Misplaced Pages

#989010

69-622: The KEGG database project was initiated in 1995 by Minoru Kanehisa , professor at the Institute for Chemical Research, Kyoto University , under the then ongoing Japanese Human Genome Program . Foreseeing the need for a computerized resource that can be used for biological interpretation of genome sequence data , he started developing the KEGG PATHWAY database. It is a collection of manually drawn KEGG pathway maps representing experimental knowledge on metabolism and various other functions of

138-572: A binary classifier for each GO term, which are then joined to make predictions on individual GO terms (forming a multiclass classifier ) for which confidence scores are later obtained. The support vector machine (SVM) is the most widely used binary classifier in functional annotation; however, other algorithms, such as k-nearest neighbors (kNN) and convolutional neural network (CNN), have also been employed. Binary or multiclass classification methods for functional annotation generally produce less accurate results because they do not take into account

207-541: A directed acyclic graph , in which every node is a particular function, and every edge (or arrow) between two nodes indicates a parent-child or subcategory-category relationship. As of 2020, GO is the most widely used controlled vocabulary for functional annotation of genes, followed by the MIPS Functional Catalog (FunCat). Some conventional methods for functional annotation are homology -based, which rely on local alignment search tools. Its premise

276-649: A genome browser requires a descriptive output file, which should describe the intron - exon structures of each annotation, their start and stop codons , UTRs and alternative transcripts, and ideally should include information about the sequence alignments and gene predictions that support each gene model. Some commonly used formats for describing annotations are GenBank, GFF3 , GTF, BED and EMBL. Some of these formats use controlled vocabularies and ontologies to define their descriptive terminologies and guarantee interoperability between analysis and visualization tools. Genomic browsers are software products that simplify

345-418: A post-transcriptional process in which introns (non-coding regions) are removed and exons (coding regions) are joined. Therefore, eukaryotic coding sequences (CDS) are discontinuous, and, to ensure their proper identification, intronic regions must be filtered. To do so, annotation pipelines must find the exon-intron boundaries, and multiple methodologies have been developed for this purpose. One solution

414-576: A controlled vocabulary (or ontology) to name the predicted functional features. However, because there are numerous ways to define gene functions, the annotation process may be hindered when it is performed by different research groups. As such, a standardized controlled vocabulary must be employed, the most comprehensive of which is the Gene Ontology (GO). It classifies functional properties into one of three categories (molecular function, biological process, and cellular component) and organizes them in

483-656: A disruption in their open reading frame (ORF), making them untranslatable . They may be identified using one of the following two methods: Segmental duplications are DNA segments of more than 1000 base pairs that are repeated in the genome with more than 90% sequence identity. Two strategies used for their identification are WGAC and WSSD: DNA binding sites are regions in the genome sequence that bind to and interact with specific proteins. They play an important role in DNA replication and repair , transcriptional regulation , and viral infection . Binding site prediction involves

552-619: A lower level. The advent of complete genomes in the 1990s (the first one being the genome of Haemophilus influenzae sequenced in 1995) introduced a second generation of annotators. Just like in the previous generation, they performed annotation through ab initio methods, but now applied on a genome-wide scale. Markov models are the driving force behind many algorithms used within annotators of this generation; these models can be thought of as directed graphs where nodes represent different genomic signals (such as transcription and translation start sites) connected by arrows representing

621-452: A major challenge for scientists investigating the human and other genomes. Structural annotation describes the precise location of the different elements in a genome, such as open reading frames (ORFs), coding sequences (CDS), exons , introns , repeats , splice sites , regulatory motifs , start and stop codons , and promoters . The main steps of structural annotation are: The first step of structural annotation consists in

690-406: A more accurate term. CDS predictors detect genome features through methods called sensors , which include signal sensors that identify functional site signals such as promoters and polyA sites , and content sensors that classify DNA sequences into coding and noncoding content. Whereas prokaryotic CDS predictors mostly deal with open reading frames (ORFs), which are segments of DNA between

759-708: A protein family. Functional annotation can be performed through probabilistic methods. The distribution of hydrophilic and hydrophobic amino acids indicates whether a protein is located in a solution or membrane. Specific sequence motifs provide information on posttranslational modifications and final location of any given protein. Probabilistic methods may be paired with a controlled vocabulary, such as GO; for example, protein-protein interaction (PPI) networks usually place proteins with similar functions close to each other. Machine learning methods are also used to generate functional annotations for novel proteins based on GO terms. Generally, they consist in constructing

