Law, J. N. | Search bioRxiv

bioRxiv2019

Accurate and Efficient Gene Function Prediction using a Multi-Bacterial Network

MotivationNearly 40% of the genes in sequenced genomes have no experimentally- or computationally-derived functional annotations. To fill this gap, we seek to develop methods for network-based gene function prediction that can integrate heterogeneous data for multiple species with experimentally-based functional annotations and systematically transfer them to newly-sequenced organisms on a genomewide scale. However, the large size of such networks pose a challenge for the scalability of current methods. ResultsWe develop a label propagation algorithm called FastSinkSource. By formally bounding its the rate of progress, we decrease the running time by a factor of 100 without sacrificing accuracy. We systematically evaluate many approaches to construct multi-species bacterial networks and apply FastSinkSource and other state-of-the-art methods to these networks. We find that the most accurate and efficient approach is to pre-compute annotation scores for species with experimental annotations, and then to transfer them to other organisms. In this manner, FastSinkSource runs in under three minutes for 200 bacterial species. Availability and ImplementationPython implementations of each algorithm and all data used in this research are available at http://bioinformatics.cs.vt.edu/~jeffl/supplements/2020-fastsinksource. Contactmurali@cs.vt.edu Supplementary InformationA supplementary file is available at bioRxiv online.

systems biology↗

bioRxiv2019

Benchmarking algorithms for gene regulatory network inference from single-cell transcriptomic data

We present a comprehensive evaluation of state-of-the-art algorithms for inferring gene regulatory networks (GRNs) from single-cell gene expression data. We develop a systematic framework called BEELINE for this purpose. We use synthetic networks with predictable cellular trajectories as well as curated Boolean models to serve as the ground truth for evaluating the accuracy of GRN inference algorithms. We develop a strategy to simulate single-cell gene expression data from these two types of networks that avoids the pitfalls of previously-used methods. We selected 12 representative GRN inference algorithms. We found that the accuracy of these methods (measured in terms of AUROC and AUPRC) was moderate, by and large, although the methods were better in recovering interactions in the synthetic networks than the Boolean models. Techniques that did not require pseudotime-ordered cells were more accurate, in general. The observation that the endpoints of many false positive edges were connected by paths of length two in the Boolean models suggested that indirect effects may be predominant in the outputs of the algorithms we tested. The predicted networks were considerably inconsistent with each other, indicating that combining GRN inference algorithms using ensembles is likely to be challenging. Based on the results, we present some recommendations to users of GRN inference algorithms, including suggestions on how to create simulated gene expression datasets for testing them. BEELINE, which is available at http://github.com/murali-group/BEELINE under an open-source license, will aid in the future development of GRN inference algorithms for single-cell transcriptomic data.

bioinformatics↗