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

From GPUs to AI and quantum: three waves of acceleration in bioinformatics

The enormous growth in the amount of data generated by the life sciences is continuously shifting the field from model-driven science towards data-driven science. The need for efficient processing has led to the adoption of massively parallel accelerators such as graphics processing units (GPUs). Consequently, the development of bioinformatics methods nowadays often heavily depends on the effective use of these powerful technologies. Furthermore, progress in computational techniques and architectures continues to be highly dynamic, involving novel deep neural network models and artificial intelligence (AI) accelerators, and potentially quantum processing units in the future. These are expected to be disruptive for the life sciences as a whole and for drug discovery in particular. Here, we identify three waves of acceleration and their applications in a bioinformatics context: (i) GPU computing, (ii) AI and (iii) next-generation quantum computers.

The macroevolutionary singularity of snakes

Snakes and lizards (Squamata) represent a third of terrestrial vertebrates and exhibit spectacular innovations in locomotion, feeding, and sensory processing. However, the evolutionary drivers of this radiation remain poorly known. We infer potential causes and ultimate consequences of squamate macroevolution by combining individual-based natural history observations (>60,000 animals) with a comprehensive time-calibrated phylogeny that we anchored with genomic data (5400 loci) from 1018 species. Due to shifts in the dynamics of speciation and phenotypic evolution, snakes have transformed the trophic structure of animal communities through the recurrent origin and diversification of specialized predatory strategies. Squamate biodiversity reflects a legacy of singular events that occurred during the early history of snakes and reveals the impact of historical contingency on vertebrate biodiversity.

DNA binding and RAD51 engagement by the BRCA2 C-terminus orchestrate DNA repair and replication fork preservation

The tumor suppressor BRCA2 participates in DNA double-strand break repair by RAD51-dependent homologous recombination and protects stressed DNA replication forks from nucleolytic attack. We demonstrate that the C-terminal Recombinase Binding (CTRB) region of BRCA2, encoded by gene exon 27, harbors a DNA binding activity. CTRB alone stimulates the DNA strand exchange activity of RAD51 and permits the utilization of RPA-coated ssDNA by RAD51 for strand exchange. Moreover, CTRB functionally synergizes with the Oligonucleotide Binding fold containing DNA binding domain and BRC4 repeat of BRCA2 in RPA-RAD51 exchange on ssDNA. Importantly, we show that the DNA binding and RAD51 interaction attributes of the CTRB are crucial for homologous recombination and protection of replication forks against MRE11-mediated attrition. Our findings shed light on the role of the CTRB region in genome repair, reveal remarkable functional plasticity of BRCA2, and help explain why deletion of Brca2 exon 27 impacts upon embryonic lethality.

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.

Spotlight on alternative frame coding: Two long overlapping genes in Pseudomonas aeruginosa are translated and under purifying selection

The existence of overlapping genes (OLGs) with significant coding overlaps revolutionizes our understanding of genomic complexity. We report two exceptionally long (957 nt and 1536 nt), evolutionarily novel, translated antisense open reading frames (ORFs) embedded within annotated genes in the pathogenic Gram-negative bacterium Pseudomonas aeruginosa. Both OLG pairs show sequence features consistent with being genes and transcriptional signals in RNA sequencing. Translation of both OLGs was confirmed by ribosome profiling and mass spectrometry. Quantitative proteomics of samples taken during different phases of growth revealed regulation of protein abundances, implying biological functionality. Both OLGs are taxonomically restricted, and likely arose by overprinting within the genus. Evidence for purifying selection further supports functionality. The OLGs reported here, designated olg1 and olg2, are the longest yet proposed in prokaryotes and are among the best attested in terms of translation and evolutionary constraint. These results highlight a potentially large unexplored dimension of prokaryotic genomes.

Ecological diversification reveals routes of pathogen emergence in endemic Vibrio vulnificus populations

