Database Search Tools Research Articles

SigAlign: an alignment algorithm guided by explicit similarity criteria.

In biological sequence alignment, prevailing heuristic aligners achieve high-throughput by several approximation techniques, but at the cost of sacrificing the clarity of output criteria and creating complex parameter spaces. To surmount these challenges, we introduce 'SigAlign', a novel alignment algorithm that employs two explicit cutoffs for the results: minimum length and maximum penalty per length, alongside three affine gap penalties. Comparative analyses of SigAlign against leading database search tools (BLASTn, MMseqs2) and read mappers (BWA-MEM, bowtie2, HISAT2, minimap2) highlight its performance in read mapping and database searches. Our research demonstrates that SigAlign not only provides high sensitivity with a non-heuristic approach, but also surpasses the throughput of existing heuristic aligners, particularly for high-accuracy reads or genomes with few repetitive regions. As an open-source library, SigAlign is poised to become a foundational component to provide a transparent and customizable alignment process to new analytical algorithms, toolsand pipelines in bioinformatics.

A learned score function improves the power of mass spectrometry database search.

One of the core problems in the analysis of protein tandem mass spectrometry data is the peptide assignment problem: determining, for each observed spectrum, the peptide sequence that was responsible for generating the spectrum. Two primary classes of methods are used to solve this problem: database search and de novo peptide sequencing. State-of-the-art methods for de novo sequencing use machine learning methods, whereas most database search engines use hand-designed score functions to evaluate the quality of a match between an observed spectrum and a candidate peptide from the database. We hypothesized that machine learning models for de novo sequencing implicitly learn a score function that captures the relationship between peptides and spectra, and thus may be re-purposed as a score function for database search. Because this score function is trained from massive amounts of mass spectrometry data, it could potentially outperform existing, hand-designed database search tools. To test this hypothesis, we re-engineered Casanovo, which has been shown to provide state-of-the-art de novo sequencing capabilities, to assign scores to given peptide-spectrum pairs. We then evaluated the statistical power of this Casanovo score function, Casanovo-DB, to detect peptides on a benchmark of three mass spectrometry runs from three different species. In addition, we show that re-scoring with the Percolator post-processor benefits Casanovo-DB more than other score functions, further increasing the number of detected peptides.

A large-scale assessment of sequence database search tools for homology-based protein function prediction.

Sequence database searches followed by homology-based function transfer form one of the oldest and most popular approaches for predicting protein functions, such as Gene Ontology (GO) terms. These searches are also a critical component in most state-of-the-art machine learning and deep learning-based protein function predictors. Although sequence search tools are the basis of homology-based protein function prediction, previous studies have scarcely explored how to select the optimal sequence search tools and configure their parameters to achieve the best function prediction. In this paper, we evaluate the effect of using different options from among popular search tools, as well as the impacts of search parameters, on protein function prediction. When predicting GO terms on a large benchmark dataset, we found that BLASTp and MMseqs2 consistently exceed the performance of other tools, including DIAMOND-one of the most popular tools for function prediction-under default search parameters. However, with the correct parameter settings, DIAMOND can perform comparably to BLASTp and MMseqs2 in function prediction. Additionally, we developed a new scoring function to derive GO prediction from homologous hits that consistently outperform previously proposed scoring functions. These findings enable the improvement of almost all protein function prediction algorithms with a few easily implementable changes in their sequence homolog-based component. This study emphasizes the critical role of search parameter settings in homology-based function transfer and should have an important contribution to the development of future protein function prediction algorithms.

Accelerating research processes with Scopus AI: A place branding case study

Academic databases play a crucial role in advancing science by hosting a vast array of peer-reviewed literature. However, academic database search tools involve a relatively slow and rather unintuitive process of searching and evaluating content. To address these challenges, in January 2024, Elsevier introduced Scopus AI, a generative artificial intelligence that synthesises evidence indexed in Scopus based on prompts. This study assesses the utility of Scopus AI (in its beta version at the time of the research), within the context of a doctoral thesis through a specific case study. By employing a relational prompt and three follow-up questions, the study aims to pinpoint intersections between different topics within the realm of Social Sciences and, more specifically, Communication, with a case on place branding. The consolidated result provides an initial list of references, offers a comprehensive overview, and allows to generate a meta-synthesis based on the summaries provided by each prompt. Scopus AI (beta) presents features that enable researchers to identify influential authors and works, explore relevant keywords, review recent literature, and identify potential research gaps. Although Scopus AI has some limitations, such as the dependence on the abstracts of documents indexed in Scopus, the simplification of concepts, or the relative disconnection between arguments, the results demonstrate the value of this tool in accelerating research processes, as it synthesises research in a given area, maps its main characteristics and allows for information discovery.

