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At least 19 records

An FPGA-based hardware accelerator supporting sensitive sequence homology filtering with profile hidden Markov models

Abstract Background Sequence alignment lies at the heart of genome sequence annotation. While the BLAST suite of alignment tools has long held an important role in alignment-based sequence database search, greater sensitivity is achieved through the use of profile hidden Markov models (pHMMs). Here, we describe an FPGA hardware accelerator, called HAVAC, that targets a key bottleneck step (SSV) in the analysis pipeline of the popular pHMM alignment tool, HMMER. Results The HAVAC kernel calculates the SSV matrix at 1739 GCUPS on a $$\sim$$ ∼ $3000 Xilinx Alveo U50 FPGA accelerator card, $$\sim$$ ∼ 227× faster than the optimized SSV implementation in nhmmer . Accounting for PCI-e data transfer data processing, HAVAC is 65× faster than nhmmer’s SSV with one thread and 35× faster than nhmmer with four threads, and uses $$\sim$$ ∼ 31% the energy of a traditional high end Intel CPU. Conclusions HAVAC demonstrates the potential offered by FPGA hardware accelerators to produce dramatic speed gains in sequence annotation and related bioinformatics applications. Because these computations are performed on a co-processor, the host CPU remains free to simultaneously compute other aspects of the analysis pipeline.

59 BASIC BIOLOGICAL SCIENCES↗

Towards Scalable Optimal Sequence Homology Detection

Abstract—The field of bioinformatics and computational biol- ogy is experiencing a data revolution — experimental techniques to procure data have increased in throughput, improved in accuracy and reduced in costs. This has spurred an array of high profile sequencing and data generation projects. While the data repositories represent untapped reservoirs of rich information critical for scientific breakthroughs, the analytical software tools that are needed to analyze large volumes of such sequence data have significantly lagged behind in their capacity to scale. In this paper, we address homology detection, which is a funda- mental problem in large-scale sequence analysis with numerous applications. We present a scalable framework to conduct large- scale optimal homology detection on massively parallel super- computing platforms. Our approach employs distributed memory work stealing to effectively parallelize optimal pairwise alignment computation tasks. Results on 120,000 cores of the Hopper Cray XE6 supercomputer demonstrate strong scaling and up to 2.42 × 107 optimal pairwise sequence alignments computed per second (PSAPS), the highest reported in the literature.

Daily, Jeffrey A.↗

Deletion of the Clostridium thermocellum recA gene reveals that it is required for thermophilic plasmid replication but not plasmid integration at homologous DNA sequences

Abstract A limitation to the engineering of cellulolytic thermophiles is the availability of functional, thermostable (≥ 60 °C) replicating plasmid vectors for rapid expression and testing of genes that provide improved or novel fuel molecule production pathways. A series of plasmid vectors for genetic manipulation of the cellulolytic thermophile Caldicellulosiruptor bescii has recently been extended to Clostridium thermocellum, another cellulolytic thermophile that very efficiently solubilizes plant biomass and produces ethanol. While the C. bescii pBAS2 replicon on these plasmids is thermostable, the use of homologous promoters, signal sequences and genes led to undesired integration into the bacterial chromosome, a result also observed with less thermostable replicating vectors. In an attempt to overcome undesired plasmid integration in C. thermocellum, a deletion of recA was constructed. As expected, C. thermocellum ∆recA showed impaired growth in chemically defined medium and an increased susceptibility to UV damage. Interestingly, we also found that recA is required for replication of the C. bescii thermophilic plasmid pBAS2 in C. thermocellum, but it is not required for replication of plasmid pNW33N. In addition, the C. thermocellum recA mutant retained the ability to integrate homologous DNA into the C. thermocellum chromosome. These data indicate that recA can be required for replication of certain plasmids, and that a recA-independent mechanism exists for the integration of homologous DNA into the C. thermocellum chromosome. Understanding thermophilic plasmid replication is not only important for engineering of these cellulolytic thermophiles, but also for developing genetic systems in similar new potentially useful non-model organisms.

