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Pinson, S.

Publications and source records attributed to Pinson, S..

2 recordsLinked to original sources

Detecting aberrant splicing events in short-read RNA-seq with SAMI, an UMI-aware Nextflow pipeline

SummaryDespite RNA-sequencing solutions have existed for 15 years, successfully replacing micro-arrays for gene expression profiling, their potential to analyse splicing is still to be achieved in clinical settings. Powerful tools were developed to quantify known iso-forms, to compare sample groups or to identify outliers in large datasets, but most fail to address the most common situation in routine diagnostics: identifying non-recurring events in low-dimension data. To fill this gap, we developed SAMI (Splicing Analysis with Molecular Indexes), an UMI-aware pipeline focusing on splicing events which differ from provided annotation. The ability of SAMI and concurrent software to detect intragenic splicing aberrations and gene fusions was assessed, both on real data from a commercial control sample and simulated data generated with ASimulatoR. Availability and ImplementationNextflow pipeline and Singularity container recipe freely available under GPL 3 licence at https://github.com/HCL-HUBL/SAMI Contactsylvain.mareschal@chu-lyon.fr

bioinformatics↗

Counting sperm whales and visualising their dive profiles using two-hydrophone recordings and an automated click detector algorithm in a longline depredation context.

Odontocetes depredating fish caught on longlines is a serious socio-economic and conservation issue. A good understanding of the depredation behaviour by odontocetes is therefore required. Within this purpose, a method is described to follow diving behaviour of sperm whales, considered as proxy of their foraging activity. The study case took place around Kerguelen Islands from the Patagonian toothfish fishery. The method uses the coherence between direct-path sperm whale clicks, recorded by two synchronized hydrophones, to distinguish them from decoherent clicks that are reflected by the water surface or seefloor (due to surface roughness). Its low computational cost permits to process large dataset and bring new insights on sperm whales behaviour. Detection of sperm whale clicks permits to estimate the number of sperm whales and to assess their diving behaviour. Three diving behaviour were identified as "Water Column" (individual goes down and up), "Water Wander" (individual seems to go up and down multiple times in the water column), and "Seafloor" (individual spend time on the seabed). Results suggest that sperm whales have different diving behaviours with specific dives as they are either "interacting" or "not-interacting" with a hauling vessel.

animal behavior and cognition↗