Probalign: multiple sequence alignment using partition function posterior probabilities

Infrared: a declarative tree decomposition-powered framework for bioinformatics

MOTIVATION

Many bioinformatics problems can be approached as optimization or controlled sampling tasks, and solved exactly and efficiently using Dynamic Programming (DP). However, such exact methods are typically tailored towards specific settings, complex to develop, and hard to implement and adapt to problem variations.

METHODS

We introduce the Infrared framework to overcome such hindrances for a large class of problems. Its underlying paradigm is tailored toward problems that can be declaratively formalized as sparse feature networks, a generalization of constraint networks. Classic Boolean constraints specify a search space, consisting of putative solutions whose evaluation is performed through a combination of features. Problems are then solved using generic cluster tree elimination algorithms over a tree decomposition of the feature network. Their overall complexities are linear on the number of variables, and only exponential in the treewidth of the feature network. For sparse feature networks, associated with low to moderate treewidths, these algorithms allow to find optimal solutions, or generate controlled samples, with practical empirical efficiency.

RESULTS

Implementing these methods, the Infrared software allows Python programmers to rapidly develop exact optimization and sampling applications based on a tree decomposition-based efficient processing. Instead of directly coding specialized algorithms, problems are declaratively modeled as sets of variables over finite domains, whose dependencies are captured by constraints and functions. Such models are then automatically solved by generic DP algorithms. To illustrate the applicability of Infrared in bioinformatics and guide new users, we model and discuss variants of bioinformatics applications. We provide reimplementations and extensions of methods for RNA design, RNA sequence-structure alignment, parsimony-driven inference of ancestral traits in phylogenetic trees/networks, and design of coding sequences. Moreover, we demonstrate multidimensional Boltzmann sampling. These applications of the framework-together with our novel results-underline the practical relevance of Infrared. Remarkably, the achieved complexities are typically equivalent to the ones of specialized algorithms and implementations.

AVAILABILITY

Infrared is available at https://amibio.gitlabpages.inria.fr/Infrared with extensive documentation, including various usage examples and API reference; it can be installed using Conda or from source.

Evolutionary Structure Conservation and Covariance Scores

Effective homology search for non-coding RNAs is frequently not possible via sequence similarity alone. Current methods leverage evolutionary information like structure conservation or covariance scores to identify homologs in organisms that are phylogenetically more distant. In this chapter, we introduce the theoretical background of evolutionary structure conservation and covariance score, and we show hands-on how current methods in the field are applied on example datasets.

Experimental and computational analysis of the ancestry of an evolutionary young enzyme from histidine biosynthesis

The conservation of fold and chemistry of the enzymes associated with histidine biosynthesis suggests that this pathway evolved prior to the diversification of Bacteria, Archaea, and Eukaryotes. The only exception is the histidinol phosphate phosphatase (HolPase). So far, non-homologous HolPases that possess distinct folds and belong to three different protein superfamilies have been identified in various phylogenetic clades. However, their evolution has remained unknown to date. Here, we analyzed the evolutionary history of the HolPase from γ-Proteobacteria (HisB-N). It has been argued that HisB-N and its closest homologue d-glycero-d-manno-heptose-1,7-bisphosphate 7-phosphatase (GmhB) have emerged from the same promiscuous ancestral phosphatase. GmhB variants catalyze the hydrolysis of the anomeric d-glycero-d-manno-heptose-1,7-bisphosphate (αHBP or βHBP) with a strong preference for one anomer (αGmhB or βGmhB). We found that HisB-N from Escherichia coli shows promiscuous activity for βHBP but not αHBP, while βGmhB from Crassaminicella sp. shows promiscuous activity for HolP. Accordingly, a combined phylogenetic tree of αGmhBs, βGmhBs, and HisB-N sequences revealed that HisB-Ns form a compact subcluster derived from βGmhBs. Ancestral sequence reconstruction and in vitro analysis revealed a promiscuous HolPase activity in the resurrected enzymes prior to functional divergence of the successors. The following increase in catalytic efficiency of the HolP turnover is reflected in the shape and electrostatics of the active site predicted by AlphaFold. An analysis of the phylogenetic tree led to a revised evolutionary model that proposes the horizontal gene transfer of a promiscuous βGmhB from δ- to γ-Proteobacteria where it evolved to the modern HisB-N.

MLProbs: A Data-Centric Pipeline for Better Multiple Sequence Alignment

In this paper, we explore using the data-centric approach to tackle the Multiple Sequence Alignment (MSA) construction problem. Unlike the algorithm-centric approach, which reduces the construction problem to a combinatorial optimization problem based on an abstract mathematical model, the data-centric approach explores using classification models trained from existing benchmark data to guide the construction. We identified two simple classifications to help us choose a better alignment tool and determine whether and how much to carry out realignment. We show that shallow machine-learning algorithms suffice to train sensitive models for these classifications. Based on these models, we implemented a new multiple sequence alignment pipeline, called MLProbs. Compared with 10 other popular alignment tools over four benchmark databases (namely, BAliBASE, OXBench, OXBench-X and SABMark), MLProbs consistently gives the highest TC score. More importantly, MLProbs shows non-trivial improvement for protein families with low similarity; in particular, when evaluated against the 1,356 protein families with similarity ≤ 50%, MLProbs achieves a TC score of 56.93, while the next best three tools are in the range of [55.41, 55.91] (increased by more than 1.8%). We also compared the performance of MLProbs and other MSA tools in two real-life applications - Phylogenetic Tree Construction Analysis and Protein Secondary Structure Prediction - and MLProbs also had the best performance. In our study, we used only shallow machine-learning algorithms to train our models. It would be interesting to study whether deep-learning methods can help make further improvements, so we suggest some possible research directions in the conclusion section.

Dynamic genome evolution in a model fern

The large size and complexity of most fern genomes have hampered efforts to elucidate fundamental aspects of fern biology and land plant evolution through genome-enabled research. Here we present a chromosomal genome assembly and associated methylome, transcriptome and metabolome analyses for the model fern species Ceratopteris richardii. The assembly reveals a history of remarkably dynamic genome evolution including rapid changes in genome content and structure following the most recent whole-genome duplication approximately 60 million years ago. These changes include massive gene loss, rampant tandem duplications and multiple horizontal gene transfers from bacteria, contributing to the diversification of defence-related gene families. The insertion of transposable elements into introns has led to the large size of the Ceratopteris genome and to exceptionally long genes relative to other plants. Gene family analyses indicate that genes directing seed development were co-opted from those controlling the development of fern sporangia, providing insights into seed plant evolution. Our findings and annotated genome assembly extend the utility of Ceratopteris as a model for investigating and teaching plant biology.

Phylogenies of the 16S rRNA gene and its hypervariable regions lack concordance with core genome phylogenies

BACKGROUND

The 16S rRNA gene is used extensively in bacterial phylogenetics, in species delineation, and now widely in microbiome studies. However, the gene suffers from intragenomic heterogeneity, and reports of recombination and an unreliable phylogenetic signal are accumulating. Here, we compare core gene phylogenies to phylogenies constructed using core gene concatenations to estimate the strength of signal for the 16S rRNA gene, its hypervariable regions, and all core genes at the intra- and inter-genus levels. Specifically, we perform four intra-genus analyses (Clostridium, n = 65; Legionella, n = 47; Staphylococcus, n = 36; and Campylobacter, n = 17) and one inter-genus analysis [41 core genera of the human gut microbiome (31 families, 17 orders, and 12 classes), n = 82].

