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MOTIVATION: The alignment of sequencing reads is a critical step in the characterization of ancient genomes. However, reference bias and spurious mappings pose a significant challenge, particularly as cutting-edge wet lab methods generate datasets that push the boundaries of alignment tools. Reference bias occurs when reference alleles are favoured over alternative alleles during mapping, whereas spurious mappings stem from either contamination or when endogenous reads fail to align to their correct position. Previous work has shown that these phenomena are correlated with read length but a more thorough investigation of reference bias and spurious mappings for ancient DNA has been lacking. Here, we use a range of empirical and simulated palaeogenomic datasets to investigate the impacts of mapping tools, quality thresholds, and reference genome on mismatch rates across read lengths. RESULTS: For these analyses, we introduce AMBER, a new bioinformatics tool for assessing the quality of ancient DNA mapping directly from BAM-files and informing on reference bias, read length cut-offs and reference selection. AMBER rapidly and simultaneously computes the sequence read mapping bias in the form of the mismatch rates per read length, cytosine deamination profiles at both CpG and non-CpG sites, fragment length distributions, and genomic breadth and depth of coverage. Using AMBER, we find that mapping algorithms and quality threshold choices dictate reference bias and rates of spurious alignment at different read lengths in a predictable manner, suggesting that optimized mapping parameters for each read length will be a key step in alleviating reference bias and spurious mappings. AVAILABILITY AND IMPLEMENTATION: AMBER is available for noncommercial use on GitHub (https://github.com/tvandervalk/AMBER.git). Scripts used to generate and analyse simulated datasets are available on Github (https://github.com/sdolenz/refbias_scripts).

Network alignment aims to uncover topologically similar regions in the protein-protein interaction (PPI) networks of two or more species under the assumption that topologically similar regions tend to perform similar functions. Although there exist a plethora of both network alignment algorithms and measures of topological similarity, currently no gold standard exists for evaluating how well either is able to uncover functionally similar regions. Here we propose a formal, mathematically and statistically rigorous method for evaluating the statistical significance of shared GO terms in a global, 1-to-1 alignment between two PPI networks. Given an alignment in which k aligned protein pairs share a particular GO term g, we use a combinatorial argument to precisely quantify the p-value of that alignment with respect to g compared to a random alignment. The p-value of the alignment with respect to all GO terms, including their inter-relationships, is approximated using the Empirical Browns Method. We note that, just as with BLASTs p-values, this method is not designed to guide an alignment algorithm towards a solution; instead, just as with BLAST, an alignment is guided by a scoring matrix or function; the p-values herein are computed after the fact, providing independent feedback to the user on the biological quality of the alignment that was generated by optimizing the scoring function. Importantly, we demonstrate that among all GO-based measures of network alignments, ours is the only one that correlates with the precision of GO annotation predictions, paving the way for network alignment-based protein function prediction.

In this study, we utilize Protein Residue Networks (PRNs), constructed using Local Spatial Pattern (LSP) alignment, to explore the dynamic behavior of Catabolite Activator Protein (CAP) upon the sequential binding of cAMP. We employed the Degree Centrality of these PRNs to investigate protein dynamics on a sub-nanosecond time scale, hypothesizing that it would reflect changes in CAPs entropy related to its thermal motions. We show that the binding of the first cAMP led to an increase in stability in the Cyclic-Nucleotide Binding Domain A (CNBD-A) and destabilization in CNBD-B, agreeing with previous reports explaining the negative cooperativity of cAMP binding in terms of an entropy-driven allostery. LSP-based PRNs also allow for the study of Betweenness Centrality, another graph-theoretical characteristic of PRNs, providing insights into global residue connectivity within CAP. Using this approach, we were able to correctly identify amino acids that were shown to be critical in mediating allosteric interactions in CAP. The agreement between our studies and previous experimental reports validates our method, particularly with respect to the reliability of Degree Centrality as a proxy for entropy related to protein thermal dynamics. Because LSP-based PRNs can be easily extended to include dynamics of small organic molecules, polynucleotides, or other allosteric proteins, the methods presented here mark a significant advancement in the field, positioning them as vital tools for a fast, cost-effective, and accurate analysis of entropy-driven allostery and identification of allosteric hotspots.

MOTIVATION: Methods for concept recognition (CR) in clinical texts have largely been tested on abstracts or articles from the medical literature. However, texts from electronic health records (EHRs) frequently contain spelling errors, abbreviations, and other nonstandard ways of representing clinical concepts. RESULTS: Here, we present a method inspired by the BLAST algorithm for biosequence alignment that screens texts for potential matches on the basis of matching k-mer counts and scores candidates based on conformance to typical patterns of spelling errors derived from 2.9 million clinical notes. Our method, the Term-BLAST-like alignment tool (TBLAT) leverages a gold standard corpus for typographical errors to implement a sequence alignment-inspired method for efficient entity linkage. We present a comprehensive experimental comparison of TBLAT with five widely used tools. Experimental results show an increase of 10% in recall on scientific publications and 20% increase in recall on EHR records (when compared against the next best method), hence supporting a significant enhancement of the entity linking task. The method can be used stand-alone or as a complement to existing approaches. AVAILABILITY AND IMPLEMENTATION: Fenominal is a Java library that implements TBLAT for named CR of Human Phenotype Ontology terms and is available at https://github.com/monarch-initiative/fenominal under the GNU General Public License v3.0.

The results of tertiary structure assessment at CASP15 are reported. For the first time, recognizing the outstanding performance of AlphaFold 2 (AF2) at CASP14, all single-chain predictions were assessed together, irrespective of whether a template was available. At CASP15, there was no single stand-out group, with most of the best-scoring groups-led by PEZYFoldings, UM-TBM, and Yang Server-employing AF2 in one way or another. Many top groups paid special attention to generating deep Multiple Sequence Alignments (MSAs) and testing variant MSAs, thereby allowing them to successfully address some of the hardest targets. Such difficult targets, as well as lacking templates, were typically proteins with few homologues. Local divergence between prediction and target correlated with localization at crystal lattice or chain interfaces, and with regions exhibiting high B-factor factors in crystal structure targets, and should not necessarily be considered as representing error in the prediction. However, analysis of exposed and buried side chain accuracy showed room for improvement even in the latter. Nevertheless, a majority of groups produced high-quality predictions for most targets, which are valuable for experimental structure determination, functional analysis, and many other tasks across biology. These include those applying methods similar to those used to generate major resources such as the AlphaFold Protein Structure Database and the ESM Metagenomic atlas: the confidence estimates of the former were also notably accurate.

Introduction: Porcine reproductive and respiratory syndrome (PRRS) is a significant threat to the global swine industry, and its prevalence in Thailand spans over two decades. Methods: To understand the genetic variation and recombination of the PRRS virus (PRRSV) GP5 gene in Thailand, we retrieved 726 GP5 gene sequences from the NCBI database. Phylogenetic trees were constructed using the neighbor-joining (NJ) and maximum likelihood (ML) methods, and recombination analysis was performed. Results: Homology analysis was conducted on 83 PRRSV-1 and 83 PRRSV-2 strains. Phylogenetic analysis revealed the prevalence of both PRRSV-1 and PRRSV-2 strains in Thailand, with the latter exhibiting wider distribution. PRRSV-1 strains clustered into clades A, D, and H, while PRRSV-2 strains grouped into lineages 1, 5, and sublineage 8.7, further divided into 8.7/HP and 8.7/NA sublineages. Sublineage 8.7/NA strains accounted for a significant proportion of circulating PRRSV-2 strains. Homology analysis showed nucleotide and amino acid similarities ranging from 75.4 to 100.0% and 41.3 to 100.0% for PRRSV-1, and 78.6 to 100.0% and 70.8 to 100.0% for PRRSV-2 strains. Amino acid sequence alignments revealed mutations, insertions, and deletions in PRRSV-1 GP5, and key residue mutations in PRRSV-2 GP5 associated with biological functions. Recombination analysis identified two recombination events within PRRSV-2 sublineage 8.7 strains. Discussion: These findings confirm the variability of the GP5 protein. This study enhances our understanding of PRRSV prevalence and genetic variation in Thailand, contributing valuable insights for PRRS prevention and control. [ABSTRACT FROM AUTHOR]

