BACKGROUND: Accurate taxonomic classification in genome databases is essential for reliable biological research and effective data sharing. Mislabeling or inaccuracies in genome annotations can lead to incorrect scientific conclusions and hinder the reproducibility of research findings. Despite advances in genome analysis techniques, challenges persist in ensuring precise and reliable taxonomic assignments. Existing tools for genome verification often involve extensive computational resources or lengthy processing times, which can limit their accessibility and scalability for large-scale projects. There is a need for more efficient, user-friendly solutions that can handle diverse datasets and provide accurate results with minimal computational demands. This work aimed to address these challenges by introducing a novel tool that enhances taxonomic accuracy, offers a user-friendly interface, and supports large-scale analyses. RESULTS: We introduce a novel tool for the quality control and taxonomic classification tool of prokaryotic genomes, called DFAST_QC, which is available as both a command-line tool and a web service. DFAST_QC can quickly identify species based on NCBI and GTDB taxonomies by combining genome-distance calculations using MASH with ANI calculations using Skani. We evaluated DFAST_QC's performance in species identification and found it to be highly consistent with existing taxonomic standards, successfully identifying species across diverse datasets. In several cases, DFAST_QC identified potential mislabeling of species names in public databases and highlighted discrepancies in current classifications, demonstrating its capability to uncover errors and enhance taxonomic accuracy. Additionally, the tool's efficient design allows it to operate smoothly on local machines with minimal computational requirements, making it a practical choice for large-scale genome projects. CONCLUSIONS: DFAST_QC is a reliable and efficient tool for accurate taxonomic identification and genome quality control, well-suited for large-scale genomic studies. Its compatibility with limited-resource environments, combined with its user-friendly design, ensures seamless integration into existing workflows. DFAST_QC's ability to refine species assignments in public databases highlights its value as a complementary tool for maintaining and enhancing the accuracy of taxonomic data in genomic research. The web version is available at https://dfast.ddbj.nig.ac.jp/dqc/submit/ , and the source code for local use can be found at https://github.com/nigyta/dfast_qc .

A Recurrent Neural Network (RNN) for Video Super Resolution (VSR) is generally trained with randomly clipped and cropped short videos extracted from original training videos due to various challenges in learning RNNs. However, since this RNN is optimized to super-resolve short videos, VSR of long videos is degraded due to the domain gap. Our preliminary experiments reveal that such degradation changes depending on the video properties, such as the video length and dynamics. To avoid this degradation, this paper proposes the training strategy of RNN for VSR that can work efficiently and stably independently of the video length and dynamics. The proposed training strategy stabilizes VSR by training a VSR network with various RNN hidden states changed depending on the video properties. Since computing such a variety of hidden states is time-consuming, this computational cost is reduced by reusing the hidden states for efficient training. In addition, training stability is further improved with frame-number conditioning. Our experimental results demonstrate that the proposed method performed better than base methods in videos with various lengths and dynamics.

Motion representation is one of the most important elements in producing realistic virtual humans in computer graphics. Although manual production techniques, such as keyframe interpolation, enable animators to freely synthesize motion, manually creating motions as detailed as those obtained by motion capture requires substantial skill and time. Therefore, this paper proposes a method to obtain high-detail motion with high expressive power from inexpressive and low-detail motion. The method entails deep-learning based detailing of motion representations, by using a motion enhancement network built on a recurrent neural network with a long short-term memory. This method may potentially produce high-detail motion resembling actual human motions from arbitrary low-detail motions created by novice users using keyframing techniques. Our experiments demonstrate the synthesis of high-detail motion, including the amount of movement, from low-detail motion of walking and talking.

