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            "name": "DeepSTABp",
            "description": "An AI based web tool to predict the melting temperature (Tm) of proteins based on their amino acid sequence and various growth conditions.",
            "homepage": "https://csb-deepstabp.bio.rptu.de",
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                            "term": "Variant effect prediction"
                        },
                        {
                            "uri": "http://edamontology.org/operation_3092",
                            "term": "Protein feature detection"
                        }
                    ],
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                        {
                            "data": {
                                "uri": "http://edamontology.org/data_2976",
                                "term": "Protein sequence"
                            },
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                            "data": {
                                "uri": "http://edamontology.org/data_0897",
                                "term": "Protein property"
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                "Web application",
                "Desktop application"
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                {
                    "uri": "http://edamontology.org/topic_0130",
                    "term": "Protein folding, stability and design"
                },
                {
                    "uri": "http://edamontology.org/topic_0121",
                    "term": "Proteomics"
                },
                {
                    "uri": "http://edamontology.org/topic_0154",
                    "term": "Small molecules"
                },
                {
                    "uri": "http://edamontology.org/topic_0080",
                    "term": "Sequence analysis"
                },
                {
                    "uri": "http://edamontology.org/topic_0601",
                    "term": "Protein modifications"
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                "JavaScript"
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            "link": [
                {
                    "url": "https://github.com/CSBiology/deepStabP",
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                    "url": "https://git.nfdi4plants.org/f_jung/deepstabp",
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                {
                    "doi": "10.3390/IJMS24087444",
                    "pmid": "37108605",
                    "pmcid": "PMC10138888",
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                    "metadata": {
                        "title": "DeepSTABp: A Deep Learning Approach for the Prediction of Thermal Protein Stability",
                        "abstract": "Proteins are essential macromolecules that carry out a plethora of biological functions. The thermal stability of proteins is an important property that affects their function and determines their suitability for various applications. However, current experimental approaches, primarily thermal proteome profiling, are expensive, labor-intensive, and have limited proteome and species coverage. To close the gap between available experimental data and sequence information, a novel protein thermal stability predictor called DeepSTABp has been developed. DeepSTABp uses a transformer-based protein language model for sequence embedding and state-of-the-art feature extraction in combination with other deep learning techniques for end-to-end protein melting temperature prediction. DeepSTABp can predict the thermal stability of a wide range of proteins, making it a powerful and efficient tool for large-scale prediction. The model captures the structural and biological properties that impact protein stability, and it allows for the identification of the structural features that contribute to protein stability. DeepSTABp is available to the public via a user-friendly web interface, making it accessible to researchers in various fields.",
                        "date": "2023-04-01T00:00:00Z",
                        "citationCount": 0,
                        "authors": [
                            {
                                "name": "Jung F."
                            },
                            {
                                "name": "Frey K."
                            },
                            {
                                "name": "Zimmer D."
                            },
                            {
                                "name": "Muhlhaus T."
                            }
                        ],
                        "journal": "International Journal of Molecular Sciences"
                    }
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            "credit": [
                {
                    "name": "Timo Mühlhaus",
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            "description": "Identifying snoRNA-disease associations based on multiple biological data by ranking framework.",
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                            "term": "Nucleic acid sequence analysis"
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                                "uri": "http://edamontology.org/data_3495",
                                "term": "RNA sequence"
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                    "uri": "http://edamontology.org/topic_0659",
                    "term": "Functional, regulatory and non-coding RNA"
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                {
                    "uri": "http://edamontology.org/topic_0634",
                    "term": "Pathology"
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                    "term": "Laboratory techniques"
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                    "doi": "10.1109/TCBB.2023.3258448",
                    "pmid": "37030816",
                    "pmcid": null,
                    "type": [],
                    "version": null,
                    "note": null,
                    "metadata": {
                        "title": "iSnoDi-MDRF: identifying snoRNA-disease associations based on multiple biological data by ranking framework",
                        "abstract": "Accumulating evidence indicates that the dysregulation of small nucleolar RNAs (snoRNAs) is relevant with diseases. Identifying snoRNA-disease associations by computational methods is desired for biologists, which can save considerable costs and time compared biological experiments. However, it still faces some challenges as followings: (i) Many snoRNAs are detected in recent years, but only a few snoRNAs have been proved to be associated with diseases; (ii) Computational predictors trained with only a few known snoRNA-disease associations fail to accurately identify the snoRNA-disease associations. In this study, we propose a ranking framework, called iSnoDi-MDRF, to identify potential snoRNA-disease associations based on multiple biological data, which has the following highlights: (i) iSnoDi-MDRF integrates ranking framework, which is not only able to identify potential associations between known snoRNAs and diseases, but also can identify diseases associated with new snoRNAs. (ii) Known gene-disease associations are employed to help train a mature model for predicting snoRNA-disease association. Experimental results illustrate that iSnoDi-MDRF is very suitable for identifying potential snoRNA-disease associations. The web server of iSnoDi-MDRF predictor is freely available at <uri>http://bliulab.net/iSnoDi-MDRF/</uri>.",
                        "date": "2023-01-01T00:00:00Z",
                        "citationCount": 0,
                        "authors": [
                            {
                                "name": "Zhang W."
