Once the MODs annotations have been integrated into our database, UniProt-GOA will provide the MOD with a file in the GAF2.0 format containing the entire set of GO annotations that match the taxon identifier(s) the MOD is responsible for as well as any additional annotations the MOD has created to other taxons. When importing the annotations back into their own database, the MOD can either note the updates made in this set from the changes in the date attached to each annotation (dates indicate when the last edit was made to the annotation) or they can carry out a full delete and reload of their GO annotation set. Any annotations that we cannot accept from the MOD, but which the MOD wants to keep can be appended to the supplied GAF by the MOD, e.g. annotations to non-coding RNAs, annotations using internal references that arent mapped to a GO_REF, IEA annotations, etc. UniProt-GOA will not store the annotations that are excluded, so it is up to the MOD to keep a record of these. If required, ...

BACKGROUND: As a major stored-product pest insect, Liposcelis entomophila has developed high levels of resistance to various insecticides in grain storage systems. However, the molecular mechanisms underlying resistance and environmental stress have not been characterized. To date, there is a lack of genomic information for this species. Therefore, studies aimed at profiling the L. entomophila transcriptome would provide a better understanding of the biological functions at the molecular levels.. METHODOLOGY/PRINCIPAL FINDINGS: We applied Illumina sequencing technology to sequence the transcriptome of L. entomophila. A total of 54,406,328 clean reads were obtained and that de novo assembled into 54,220 unigenes, with an average length of 571 bp. Through a similarity search, 33,404 (61.61%) unigenes were matched to known proteins in the NCBI non-redundant (Nr) protein database. These unigenes were further functionally annotated with gene ontology (GO), cluster of orthologous groups of proteins ...

Q1. If there are duplicate manual annotations from both the MOD and UniProt, how will that be handled? A1. The UniProt-GOA database can handle duplicate annotations that differ only in source, therefore we will display duplicate annotations. We will be supplying all annotations to the species indicated in the file, regardless of which group created the annotation, so it would be up to each group to decide which they want to keep. However, if annotations from other groups are retained, attribution of these annotations must stay as the original source. Q2. Some MODs update their databases on a nightly basis and would therefore like to have more frequent data releases. Is that possible? A2. The default for supplying annotation files to groups is once every two weeks. If any group would like their file more often, we are happy to consider this within reason. There are certain times of the week when it is not possible to generate files (including at weekends) due to scheduling conflicts with other ...

Recent advances in global gene expression measurement and the development of large- scale public repositories for storage of such data have made a wealth of information available to researchers. While one gene expression study may lack sufficient replicates to make statistically significant pronouncements, the combination of studies through meta-analysis can yield results with a much greater likelihood of accuracy. In order to combine multiple sets of data, one must first address the issue of cross-comparison between global gene expression platforms, as well as resolve the issue of repeated measures (multiple probes representing the same gene) within each platform. In this work, I present computational methods for probe reannotation and scoring and for redundant probe consolidation that together allow for greatly improved access to data for meta-analysis. I also present an example of the application of these methods, in the analysis of the gene expression regulated by estrogen across multiple ...

Annotation: Augments the information the viewer can immediately see about the data with notes, sources, or other useful information. Ive been looking for data labeling for computer vision data. Hire a Netguru team to help you implement Data Annotation solutions. You can compare the annotations and privilege levels across vCenter Server instances and host machines. An up to date and manually curated list of top data annotation companies from all over the world. Image annotation describes the classification of information that is of relevance to an image. Genome and genome annotation. The annotations automatically save for the loaded security next time that security is pulled up. Image annotation. Ngene empowers LabVIEW development environment with Machine Learning/Deep Learning tools. ai provides high-quality training and validation data to enable mobility companies to develop with confidence computer vision and machine learning models that reliably and safely power autonomous vehicles. ...

Hemarthria R. Br. is an important genus of perennial forage grasses that is widely used in subtropical and tropical regions. Hemarthria grasses have made remarkable contributions to the development of animal husbandry and agro-ecosystem maintenance; however, there is currently a lack of comprehensive genomic data available for these species. In this study, we used Illumina high-throughput deep sequencing to characterize of two agriculturally important Hemarthria materials, H. compressa Yaan and H. altissima 1110. Sequencing runs that used each of four normalized RNA samples from the leaves or roots of the two materials yielded more than 24 million high-quality reads. After de novo assembly, 137,142 and 77,150 unigenes were obtained for Yaan and 1110, respectively. In addition, a total of 86,731 Yawn and 48,645 1110 unigenes were successfully annotated. After consolidating the unigenes for both materials, 42,646 high-quality SNPs were identified in 10,880 unigenes and 10,888 SSRs were ...

