Min Shi (141075), Yongshun Gong (14856776), Tiantian Xu (3636328) et al. · Figshare · 2023
<div><p>High utility sequential pattern (HUSP) mining aims to mine actionable patterns with high utilities, widely applied in real-world learning scenarios such as market basket analysis, scenic route planning and click-stream analysis. The existing HUSP mining algorithms mainly attempt to improve computation efficiency while maintaining the algorithm stability in the setting of large-scale data. Although these methods have made some progress, they ignore the relationship between additional items and underlying sequences, which directly leads to the generation of redundant sequential patterns sharing the same underlying sequence. Hence, the mined patterns’ actionability is limited, which significantly compromises the performance of patterns in real-world applications. To address this problem, we present a new method named Combined Utility-Association Sequential Pattern Mining (CUASPM) by incorporating item/sequence relations, which can effectively remove redundant patterns and extract high discriminative and strongly associated sequential pattern combinations with high utilities. Specifically, we introduce the concept of actionable combined mining into HUSP mining for the first time and develop a novel tree structure to select discriminative high utility sequential patterns (HUSPs) for downstream tasks. Furthermore, two efficient strategies (i.e., global and local strategies) are presented to facilitate mining HUSPs while guaranteeing utility growth and high levels of association. Last, two parameters are introduced to evaluate the interestingness of patterns to choose the most useful actionable combined HUSPs (ACHUSPs). Extensive experimental results demonstrate that the proposed CUASPM outperforms the baselines in terms of execution time, memory usage, mining high discriminative and strongly associated HUSPs.</p></div>
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Johannes Zimmermann, Silvio Waschina · Zenodo (CERN European Organization for Nuclear Research) · 2023
The repository contains the protein sequences used by gapseq [https://github.com/jotech/gapseq] to predict the presence of metabolic reactions and to construct metabolic models.
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Simon Malesys, Rachel Torchet, Bertrand Saunier et al. · NAR Genomics and Bioinformatics · 2024
Antibodies play a crucial role in the humoral immune response against health threats, such as viral infections. Although the theoretical number of human immunoglobulins is well over a trillion, the total number of unique antibody protein sequences accessible in databases is much lower than the number found in a single individual. Training AI (Artificial Intelligence) models, for example to assist in developing serodiagnoses or antibody-based therapies, requires building datasets according to strict criteria to include as many standardized antibody sequences as possible. However, the available sequences are scattered across partially redundant databases, making it difficult to compile them into single non-redundant datasets. Here, we introduce ABSD (AntiBody Sequence Database, https://absd.pasteur.cloud), which contains data from major publicly available resources, creating the largest standardized, automatically updated and non-redundant source of public antibody sequences. This user-friendly and open website enables users to generate lists of antibodies based on selected criteria and download the unique sequence pairs of their variable regions.
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Yang, Yu-Hsuan · Zenodo (CERN European Organization for Nuclear Research) · 2025
This dataset contains flanking sequences of T Cell Receptor (TCR) alleles, generated from high-quality genome assemblies provided by the Human Pangenome Reference Consortium (HPRC). Flanking sequences of two lengths (50 bp and 200 bp) were grouped by alleles, and distinct flanking sequences within the same allele are labeled by appending “&flank” to the allele name for ease of identification. File Descriptions: FASTA FilesThese files contain the flanking sequences for TCR alleles, grouped by allele and categorized by length (50 bp or 200 bp): TCRV_cluster_50.fasta: 50 bp flanking sequences for TCRV alleles. TCRV_cluster_200.fasta: 200 bp flanking sequences for TCRV alleles. TCRJ_cluster_50.fasta: 50 bp flanking sequences for TCRJ alleles. TCRJ_cluster_200.fasta: 200 bp flanking sequences for TCRJ alleles. TCRD_cluster_50.fasta: 50 bp flanking sequences for TCRD alleles. TCRD_cluster_200.fasta: 200 bp flanking sequences for TCRD alleles. CSV FilesThese files summarize the haplotypes (h1 for paternal and h2 for maternal) observed in the HPRC dataset and the corresponding flanking sequence groupings for each allele: TCRV_cluster_summary_50.csv: Summary of 50 bp flanking sequence groups for TCRV alleles. TCRV_cluster_summary_200.csv: Summary of 200 bp flanking sequence groups for TCRV alleles. TCRJ_cluster_summary_50.csv: Summary of 50 bp flanking sequence groups for TCRJ alleles. TCRJ_cluster_summary_200.csv: Summary of 200 bp flanking sequence groups for TCRJ alleles. TCRD_cluster_summary_50.csv: Summary of 50 bp flanking sequence groups for TCRD alleles. TCRD_cluster_summary_200.csv: Summary of 200 bp flanking sequence groups for TCRD alleles. The genome assemblies were sourced from the official HPRC GitHub repository:https://github.com/human-pangenomics/HPP_Year1_Assemblies.
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B. F. Francis Ouellette · Methods of biochemical analysis · 1998
This chapter contains sections titled: Introduction Primary and Secondary Databases Format vs. Content: Computers vs. Humans The Database The GenBank Flatfile: A Dissection Conclusions
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A. Murat Eren · Figshare · 2017
Amino acid sequences occurring in all scaffolds >1,000 in each co-assembly of TARA Oceans metagenomic sets.