A Comprehensive Summary of the Meta-Analyses and Systematic Reviews on Platelet-Rich Plasma Therapies for Knee Osteoarthritis.

Osteoarthritis (OA), including that of the knee joint, represents a significant proportion of musculoskeletal injuries in the Canadian Armed Forces (CAF) due to the frequent, high-stress physical activity for which member participation is necessary. Platelet-rich plasma (PRP) is a conservative, autologous treatment that has the potential to relieve symptoms and improve functionality of military members to decrease the impact of the disease and ultimately strengthen the CAF. A search of systematic reviews and meta-analyses was conducted to determine the efficacy of PRP injections in treating knee OA. The Scopus database, PubMed database, and Omni academic search tools were scoped for relevant publications. English literature, published up to and including March 2023, that investigated only clinically randomized controlled trials (RCTs) was eligible for inclusion. The results of network meta-analyses were investigated and summarized independent of reviews and non-network meta-analyses. A total of 225 unique systematic reviews and meta-analyses were initially identified, of which 39 publications, including 7 network meta-analyses, adhered to the defined inclusion and exclusion criteria. PRP was found to significantly alleviate symptoms of pain based on the visual analog scale and Western Ontario and McMaster Universities Arthritis Index pain scores within the 12-month follow-up. Function, activity, sport, quality of life, and stiffness were additionally determined to generally improve to a greater extent from PRP treatment compared to controls, while adverse effects were minor and temporary. PRP placed in the top 3 in 9 reported surface under the cumulative ranking curves, while individually reported rankings of leukocyte-poor and leukocyte-rich PRP both placed in the top 4. The clinical recommendations made were generally positive, with 17 publications acknowledging the benefits of PRP, 3 supporting possible efficacy, and an additional 8 recommending that it be an option for the conservative treatment of knee OA. The results of this review support the efficacy of PRP for relieving symptoms of pain and improving function, stiffness, and quality of life for patients experiencing knee OA within 12 months. As a result, leukocyte-poor-PRP could be considered for members of the CAF with mild to moderate knee OA (Kellgren-Lawrence grades 1-3) to slow the progression of OA and extend the military careers of CAF members. There continues to be a need for future studies to investigate the longer-term effects of PRP to verify sustained benefits at follow-up points greater than 12 months, including findings of improvement in a delayed fashion at the 3- and 6-month timeframe compared to hyaluronic acid treatment.

Target-decoy false discovery rate estimation using Crema.

Assigning statistical confidence estimates to discoveries produced by a tandem mass spectrometry proteomics experiment is critical to enabling principled interpretation of the results and assessing the cost/benefit ratio of experimental follow-up. The most common technique for computing such estimates is to use target-decoy competition (TDC), in which observed spectra are searched against a database of real (target) peptides and a database of shuffled or reversed (decoy) peptides. TDC procedures for estimating the false discovery rate (FDR) at a given score threshold have been developed for application at the level of spectra, peptides, or proteins. Although these techniques are relatively straightforward to implement, it is common in the literature to skip over the implementation details or even to make mistakes in how the TDC procedures are applied in practice. Here we present Crema, an open-source Python tool that implements several TDC methods of spectrum-, peptide- and protein-level FDR estimation. Crema is compatible with a variety of existing database search tools and provides a straightforward way to obtain robust FDR estimates.

PeposX-Exhaust: A lightweight and efficient tool for identification of short peptides

Short peptides have become the focus of recent research due to their variable bioactivities, good digestibility and wide existences in food-derived protein hydrolysates. However, due to the high complexity of the samples, identifying short peptides still remains a challenge. In this work, a tool, named PeposX-Exhaust, was developed for short peptide identification. Through validation with known peptides, PeposX-Exhaust identified all the submitted spectra and the accuracy rate reached 75.36%, and the adjusted accuracy rate further reached 98.55% when with top 5 candidates considered. Compared with other tools, the accuracy rate by PeposX-Exhaust was at least 70% higher than two database-search tools and 15% higher than the other two de novo-sequencing tools, respectively. For further application, the numbers of short peptides identified from soybean, walnut, collagen and bonito protein hydrolysates reached 1145, 628, 746 and 681, respectively. This fully demonstrated the superiority of the tool in short peptide identification.