SUMMARY Structural prediction by artificial intelligence can be powerful new instruments to discover novel protein–protein interactions, but the community still grapples with the implementation, opportunities and limitations. Here, we discuss and re‐analyse our in silico screen for novel pathogen‐secreted inhibitors of immune hydrolases to illustrate the power and limitations of structural predictions. We discuss strategies of curating sequences, including controls, and reusing sequence alignments and highlight important limitations caused by different platforms, sequence depth and computing times. We hope these experiences will support similar interactomic screens by the research community. [ABSTRACT FROM AUTHOR]

Introduction: The dissemination of strains producing tetracyclines monooxygenase Tet(X) from breeding farms to the natural environment poses a potential threat to public health. Methods: Antimicrobial susceptibility testing and WGS were performed to identify resistance phenotypes and genotypes. Cloning experiments, sequence alignment, and homology modeling were used to characterize the function and formation mechanisms of the recombinant variant. The mobilization potential of Tet(X) was assessed by collinearity analysis, conjugation experiments, and phylogenetic analysis. Results: Three tet(X)-producing Elizabethkingia meningoseptica strains were isolated from bullfrog breeding ponds, the sewage outlet, and downstream river in Zhejiang Province, China. These strains carry a novel Tet(X) variant, differing from Tet(X6) by seven residues, and possess the ability to degrade tetracyclines. Interestingly, the novel Tet(X) is a recombinant variant formed by homologous recombination of Tet(X6) and the C-terminal of Tet(X2). Further analysis revealed that Tet(X6) formed several Tet(X) variants, including Tet(X5), through homologous recombination. The novel tet(X) gene is located on a circularizable integrative and conjugative element (ICEEmeChn3), with ISwz1 participating in the recombination of its multi-drug resistance region, potentially facilitating the mobilization and recombination of tet(X) in early hosts. These three strains were clonally transmitted and shared a close genetic relationship (SNP < 62) with a clinically-sourced strain isolated from the same province. Discussion: To our knowledge, this is the first report of homologous recombination between Tet(X) variants with differing activities. These clonal strains provide evidence of the transmission of tet(X)-positive strains from aquaculture sewage to the natural environment, highlighting the need to strengthen the monitoring and management of this emerging farming model. [ABSTRACT FROM AUTHOR]

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BioLadder is an online data analysis platform designed for proteomics research. It offers a variety of proteomics analysis modules, as well as modules for other omics data analysis. The platform includes experimental data analysis modules and common data analysis modules. It provides functions for data visualization, preprocessing, quantitative analysis, sequence analysis, abundance mapping, classification analysis, and function analysis. The platform is user-friendly, with simplified input formats, specialized default parameters for proteomics, diverse adjustment methods, powerful color schemes, comprehensive help information, and real-time assistance. The platform is developed using the Vue.js and Laravel frameworks, with data persistence managed by MySQL. The authors of the platform are affiliated with Qinglian Biotech Co., Ltd. The work was supported by the National Key Research Program of China. [Extracted from the article]

Rabies, a viral disease that causes lethal encephalitis, kills ≈59,000 persons worldwide annually, despite availability of effective countermeasures. Rabies is endemic in Kenya and is mainly transmitted to humans through bites from rabid domestic dogs. We analyzed 164 brain stems collected from rabid animals in western and eastern Kenya and evaluated the phylogenetic relationships of rabies virus (RABV) from the 2 regions. We also analyzed RABV genomes for potential amino acid changes in the vaccine antigenic sites of nucleoprotein and glycoprotein compared with RABV vaccine strains commonly used in Kenya. We found that RABV genomes from eastern Kenya overwhelmingly clustered with the Africa-1b subclade and RABV from western Kenya clustered with Africa-1a. We noted minimal amino acid variances between the wild and vaccine virus strains. These data confirm minimal viral migration between the 2 regions and that rabies endemicity is the result of limited vaccine coverage rather than limited efficacy. [ABSTRACT FROM AUTHOR]

Many α-agarases have been characterized and are utilized for producing agarooligosaccharides through the degradation of agar and agarose, which are considered valuable for applications in the food and medicine industries. However, the catalytic mechanism and product transformation process of α-agarase remain unclear, limiting further enzyme engineering for industrial applications. In this study, an α-agarase from Catenovulum maritimus STB14 (Cm-AGA) was employed to degrade agarose oligosaccharides (AGOs) with varying degrees of polymerization (DPs) to investigate the catalytic mechanism of α-agarases. The results demonstrated that Cm-AGA could degrade agarose into agarotetraose and agarohexaose. The reducing ends of agarotetraose and agarohexaose spontaneously release unstable 3,6-anhydro-α-l-galactose molecules, which were further degraded into agarotriose and agaropentose. Cm-AGA cannot act on α-1,3-glucoside bonds in agarotriose, agarotetraose, neoagarobiose, and neoagarotetraose but can act on AGOs with a DP greater than four. The product analysis was further verified by β-galactosidase hydrolysis, which specifically cleaves the non-reducing glycosidic bond of agarooligosaccharides. Multiple sequence alignment results showed that two conserved residues, Asp994 and Glu1129, were proposed as catalytic residues and were further identified by site-directed mutagenesis. Molecular docking of Cm-AGA with agaroheptose revealed the potential substrate binding mode of the α-agarase. These findings enhance the understanding of Cm-AGA's catalytic mode and could guide enzyme engineering for modulating the production of agarooligosaccharides. [ABSTRACT FROM AUTHOR]

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 ∼ $3000 Xilinx Alveo U50 FPGA accelerator card, ∼ 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 ∼ 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. [ABSTRACT FROM AUTHOR]

Loquat leaves exhibiting obvious yellowing, blistering, mosaic, leaf upward cupping, crinkle, and leaf narrowing were identified in Panzhihua City, Sichuan Province, China. High-throughput sequencing (HTS) with the ribo-depleted cDNA library was employed to identify the virome in the loquat samples; only tomato mosaic virus (ToMV) and citrus exocortis viroid (CEVd) were identified in the transcriptome data. The complete genome sequence of ToMV and CEVd were obtained from the loquat leaves. The full-length genome of the ToMV-loquat is 6376 nt and comprises four open reading frames (ORFs) encoding 183 kDa protein, RNA-dependent RNA polymerase (RdRp), movement protein (MP), and coat protein (CP), respectively. A pairwise identity analysis showed that the complete sequence of the ToMV-loquat had a nucleotide identity between 98.5 and 99.3% with other ToMV isolates. A phylogenetic analysis indicated that ToMV-loquat was more closely related to ToMV-IFA9 (GenBank No. ON156781). A CEVd sequence with 361 nt in length was amplified based on the HTS contigs, sequence alignment indicated CEVd-loquat had the highest identity with the strain of CEVd-Balad (GenBank No. PP869624), phylogenetic analysis showed that CEVd-loquat was more closely related to CEVd-lettuce (GenBank No. ON993891). This significant discovery marks the first documentation and characterization of ToMV and CEVd infecting loquat plants, shedding light on potential threats to loquat cultivation and providing insights for disease management strategies. [ABSTRACT FROM AUTHOR]

