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MIPhy: identify and quantify rapidly evolving members of large gene families.

ABSTRACT: After transitioning to a new environment, species often exhibit rapid phenotypic innovation. One of the fastest mechanisms for this is duplication followed by specialization of existing genes. When this happens to a member of a gene family, it tends to leave a detectable phylogenetic signature of lineage-specific expansions and contractions. These can be identified by analyzing the gene family across several species and identifying patterns of gene duplication and loss that do not correlate with the known relationships between those species. This signature, termed phylogenetic instability, has been previously linked to adaptations that change the way an organism samples and responds to its environment; conversely, low phylogenetic instability has been previously linked to proteins with endogenous functions. With the increase in genome-level data, there is a need to identify and quantify phylogenetic instability. Here, we present Minimizing Instability in Phylogenetics (MIPhy), a tool that solves this problem by quantifying the incongruence of a gene's evolutionary history. The motivation behind MIPhy was to produce a tool to aid in interpreting phylogenetic trees. It can predict which members of a gene family are under adaptive evolution, working only from a gene tree and the relationship between the species under consideration. While it does not conduct any estimation of positive selection-which is the typical indication of adaptive evolution-the results tend to agree. We demonstrate the usefulness of MIPhy by accurately predicting which members of the mammalian cytochrome P450 gene superfamily metabolize xenobiotics and which metabolize endogenous compounds. Our predictions correlate very well with known substrate specificities of the human enzymes. We also analyze the Caenorhabditis collagen gene family and use MIPhy to predict genes that produce an observable phenotype when knocked down in C. elegans, and show that our predictions correlate well with existing knowledge. The software can be downloaded and installed from https://github.com/dave-the-scientist/miphy and is also available as an online web tool at http://www.miphy.wasmuthlab.org.

SUBMITTER: Curran DM

PROVIDER: S-EPMC5983006 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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MIPhy: identify and quantify rapidly evolving members of large gene families.

Curran David M DM Gilleard John S JS Wasmuth James D JD

PeerJ 20180529

After transitioning to a new environment, species often exhibit rapid phenotypic innovation. One of the fastest mechanisms for this is duplication followed by specialization of existing genes. When this happens to a member of a gene family, it tends to leave a detectable phylogenetic signature of lineage-specific expansions and contractions. These can be identified by analyzing the gene family across several species and identifying patterns of gene duplication and loss that do not correlate with ...[more]

PMID: 29868279

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Project description:BackgroundClustering sequences into groups of putative homologs (families) is a critical first step in many areas of comparative biology and bioinformatics. The performance of clustering approaches in delineating biologically meaningful families depends strongly on characteristics of the data, including content bias and degree of divergence. New, highly scalable methods have recently been introduced to cluster the very large datasets being generated by next-generation sequencing technologies. However, there has been little systematic investigation of how characteristics of the data impact the performance of these approaches.ResultsUsing clusters from a manually curated dataset as reference, we examined the performance of a widely used graph-based Markov clustering algorithm (MCL) and a greedy heuristic approach (UCLUST) in delineating protein families coded by three sets of bacterial genomes of different G+C content. Both MCL and UCLUST generated clusters that are comparable to the reference sets at specific parameter settings, although UCLUST tends to under-cluster compositionally biased sequences (G+C content 33% and 66%). Using simulated data, we sought to assess the individual effects of sequence divergence, rate heterogeneity, and underlying G+C content. Performance decreased with increasing sequence divergence, decreasing among-site rate variation, and increasing G+C bias. Two MCL-based methods recovered the simulated families more accurately than did UCLUST. MCL using local alignment distances is more robust across the investigated range of sequence features than are greedy heuristics using distances based on global alignment.ConclusionsOur results demonstrate that sequence divergence, rate heterogeneity and content bias can individually and in combination affect the accuracy with which MCL and UCLUST can recover homologous protein families. For application to data that are more divergent, and exhibit higher among-site rate variation and/or content bias, MCL may often be the better choice, especially if computational resources are not limiting.

Project description:Here we focus on identifying conserved elements that are required for Chaserr function, a lncRNA that we recently characterized as being essential for mouse viability (https://doi.org/10.1038/s41467-019-13075-8). Chaserr is highly conserved in different species, and is located in close proximity (<2kb) to the transcription start site of CHD2. It acts in concert with the CHD2 protein to maintain proper Chd2 expression levels. Using the LncLOOM framework we identified AUGG enriched conserved elements which are located in the last exon of mouse Chaserr. In order to identify proteins that potentially bind to these conserved regions, we used in vitro transcription to generate the following biotinylated RNAs; along with their antisense controls: 1) The WT sequence, containing the first 322 nt of the last exon of Chaserr (bases 764–1086 of the NR_037601 isoform). An antisense control fragment was also prepared, labelled as AWT 2) The mutant conserved (MC) sequence, that contained AUGG→UACC mutations in four conserved motifs that were found using LncLOOM. An antisense control fragment was also prepared, labelled as AMC 3) The muatant all (MA) sequence in which all seven of the AUGG sites in the last exon were mutated to UACC. An antisense control fragment was also prepared, labelled as AMA 4) We also included a No Probe Control, which did not contain an RNA fragment. The fragments were incubated with N2a cell lysates followed by RNA-pulldown and Mass Spec analysis. The experiment was performed in triplicate. A total of 938 proteins were found to associate with the WT sequence: 74 of these were enriched ≥3-fold compared to the antisense sequence.We identified 9 proteins that had ≥2-fold higher recovery when using the WT sequence compared to both mutants. Included in these proteins was DHX36, which showed the highest enrichment compared to the mutated sequences. We validated the association of DHX36 with Chaserr through RNA immunoprecipitation experiments.

Dataset Information

MIPhy: identify and quantify rapidly evolving members of large gene families.

Publications

MIPhy: identify and quantify rapidly evolving members of large gene families.

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