MosaicFinder: identification of fused gene families in sequence similarity networks

Abstract

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 highfalse-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 Cþþ 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.

Evolution by Pervasive Gene Fusion in Antibiotic Resistance and Antibiotic Synthesizing Genes

“…FusedTriplets.py was used in this study as a method for detecting composite genes however other technologies also exist. Another interesting fusion detection program currently available is MosaicFinder [32]. A comparison of both these programs was carried out and the results show that the two methods produce overlapping, but non-identical sets of inferences of fused genes (data not shown).…”

“…The sequence similarity network (SSN) was searched for composite genes using the Python script FusedTriplets.py [32], set at an E-value threshold of 1e−20. FusedTriplets identifies the full set of non-transitive triplets.…”

“…The GCC contains 348 nodes, which is 82% of the entire network. The network was searched for composite genes using the program FusedTriplets.py [32] with a threshold set at 1e−15. FusedTriplets.py detected 13 composite genes within the SSN, all of who are located within the GCC of the network (see Figure 1a).…”

The origins of microbial adaptations: how introgressive descent, egalitarian evolutionary transitions and expanded kin selection shape the network of life

“…However, many genes originate from the composition of genetic material from sequences belonging to different gene families. Eukaryotes are the main creators of composite genes (in terms of the proportion of composite genes in their genomes) (Haggerty et al, 2013), yet in terms of absolute numbers, mobile genetic elements operate the most massive gene remodeling on Earth (Jachiet et al, 2013). Therefore, numerous genes display family resemblances (Halary et al, 2013): true similarity caused by introgression between non-homologous sequences.…”

A Pluralistic Account of Homology: Adapting the Models to the Data

“…For the most part, such pluralistic homology relationships have been depicted using connected components in sequence similarity networks (Dagan et al 2008; Dagan and Martin 2009; Dagan 2011; Kloesges et al 2011; Bapteste et al 2012; Jachiet et al 2013; also, see figs. 2–5 in this manuscript).…”