Dimeric indole‐containing diketopiperazines (di‐DKPs) are a diverse group of natural products produced through cytochrome P450‐catalyzed C–C or C–N coupling reactions. The regio‐ and stereoselectivity of these reactions plays a significant role in the structural diversity of di‐DKPs. Despite their pivotal role, the mechanisms governing the selectivity in fungi are not fully understood. Employing bioinformatics analysis and heterologous expression experiments, five undescribed P450 enzymes (AmiP450, AcrP450, AtP450, AcP450, and AtuP450) responsible for the regio‐ and stereoselective dimerization of diketopiperazines (DKPs) in fungi are identified. The function of these P450s is consistent with phylogenetic analysis, highlighting their dominant role in controlling the dimerization modes. Combinatorial biosynthesis‐based pathway reconstitution of non‐native gene clusters expands the chemical space of fungal di‐DKPs and reveals that the regioselectivity is influenced by the substrate. Furthermore, multiple sequence alignment and molecular docking of these enzymes demonstrate a C‐terminal variable region near the substrate tunnel entrance in AtuP450 that is crucial for its regioselectivity. These findings not only reveal the secret of fungal di‐DKPs diversity but also deepen understanding of the mechanisms and catalytic specificity involved in P450‐catalyzed dimerization reactions. [ABSTRACT FROM AUTHOR]

The marine prasinophyte green algae Pycnococcus provasolii and Pseudoscourfieldia marina represent the only extant genera and known species of the Pycnococcaceae. However, their taxonomic status needs to be reassessed, owing to the very close relationship inferred from previous sequence comparisons of individual genes. Although Py. provasolii and Ps. marina are morphologically different, their plastid rbcL and nuclear small subunit rRNA genes were observed to be nearly or entirely identical in sequence, thus leading to the hypothesis that they represent distinct growth forms or alternate life‐cycle stages of the same organism. To evaluate this hypothesis, we used organelle genomes as molecular markers. The plastome and mitogenome of Ps. marina UIO 007 were sequenced and compared with those available for two isolates of Py. provasolii (CCMP 1203 and CCAP 190/2). The Ps. marina organelle genomes proved to be almost identical in size and had the same gene content and gene order as their Py. provasolii counterparts. Single nucleotide substitutions and insertions/deletions were localized using genome‐scale sequence alignments. Over 99.70% sequence identities were observed in all pairwise comparisons of plastomes and mitogenomes. Alignments of both organelle genomes revealed that Ps. marina UIO 007 is closer to Py. provasolii CCAP 190/2 than are the two Py. provasolii strains to one another. Therefore, our results are not consistent with the placement of Ps. marina and Py. provasolii strains into distinct genera. We propose a taxonomic revision of the Pycnococcaceae and the erection of a new class of Chlorophyta, the Pseudoscourfieldiophyceae. [ABSTRACT FROM AUTHOR]

Background: Orf, also known as contagious ecthyma (CE), is an acute, contagious zoonotic disease caused by the orf virus (ORFV). The F1L protein is a major immunodominant protein on the surface of ORFV and can induce the production of neutralizing antibodies. Methods: The prokaryotic expression system was used to produce the recombinant F1L protein of ORFV, which was subsequently purified and used to immunize mice. Positive hybridoma clones were screened using an indirect enzyme-linked immunosorbent assay (ELISA). The reactivity and specificity of the monoclonal antibody (mAb) were verified through Western blot and indirect immunofluorescence (IFA). The linear antigenic epitope specific to the mAb was identified through Western blot, using truncated F1L proteins expressed in eukaryotic cells. A multiple sequence alignment of the ORFV reference strains was performed to evaluate the degree of conservation of the identified epitope. Results: After three rounds of subcloning, a mAb named Ba-F1L was produced. Ba-F1L was found to react with both the exogenously expressed F1L protein and the native F1L protein from ORFV-infected cells, as confirmed by Western blot and IFA. The mAb recognized the core epitope 103CKSTCPKEM111, which is highly conserved among various ORFV strains, as shown by homologous sequence alignment. Conclusion: The mAb produced in the present study can be used as a diagnostic reagent for detecting ORFV and as a basic tool for exploring the mechanisms of orf pathogenesis. In addition, the identified linear epitope may be valuable for the development of epitope-based vaccines. [ABSTRACT FROM AUTHOR]

Motivation Protein language models offer a new perspective for addressing challenges in structural biology, while relying solely on sequence information. Recent studies have investigated their effectiveness in forecasting shifts in thermodynamic stability caused by single amino acid mutations, a task known for its complexity due to the sparse availability of data, constrained by experimental limitations. To tackle this problem, we introduce two key novelties: leveraging a protein language model that incorporates Multiple Sequence Alignments to capture evolutionary information, and using a recently released mega-scale dataset with rigorous data preprocessing to mitigate overfitting. Results We ensure comprehensive comparisons by fine-tuning various pretrained models, taking advantage of analyses such as ablation studies and baselines evaluation. Our methodology introduces a stringent policy to reduce the widespread issue of data leakage, rigorously removing sequences from the training set when they exhibit significant similarity with the test set. The MSA Transformer emerges as the most accurate among the models under investigation, given its capability to leverage co-evolution signals encoded in aligned homologous sequences. Moreover, the optimized MSA Transformer outperforms existing methods and exhibits enhanced generalization power, leading to a notable improvement in predicting changes in protein stability resulting from point mutations. Availability and implementation Code and data at https://github.com/RitAreaSciencePark/PLM4Muts. [ABSTRACT FROM AUTHOR]

Motivation Currently used methods for estimating branch support in phylogenetic analyses often rely on the classic Felsenstein's bootstrap, parametric tests, or their approximations. As these branch support scores are widely used in phylogenetic analyses, having accurate, fast, and interpretable scores is of high importance. Results Here, we employed a data-driven approach to estimate branch support values with a probabilistic interpretation. To this end, we simulated thousands of realistic phylogenetic trees and the corresponding multiple sequence alignments. Each of the obtained alignments was used to infer the phylogeny using state-of-the-art phylogenetic inference software, which was then compared to the true tree. Using these extensive data, we trained machine-learning algorithms to estimate branch support values for each bipartition within the maximum-likelihood trees obtained by each software. Our results demonstrate that our model provides fast and more accurate probability-based branch support values than commonly used procedures. We demonstrate the applicability of our approach on empirical datasets. Availability and implementation The data supporting this work are available in the Figshare repository at https://doi.org/10.6084/m9.figshare.25050554.v1 , and the underlying code is accessible via GitHub at https://github.com/noaeker/bootstrap_repo. [ABSTRACT FROM AUTHOR]

Summary Multiple sequence alignment is an important problem in computational biology with applications that include phylogeny and the detection of remote homology between protein sequences. UPP is a popular software package that constructs accurate multiple sequence alignments for large datasets based on ensembles of hidden Markov models (HMMs). A computational bottleneck for this method is a sequence-to-HMM assignment step, which relies on the precise computation of probability scores on the HMMs. In this work, we show that we can speed up this assignment step significantly by replacing these HMM probability scores with alternative scores that can be efficiently estimated. Our proposed approach utilizes a multi-armed bandit algorithm to adaptively and efficiently compute estimates of these scores. This allows us to achieve similar alignment accuracy as UPP with a significant reduction in computation time, particularly for datasets with long sequences. Availability and implementation The code used to produce the results in this paper is available on GitHub at: https://github.com/ilanshom/adaptiveMSA. [ABSTRACT FROM AUTHOR]