                            },
                            {
                                "name": "Liu B."
                            }
                        ],
                        "journal": "IEEE/ACM Transactions on Computational Biology and Bioinformatics"
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            "credit": [
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                    "name": "Bin Liu",
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            "name": "ACP-MLC",
            "description": "A two-level prediction engine for identification of anticancer peptides and multi-label classification of their functional types.",
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                            "uri": "http://edamontology.org/operation_3631",
                            "term": "Peptide identification"
                        },
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                            "uri": "http://edamontology.org/operation_3936",
                            "term": "Feature selection"
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                    "input": [
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                                "term": "Protein sequence"
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                "Desktop application"
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            "topic": [
                {
                    "uri": "http://edamontology.org/topic_0154",
                    "term": "Small molecules"
                },
                {
                    "uri": "http://edamontology.org/topic_0121",
                    "term": "Proteomics"
                },
                {
                    "uri": "http://edamontology.org/topic_3474",
                    "term": "Machine learning"
                },
                {
                    "uri": "http://edamontology.org/topic_2640",
                    "term": "Oncology"
                }
            ],
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                "Windows"
            ],
            "language": [
                "Python"
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                    "url": "https://www.amazon.com/clouddrive/share/dW6uIgXPXfNBCLIcMcMJiKJ9rr5aXaJAo78VgGKVKRH",
                    "type": "Software package",
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                    "url": "https://github.com/Nicole-DH/ACP-MLC/blob/master/Manual.pdf",
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            "publication": [
                {
                    "doi": "10.1016/J.COMPBIOMED.2023.106844",
                    "pmid": "37058760",
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                    "type": [],
                    "version": null,
                    "note": null,
                    "metadata": {
                        "title": "ACP-MLC: A two-level prediction engine for identification of anticancer peptides and multi-label classification of their functional types",
                        "abstract": "Anticancer peptides (ACPs), a series of short bioactive peptides, are promising candidates in fighting against cancer due to their high activity, low toxicity, and not likely cause drug resistance. The accurate identification of ACPs and classification of their functional types is of great importance for investigating their mechanisms of action and developing peptide-based anticancer therapies. Here, we provided a computational tool, called ACP-MLC, to address binary classification and multi-label classification of ACPs for a given peptide sequence. Briefly, ACP-MLC is a two-level prediction engine, in which the 1st-level model predicts whether a query sequence is an ACP or not by random forest algorithm, and the 2nd-level model predicts which tissue types the sequence might target by the binary relevance algorithm. Development and evaluation by high-quality datasets, our ACP-MLC yielded an area under the receiver operating characteristic curve (AUC) of 0.888 on the independent test set for the 1st-level prediction, and obtained 0.157 hamming loss, 0.577 subset accuracy, 0.802 F1-scoremacro, and 0.826 F1-scoremicro on the independent test set for the 2nd-level prediction. A systematic comparison demonstrated that ACP-MLC outperformed existing binary classifiers and other multi-label learning classifiers for ACP prediction. Finally, we interpreted the important features of ACP-MLC by the SHAP method. User-friendly software and the datasets are available at https://github.com/Nicole-DH/ACP-MLC. We believe that the ACP-MLC would be a powerful tool in ACP discovery.",
                        "date": "2023-05-01T00:00:00Z",
                        "citationCount": 0,
                        "authors": [
                            {
                                "name": "Deng H."