59 BASIC BIOLOGICAL SCIENCES↗

Transposon insertional mutagenesis in Saccharomyces uvarum reveals trans-acting effects influencing species-dependent essential genes

To understand how complex genetic networks perform and regulate diverse cellular processes, the function of each individual component must be defined. Comprehensive phenotypic studies of mutant alleles have been successful in model organisms in determining what processes depend on the normal function of a gene. These results are often ported to newly sequenced genomes by using sequence homology. However, sequence similarity does not always mean identical function or phenotype, suggesting that new methods are required to functionally annotate newly sequenced species. We have implemented here comparative analysis by high-throughput experimental testing of gene dispensability in Saccharomyces uvarum, a sister species of Saccharomyces cerevisiae. We created haploid and heterozygous diploid Tn7 insertional mutagenesis libraries in S. uvarum to identify species-dependent essential genes, with the goal of detecting genes with divergent functions and/or different genetic interactions. Comprehensive gene dispensability comparisons with S. cerevisiae predicted diverged dispensability at 12% of conserved orthologs, and validation experiments confirmed 22 differentially essential genes. Despite their differences in essentiality, these genes were capable of cross-species complementation, demonstrating that trans-acting factors that are background-dependent contribute to differential gene essentiality. This study shows that direct experimental testing of gene disruption phenotypes across species can inform comparative genomic analyses and improve gene annotations. Our method can be widely applied in microorganisms to further our understanding of genome evolution.

59 BASIC BIOLOGICAL SCIENCES↗

High wax ester and triacylglycerol biosynthesis potential in coastal sediments of Antarctic and Subantarctic environments

The wax ester (WE) and triacylglycerol (TAG) biosynthetic potential of marine microorganisms is poorly understood at the microbial community level. The goal of this work was to uncover the prevalence and diversity of bacteria with the potential to synthesize these neutral lipids in coastal sediments of two high latitude environments, and to characterize the gene clusters related to this process. Homolog sequences of the key enzyme, the wax ester synthase/acyl-CoA:diacylglycerol acyltransferase (WS/DGAT) were retrieved from 13 metagenomes, including subtidal and intertidal sediments of a Subantarctic environment (Ushuaia Bay, Argentina), and subtidal sediments of an Antarctic environment (Potter Cove, Antarctica). The abundance of WS/DGAT homolog sequences in the sediment metagenomes was 1.23 ± 0.42 times the abundance of 12 single-copy genes encoding ribosomal proteins, higher than in seawater (0.13 ± 0.31 times in 338 metagenomes). Homolog sequences were highly diverse, and were assigned to the Pseudomonadota, Actinomycetota, Bacteroidota and Acidobacteriota phyla. The genomic context of WS/DGAT homologs included sequences related to WE and TAG biosynthesis pathways, as well as to other related pathways such as fatty-acid metabolism, suggesting carbon recycling might drive the flux to neutral lipid synthesis. These results indicate the presence of abundant and taxonomically diverse bacterial populations with the potential to synthesize lipid storage compounds in marine sediments, relating this metabolic process to bacterial survival.

59 BASIC BIOLOGICAL SCIENCES↗

Scalable Parallel Methods for Analyzing Metagenomics Data at Extreme Scale

The field of bioinformatics and computational biology is currently experiencing a data revolution. The exciting prospect of making fundamental biological discoveries is fueling the rapid development and deployment of numerous cost-effective, high-throughput next-generation sequencing technologies. The result is that the DNA and protein sequence repositories are being bombarded with new sequence information. Databases are continuing to report a Moore’s law-like growth trajectory in their database sizes, roughly doubling every 18 months. In what seems to be a paradigm-shift, individual projects are now capable of generating billions of raw sequence data that need to be analyzed in the presence of already annotated sequence information. While it is clear that data-driven methods, such as sequencing homology detection, are becoming the mainstay in the field of computational life sciences, the algorithmic advancements essential for implementing complex data analytics at scale have mostly lagged behind. Sequence homology detection is central to a number of bioinformatics applications including genome sequencing and protein family characterization. Given millions of sequences, the goal is to identify all pairs of sequences that are highly similar (or “homologous”) on the basis of alignment criteria. While there are optimal alignment algorithms to compute pairwise homology, their deployment for large-scale is currently not feasible; instead, heuristic methods are used at the expense of quality. In this dissertation, we present the design and evaluation of a parallel implementation for conducting optimal homology detection on distributed memory supercomputers. Our approach uses a combination of techniques from asynchronous load balancing (viz. work stealing, dynamic task counters), data replication, and exact-matching filters to achieve homology detection at scale. Results for a collection of 2.56M sequences show parallel efficiencies of ~75-100% on up to 8K cores, representing a time-to-solution of 33 seconds. We extend this work with a detailed analysis of single-node sequence alignment performance using the latest CPU vector instruction set extensions. Preliminary results reveal that current sequence alignment algorithms are unable to fully utilize widening vector registers.