SECTION 10

#1732786786990

828-515: A staff scientist at Los Alamos in 1981. While at Los Alamos, he was one of the developers of the GenBank database of all publicly available nucleotide sequences and their protein translations. Kanehisa joined Kyoto University as an associate professor in 1985, becoming a professor in 1987. In 1995, Kanehisa started the KEGG (Kyoto Encyclopedia of Genes and Genomes) database project. Foreseeing

897-519: A strain capable of using DDT as its sole carbon and energy source, to mention a few examples. Genes in a eukaryotic genome can be annotated using various annotation tools such as FINDER. A modern annotation pipeline can support a user-friendly web interface and software containerization such as MOSGA. Modern annotation pipelines for prokaryotic genomes are Bakta, Prokka and PGAP. The National Center for Biomedical Ontology develops tools for automated annotation of database records based on

966-522: Is being used by researchers to establish a disease-gene relationship, as GO helps in the identification of novel genes, the alterations in their expression, distribution and function under a different set of conditions, such as diseased versus healthy. Databases of this disease-gene relationships of different organisms have been created, such as Plant-Pathogen Ontology, Plant-Associated Microbe Gene Ontology or DisGeNET. And some others have been implemented in pre-existing databases like Rat Disease Ontology in

1035-458: Is classified into two categories: structural annotation , which identifies and demarcates elements in a genome, and functional annotation , which assigns functions to these elements. This is not the only way in which it has been categorized, as several alternatives, such as dimension-based and level-based classifications, have also been proposed. The first generation of genome annotators used local ab initio methods, which are based solely on

1104-497: Is designed to link genes in the genome to gene products (mostly proteins ) in the pathway. This has enabled the analysis called KEGG pathway mapping, whereby the gene content in the genome is compared with the KEGG PATHWAY database to examine which pathways and associated functions are likely to be encoded in the genome. In 1999, Kanehisa was elected the first president of the Japanese Society for Bioinformatics . He

1173-489: Is divided in coding and noncoding regions, and the last step of structural annotation consists in identifying these features within the genome. In fact, the primary task in genome annotation is gene prediction , which is why numerous methods have been developed for this purpose. Gene prediction is a misleading term, as most gene predictors only identify coding sequences (CDS) and do not report untranslated regions (UTRs); for this reason, CDS prediction has been proposed as

1242-776: Is known for developing the KEGG bioinformatics database. In 2018 he was listed on a list of Clarivate Citation Laureates for the Nobel Prize in Physiology or Medicine for "contributions to bioinformatics, specifically for his development of the Kyoto Encyclopedia of Genes and Genomes (KEGG)". Kanehisa studied at the University of Tokyo , gaining his Doctor of Science degree in physics in 1976. Following postdoctoral studies at Johns Hopkins School of Medicine and Los Alamos National Laboratory , he became

1311-441: Is that high sequence conservation between two genomic elements implies that their function is conserved as well. Pairs of homologous sequences that appeared through paralogy , orthology , or xenology usually perform a similar function. However, orthologous sequences should be treated with caution because of two reasons: (1) they might have different names depending on when they were originally annotated, and (2) they may not perform

1380-520: Is the KEGG BRITE database. It is an ontology database containing hierarchical classifications of various entities including genes, proteins, organisms, diseases, drugs, and chemical compounds. While KEGG PATHWAY is limited to molecular interactions and reactions of these entities, KEGG BRITE incorporates many different types of relationships. Several months after the KEGG project was initiated in 1995,

1449-564: Is to use known exon boundaries for alignment; for instance, many introns begin with GT and end with AG. This approach, however, cannot detect novel boundaries, so alternatives like machine learning algorithms exist that are trained on known exon boundaries and quality information to predict new ones. Predictors of new exon boundaries usually require efficient data-compression and alignment algorithms, but they are prone to failure in boundaries located in regions with low sequence coverage or high error-rates produced during sequencing. A genome

SECTION 20

#1732786786990

1518-527: The Insertion Sequence (IS) Finder database. This analysis concluded in the localization of the upper pathway genes of naphthalene degradation, right next to the genes encoding tRNA-Gly and integrase, as well as the identification of the genes encoding enzymes involved in the degradation of salicylate , benzoate , 4-hydroxybenzoate , phenylacetic acid , hydroxyphenyl acetic acid, and the recognition of an operon involved in glucose transport in