                            },
                            {
                                "name": "Ding M."
                            },
                            {
                                "name": "Wang Y."
                            },
                            {
                                "name": "Li W."
                            },
                            {
                                "name": "Liu G."
                            },
                            {
                                "name": "Tang Y."
                            }
                        ],
                        "journal": "Computers in Biology and Medicine"
                    }
                }
            ],
            "credit": [
                {
                    "name": "Yun Tang",
                    "email": "ytang234@ecust.edu.cn",
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            "name": "smalldisco",
            "description": "Pipeline for siRNA discovery and 3' tail identification.",
            "homepage": "https://doi.org/10.5281/zenodo.7799621",
            "biotoolsID": "smalldisco",
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                            "uri": "http://edamontology.org/operation_3192",
                            "term": "Sequence trimming"
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                        {
                            "uri": "http://edamontology.org/operation_2008",
                            "term": "siRNA duplex prediction"
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                        {
                            "uri": "http://edamontology.org/operation_3198",
                            "term": "Read mapping"
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                    ],
                    "input": [
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_3495",
                                "term": "RNA sequence"
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                                    "uri": "http://edamontology.org/format_2572",
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                                "uri": "http://edamontology.org/data_1255",
                                "term": "Sequence features"
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                                    "term": "GFF"
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                    "term": "Gene transcripts"
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                    "doi": "10.1093/G3JOURNAL/JKAD092",
                    "pmid": "37094819",
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                    "type": [],
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                    "metadata": {
                        "title": "smalldisco, a pipeline for siRNA discovery and 3′ tail identification",
                        "abstract": "Capturing and sequencing small RNAs is standard practice; however, identification of a group of these small RNAs—small interfering RNAs (siRNAs)—has been more difficult. We present smalldisco, a command-line tool for small interfering RNA discovery and annotation from small RNA-seq datasets. smalldisco can distinguish short reads that map antisense to an annotated genomic feature (e.g. exons or mRNAs), annotate these siRNAs, and quantify their abundance. smalldisco also uses the program Tailor to quantify 3′ nontemplated nucleotides of siRNAs or any small RNA species. smalldisco and supporting documentation are available for download from GitHub (https://github.com/ianvcaldas/smalldisco) and archived in Zenodo",
                        "date": "2023-06-01T00:00:00Z",
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                        "authors": [
                            {
                                "name": "Caldas I.V."
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                                "name": "Ahmed-Braimah Y.H."
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                            {
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            "name": "A2TEA",
            "description": "A2TEA is a software workflow facilitating identification of candidate genes for stress adaptation based on comparative genomics and transcriptomics. It combines differential gene expression with gene family expansion as an indicator for the evolution of adaptive traits.",
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                            "term": "Differential gene expression profiling"
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                            "uri": "http://edamontology.org/operation_3501",
                            "term": "Enrichment analysis"
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                    "metadata": {
                        "title": "ViReMa: a virus recombination mapper of next-generation sequencing data characterizes diverse recombinant viral nucleic acids",
                        "abstract": "Background: Genetic recombination is a tremendous source of intrahost diversity in viruses and is critical for their ability to rapidly adapt to new environments or fitness challenges. While viruses are routinely characterized using high-throughput sequencing techniques, characterizing the genetic products of recombination in next-generation sequencing data remains a challenge. Viral recombination events can be highly diverse and variable in nature, including simple duplications and deletions, or more complex events such as copy/snap-back recombination, intervirus or intersegment recombination, and insertions of host nucleic acids. Due to the variable mechanisms driving virus recombination and the different selection pressures acting on the progeny, recombination junctions rarely adhere to simple canonical sites or sequences. Furthermore, numerous different events may be present simultaneously in a viral population, yielding a complex mutational landscape. Findings: We have previously developed an algorithm called ViReMa (Virus Recombination Mapper) that bootstraps the bowtie short-read aligner to capture and annotate a wide range of recombinant species found within virus populations. Here, we have updated ViReMa to provide an “error density” function designed to accurately detect recombination events in the longer reads now routinely generated by the Illumina platforms and provide output reports for multiple types of recombinant species using standardized formats. We demonstrate the utility and flexibility of ViReMa in different settings to report deletion events in simulated data from Flock House virus, copy-back RNA species in Sendai viruses, short duplication events in HIV, and virus-to-host recombination in an archaeal DNA virus.",
                        "date": "2023-01-01T00:00:00Z",
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                        "authors": [
                            {
                                "name": "Sotcheff S."