RESULTS

At both taxonomic levels, the 16S rRNA gene was recombinant and subject to horizontal gene transfer. At the intra-genus level, the gene showed one of the lowest levels of concordance with the core genome phylogeny (50.7% average). Concordance for hypervariable regions was lower still, with entropy masking providing little to no benefit. A major factor influencing concordance was SNP count, which showed a positive logarithmic association. Using this relationship, we determined that 690 ± 110 SNPs were required for 80% concordance (average 16S rRNA gene SNP count was 254). We also found a wide range in 16S-23S-5S rRNA operon copy number among genomes (1-27). At the inter-genus level, concordance for the whole 16S rRNA gene was markedly higher (73.8% - 10th out of 49 loci); however, the most concordant hypervariable regions (V4, V3-V4, and V1-V2) ranked in the third quartile (62.5 to 60.0%).

CONCLUSIONS

Ramifications of a poor phylogenetic performance for the 16S rRNA gene are far reaching. For example, in addition to incorrect species/strain delineation and phylogenetic inference, it has the potential to confound community diversity metrics if phylogenetic information is incorporated - for example, with popular approaches such as Faith's phylogenetic diversity and UniFrac. Our results highlight the problematic nature of these approaches and their use (along with entropy masking) is discouraged. Lastly, the wide range in 16S rRNA gene copy number among genomes also has a strong potential to confound diversity metrics. Video Abstract.

Multiobjective Formulation of Multiple Sequence Alignment for Phylogeny Inference

Multiple sequence alignment (MSA) is a preliminary task for estimating phylogenies. It is used for homology inference among the sequences of a set of species. Generally, the MSA task is handled as a single-objective optimization process. The alignments computed under one criterion may be different from the alignments generated by other criteria, inferring discordant homologies and thus leading to different hypothesized evolutionary histories relating the sequences. The multiobjective (MO) formulation of MSA has recently been advocated by several researchers, to address this issue. An MO approach independently optimizes multiple (often conflicting) objective functions at the same time and outputs a set of competitive alignments. However, no conceptual or experimental rational from a real-world application perspective has been reported so far for any MO formulation of MSA. This article work investigates the impact of MO formulation in the context of an important scientific problem, namely, phylogeny estimation. Employing popular evolutionary MO algorithms, we show that: 1) trees inferred based on alignments produced by the existing MSA methods used in practice are substantially worse in quality than the trees inferred based on the alignment's output by an MO algorithm and 2) even high-quality alignments (according to popular measures available in the literature) may fail to achieve acceptable accuracy in generating phylogenetic trees. Thus, we essentially ask the following natural question: "can a phylogeny-aware (i.e., application-aware) metric guide in selecting appropriate MO formulations to ensure better phylogeny estimation?" Here, we report a carefully designed extensive experimental study that positively answers this question.

Developments in Algorithms for Sequence Alignment: A Review

The continuous development of sequencing technologies has enabled researchers to obtain large amounts of biological sequence data, and this has resulted in increasing demands for software that can perform sequence alignment fast and accurately. A number of algorithms and tools for sequence alignment have been designed to meet the various needs of biologists. Here, the ideas that prevail in the research of sequence alignment and some quality estimation methods for multiple sequence alignment tools are summarized.

A survey on the algorithm and development of multiple sequence alignment

Multiple sequence alignment (MSA) is an essential cornerstone in bioinformatics, which can reveal the potential information in biological sequences, such as function, evolution and structure. MSA is widely used in many bioinformatics scenarios, such as phylogenetic analysis, protein analysis and genomic analysis. However, MSA faces new challenges with the gradual increase in sequence scale and the increasing demand for alignment accuracy. Therefore, developing an efficient and accurate strategy for MSA has become one of the research hotspots in bioinformatics. In this work, we mainly summarize the algorithms for MSA and its applications in bioinformatics. To provide a structured and clear perspective, we systematically introduce MSA's knowledge, including background, database, metric and benchmark. Besides, we list the most common applications of MSA in the field of bioinformatics, including database searching, phylogenetic analysis, genomic analysis, metagenomic analysis and protein analysis. Furthermore, we categorize and analyze classical and state-of-the-art algorithms, divided into progressive alignment, iterative algorithm, heuristics, machine learning and divide-and-conquer. Moreover, we also discuss the challenges and opportunities of MSA in bioinformatics. Our work provides a comprehensive survey of MSA applications and their relevant algorithms. It could bring valuable insights for researchers to contribute their knowledge to MSA and relevant studies.

A Comprehensive Analysis of Fibrillar Collagens in Lamprey Suggests a Conserved Role in Vertebrate Musculoskeletal Evolution

Vertebrates have distinct tissues which are not present in invertebrate chordates nor other metazoans. The rise of these tissues also coincided with at least one round of whole-genome duplication as well as a suite of lineage-specific segmental duplications. Understanding whether novel genes lead to the origin and diversification of novel cell types, therefore, is of great importance in vertebrate evolution. Here we were particularly interested in the evolution of the vertebrate musculoskeletal system, the muscles and connective tissues that support a diversity of body plans. A major component of the musculoskeletal extracellular matrix (ECM) is fibrillar collagens, a gene family which has been greatly expanded upon in vertebrates. We thus asked whether the repertoire of fibrillar collagens in vertebrates reflects differences in the musculoskeletal system. To test this, we explored the diversity of fibrillar collagens in lamprey, a jawless vertebrate which diverged from jawed vertebrates (gnathostomes) more than five hundred million years ago and has undergone its own gene duplications. Some of the principal components of vertebrate hyaline cartilage are the fibrillar collagens type II and XI, but their presence in cartilage development across all vertebrate taxa has been disputed. We particularly emphasized the characterization of genes in the lamprey hyaline cartilage, testing if its collagen repertoire was similar to that in gnathostomes. Overall, we discovered thirteen fibrillar collagens from all known gene subfamilies in lamprey and were able to identify several lineage-specific duplications. We found that, while the collagen loci have undergone rearrangement, the Clade A genes have remained linked with the hox clusters, a phenomenon also seen in gnathostomes. While the lamprey muscular tissue was largely similar to that seen in gnathostomes, we saw considerable differences in the larval lamprey skeletal tissue, with distinct collagen combinations pertaining to different cartilage types. Our gene expression analyses were unable to identify type II collagen in the sea lamprey hyaline cartilage nor any other fibrillar collagen during chondrogenesis at the stages observed, meaning that sea lamprey likely no longer require these genes during early cartilage development. Our findings suggest that fibrillar collagens were multifunctional across the musculoskeletal system in the last common ancestor of vertebrates and have been largely conserved, but these genes alone cannot explain the origin of novel cell types.

Accurate consistency-based MSA reducing the memory footprint

BACKGROUND AND OBJECTIVE

The emergence of Next-Generation sequencing has created a push for faster and more accurate multiple sequence alignment tools. The growing number of sequences and their longer sizes, which require the use of increased system resources and produce less accurate results, are heavily challenging to these applications. Consistency-based methods have the most intensive CPU and memory usage requirements. We hypothesize that library reductions can enhance the scalability and performance of consistency-based multiple sequence alignment tools; however, we have previously shown a noticeable impact on the accuracy when extreme reductions were performed.

