BACKGROUND: Insight into the rigidity or flexibility of molecular structures is integral for a series of common research questions in molecular biology, including the identification of functional regions, simulated protein unfolding, or tracking and prediction of conformational changes in proteins over time. Determining rigidity in 3-dimensional space is a difficult problem in general. For a well-defined subclass of frameworks, however, this task can be solved in polynomial time with the help of constraint counting algorithms known as pebble games. Although this approach is well established, no easy-to-use implementation of the pebble game algorithm in the context of general graph analysis and molecular rigidity is currently available to researchers. RESULTS: To close this gap, we developed TRAMbio, a Python-based software tool for Topological Rigidity Analysis in Molecular Biology. We summarize and discuss the theoretical foundation of the pebble game and how it can be applied to molecular rigidity. CONCLUSIONS: TRAMbio performs well even on large molecules and on discrete time series of protein movement. Results are accessible for both bioinformaticians and biologists, as rigid components can be rendered using standard molecular visualization platforms.

MOTIVATION: Accurate assembly of RNA-seq is a crucial step in many analytic tasks such as gene annotation or expression studies. Despite ongoing research, progress on traditional single sample assembly has brought no major breakthrough. Multi-sample RNA-Seq experiments provide more information than single sample datasets and thus constitute a promising area of research. Yet, this advantage is challenging to utilize due to the large amount of accumulating errors. RESULTS: We present an extension to Ryūtō enabling the reconstruction of consensus transcriptomes from multiple RNA-seq datasets, incorporating consensus calling at low level features. We report stable improvements already at three replicates. Ryūtō outperforms competing approaches, providing a better and user-adjustable sensitivity-precision trade-off. Ryūtō's unique ability to utilize a (incomplete) reference for multi sample assemblies greatly increases precision. We demonstrate benefits for differential expression analysis.Ryūtō consistently improves assembly on replicates of the same tissue independent of filter settings, even when mixing conditions or time series. Consensus voting in Ryūtō is especially effective at high precision assembly, while Ryūtō's conventional mode can reach higher recall. AVAILABILITY AND IMPLEMENTATION: Ryūtō is available at https://github.com/studla/RYUTO. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Ongoing developments in genome sequencing have caused a fundamental paradigm shift in the field in recent years. With ever lower sequencing costs, projects are no longer limited by available raw data, but rather by computational demands. The high complexity of eukaryotic genomes in concordance with increasing data sizes creates unique demands on methods to assemble full genomes. We describe a new approach to assemble genomes from a combination of low-coverage short and long reads. LazyB starts from a bipartite overlap graph between long reads and restrictively filtered short-read unitigs, which are then reduced to a long-read overlap graph G. Instead of the more conventional approach of removing tips, bubbles, and other local features, LazyB stepwisely extracts subgraphs whose global properties approach a disjoint union of paths. First, a consistently oriented subgraph is extracted, which in a second step is reduced to a directed acyclic graph. In the next step, properties of proper interval graphs are used to extract contigs as maximum weight paths. These are translated into genomic sequences only in the final step. A prototype implementation of LazyB, entirely written in python, not only yields significantly more accurate assemblies of the yeast and fruit fly genomes compared to state-of-the-art pipelines but also requires much less computational effort. Our findings demonstrate a new low-cost method that enables the assembly of even large genomes with low computational effort.

BACKGROUND: The rapid increase in High-throughput sequencing of RNA (RNA-seq) has led to tremendous improvements in the detection and reconstruction of both expressed coding and non-coding RNA transcripts. Yet, the complete and accurate annotation of the complex transcriptional output of not only the human genome has remained elusive. One of the critical bottlenecks in this endeavor is the computational reconstruction of transcript structures, due to high noise levels, technological limits, and other biases in the raw data. RESULTS: We introduce several new and improved algorithms in a novel workflow for transcript assembly and quantification. We propose an extension of the common splice graph framework that combines aspects of overlap and bin graphs and makes it possible to efficiently use both multi-splice and paired-end information to the fullest extent. Phasing information of reads is used to further resolve loci. The decomposition of read coverage patterns is modeled as a minimum-cost flow problem to account for the unavoidable non-uniformities of RNA-seq data. CONCLUSION: Its performance compares favorably with state of the art methods on both simulated and real-life datasets. Ryūtō calls 1-4% more true transcripts, while calling 5-35% less false predictions compared to the next best competitor.