                            },
                            {
                                "name": "Zhou Y."
                            },
                            {
                                "name": "Yeung J."
                            },
                            {
                                "name": "Sun Y."
                            },
                            {
                                "name": "Johnson J.E."
                            },
                            {
                                "name": "Torbett B.E."
                            },
                            {
                                "name": "Routh A.L."
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                    "name": "Andrew L Routh",
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                            "uri": "http://edamontology.org/operation_3672",
                            "term": "Gene functional annotation"
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                            "data": {
                                "uri": "http://edamontology.org/data_2976",
                                "term": "Protein sequence"
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                    "term": "Ontology and terminology"
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                    "uri": "http://edamontology.org/topic_3520",
                    "term": "Proteomics experiment"
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                {
                    "uri": "http://edamontology.org/topic_0736",
                    "term": "Protein folds and structural domains"
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                {
                    "doi": "10.1093/BIOINFORMATICS/BTAD226",
                    "pmid": "37084276",
                    "pmcid": "PMC10185405",
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                    "note": null,
                    "metadata": {
                        "title": "FAS: assessing the similarity between proteins using multi-layered feature architectures",
                        "abstract": "Motivation: Protein sequence comparison is a fundamental element in the bioinformatics toolkit. When sequences are annotated with features such as functional domains, transmembrane domains, low complexity regions or secondary structure elements, the resulting feature architectures allow better informed comparisons. However, many existing schemes for scoring architecture similarities cannot cope with features arising from multiple annotation sources. Those that do fall short in the resolution of overlapping and redundant feature annotations. Results: Here, we introduce FAS, a scoring method that integrates features from multiple annotation sources in a directed acyclic architecture graph. Redundancies are resolved as part of the architecture comparison by finding the paths through the graphs that maximize the pair-wise architecture similarity. In a large-scale evaluation on more than 10 000 human-yeast ortholog pairs, architecture similarities assessed with FAS are consistently more plausible than those obtained using e-values to resolve overlaps or leaving overlaps unresolved. Three case studies demonstrate the utility of FAS on architecture comparison tasks: benchmarking of orthology assignment software, identification of functionally diverged orthologs, and diagnosing protein architecture changes stemming from faulty gene predictions. With the help of FAS, feature architecture comparisons can now be routinely integrated into these and many other applications. Availability and implementation: FAS is available as python package: https://pypi.org/project/greedyFAS/.",
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                            {
                                "name": "Dosch J."
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                            {
                                "name": "Tran V."
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                            {
                                "name": "Ebersberger I."
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                        "journal": "Bioinformatics"
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            "description": "Identification of therapeutic peptides and their types using two-layer ensemble learning framework.",
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                            "uri": "http://edamontology.org/operation_3631",
                            "term": "Peptide identification"
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                            "uri": "http://edamontology.org/operation_3937",
                            "term": "Feature extraction"
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                    "input": [
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                            "data": {
                                "uri": "http://edamontology.org/data_2976",
                                "term": "Protein sequence"
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                                    "term": "FASTA"
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                                "term": "Sequence similarity score"
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                    "term": "Small molecules"
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                    "term": "Machine learning"
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                    "uri": "http://edamontology.org/topic_0121",
                    "term": "Proteomics"
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                {
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                    "pmid": "37010503",
                    "pmcid": "PMC10076046",
                    "type": [
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                    "metadata": {
                        "title": "PreTP-2L: identification of therapeutic peptides and their types using two-layer ensemble learning framework",
                        "abstract": "Motivation: Therapeutic peptides play an important role in immune regulation. Recently various therapeutic peptides have been used in the field of medical research, and have great potential in the design of therapeutic schedules. Therefore, it is essential to utilize the computational methods to predict the therapeutic peptides. However, the therapeutic peptides cannot be accurately predicted by the existing predictors. Furthermore, chaotic datasets are also an important obstacle of the development of this important field. Therefore, it is still challenging to develop a multi-classification model for identification of therapeutic peptides and their types. Results: In this work, we constructed a general therapeutic peptide dataset. An ensemble-learning method named PreTP-2L was developed for predicting various therapeutic peptide types. PreTP-2L consists of two layers. The first layer predicts whether a peptide sequence belongs to therapeutic peptide, and the second layer predicts if a therapeutic peptide belongs to a particular species.",
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                            {
                                "name": "Guo Y."