59 BASIC BIOLOGICAL SCIENCES↗

Extracting data from the muck: deriving biological insight from complex microbial communities and non-model organisms with next generation sequencing

It is becoming increasingly clear that microbes within microbial communities, for which cultured isolates have not yet been obtained, have an immense, untapped reservoir of enzymes that could help address grand challenges in human health, energy, and sustainability. Despite the obstacles associated with culturing these microbes, recent advances in next-generation sequencing (NGS) have made it possible to explore complex microbial communities in their native context for the first time. Key to extracting meaning from rapidly growing NGS datasets are bioinformatics tools that assemble the sequence data, annotate homologous sequences and interrogate it to reveal regulatory patterns. Complementing this are advances in proteomics that can link NGS data to biological function. Finally, this combination of next generation sequencing, proteomics and bioinformatic analysis forms a powerful tool to study non-model microbes, which will transform what we know about these dynamic systems.

59 BASIC BIOLOGICAL SCIENCES↗

j5 v2.8.4

j5 automates and optimizes the design of the molecular biological process of cloning/constructing DNA. j5 enables users to benefit from (combinatorial) multi-part scar-less SLIC, Gibson, CPEC, Golden Gate assembly, or variants thereof, for which automation software does not currently exist, without the intense labor currently associated with the process. j5 inputs a list of the DNA sequences to be assembled, along with a Genbank, FASTA, jbei-seq, or SBOL v1.1 format sequence file for each DNA source. Given the list of DNA sequences to be assembled, j5 first determines the cost-minimizing assembly strategy for each part (direct synthesis, PCR/SOE, or oligo-embedding), designs DNA oligos with Primer3, adds flanking homology sequences (SLIC, Gibson, and CPEC; optimized with Primer3 for CPEC) or optimized overhang sequences (Golden Gate) to the oligos and direct synthesis pieces, and utilizes BLAST to check against oligo mis-priming and assembly piece incompatibility events. After identifying DNA oligos that are already contained within a local collection for reuse, the program estimates the total cost of direct synthesis and new oligos to be ordered. In the instance that j5 identifies putative assembly piece incompatibilities (multiple pieces with high flanking sequence homology), the program suggests hierarchical subassemblies where possible. The program outputs a comma-separated value (CSV) file, viewable via Excel or other spreadsheet software, that contains assembly design information (such as the PCR/SOE reactions to perform, their anticipated sizes and sequences, etc.) as well as a properly annotated genbank file containing the sequence resulting from the assembly, and appends the local oligo library with the oligos to be ordered j5 condenses multiple independent assembly projects into 96-well format for high-throughput liquid-handling robotics platforms, and generates configuration files for the PR-PR biology-friendly robot programming language. j5 thus provides a new way to design DNA assembly procedures much more productively and efficiently, not only in terms of time, but also in terms of cost. To a large extent, however, j5 does not allow people to do something that could not be done before by hand given enough time and effort. An exception to this is that, since the very act of using j5 to design the DNA assembly process standardizes the experimental details and workflow, j5 enables a single person to concurrently perform the independent DNA construction tasks of an entire group of researchers. Currently, this is not readily possible, since separate researchers employ disparate design strategies and workflows, and furthermore, their designs and workflows are very infrequently fully captured in an electronic format which is conducive to automation.