The Gene Ontology project integrates data about the function of gene products across a diverse range of organisms, allowing the transfer of knowledge from model organisms to humans, and enabling computational analyses for interpretation of high-throughput experimental and clinical data. The core data structure is the annotation, an association between a gene product and a term from one of the three ontologies comprising the GO. Historically, it has not been possible to provide additional information about the context of a GO term, such as the target gene or the location of a molecular function. This has limited the specificity of knowledge that can be expressed by GO annotations. The GO Consortium has introduced annotation extensions that enable manually curated GO annotations to capture additional contextual details. Extensions represent effector-target relationships such as localization dependencies, substrates of protein modifiers and regulation targets of signaling pathways and transcription factors

Gene-list annotations are critical for researchers to explore the complex relationships between genes and functionalities. Currently, the annotations of a gene list are usually summarized by a table or a barplot. As such, potentially biologically important complexities such as one gene belonging to multiple annotation categories are difficult to extract. We have devised explicit and efficient visualization methods that provide intuitive methods for interrogating the intrinsic connections between biological categories and genes. We have constructed a data model and now present two novel methods in a Bioconductor package, GeneAnswers, to simultaneously visualize genes, concepts (a.k.a. annotation categories), and concept-gene connections (a.k.a. annotations): the Concept-and-Gene Network and the Concept-and-Gene Cross Tabulation. These methods have been tested and validated with microarray-derived gene lists. These new visualization methods can effectively present annotations using Gene Ontology,

AceView offers a comprehensive annotation of human and nematode genes reconstructed by co-alignment and clustering of all publicly available mRNAs and ESTs on the genome sequence. Our goals are to offer a reliable up-to-date resource on the genes, their functions, alternative variants, expression, regulation and interactions, in the hope to stimulate further validating experiments at the bench

The genome sequence of an organism is an information resource unlike any that biologists have previously had access to. But the value of the genome is only as good as its annotation. It is the annotation that bridges the gap from the sequence to the biology of the organism. The aim of high-quality annotation is to identify the key features of the genome - in particular, the genes and their products. The tools and resources for annotation are developing rapidly, and the scientific community is becoming increasingly reliant on this information for ail aspects of biological research.. ...

Genomic locations of UniProt/SwissProt annotations are labeled with a short name for the type of annotation (e.g. glyco, disulf bond, Signal peptide etc.). A click on them shows the full annotation and provides a link to the UniProt/SwissProt record for more details. TrEMBL annotations are always shown in light blue, except in the Signal Peptides, Extracellular Domains, Transmembrane Domains, and Cytoplamsic domains subtracks.. Mouse over a feature to see the full UniProt annotation comment. For variants, the mouse over will show the full name of the UniProt disease acronym. The subtracks for domains related to subcellular location are sorted from outside to inside of the cell: Signal peptide, extracellular, transmembrane, and cytoplasmic. In the UniProt Modifications track, lipoification sites are highlighted in dark blue, glycosylation sites in dark green, and phosphorylation in light green.. Duplicate annotations are removed as far as possible: if a TrEMBL annotation has the same ...

Genomic locations of UniProt/SwissProt annotations are labeled with a short name for the type of annotation (e.g. glyco, disulf bond, Signal peptide etc.). A click on them shows the full annotation and provides a link to the UniProt/SwissProt record for more details. TrEMBL annotations are always shown in light blue, except in the Signal Peptides, Extracellular Domains, Transmembrane Domains, and Cytoplamsic domains subtracks.. Mouse over a feature to see the full UniProt annotation comment. For variants, the mouse over will show the full name of the UniProt disease acronym. The subtracks for domains related to subcellular location are sorted from outside to inside of the cell: Signal peptide, extracellular, transmembrane, and cytoplasmic. In the UniProt Modifications track, lipoification sites are highlighted in dark blue, glycosylation sites in dark green, and phosphorylation in light green.. Duplicate annotations are removed as far as possible: if a TrEMBL annotation has the same ...

Function ,p>Position-independent general annotations used to be found in the General annotation (Comments) section in the previous version of the UniProtKB entry view. They provide any useful information about the protein, mostly biological knowledge. General annotations are frequently written in free text, although we increasingly try to standardize them and use controlled vocabulary wherever possible. The flat file and XML formats still group all general annotation together in a Comments section (CC, ,comment>). ,p>,a href=/help/general_annotation target=_top>More...,/a>,/p>[CC]i ...

Function ,p>Position-independent general annotations used to be found in the General annotation (Comments) section in the previous version of the UniProtKB entry view. They provide any useful information about the protein, mostly biological knowledge. General annotations are frequently written in free text, although we increasingly try to standardize them and use controlled vocabulary wherever possible. The flat file and XML formats still group all general annotation together in a Comments section (CC, ,comment>). ,p>,a href=/help/general_annotation target=_top>More...,/a>,/p>[CC]i ...

Function ,p>Position-independent general annotations used to be found in the General annotation (Comments) section in the previous version of the UniProtKB entry view. They provide any useful information about the protein, mostly biological knowledge. General annotations are frequently written in free text, although we increasingly try to standardize them and use controlled vocabulary wherever possible. The flat file and XML formats still group all general annotation together in a Comments section (CC, ,comment>). ,p>,a href=/help/general_annotation target=_top>More...,/a>,/p>[CC]i ...