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                            "term": "Mapping assembly"
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                    "metadata": {
                        "title": "ntLink: A Toolkit for De Novo Genome Assembly Scaffolding and Mapping Using Long Reads",
                        "abstract": "With the increasing affordability and accessibility of genome sequencing data, de novo genome assembly is an important first step to a wide variety of downstream studies and analyses. Therefore, bioinformatics tools that enable the generation of high-quality genome assemblies in a computationally efficient manner are essential. Recent developments in long-read sequencing technologies have greatly benefited genome assembly work, including scaffolding, by providing long-range evidence that can aid in resolving the challenging repetitive regions of complex genomes. ntLink is a flexible and resource-efficient genome scaffolding tool that utilizes long-read sequencing data to improve upon draft genome assemblies built from any sequencing technologies, including the same long reads. Instead of using read alignments to identify candidate joins, ntLink utilizes minimizer-based mappings to infer how input sequences should be ordered and oriented into scaffolds. Recent improvements to ntLink have added important features such as overlap detection, gap-filling, and in-code scaffolding iterations. Here, we present three basic protocols demonstrating how to use each of these new features to yield highly contiguous genome assemblies, while still maintaining ntLink's proven computational efficiency. Further, as we illustrate in the alternate protocols, the lightweight minimizer-based mappings that enable ntLink scaffolding can also be utilized for other downstream applications, such as misassembly detection. With its modularity and multiple modes of execution, ntLink has broad benefit to the genomics community, from genome scaffolding and beyond. ntLink is an open-source project and is freely available from https://github.com/bcgsc/ntLink. © 2023 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: ntLink scaffolding using overlap detection. Basic Protocol 2: ntLink scaffolding with gap-filling. Basic Protocol 3: Running in-code iterations of ntLink scaffolding. Alternate Protocol 1: Generating long-read to contig mappings with ntLink. Alternate Protocol 2: Using ntLink mappings for genome assembly correction with Tigmint-long. Support Protocol: Installing ntLink.",
                        "date": "2023-04-01T00:00:00Z",
                        "citationCount": 1,
                        "authors": [
                            {
                                "name": "Coombe L."
                            },
                            {
                                "name": "Warren R.L."
                            },
                            {
                                "name": "Wong J."
                            },
                            {
                                "name": "Nikolic V."
                            },
                            {
                                "name": "Birol I."
                            }
                        ],
                        "journal": "Current Protocols"
                    }
                }
            ],
            "credit": [
                {
                    "name": "Lauren Coombe",
                    "email": "lcoombe@bcgsc.ca",
                    "url": null,
                    "orcidid": null,
                    "gridid": null,
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                },
                {
                    "name": "Inanc Birol",
                    "email": "ibirol@bcgsc.ca",
                    "url": null,
                    "orcidid": null,
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                }
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            "owner": "Pub2Tools",
            "additionDate": "2023-09-15T16:26:06.061484Z",
            "lastUpdate": "2023-09-15T16:26:06.064198Z",
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            },
            "validated": 0,
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        },
        {
            "name": "AcrNET",
            "description": "Predicting anti-CRISPR with deep learning.",
            "homepage": "https://proj.cse.cuhk.edu.hk/aihlab/AcrNET/",
            "biotoolsID": "acrnet",
            "biotoolsCURIE": "biotools:acrnet",
            "version": [],
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            "relation": [],
            "function": [
                {
                    "operation": [
                        {
                            "uri": "http://edamontology.org/operation_1777",
                            "term": "Protein function prediction"
                        },
                        {
                            "uri": "http://edamontology.org/operation_0272",
                            "term": "Residue contact prediction"
                        }
                    ],
                    "input": [
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_2976",
                                "term": "Protein sequence"
                            },
                            "format": [
                                {
                                    "uri": "http://edamontology.org/format_1929",
                                    "term": "FASTA"
                                }
                            ]
                        }
                    ],
                    "output": [
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_1772",
                                "term": "Score"
                            },
                            "format": [
                                {
                                    "uri": "http://edamontology.org/format_3752",
                                    "term": "CSV"
                                }