SUMMARY: : Similarity searches of amino acid sequences against the public metagenomic data can provide users insights about the function of sequences based on the environmental distribution of similar sequences. However, a considerable reduction in the amount of data or the accuracy of the result was necessary to conduct sequence similarity searches against public metagenomic data, because of the vast data size more than Terabytes. Here, we present an ultra-fast service for the highly accurate amino acid sequence similarity search, called PZLAST, which can search the user's amino acid sequences to several Terabytes of public metagenomic sequences in ∼10-20 min. PZLAST accomplishes its search speed by using PEZY-SC2, which is a Multiple Instruction Multiple Data many-core processor. Results of PZLAST are summarized by the ontology-based environmental distribution of similar sequences. PZLAST can be used to predict the function of sequences and mine for homologs of functionally important gene sequences. AVAILABILITY AND IMPLEMENTATION: PZLAST is freely accessible at https://pzlast.riken.jp/meta without requiring registration. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Viruses are the most numerous biological entity, existing in all environments and infecting all cellular organisms. Compared with cellular life, the evolution and origin of viruses are poorly understood; viruses are enormously diverse, and most lack sequence similarity to cellular genes. To uncover viral sequences without relying on either reference viral sequences from databases or marker genes that characterize specific viral taxa, we developed an analysis pipeline for virus inference based on clustered regularly interspaced short palindromic repeats (CRISPR). CRISPR is a prokaryotic nucleic acid restriction system that stores the memory of previous exposure. Our protocol can infer CRISPR-targeted sequences, including viruses, plasmids, and previously uncharacterized elements, and predict their hosts using unassembled short-read metagenomic sequencing data. By analyzing human gut metagenomic data, we extracted 11,391 terminally redundant CRISPR-targeted sequences, which are likely complete circular genomes. The sequences included 2,154 tailed-phage genomes, together with 257 complete crAssphage genomes, 11 genomes larger than 200 kilobases, 766 genomes of Microviridae species, 56 genomes of Inoviridae species, and 95 previously uncharacterized circular small genomes that have no reliably predicted protein-coding gene. We predicted the host(s) of approximately 70% of the discovered genomes at the taxonomic level of phylum by linking protospacers to taxonomically assigned CRISPR direct repeats. These results demonstrate that our protocol is efficient for de novo inference of CRISPR-targeted sequences and their host prediction.

As data for microbial community structures found in various environments has increased, studies have examined the relationship between environmental labels given to retrieved microbial samples and their community structures. However, because environments continuously change over time and space, mixed states of some environments and its effects on community formation should be considered, instead of evaluating effects of discrete environmental categories. Here we applied a hierarchical Bayesian model to paired datasets containing more than 30,000 samples of microbial community structures and sample description documents. From the training results, we extracted latent environmental topics that associate co-occurring microbes with co-occurring word sets among samples. Topics are the core elements of environmental mixtures and the visualization of topic-based samples clarifies the connections of various environments. Based on the model training results, we developed a web application, LEA (Latent Environment Allocation), which provides the way to evaluate typicality and heterogeneity of microbial communities in newly obtained samples without confining environmental categories to be compared. Because topics link words and microbes, LEA also enables to search samples semantically related to the query out of 30,000 microbiome samples.

We propose a new corner estimation technique for 3D point cloud on convex polyhedral surfaces acquired by a 3D scanner. A corner is an important property of objects, and it is used in various technologies such as 2D or 3D object recognition system. The Harris corner detector and SUSAN are widely used as a method of finding corners in a 3D point cloud. In these methods, however, high processing cost will often be a problem. In this paper, we propose a new corner estimation technique using Delaunay tetrahedralization. This technique is able to estimate corners of convex polyhedral surfaces efficiently by using characteristic tetrahedrons generated by Delaunay tetrahedralization. In an experiment, we compared the proposed technique with the Harris corner detector. As a result of the experiment, for convex polyhedral surfaces used in the experiment, it is confirmed that the proposed technique can estimate corners with less cost than the half of the Harris corner detector without serious loss of the accuracy of each corner location.

Template matching is a technique for finding a part of reference image which matches a template image. This paper presents a new fast template matching algorithm which can detect the most similar position. In the proposed method, first, an effective initial threshold is calculated using Winner Update Algorithm. Next, very fast template matching is achieved by using this initial threshold in Multilevel Successive Elimination Algorithm. Furthermore, Brick Partitioning which is a new partitioning method is used to reduce the computational cost of comparing a template with each position within reference image. Experimental results indicate that the proposed method can search faster than previous methods.

BACKGROUND: Computational predictions of catalytic function are vital for in-depth understanding of enzymes. Because several novel approaches performing better than the common BLAST tool are rarely applied in research, we hypothesized that there is a large gap between the number of known annotated enzymes and the actual number in the protein universe, which significantly limits our ability to extract additional biologically relevant functional information from the available sequencing data. To reliably expand the enzyme space, we developed DomSign, a highly accurate domain signature-based enzyme functional prediction tool to assign Enzyme Commission (EC) digits. RESULTS: DomSign is a top-down prediction engine that yields results comparable, or superior, to those from many benchmark EC number prediction tools, including BLASTP, when a homolog with an identity >30% is not available in the database. Performance tests showed that DomSign is a highly reliable enzyme EC number annotation tool. After multiple tests, the accuracy is thought to be greater than 90%. Thus, DomSign can be applied to large-scale datasets, with the goal of expanding the enzyme space with high fidelity. Using DomSign, we successfully increased the percentage of EC-tagged enzymes from 12% to 30% in UniProt-TrEMBL. In the Kyoto Encyclopedia of Genes and Genomes bacterial database, the percentage of EC-tagged enzymes for each bacterial genome could be increased from 26.0% to 33.2% on average. Metagenomic mining was also efficient, as exemplified by the application of DomSign to the Human Microbiome Project dataset, recovering nearly one million new EC-labeled enzymes. CONCLUSIONS: Our results offer preliminarily confirmation of the existence of the hypothesized huge number of "hidden enzymes" in the protein universe, the identification of which could substantially further our understanding of the metabolisms of diverse organisms and also facilitate bioengineering by providing a richer enzyme resource. Furthermore, our results highlight the necessity of using more advanced computational tools than BLAST in protein database annotations to extract additional biologically relevant functional information from the available biological sequences.