METHODS

In this study, we propose Matrix-Based T-Coffee, a consistency-based method that uses library reductions in conjunction with a complementary objective function. The proposed method, implemented in T-Coffee, can mitigate the accuracy loss caused by low memory resources.

RESULTS

The use of a complementary objective function with a library reduction of ≥30% improved the accuracy of T-Coffee. Interestingly, ≥50% library reduction achieved lower execution times and better overall scalability.

CONCLUSIONS

Matrix-Based T-Coffee benefits from accurate alignments while achieving better scalability. This leads to a reduction in memory footprint and execution time. In addition, these enhancements could be applied to other aligners based on consistency.

Metaheuristics for multiple sequence alignment: A systematic review

The Multiple Sequence Alignment (MSA) is a key task in bioinformatics, because it is used in different important biological analysis, such as function and structure prediction of unknown proteins. There are several approaches to perform MSA and the use of metaheuristics stands out because of the search ability of these methods, which generally leads to good results in a reasonable amount of time. This paper presents a Systematic Literature Review (SLR) on metaheuristics for MSA, compiling relevant works published between 2014 and 2019. The results of our SLR show the constant interest in this subject, due to the several recent publications that use different metaheuristics to obtain more accurate alignments. Moreover, the final results of our SLR show a multi-objective and hybrid approaches trends, which generally leads these methods to achieve even better results. Thus, we show in this work how the use of metaheuristics to perform MSA still remains an important and promising open research field.

Multiple sequence alignment using enhanced bird swarm align algorithm

Discovering structural, functional and evolutionary information in biological sequences have been considered as a core research area in Bioinformatics. Multiple Sequence Alignment (MSA) tries to align all sequences in a given query set to provide us ease in annotation of new sequences. Traditional methods to find the optimal alignment are computationally expensive in real time. This research presents an enhanced version of Bird Swarm Algorithm (BSA), based on bio inspired optimization. Enhanced Bird Swarm Align Algorithm (EBSAA) is proposed for multiple sequence alignment problem to determine the optimal alignment among different sequences. Twenty-one different datasets have been used in order to compare performance of EBSAA with Genetic Algorithm (GA) and Particle Swarm Align Algorithm (PSAA). The proposed technique results in better alignment as compared to GA and PSAA in most of the cases.

Direct molecular evidence for an ancient, conserved developmental toolkit controlling post-transcriptional gene regulation in land plants

In plants, miRNA production is orchestrated by a suite of proteins that control transcription of the pri-miRNA gene, post-transcriptional processing and nuclear export of the mature miRNA. Post-transcriptional processing of miRNAs is controlled by a pair of physically interacting proteins, hyponastic leaves 1 (HYL1) and Dicer-like 1 (DCL1). However, the evolutionary history and structural basis of the HYL1–DCL1 interaction is unknown. Here we use ancestral sequence reconstruction and functional characterization of ancestral HYL1 in vitro and in Arabidopsis thaliana to better understand the origin and evolution of the HYL1–DCL1 interaction and its impact on miRNA production and plant development. We found the ancestral plant HYL1 evolved high affinity for both double-stranded RNA (dsRNA) and its DCL1 partner before the divergence of mosses from seed plants (∼500 Ma), and these high-affinity interactions remained largely conserved throughout plant evolutionary history. Structural modeling and molecular binding experiments suggest that the second of two dsRNA-binding motifs (DSRMs) in HYL1 may interact tightly with the first of two C-terminal DCL1 DSRMs to mediate the HYL1–DCL1 physical interaction necessary for efficient miRNA production. Transgenic expression of the nearly 200 Ma-old ancestral flowering-plant HYL1 in A. thaliana was sufficient to rescue many key aspects of plant development disrupted by HYL1⁻ knockout and restored near-native miRNA production, suggesting that the functional partnership of HYL1–DCL1 originated very early in and was strongly conserved throughout the evolutionary history of terrestrial plants. Overall, our results are consistent with a model in which miRNA-based gene regulation evolved as part of a conserved plant “developmental toolkit.”

Alignment-Integrated Reconstruction of Ancestral Sequences Improves Accuracy

Ancestral sequence reconstruction (ASR) uses an alignment of extant protein sequences, a phylogeny describing the history of the protein family and a model of the molecular-evolutionary process to infer the sequences of ancient proteins, allowing researchers to directly investigate the impact of sequence evolution on protein structure and function. Like all statistical inferences, ASR can be sensitive to violations of its underlying assumptions. Previous studies have shown that, whereas phylogenetic uncertainty has only a very weak impact on ASR accuracy, uncertainty in the protein sequence alignment can more strongly affect inferred ancestral sequences. Here, we show that errors in sequence alignment can produce errors in ASR across a range of realistic and simplified evolutionary scenarios. Importantly, sequence reconstruction errors can lead to errors in estimates of structural and functional properties of ancestral proteins, potentially undermining the reliability of analyses relying on ASR. We introduce an alignment-integrated ASR approach that combines information from many different sequence alignments. We show that integrating alignment uncertainty improves ASR accuracy and the accuracy of downstream structural and functional inferences, often performing as well as highly accurate structure-guided alignment. Given the growing evidence that sequence alignment errors can impact the reliability of ASR studies, we recommend that future studies incorporate approaches to mitigate the impact of alignment uncertainty. Probabilistic modeling of insertion and deletion events has the potential to radically improve ASR accuracy when the model reflects the true underlying evolutionary history, but further studies are required to thoroughly evaluate the reliability of these approaches under realistic conditions.

Lamprey lecticans link new vertebrate genes to the origin and elaboration of vertebrate tissues

The evolution of vertebrates from an invertebrate chordate ancestor involved the evolution of new organs, tissues, and cell types. It was also marked by the origin and duplication of new gene families. If, and how, these morphological and genetic innovations are related is an unresolved question in vertebrate evolution. Hyaluronan is an extracellular matrix (ECM) polysaccharide important for water homeostasis and tissue structure. Vertebrates possess a novel family of hyaluronan binding proteins called Lecticans, and studies in jawed vertebrates (gnathostomes) have shown they function in many of the cells and tissues that are unique to vertebrates. This raises the possibility that the origin and/or expansion of this gene family helped drive the evolution of these vertebrate novelties. In order to better understand the evolution of the lectican gene family, and its role in the evolution of vertebrate morphological novelties, we investigated the phylogeny, genomic arrangement, and expression patterns of all lecticans in the sea lamprey (Petromyzon marinus), a jawless vertebrate. Though both P. marinus and gnathostomes each have four lecticans, our phylogenetic and syntenic analyses are most consistent with the independent duplication of one of more lecticans in the lamprey lineage. Despite the likely independent expansion of the lamprey and gnathostome lectican families, we find highly conserved expression of lecticans in vertebrate-specific and mesenchyme-derived tissues. We also find that, unlike gnathostomes, lamprey expresses its lectican paralogs in distinct subpopulations of head skeleton precursors, potentially reflecting an ancestral diversity of skeletal tissue types. Together, these observations suggest that the ancestral pre-duplication lectican had a complex expression pattern, functioned to support mesenchymal histology, and likely played a role in the evolution of vertebrate-specific cell and tissue types.

