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Background: The constant progress in sequencing technology leads to ever increasing amounts of genomic data. In the light of current evidence transposable elements (TEs for short) are becoming useful tools for learning about the evolution of host genome. Therefore the software for genome-wide detection and analysis of TEs is of great interest. Results: Here we describe the computational tool for mining, classifying and storing TEs from newly sequenced genomes. This is an online, web-based, user-friendly service, enabling users to upload their own genomic data, and perform de-novo searches for TEs. The detected TEs are automatically analyzed, compared to reference databases, annotated, clustered into families, and stored in TEs repository. Also, the genome-wide nesting structure of found elements are detected and analyzed by new method for inferring evolutionary history of TEs. We illustrate the functionality of our tool by performing a full-scale analyses of TE landscape in Medicago truncatula genome. Conclusions: TRANScendence is an effective tool for the de-novo annotation and classification of transposable elements in newly-acquired genomes. Its streamlined interface makes it well-suited for evolutionary studies. [ABSTRACT FROM AUTHOR]

The linear regression of mass spectra is a computational problem defined as fitting a linear combination of reference spectra to an experimental one. It is typically used to estimate the relative quantities of selected ions. In this work, we study this problem in an abstract setting to develop new approaches applicable to a diverse range of experiments.To overcome the sensitivity of the ordinary least‐squares regression to measurement inaccuracies, we base our methods on a non‐conventional spectral dissimilarity measure, known as the Wasserstein or the Earth Mover's distance. This distance is based on the notion of the cost of transporting signal between mass spectra, which renders it naturally robust to measurement inaccuracies in the mass domain.Using a data set of 200 mass spectra, we show that our approach is capable of estimating ion proportions accurately without extensive preprocessing of spectra required by other methods. The conclusions are further substantiated using data sets simulated in a way that mimics most of the measurement inaccuracies occurring in real experiments.We have developed a linear regression algorithm based on the notion of the cost of transporting signal between spectra. Our implementation is available in a Python 3 package called masserstein, which is freely available at https://github.com/mciach/masserstein. [ABSTRACT FROM AUTHOR]

Exactly half a century has passed since the launch of the first documented research project (1965 Dendral) on computer-assisted organic synthesis. Many more programs were created in the 1970s and 1980s but the enthusiasm of these pioneering days had largely dissipated by the 2000s, and the challenge of teaching the computer how to plan organic syntheses earned itself the reputation of a "mission impossible". This is quite curious given that, in the meantime, computers have "learned" many other skills that had been considered exclusive domains of human intellect and creativity-for example, machines can nowadays play chess better than human world champions and they can compose classical music pleasant to the human ear. Although there have been no similar feats in organic synthesis, this Review argues that to concede defeat would be premature. Indeed, bringing together the combination of modern computational power and algorithms from graph/network theory, chemical rules (with full stereo- and regiochemistry) coded in appropriate formats, and the elements of quantum mechanics, the machine can finally be "taught" how to plan syntheses of non-trivial organic molecules in a matter of seconds to minutes. The Review begins with an overview of some basic theoretical concepts essential for the big-data analysis of chemical syntheses. It progresses to the problem of optimizing pathways involving known reactions. It culminates with discussion of algorithms that allow for a completely de novo and fully automated design of syntheses leading to relatively complex targets, including those that have not been made before. Of course, there are still things to be improved, but computers are finally becoming relevant and helpful to the practice of organic-synthetic planning. Paraphrasing Churchill's famous words after the Allies' first major victory over the Axis forces in Africa, it is not the end, it is not even the beginning of the end, but it is the end of the beginning for the computer-assisted synthesis planning. The machine is here to stay., (© 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)