BACKGROUND: Metagenomics is a powerful methodology to study microbial communities, but it is highly dependent on nucleotide sequence similarity searching against sequence databases. Metagenomic analyses with next-generation sequencing technologies produce enormous numbers of reads from microbial communities, and many reads are derived from microbes whose genomes have not yet been sequenced, limiting the usefulness of existing sequence similarity search tools. Therefore, there is a clear need for a sequence similarity search tool that can rapidly detect weak similarity in large datasets. RESULTS: We developed a tool, which we named CLAST (CUDA implemented large-scale alignment search tool), that enables analyses of millions of reads and thousands of reference genome sequences, and runs on NVIDIA Fermi architecture graphics processing units. CLAST has four main advantages over existing alignment tools. First, CLAST was capable of identifying sequence similarities ~80.8 times faster than BLAST and 9.6 times faster than BLAT. Second, CLAST executes global alignment as the default (local alignment is also an option), enabling CLAST to assign reads to taxonomic and functional groups based on evolutionarily distant nucleotide sequences with high accuracy. Third, CLAST does not need a preprocessed sequence database like Burrows-Wheeler Transform-based tools, and this enables CLAST to incorporate large, frequently updated sequence databases. Fourth, CLAST requires <2 GB of main memory, making it possible to run CLAST on a standard desktop computer or server node. CONCLUSIONS: CLAST achieved very high speed (similar to the Burrows-Wheeler Transform-based Bowtie 2 for long reads) and sensitivity (equal to BLAST, BLAT, and FR-HIT) without the need for extensive database preprocessing or a specialized computing platform. Our results demonstrate that CLAST has the potential to be one of the most powerful and realistic approaches to analyze the massive amount of sequence data from next-generation sequencing technologies.

BACKGROUND: Understanding the community structure of microbes is typically accomplished by sequencing 16S ribosomal RNA (16S rRNA) genes. These community data can be represented by constructing a phylogenetic tree and comparing it with other samples using statistical methods. However, owing to high computational complexity, these methods are insufficient to effectively analyze the millions of sequences produced by new sequencing technologies such as pyrosequencing. RESULTS: We introduce a web tool named VITCOMIC (VIsualization tool for Taxonomic COmpositions of MIcrobial Community) that can analyze millions of bacterial 16S rRNA gene sequences and calculate the overall taxonomic composition for a microbial community. The 16S rRNA gene sequences of genome-sequenced strains are used as references to identify the nearest relative of each sample sequence. With this information, VITCOMIC plots all sequences in a single figure and indicates relative evolutionary distances. CONCLUSIONS: VITCOMIC yields a clear representation of the overall taxonomic composition of each sample and facilitates an intuitive understanding of differences in community structure between samples. VITCOMIC is freely available at http://mg.bio.titech.ac.jp/vitcomic/.

Story-based VR system is important for many applications such as entertainment and education. In this paper, we propose the key action control of multiple human characters. Key actions are important actions that define the structure of story. Improvisational actions can be considered as variations that satisfy key actions. We applied the key action technique to the story construction kit and the interactive drama systems including multiple character interactions.

Editing human motion signals are difficult because of its multi-dimensional nature. In this paper, we propose a new method for analysis and synthesis of human motion using ICA. We extract several independent motion signals from human movement. The result of the extracted motion signal corresponds to the perceptually meaningful motion features that characterize the complex human actions. We show that generating various human motions can be much easier by editing motion feature extracted by ICA.

As sub-micron design rules are utilized for IC fabrication, wiring is becoming an important issue in the register-transfer synthesis of high-speed application-specific integrated circuits. This paper proposes a new algorithm that incorporates performance-driven placement in module selection phase of the synthesis. The algorithm not only efficiently exploits multiple module implementations in the design library, but also finds the module placement which minimizes wiring delay. Experimental results on a practical size example show that considering both module and wiring issues, the algorithm is able to improve the design performance more than 20%.