Revisiting Evaluation of Multiple Sequence Alignment Methods

Multiple sequence alignment is a core first step in many bioinformatics analyses, and errors in these alignments can have negative consequences for scientific studies. In this article, we review some of the recent literature evaluating multiple sequence alignment methods and identify specific challenges that arise when performing these evaluations. In particular, we discuss the different trends observed in simulation studies and when using biological benchmarks. Overall, we find that multiple sequence alignment, far from being a "solved problem," would benefit from new attention.

Aethionema arabicum genome annotation using PacBio full-length transcripts provides a valuable resource for seed dormancy and Brassicaceae evolution research

Aethionema arabicum is an important model plant for Brassicaceae trait evolution, particularly of seed (development, regulation, germination, dormancy) and fruit (development, dehiscence mechanisms) characters. Its genome assembly was recently improved but the gene annotation was not updated. Here, we improved the Ae. arabicum gene annotation using 294 RNA-seq libraries and 136 307 full-length PacBio Iso-seq transcripts, increasing BUSCO completeness by 11.6% and featuring 5606 additional genes. Analysis of orthologs showed a lower number of genes in Ae. arabicum than in other Brassicaceae, which could be partially explained by loss of homeologs derived from the At-α polyploidization event and by a lower occurrence of tandem duplications after divergence of Aethionema from the other Brassicaceae. Benchmarking of MADS-box genes identified orthologs of FUL and AGL79 not found in previous versions. Analysis of full-length transcripts related to ABA-mediated seed dormancy discovered a conserved isoform of PIF6-β and antisense transcripts in ABI3, ABI4 and DOG1, among other cases found of different alternative splicing between Turkey and Cyprus ecotypes. The presented data allow alternative splicing mining and proposition of numerous hypotheses to research evolution and functional genomics. Annotation data and sequences are available at the Ae. arabicum DB (https://plantcode.online.uni-marburg.de/aetar_db).

Fast and Accurate Multiple Sequence Alignment with MSAProbs-MPI

Multiple sequence alignment (MSA) is a central step in many bioinformatics and computational biology analyses. Although there exist many methods to perform MSA, most of them fail when dealing with large datasets due to their high computational cost. MSAProbs-MPI is a publicly available tool ( http://msaprobs.sourceforge.net ) that provides highly accurate results in relatively short runtime thanks to exploiting the hardware resources of multicore clusters. In this chapter, I explain the statistical and biological concepts employed in MSAProbs-MPI to complete the alignments, as well as the high-performance computing techniques used to accelerate it. Moreover, I provide some hints about the configuration parameters that should be used to guarantee high-performance executions.

SpliVert: A Protein Multiple Sequence Alignment Refinement Method Based on Splitting-Splicing Vertically

BACKGROUND

Multiple Sequence Alignment (MSA) is a fundamental task in bioinformatics and is required for many biological analysis tasks. The more accurate the alignments are, the more credible the downstream analyses. Most protein MSA algorithms realign an alignment to refine it by dividing it into two groups horizontally and then realign the two groups. However, this strategy does not consider that different regions of the sequences have different conservation; this property may lead to incorrect residue-residue or residue-gap pairs, which cannot be corrected by this strategy.

OBJECTIVE

In this article, our motivation is to develop a novel refinement method based on splitting- splicing vertically.

METHODS

Here, we present a novel refinement method based on splitting-splicing vertically, called SpliVert. For an alignment, we split it vertically into 3 parts, remove the gap characters in the middle, realign the middle part alone, and splice the realigned middle parts with the other two initial pieces to obtain a refined alignment. In the realign procedure of our method, the aligner will only focus on a certain part, ignoring the disturbance of the other parts, which could help fix the incorrect pairs.

RESULTS

We tested our refinement strategy for 2 leading MSA tools on 3 standard benchmarks, according to the commonly used average SP (and TC) score. The results show that given appropriate proportions to split the initial alignment, the average scores are increased comparably or slightly after using our method. We also compared the alignments refined by our method with alignments directly refined by the original alignment tools. The results suggest that using our SpliVert method to refine alignments can also outperform direct use of the original alignment tools.

CONCLUSION

The results reveal that splitting vertically and realigning part of the alignment is a good strategy for the refinement of protein multiple sequence alignments.

A two-way molecular dialogue between embryo and endosperm is required for seed development

The plant embryonic cuticle is a hydrophobic barrier deposited de novo by the embryo during seed development. At germination, it protects the seedling from water loss and is, thus, critical for survival. Embryonic cuticle formation is controlled by a signaling pathway involving the ABNORMAL LEAF SHAPE1 subtilase and the two GASSHO receptor-like kinases. We show that a sulfated peptide, TWISTED SEED1 (TWS1), acts as a GASSHO ligand. Cuticle surveillance depends on the action of the subtilase, which, unlike the TWS1 precursor and the GASSHO receptors, is not produced in the embryo but in the neighboring endosperm. Subtilase-mediated processing of the embryo-derived TWS1 precursor releases the active peptide, triggering GASSHO-dependent cuticle reinforcement in the embryo. Thus, a bidirectional molecular dialogue between embryo and endosperm safeguards cuticle integrity before germination.

Independent origin of MIRNA genes controlling homologous target genes by partial inverted duplication of antisense-transcribed sequences

Some microRNAs (miRNAs) are key regulators of developmental processes, mainly by controlling the accumulation of transcripts encoding transcription factors that are important for morphogenesis. MADS-box genes encode a family of transcription factors which control diverse developmental processes in flowering plants. Here we study the convergent evolution of two MIRNA (MIR) gene families, named MIR444 and MIR824, targeting members of the same clade of MIKC^C -group MADS-box genes. We show that these two MIR genes most likely originated independently in monocots (MIR444) and in Brassicales (eudicots, MIR824). We provide evidence that, in both cases, the future target gene was transcribed in antisense prior to the evolution of the MIR genes. Both MIR genes then likely originated by a partial inverted duplication of their target genes, resulting in natural antisense organization of the newly evolved MIR gene and its target gene at birth. We thus propose a model for the origin of MIR genes, MEPIDAS (MicroRNA Evolution by Partial Inverted Duplication of Antisense-transcribed Sequences). MEPIDAS is a refinement of the inverted duplication hypothesis. According to MEPIDAS, a MIR gene evolves at a genomic locus at which the future target gene is also transcribed in the antisense direction. A partial inverted duplication at this locus causes the antisense transcript to fold into a stem-loop structure that is recognized by the miRNA biogenesis machinery to produce a miRNA that regulates the gene at this locus. Our analyses exemplify how to elucidate the origin of conserved miRNAs by comparative genomics and will guide future studies. OPEN RESEARCH BADGE: This article has earned an Open Data Badge for making publicly available the digitally-shareable data necessary to reproduce the reported results. The data is available at https://www.ncbi.nlm.nih.gov/genbank/.