                            ]
                        }
                    ],
                    "note": null,
                    "cmd": null
                }
            ],
            "toolType": [
                "Web application"
            ],
            "topic": [
                {
                    "uri": "http://edamontology.org/topic_2275",
                    "term": "Molecular modelling"
                },
                {
                    "uri": "http://edamontology.org/topic_3474",
                    "term": "Machine learning"
                },
                {
                    "uri": "http://edamontology.org/topic_0160",
                    "term": "Sequence sites, features and motifs"
                },
                {
                    "uri": "http://edamontology.org/topic_3293",
                    "term": "Phylogenetics"
                }
            ],
            "operatingSystem": [
                "Mac",
                "Linux",
                "Windows"
            ],
            "language": [
                "Python"
            ],
            "license": null,
            "collectionID": [],
            "maturity": null,
            "cost": "Free of charge",
            "accessibility": "Open access",
            "elixirPlatform": [],
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            "elixirCommunity": [],
            "link": [
                {
                    "url": "https://github.com/banma12956/AcrNET",
                    "type": [
                        "Repository"
                    ],
                    "note": null
                }
            ],
            "download": [],
            "documentation": [],
            "publication": [
                {
                    "doi": "10.1093/BIOINFORMATICS/BTAD259",
                    "pmid": "37084259",
                    "pmcid": "PMC10174705",
                    "type": [
                        "Primary"
                    ],
                    "version": null,
                    "note": null,
                    "metadata": {
                        "title": "AcrNET: predicting anti-CRISPR with deep learning",
                        "abstract": "Motivation: As an important group of proteins discovered in phages, anti-CRISPR inhibits the activity of the immune system of bacteria (i.e. CRISPR-Cas), offering promise for gene editing and phage therapy. However, the prediction and discovery of anti-CRISPR are challenging due to their high variability and fast evolution. Existing biological studies rely on known CRISPR and anti-CRISPR pairs, which may not be practical considering the huge number. Computational methods struggle with prediction performance. To address these issues, we propose a novel deep neural network for anti-CRISPR analysis (AcrNET), which achieves significant performance. Results: On both the cross-fold and cross-dataset validation, our method outperforms the state-of-the-art methods. Notably, AcrNET improves the prediction performance by at least 15% regarding the F1 score for the cross-dataset test problem comparing with state-of-art Deep Learning method. Moreover, AcrNET is the first computational method to predict the detailed anti-CRISPR classes, which may help illustrate the anti-CRISPR mechanism. Taking advantage of a Transformer protein language model ESM-1b, which was pre-trained on 250 million protein sequences, AcrNET overcomes the data scarcity problem. Extensive experiments and analysis suggest that the Transformer model feature, evolutionary feature, and local structure feature complement each other, which indicates the critical properties of anti-CRISPR proteins. AlphaFold prediction, further motif analysis, and docking experiments further demonstrate that AcrNET can capture the evolutionarily conserved pattern and the interaction between anti-CRISPR and the target implicitly.",
                        "date": "2023-05-01T00:00:00Z",
                        "citationCount": 1,
                        "authors": [
                            {
                                "name": "Li Y."
                            },
                            {
                                "name": "Wei Y."
                            },
                            {
                                "name": "Xu S."
                            },
                            {
                                "name": "Tan Q."
                            },
                            {
                                "name": "Zong L."
                            },
                            {
                                "name": "Wang J."
                            },
                            {
                                "name": "Wang Y."
                            },
                            {
                                "name": "Chen J."
                            },
                            {
                                "name": "Hong L."
                            },
                            {
                                "name": "Li Y."
                            }
                        ],
                        "journal": "Bioinformatics"
                    }
                }
            ],
            "credit": [
                {
                    "name": "Yu Li",
                    "email": "liyu@cse.cuhk.edu.hk",
                    "url": null,
                    "orcidid": "https://orcid.org/0000-0002-3664-6722",
                    "gridid": null,
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                }
            ],
            "community": null,
            "owner": "Pub2Tools",
            "additionDate": "2023-09-15T11:15:49.737634Z",
            "lastUpdate": "2023-09-15T11:16:49.084749Z",
            "editPermission": {
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            },
            "validated": 0,
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            "confidence_flag": "tool"
        }
    ]
}