Motivation Function annotations of gene products, and phenotype annotations of genotypes, provide valuable information about molecular mechanisms that can be utilized by computational methods to identify functional and phenotypic relatedness, improve our understanding of disease and pathobiology, and lead to discovery of drug targets. Identifying functions and phenotypes commonly requires experiments which are time-consuming and expensive to carry out; creating the annotations additionally requires a curator to make an assertion based on reported evidence. Support to validate the mutual consistency of functional and phenotype annotations as well as a computational method to predict phenotypes from function annotations, would greatly improve the utility of function annotations. Results We developed a novel ontology-based method to validate the mutual consistency of function and phenotype annotations. We apply our method to mouse and human annotations, and identify several inconsistencies that can ...

The Distributed Annotation System, or DAS, is a protocol for exchanging and retrieving sequence annotations, possibly from multiple sources. With DAS you dont have to store annotation data to use or display it. You only have to know how to retrieve it from a DAS server. See the BioDas web site for a full explanation of DAS ...

GO annotations: Mouse from MGI; Human from GO Annotations @ EBI (GOA); Rat from RGD; Chicken from GOA; Fly from FlyBase; Pfalc from PlasmoDB; Worm from WormBase; Dicty from dictyBase; Yeast from SGD; Zfin from ZFIN; Tair from TAIR/TIGR; Rice from Gramene; Pombe from Sanger GeneDB ...

The integration of genome annotations is critical to the identification of genetic variants that are relevant to studies of disease or other traits. However, comprehensive variant annotation with diverse file formats is difficult with existing methods. Here we describe vcfanno, which flexibly extracts and summarizes attributes from multiple annotation files and integrates the annotations within the INFO column of the original VCF file. By leveraging a parallel

We have set up the Gene Search page, users can submit gene locus, GO or InterPro category, or functional information, the server will return detailed gene annotation, including predicted functional information, homologs in Arabidopsis thaliana and Oryza sativa, domain assignment, GO and Mapman annotation, etc.. ...

Annotations to Nabokov works: The annotations maintain a consistent format but the depth of annotations, and the degree of interpretation, vary according to the annotators. Please edit typos as you notice them, but contact the annotators to propose more substantial changes. Ada Headnote by Brian Boyd AdaOnline by Brian Boyd (separate website): Annotations (full, interlinked, and with images) from Pt 1 Ch 1 to Pt 2 Ch 3

Ab initio annotation of sequences in Human genome draft: (49171 Genes and 282378 exons) The nucleotide sequence of nearly 90% of the Human genome (3 GB) has been determined in worldwide sequencing community. We annotated these sequences predicting genes by one of the most accurate FGENESH program (at http://www.softberry.com/nucleo.html) and annotated similarity of each exon with the PfamA protein domain database. The complete results of this analysis are presented in Table 1 and can be seen in the InfoGene database at: http://www.softberry.com/inf/infodb.html where the Infogen Java viewer can by used to visualize the predictions along the chromosomes and by Action meny and Obtain Locus to get Prediction data Blast search against the predicted Human proteins is provided at: httpd: //www.softberry.com/scan.html . The sequences of exons and gene annotation data can be copied for using them locally or to create microarray oligos: ,Human genome predicted genes/exons ,Predicted amino acid sequences ...

The definition of a protein coding domain that we used here is a contiguous stretch of DNA that, when transcribed, produces an mRNA that specifies the amino acid sequence of a protein. The T7 protein coding domains were first characterized by the isolation and analysis of randomly generated amber mutants. Nineteen genes were identified by mapping mutants that disrupt T7 DNA synthesis, particle maturation, and lysis (Studier, 1969; Haussman & Gomez, 1967; Haussman & LaRue, 1969). Two additional genes, T7 DNA ligase and protein kinase, were isolated via loss of function and deletion, respectively (Masamune et al, 1971); the genetic analysis of ligase and kinase mutants was carried out using mutant host strains that do not support the growth of ligase or kinase defective phage (Studier, 1969). Up to thirty T7 proteins were observed by pulsing phage-infected cells with radioactive amino acids (Studier & Maizel, 1969; Studier, 1973). Further experiments, such as electrophoretic mobility shifts of ...

FatiGO is a web-accessible application that functions in much the same way as DAVIDs GoCharts, including the ability to specify term-specificity level. Unlike DAVID, FatiGO does not allow the setting of a minimum hit threshold for simplified viewing of only the most highly represented functional categories. Likewise, FatiGO limits the graphical output to only one top-level GO category at a time, whereas DAVID allows the combined viewing of biological process, molecular function, and cellular component annotations simultaneously. FatiGOs static barchart output looks very similar to DAVIDs GoChart; an important distinction is that DAVIDs GoCharts are dynamic, allowing users to drill-down and traverse the GO hierarchy for any subset of genes, view the underlying chart data and associated annotations, and link out to external data repositories including LocusLink and QuickGO. As shown in Table 3 the majority of accession types accepted and functional annotations offered by DAVID are not ...

