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How many sequences is clasiffied in cazy database? 


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The CAZy database classifies a significant number of sequences related to carbohydrate-active enzymes. As of September 2008, the CAZy database contained over 6400 proteins with assigned EC numbers and 700 proteins with a PDB structure . This database has been continuously updated since 1998 and provides a classification of CAZyme modules based on experimentally characterized proteins, with families populated by related sequences from public databases . The CAZy database has been instrumental in improving functional predictions for numerous genome projects and offers a stable nomenclature for enzymes involved in carbohydrate metabolism . The rapid increase in sequences within the CAZy database due to metagenomics advancements has facilitated better refinement of CAZy families into subfamilies, enhancing the prediction of enzyme functions and molecular engineering possibilities .

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The CAZy database classifies sequences based on families, linking them to enzyme specificity and structure; the exact number of sequences is not specified in the provided information.
Open accessJournal ArticleDOI
Štefan Janeček, Birte Svensson 
01 Jan 2022-Amylase
16 Citations
There are 172 GH families in the CAZy database, with four families (GH13, GH57, GH119, and GH126) classified as α-amylase GH families.
The CAZy database classifies over 6400 proteins with assigned EC numbers and 700 proteins with a PDB structure, specializing in enzymes related to complex carbohydrates and glycoconjugates.
The CAZy database classifies sequences of Carbohydrate-Active Enzymes (CAZymes) based on experimentally characterized protein modules, with continuous updates and expert curation since 1998.
The CAZy database contains a rapidly increasing number of sequences due to metagenomics advancements, facilitating the refinement of CAZy families into subfamilies for improved function prediction and molecular engineering.

Related Questions

What are clans in Cazy database and where there are?5 answersClans in the CAZy database refer to groups of families that are evolutionarily related and share a common ancestor. These clans are based on sequence similarities among the carbohydrate-active enzymes (CAZymes) present in the database. The CAZy database classifies CAZymes into families, and these families are further grouped into clans if they exhibit significant sequence similarities, indicating a shared evolutionary history. Clans provide a higher level of organization above families, helping to understand the relationships and evolutionary origins of different CAZyme families. The concept of clans enhances the classification and analysis of CAZymes, contributing to the comprehensive understanding of how these enzymes function and evolve within the context of carbohydrate metabolism.
What are CAZYymes?4 answersCAZymes, or Carbohydrate-Active enZymes, are enzymes that are involved in the assembly, modification, and breakdown of carbohydrates and glycoconjugates. They play important roles in various biological processes such as energy metabolism, cell structure maintenance, signaling, and pathogen recognition. CAZymes have been extensively studied and characterized due to their significance in carbohydrate metabolism in different biological contexts. The CAZy database is a valuable resource that provides a sequence-based classification of CAZymes and offers access to relevant genomic, structural, and biochemical data on these enzymes. It classifies CAZymes into distinct families based on amino acid sequence similarity and provides annotations for each family. The database has been continuously updated and provides a comprehensive understanding of CAZymes and their functions.
What is DNA DATABASE?4 answersA DNA database is a comprehensive collection of information about DNA sequences and their associated properties. It includes data on restriction-modification (RM) systems, DNA methyltransferases (MTases), DNA profiles, and DNA modifications. REBASE is a well-curated database that provides information on RM system components, including recognition and cleavage sites for restriction enzymes and DNA MTases. DNA databases, such as CODIS, are used in the criminal justice system to store genetic profiles for identification purposes. DNAmod is a database specifically focused on cataloging DNA modifications, providing information on their chemical properties, structures, occurrence in nature, and sequencing methods. DNA barcode databases, such as INSDC, BOLD, and MMDBD, are used for species-level identification and mapping biodiversity resources. These databases play a crucial role in standardizing DNA sequencing, facilitating research, and promoting global standards in data management and analysis.
How to know which RNA sequence coded a protien?5 answersRNA sequences that code for proteins can be identified using various methods. One approach is to use long-read RNA sequencing techniques such as nanopore and SMRT sequencing, which can provide the full-length structure of RNA molecules. Another method involves using postlabeling procedures combined with fingerprinting and sequencing techniques. This approach uses in vitro labeling and partial digestion of RNA, followed by 5′-32P-labeling and two-dimensional polyacrylamide gel electrophoresis. The resulting fingerprints can be used to deduce the nucleotide sequences of the corresponding RNA fragments. These methods enable the identification of specific RNA sequences that are responsible for coding proteins.
Sequence data generated from ngs and database?5 answersNext generation sequencing (NGS) technologies have revolutionized the generation of sequence data, allowing for the analysis of nucleic acid and protein sequences on a large scale. NGS data analysis has been used to detect known viruses and discover novel viruses in various samples, including clinical and environmental samples. In addition, NGS data has been utilized to identify genetic variants and mutations associated with human diseases, such as cancer. To facilitate the analysis of NGS data, databases have been developed to collect and curate available human NGS data from published literature. These databases provide a comprehensive resource for researchers to access and analyze NGS data, aiding in the identification of disease-associated variants and the understanding of molecular sequence organization and evolution.
What algorithms are used in DNA sequencing?5 answersDNA sequencing utilizes various machine learning algorithms to analyze genetic information. These algorithms include SVM, CNN, LSTM, Random Forest Classifier, Adaboost, Naive Bayes, Multilayer Perceptron, XGB Classifier, and KNN. These techniques are applied to sequence DNA in human datasets, allowing researchers and doctors to identify genetic variants and mutations associated with specific disorders. By understanding a patient's unique genetic profile, drug makers can target specific subgroups of individuals with similar genetic makeup, leading to more precise and customized treatments. Additionally, string-matching algorithms are used to locate specific patterns within DNA sequences, addressing the challenges of pattern and text lengths in DNA matching. These algorithms play an integral role in the biomedical research process, aiding in the development of personalized medicine and improving treatment outcomes.

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