1587-427: The cell and the organism . Each pathway map contains a network of molecular interactions and reactions and is designed to link genes in the genome to gene products (mostly proteins ) in the pathway. This has enabled the analysis called KEGG pathway mapping, whereby the gene content in the genome is compared with the KEGG PATHWAY database to examine which pathways and associated functions are likely to be encoded in

1656-433: The human genome are composed of repetitive elements. Identifying repeats is difficult for two main reasons: they are poorly conserved, and their boundaries are not clearly-defined. Because of this, repeat libraries must be built for the genome of interest, which can be accomplished with one of the following methods: After the repetitive regions in a genome have been identified, they are masked. Masking means replacing

1725-432: The pangenome ; by doing so, for instance, annotation pipelines ensure that core genes of a clade are also found in new genomes of the same clade. Both annotation strategies constitute the fourth generation of genome annotators. By the 2010s, the genome sequences of more than a thousand-human individuals (through the 1000 Genomes Project ) and several model organisms became available. As such, genome annotation remains

1794-602: The start and stop codons , eukaryotic CDS predictors are faced with a more difficult problem because of the complex organization of eukaryotic genes. CDS prediction methods can be classified into three broad categories: Functional annotation assigns functions to the genomic elements found by structural annotation, by relating them to biological processes such as the cell cycle , cell death , development , metabolism , etc. It may also be used as an additional quality check by identifying elements that may have been annotated by error. Functional annotation of genes requires

1863-552: The KEGG MODULE database are higher-resolution, localized wiring diagrams, representing tighter functional units within a pathway map, such as subpathways conserved among specific organism groups and molecular complexes. KEGG modules are defined as characteristic gene sets that can be linked to specific metabolic capacities and other phenotypic features, so that they can be used for automatic interpretation of genome and metagenome data. Another database that supplements KEGG PATHWAY

1932-548: The Rat Genome database. A great diversity of catabolic enzymes involved in hydrocarbon degradation by some bacterial strains are encoded by genes located in their mobile genetic elements (MGEs). The study of these elements is of great importance in the field of bioremediation, since recently the inoculation of wild or genetically modified strains with these MGEs has been sought in order to acquire these hydrocarbon degradation capacities. In 2013, Phale et al. published

2001-512: The US, and Europe. They are distinguished by chemical structures and/or chemical components and associated with target molecules, metabolizing enzymes , and other molecular interaction network information in the KEGG pathway maps and the BRITE hierarchies. This enables an integrated analysis of drug interactions with genomic information. Crude drugs and other health-related substances, which are outside

2070-482: The analysis and visualization of large genomic sequence and annotation data to gain biological insight, via a graphical interface. Genomic browsers can be divided into web-based genomic browsers and stand-alone genomic browsers . The former use information from databases and can be classified into multiple-species (integrate sequence and annotations of multiple organisms and promote cross-species comparative analysis) and species-specific (focus on one organism and

2139-411: The analyzed genome, that is, aligning all known expressed sequence tags (ESTs), RNAs and proteins of the organism being annotated with the genome. Although it is optional, it can improve gene sequence elucidation because RNAs and proteins are direct products of coding sequences. If RNA-Seq data is available, it may be used to annotate and quantify all of the genes and their isoforms located in

KEGG - Misplaced Pages Continue

2208-569: The annotation standards used by the Sanger Institute's Human and Vertebrate Analysis Project (HAVANA). Annotation projects often rely on previous annotations of an organism's genome; however, these older annotations may contain errors that can propagate to new annotations. As new genome analysis technologies are developed and richer databases become available, the annotation of some older genomes may be updated. This process, known as reannotation, can provide users with new information about

2277-424: The annotations for particular species). The latter are not necessarily linked to a specific genome database but are general-purpose browsers that can be downloaded and installed as an application on a local computer. Comparative genomics aims to identify similarities and differences in genomic features, as well as to examine evolutionary relationships between organisms. Visualization tools capable of illustrating

2346-526: The category of approved drugs, are stored in the KEGG ENVIRON database. The databases in the health information category are collectively called KEGG MEDICUS, which also includes package inserts of all marketed drugs in Japan. In July 2011 KEGG introduced a subscription model for FTP download due to a significant cutback of government funding. KEGG continues to be freely available through its website, but

2415-412: The comparative behavior between two or more genomes are essential for this approach, and can be classified into three categories based on the representation of the relationships between the compared genomes: The quality of the sequence assembly influences the quality of the annotation, so it is important to assess assembly quality before performing the subsequent annotation steps. In order to quantify