PacBio calls their technology SMRT sequencing - single molecule, real-time. Unlike most other sequencing technologies, it doesnt require clonal amplification of DNA - it sequences single molecules. The real-time nature of PacBio leads to three distinct advantages. First, the reads are quite fast, with runs lasting from 30 minutes to three hours (rather than days). Second, the reads are substantially longer than most other commercially available sequencing platforms (including Sanger-based sequencers), with a mean of ~15 kb. Third, the movie captures information about the rate of nucleotide incorporation, which can be used to determine the modification status of the template nucleotide (e.g. 5-mC, 5-hmC, etc.). The raw read error rate is substantially higher at around 14% compared with the 0.1 to 1% error rate of other leading systems. However, unlike the others, the error model is stochastic, so very high quality reads across all bases can be achieved in the consensus sequence. Additionally, ...

Methods, systems, and articles of manufacture that may be used to create and share annotations for query components, such as query conditions, in an effort to share domain knowledge, are provided. The annotations may be created by users with particular domain knowledge and may contain information useful to other users when building queries including the annotated query components. An annotation may indicate a particular format or syntax for an associated query component. In some cases, a replacement to the associated query component is suggested.

Hi What must I do if I want to delete all annotation objects in a MedicalViewerCell before drawing a new annotation object so that there is only one annotation object in MedicalViewerCell at any...

Why does the choice of a gene model have so dramatic an effect on gene quantification? Below, we chose a few extreme or representative cases to provide possible explanations. In the liver sample, the expression levels for these exemplary genes for both Ensembl and RefGene were summarized in Table 2 (read length = 75 bp). PIK3CA (phosphatidylinositol-4,5-bisphosphate 3-kinase, catalytic subunit alpha) uses ATP to phosphorylate PtdIns, PtdIns4P, and PtdIns(4,5)P2. In the liver sample, there were 1094 reads mapped to PIK3CA in Ensembl annotation, while only 492 reads were mapped in RefGene. The PIK3CA gene definition in both Ensembl and RefGene, and the mapping profile of RNA-Seq reads were shown in Figure 6. Clearly, the difference in gene definition gives rise to the observed discrepancy in quantification ...

Dear Ernesto, our curation protocol found a mouse gene annotation in this pathway. There seems to be a human ortholog: ENSG00000020922 Can you let me know if the mouse gene is there deliberately or if they Ensembl identifier can be updated? Thanks, Egon ...

You may suggest updates to the annotation of this entry using this form. Suggestions will be sent to our curators for review and, if acceptable, will be included in the next public release of InterPro. It is helpful if you can include literature references supporting your annotation suggestion. ...

You may suggest updates to the annotation of this entry using this form. Suggestions will be sent to our curators for review and, if acceptable, will be included in the next public release of InterPro. It is helpful if you can include literature references supporting your annotation suggestion. ...

Author Summary Understanding gene function-how individual genes contribute to the biology of an organism at the molecular, cellular and organism levels-is one of the primary aims of biomedical research. It has been a longstanding tenet of model organism research that experimental knowledge obtained in one organism is often applicable to other organisms, particularly if the organisms share the relevant genes because they inherited them from their common ancestor. Nevertheless this tenet is, like any hypothesis, not beyond question. A recent paper has termed this hypothesis a

Quick Read:Who would have thought what had begun as a traditional 17th century readers habit of jotting down some thoughts and gossip on the margins of a sheet of paper could turn out to become a concept for world domination? Annotations are in vogue today! In the 17th and 18th centuries, it was traditional for…

Integrating protein annotations for the in silico prioritization of putative drug target... - Free PDF Download - 130 pages - year: 2013

In metagenomics datasets, it is standard practice to correct samples for (a) differences in sequencing effort (library size) and (b) normalise gene counts based on the total annotated hits per sample to obtain relative abundances. However, most databases on functional genes such as SEED or KEGG are biased, such that genes involved in central metabolism are better annotated. Hence, categories such as Carbohydrate metabolism and protein synthesis often dominate function profiles as result of this bias. Most articles do not correct for this database bias. What are the common ways of accounting for this bias?. ...

geneid - Gene prediction tool, it can also introduce homology and annotation evidences and produce a reannotation of a genomic sequence. A pthreads parallel version also available ...

Function ,p>Position-independent general annotations used to be found in the General annotation (Comments) section in the previous version of the UniProtKB entry view. They provide any useful information about the protein, mostly biological knowledge. General annotations are frequently written in free text, although we increasingly try to standardize them and use controlled vocabulary wherever possible. The flat file and XML formats still group all general annotation together in a Comments section (CC, ,comment>). ,p>,a href=/help/general_annotation target=_top>More...,/a>,/p>[CC]i ...

