Summary: L1 Chimeric Transcripts (LCTs) are initiated by repeated LINE-1 element antisense promoters and include the L1 5'UTR sequence in antisense orientation followed by the adjacent genomic region. LCTs have been characterized mainly using bioinformatics approaches to query dbEST. To take advantage of NGS data to unravel the transcriptome composition, we developed Chimeric LIne Finder (CLIFinder), a new bioinformatics tool. Using stranded paired-end RNA-seq data, we demonstrated that CLIFinder can identify genome-wide transcribed chimera sequences corresponding to potential LCTs. Moreover, CLIFinder can be adapted to study transcription from other repeat types. Availability and implementation: The code is available at: https://github.com/GReD-Clermont/CLIFinder; and for Galaxy users, it is directly accessible in the tool shed at: https://toolshed.g2.bx.psu.edu/view/clifinder/clifinder/. Contact: [email protected]. Supplementary information: Supplementary data are available at Bioinformatics online.

MOTIVATION: Gene fusion is an important evolutionary process. It can yield valuable information to infer the interactions and functions of proteins. Fused genes have been identified as non-transitive patterns of similarity in triplets of genes. To be computationally tractable, this approach usually imposes an a priori distinction between a dataset in which fused genes are searched for, and a dataset that may have provided genetic material for fusion. This reduces the 'genetic space' in which fusion can be discovered, as only a subset of triplets of genes is investigated. Moreover, this approach may have a high-false-positive rate, and it does not identify gene families descending from a common fusion event. RESULTS: We represent similarities between sequences as a network. This leads to an efficient formulation of previous methods of fused gene identification, which we implemented in the Python program FusedTriplets. Furthermore, we propose a new characterization of families of fused genes, as clique minimal separators of the sequence similarity network. This well-studied graph topology provides a robust and fast method of detection, well suited for automatic analyses of big datasets. We implemented this method in the C++ program MosaicFinder, which additionally uses local alignments to discard false-positive candidates and indicates potential fusion points. The grouping into families will help distinguish sequencing or prediction errors from real biological fusions, and it will yield additional insight into the function and history of fused genes. AVAILABILITY: FusedTriplets and MosaicFinder are published under the GPL license and are freely available with their source code at this address: http://sourceforge.net/projects/mosaicfinder. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.