THE ROLE OF SCHOOL LITERACY MOVEMENT (SLM) IN INCREASING STUDENT INTEREST IN READING: A LITERATURE REVIEW

This study was prompted by students' lack of interest in reading and literacy abilities in Indonesia. In 2015, Indonesia ranked 62 out of 76 countries in the PISA survey. The government created the School Literacy Movement (SLM) program in 2016, in response to students' low literacy scores and reading interest. The government expects that this program will increase reading enthusiasm in Indonesia. The objective of this study was to investigate the impact of the SLM on students' reading interest. The method utilized is a literature research or review. A literature study is a research design used to collect data sources on a certain topic. As the stage of searching for literary sources, data collecting for the study of literature is carried out using a database search tool. This research is conducted by synthesizing conceptual or empirical articles pertinent to the study to be conducted. It begins with the analysis of articles pertinent to the topic to be reviewed, this entails discussing and reviewing the articles by identifying and classifying them based on the elements to be reviewed from multiple articles that discuss essentially the same subject. According to the papers discovered and evaluated, the school literacy movement has a positive role on students' reading interest, this is proven by the increase of library visits, the increase in problem-solving skills in the learning process, the increase in students' capacity to write Wall Print Magazines, and the increase in the number of students who read before class. As a result, it can be inferred that SLM has a role in enhancing children's interest in reading through various school-based programs and activities.

The molecular epidemiology and antimicrobial resistance of Staphylococcus pseudintermedius canine clinical isolates submitted to a veterinary diagnostic laboratory in South Africa.

Staphylococcus pseudintermedius is an important cause of clinical infections in small-animal-veterinary medicine. Evolutionary changes of strains using multilocus sequence typing (MLST) have been observed among S. pseudintermedius in European countries and the United States. However, there are limited or no studies on the detection of methicillin resistant Staphylococcus pseudintermedius (MRSP) and predominating MLST strains in South Africa. Therefore, this study aimed to determine the molecular epidemiology of S. pseudintermedius in South Africa. Twenty-six, non-duplicate, clinical isolates from dogs were obtained as convenience samples from four provinces in South Africa. The Kirby Bauer disk diffusion test was used to determine antimicrobial susceptibility. We used Resfinder and the Comprehensive Antibiotic Resistance Database (CARD) to detect antimicrobial resistance genes. Virulence genes were identified using the virulence factor database and Basic Local Alignment Search Tool (BLASTN) on Geneious prime. geoBURST analysis was used to study relationships between MLST. Finally, the maximum likelihood phylogeny was determined using Randomized Axelerated Maximum Likelihood (RAxML). Twenty-three isolates were confirmed as S. pseudintermedius of which 14 were MRSP. In addition to β-lactam antimicrobials, MRSP isolates were resistant to tetracycline (85.7%), doxycycline (92.8%), kanamycin (92.8%), and gentamicin (85.7%). The isolates harbored antimicrobial resistance genes (tetM, ermB, drfG, cat, aac(6')-Ie-aph(2")-Ia, ant(6)-Ia, and aph(3')-III) and virulence genes (AdsA, geh, icaA, and lip). MLST analysis showed that ST2228, ST2229, ST2230, ST2231, ST2232, ST2318, ST2326 and ST2327 are unique sequence types in South Africa. Whereas, previously reported major STs including ST45, ST71, ST181, ST551 and ST496 were also detected. The geoBURST and phylogenetic analysis suggests that the isolates in South Africa are likely genetically related to isolates identified in other countries. Highly resistant MRSP strains (ST496, ST71, and ST45) were reported that could present challenges in the treatment of canine infections in South Africa. Hence, we have gained a better understanding of the epidemiology of MRSP in the African continent, the genes involved in resistance and virulence factors associated with these organisms.

