Engineering Disulfide Cross‐Links in RNA Via Air Oxidation

This unit presents protocols for the synthesis of alkylthiol‐modified ribonucleosides, their incorporation into synthetic RNA, and the formation of intramolecular disulfide bonds in RNA by air oxidation. The disulfide bonds can be formed in quantitative yields between thiols positioned in close proximity by virtue of either the secondary or tertiary structure of the RNA. Disulfide cross‐links are useful tools to probe solution structures of RNA, to monitor dynamic motion, to stabilize folded RNAs, and to study the process of tertiary structure folding.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/143806/1/cpnc0504.pd

NCBI reference sequences (RefSeq): a curated non-redundant sequence database of genomes, transcripts and proteins

NCBI's reference sequence (RefSeq) database () is a curated non-redundant collection of sequences representing genomes, transcripts and proteins. The database includes 3774 organisms spanning prokaryotes, eukaryotes and viruses, and has records for 2 879 860 proteins (RefSeq release 19). RefSeq records integrate information from multiple sources, when additional data are available from those sources and therefore represent a current description of the sequence and its features. Annotations include coding regions, conserved domains, tRNAs, sequence tagged sites (STS), variation, references, gene and protein product names, and database cross-references. Sequence is reviewed and features are added using a combined approach of collaboration and other input from the scientific community, prediction, propagation from GenBank and curation by NCBI staff. The format of all RefSeq records is validated, and an increasing number of tests are being applied to evaluate the quality of sequence and annotation, especially in the context of complete genomic sequence

Entrez Gene: gene-centered information at NCBI

Entrez Gene () is NCBI's database for gene-specific information. Entrez Gene includes records from genomes that have been completely sequenced, that have an active research community to contribute gene-specific information or that are scheduled for intense sequence analysis. The content of Entrez Gene represents the result of both curation and automated integration of data from NCBI's Reference Sequence project (RefSeq), from collaborating model organism databases and from other databases within NCBI. Records in Entrez Gene are assigned unique, stable and tracked integers as identifiers. The content (nomenclature, map location, gene products and their attributes, markers, phenotypes and links to citations, sequences, variation details, maps, expression, homologs, protein domains and external databases) is provided via interactive browsing through NCBI's Entrez system, via NCBI's Entrez programing utilities (E-Utilities), and for bulk transfer by ftp

Entrez Gene: gene-centered information at NCBI

Entrez Gene (http://www.ncbi.nlm.nih.gov/gene) is National Center for Biotechnology Information (NCBI)’s database for gene-specific information. Entrez Gene maintains records from genomes which have been completely sequenced, which have an active research community to submit gene-specific information, or which are scheduled for intense sequence analysis. The content represents the integration of curation and automated processing from NCBI’s Reference Sequence project (RefSeq), collaborating model organism databases, consortia such as Gene Ontology and other databases within NCBI. Records in Entrez Gene are assigned unique, stable and tracked integers as identifiers. The content (nomenclature, genomic location, gene products and their attributes, markers, phenotypes and links to citations, sequences, variation details, maps, expression, homologs, protein domains and external databases) is available via interactive browsing through NCBI’s Entrez system, via NCBI’s Entrez programming utilities (E-Utilities) and for bulk transfer by FTP

NCBI Reference Sequences (RefSeq): current status, new features and genome annotation policy

The National Center for Biotechnology Information (NCBI) Reference Sequence (RefSeq) database is a collection of genomic, transcript and protein sequence records. These records are selected and curated from public sequence archives and represent a significant reduction in redundancy compared to the volume of data archived by the International Nucleotide Sequence Database Collaboration. The database includes over 16 000 organisms, 2.4 × 106 genomic records, 13 × 106 proteins and 2 × 106 RNA records spanning prokaryotes, eukaryotes and viruses (RefSeq release 49, September 2011). The RefSeq database is maintained by a combined approach of automated analyses, collaboration and manual curation to generate an up-to-date representation of the sequence, its features, names and cross-links to related sources of information. We report here on recent growth, the status of curating the human RefSeq data set, more extensive feature annotation and current policy for eukaryotic genome annotation via the NCBI annotation pipeline. More information about the resource is available online (see http://www.ncbi.nlm.nih.gov/RefSeq/)

NCBI Reference Sequences: current status, policy and new initiatives

NCBI's Reference Sequence (RefSeq) database (http://www.ncbi.nlm.nih.gov/RefSeq/) is a curated non-redundant collection of sequences representing genomes, transcripts and proteins. RefSeq records integrate information from multiple sources and represent a current description of the sequence, the gene and sequence features. The database includes over 5300 organisms spanning prokaryotes, eukaryotes and viruses, with records for more than 5.5 × 106 proteins (RefSeq release 30). Feature annotation is applied by a combination of curation, collaboration, propagation from other sources and computation. We report here on the recent growth of the database, recent changes to feature annotations and record types for eukaryotic (primarily vertebrate) species and policies regarding species inclusion and genome annotation. In addition, we introduce RefSeqGene, a new initiative to support reporting variation data on a stable genomic coordinate system