Nitraria sibirica Pall., a typical halophyte of great ecological value, is widely distributed in desert, saline, and coastal saline-alkali environments. Consequently, researching the salt tolerance mechanism of N. sibirica Pall. has great significance to the cultivation and utilization of salt-tolerant plants. In this research, RNA-seq, digital gene expression (DGE), and high flux element analysis technologies were used to investigate the molecular and physiological mechanisms related to salt tolerance of N. sibirica Pall. Integrative analysis and de novo transcriptome assembly generated 137,421 unigenes. In total, 58,340 and 34,033 unigenes were annotated with gene ontology (GO) terms and mapped in Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, respectively. Three differentially expressed genes (DEGs) libraries were subsequently constructed from the leaves of N. sibirica Pall. seedlings under different treatments: control (CK), light short-term salt stress (CL2), and heavy long-term salt

The Ensembl human gene annotations have been updated using Ensembls automatic annotation pipeline. The updated annotation incorporates new protein and cDNA sequences which have become publicly available since the last GRCh37 genebuild (March 2009).. In release 67 (May 2012), we continue to display a joint gene set based on the merge between the automatic annotation from Ensembl and the manually curated annotation from Havana. This refined gene set corresponds to GENCODE release 12. The Consensus Coding Sequence (CCDS) identifiers have also been mapped to the annotations. More information about the CCDS project. Updated manual annotation from Havana is merged into the Ensembl annotation every release. Transcripts from the two annotation sources are merged if they share the same internal exon-intron boundaries (i.e. have identical splicing pattern) with slight differences in the terminal exons allowed. Importantly, all Havana transcripts are included in the final Ensembl/Havana merged (GENCODE) ...

The CASAM multimedia annotation system implements a model of cooperative annotation between a human annotator and automated components. The aim is that they work asynchronously but together. The system focuses upon the areas where automated recognition and reasoning are most effective and the user is able to work in the areas where their unique skills are required. The systems reasoning is influenced by the annotations provided by the user and, similarly, the user can see the systems work and modify and, implicitly, direct it. The CASAM system interacts with the user by providing a window onto the current state of annotation, and by generating requests for information which are important for the final annotation or to constrain its reasoning. The user can modify the annotation, respond to requests and also add their own annotations. The objective is that the human annotators time is used more effectively and that the result is an annotation that is both of higher quality and produced more ...

To the best of our knowledge, this is first study to address the variation of human-annotated 3D facial landmarks. Understanding the variation of manual annotations is important as components of registration, recognition, and machine learning are influenced by manual annotation errors. However, the current literature is sparse in area pertaining to 3D facial morphology and variation. We expect that an increase in the availability, accuracy, user friendliness (i.e. fewer operator demands) of 3D imaging scanners will probe the use of shape models in clinical diagnostics, as seen for example in orthopedic surgery [24]. However, to assess the putative clinical impact of such tools, it is important to understand the variability embedded in manual annotation. Our analysis focused on facial morphology, suggests a procedure to retrieve a dense correspondence mesh of the face with low variance and minimal human operator assigned annotation points.. We first address the variability of 73 facial 3D ...

How will this virtual institute work? It will be divided into nodes, each focused on one aspect of genome annotation. The annotations generated will be integrated and made freely accessible to all through a single portal on the web, and will be used as a means of guiding future experimental work. Experimental validation of a statistically significant subset of computational predictions will be an integral part of the process, leading to an iterative improvement in methods, explains Thornton. The annotations will be integrated using DAS (Distributed Annotation System), an Open Source system developed by researcher Lincoln Stein and colleagues at Cold Spring Harbor Laboratory (NY, USA) for exchanging annotations on genomic sequence data. DAS heralds a new era for database structure, where information is distributed by a network rather than a single site, explains Søren Brunak. Meetings and workshops organized by the institute will encourage cooperation and reduce duplication of effort. They ...

Abstract Background While studies of non-model organisms are critical for many research areas, such as evolution, development, and environmental biology, they present particular challenges for both experimental and computational genomic level research. Resources such as mass-produced microarrays and the computational tools linking these data to functional annotation at the system and pathway level are rarely available for non-model species. This type of systems-level analysis is critical to the understanding of patterns of gene expression that underlie biological processes. Results We describe a bioinformatics pipeline known as FunnyBase that has been used to store, annotate, and analyze 40,363 expressed sequence tags (ESTs) from the heart and liver of the fish, Fundulus heteroclitus. Primary annotations based on sequence similarity are linked to networks of systematic annotation in Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) and can be queried and computationally ...

