A metaanalysis of bat phylogenetics and positive selection based on genomes and transcriptomes from 18 species

Historically, the evolution of bats has been analyzed using a small number of genetic loci for many species or many genetic loci for a few species. Here we present a phylogeny of 18 bat species, each of which is represented in 1,107 orthologous gene alignments used to build the tree. We generated a transcriptome sequence of Hypsignathus monstrosus, the African hammer-headed bat, and additional transcriptome sequence for Rousettus aegyptiacus, the Egyptian fruit bat. We then combined these data with existing genomic and transcriptomic data from 16 other bat species. In the analysis of such datasets, there is no clear consensus on the most reliable computational methods for the curation of quality multiple sequence alignments since these public datasets represent multiple investigators and methods, including different source materials (chromosomal DNA or expressed RNA). Here we lay out a systematic analysis of parameters and produce an advanced pipeline for curating orthologous gene alignments from combined transcriptomic and genomic data, including a software package: the Mismatching Isoform eXon Remover (MIXR). Using this method, we created alignments of 11,677 bat genes, 1,107 of which contain orthologs from all 18 species. Using the orthologous gene alignments created, we assessed bat phylogeny and also performed a holistic analysis of positive selection acting in bat genomes. We found that 181 genes have been subject to positive natural selection. This list is dominated by genes involved in immune responses and genes involved in the production of collagens.

Download Full-text

Positive natural selection in primate genes of the type I interferon response

BMC Ecology and Evolution ◽

10.1186/s12862-021-01783-z ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Elena N. Judd ◽

Alison R. Gilchrist ◽

Nicholas R. Meyerson ◽

Sara L. Sawyer

Keyword(s):

Natural Selection ◽

Positive Selection ◽

Type I Interferon ◽

Interferon Response ◽

Type I ◽

Sequence Alignments ◽

Multiple Sequence ◽

Multiple Sequence Alignments ◽

Interferon Stimulated Genes ◽

Interferon Induction

Abstract Background The Type I interferon response is an important first-line defense against viruses. In turn, viruses antagonize (i.e., degrade, mis-localize, etc.) many proteins in interferon pathways. Thus, hosts and viruses are locked in an evolutionary arms race for dominance of the Type I interferon pathway. As a result, many genes in interferon pathways have experienced positive natural selection in favor of new allelic forms that can better recognize viruses or escape viral antagonists. Here, we performed a holistic analysis of selective pressures acting on genes in the Type I interferon family. We initially hypothesized that the genes responsible for inducing the production of interferon would be antagonized more heavily by viruses than genes that are turned on as a result of interferon. Our logic was that viruses would have greater effect if they worked upstream of the production of interferon molecules because, once interferon is produced, hundreds of interferon-stimulated proteins would activate and the virus would need to counteract them one-by-one. Results We curated multiple sequence alignments of primate orthologs for 131 genes active in interferon production and signaling (herein, “induction” genes), 100 interferon-stimulated genes, and 100 randomly chosen genes. We analyzed each multiple sequence alignment for the signatures of recurrent positive selection. Counter to our hypothesis, we found the interferon-stimulated genes, and not interferon induction genes, are evolving significantly more rapidly than a random set of genes. Interferon induction genes evolve in a way that is indistinguishable from a matched set of random genes (22% and 18% of genes bear signatures of positive selection, respectively). In contrast, interferon-stimulated genes evolve differently, with 33% of genes evolving under positive selection and containing a significantly higher fraction of codons that have experienced selection for recurrent replacement of the encoded amino acid. Conclusion Viruses may antagonize individual products of the interferon response more often than trying to neutralize the system altogether.

Download Full-text

Faculty Opinions recommendation of Evolutionary profiles from the QR factorization of multiple sequence alignments.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.1024515.296730 ◽

2005 ◽

Author(s):

Anne-Catherine Dock-Bregeon

Keyword(s):

Qr Factorization ◽

Sequence Alignments ◽

Multiple Sequence ◽

Multiple Sequence Alignments

Download Full-text

Faculty Opinions recommendation of Protein contact prediction by integrating deep multiple sequence alignments, coevolution and machine learning.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.732011981.793542976 ◽

2018 ◽

Author(s):

Chandra Verma ◽

Suryani Lukman

Keyword(s):

Machine Learning ◽

Sequence Alignments ◽

Multiple Sequence ◽

Contact Prediction ◽

Multiple Sequence Alignments

Download Full-text

SNN-SB: Combining Partial Alignment Using Modified SNN Algorithm with Segment-Based for Multiple Sequence Alignments

Journal of Physics Conference Series ◽

10.1088/1742-6596/1962/1/012048 ◽

2021 ◽

Vol 1962 (1) ◽

pp. 012048

Author(s):

Aziz Nasser Boraik Ali ◽

Hassan Pyar Ali Hassan ◽

Hesham Bahamish

Keyword(s):

Sequence Alignments ◽

Multiple Sequence ◽

Multiple Sequence Alignments ◽

Partial Alignment

Download Full-text

DNCON2_Inter: predicting interchain contacts for homodimeric and homomultimeric protein complexes using multiple sequence alignments of monomers and deep learning

Scientific Reports ◽

10.1038/s41598-021-91827-7 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Farhan Quadir ◽

Raj S. Roy ◽

Randal Halfmann ◽

Jianlin Cheng

Keyword(s):

Deep Learning ◽

Tertiary Structure ◽

Protein Complexes ◽

Complex Structure ◽

Great Success ◽

Sequence Alignments ◽

Multiple Sequence ◽

Multiple Sequence Alignments ◽

Residue Contacts ◽

Evolutionary Features

AbstractDeep learning methods that achieved great success in predicting intrachain residue-residue contacts have been applied to predict interchain contacts between proteins. However, these methods require multiple sequence alignments (MSAs) of a pair of interacting proteins (dimers) as input, which are often difficult to obtain because there are not many known protein complexes available to generate MSAs of sufficient depth for a pair of proteins. In recognizing that multiple sequence alignments of a monomer that forms homomultimers contain the co-evolutionary signals of both intrachain and interchain residue pairs in contact, we applied DNCON2 (a deep learning-based protein intrachain residue-residue contact predictor) to predict both intrachain and interchain contacts for homomultimers using multiple sequence alignment (MSA) and other co-evolutionary features of a single monomer followed by discrimination of interchain and intrachain contacts according to the tertiary structure of the monomer. We name this tool DNCON2_Inter. Allowing true-positive predictions within two residue shifts, the best average precision was obtained for the Top-L/10 predictions of 22.9% for homodimers and 17.0% for higher-order homomultimers. In some instances, especially where interchain contact densities are high, DNCON2_Inter predicted interchain contacts with 100% precision. We also developed Con_Complex, a complex structure reconstruction tool that uses predicted contacts to produce the structure of the complex. Using Con_Complex, we show that the predicted contacts can be used to accurately construct the structure of some complexes. Our experiment demonstrates that monomeric multiple sequence alignments can be used with deep learning to predict interchain contacts of homomeric proteins.

Download Full-text