Initiatives et mécanismes correcteurs

peer reviewe

AMAW: automated gene annotation for non-model eukaryotic genomes

peer reviewedBackground: The annotation of genomes is a crucial step regarding the analysis of new genomic data and resulting insights, and this especially for emerging organisms which allow researchers to access unexplored lineages, so as to expand our knowledge of poorly represented taxonomic groups. Complete pipelines for eukaryotic genome annotation have been proposed for more than a decade, but the issue is still challenging. One of the most widely used tools in the field is MAKER2, an annotation pipeline using experimental evidence (mRNA-seq and proteins) and combining different gene prediction tools. MAKER2 enables individual laboratories and small-scale projects to annotate non-model organisms for which pre-existing gene models are not available. The optimal use of MAKER2 requires gathering evidence data (by searching and assembling transcripts, and/or collecting homologous proteins from related organisms), elaborating the best annotation strategy (training of gene models) and efficiently orchestrating the different steps of the software in a grid computing environment, which is tedious, time-consuming and requires a great deal of bioinformatic skills. Methods: To address these issues, we present AMAW (Automated MAKER2 Annotation Wrapper), a wrapper pipeline for MAKER2 that automates the above-mentioned tasks. Importantly, AMAW also exists as a Singularity container recipe easy to deploy on a grid computer, thereby overcoming the tricky installation of MAKER2. Use case: The performance of AMAW is illustrated through the annotation of a selection of 32 protist genomes, for which we compared its annotations with those produced with gene models directly available in AUGUSTUS. Conclusions: Importantly, AMAW also exists as a Singularity container recipe easy to deploy on a grid computer, thereby overcoming the tricky installation of MAKER

Detecting SIMDization Opportunities through Static/Dynamic Dependence Analysis

International audienceUsing SIMD instructions is essential in modern processor architecture for high performance computing. Compilers automatic vectorization shows limited efficiency in general, due to conservative dependence analysis, complex control flow or indexing. This paper presents a technique to detect SIMDization opportunities, complementing in a more detailed way compiler optimization reports. The method is based on static and dynamic dependence analysis, able to analyze codes not vectorized by a compiler. This method generates user-hints to help vectorize applications. We show on TSVC benchmark the benefits of this approach.L'utilisation des instructions SIMD est essentielle pour obtenir de bonnes performances de calcul sur les processeurs d'architecture moderne. La vectorisation automatique proposée par les compilateurs s'avère d'efficacité limitée en général, du fait d'une analyse de dépendances conservatrice, de flots de contrôle ou d'indices complexes. Cet article présente une technique de détection des opportunités de SIMDisation, complétant de façon plus détaillée les rapports d'optimisation des compilateurs. Cette méthode est basée sur l'analyse statique et dynamique conjointe des dépendances. Elle est capable d'analyser des codes non vectorisés par un compilateur. Cette méthode génère des suggestions à destination de l'utilisateur, afin de l'aider à vectoriser des applications. Nous montrons les bénéfices de cette approche sur le benchmark TSVC

Progressive fibrosing interstitial lung disease in rheumatoid arthritis: A retrospective study

peer reviewedBackground and objectiveRheumatoid arthritis associated-interstitial lung disease (RA-ILD) is the most common pulmonary manifestation of rheumatoid arthritis (RA) and an important cause of mortality. In patients suffering from interstitial lung diseases (ILD) from different etiologies (including RA-ILD), a significant proportion is exhibiting a fibrotic progression despite immunosuppressive therapies, defined as progressive fibrosing interstitial lung disease (PF-ILD). Here, we report the frequency of RA-ILD and PF-ILD in all RA patients’ cohort at University Hospital of Liège and compare their characteristics and outcomes.MethodsPatients were retrospectively recruited from 2010 to 2020. PF-ILD was defined based on functional, clinical and/or iconographic progression criteria within 24 months despite specific anti-RA treatment.ResultsOut of 1,500 RA patients, about one third had high-resolution computed tomography (HRCT) performed, 89 showed RA-ILD and 48 PF-ILD. RA-ILD patients were significantly older than other RA patients (71 old of median age vs. 65, p < 0.0001), with a greater proportion of men (46.1 vs. 27.7%, p < 0.0001) and of smoking history. Non-specific interstitial pneumonia pattern was more frequent than usual interstitial pneumonia among RA-ILD (60.7 vs. 27.0%) and PF-ILD groups (60.4 vs. 31.2%). The risk of death was 2 times higher in RA-ILD patients [hazard ratio 2.03 (95% confidence interval 1.15–3.57), p < 0.01] compared to RA.ConclusionWe identified a prevalence of PF-ILD of 3% in a general RA population. The PF-ILD cohort did not seem to be different in terms of demographic characteristics and mortality compared to RA-ILD patients who did not exhibit the progressive phenotype yet

Open versus laparoscopically-assisted oesophagectomy for cancer: a multicentre randomised controlled phase III trial - the MIRO trial