Upon the completion of whole genome sequencing, thorough genome annotation that associates genome sequences with biological meanings is essential. Genome annotation depends on the availability of transcript information as well as orthology information. In teleost fish, genome annotation is seriously hindered by genome duplication. Because of gene duplications, one cannot establish orthologies simply by homology comparisons. Rather intense phylogenetic analysis or structural analysis of orthologies is required for the identification of genes. To conduct phylogenetic analysis and orthology analysis, full-length transcripts are essential. Generation of large numbers of full-length transcripts using traditional transcript sequencing is very difficult and extremely costly. In this work, we took advantage of a doubled haploid catfish, which has two sets of identical chromosomes and in theory there should be no allelic variations. As such, transcript sequences generated from next-generation sequencing can be

Scientific literature is a central repository of scientific knowledge - every important scientific discovery has been published in it. As such, scientific literature has become a main target of data mining, and in particular, text mining. However, the unstructured, or covertly structured, nature of natural language texts poses a major barrier to accessing the contents of literature. The technology of literature annotation thus has played a central role for text mining. While literature annotation still requires enormous effort despite a number of years of concentrated experience, the productivity of literature annotation is recently significantly improved, and there are quite a few groups producing annotations in large scale. While many groups have released those data sets of literature annotation to the public, however, the way of sharing those widely valuable resources still remains at a primitive level, e.g., relying on individual exchange of archived files.. Meanwhile, the advancement of ...

Background Distinguishing between individuals is critical to those conducting animal/plant breeding, food safety/quality research, diagnostic and clinical testing, and evolutionary biology studies. Classical genetic identification studies are based on marker polymorphisms, but polymorphism-based techniques are time and labor intensive and often cannot distinguish between closely related individuals. Illumina sequencing technologies provide the detailed sequence data required for rapid and efficient differentiation of related species, lines/cultivars, and individuals in a cost-effective manner. Here we describe the use of Illumina high-throughput exome sequencing, coupled with SNP mapping, as a rapid means of distinguishing between related cultivars of the lignocellulosic bioenergy crop giant miscanthus (Miscanthus × giganteus). We provide the first exome sequence database for Miscanthus species complete with Gene Ontology (GO) functional annotations. Results A SNP comparative analysis of rhizome

ISSUE-72 (Annotation Semantics): REPORTED: lack of annotation semantics is not backwardly compatible http://www.w3.org/2007/OWL/tracker/issues/ Raised by: Jeremy Carroll On product: The semantics doc explicitly gives no semantics to annotations. This is not backwardly compatible with OWL 1.0 in which annotations have the RDFS semantics ...

We developed an annotation tool-Label360 to solve the distortion and instance matching issues across different viewing aspects in spherical image annotations. A post-processing algorithm was introduced to generate distortion-free annotations on equirectangular images. Two experiments were conducted to examine the consistency of annotations using Label360 and to compare labeling efficiency with LabelMe. Our tool obtained a mean intersection over union (mIoU) of 0.92 in the consistency test and has 1.45x the annotation speed of LabelMe. This demonstrates that Label360 is efficient for annotating instance-aware semantic segmentation labels on spherical images.

We would like to get your feedback about GenDB. Please take a few seconds to fill out our survey.. GenDB is a genome annotation system for prokaryotic genomes. The system has been developed as an extensible and user friendly framework for both bioinformatics researchers and biologists to use in their genome projects. The GenDB annotation engine will automatically identify, classify and annotate genes using a large collection of software tools. Many groups view this automatic annotation as the first step that needs to be followed by expert annotation of the genome.. GenDB offers user interfaces that allow expert annotation with large, geo-graphically dispersed teams of experts. Genes to be annotated can be categorized by functional class or gene location. A number of naming schemes (aka ontologies or functional classification schemes) are supported: EC numbers, GO, COG. In addition to its use as a production genome annotation system, it can be employed as a flexible framework for the large-scale ...

I previously created a C.bairdi de novo transcriptome assembly v4.0 with Trinity from all our C.bairdi RNAseq reads which had BLASTx matches to the C.opilio genome and decided to assess its

The ever-increasing number of sequenced and annotated genomes has made management of their annotations a significant undertaking, especially for large eukaryotic genomes containing many thousands of genes. Typically, changes in gene and transcript numbers are used to summarize changes from release to release, but these measures say nothing about changes to individual annotations, nor do they provide any means to identify annotations in need of manual review. In response, we have developed a suite of quantitative measures to better characterize changes to a genomes annotations between releases, and to prioritize problematic annotations for manual review. We have applied these measures to the annotations of five eukaryotic genomes over multiple releases - H. sapiens, M. musculus, D. melanogaster, A. gambiae, and C. elegans. Our results provide the first detailed, historical overview of how these genomes annotations have changed over the years, and demonstrate the usefulness of these measures for genome

WebApollo is a browser-based tool for visualization and editing of sequence annotations. It is designed for distributed community annotation efforts, where numerous people may be working on the same sequences in geographically different locations; real-time updating keeps all users in sync during the editing process. The features of WebApollo include: *History tracking, including browsing of an annotations edit history and full undo/redo functions *Real time updating: edits in one client are instantly pushed to all other clients *Convenient management of user login, authentication, and edit permissions *Two-stage curation process: edit within a temporary workspace, then publish to a curated database *Ability to add comments, either chosen from a pre-defined set of comments or as freeform text. *Ability to add dbxrefs [database crossreferences] -- e.g. for GO functional annotation *Can set start of translation for a transcript or let server determine automatically *Flagging of non-canonical ...