MSBooster: improving peptide identification rates using deep learning-based features

Peptide identification in liquid chromatography-tandem mass spectrometry (LC-MS/MS) experiments relies on computational algorithms for matching acquired MS/MS spectra against sequences of candidate peptides using database search tools, such as MSFragger. Here, we present a new tool, MSBooster, for rescoring peptide-to-spectrum matches using additional features incorporating deep learning-based predictions of peptide properties, such as LC retention time, ion mobility, and MS/MS spectra. We demonstrate the utility of MSBooster, in tandem with MSFragger and Percolator, in several different workflows, including nonspecific searches (immunopeptidomics), direct identification of peptides from data independent acquisition data, single-cell proteomics, and data generated on an ion mobility separation-enabled timsTOF MS platform. MSBooster is fast, robust, and fully integrated into the widely used FragPipe computational platform.

The Effect of Quantum Teaching Method on Motivation and Arabic Language Learning Outcomes (Literature Study)

This study analyzes the effectiveness of the Quantum Teaching method in improving motivation and learning outcomes in Arabic. Researchers use descriptive qualitative research methods with a systematic approach to conduct data analysis in a simplified approach. Data was collected through database search tools and literature search stages using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) method. Article search strategies are carried out through international databases, such as Open Knowledge Maps, ResearchGate, and Google Scholar. The summary of this literature review is done using an analysis matrix. The results showed that applying the Quantum Teaching method significantly improved students' Arabic proficiency and learning outcomes. Quantum Teaching effectively improves student learning outcomes, motivation, and engagement. This method creates a comfortable, enjoyable learning environment and strengthens the interaction between teachers and students. In addition, Quantum Teaching also improves students' memory and learning potential.

Mzion enables deep and precise identification of peptides in data-dependent acquisition proteomics

Sensitive and reliable identification of proteins and peptides pertains the basis of proteomics. We introduce Mzion, a new database search tool for data-dependent acquisition (DDA) proteomics. Our tool utilizes an intensity tally strategy and achieves generally a higher performance in terms of depth and precision across 20 datasets, ranging from large-scale to single-cell proteomics. Compared to several other search engines, Mzion matches on average 20% more peptide spectra at tryptic enzymatic specificity and 80% more at no enzymatic specificity from six large-scale, global datasets. Mzion also identifies more phosphopeptide spectra that can be explained by fewer proteins, demonstrated by six large-scale, local datasets corresponding to the global data. Our findings highlight the potential of Mzion for improving proteomic analysis and advancing our understanding of protein biology.

Molecular identification and sequencing of Pseudomonas aeruginosa 16S rRNA gene isolated from a variety of raw cow milk samplesin Iraq

Pseudomonas aeruginosa is Gram-negative bacterium which is notably known as a pathogen in humans, animals, and plants.Suspect raw milk samples were analyzed to amplify the 16S rRNA gene and confirm Pseudomonas aeruginosa strains. This study was conducted at different farmland in diwaniya and al-Najaf., a total of 70 samples were collected (28 from mastitic cows and 42 from apparently healthy cows ) the detection of the 16S rRNA gene in this study revealed 11 ( positive isolates of Pseudomonas were identified from samples after culture on cetrimid agar. New sets of primer pairs were designed using the NCBI database search tool. The phylogenetic relationship between different strains of Pseudomonas aeruginosa has been studied through using 16S rRNA gene region sequencing. A distance tree was constructed to determine the genetic similarity between species. as result of the 9 isolate gene sequencing, it was determined, and submitted in GenBank accession numbers (MZ799357, MZ817077, MZ820788, MZ820878 , MZ821016, MZ823355, MZ823356 MZ823387. MZ823389) , in conclusion the PCR is a reliable technique that identifies Pseudomonas aeruginosa at the molecular level

Parkinson’s Disease Gene Biomarkers Screened by the LASSO and SVM Algorithms

Parkinson's disease (PD) is a common progressive neurodegenerative disorder. Various evidence has revealed the possible penetration of peripheral immune cells in the substantia nigra, which may be essential for PD. Our study uses machine learning (ML) to screen for potential PD genetic biomarkers. Gene expression profiles were screened from the Gene Expression Omnibus (GEO). Differential expression genes (DEGs) were selected for the enrichment analysis. A protein-protein interaction (PPI) network was built with the STRING database (Search Tool for the Retrieval of Interacting Genes), and two ML approaches, namely least absolute shrinkage and selection operator (LASSO) and support vector machine recursive feature elimination (SVM-RFE), were employed to identify candidate genes. The external validation dataset further tested the expression degree and diagnostic value of candidate biomarkers. To assess the validity of the diagnosis, we determined the receiver operating characteristic (ROC) curve. A convolution tool was employed to evaluate the composition of immune cells by CIBERSORT, and we performed correlation analyses on the basis of the training dataset. Twenty-seven DEGs were screened in the PD and control samples. Our results from the enrichment analysis showed a close association with inflammatory and immune-associated diseases. Both the LASSO and SVM algorithms screened eight and six characteristic genes. AGTR1, GBE1, TPBG, and HSPA6 are overlapping hub genes strongly related to PD. Our results of the area under the ROC (AUC), including AGTR1 (AUC = 0.933), GBE1 (AUC = 0.967), TPBG (AUC = 0.767), and HSPA6 (AUC = 0.633), suggested that these genes have good diagnostic value, and these genes were significantly associated with the degree of immune cell infiltration. AGTR1, GBE1, TPBG, and HSPA6 were identified as potential biomarkers in the diagnosis of PD and provide a novel viewpoint for further study on PD immune mechanism and therapy.