Hillson, Nathan↗

The enterococcal cytolysin synthetase has an unanticipated lipid kinase fold

The enterococcal cytolysin is a virulence factor consisting of two post-translationally modified peptides that synergistically kill human immune cells. Both peptides are made by CylM, a member of the LanM lanthipeptide synthetases. CylM catalyzes seven dehydrations of Ser and Thr residues and three cyclization reactions during the biosynthesis of the cytolysin large subunit. We present here the 2.2 Å resolution structure of CylM, the first structural information on a LanM. Unexpectedly, the structure reveals that the dehydratase domain of CylM resembles the catalytic core of eukaryotic lipid kinases, despite the absence of clear sequence homology. The kinase and phosphate elimination active sites that affect net dehydration are immediately adjacent to each other. Characterization of mutants provided insights into the mechanism of the dehydration process. The structure is also of interest because of the interactions of human homologs of lanthipeptide cyclases with kinases such as mammalian target of rapamycin.

Dong, Shi-Hui↗

The crystal structure of dGTPase reveals the molecular basis of dGTP selectivity

Deoxynucleotide triphosphohydrolases (dNTPases) play a critical role in cellular survival and DNA replication through the proper maintenance of cellular dNTP pools. While the vast majority of these enzymes display broad activity toward canonical dNTPs, such as the dNTPase SAMHD1 that blocks reverse transcription of retroviruses in macrophages by maintaining dNTP pools at low levels, Escherichia coli ( Ec )-dGTPase is the only known enzyme that specifically hydrolyzes dGTP. However, the mechanism behind dGTP selectivity is unclear. Here we present the free-, ligand (dGTP)- and inhibitor (GTP)-bound structures of hexameric Ec- dGTPase, including an X-ray free-electron laser structure of the free Ec- -dGTPase enzyme to 3.2 Å. To obtain this structure, we developed a method that applied UV-fluorescence microscopy, video analysis, and highly automated goniometer-based instrumentation to map and rapidly position individual crystals randomly located on fixed target holders, resulting in the highest indexing rates observed for a serial femtosecond crystallography experiment. Our structures show a highly dynamic active site where conformational changes are coupled to substrate (dGTP), but not inhibitor binding, since GTP locks dGTPase in its apo- form. Moreover, despite no sequence homology, Ec- dGTPase and SAMHD1 share similar active-site and HD motif architectures; however, Ec- dGTPase residues at the end of the substrate-binding pocket mimic Watson–Crick interactions providing guanine base specificity, while a 7-Å cleft separates SAMHD1 residues from dNTP bases, abolishing nucleotide-type discrimination. Furthermore, the structures shed light on the mechanism by which long distance binding (25 Å) of single-stranded DNA in an allosteric site primes the active site by conformationally “opening” a tyrosine gate allowing enhanced substrate binding.

Barnes, Christopher O.↗

End-to-end learning of multiple sequence alignments with differentiable Smith–Waterman

Abstract Motivation Multiple sequence alignments (MSAs) of homologous sequences contain information on structural and functional constraints and their evolutionary histories. Despite their importance for many downstream tasks, such as structure prediction, MSA generation is often treated as a separate pre-processing step, without any guidance from the application it will be used for. Results Here, we implement a smooth and differentiable version of the Smith–Waterman pairwise alignment algorithm that enables jointly learning an MSA and a downstream machine learning system in an end-to-end fashion. To demonstrate its utility, we introduce SMURF (Smooth Markov Unaligned Random Field), a new method that jointly learns an alignment and the parameters of a Markov Random Field for unsupervised contact prediction. We find that SMURF learns MSAs that mildly improve contact prediction on a diverse set of protein and RNA families. As a proof of concept, we demonstrate that by connecting our differentiable alignment module to AlphaFold2 and maximizing predicted confidence, we can learn MSAs that improve structure predictions over the initial MSAs. Interestingly, the alignments that improve AlphaFold predictions are self-inconsistent and can be viewed as adversarial. This work highlights the potential of differentiable dynamic programming to improve neural network pipelines that rely on an alignment and the potential dangers of optimizing predictions of protein sequences with methods that are not fully understood. Availability and implementation Our code and examples are available at: https://github.com/spetti/SMURF. Supplementary information Supplementary data are available at Bioinformatics online.