2484-641: The corresponding genome, providing not only their locations, but also their rates of expression. However, transcripts provide insufficient information for gene prediction because they might be unobtainable from some genes, they may encode operons of more than one gene, and their start and stop codons cannot be determined due to frameshifts and translation initiation factors . To solve this problem, proteogenomics based approaches are employed, which utilize information from expressed proteins often derived from mass spectrometry . Annotation of eukaryotic genomes has an extra layer of difficulty due to RNA splicing ,

2553-433: The depth of analysis reported in literature for different genomes vary widely, with some reports including additional information that goes beyond a simple annotation. Furthermore, due to the size and complexity of sequenced genomes, DNA annotation is not performed manually, but is instead automated by computational means. However, the conclusions drawn from the obtained results require manual expert analysis. DNA annotation

2622-527: The dual aspects of the metabolic network: the genomic network of how genome-encoded enzymes are connected to catalyze consecutive reactions and the chemical network of how chemical structures of substrates and products are transformed by these reactions. A set of enzyme genes in the genome will identify enzyme relation networks when superimposed on the KEGG pathway maps, which in turn characterize chemical structure transformation networks allowing interpretation of biosynthetic and biodegradation potentials of

2691-558: The enzyme nomenclature. Currently, there are additional databases: KEGG GLYCAN for glycans and two auxiliary reaction databases called RPAIR (reactant pair alignments) and RCLASS (reaction class). KEGG COMPOUND has also been expanded to contain various compounds such as xenobiotics , in addition to metabolites. In KEGG, diseases are viewed as perturbed states of the biological system caused by perturbants of genetic factors and environmental factors, and drugs are viewed as different types of perturbants. The KEGG PATHWAY database includes not only

2760-514: The first report of the completely sequenced bacterial genome was published. Since then all published complete genomes are accumulated in KEGG for both eukaryotes and prokaryotes . The KEGG GENES database contains gene/protein-level information and the KEGG GENOME database contains organism-level information for these genomes. The KEGG GENES database consists of gene sets for the complete genomes, and genes in each set are given annotations in

2829-513: The form of establishing correspondences to the wiring diagrams of KEGG pathway maps, KEGG modules, and BRITE hierarchies. These correspondences are made using the concept of orthologs . The KEGG pathway maps are drawn based on experimental evidence in specific organisms but they are designed to be applicable to other organisms as well, because different organisms, such as human and mouse, often share identical pathways consisting of functionally identical genes, called orthologous genes or orthologs. All

KEGG - Misplaced Pages Continue

2898-561: The genes in the KEGG GENES database are being grouped into such orthologs in the KEGG ORTHOLOGY (KO) database. Because the nodes (gene products) of KEGG pathway maps, as well as KEGG modules and BRITE hierarchies, are given KO identifiers, the correspondences are established once genes in the genome are annotated with KO identifiers by the genome annotation procedure in KEGG. The KEGG metabolic pathway maps are drawn to represent

2967-560: The genome annotation of a strain of Pseudomonas putida (CSV86), a bacterium known for its preference of naphthalene and other aromatic compounds over glucose as a carbon and energy source. In order to find the MGEs of this bacterium, its genome was annotated using RAST and the NCBI Prokaryotic Genome Annotation Pipeline (PGAP), and the identification of nine mobile elements was possible with

3036-413: The genome, including details about genes and protein functions. Re-annotation is therefore a useful approach in quality control. Community annotation consists in the engagement of a community (both scientific and nonscientific) in genome annotation projects. It can be classified into the following six categories: A community annotation is said to be supervised when there is a coordinator who manages

3105-534: The genome. According to the developers, KEGG is a "computer representation" of the biological system . It integrates building blocks and wiring diagrams of the system—more specifically, genetic building blocks of genes and proteins, chemical building blocks of small molecules and reactions, and wiring diagrams of molecular interaction and reaction networks. This concept is realized in the following databases of KEGG, which are categorized into systems, genomic, chemical, and health information. The KEGG PATHWAY database,

3174-409: The identification and masking of repeats , which include low-complexity sequences (such as AGAGAGAG, or monopolymeric segments like TTTTTTTTT), and transposons (which are larger elements with several copies across the genome). Repeats are a major component of both prokaryotic and eukaryotic genomes; for instance, between 0% and over 42% of prokaryotic genomes consist of repeats and three quarters of