This study presents a comprehensive analysis of the dimerization interfaces of fly GSTs through sequence alignment. Our investigation revealed GSTE1 as a particularly intriguing target, providing valuable insights into the variations within Delta and Epsilon GST interfaces. The X-ray structure of GSTE1 was determined, unveiling remarkable thermal stability and a distinctive dimerization interface. Utilizing circular dichroism, we assessed the thermal stability of GSTE1 and other Drosophila GSTs with resolved X-ray structures. The subsequent examination of GST dimer stability correlated with the dimerization interface supported by findings from X-ray structural analysis and thermal stability measurements. Our discussion extends to the broader context of GST dimer interfaces, offering a generalized perspective on their stability. This research enhances our understanding of the structural and thermodynamic aspects of GST dimerization, contributing valuable insights to the field. [ABSTRACT FROM AUTHOR]

Bats, with their virus tolerance, social behaviors, and mobility, are reservoirs for emerging viruses, including coronaviruses (CoVs) known for genetic flexibility. Studying the cophylogenetic link between bats and CoVs provides vital insights into transmission dynamics and host adaptation. Prior research has yielded valuable insights into phenomena such as host switching, cospeciation, and other dynamics concerning the interaction between CoVs and bats. Nonetheless, a distinct gap exists in the current literature concerning a comparative cophylogenetic analysis focused on elucidating the contributions of sequence fragments to the co-evolution between hosts and viruses. In this study, we analyzed the cophylogenetic patterns of 69 host–virus connections. Among the 69 host–virus links examined, 47 showed significant cophylogeny based on ParaFit and PACo analyses, affirming strong associations. Focusing on two proteins, ORF1ab and spike, we conducted a comparative analysis of host and CoV phylogenies. For ORF1ab, the specific window ranged in multiple sequence alignment (positions 520–680, 770–870, 2930–3070, and 4910–5080) exhibited the lowest Robinson–Foulds (RF) distance (i.e., 84.62%), emphasizing its higher contribution in the cophylogenetic association. Similarly, within the spike region, distinct window ranges (positions 0–140, 60–180, 100–410, 360–550, and 630–730) displayed the lowest RF distance at 88.46%. Our analysis identified six recombination regions within ORF1ab (positions 360–1390, 550–1610, 680–1680, 700–1710, 2060–3090, and 2130–3250), and four within the spike protein (positions 10–510, 50–560, 170–710, and 230–730). The convergence of minimal RF distance regions with combination regions robustly affirms the pivotal role of recombination in viral adaptation to host selection pressures. Furthermore, horizontal gene transfer reveals prominent instances of partial gene transfer events, occurring not only among variants within the same host species but also crossing host species boundaries. This suggests a more intricate pattern of genetic exchange. By employing a multifaceted approach, our comprehensive strategy offers a nuanced understanding of the intricate interactions that govern the co-evolutionary dynamics between bat hosts and CoVs. This deeper insight enhances our comprehension of viral evolution and adaptation mechanisms, shedding light on the broader dynamics that propel viral diversity. [ABSTRACT FROM AUTHOR]

Modern genomic datasets, like those generated under the 1000 Genome Project, contain millions of variants belonging to known haplotypes. Although these datasets are more representative than a single reference sequence and can alleviate issues like reference bias, they are significantly more computationally burdensome to work with, often involving large-indexed genome graph data structures for tasks such as read mapping. The construction, preprocessing, and mapping algorithms can require substantial computational resources depending on the size of these variant sets. Moreover, the accuracy of mapping algorithms has been shown to decrease when working with complete variant sets. Therefore, a drastically reduced set of variants that preserves important properties of the original set is desirable. This work provides a technique for finding a minimal subset of variants S such that for given parameters α and δ, all substrings up to length α in the haplotypes are guaranteed to be still alignable to the appropriate locations with either Hamming or edit distance at most δ, using only S. Our contributions include showing the NP-hardness and inapproximability of these optimization problems and providing Integer Linear Programming (ILP) formulations. Our edit distance ILP formulation carefully decomposes the problem according to variant locations, which allows it to scale to support all of chromosome 22's variants from the 1000 Genome Project. Our experiments also demonstrate a significant reduction in the number of variants. For example, for moderately long reads, e.g., α = 1000, over 75% of the variants can be removed while preserving read mappability with edit distance at most one. [ABSTRACT FROM AUTHOR]

Most sequence sketching methods work by selecting specific k-mers from sequences so that the similarity between two sequences can be estimated using only the sketches. Because estimating sequence similarity is much faster using sketches than using sequence alignment, sketching methods are used to reduce the computational requirements of computational biology software. Applications using sketches often rely on properties of the k-mer selection procedure to ensure that using a sketch does not degrade the quality of the results compared with using sequence alignment. Two important examples of such properties are locality and window guarantees, the latter of which ensures that no long region of the sequence goes unrepresented in the sketch. A sketching method with a window guarantee, implicitly or explicitly, corresponds to a decycling set of the de Bruijn graph, which is a set of unavoidable k-mers. Any long enough sequence, by definition, must contain a k-mer from any decycling set (hence, the unavoidable property). Conversely, a decycling set also defines a sketching method by choosing the k-mers from the set as representatives. Although current methods use one of a small number of sketching method families, the space of decycling sets is much larger and largely unexplored. Finding decycling sets with desirable characteristics (e.g., small remaining path length) is a promising approach to discovering new sketching methods with improved performance (e.g., with small window guarantee). The Minimum Decycling Sets (MDSs) are of particular interest because of their minimum size. Only two algorithms, by Mykkeltveit and Champarnaud, are previously known to generate two particular MDSs, although there are typically a vast number of alternative MDSs. We provide a simple method to enumerate MDSs. This method allows one to explore the space of MDSs and to find MDSs optimized for desirable properties. We give evidence that the Mykkeltveit sets are close to optimal regarding one particular property, the remaining path length. A number of conjectures and computational and theoretical evidence to support them are presented. Code available at https://github.com/Kingsford-Group/mdsscope [ABSTRACT FROM AUTHOR]

Tandem repeat sequences are difficult part in genome construction, due to the high similarity between repeated units and the ambiguity in copy numbers, it often result in multiple candidate positions during sequence alignment. The challenge lies in rapidly and accurately filtering out the correct alignment positions. Existing methods address this issue by using seeds (short sequences selected from sequencing fragments) to locate and extend candidate alignment positions, but overlook the distinctive characteristics of tandem repeat sequences when selecting seeds. To tackle the problem, this paper proposed a position filtering method for tandem repeat sequence alignment, which filtered alignment results by calculating the similarity of rare kmer sequences. Additionally, it implemented a strategy of merging rare kmers to expedite computation, coupled with a fuzzy search based on edit distance to enhance filtering information density. Experimental results demonstrate that this approach improves both the recall and accuracy of alignment results on simulated datasets while achieving approximately a 2-fold increase in computational speed compared to existing methods, with notable parallel acceleration effects. [ABSTRACT FROM AUTHOR]

Predicting which proteins interact together from amino acid sequences is an important task. We develop a method to pair interacting protein sequences which leverages the power of protein language models trained on multiple sequence alignments (MSAs), such as MSA Transformer and the EvoFormer module of AlphaFold. We formulate the problem of pairing interacting partners among the paralogs of two protein families in a differentiable way. We introduce a method called Differentiable Pairing using Alignment-based Language Models (DiffPALM) that solves it by exploiting the ability of MSA Transformer to fill in masked amino acids in multiple sequence alignments using the surrounding context. MSA Transformer encodes coevolution between functionally or structurally coupled amino acids within protein chains. It also captures inter-chain coevolution, despite being trained on single-chain data. Relying on MSA Transformer without fine-tuning, DiffPALM outperforms existing coevolutionbased pairing methods on difficult benchmarks of shallow multiple sequence alignments extracted from ubiquitous prokaryotic protein datasets. It also outperforms an alternative method based on a state-of-the-art protein language model trained on single sequences. Paired alignments of interacting protein sequences are a crucial ingredient of supervised deep learning methods to predict the three-dimensional structure of protein complexes. Starting from sequences paired by DiffPALM substantially improves the structure prediction of some eukaryotic protein complexes by AlphaFold-Multimer. It also achieves competitive performance with using orthology-based pairing. [ABSTRACT FROM AUTHOR]