<p>Abstract</p> <p>Background</p> <p>Open transthoracic oesophagectomy is the standard treatment for infracarinal resectable oesophageal carcinomas, although it is associated with high mortality and morbidity rates of 2 to 10% and 30 to 50%, respectively, for both the abdominal and thoracic approaches. The worldwide popularity of laparoscopic techniques is based on promising results, including lower postoperative morbidity rates, which are related to the reduced postoperative trauma. We hypothesise that the laparoscopic abdominal approach (laparoscopic gastric mobilisation) in oesophageal cancer surgery will decrease the major postoperative complication rate due to the reduced surgical trauma.</p> <p>Methods/Design</p> <p>The MIRO trial is an open, controlled, prospective, randomised multicentre phase III trial. Patients in study arm A will receive laparoscopic-assisted oesophagectomy, i.e., a transthoracic oesophagectomy with two-field lymphadenectomy and laparoscopic gastric mobilisation. Patients in study arm B will receive the same procedure, but with the conventional open abdominal approach. The primary objective of the study is to evaluate the major postoperative 30-day morbidity. Secondary objectives are to assess the overall 30-day morbidity, 30-day mortality, 30-day pulmonary morbidity, disease-free survival, overall survival as well as quality of life and to perform medico-economic analysis. A total of 200 patients will be enrolled, and two safety analyses will be performed using 25 and 50 patients included in arm A.</p> <p>Discussion</p> <p>Postoperative morbidity remains high after oesophageal cancer surgery, especially due to major pulmonary complications, which are responsible for 50% of the postoperative deaths. This study represents the first randomised controlled phase III trial to evaluate the benefits of the minimally invasive approach with respect to the postoperative course and oncological outcomes in oesophageal cancer surgery.</p> <p>Trial Registration</p> <p><a href="http://www.clinicaltrials.gov/ct2/show/NCT00937456">NCT00937456</a> (ClinicalTrials.gov)</p

The GEN-ERA toolbox: unified and reproducible workflows for research in microbial genomics.

peer reviewed[en] BACKGROUND: Microbial culture collections play a key role in taxonomy by studying the diversity of their strains and providing well-characterized biological material to the scientific community for fundamental and applied research. These microbial resource centers thus need to implement new standards in species delineation, including whole-genome sequencing and phylogenomics. In this context, the genomic needs of the Belgian Coordinated Collections of Microorganisms were studied, resulting in the GEN-ERA toolbox. The latter is a unified cluster of bioinformatic workflows dedicated to both bacteria and small eukaryotes (e.g., yeasts). FINDINGS: This public toolbox allows researchers without a specific training in bioinformatics to perform robust phylogenomic analyses. Hence, it facilitates all steps from genome downloading and quality assessment, including genomic contamination estimation, to tree reconstruction. It also offers workflows for average nucleotide identity comparisons and metabolic modeling. TECHNICAL DETAILS: Nextflow workflows are launched by a single command and are available on the GEN-ERA GitHub repository (https://github.com/Lcornet/GENERA). All the workflows are based on Singularity containers to increase reproducibility. TESTING: The toolbox was developed for a diversity of microorganisms, including bacteria and fungi. It was further tested on an empirical dataset of 18 (meta)genomes of early branching Cyanobacteria, providing the most up-to-date phylogenomic analysis of the Gloeobacterales order, the first group to diverge in the evolutionary tree of Cyanobacteria. CONCLUSION: The GEN-ERA toolbox can be used to infer completely reproducible comparative genomic and metabolic analyses on prokaryotes and small eukaryotes. Although designed for routine bioinformatics of culture collections, it can also be used by all researchers interested in microbial taxonomy, as exemplified by our case study on Gloeobacterales

An externally validated fully automated deep learning algorithm to classify COVID-19 and other pneumonias on chest computed tomography.

peer reviewedPurpose: In this study, we propose an artificial intelligence (AI) framework based on three-dimensional convolutional neural networks to classify computed tomography (CT) scans of patients with coronavirus disease 2019 (COVID-19), influenza/community-acquired pneumonia (CAP), and no infection, after automatic segmentation of the lungs and lung abnormalities. Methods: The AI classification model is based on inflated three-dimensional Inception architecture and was trained and validated on retrospective data of CT images of 667 adult patients (no infection n=188, COVID-19 n=230, influenza/CAP n=249) and 210 adult patients (no infection n=70, COVID-19 n=70, influenza/CAP n=70), respectively. The model's performance was independently evaluated on an internal test set of 273 adult patients (no infection n=55, COVID-19 n= 94, influenza/CAP n=124) and an external validation set from a different centre (305 adult patients: COVID-19 n=169, no infection n=76, influenza/CAP n=60). Results: The model showed excellent performance in the external validation set with area under the curve of 0.90, 0.92 and 0.92 for COVID-19, influenza/CAP and no infection, respectively. The selection of the input slices based on automatic segmentation of the abnormalities in the lung reduces analysis time (56 s per scan) and computational burden of the model. The Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) score of the proposed model is 47% (15 out of 32 TRIPOD items). Conclusion: This AI solution provides rapid and accurate diagnosis in patients suspected of COVID-19 infection and influenza