The three laureates of this 10th edition of the SIB Bioinformatics Awards were announced during the [BC]2 Basel Computational Biology Conference held in Basel from 9 to 11 September. Eleonora Porcu (Early Career Bioinformatician Award), Jochen Singer (Best Swiss Bioinformatics Graduate Paper Award) and Sten Linnarsson for the resource Velocyto (Bioinformatics Resource Innovation Award) each gave a talk on this occasion, recorded as part of a special edition of our ‘in silico talks’ series.
Identifying functionally relevant genes for complex traits
The Early Career Bioinformatician Award went to Postdoc scientist Eleonora Porcu at the SIB Group of Zoltán Kutalik at the University of Lausanne (UNIL) and CHUV and the Group of Alexandre Reymond at UNIL, for her outstanding early career in computational biology, and in particular her work on the development of a new approach to unravel genetic determinants of complex and clinical traits. Using transcriptome-wide summary statistics-based Mendelian randomization approach (TWMR), applied to 43 human traits, she and her colleagues were able to uncover hundreds of previously unreported gene–trait associations.
Before joining the labs of Kutalik and Reymond in 2016, Eleonora obtained a PhD in Biomedical Science at the University of Sassari, during which she spent more than two years at the University of Michigan where she improved her knowledge in rare variants test, next generation sequencing and imputation strategies.
Open the video in YouTube to access its description with more information about the speaker, individual chapters, etc.
REFERENCE
Porcu E et al. Mendelian randomization integrating GWAS and eQTL data reveals genetic determinants of complex and clinical traits, Nature Communications 2019, doi: 10.1038/s41467-019-10936-0
Overcoming noisy data to reconstruct tumor evolution
Started in 2008 as an initiative to distinguish young bioinformaticians in Switzerland, the SIB Bioinformatics Awards have gone a long way since: from a single national award to three different prizes today, each with a dedicated jury panel composed of Swiss and international leading scientists, honoring 1) international early career bioinformaticians (Early Career Bioinformatician Award), 2) excellency within the Swiss PhD community (Best Swiss Bioinformatics Graduate Paper Award) and 3) innovative bioinformatics resources (Bioinformatics Resource Innovation Award).
The Best Swiss Bioinformatics Graduate Paper Award went to Jochen Singer, for his paper “Single-cell mutation identification via phylogenetic inference”, published together with his colleagues from Niko Beerenwinkel’s lab of the ETH Zurich in Nature Communications in 2018.
In this paper, Singer and colleagues propose a new approach to call mutations in individual cells despite the high allelic imbalance, drop-out rates and missing data that are characteristic for single-cell sequencing data. They do so by leveraging the evolutionary relationships among cells and reconstructing their lineage tree. Their method, which performs favourably to other available tools, opens the way to a better understanding of tumor heterogeneity, and to improved targeted cancer therapies.
Open the video in YouTube to access its description with more information about the speaker, individual chapters, etc.
REFERENCE
Singer J et al. Single-cell mutation identification via phylogenetic inference. Nature communications 2018. https://doi.org/10.1038/s41467-018-07627-7
Inferring the fate of single cells using Velocyto
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The resource Velocyto, wich was awarded the Bioinformatics Resource Innovation Award, offers a new framework for the analysis of RNA velocity in single cells, in order to quantitatively infer cell fates. The talk was delivered by Sten Linnarsson, Professor at the Karolinska Institute, Sweden, on behalf of the team that contributed to the development of the tool and which includes EPFL’s Group Leader Gioele La Manno, Prof. Peter Kharchenko (Harvard) and Ruslan Soldatov (Harvard).
The framework takes advantage of the fact that mRNA goes through stages of maturation throughout its lifetime, from immature (unspliced) to mature (spliced) to move from a static to a dynamic inference of cell fate. It uses the derived RNA velocity as a proxy to quantitatively predict cell fates. As Linnarsson puts it, RNA velocity is akin to the ‘motion blur’ effect in photography, which depicts the trajectory of an object based on a snapshot in time. The Velocyto package, free and open source, also includes tools for quality control, summarizing, data smoothing, etc. and is available for Python and R.
Open the video in YouTube to access its description with more information about the speaker, individual chapters, etc.
REFERENCE
La Manno G et al. RNA velocity of single cells. Nature 2018 https://doi.org/10.1038/s41586-018-0414-6
Eleonora Porcu, laureate of the Early Career Bioinformatician Award & Jochen Singer, laureate of the Best Swiss Bioinformatics Graduate Paper Award. Sten Linnarsson, who presented the Velocyto tool - laureate of the Bioinformatics Resource Innovation Award - gave his talk remotely from Sweden.