Pathogen emergence is a complex phenomenon that, despite its public health relevance, remains poorly understood. Vibrio vulnificus, an emergent human pathogen, can cause a deadly septicaemia with over 50% mortality rate. To date, the ecological drivers that lead to the emergence of clinical strains and the unique genetic traits that allow these clones to colonize the human host remain mostly unknown. We recently surveyed a large estuary in eastern Florida, where outbreaks of the disease frequently occur, and found endemic populations of the bacterium. We established two sampling sites and observed strong correlations between location and pathogenic potential. One site is significantly enriched with strains that belong to one phylogenomic cluster (C1) in which the majority of clinical strains belong. Interestingly, strains isolated from this site exhibit phenotypic traits associated with clinical outcomes, whereas strains from the second site belong to a cluster that rarely causes disease in humans (C2). Analyses of C1 genomes indicate unique genetic markers in the form of clinical-associated alleles with a potential role in virulence. Finally, metagenomic and physicochemical analyses of the sampling sites indicate that this marked cluster distribution and genetic traits are strongly associated with distinct biotic and abiotic factors (e.g., salinity, nutrients, or biodiversity), revealing how ecosystems generate selective pressures that facilitate the emergence of specific strains with pathogenic potential in a population. This knowledge can be applied to assess the risk of pathogen emergence from environmental sources and integrated toward the development of novel strategies for the prevention of future outbreaks.

RaFAH: Host prediction for viruses of Bacteria and Archaea based on protein content

Culture-independent approaches have recently shed light on the genomic diversity of viruses of prokaryotes. One fundamental question when trying to understand their ecological roles is: which host do they infect? To tackle this issue we developed a machine-learning approach named Random Forest Assignment of Hosts (RaFAH), that uses scores to 43,644 protein clusters to assign hosts to complete or fragmented genomes of viruses of Archaea and Bacteria. RaFAH displayed performance comparable with that of other methods for virus-host prediction in three different benchmarks encompassing viruses from RefSeq, single amplified genomes, and metagenomes. RaFAH was applied to assembled metagenomic datasets of uncultured viruses from eight different biomes of medical, biotechnological, and environmental relevance. Our analyses led to the identification of 537 sequences of archaeal viruses representing unknown lineages, whose genomes encode novel auxiliary metabolic genes, shedding light on how these viruses interfere with the host molecular machinery. RaFAH is available at https://sourceforge.net/projects/rafah/.

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RaFAH was developed to predict the hosts of viruses of Bacteria and Archaea

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RaFAH displayed comparable or superior performance to other host-prediction tools

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RaFAH performed well across viromes from eight different ecosystems

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RaFAH identified hundreds of genomic sequences as derived from viruses of Archaea

Viruses that infect Bacteria and Archaea are ubiquitous and extremely abundant. Recent advances have led to the discovery of many thousands of complete and partial genomes of these biological entities. Understanding the biology of these viruses and how they influence their ecosystems depends on knowing which hosts they infect. We developed a tool that uses data from complete or fragmented genomes to predict the hosts of viruses using a machine-learning approach. Our tool, RaFAH, displayed performance comparable with or superior to that of other host-prediction tools. In addition, it identified hundreds of sequences as derived from the genomes of viruses of Archaea, which are one of the least characterized fractions of the global virosphere.

Parallelization of MAFFT for large-scale multiple sequence alignments

Summary

We report an update for the MAFFT multiple sequence alignment program to enable parallel calculation of large numbers of sequences. The G-INS-1 option of MAFFT was recently reported to have higher accuracy than other methods for large data, but this method has been impractical for most large-scale analyses, due to the requirement of large computational resources. We introduce a scalable variant, G-large-INS-1, which has equivalent accuracy to G-INS-1 and is applicable to 50 000 or more sequences.

Availability and implementation

This feature is available in MAFFT versions 7.355 or later at https://mafft.cbrc.jp/alignment/software/mpi.html.

Supplementary information

Supplementary data are available at Bioinformatics online.

FAMSA: Fast and accurate multiple sequence alignment of huge protein families

Rapid development of modern sequencing platforms has contributed to the unprecedented growth of protein families databases. The abundance of sets containing hundreds of thousands of sequences is a formidable challenge for multiple sequence alignment algorithms. The article introduces FAMSA, a new progressive algorithm designed for fast and accurate alignment of thousands of protein sequences. Its features include the utilization of the longest common subsequence measure for determining pairwise similarities, a novel method of evaluating gap costs, and a new iterative refinement scheme. What matters is that its implementation is highly optimized and parallelized to make the most of modern computer platforms. Thanks to the above, quality indicators, i.e. sum-of-pairs and total-column scores, show FAMSA to be superior to competing algorithms, such as Clustal Omega or MAFFT for datasets exceeding a few thousand sequences. Quality does not compromise on time or memory requirements, which are an order of magnitude lower than those in the existing solutions. For example, a family of 415519 sequences was analyzed in less than two hours and required no more than 8 GB of RAM. FAMSA is available for free at http://sun.aei.polsl.pl/REFRESH/famsa.