RNA-Seq as an Effective Tool for Modern Transcriptomics, A Review-based Study

Transcriptome analysis is a useful method for identification and understanding genes. Finding genes that are differentially expressed between conditions is a crucial aspect of transcriptomics. The discovery of RNA seq has been revolutionized next-generation sequencing technology. The fact that RNA sequencing does not requires gene probes and provides a precise measure of gene expression over a much wider range proved its credibility over other common techniques. The expressed gene profile and transcriptome data are stored in a database and could be accessed freely. During RNA seq short read mapping to the reference transcriptome (the set of all known transcript RNA sequences for a species) or genome in the database, a variety of database search tools and alignment methods become visible. There are a variety of applications that help align short reads generated by fragment sequencing. The study of expressed genes is aided by quantifying reads that align to the reference genome or transcriptome. RNA sequencing gives crucial information regarding alternative splicing and gene isoforms, in addition to differential gene expression.

Leveraging the timing and frequency of patient decision aids in longitudinal shared decision-making: A narrative review and applied model.

IntroductionShared decision‐making (SDM) is intended to increase patient‐centredness of medical decision‐making for patients with acute and chronic conditions. Concurrently, patient decision aids (PtDAs) can supplement SDM by providing information to guide communication between patients and healthcare providers. Because of the prevalence of chronic conditions, where decisions may be extended or recurring, we sought to explore how effectively these tools have been leveraged in this context.MethodsWe conducted a narrative review of the literature on both SDM and PtDAs, searching PubMed and Boston University's library database search tool for English‐language articles published from January 2005 until March 2021. Additional search terms focused on temporality. Drawing from our findings, we developed a combined framework to highlight areas for future research using the discussion of end‐of‐life decisions as an exemplar to illustrate its relevance to chronic care contexts.ResultsAfter screening 57 articles, we identified 25 articles that fulfilled the inclusion criteria on SDM, PtDA use and temporality for chronic care. The literature on SDM highlighted time outside of the medical visit and opportunity to include outside decision partners as important elements of the process. PtDAs were commonly evaluated for process‐related and proximal outcomes, but less often for distal outcomes. Early evidence points to the value of comparative outcome evaluation based on the timing of PtDA distribution.ConclusionOur review of the literature on SDM and PtDAs reveals less attention to the timing of PtDAs relative to that of SDM. We highlight the need for further study of timing in PtDA use to improve longitudinal SDM for chronic care. The model that we propose in our discussion provides a starting point for future research on PtDA efficacy.Patient or Public ContributionFive patient consultants provided input and feedback on the development and utility of our model.

IntroSpect: Motif-Guided Immunopeptidome Database Building Tool to Improve the Sensitivity of HLA I Binding Peptide Identification by Mass Spectrometry.

Although database search tools originally developed for shotgun proteome have been widely used in immunopeptidomic mass spectrometry identifications, they have been reported to achieve undesirably low sensitivities or high false positive rates as a result of the hugely inflated search space caused by the lack of specific enzymic digestions in immunopeptidome. To overcome such a problem, we developed a motif-guided immunopeptidome database building tool named IntroSpect, which is designed to first learn the peptide motifs from high confidence hits in the initial search, and then build a targeted database for refined search. Evaluated on 18 representative HLA class I datasets, IntroSpect can improve the sensitivity by an average of 76%, compared to conventional searches with unspecific digestions, while maintaining a very high level of accuracy (~96%), as confirmed by synthetic validation experiments. A distinct advantage of IntroSpect is that it does not depend on any external HLA data, so that it performs equally well on both well-studied and poorly-studied HLA types, unlike the previously developed method SpectMHC. We have also designed IntroSpect to keep a global FDR that can be conveniently controlled, similar to a conventional database search. Finally, we demonstrate the practical value of IntroSpect by discovering neoepitopes from MS data directly, an important application in cancer immunotherapies. IntroSpect is freely available to download and use.