59 BASIC BIOLOGICAL SCIENCES↗

A Universally Primed-Polymerase Chain Reaction (UP-PCR) Marker to Discriminate Clonostachys rosea ACM941 from Related Strains

Clonostachys rosea strain ACM941 is an effective biocontrol agent against several crop diseases including Fusarium head blight. In anticipation of its increased relevance going forward, the development of a reliable DNA-based molecular marker to track it is essential. Universally primed-PCR (UP-PCR) has been used successfully to differentiate other C. rosea strains. Herein, the development of a UP-PCR marker for ACM941 is described. A combination of two primers (AS15 and L45) produced a ~450 bp fragment that was unique to ACM941 compared to other commercial biocontrol agents. Primers subsequently designed based on the obtained fragment also produced a similarly unique band from ACM941 alone. BLAST analysis of the amplified sequence did not yield any homologous sequence in available online databases or within the closely related C. rosea IK726 and CBS125111 strains’ genomes. The specificity of this marker for ACM941 was validated against ten additional C. rosea strains isolated from Canada, with ACM941 producing the brightest band. Taken together, these results imply that the UP-PCR primers AS15 and L45 and the amplified fragment can be used to detect and monitor the ACM941 strain after its release into the environment.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Structural Models and Sequence Alignment Results of the Rhodospirillum rubrum Proteome

This dataset contains the structural models for the primary transcripts of the Rhodospirillum rubrum proteome as well as sequence alignment results for a subset of the encoded proteins. For each protein, the five models inferred from AlphaFold 2 are provided. The largest pTM-scoring model for each protein was energy minimized; this minimized structure as well as its AlphaFold pickle output file are also provided. This set of structures represent an alternate source of models for the R. rubrum proteome to those available in the AlphaFold Protein Structure Database. For proteins that have been annotated as hypothetical, sequence alignment results from the HHblits and SAdLSA alignment methods are provided. These methods are often more capable to resolve sequence homology than other methods. Therefore, the results from both HHblits and SAdLSA are provided to identify possible homologs for these challenging proteins. Numerous sequence databases are utilized for these alignments. References AlphaFold v2 Multimer: https://doi.org/10.1101/2021.10.04.463034. References HHBlits: https://doi.org/10.1186/s12859-019-3019-7. References SAdLSA: https://doi.org/10.3389/fbinf.2021.689960.

59 BASIC BIOLOGICAL SCIENCES↗

Structural Models and Sequence Alignment Results of the Desulfovibrio vulgaris Proteome

This dataset contains the structural models for the primary transcripts of the Desulfovibrio vulgaris proteome as well as sequence alignment results for a subset of the encoded proteins. For each protein, the five models inferred from AlphaFold 2 are provided. The largest pTM-scoring model for each protein was energy minimized; this minimized structure as well as its AlphaFold pickle output file are also provided. This set of structures represent an alternate source of models for the D. vulgaris proteome to those available in the AlphaFold Protein Structure Database (AFDB). This is a bit more complicated since the proteins reporting in the AFDB originate from an outdated form of the D. vulgaris sequence. The different versions of the D. vulgaris gene annotation are collected in the Chronology subdirectory; further consideration of these changes on the structural space of the proteome are currently underway. For proteins that have been annotated as hypothetical, sequence alignment results from the HHblits and SAdLSA alignment methods are provided. These methods are often more capable to resolve sequence homology than other methods. Therefore, the results from both HHblits and SAdLSA are provided to identify possible homologs for these challenging proteins. Numerous sequence databases are utilized for these alignments. References AlphaFold v2 Multimer: https://doi.org/10.1101/2021.10.04.463034. References HHblits: hhtps://doi.org/10.1186/s12859-019-3019-7. References SAdLSA: hhtps://doi.org/10.3389/fbinf.2021.689960.

59 BASIC BIOLOGICAL SCIENCES↗

luoyunan/ECNet: First release

ECNet (evolutionary context-integrated neural network) is a deep learning model that guides protein engineering by predicting protein fitness from the sequence. It integrates local evolutionary context from homologous sequences that explicitly model residue-residue epistasis for the protein of interest with the global evolutionary context that encodes rich semantic and structural features from the enormous protein sequence universe.

Luo, Yunan↗