3243-458: The incorrect ones. As more sequenced genomes began to be available in early and mid 2000s, coupled with the numerous protein sequences that were obtained experimentally, genome annotators began employing homology based methods, launching the third generation of genome annotation. These new methods allowed annotators not only to infer genomic elements through statistical means (as in previous generations) but could also perform their task by comparing

3312-693: The information that can be extracted from the DNA sequence on a local scale, that is, one open reading frame (ORF) at a time. They appeared as a necessity to handle the enormous amount of data produced by the Maxam-Gilbert and Sanger DNA sequencing techniques developed in the late 1970s. The first software used to analyze sequencing reads is the Staden Package , created by Rodger Staden in 1977. It performed several tasks related to annotation, such as base and codon counts. In fact, codon usage

3381-705: The interrelations between GO terms. More advanced methods that consider these interrelations do so by either a flat or hierarchical approach, which are distinguished by the fact that the former does not take into account the ontology structure, while the latter does. Some of these methods compress the GO terms by matrix factorization or by hashing , thus boosting their performance. Noncoding sequences (ncDNA) are those that do not code for proteins. They include elements such as pseudogenes, segmental duplications, binding sites and RNA genes. Pseudogenes are mutated copies of protein-coding genes that lost their coding function due to

3450-474: The latter has been less successful than the former presumably due to a lack of time, motivation, incentive and/or communication. Misplaced Pages has multiple WikiProjects aimed at improving annotation. The Gene WikiProject , for instance, operates a bot that harvests gene data from research databases and creates gene stubs on that basis. The RNA WikiProject seeks to write articles that describe individual RNAs and RNA families in an accessible way. Gene Ontology

3519-404: The letters of the nucleotides (A, C, G, or T) with other letters. By doing so, these regions will be marked as repetitive and downstream analyses will treat them accordingly. Repetitive regions may produce performance issues if they are not masked, and may even produce false evidence for gene annotation (for example, treating an open reading frame (ORF) in a transposon as an exon ) Depending on

SECTION 50

#1732786786990

3588-586: The letters used for replacement, masking can be classified as soft or hard: in soft masking , repetitive regions are indicated with lowercase letters (a, c, g, or t), whereas in hard masking , the letters of these regions are replaced with N's. This way, for example, soft masking can be used to exclude word matches and avoid initiating an alignment in those regions, and hard masking, apart from all of this, can also exclude masked regions from alignment scores. The next step after genome masking usually involves aligning all available transcript and protein evidence with

3657-435: The locations of genes and all the coding regions in a genome and determines what those genes do. Annotation is performed after a genome is sequenced and assembled , and is a necessary step in genome analysis before the sequence is deposited in a database and described in a published article. Although describing individual genes and their products or functions is sufficient to consider this description as an annotation,

3726-408: The need for a computerized resource that can be used for biological interpretation of genome sequence data , he started developing the KEGG PATHWAY database. It is a collection of manually drawn KEGG pathway maps representing experimental knowledge on metabolism and various other functions of the cell and the organism . Each pathway map contains a network of molecular interactions and reactions and

3795-565: The normal states but also the perturbed states of the biological systems. However, disease pathway maps cannot be drawn for most diseases because molecular mechanisms are not well understood. An alternative approach is taken in the KEGG DISEASE database, which simply catalogs known genetic factors and environmental factors of diseases. These catalogs may eventually lead to more complete wiring diagrams of diseases. The KEGG DRUG database contains active ingredients of approved drugs in Japan,

3864-508: The organism. Alternatively, a set of metabolites identified in the metabolome will lead to the understanding of enzymatic pathways and enzyme genes involved. The databases in the chemical information category, which are collectively called KEGG LIGAND, are organized by capturing knowledge of the chemical network. In the beginning of the KEGG project, KEGG LIGAND consisted of three databases: KEGG COMPOUND for chemical compounds, KEGG REACTION for chemical reactions, and KEGG ENZYME for reactions in

3933-416: The project by requesting the annotation of specific items to a select number of experts. On the other hand, when anyone can enter a project and coordination is accomplished in a decentralized manner, it is called unsupervised community annotation. Supervised community annotation is short-lived and limited to the duration of the event, whereas the unsupervised counterpart does not have this limitation. However,

4002-413: The quality of a genome annotation, three metrics have been used: recall , precision and accuracy ; although these measures are not explicitly used in annotation projects, but rather in discussions of prediction accuracy. Community annotation approaches are great techniques for quality control and standardization in genome annotation. An annotation jamboree that took part in 2002, led to the creation of