LMAP_S: Lightweight Multigene Alignment and Phylogeny eStimation

Background

Recent advances in genome sequencing technologies and the cost drop in high-throughput sequencing continue to give rise to a deluge of data available for downstream analyses. Among others, evolutionary biologists often make use of genomic data to uncover phenotypic diversity and adaptive evolution in protein-coding genes. Therefore, multiple sequence alignments (MSA) and phylogenetic trees (PT) need to be estimated with optimal results. However, the preparation of an initial dataset of multiple sequence file(s) (MSF) and the steps involved can be challenging when considering extensive amount of data. Thus, it becomes necessary the development of a tool that removes the potential source of error and automates the time-consuming steps of a typical workflow with high-throughput and optimal MSA and PT estimations.

Results

We introduce LMAP_S (Lightweight Multigene Alignment and Phylogeny eStimation), a user-friendly command-line and interactive package, designed to handle an improved alignment and phylogeny estimation workflow: MSF preparation, MSA estimation, outlier detection, refinement, consensus, phylogeny estimation, comparison and editing, among which file and directory organization, execution, manipulation of information are automated, with minimal manual user intervention. LMAP_S was developed for the workstation multi-core environment and provides a unique advantage for processing multiple datasets. Our software, proved to be efficient throughout the workflow, including, the (unlimited) handling of more than 20 datasets.

Conclusions

We have developed a simple and versatile LMAP_S package enabling researchers to effectively estimate multiple datasets MSAs and PTs in a high-throughput fashion. LMAP_S integrates more than 25 software providing overall more than 65 algorithm choices distributed in five stages. At minimum, one FASTA file is required within a single input directory. To our knowledge, no other software combines MSA and phylogeny estimation with as many alternatives and provides means to find optimal MSAs and phylogenies. Moreover, we used a case study comparing methodologies that highlighted the usefulness of our software. LMAP_S has been developed as an open-source package, allowing its integration into more complex open-source bioinformatics pipelines. LMAP_S package is released under GPLv3 license and is freely available at https://lmap-s.sourceforge.io/.

ProbPFP: a multiple sequence alignment algorithm combining hidden Markov model optimized by particle swarm optimization with partition function

BACKGROUND

During procedures for conducting multiple sequence alignment, that is so essential to use the substitution score of pairwise alignment. To compute adaptive scores for alignment, researchers usually use Hidden Markov Model or probabilistic consistency methods such as partition function. Recent studies show that optimizing the parameters for hidden Markov model, as well as integrating hidden Markov model with partition function can raise the accuracy of alignment. The combination of partition function and optimized HMM, which could further improve the alignment's accuracy, however, was ignored by these researches.

RESULTS

A novel algorithm for MSA called ProbPFP is presented in this paper. It intergrate optimized HMM by particle swarm with partition function. The algorithm of PSO was applied to optimize HMM's parameters. After that, the posterior probability obtained by the HMM was combined with the one obtained by partition function, and thus to calculate an integrated substitution score for alignment. In order to evaluate the effectiveness of ProbPFP, we compared it with 13 outstanding or classic MSA methods. The results demonstrate that the alignments obtained by ProbPFP got the maximum mean TC scores and mean SP scores on these two benchmark datasets: SABmark and OXBench, and it got the second highest mean TC scores and mean SP scores on the benchmark dataset BAliBASE. ProbPFP is also compared with 4 other outstanding methods, by reconstructing the phylogenetic trees for six protein families extracted from the database TreeFam, based on the alignments obtained by these 5 methods. The result indicates that the reference trees are closer to the phylogenetic trees reconstructed from the alignments obtained by ProbPFP than the other methods.

CONCLUSIONS

We propose a new multiple sequence alignment method combining optimized HMM and partition function in this paper. The performance validates this method could make a great improvement of the alignment's accuracy.

A Dead Gene Walking: Convergent Degeneration of a Clade of MADS-Box Genes in Crucifers

Genes are "born," and eventually they "die." These processes shape the phenotypic evolution of organisms and are hence of great biological interest. If genes die in plants, they generally do so quite rapidly. Here, we describe the fate of GOA-like genes that evolve in a dramatically different manner. GOA-like genes belong to the subfamily of Bsister genes of MIKC-type MADS-box genes. Typical MIKC-type genes encode conserved transcription factors controlling plant development. We show that ABS-like genes, a clade of Bsister genes, are indeed highly conserved in crucifers (Brassicaceae) maintaining the ancestral function of Bsister genes in ovule and seed development. In contrast, their closest paralogs, the GOA-like genes, have been undergoing convergent gene death in Brassicaceae. Intriguingly, erosion of GOA-like genes occurred after millions of years of coexistence with ABS-like genes. We thus describe Delayed Convergent Asymmetric Degeneration, a so far neglected but possibly frequent pattern of duplicate gene evolution that does not fit classical scenarios. Delayed Convergent Asymmetric Degeneration of GOA-like genes may have been initiated by a reduction in the expression of an ancestral GOA-like gene in the stem group of Brassicaceae and driven by dosage subfunctionalization. Our findings have profound implications for gene annotations in genomics, interpreting patterns of gene evolution and using genes in phylogeny reconstructions of species.

Evaluating Statistical Multiple Sequence Alignment in Comparison to Other Alignment Methods on Protein Data Sets

The estimation of multiple sequence alignments of protein sequences is a basic step in many bioinformatics pipelines, including protein structure prediction, protein family identification, and phylogeny estimation. Statistical coestimation of alignments and trees under stochastic models of sequence evolution has long been considered the most rigorous technique for estimating alignments and trees, but little is known about the accuracy of such methods on biological benchmarks. We report the results of an extensive study evaluating the most popular protein alignment methods as well as the statistical coestimation method BAli-Phy on 1192 protein data sets from established benchmarks as well as on 120 simulated data sets. Our study (which used more than 230 CPU years for the BAli-Phy analyses alone) shows that BAli-Phy has better precision and recall (with respect to the true alignments) than the other alignment methods on the simulated data sets but has consistently lower recall on the biological benchmarks (with respect to the reference alignments) than many of the other methods. In other words, we find that BAli-Phy systematically underaligns when operating on biological sequence data but shows no sign of this on simulated data. There are several potential causes for this change in performance, including model misspecification, errors in the reference alignments, and conflicts between structural alignment and evolutionary alignments, and future research is needed to determine the most likely explanation. We conclude with a discussion of the potential ramifications for each of these possibilities. [BAli-Phy; homology; multiple sequence alignment; protein sequences; structural alignment.]

Resolving Phylogenetic Relationships for Streptococcus mitis and Streptococcus oralis through Core- and Pan-Genome Analyses

Taxonomic and phylogenetic relationships of Streptococcus mitis and Streptococcus oralis have been difficult to establish biochemically and genetically. We used core-genome analyses of S. mitis and S. oralis, as well as the closely related species Streptococcus pneumoniae and Streptococcus parasanguinis, to clarify the phylogenetic relationships between S. mitis and S. oralis, as well as within subclades of S. oralis. All S. mitis (n = 67), S. oralis (n = 89), S. parasanguinis (n = 27), and 27 S. pneumoniae genome assemblies were downloaded from NCBI and reannotated. All genes were delineated into homologous clusters and maximum-likelihood phylogenies built from putatively nonrecombinant core gene sets. Population structure was determined using Bayesian genome clustering, and patristic distance was calculated between populations. Population-specific gene content was assessed using a phylogenetic-based genome-wide association approach. Streptococcus mitis and S. oralis formed distinct clades, but species mixing suggests taxonomic misassignment. Patristic distance between populations suggests that S. oralis subsp. dentisani is a distinct species, whereas S. oralis subsp. tigurinus and subsp. oralis are supported as subspecies, and that S. mitis comprises two subspecies. None of the genes within the pan-genomes of S. mitis and S. oralis could be statistically correlated with either, and the dispensable genomes showed extensive variation among isolates. These are likely important factors contributing to established overlap in biochemical characteristics for these taxa. Based on core-genome analysis, the substructure of S. oralis and S. mitis should be redefined, and species assignments within S. oralis and S. mitis should be made based on whole-genome analysis to be robust to misassignment.