Human immunodeficiency virus type 1, human protein interaction database at NCBI

The ‘Human Immunodeficiency Virus Type 1 (HIV-1), Human Protein Interaction Database’, available through the National Library of Medicine at www.ncbi.nlm.nih.gov/RefSeq/HIVInteractions, was created to catalog all interactions between HIV-1 and human proteins published in the peer-reviewed literature. The database serves the scientific community exploring the discovery of novel HIV vaccine candidates and therapeutic targets. To facilitate this discovery approach, the following information for each HIV-1 human protein interaction is provided and can be retrieved without restriction by web-based downloads and ftp protocols: Reference Sequence (RefSeq) protein accession numbers, Entrez Gene identification numbers, brief descriptions of the interactions, searchable keywords for interactions and PubMed identification numbers (PMIDs) of journal articles describing the interactions. Currently, 2589 unique HIV-1 to human protein interactions and 5135 brief descriptions of the interactions, with a total of 14 312 PMID references to the original articles reporting the interactions, are stored in this growing database. In addition, all protein–protein interactions documented in the database are integrated into Entrez Gene records and listed in the ‘HIV-1 protein interactions’ section of Entrez Gene reports. The database is also tightly linked to other databases through Entrez Gene, enabling users to search for an abundance of information related to HIV pathogenesis and replication

Transmembrane Protein Oxygen Content and Compartmentalization of Cells

Recently, there was a report that explored the oxygen content of transmembrane proteins over macroevolutionary time scales where the authors observed a correlation between the geological time of appearance of compartmentalized cells with atmospheric oxygen concentration. The authors predicted, characterized and correlated the differences in the structure and composition of transmembrane proteins from the three kingdoms of life with atmospheric oxygen concentrations in geological timescale. They hypothesized that transmembrane proteins in ancient taxa were selectively excluding oxygen and as this constraint relaxed over time with increase in the levels of atmospheric oxygen the size and number of communication-related transmembrane proteins increased. In summary, they concluded that compartmentalized and non-compartmentalized cells can be distinguished by how oxygen is partitioned at the proteome level. They derived this conclusion from an analysis of 19 taxa. We extended their analysis on a larger sample of taxa comprising 309 eubacterial, 34 archaeal, and 30 eukaryotic complete proteomes and observed that one can not absolutely separate the two groups of cells based on partition of oxygen in their membrane proteins. In addition, the origin of compartmentalized cells is likely to have been driven by an innovation than happened 2700 million years ago in the membrane composition of cells that led to the evolution of endocytosis and exocytosis rather than due to the rise in concentration of atmospheric oxygen

Automatic Assignment of EC Numbers

A wide range of research areas in molecular biology and medical biochemistry require a reliable enzyme classification system, e.g., drug design, metabolic network reconstruction and system biology. When research scientists in the above mentioned areas wish to unambiguously refer to an enzyme and its function, the EC number introduced by the Nomenclature Committee of the International Union of Biochemistry and Molecular Biology (IUBMB) is used. However, each and every one of these applications is critically dependent upon the consistency and reliability of the underlying data for success. We have developed tools for the validation of the EC number classification scheme. In this paper, we present validated data of 3788 enzymatic reactions including 229 sub-subclasses of the EC classification system. Over 80% agreement was found between our assignment and the EC classification. For 61 (i.e., only 2.5%) reactions we found that their assignment was inconsistent with the rules of the nomenclature committee; they have to be transferred to other sub-subclasses. We demonstrate that our validation results can be used to initiate corrections and improvements to the EC number classification scheme

A gene signature for post-infectious chronic fatigue syndrome

Background: At present, there are no clinically reliable disease markers for chronic fatigue syndrome. DNA chip microarray technology provides a method for examining the differential expression of mRNA from a large number of genes. Our hypothesis was that a gene expression signature, generated by microarray assays, could help identify genes which are dysregulated in patients with post-infectious CFS and so help identify biomarkers for the condition. Methods: Human genome-wide Affymetrix GeneChip arrays (39,000 transcripts derived from 33,000 gene sequences) were used to compare the levels of gene expression in the peripheral blood mononuclear cells of male patients with post-infectious chronic fatigue (n = 8) and male healthy control subjects (n = 7). Results: Patients and healthy subjects differed significantly in the level of expression of 366 genes. Analysis of the differentially expressed genes indicated functional implications in immune modulation, oxidative stress and apoptosis. Prototype biomarkers were identified on the basis of differential levels of gene expression and possible biological significance Conclusion: Differential expression of key genes identified in this study offer an insight into the possible mechanism of chronic fatigue following infection. The representative biomarkers identified in this research appear promising as potential biomarkers for diagnosis and treatment