Preparation of DNA polymorphism datasets for analysis is an important step in evolutionary genetic and molecular ecology studies. Ever‐growing dataset sizes make this step time consuming, but few convenient software tools are available to facilitate processing of large‐scale datasets including thousands of sequence alignments. Here I report "processor of sequences v4" (proSeq4)—a user‐friendly multiplatform software for preparation and evolutionary genetic analyses of genome‐ or transcriptome‐scale sequence polymorphism datasets. The program has an easy‐to‐use graphic user interface and is designed to process and analyse many thousands of datasets. It supports over two dozen file formats, includes a flexible sequence editor and various tools for data visualization, quality control and most commonly used evolutionary genetic analyses, such as NJ‐phylogeny reconstruction, DNA polymorphism analyses and coalescent simulations. Command line tools (e.g. vcf2fasta) are also provided for easier integration into bioinformatic pipelines. Apart of molecular ecology and evolution research, proSeq4 may be useful for teaching, e.g. for visual illustration of different shapes of phylogenies generated with coalescent simulations in different scenarios. ProSeq4 source code and binaries for Windows, MacOS and Ubuntu are available from https://sourceforge.net/projects/proseq/. [ABSTRACT FROM AUTHOR]

Pathogenic bacteria represent a formidable threat to human health, necessitating substantial resources for prevention and treatment. With the escalating concern regarding antibiotic resistance, there is a pressing need for innovative approaches to combat these pathogens. Repurposing existing drugs offers a promising solution. Our present work hypothesizes that proteins harboring ligand-binding pockets with similar chemical environments may be able to bind the same drug. To facilitate this drug-repurposing strategy against pathogenic bacteria, we introduce an online server, PharmaRedefine. Leveraging a combination of sequence and structure alignment and protein pocket similarity analysis, this platform enables the prediction of potential targets in representative bacteria for specific FDA-approved drugs. This novel approach holds tremendous potential for drug repositioning that effectively combat infections caused by pathogenic bacteria. PharmaRedefine is freely available at http://guolab.mpu.edu.mo/pharmredefine. [ABSTRACT FROM AUTHOR]

The current trend in phylogenetic and evolutionary analyses predominantly relies on omic data. However, prior to core analyses, traditional methods typically involve intricate and time-consuming procedures, including assembly from high-throughput reads, decontamination, gene prediction, homology search, orthology assignment, multiple sequence alignment, and matrix trimming. Such processes significantly impede the efficiency of research when dealing with extensive data sets. In this study, we develop PhyloAln, a convenient reference-based tool capable of directly aligning high-throughput reads or complete sequences with existing alignments as a reference for phylogenetic and evolutionary analyses. Through testing with simulated data sets of species spanning the tree of life, PhyloAln demonstrates consistently robust performance compared with other reference-based tools across different data types, sequencing technologies, coverages, and species, with percent completeness and identity at least 50 percentage points higher in the alignments. Additionally, we validate the efficacy of PhyloAln in removing a minimum of 90% foreign and 70% cross-contamination issues, which are prevalent in sequencing data but often overlooked by other tools. Moreover, we showcase the broad applicability of PhyloAln by generating alignments (completeness mostly larger than 80%, identity larger than 90%) and reconstructing robust phylogenies using real data sets of transcriptomes of ladybird beetles, plastid genes of peppers, or ultraconserved elements of turtles. With these advantages, PhyloAln is expected to facilitate phylogenetic and evolutionary analyses in the omic era. The tool is accessible at https://github.com/huangyh45/PhyloAln. [ABSTRACT FROM AUTHOR]

Most algorithms that are used to predict the effects of variants rely on evolutionary conservation. However, a majority of such techniques compute evolutionary conservation by solely using the alignment of multiple sequences while overlooking the evolutionary context of substitution events. We had introduced PHACT, a scoring-based pathogenicity predictor for missense mutations that can leverage phylogenetic trees, in our previous study. By building on this foundation, we now propose PHACTboost, a gradient boosting tree–based classifier that combines PHACT scores with information from multiple sequence alignments, phylogenetic trees, and ancestral reconstruction. By learning from data, PHACTboost outperforms PHACT. Furthermore, the results of comprehensive experiments on carefully constructed sets of variants demonstrated that PHACTboost can outperform 40 prevalent pathogenicity predictors reported in the dbNSFP, including conventional tools, metapredictors, and deep learning–based approaches as well as more recent tools such as AlphaMissense, EVE, and CPT-1. The superiority of PHACTboost over these methods was particularly evident in case of hard variants for which different pathogenicity predictors offered conflicting results. We provide predictions of 215 million amino acid alterations over 20,191 proteins. PHACTboost is available at https://github.com/CompGenomeLab/PHACTboost. PHACTboost can improve our understanding of genetic diseases and facilitate more accurate diagnoses. [ABSTRACT FROM AUTHOR]

Phylogenetic methods are widely used to reconstruct the evolutionary relationships among species and individuals. However, recombination can obscure ancestral relationships as individuals may inherit different regions of their genome from different ancestors. It is, therefore, often necessary to detect recombination events, locate recombination breakpoints, and select recombination-free alignments prior to reconstructing phylogenetic trees. While many earlier studies have examined the power of different methods to detect recombination, very few have examined the ability of these methods to accurately locate recombination breakpoints. In this study, we simulated genome sequences based on ancestral recombination graphs and explored the accuracy of three popular recombination detection methods: MaxChi, 3SEQ, and Genetic Algorithm Recombination Detection. The accuracy of inferred breakpoint locations was evaluated along with the key factors contributing to variation in accuracy across datasets. While many different genomic features contribute to the variation in performance across methods, the number of informative sites consistent with the pattern of inheritance between parent and recombinant child sequences always has the greatest contribution to accuracy. While partitioning sequence alignments based on identified recombination breakpoints can greatly decrease phylogenetic error, the quality of phylogenetic reconstructions depends very little on how breakpoints are chosen to partition the alignment. Our work sheds light on how different features of recombinant genomes affect the performance of recombination detection methods and suggests best practices for reconstructing phylogenies based on recombination-free alignments. [ABSTRACT FROM AUTHOR]

Despite having important biological implications, insertion, and deletion (indel) events are often disregarded or mishandled during phylogenetic inference. In multiple sequence alignment, indels are represented as gaps and are estimated without considering the distinct evolutionary history of insertions and deletions. Consequently, indels are usually excluded from subsequent inference steps, such as ancestral sequence reconstruction and phylogenetic tree search. Here, we introduce indel-aware parsimony (indelMaP), a novel way to treat gaps under the parsimony criterion by considering insertions and deletions as separate evolutionary events and accounting for long indels. By identifying the precise location of an evolutionary event on the tree, we can separate overlapping indel events and use affine gap penalties for long indel modeling. Our indel-aware approach harnesses the phylogenetic signal from indels, including them into all inference stages. Validation and comparison to state-of-the-art inference tools on simulated data show that indelMaP is most suitable for densely sampled datasets with closely to moderately related sequences, where it can reach alignment quality comparable to probabilistic methods and accurately infer ancestral sequences, including indel patterns. Due to its remarkable speed, our method is well suited for epidemiological datasets, eliminating the need for downsampling and enabling the exploitation of the additional information provided by dense taxonomic sampling. Moreover, indelMaP offers new insights into the indel patterns of biologically significant sequences and advances our understanding of genetic variability by considering gaps as crucial evolutionary signals rather than mere artefacts. [ABSTRACT FROM AUTHOR]