A member of the CONSTANS-Like protein family is a putative regulator of reactive oxygen species homeostasis and spaceflight physiological adaptation

A feature of the physiological adaptation to spaceflight in Arabidopsis thaliana (Arabidopsis) is the induction of reactive oxygen species (ROS)-associated gene expression. The patterns of ROS-associated gene expression vary among Arabidopsis ecotypes, and the role of ROS signalling in spaceflight acclimation is unknown. What could differences in ROS gene regulation between ecotypes on orbit reveal about physiological adaptation to novel environments? Analyses of ecotype-dependent responses to spaceflight resulted in the elucidation of a previously uncharacterized gene (OMG1) as being ROS-associated. The OMG1 5' flanking region is an active promoter in cells where ROS activity is commonly observed, such as in pollen tubes, root hairs, and in other tissues upon wounding. qRT-PCR analyses revealed that upon wounding on Earth, OMG1 is an apparent transcriptional regulator of MYB77 and GRX480, which are associated with the ROS pathway. Fluorescence-based ROS assays show that OMG1 affects ROS production. Phylogenetic analysis of OMG1 and closely related homologs suggests that OMG1 is a distant, unrecognized member of the CONSTANS-Like protein family, a member that arose via gene duplication early in the angiosperm lineage and subsequently lost its first DNA-binding B-box1 domain. These data illustrate that members of the rapidly evolving COL protein family play a role in regulating ROS pathway functions, and their differential regulation on orbit suggests a role for ROS signalling in spaceflight physiological adaptation.

High-Throughput Reconstruction of Ancestral Protein Sequence, Structure, and Molecular Function

Ancestral protein sequence reconstruction is a powerful technique for explicitly testing hypotheses about the evolution of molecular function, allowing researchers to meticulously dissect how historical changes in protein sequence impacted functional repertoire by altering the protein's 3D structure. These techniques have provided concrete, experimentally validated insights into ancient evolutionary processes and help illuminate the complex relationship between protein sequence, structure, and function. Inferring the protein family phylogenies on which ancestral sequence reconstruction depends and reconstructing the sequences, themselves, are amenable to high-throughput computational analysis. However, determining the structures of ancestral-reconstructed proteins and characterizing their functions typically rely on time-consuming and expensive laboratory analyses, limiting most current studies to examining a relatively small number of specific hypotheses. For this reason, we have little detailed, unbiased information about how molecular function evolves across large protein family phylogenies. Here we describe a generalized protocol that integrates ancestral sequence reconstruction with structural homology modeling and structure-based molecular affinity prediction to characterize historical changes in protein function across families with thousands of individual sequences. We highlight key steps in the analysis protocol requiring particularly careful attention to avoid introducing potential errors as well as steps for which computationally efficient subroutines can be substituted for more intensive approaches, allowing researchers to scale the analysis up or down, depending on available resources and requirements for reproducibility and scientific rigor. In our view, this approach provides a compelling compliment to more laboratory-intensive procedures, generating important contextual information that can help guide detailed experiments.

A hybrid solver for protein multiple sequence alignment problem

In this work, a novel hybrid model called PSOSA for solving multiple sequence alignment (MSA) problem is proposed. The developed approach is a combination between particle swarm optimization (PSO) algorithm and simulated annealing (SA) technique. In our PSOSA approach, PSO is exploited in global search, but it is easily trapped into local optimum and may lead to premature convergence. SA is incorporated as local improvement approach to overcome local optimum problem and intensify the search in local regions to improve solution quality. Numerical results on BAliBASE benchmark have shown the effectiveness of the proposed method and its ability to achieve good quality solutions when compared with those given by other existing methods.

SVM-dependent pairwise HMM: an application to protein pairwise alignments

Motivation

Methods able to provide reliable protein alignments are crucial for many bioinformatics applications. In the last years many different algorithms have been developed and various kinds of information, from sequence conservation to secondary structure, have been used to improve the alignment performances. This is especially relevant for proteins with highly divergent sequences. However, recent works suggest that different features may have different importance in diverse protein classes and it would be an advantage to have more customizable approaches, capable to deal with different alignment definitions.

Results

Here we present Rigapollo, a highly flexible pairwise alignment method based on a pairwise HMM-SVM that can use any type of information to build alignments. Rigapollo lets the user decide the optimal features to align their protein class of interest. It outperforms current state of the art methods on two well-known benchmark datasets when aligning highly divergent sequences.

Availability and implementation

A Python implementation of the algorithm is available at http://ibsquare.be/rigapollo.

Contact

wim.vranken@vub.be.

Supplementary information

Supplementary data are available at Bioinformatics online.

Scalable Consistency in T-Coffee Through Apache Spark and Cassandra Database

Next-generation sequencing, also known as high-throughput sequencing, has increased the volume of genetic data processed by sequencers. In the bioinformatic scientific area, highly rated multiple sequence alignment tools, such as MAFFT, ProbCons, and T-Coffee (TC), use the probabilistic consistency as a prior step to the progressive alignment stage to improve the final accuracy. However, such methods are severely limited by the memory required to store the consistency information. Big data processing and persistence techniques are used to manage and store the huge amount of information that is generated. Although these techniques have significant advantages, few biological applications have adopted them. In this article, a novel approach named big data tree-based consistency objective function for alignment evaluation (BDT-Coffee) is presented. BDT-Coffee is based on the integration of consistency information through Cassandra database in TC, previously generated by the MapReduce processing paradigm, to enable large data sets to be processed with the aim of improving the performance and scalability of the original algorithm.

Pairwise alignment for very long nucleic acid sequences

Sequence alignment is one of the fundamental problems in computational biology and has numerous applications. The Smith-Waterman algorithm generates optimal local alignment for pairwise alignment task and has become a standard algorithm in its field. However, the current version of the Smith-Waterman algorithm demands a significant amount of memory and is not suitable for alignment of very long sequences. On the hand, the recent DNA sequencing technologies have produced vast amounts of biological sequences. Some nucleic acid sequences are very long and cannot employ the Smith-Waterman algorithm. To this end, this study proposes the PAAVLS algorithm that follows the dynamic programming technique employed by the Smith-Waterman algorithm and largely reduces the demand of memory. The proposed PAAVLS algorithm can be employed for alignment of very long sequences, i.e., sequences contain more than 100,000,000 nucleotides, on a personal computer. Additionally, the running time of the proposed PAAVLS algorithm is comparable with the running time of the standard Smith-Waterman algorithm.

