@article{Videm_Kumar_Zharkov-ChiRA_integ_frame-2021,
author = {Videm, Pavankumar and Kumar, Anup and Zharkov, Oleg and 
          Grüning, Björn Andreas and Backofen, Rolf},
title = {{ChiRA}: an integrated framework for chimeric read analysis 
         from {RNA}-{RNA} interactome and {RNA} structurome data},
journal = {Gigascience},
year = {2021},
doi = {10.1093/gigascience/giaa158},
volume = {10},
user = {videmp},
pmid = {33511995},
pages = {},
number = {2},
issn = {2047-217X},
abstract = {BACKGROUND: With the advances in next-generation sequencing 
            technologies, it is possible to determine RNA-RNA 
            interaction and RNA structure predictions on a genome-wide 
            level. The reads from these experiments usually are 
            chimeric, with each arm generated from one of the 
            interaction partners. Owing to short read lengths, often 
            these sequenced arms ambiguously map to multiple locations. 
            Thus, inferring the origin of these can be quite 
            complicated. Here we present ChiRA, a generic framework for 
            sensitive annotation of these chimeric reads, which in turn 
            can be used to predict the sequenced hybrids. RESULTS: 
            Grouping reference loci on the basis of aligned common reads 
            and quantification improved the handling of the multi-mapped 
            reads in contrast to common strategies such as the selection 
            of the longest hit or a random choice among all hits. On 
            benchmark data ChiRA improved the number of correct 
            alignments to the reference up to 3-fold. It is shown that 
            the genes that belong to the common read loci share the same 
            protein families or similar pathways. In published data, 
            ChiRA could detect 3 times more new interactions compared to 
            existing approaches. In addition, ChiRAViz can be used to 
            visualize and filter large chimeric datasets intuitively. 
            CONCLUSION: ChiRA tool suite provides a complete analysis 
            and visualization framework along with ready-to-use Galaxy 
            workflows and tutorials for RNA-RNA interactome and 
            structurome datasets. Common read loci built by ChiRA can 
            rescue multi-mapped reads on paralogous genes without 
            requiring any information on gene relations. We showed that 
            ChiRA is sensitive in detecting new RNA-RNA interactions 
            from published RNA-RNA interactome datasets.}
}