The thermotolerant yeast Pichia kudriavzevii (previously known as Issatchenkia orientalis), can produce ethanol from a variety of carbon sources and grows at around 45 °C. Thus, this yeast is considered a useful biocatalyst for producing ethanol from lignocellulose through simultaneous saccharification and fermentation (SSF). SSF has several advantages, such as a simplified manufacturing process, ease of operation and reduced energy input. Using P. kudriavzevii NBRC1279 and NBRC1664, we previously succeeded in producing ethanol through SSF; however, the extent to which inhibitors by-produced from lignocellulose hydrolysis affect the growth and ethanol productivity of the two strains remains to be investigated. In this study, to better understand the inhibitor tolerance capacity of the two strains, spot assay, growth experiment, real-time quantitative PCR (RT-qPCR) analysis and multiple sequence alignment analysis were carried out. When P. kudriavzevii NBRC1279 and NBRC1664, as well as Saccharomyces cerevisiae BY4742 as a control, were cultured on SCD plates containing 17% ethanol, 42 mM furfural, 56 mM 5-hydroxymethylfurfural (HMF) or 10 mM vanillin, only P. kudriavzevii NBRC1664 was able to grow under all conditions. Moreover, the inhibitor tolerance capacity of P. kudriavzevii NBRC1664 was greater than those of other strains using SCD medium containing the same concentrations of various inhibitors. When an RT-qPCR analysis of seven gene sequences from aldehyde dehydrogenase and the aldehyde dehydrogenase family protein (ADHF) was performed using P. kudriavzevii NBRC1664 cultivated in the presence of 56 mM HMF, ADHF1 and ADHF2 were up-regulated in the early logarithmic growth phase. Moreover, a multiple sequence alignment of the amino acid sequences of ADHF1, ADHF2 and the known ADH suggested that ADHF1 and ADHF2 may catalyze the reversible NAD+-dependent oxidation of HMF. Our data may be useful for future studies on the metabolic engineering of more useful strains for ethanol production from lignocellulose. [ABSTRACT FROM AUTHOR]

Tea plant (Camellia sinensis [L.]) is one of the most important crops in China, and tea branch is an important agronomic trait that determines the yield of tea plant. In previous work focused on GWAS that detecting GWAS signals related to plant architecture through whole genome re‐sequencing of ancient tea plants, a gene locus TEA 029928 significantly related to plant type was found. Sequence alignment results showed that this gene belonged to the F‐box family. We named it CsBRC. CsBRC‐GFP fusion proteins were mainly localized in the plasma membrane. By comparing the phenotypes of CsBRC transgenic tobacco and WT tobacco, it was found that the number of branches of transgenic tobacco was significantly higher than that of wild‐type tobacco. Through RNA‐seq analysis, it was found that CsBRC affects the branching development of plants by regulating the expression of genes related to brassinosteroid synthesis pathway in plants. In addition, overexpression of CsBRC in rice could increase tiller number, grain length and width, and 1,000‐grain weight. [ABSTRACT FROM AUTHOR]

Seafood is a prime target for fraudulent activities due to the complexity of its supply chain, high demand, and difficult discrimination among species once morphological characteristics are removed. Instances of seafood fraud are expected to increase due to growing demand. This manuscript reviews the application of DNA‐based methods for commercial fish authentication and identification from 2000 to 2023. It explores (1) the most common types of commercial fish used in assay development, (2) the type of method used, (3) the gene region most often targeted, (4) provides a case study of currently published assays or primer‐probe pairs used for DNA amplification, for specificity, and (5) makes recommendations for ensuring standardized assay‐based reporting for future studies. A total of 313 original assays for the detection and authentication of commercial fish species from 191 primary articles published over the last 23 years were examined. The most explored DNA‐based method was real‐time polymerase chain reaction (qPCR), followed by DNA sequencing. The most targeted gene regions were cytb (cytochrome b) and COI (cytochrome c oxidase 1). Tuna was the most targeted commercial fish species. A case study of published tuna assays (n = 19) targeting the cytb region found that most assays were not species‐specific through in silico testing. This was conducted by examining the primer mismatch for each assay using multiple sequence alignment. Therefore, there is need for more standardized DNA‐based assay reporting in the literature to ensure specificity, reproducibility, and reliability of results. Factors, such as cost, sensitivity, quality of the DNA, and species, should be considered when designing assays. [ABSTRACT FROM AUTHOR]

Background: With the surge in genomic data driven by advancements in sequencing technologies, the demand for efficient bioinformatics tools for sequence analysis has become paramount. BLAST-like alignment tool (BLAT), a sequence alignment tool, faces limitations in performance efficiency and integration with modern programming environments, particularly Python. This study introduces PxBLAT, a Python-based framework designed to enhance the capabilities of BLAT, focusing on usability, computational efficiency, and seamless integration within the Python ecosystem. Results: PxBLAT demonstrates significant improvements over BLAT in execution speed and data handling, as evidenced by comprehensive benchmarks conducted across various sample groups ranging from 50 to 600 samples. These experiments highlight a notable speedup, reducing execution time compared to BLAT. The framework also introduces user-friendly features such as improved server management, data conversion utilities, and shell completion, enhancing the overall user experience. Additionally, the provision of extensive documentation and comprehensive testing supports community engagement and facilitates the adoption of PxBLAT. Conclusions: PxBLAT stands out as a robust alternative to BLAT, offering performance and user interaction enhancements. Its development underscores the potential for modern programming languages to improve bioinformatics tools, aligning with the needs of contemporary genomic research. By providing a more efficient, user-friendly tool, PxBLAT has the potential to impact genomic data analysis workflows, supporting faster and more accurate sequence analysis in a Python environment. [ABSTRACT FROM AUTHOR]

Background: Artemisia selengensis, classified within the genus Artemisia of the Asteraceae family, is a perennial herb recognized for its dual utility in culinary and medicinal domains. There are few studies on the chloroplast genome of A. selengensis, and the phylogeographic classification is vague, which makes phylogenetic analysis and evolutionary studies very difficult. Results: The chloroplast genomes of 10 A. selengensis in this study were highly conserved in terms of gene content, gene order, and gene intron number. The genome lengths ranged from 151,148 to 151,257 bp and were typical of a quadripartite structure with a total GC content of approximately 37.5%. The chloroplast genomes of all species encode 133 genes, including 88 protein-coding genes, 37 tRNA genes, and 8 rRNA genes. Due to the contraction and expansion of the inverted repeats (IR), the overlap of ycf1 and ndhF genes occurred at the inverted repeats B (IRB) and short single copy sequence (SSC) boundaries. According to a codon use study, the frequent base in the chloroplast genome of A. selengensis' third codon position was A/T. The number of SSR repeats was 42–44, most of which were single nucleotide A/T repeats. Sequence alignment analysis of the chloroplast genome showed that variable regions were mainly distributed in single copy regions, nucleotide diversity values of 0 to 0.009 were calculated by sliding window analysis, 8 mutation hotspot regions were detected, and coding regions were more conserved than non-coding regions. Analysis of non-synonymous substitution (Ka) and synonymous substitution (Ks) revealed that accD, rps12, petB, and atpF genes were affected by positive selection and no genes were affected by neutral selection. Based on the findings of the phylogenetic analysis, Artemisia selengensis was sister to the genus Artemisia Chrysanthemum and formed a monophyletic group with other Artemisia genera. Conclusions: In this research, the present study systematically compared the chloroplast genomic features of A. selengensis and provided important information for the study of the chloroplast genome of A. selengensis and the evolutionary relationships among Asteraceae species. [ABSTRACT FROM AUTHOR]