Unifying the global phylogeny and environmental distribution of ammonia-oxidising archaea based on amoA genes

Ammonia-oxidising archaea (AOA) are ubiquitous and abundant in nature and play a major role in nitrogen cycling. AOA have been studied intensively based on the amoA gene (encoding ammonia monooxygenase subunit A), making it the most sequenced functional marker gene. Here, based on extensive phylogenetic and meta-data analyses of 33,378 curated archaeal amoA sequences, we define a highly resolved taxonomy and uncover global environmental patterns that challenge many earlier generalisations. Particularly, we show: (i) the global frequency of AOA is extremely uneven, with few clades dominating AOA diversity in most ecosystems; (ii) characterised AOA do not represent most predominant clades in nature, including soils and oceans; (iii) the functional role of the most prevalent environmental AOA clade remains unclear; and (iv) AOA harbour molecular signatures that possibly reflect phenotypic traits. Our work synthesises information from a decade of research and provides the first integrative framework to study AOA in a global context.

A Characteristic-Based Framework for Multiple Sequence Aligners

The multiple sequence alignment is a well-known bioinformatics problem that consists in the alignment of three or more biological sequences (protein or nucleic acid). In the literature, a number of tools have been proposed for dealing with this biological sequence alignment problem, such as progressive methods, consistency-based methods, or iterative methods; among others. These aligners often use a default parameter configuration for all the input sequences to align. However, the default configuration is not always the best choice, the alignment accuracy of the tool may be highly boosted if specific parameter configurations are used, depending on the biological characteristics of the input sequences. In this paper, we propose a characteristic-based framework for multiple sequence aligners. The idea of the framework is, given an input set of unaligned sequences, extract its characteristics and run the aligner with the best parameter configuration found for another set of unaligned sequences with similar characteristics. In order to test the framework, we have used the well-known multiple sequence comparison by log-expectation (MUSCLE) v3.8 aligner with different benchmarks, such as benchmark alignments database v3.0, protein reference alignment benchmark v4.0, and sequence alignment benchmark v1.65. The results shown that the alignment accuracy and conservation of MUSCLE might be greatly improved with the proposed framework, specially in those scenarios with a low percentage of identity. The characteristic-based framework for multiple sequence aligners is freely available for downloading at http://arco.unex.es/arl/fwk-msa/cbf-msa.zip.

DotAligner: identification and clustering of RNA structure motifs

The diversity of processed transcripts in eukaryotic genomes poses a challenge for the classification of their biological functions. Sparse sequence conservation in non-coding sequences and the unreliable nature of RNA structure predictions further exacerbate this conundrum. Here, we describe a computational method, DotAligner, for the unsupervised discovery and classification of homologous RNA structure motifs from a set of sequences of interest. Our approach outperforms comparable algorithms at clustering known RNA structure families, both in speed and accuracy. It identifies clusters of known and novel structure motifs from ENCODE immunoprecipitation data for 44 RNA-binding proteins.

Bacterial Foraging Optimization -Genetic Algorithm for Multiple Sequence Alignment with Multi-Objectives

This research work focus on the multiple sequence alignment, as developing an exact multiple sequence alignment for different protein sequences is a difficult computational task. In this research, a hybrid algorithm named Bacterial Foraging Optimization-Genetic Algorithm (BFO-GA) algorithm is aimed to improve the multi-objectives and carrying out measures of multiple sequence alignment. The proposed algorithm employs multi-objectives such as variable gap penalty minimization, maximization of similarity and non-gap percentage. The proposed BFO-GA algorithm is measured with various MSA methods such as T-Coffee, Clustal Omega, Muscle, K-Align, MAFFT, GA, ACO, ABC and PSO. The experiments were taken on four benchmark datasets such as BAliBASE 3.0, Prefab 4.0, SABmark 1.65 and Oxbench 1.3 databases and the outcomes prove that the proposed BFO-GA algorithm obtains better statistical significance results as compared with the other well-known methods. This research study also evaluates the practicability of the alignments of BFO-GA by applying the optimal sequence to predict the phylogenetic tree by using ClustalW2 Phylogeny tool and compare with the existing algorithms by using the Robinson-Foulds (RF) distance performance metric. Lastly, the statistical implication of the proposed algorithm is computed by using the Wilcoxon Matched-Pair Signed- Rank test and also it infers better results.

QuickProbs 2: Towards rapid construction of high-quality alignments of large protein families

The ever-increasing size of sequence databases caused by the development of high throughput sequencing, poses to multiple alignment algorithms one of the greatest challenges yet. As we show, well-established techniques employed for increasing alignment quality, i.e., refinement and consistency, are ineffective when large protein families are investigated. We present QuickProbs 2, an algorithm for multiple sequence alignment. Based on probabilistic models, equipped with novel column-oriented refinement and selective consistency, it offers outstanding accuracy. When analysing hundreds of sequences, Quick-Probs 2 is noticeably better than ClustalΩ and MAFFT, the previous leaders for processing numerous protein families. In the case of smaller sets, for which consistency-based methods are the best performing, QuickProbs 2 is also superior to the competitors. Due to low computational requirements of selective consistency and utilization of massively parallel architectures, presented algorithm has similar execution times to ClustalΩ, and is orders of magnitude faster than full consistency approaches, like MSAProbs or PicXAA. All these make QuickProbs 2 an excellent tool for aligning families ranging from few, to hundreds of proteins.

Comparative transcriptomics of elasmobranchs and teleosts highlight important processes in adaptive immunity and regional endothermy

BACKGROUND

Comparative genomic and/or transcriptomic analyses involving elasmobranchs remain limited, with genome level comparisons of the elasmobranch immune system to that of higher vertebrates, non-existent. This paper reports a comparative RNA-seq analysis of heart tissue from seven species, including four elasmobranchs and three teleosts, focusing on immunity, but concomitantly seeking to identify genetic similarities shared by the two lamnid sharks and the single billfish in our study, which could be linked to convergent evolution of regional endothermy.

RESULTS

Across seven species, we identified an average of 10,877 Swiss-Prot annotated genes from an average of 32,474 open reading frames within each species' heart transcriptome. About half of these genes were shared between all species while the remainder included functional differences between our groups of interest (elasmobranch vs. teleost and endotherms vs. ectotherms) as revealed by Gene Ontology (GO) and selection analyses. A repeatedly represented functional category, in both the uniquely expressed elasmobranch genes (total of 259) and the elasmobranch GO enrichment results, involved antibody-mediated immunity, either in the recruitment of immune cells (Fc receptors) or in antigen presentation, including such terms as "antigen processing and presentation of exogenous peptide antigen via MHC class II", and such genes as MHC class II, HLA-DPB1. Molecular adaptation analyses identified three genes in elasmobranchs with a history of positive selection, including legumain (LGMN), a gene with roles in both innate and adaptive immunity including producing antigens for presentation by MHC class II. Comparisons between the endothermic and ectothermic species revealed an enrichment of GO terms associated with cardiac muscle contraction in endotherms, with 19 genes expressed solely in endotherms, several of which have significant roles in lipid and fat metabolism.

CONCLUSIONS

This collective comparative evidence provides the first multi-taxa transcriptomic-based perspective on differences between elasmobranchs and teleosts, and suggests various unique features associated with the adaptive immune system of elasmobranchs, pointing in particular to the potential importance of MHC Class II. This in turn suggests that expanded comparative work involving additional tissues, as well as genome sequencing of multiple elasmobranch species would be productive in elucidating the regulatory and genome architectural hallmarks of elasmobranchs.