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Re3data.Org · Registry of Research Repositories · 2013
The Reference Sequence (RefSeq) collection provides a comprehensive, integrated, non-redundant, well-annotated set of sequences, including genomic DNA, transcripts, and proteins. RefSeq sequences form a foundation for medical, functional, and diversity studies. They provide a stable reference for genome annotation, gene identification and characterization, mutation and polymorphism analysis (especially RefSeqGene records), expression studies, and comparative analyses.
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2013
had and still has as its primary goal the creation of richer descriptions of our collections of genomes and metagenomes through the development of standards and tools for supporting compliance and exchange of contextual information [1]. Members of the GSC include representatives from the major sequence centers, bioinformatics centers, a range of research institutions and from the International
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Osamu Ogasawara, Jun Mashima, Yuichi Kodama et al. · 2012
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Kayala, Matthew, Kambhampati, Akhila, Forschner, Stephanie et al. · Journal of Media Literacy Education · 2006
1. Introduction The sequencing of nucleic acids and the subsequent analysis of their encoded the genes and proteins has revolutionized biological sciences and allowed for the determination of evolutionary origins and biochemical potential of organisms. While the technology to determine molecular sequence data has become extremely powerful in terms of both automation and thoughput, sequence analysis technology has lagged behind, unable to efficiently keep up with the vast amount of sequence data being generated both in individual labs and by researchers worldwide. The foundation of robust sequence analysis is dependent on the building and maintaining of project sequence databases (PSD) specific to the interest of the researcher. Laboratory generated sequence data is combined with sequences of similar origin or function from public databases, such as the National Center for Biotechnology Information (NCBI). Currently, one must perform a single BLAST search on each individual sequence to obtain phylogenetically related sequences within the desired confidence value. Each related sequence file must then be opened independently and the pertinent information is essentially ‘copy and pasted’ by hand into a document or excel spreadsheet. From this document, sequences which are to make up the database must be imported into the preferred program for phylogenetic analysis. It is only then that analysis of the laboratory generated sequence can be preformed. Since the NCBI database is updated continuously with new sequences being generated each day, it is impossible to keep a PSD up-to-date. The time consumed trying to build and maintain the PSD is unacceptable in a modern lab. The capacity of performing multiple BLAST searches at once needs to be combined with automated extraction of the resulting relevant information into a format accessible by many subsequent applications. Also, as NCBI is updated, the searches must be repeated and new results added to the database without creating redundancy. It is also necessary to add lab generated sequence data to the PSD in a format matching that of the public databases. This type of database management program will save researchers valuable time and streamline the analysis of gene and protein sequences, especially if the software is made to be user-friendly and intuitive enough to be used by any biologist, even a novice computer user. Appendices Table of Contents: PSD Formal Specification StaticDiagrams PSD Overall Architecture psd.commobjects Class Diagram psd.client.ui Class Diagram psd.client.models Class Diagram psd.client.si Class Diagram psd.server.servlets Class Diagram psd.server.registries Class Diagram psd.server.sql Class Diagram psd.server.blastmanager Class Diagram psd.server.filemanager Class Diagram PSDDatabaseSchema Sequence Diagrams Client Initialization Initializer Servlet LGF Servlet DF Servlet SI Servlet Admin Servlet Use Case Diagrams Original Project Plan
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Takehide Kosuge, Jun Mashima, Yuichi Kodama et al. · 2013
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M. O. Dayhoff, R. M. Schwartz, H.R. CHEN et al. · DNA · 1981
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K. G. Nicholson, R. G. Webster, A. J. Hay et al. · 2016
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Carol A. Harger · Nucleic Acids Research · 2000
The Genome Sequence DataBase (GSDB) is a database of publicly available nucleotide sequences and their associated biological and bibliographic information. Several notable changes have occurred in the past year: GSDB stopped accepting data submissions from researchers; ownership of data submitted to GSDB was transferred to GenBank; sequence analysis capabilities were expanded to include Smith-Waterman and Frame Search; and Sequence Viewer became available to Mac users. The content of GSDB remains up-to-date because publicly available data is acquired from the International Nucleotide Sequence Database Collaboration databases (IC) on a nightly basis. This allows GSDB to continue providing researchers with the ability to analyze, query and retrieve nucleotide sequences in the database. GSDB and its related tools are freely accessible from the URL: http://www.ncgr.org
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Li Chenyi · Zenodo (CERN European Organization for Nuclear Research) · 2022
This 16S rRNA database was curated for honeybee gut microbiome.
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Weina Li (484433), Jiadong Ren (3826267) · Figshare · 2018
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Eren, A. Murat · Figshare · 2017
Amino acid sequences occurring in all scaffolds >1,000 in each co-assembly of TARA Oceans metagenomic sets.
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Adam Stark · Bulletin of Miscellaneous Information (Royal Gardens Kew)
A database of guitar-only audio files with accompanying annotations including beat locations and information about repeated content. The names of authors/composers of each audio file can be found in the file 'Audio File Author Information'. Attributions for derived works should be to the relevant author/composer.
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Paul M. Selzer, Andreas Rohwer, Richard J. Marhöfer · Applied Bioinformatics · 2008
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