Objective: Defining whether a suspected case was due to scrub typhus through laboratory testing, to understand the prevalence of scrub typhus in Shijiazhuang City, Hebei Province. Methods: An epidemiological investigation was conducted on the suspected case, utilizing Weil-Felix test and indirect immunofluorescence assay (IFA) to detect specific antibodies against O. tsutsugamushi in serum specimens. Additionally, PCR amplification of the 56-kDa and groEL genes was performed, followed by constructing a phylogenetic tree to identify the genotype. Results: The acute phase titer of the Weil-Felix test for the case was 1:160, which increased to 1:320 in the recovery phase. IFA assay revealed IgG titers against O. tsutsugamushi of 1:64 in the acute phase and 1:256 in the recovery phase. Sequence alignment of the PCR amplified fragment showed the highest similarity with the O. tsutsugamushi genotype. Kawasaki sequence, ranging from 99.71 to 100.00%. The strain exhibited the closest genetic relationship with the known O. tsutsugamushi Kawasaki genotype. Conclusion: This study confirms the presence of O. tsutsugamushi in Shijiazhuang City, Hebei Province, with the identified strain belonging to the Kawasaki genotype, marking the first diagnosis of this strain in the region. [ABSTRACT FROM AUTHOR]

Microsatellites, known as simple sequence repeats (SSRs), are short tandem repeats of 1 to 6 nucleotide motifs found in all genomes, particularly eukaryotes. They are widely used as co-dominant markers in genetic analyses and molecular breeding. Triticeae, a tribe of grasses, includes major cereal crops such as bread wheat, barley, and rye, as well as abundant forage and lawn grasses, playing a crucial role in global food production and agriculture. To enhance genetic work and expedite the improvement of Triticeae crops, we have developed TriticeaeSSRdb, an integrated and user-friendly database. It contains 3,891,705 SSRs from 21 species and offers browsing options based on genomic regions, chromosomes, motif types, and repeat motif sequences. Advanced search functions allow personalized searches based on chromosome location and length of SSR. Users can also explore the genes associated with SSRs, design customized primer pairs for PCR validation, and utilize practical tools for wholegenome browsing, sequence alignment, and in silico SSR prediction from local sequences. We continually update TriticeaeSSRdb with additional species and practical utilities. We anticipate that this database will greatly facilitate trait genetic analyses and enhance molecular breeding strategies for Triticeae crops. Researchers can freely access the database at http://triticeaessrdb.com/. [ABSTRACT FROM AUTHOR]

Background: This computational analysis investigated sequence complementarities between the TRIM33 gene and human noncoding (nc)RNAs and characterized their interactions in the context of paraneoplastic dermatomyositis. Methods: TRIM33 FASTA sequence (NCBI Reference Sequence: NC_000001.11) was used for BLASTN analysis against Human GRCh38 in the Ensembl.org database. Retrieved ncRNAs showing hits to TRIM33 were searched in the GeneCards.org database and further analyzed through RNAInter, QmRLFS-finder, Spliceator, and NcPath enrichment analysis. Results: A total of 100 hits were found, involving the lncRNAs NNT-AS1, MKLN1-AS, LINC01206, and PAXBP1-AS1, whose dysregulation has been reported in either cancer or dermatomyositis. Additionally, the lncRNAs NNT-AS1 and PAXBP1-AS1 may interact with microRNA-142-3p, reducing its expression and increasing that of TRIM33. Sequence complementarity affected only TRIM33 intron 1, possibly resulting in alternatively spliced isoforms of TIF1γ with increased immunogenicity. The results also revealed nucleotide alignment between TRIM33 and the gene regulatory elements of 28 ncRNA genes involved in immune pathways. Conclusions: This pivotal study demonstrates sequence complementarity between TRIM33 and human ncRNAs dysregulated in cancer and dermatomyositis. This scenario may lead to the overproduction of more immunogenic TIF1γ variants in tumors and the stimulation of autoimmunity. Further experimental analyses using targeted methods such as Western blot or Chip-Seq are required to confirm these data. [ABSTRACT FROM AUTHOR]

ω-Transaminases (ω-TAs) are significant candidate enzymes in the biosynthesis of chiral amines for the pharmaceutical and chemical industries. Novel thermostable ω-TAs for potential use in these industries can be discovered using homologous sequence alignment. In this study, we used well-studied B3-TA from hot spring metagenomes, the Sbv333-TA from Streptomyces sp., and TATR from Thermomicrobium roseum as starting templates to BLAST search, and found a yet unidentified thermostable ω-TA (TATP) from Thermorudis peleae. We cloned TATP into vector pET28a, leading to pET28a-TATP, and overexpressed it in Escherichia coli. The enzyme showed the highest activity at pH 8.8 and 73.6 °C, with remarkable thermostability and tolerance toward organic solvents methanol and ethanol. Substrate specificity analysis showed that TATP enzyme is active toward a broad range of substrates including glyoxylate, pyruvate, 2-phenylpropionaldehyde, 2-oxobutyrate, propionaldehyde, acetaldehyde, ethyl acetoacetate, etc. Especially, TATP enzyme presented relatively good activity toward pyruvate and glyoxylate. In addition, the active sites of TATP were analyzed via the approaches of protein sequence alignment, three-dimensional structure simulation, and coenzyme pyridoxamine phosphate docking, which provided a guideline for further enzyme engineering and suggested a potential industrial thermostable ω-TA for chiral amines synthesis. [ABSTRACT FROM AUTHOR]

The article discusses the development of an AI-based algorithm called DMFold for predicting the structure of protein-protein complexes. Proteins carry out important biological functions through their interactions with other proteins, known as protein-protein interactions (PPIs). Determining the three-dimensional structures of PPI complexes is crucial for understanding their functions and developing drugs that target PPIs. The DMFold algorithm constructs precise multiple sequence alignments (MSAs) and utilizes co-evolutionary information to predict the structure of PPI complexes. In blind tests, DMFold outperformed other methods, including the widely acclaimed AlphaFold2, in predicting the structures of protein complexes. The algorithm has the potential to enhance protein function annotations and aid in the discovery of drugs targeting PPI-related diseases. [Extracted from the article]

Bioinformatics and computational biology are two fields that have been exploiting GPUs for more than two decades, with being CUDA the most used programming language for them. However, as CUDA is an NVIDIA proprietary language, it implies a strong portability restriction to a wide range of heterogeneous architectures, like AMD or Intel GPUs. To face this issue, the Khronos group has recently proposed the SYCL standard, which is an open, royalty-free, cross-platform abstraction layer that enables the programming of a heterogeneous system to be written using standard, single-source C++ code. Over the past few years, several implementations of this SYCL standard have emerged, being oneAPI the one from Intel. This paper presents the migration process of the SW# suite, a biological sequence alignment tool developed in CUDA, to SYCL using Intel's oneAPI ecosystem. The experimental results show that SW# was completely migrated with a small programmer intervention in terms of hand-coding. In addition, it was possible to port the migrated code between different architectures (considering multiple vendor GPUs and also CPUs), with no noticeable performance degradation on five different NVIDIA GPUs. Moreover, performance remained stable when switching to another SYCL implementation. As a consequence, SYCL and its implementations can offer attractive opportunities for the bioinformatics community, especially considering the vast existence of CUDA-based legacy codes. [ABSTRACT FROM AUTHOR]

