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April 21, 2020 at 11:00AM EDT!
The architecture of mammalian genes enables the production of multiple transcripts that greatly expand the coding capacity of our genomes. Understanding how these transcripts are regulated is of particular importance in cancer genomics, as their aberrant regulation contributes to the ~10 million cancer-related deaths each year. We recently described a phenomenon called exon-mediated activation of transcription starts (EMATS) in which the splicing of internal exons impacts the spectrum of promoters used and expression level of the host gene. We showed that targeted-inhibition of splicing reduces the usage of promoters and suppresses gene expression, while evolutionary creation of a new splice site can activate cryptic promoters. My findings support a model in which splicing factors recruit transcription machinery to influence promoter choice and regulate the expression of thousands of mammalian genes.
DNCON2: improved protein contact prediction using two-level deep convolutional neural networks
by Jianlin Cheng
April 22, 2020 at 11:00 AM EDT!
Significant improvements in the prediction of protein residue-residue contacts are observed in the recent years. These contacts, predicted using a variety of coevolution-based and machine learning methods, are the key contributors to the recent progress in ab initio protein structure prediction, as demonstrated in the recent CASP experiments. Continuing the development of new methods to reliably predict contact maps is essential to further improve ab initio structure prediction.
In this paper we discuss DNCON2, an improved protein contact map predictor based on two-level deep convolutional neural networks. It consists of six convolutional neural networks-the first five predict contacts at 6, 7.5, 8, 8.5 and 10 Å distance thresholds, and the last one uses these five predictions as additional features to predict final contact maps. On the free-modeling datasets in CASP10, 11 and 12 experiments, DNCON2 achieves mean precisions of 35, 50 and 53.4%, respectively, higher than 30.6% by MetaPSICOV on CASP10 dataset, 34% by MetaPSICOV on CASP11 dataset and 46.3% by Raptor-X on CASP12 dataset, when top L/5 long-range contacts are evaluated. We attribute the improved performance of DNCON2 to the inclusion of short- and medium-range contacts into training, two-level approach to prediction, use of the state-of-the-art optimization and activation functions, and a novel deep learning architecture that allows each filter in a convolutional layer to access all the input features of a protein of arbitrary length.
The source code of DNCON2 is available at https://github.com/multicom-toolbox/DNCON2/
RAxML-NG: a fast, scalable and user-friendly tool for maximum likelihood phylogenetic inference
by Alexey Kozlov
July 22, 2020!
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.
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