Resurrecting ancestral structural dynamics of an antiviral immune receptor: adaptive binding pocket reorganization repeatedly shifts RNA preference

Background

Although resurrecting ancestral proteins is a powerful tool for understanding the molecular-functional evolution of gene families, nearly all studies have examined proteins functioning in relatively stable biological processes. The extent to which more dynamic systems obey the same ‘rules’ governing stable processes is unclear. Here we present the first detailed investigation of the functional evolution of the RIG-like receptors (RLRs), a family of innate immune receptors that detect viral RNA in the cytoplasm.

Results

Using kinetic binding assays and molecular dynamics simulations of ancestral proteins, we demonstrate how a small number of adaptive protein-coding changes repeatedly shifted the RNA preference of RLRs throughout animal evolution by reorganizing the shape and electrostatic distribution across the RNA binding pocket, altering the hydrogen bond network between the RLR and its RNA target. In contrast to observations of proteins involved in metabolism and development, we find that RLR-RNA preference ‘flip flopped’ between two functional states, and shifts in RNA preference were not always coupled to gene duplications or speciation events. We demonstrate at least one reversion of RLR-RNA preference from a derived to an ancestral function through a novel structural mechanism, indicating multiple structural implementations of similar functions.

Conclusions

Our results suggest a model in which frequent shifts in selection pressures imposed by an evolutionary arms race preclude the long-term functional optimization observed in stable biological systems. As a result, the evolutionary dynamics of immune receptors may be less constrained by structural epistasis and historical contingency.

Electronic supplementary material

The online version of this article (doi:10.1186/s12862-016-0818-6) contains supplementary material, which is available to authorized users.

IBBOMSA: An Improved Biogeography-based Approach for Multiple Sequence Alignment

In bioinformatics, multiple sequence alignment (MSA) is an NP-hard problem. Hence, nature-inspired techniques can better approximate the solution. In the current study, a novel biogeography-based optimization (NBBO) is proposed to solve an MSA problem. The biogeography-based optimization (BBO) is a new paradigm for optimization. But, there exists some deficiencies in solving complicated problems such as low population diversity and slow convergence rate. NBBO is an enhanced version of BBO, in which, a new migration operation is proposed to overcome the limitations of BBO. The new migration adopts more information from other habitats, maintains population diversity, and preserves exploitation ability. In the performance analysis, the proposed and existing techniques such as VDGA, MOMSA, and GAPAM are tested on publicly available benchmark datasets (ie, Bali base). It has been observed that the proposed method shows the superiority/competitiveness with the existing techniques.

Analysis of gene gain and loss in the evolution of predatory bacteria

Predatory bacteria are ubiquitously distributed in nature in including in aquatic environments, sewage, intestinal tracts of animals and humans, rhizophere and, soils. However, our understanding of their evolutionary history is limited. Results of recent studies have shown that acquiring novel genes is a major force driving bacterial evolution. Therefore, to gain a better understanding of the impact of gene gain and loss in the evolution of bacterial predators, this study employed comparative genomic approaches to identify core-set gene families and species-specific gene families, and model gene gain and loss events among 11 genomes that represented diverse lineages. In total, 1977 gene families were classified. Of these 509 (pattern 11111111111) were present all of the 11 species. Among the non-core set gene families, 52 were present only in saltwater bacteria predators and had no ortholog in the other genomes. Similarly 109 and 44 were present only in the genomes of Micavibrio spp. and Bdellovibrio spp., respectively. In this study, the gain loss mapping engine GLOOME was selected to analyze and estimate the expectations and probabilities of both gain and loss events in the predatory bacteria. In total, 354 gene families were involved in significant gene gain events, and 407 gene families were classified into gene loss events with high supported value. Moreover, 18 families from the core set gene family were identified as putative genes under positive selection. The results of this study suggest that acquisition of particular genes that encode functional proteins in metabolism and cellular processes and signaling, especially ABC systems, may help bacterial predators adapt to surrounding environmental changes and present different predation strategies for survival in their habitats.

Multiple sequence alignment using multi-objective based bacterial foraging optimization algorithm

Multiple sequence alignment (MSA) is a widespread approach in computational biology and bioinformatics. MSA deals with how the sequences of nucleotides and amino acids are sequenced with possible alignment and minimum number of gaps between them, which directs to the functional, evolutionary and structural relationships among the sequences. Still the computation of MSA is a challenging task to provide an efficient accuracy and statistically significant results of alignments. In this work, the Bacterial Foraging Optimization Algorithm was employed to align the biological sequences which resulted in a non-dominated optimal solution. It employs Multi-objective, such as: Maximization of Similarity, Non-gap percentage, Conserved blocks and Minimization of gap penalty. BAliBASE 3.0 benchmark database was utilized to examine the proposed algorithm against other methods In this paper, two algorithms have been proposed: Hybrid Genetic Algorithm with Artificial Bee Colony (GA-ABC) and Bacterial Foraging Optimization Algorithm. It was found that Hybrid Genetic Algorithm with Artificial Bee Colony performed better than the existing optimization algorithms. But still the conserved blocks were not obtained using GA-ABC. Then BFO was used for the alignment and the conserved blocks were obtained. The proposed Multi-Objective Bacterial Foraging Optimization Algorithm (MO-BFO) was compared with widely used MSA methods Clustal Omega, Kalign, MUSCLE, MAFFT, Genetic Algorithm (GA), Ant Colony Optimization (ACO), Artificial Bee Colony (ABC), Particle Swarm Optimization (PSO) and Hybrid Genetic Algorithm with Artificial Bee Colony (GA-ABC). The final results show that the proposed MO-BFO algorithm yields better alignment than most widely used methods.

Effective comparative analysis of protein-protein interaction networks by measuring the steady-state network flow using a Markov model

Background

Comparative analysis of protein-protein interaction (PPI) networks provides an effective means of detecting conserved functional network modules across different species. Such modules typically consist of orthologous proteins with conserved interactions, which can be exploited to computationally predict the modules through network comparison.

Results

In this work, we propose a novel probabilistic framework for comparing PPI networks and effectively predicting the correspondence between proteins, represented as network nodes, that belong to conserved functional modules across the given PPI networks. The basic idea is to estimate the steady-state network flow between nodes that belong to different PPI networks based on a Markov random walk model. The random walker is designed to make random moves to adjacent nodes within a PPI network as well as cross-network moves between potential orthologous nodes with high sequence similarity. Based on this Markov random walk model, we estimate the steady-state network flow – or the long-term relative frequency of the transitions that the random walker makes – between nodes in different PPI networks, which can be used as a probabilistic score measuring their potential correspondence. Subsequently, the estimated scores can be used for detecting orthologous proteins in conserved functional modules through network alignment.

Conclusions

Through evaluations based on multiple real PPI networks, we demonstrate that the proposed scheme leads to improved alignment results that are biologically more meaningful at reduced computational cost, outperforming the current state-of-the-art algorithms. The source code and datasets can be downloaded from http://www.ece.tamu.edu/~bjyoon/CUFID.