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An information theoretic algorithm that prunes away potentially indirect interactions allows for improved reconstruction of biological networks.

As an increasing number of eukaryotic genomes are being sequenced, comparative studies aimed at detecting regulatory elements in intergenic sequences are becoming more prevalent. Most comparative methods for transcription factor (TF) binding site discovery make use of global or local alignments of orthologous regulatory regions to assess whether a particular DNA site is conserved across related organisms, and thus more likely to be functional. Since binding sites are usually short, sometimes degenerate, and often independent of orientation, alignment algorithms may not align them correctly. Here, we present a novel, alignment-free approach for using conservation information for TF binding site discovery. We relax the definition of conserved sites: we consider a DNA site within a regulatory region to be conserved in an orthologous sequence if it occurs anywhere in that sequence, irrespective of orientation. We use this definition to derive informative priors over DNA sequence positions, and incorporate these priors into a Gibbs sampling algorithm for motif discovery. Our approach is simple and fast. It requires neither sequence alignments nor the phylogenetic relationships between the orthologous sequences, yet it is more effective on real biological data than methods that do.

We recently developed an approach for testing the accuracy of network inference algorithms by applying them to biologically realistic simulations with known network topology. Here, we seek to determine the degree to which the network topology and data sampling regime influence the ability of our Bayesian network inference algorithm, NETWORKINFERENCE, to recover gene regulatory networks. NETWORKINFERENCE performed well at recovering feedback loops and multiple targets of a regulator with small amounts of data, but required more data to recover multiple regulators of a gene. When collecting the same number of data samples at different intervals from the system, the best recovery was produced by sampling intervals long enough such that sampling covered propagation of regulation through the network but not so long such that intervals missed internal dynamics. These results further elucidate the possibilities and limitations of network inference based on biological data.

Single cell experiments provide an unprecedented opportunity to reconstruct a sequence of changes in a biological process from individual “snapshots” of cells. However, nonlinear gene expression changes, genes unrelated to the process, and the possibility of branching trajectories make this a challenging problem. We develop SLICER (Selective Locally Linear Inference of Cellular Expression Relationships) to address these challenges. SLICER can infer highly nonlinear trajectories, select genes without prior knowledge of the process, and automatically determine the location and number of branches and loops. SLICER recovers the ordering of points along simulated trajectories more accurately than existing methods. We demonstrate the effectiveness of SLICER on previously published data from mouse lung cells and neural stem cells. Electronic supplementary material The online version of this article (doi:10.1186/s13059-016-0975-3) contains supplementary material, which is available to authorized users.

In the search for diagnostic and therapeutic strategies for lung cancer, matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) has been evinced as a new and promising discovery platform to generate protein expression profiles in search of overexpressed proteins in lung tumors. Data from MALDI-TOF spectra require considerable signal processing such as noise removal and baseline level error correction. In this study, we discuss our preliminary approaches to these issues. We further present a novel algorithm that identified two proteins that are differentially expressed between tumor and normal tissues in patients with lung cancer. The overexpression of these two proteins was confirmed by immunoblotting, and their localization to tumor cells was studied by immunohistochemistry. Our results indicate that the proposed schemes enable accurate diagnosis based on the overexpression of a small number of identified proteins with high sensitivity, suggesting that these proteins would be helpful in further study of drug design.