4071-424: The same functional role in two different organisms. Annotators often refer to an analogous sequence when no paralogy, orthology or xenology was found. Homology-based methods have several drawbacks, such as errors in the database, low sensitivity/specificity, inability to distinguish between paralogy and homology, artificially high scores due to the presence of low complexity regions, and significant variation within

4140-405: The scanning of the sequence. To ensure a Markov model detects a genomic signal, it must first be trained on a series of known genomic signals. The output of Markov models in the context of annotation includes the probabilities of every kind of genomic element in every single part of the genome, and an accurate Markov model will assign high probabilities to correct annotations and low probabilities to

4209-444: The secondary structures of ncRNA, as they are conserved in related species even when their sequence is not. Therefore, by performing a multiple sequence alignment, more useful information can be obtained for their prediction. Homology search may also be employed to identify RNA genes, but this procedure is complicated, especially in eukaryotes, due to presence of a large number of repeats and pseudogenes. Visualization of annotations in

SECTION 60

#1732786786990

4278-664: The sequence being annotated with other already existing and validated sequences. These so-called combiner annotators, which perform both ab initio and homology-based annotation, require fast alignment algorithms to identify regions of homology . In the late 2000s, genome annotation shifted its attention towards identifying non-coding regions in DNA, which was achieved thanks to the appearance of methods to analyze transcription factor binding sites , DNA methylation sites, chromatin structure, and other RNA and regulatory region analysis techniques. Other genome annotators also began to focus on population-level studies represented by

4347-569: The strain. Gene Ontology analysis is of great importance in functional annotation, and specifically in bioremediation it can be applied to know the relationships between the genes of some microorganisms with their functions and their role in the remediation of certain contaminants. This was the approach of the investigation and identification of Halomonas zincidurans strain B6(T), a bacterium with thirty-one genes encoding resistance to heavy metals , especially zinc and Stenotrophomonas sp. DDT-1,

4416-427: The subscription model has raised discussions about sustainability of bioinformatics databases. Minoru Kanehisa Minoru Kanehisa ( 金久 實 ) (born January 23, 1948) is a Japanese bioinformatician . He is a project professor at Kyoto University , technical director of Pathway Solutions Inc and president of NPO Bioinformatics Japan. He is one of Japan's most recognized and respected bioinformatics experts and

4485-410: The textual descriptions of those records. As a general method, dcGO has an automated procedure for statistically inferring associations between ontology terms and protein domains or combinations of domains from the existing gene/protein-level annotations. A variety of software tools have been developed that allow scientists to view and share genome annotations, such as MAKER . Genome annotation

4554-489: The use of one of the following two methods: Noncoding RNA (ncRNA), produced by RNA genes, is a type of RNA that is not translated into a protein. It includes molecules such as tRNA , rRNA , snoRNA , and microRNA , as well as noncoding mRNA -like transcripts. Ab initio prediction of RNA genes in a single genome often yields inaccurate results (with an exception being miRNA), so multi-genome comparative methods are used instead. These methods are specifically concerned with

4623-761: The wiring diagram database, is the core of the KEGG resource. It is a collection of pathway maps integrating many entities including genes, proteins, RNAs, chemical compounds, glycans, and chemical reactions, as well as disease genes and drug targets, which are stored as individual entries in the other databases of KEGG. The pathway maps are classified into the following sections: The metabolism section contains aesthetically drawn global maps showing an overall picture of metabolism, in addition to regular metabolic pathway maps. The low-resolution global maps can be used, for example, to compare metabolic capacities of different organisms in genomics studies and different environmental samples in metagenomics studies. In contrast, KEGG modules in

4692-549: Was elected as a Fellow of the International Society for Computational Biology in 2013. Genome annotation In molecular biology and genetics , DNA annotation or genome annotation is the process of describing the structure and function of the components of a genome , by analyzing and interpreting them in order to extract their biological significance and understand the biological processes in which they participate. Among other things, it identifies

4761-476: Was the main strategy used by several early protein coding sequence (CDS) prediction methods, based on the assumption that the most translated regions in a genome contain codons with the most abundant corresponding tRNAs (the molecules responsible for carrying amino acids to the ribosome during protein synthesis) allowing a more efficient translation. This was also known to be the case for synonymous codons , which are often present in proteins expressed at

#989010