Citation. Florea, L., Di Francesco, V., Miller, J., Turner, R., Yao, A., Harris, M., Walenz, B., Mobarry, C., Merkulov, G. V., Charlab, R., Dew, I., Deng, Z., Istrail, S., Li, P., Sutton, G.. Gene and Alternative Splicing Annotation With AIR. Genome Res. 2005 Jan 01; 15(1). : 54-66.. PubMed Citation. Abstract. Designing effective and accurate tools for identifying the functional and structural elements in a genome remains at the frontier of genome annotation owing to incompleteness and inaccuracy of the data, limitations in the computational models, and shifting paradigms in genomics, such as alternative splicing. We present a methodology for the automated annotation of genes and their alternatively spliced mRNA transcripts based on existing cDNA and protein sequence evidence from the same species or projected from a related species using syntenic mapping information. At the core of the method is the splice graph, a compact representation of a gene, its exons, introns, and alternatively spliced ...

Retention analysis result of each fusion partner protein across 39 protein features of UniProt such as six molecule processing features, 13 region features, four site features, six amino acid modification features, two natural variation features, five experimental info features, and 3 secondary structure features. Here, because of limited space for viewing, we only show the protein feature retention information belong to the 13 regional features. All retention annotation result can be downloaded at download page. ...

Retention analysis result of each fusion partner protein across 39 protein features of UniProt such as six molecule processing features, 13 region features, four site features, six amino acid modification features, two natural variation features, five experimental info features, and 3 secondary structure features. Here, because of limited space for viewing, we only show the protein feature retention information belong to the 13 regional features. All retention annotation result can be downloaded at download page. ...

We performed a comparative analysis of five regulatory annotations, all based on diverse epigenomic signatures, to better understand their regulatory capacity and downstream transcriptional effects. We observed that stretch, super, and typical enhancers overlap enhancer chromatin states in the corresponding cell type, but overlap nonenhancer chromatin states in unrelated cell types, supporting the cell type specificity of these regulatory elements. These observations highlight H3K27ac as a good proxy for cell type-specific regulatory function. Annotations based on the H3K4me3 mark (broad domains) and TF binding (HOT regions) show a large fraction (,40%) of overlaps with promoter chromatin states across different cell types. Consistent with our observations, a recent study in the fly reported that regions bound by large numbers of TFs (such as HOT regions) are less cell type-specific (Kudron et al. 2017). While the diverse ChIP-seq data used to define regulatory annotations comes from different ...

Hi all, I used spades for assembly of bacteria-Illumina reads, and galaxy-Prokka for annotation Visualization of the annotation results showed me:. Summary of the active entries: contigs: 65. bases: 5736331. CDS: 5102. gene: 5279. misc_RNA: 52. rRNA: 9. tRNA: 115. tmRNA: 1. 1- how can I confirm that annotation results are correct? 2- I am confused, why there are no pseudogenes in my report!! Thanks for your time ...

In Python 3.9 and older, accessing the annotations dict of an object is much more complicated than in newer versions. The problem is a design flaw in these older versions of Python, specifically to do with class annotations.. Best practice for accessing the annotations dict of other objects-functions, other callables, and modules-is the same as best practice for 3.10, assuming you arent calling ...

But, by resorting to computational annotation of the function of proteins, we need to know how well can these algorithms actually perform. Enter CAFA, of which I have written before. CAFA is a community challenge that assesses the performance of protein function prediction algorithms.. How does the CAFA challenge work? Well, briefly:. 1. Target selection: we select a large number of proteins from SwissProt, UniProt-GOA and other databases. Those proteins have no experimental annotations, only computational ones. Those are the prediction targets.. 2. Prediction phase: we publish the targets. Participating CAFA teams now have four months to provide their own functional annotations, using the Gene Ontology, a controlled vocabulary describing protein functions.. 3. Growth phase: after four months, we close the predictions, and wait for another six months, or so. During those six months, some of the targets acquire experimentally-validated annotations. This typically means that biocurators have ...

Here is the first batch of annotations for The Alloy of Law. As with all of the other annotations here on the site, each annotation contains spoilers for the current chapter. Spoilers for chapters after the current one are hidden by spoiler tags. We recommend you read the book before reading the ...

Here is the first batch of annotations for The Alloy of Law. As with all of the other annotations here on the site, each annotation contains spoilers for the current chapter. Spoilers for chapters after the current one are hidden by spoiler tags. We recommend you read the book before reading the ...

