ISCB EvolCompGen COSI present

DNCON2: improved protein contact prediction using two-level deep convolutional neural networks
Presented by Alexey Kozlov, Heidelberg Institute for Theoretical Studies

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Join us Wednesday, September 30, 2020 at 11:00 AM for this jointly hosted webinar.  ISCBacademy is complimenatry for all ISCB members.  Not a Member?  You can still join for a nominal fee OR become a member and get this webinar and upcoming ones free!


Phylogenies are important for fundamental biological research, but also have numerous applications in biotechnology, agriculture and medicine. Finding the optimal tree under the popular maximum likelihood (ML) criterion is known to be NP-hard. Thus, highly optimized and scalable codes are needed to analyze constantly growing empirical datasets.

We present RAxML-NG, a from-scratch re-implementation of the established greedy tree search algorithm of RAxML/ExaML. RAxML-NG offers improved accuracy, flexibility, speed, scalability, and usability compared with RAxML/ExaML. On taxon-rich datasets, RAxML-NG typically finds higher-scoring trees than IQTree, an increasingly popular recent tool for ML-based phylogenetic inference (although IQ-Tree shows better stability). Finally, RAxML-NG introduces several new features, such as the detection of terraces in tree space and the recently introduced transfer bootstrap support metric.

The code is available under GNU GPL at RAxML-NG web service (maintained by Vital-IT) is available at