TIDD: tool-independent and data-dependent machine learning for peptide identification

BackgroundIn shotgun proteomics, database search engines have been developed to assign peptides to tandem mass (MS/MS) spectra and at the same time post-processing (or rescoring) approaches over the search results have been proposed to increase the number of confident peptide identifications. The most popular post-processing approaches such as Percolator and PeptideProphet have improved rates of peptide identifications by combining multiple scores from database search engines while applying machine learning techniques. Existing post-processing approaches, however, are limited when dealing with results from new search engines because their features for machine learning must be optimized specifically for each search engine.ResultsWe propose a universal post-processing tool, called TIDD, which supports confident peptide identifications regardless of the search engine adopted. TIDD can work for any (including newly developed) search engines because it calculates universal features that assess peptide-spectrum match quality while it allows additional features provided by search engines (or users) as well. Even though it relies on universal features independent of search tools, TIDD showed similar or better performance than Percolator in terms of peptide identification. TIDD identified 10.23–38.95% more PSMs than target-decoy estimation for MSFragger, which is not supported by Percolator. TIDD offers an easy-to-use simple graphical user interface for user convenience.ConclusionsTIDD successfully eliminated the requirement for an optimal feature engineering per database search tool, and thus, can be applied directly to any database search results including newly developed ones.

Arrhythmia Patterns in Patients on Ibrutinib.

Introduction: Ibrutinib, a Bruton's tyrosine kinase inhibitor (TKI) used primarily in the treatment of hematologic malignancies, has been associated with increased incidence of atrial fibrillation (AF), with limited data on its association with other tachyarrhythmias. There are limited reports that comprehensively analyze atrial and ventricular arrhythmia (VA) burden in patients on ibrutinib. We hypothesized that long-term event monitors could reveal a high burden of atrial and VAs in patients on ibrutinib.Methods: A retrospective data analysis at a single center using electronic medical records database search tools and individual chart review was conducted to identify consecutive patients who had event monitors while on ibrutinib therapy.Results: Seventy-two patients were included in the analysis with a mean age of 76.9 ± 9.9 years and 13 patients (18%) had a diagnosis of AF prior to the ibrutinib therapy. During ibrutinib therapy, most common arrhythmias documented were non-AF supraventricular tachycardia (n = 32, 44.4%), AF (n = 32, 44%), and non-sustained ventricular tachycardia (n = 31, 43%). Thirteen (18%) patients had >1% premature atrial contraction burden; 16 (22.2%) patients had >1% premature ventricular contraction burden. In 25% of the patients, ibrutinib was held because of arrhythmias. Overall 8.3% of patients were started on antiarrhythmic drugs during ibrutinib therapy to manage these arrhythmias.Conclusions: In this large dataset of ambulatory cardiac monitors on patients treated with ibrutinib, we report a high prevalence of atrial and VAs, with a high incidence of treatment interruption secondary to arrhythmias and related symptoms. Further research is warranted to optimize strategies to diagnose, monitor, and manage ibrutinib-related arrhythmias.

SugarDrawer: A Web-Based Database Search Tool with Editing Glycan Structures.

In life science fields, database integration is progressing and contributing to collaboration between different research fields, including the glycosciences. The integration of glycan databases has greatly progressed collaboration worldwide with the development of the international glycan structure repository, GlyTouCan. This trend has increased the need for a tool by which researchers in various fields can easily search glycan structures from integrated databases. We have developed a web-based glycan structure search tool, SugarDrawer, which supports the depiction of glycans including ambiguity, such as glycan fragments which contain underdetermined linkages, and a database search for glycans drawn on the canvas. This tool provides an easy editing feature for various glycan structures in just a few steps using template structures and pop-up windows which allow users to select specific information for each structure element. This tool has a unique feature for selecting possible attachment sites, which is defined in the Symbol Nomenclature for Glycans (SNFG). In addition, this tool can input and output glycans in WURCS and GlycoCT formats, which are the most commonly-used text formats for glycan structures.