rnaQUAST: a quality assessment tool for de novo transcriptome assemblies.

UNLABELLED: Ability to generate large RNA-Seq datasets created a demand for both de novo and reference-based transcriptome assemblers. However, while many transcriptome assemblers are now available, there is still no unified quality assessment tool for RNA-Seq assemblies. We present rnaQUAST-a tool for evaluating RNA-Seq assembly quality and benchmarking transcriptome assemblers using reference genome and gene database. rnaQUAST calculates various metrics that demonstrate completeness and correctness levels of the assembled transcripts, and outputs them in a user-friendly report. AVAILABILITY AND IMPLEMENTATION: rnaQUAST is implemented in Python and is freely available at http://bioinf.spbau.ru/en/rnaquast CONTACT: ap@bioinf.spbau.ru SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

A solution of the Crow-Kimura evolution model on fluctuating fitness landscape.

The article discusses the Crow-Kimura model in the context of random transitions between different fitness landscapes. The duration of epochs, during which the fitness landscape is constant over time, is modeled by an exponential distribution. To obtain an exact solution, a system of functional equations is required. However, to approximate the model, we consider the cases of slow or fast transitions and calculate the first-order corrections using either the transition rate or its inverse. Specifically, we focus on the case of slow transitions and find that the average fitness is equal to the average fitness for evolution on static fitness landscapes, but with the addition of a load term. We also investigate the model for a small number of genes and identify the exact transition points to the transient phase.