pages, 9 figure

Overall Survival with Osimertinib in Untreated, EGFR-Mutated Advanced NSCLC

BACKGROUND: Osimertinib is a third-generation, irreversible tyrosine kinase inhibitor of the epidermal growth factor receptor (EGFR-TKI) that selectively inhibits both EGFR-TKI-sensitizing and EGFR T790M resistance mutations. A phase 3 trial compared first-line osimertinib with other EGFR-TKIs in patients with EGFR mutation-positive advanced non-small-cell lung cancer (NSCLC). The trial showed longer progression-free survival with osimertinib than with the comparator EGFR-TKIs (hazard ratio for disease progression or death, 0.46). Data from the final analysis of overall survival have not been reported. METHODS: In this trial, we randomly assigned 556 patients with previously untreated advanced NSCLC with an EGFR mutation (exon 19 deletion or L858R allele) in a 1:1 ratio to receive either osimertinib (80 mg once daily) or one of two other EGFR-TKIs (gefitinib at a dose of 250 mg once daily or erlotinib at a dose of 150 mg once daily, with patients receiving these drugs combined in a single comparator group). Overall survival was a secondary end point. RESULTS: The median overall survival was 38.6 months (95% confidence interval [CI], 34.5 to 41.8) in the osimertinib group and 31.8 months (95% CI, 26.6 to 36.0) in the comparator group (hazard ratio for death, 0.80; 95.05% CI, 0.64 to 1.00; P = 0.046). At 3 years, 79 of 279 patients (28%) in the osimertinib group and 26 of 277 (9%) in the comparator group were continuing to receive a trial regimen; the median exposure was 20.7 months and 11.5 months, respectively. Adverse events of grade 3 or higher were reported in 42% of the patients in the osimertinib group and in 47% of those in the comparator group. CONCLUSIONS: Among patients with previously untreated advanced NSCLC with an EGFR mutation, those who received osimertinib had longer overall survival than those who received a comparator EGFR-TKI. The safety profile for osimertinib was similar to that of the comparator EGFR-TKIs, despite a longer duration of exposure in the osimertinib group. (Funded by AstraZeneca; FLAURA ClinicalTrials.gov number, NCT02296125.).status: publishe