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The high-throughput short-reads RNA-seq protocols often produce paired-endreads, with the middle portion of the fragments being unsequenced. We exploreif the full-length fragments can be computationally reconstructed from thesequenced two ends in the absence of the reference genome - a problem here werefer to as de novo bridging. Solving this problem provides longer, moreinformative RNA-seq reads, and benefits downstream RNA-seq analysis such astranscript assembly, expression quantification, and splicing differentialanalysis. However, de novo bridging is a challenging and complicated task owingto alternative splicing, transcript noises, and sequencing errors. It remainsunclear if the data provides sufficient information for accurate bridging, letalone efficient algorithms that determine the true bridges. Methods have beenproposed to bridge paired-end reads in the presence of reference genome (calledreference-based bridging), but the algorithms are far away from scaling for denovo bridging as the underlying compacted de Bruijn graph(cdBG) used in thelatter task often contains millions of vertices and edges. We designed a newtruncated Dijkstra's algorithm for this problem, and proposed a novel algorithmthat reuses the shortest path tree to avoid running the truncated Dijkstra'salgorithm from scratch for all vertices for further speeding up. Theseinnovative techniques result in scalable algorithms that can bridge allpaired-end reads in a cdBG with millions of vertices. Our experiments showedthat paired-end RNA-seq reads can be accurately bridged to a large extent. Theresulting tool is freely available athttps://github.com/Shao-Group/rnabridge-denovo.

acm-bcb ... ... : the ... acm conference on bioinformatics, computational biology and biomedicine. acm conference on bioinformatics, computational biology and biomedicine

On de novo Bridging Paired-end RNA-seq Data