On bridging paired-end RNA-seq data

Abstract Motivation The widely-used high-throughput RNA-sequencing technologies (RNA-seq) usually produce paired-end reads. We explore if full fragments can be computationally reconstructed from the sequenced two ends—a problem here we refer to as bridging . Solving this problem provides longer, more informative RNA-seq reads, and hence benefits downstream RNA-seq analysis such as transcriptome assembly and expression quantification. However, bridging is a challenging and complicated task owing to alternative splicing, transcript noises, and sequencing errors. It remains unclear if the data itself provides sufficient information for accurate bridging, let alone proper models and efficient algorithms that characterize and determine the true bridges. Algorithmic Results We studied this problem in two settings: reference-based bridging, which assumes reads alignments are available and reconstructs the alignments of full fragments, and de novo bridging, which reconstructs sequences of entire fragments from sequences of the two ends. We proposed a novel mathematical formulation that works for both settings—to seek a path in an underlying graph data structure (i.e., splice graph for reference-based bridging, and compacted de Bruijn graph for de novo bridging) such that its bottleneck weight is maximized. This formulation characterizes true bridges and is efficient in filtering out false bridges. This formulation admits optimal substructure property, and hence efficient dynamic programming algorithms can be designed. For reference-based bridging, we designed such an algorithm to calculate the top N bridging paths, followed by a voting approach to select one using the distribution of fragment length. For de novo bridging, we designed a new truncated Dijkstra’s algorithm. To further speed up, we proposed a novel algorithm that reuses the shortest path tree to avoid running the truncated Dijkstra’s algorithm from scratch for all vertices. These innovations result in scalable algorithms that can bridge all paired-end reads in a compacted de Bruijn graph with millions of vertices. Experimental Results We showed that paired-end RNA-seq reads can be accurately bridged to a large extend. Our reference-based bridging tool could correctly bridge more than 79.6% of reads. For de novo bridging, high precision was observed with varied sensitivity. We also showed that bridging can improve reference-based transcript assembly: the improvement was significant (up to 14.4% measured with adjusted precision), and universal in all combinations with different aligners and assemblers. Availability Implementations of the algorithms for reference-based and de novo bridging are available at https://github.com/Shao-Group/rnabridge-align and https://github.com/Shao-Group/rnabridge-denovo , respectively. Scripts, datasets, and documentations that can reproduce the experimental results in this manuscript are available at https://github.com/Shao-Group/rnabridge-test .