In the era of ubiquitous high‐throughput sequencing studies, there is a growing need for analysis tools that are not just performant but also comprehensive and user‐friendly enough to cater to both novice and advanced users. This article introduces SeqKit2, the next iteration of the widely used sequence analysis tool SeqKit, featuring expanded functionality, performance optimizations, and support for additional compression methods. Retaining a pragmatic subcommand architecture, SeqKit2 represents substantial enhancement through the inclusion of 19 additional subcommands, expanding its overall repertoire to a total of 38 in eight categories. The new subcommands add functionality such as amplicon processing and robust, error‐tolerant parsing of sequence records. In addition, three subcommands designed for real‐time analysis are added for periodic monitoring of properties of FASTQ and Binary Alignment/Map alignment records and real‐time streaming from multiple sequence files. The performance of SeqKit2 is benchmarked against the old version of SeqKit, Bioawk, Seqtk, and SeqFu tools. SeqKit2 consistently outperforms its predecessor, albeit with marginally higher memory usage, while maintaining competitive runtimes against other tools. With its broad functionality, proven usability, and ongoing development driven by user feedback, we hope that bioinformaticians will find SeqKit2 useful as a "Swiss army knife" of sequence and alignment processing—equally adept at facilitating ad hoc analyses and seamlessly integrating into larger pipelines. Highlights: SeqKit2 expands its capabilities, doubling the number of subcommands from 19 to 38, and adding support for three more compression file formats.SeqKit2 outperforms its predecessor and maintains competitive with other tools.SeqKit2 improves user‐friendliness with new features, like, autocompletion, progress bars, and enhanced error handling. [ABSTRACT FROM AUTHOR]

Clostridium perfringens is the main pathogen of chicken necrotic enteritis (NE) causing huge economic losses in the poultry industry. Although dietary secondary bile acid deoxycholic acid (DCA) reduced chicken NE, the accumulation of conjugated tauro-DCA (TDCA) raised concerns regarding DCA efficacy. In this study, we aimed to deconjugate TDCA by bile salt hydrolase (BSH) to increase DCA efficacy against the NE pathogen C. perfringens. Assays were conducted to evaluate the inhibition of C. perfringens growth, hydrogen sulfide (H2S) production, and virulence gene expression by TDCA and DCA. BSH activity and sequence alignment were conducted to select the bsh gene for cloning. The bsh gene from Bifidobacterium longum was PCR-amplified and cloned into plasmids pET-28a (pET-BSH) and pDR111 (pDR-BSH) for expressing the BSH protein in E. coli BL21 and Bacillus subtilis 168 (B-sub-BSH), respectively. His-tag-purified BSH from BL21 cells was evaluated by SDS-PAGE, Coomassie blue staining, and a Western blot (WB) assays. Secretory BSH from B. subtilis was analyzed by a Dot-Blot. B-sub-BSH was evaluated for the inhibition of C. perfringens growth. C. perfringens growth reached 7.8 log10 CFU/mL after 24 h culture. C. perfringens growth was at 8 vs. 7.4, 7.8 vs. 2.6 and 6 vs. 0 log10 CFU/mL in 0.2, 0.5, and 1 mM TDCA vs. DCA, respectively. Compared to TDCA, DCA reduced C. perfringens H2S production and the virulence gene expression of asrA1, netB, colA, and virT. BSH activity was observed in Lactobacillus johnsonii and B. longum under anaerobe but not L. johnsonii under 10% CO2 air. After the sequence alignment of bsh from ten bacteria, bsh from B. longum was selected, cloned into pET-BSH, and sequenced at 951 bp. After pET-BSH was transformed in BL21, BSH expression was assessed around 35 kDa using Coomassie staining and verified for His-tag using WB. After the subcloned bsh and amylase signal peptide sequence was inserted into pDR-BSH, B. subtilis was transformed and named B-sub-BSH. The transformation was evaluated using PCR with B. subtilis around 3 kb and B-sub-BSH around 5 kb. Secretory BSH expressed from B-sub-BSH was determined for His-tag using Dot-Blot. Importantly, C. perfringens growth was reduced greater than 59% log10 CFU/mL in the B-sub-BSH media precultured with 1 vs. 0 mM TDCA. In conclusion, TDCA was less potent than DCA against C. perfringens virulence, and recombinant secretory BSH from B-sub-BSH reduced C. perfringens growth, suggesting a new potential intervention against the pathogen-induced chicken NE. [ABSTRACT FROM AUTHOR]

Phylogenetic inference based on protein sequence alignment is a widely used procedure. Numerous phylogenetic algorithms have been developed, most of which have many parameters and options. Choosing a program, options, and parameters can be a nontrivial task. No benchmark for comparison of phylogenetic programs on real protein sequences was publicly available. We have developed PhyloBench, a benchmark for evaluating the quality of phylogenetic inference, and used it to test a number of popular phylogenetic programs. PhyloBench is based on natural, not simulated, protein sequences of orthologous evolutionary domains. The measure of accuracy of an inferred tree is its distance to the corresponding species tree. A number of tree-to-tree distance measures were tested. The most reliable results were obtained using the Robinson–Foulds distance. Our results confirmed recent findings that distance methods are more accurate than maximum likelihood (ML) and maximum parsimony. We tested the bayesian program MrBayes on natural protein sequences and found that, on our datasets, it performs better than ML, but worse than distance methods. Of the methods we tested, the Balanced Minimum Evolution method implemented in FastME yielded the best results on our material. Alignments and reference species trees are available at https://mouse.belozersky.msu.ru/tools/phylobench/ together with a web-interface that allows for a semi-automatic comparison of a user's method with a number of popular programs. [ABSTRACT FROM AUTHOR]

In corpus-based interpreting studies, typical challenges exist in the time-consuming and labour-intensive nature of transcribing spoken data and in identifying prosodic properties. This paper addresses these challenges by exploring methods for the automatic compilation of multimodal interpreting corpora, with a focus on English/Chinese Consecutive Interpreting. The results show that: 1) automatic transcription can achieve an accuracy rate of 95.3% in transcribing consecutive interpretations; 2) prosodic properties related to filled pauses, unfilled pauses, articulation rate, and mispronounced words can be automatically extracted using our rule-based programming; 3) mispronounced words can be effectively identified by employing Confidence Measure, with any word having a Confidence Measure lower than 0.321 considered as mispronounced; 4) automatic alignment can be achieved through the utilisation of automatic segmentation, sentence embedding, and alignment techniques. This study contributes to interpreting studies by broadening the empirical understanding of orality, enabling multimodal analyses of interpreting products, and providing a new methodological solution for the construction and utilisation of multimodal interpreting corpora. It also has implications in exploring applicability of new technologies in interpreting studies. [ABSTRACT FROM AUTHOR]

Sequence alignment is a critical factor in the variant analysis of genomic research. Since the FM (Ferrainas–Manzini) index was developed, it has proven to be a model in a compact format with efficient pattern matching and high-speed query searching, which has attracted much research interest in the field of sequence alignment. Such characteristics make it a convenient tool for handling large-scale sequence alignment projects executed with a small memory. In bioinformatics, the massive success of next-generation sequencing technology has led to an exponential growth in genomic data, presenting a computational challenge for sequence alignment. In addition, the use of a heterogeneous computing system, composed of various types of nodes, is prevalent in the field of HPC (high-performance computing), which presents a promising solution for sequence alignment. However, conventional methodologies in short-read alignment are limited in performance on current heterogeneous computing infrastructures. Therefore, we developed a parallel sequence alignment to investigate the applicability of this approach in NUMA-based (Non-Uniform Memory Access) heterogeneous architectures against traditional alignment algorithms. This proposed work combines the LF (longest-first) distribution policy with the EP (enhanced partitioning) strategy for effective load balancing and efficient parallelization among heterogeneous architectures. The newly proposed LF-EP-based FM aligner shows excellent efficiency and a significant improvement over NUMA-based heterogeneous computing platforms. We provide significantly improved performance over several popular FM aligners in many dimensions such as read length, sequence number, sequence distance, alignment speedup, and result quality. These resultant evaluation metrics cover the quality assessment, complexity analysis, and speedup evaluation of our approach. Utilizing the capabilities of NUMA-based heterogeneous computing architectures, our approach effectively provides a convenient solution for large-scale short-read alignment in the heterogeneous system. [ABSTRACT FROM AUTHOR]

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.