Diagnosis Index entries containing back-references to J Toggle navigation. The following code s above J In this context, annotation back-references refer to codes that contain: Applicable To annotations, or Code Also annotations, or Code First annotations, or Excludes1 icd, or Excludes2 annotations, or Includes annotations, or Note annotations, or Use Additional annotations. Diseases of allergt respiratory system Note Allrrgy a respiratory condition is described as occurring in more than one site and is not specifically indexed, it should be classified to allergy lower anatomic site e. Type 2 Excludes certain conditions ifd in the perinatal period P04 - P96 certain infectious and parasitic diseases AB99 complications of pregnancy, childbirth and the puerperium OO9A congenital malformations, deformations and chromosomal abnormalities QQ99 endocrine, nutritional and metabolic diseases E00 - E88 injury, poisoning and certain other consequences of external causes ST88 neoplasms CD49 smoke inhalation ...

Centromeric alpha satellite (AS) is composed of highly identical higher-order DNA repetitive sequences, which make the standard assembly process impossible. Because of this the AS repeats were severely underrepresented in previous versions of the human genome assembly showing large centromeric gaps. The latest hg38 assembly (GCA_000001405.15) employed a novel method of approximate representation of these sequences using AS reference models to fill the gaps. Therefore, a lot more of assembled AS became available for genomic analysis. We used the PERCON program previously described by us to annotate various suprachromosomal families (SFs) of AS in the hg38 assembly and presented the results of our primary analysis as an easy-to-read track for the UCSC Genome Browser. The monomeric classes, characteristic of the five known SFs, were color-coded, which allowed quick visual assessment of AS composition in whole multi-megabase centromeres down to each individual AS monomer. Such comprehensive annotation of AS

In view of the draft state of the Chinese Hamster reference genome and the incomplete annotation of noncoding RNAs, an extended reference gene model was built to use in our differential expression and methylation analysis. The resulting annotation was contained 26,270 protein coding and 78,873 noncoding transcribed regions encoding for 80,973 transcripts, including 51,193 long noncoding RNAs (lncRNAs) or processed transcripts.. ...

Dear all, I need to know if there is a key of colours and shapes for the graphical representation of annotations in proteins. for instance, if I need to have a pictorial representation of a domain or transcript then is there a standardized way to do it? So far I have seen that domains are usually represented as ellipses or rectangles, and metal bindings as non-filled circles, while active sites are red-filled circles. I am particular interested in the next type of annotations: Domain, Signal, Transit, Propeptide, Peptide, Topological domain, Intramembrane, Transmenbrane for ranges of sequences, and Metal binding, Active site, Modified residue, Lipidation, Glycosilation for point positions. I appreciate any information on this matter. Cheers, Leyla García EMBL-EBI, Cambridge, UK ...

Background. DNA sequences are pivotal for a wide array of research in biology. Large sequence databases, like GenBank, provide an amazing resource to utilize DNA sequences for large scale analyses. However, many sequences on GenBank contain more than one gene or are portions of genomes, and inconsistencies in the way genes are annotated and the numerous synonyms a single gene may be listed under provide major challenges for extracting large numbers of subsequences for comparative analysis across taxa. At present, there is no easy way to extract portions from multiple GenBank accessions based on annotations where gene names may vary extensively. Results. The R package AnnotationBustR allows users to extract sequences based on GenBank annotations through the ACNUC retrieval system given search terms of gene synonyms and accession numbers. AnnotationBustR extracts portions of interest and then writes them to a FASTA file for users to employ in their research endeavors. Conclusion. FASTA files of extracted

Next-generation sequencing (NGS) is increasingly being applied across the drug discovery and development pathway e.g. in target evaluation, patient stratification and clinical profiling. However, biological interpretation of the output of NGS is highly time-consuming, being a mostly manual process of literature searching and annotation of the gene results. This webinar will show how I2E can be used to collate a comprehensive gene profile, with key biological annotation from a combination of sources like MEDLINE, OMIM and NIH Grants.

Sequence analysis (Figure 4): The sequenced PCR product generated 801 bases of high-quality reads that were used to identify the genus of the isolated colony. The chromatogram of the sequence is available as a pdf (14R_PREMIX_JF7523_18). The NCBI BLAST analysis revealed 99% identity with bases 50-850 of the 16s RNA gene of Bacillus aerius, Bacillus stratosphericus, and Bacillus altitudinis (Figure 4) ...

This tool converts genome coordinates and genome annotation files between assemblies. The input data can be pasted into the text box or uploaded from a file. For more information, please see our LiftOver documentation. If a pair of assemblies cannot be selected from the pull-down menus, a sequential lift may still be possible. For example, to lift from mm9 to mm39, lift from Mouse mm9 to mm10 and then from mm10 to mm39 ...

This tool converts genome coordinates and genome annotation files between assemblies. The input data can be pasted into the text box or uploaded from a file. For more information, please see our LiftOver documentation. If a pair of assemblies cannot be selected from the pull-down menus, a sequential lift may still be possible. For example, to lift from mm9 to mm39, lift from Mouse mm9 to mm10 and then from mm10 to mm39 ...