A database for phylogenetic classification for proteins encoded in complete genomes. Clusters of Orthologous Groups of proteins (COGs) were delineated by comparing protein sequences encoded in complete genomes, representing major phylogenetic lineages. Each COG consists of individual proteins or groups of paralogs from at least 3 lineages and thus corresponds to an ancient conserved domain. Please be aware that COGs hasn't been updated in many years and will not be.

Background: Standard archival sequence databases have not been designed as tools for genome annotation and are far from being optimal for this purpose. We used the database of Clusters of Orthologous Groups of proteins (COGs) to reannotate the genomes of two archaea, Aeropyrum pernix, the first member of the Crenarchaea to be sequenced, and Pyrococcus abyssi. Results: A. pernix and P. abyssi proteins were assigned to COGs using the COGNITOR program; the results were verified on a case-by-case basis and augmented by additional database searches using the PSI-BLAST and TBLASTN programs. Functions were predicted for over 300 proteins from A. pernix, which could not be assigned a function using conventional methods with a conservative sequence similarity threshold, an approximately 50% increase compared to the original annotation. A. pernix shares most of the conserved core of proteins that were previously identified in the Euryarchaeota. Cluster analysis or distance matrix tree construction based on the co-occurrence of genomes in COGs showed that A. pernix forms a distinct group within the archaea, although grouping with the two species of Pyrococci, indicative of similar repertoires of conserved genes, was observed. No indication of a specific relationship between Crenarchaeota and eukaryotes was obtained in these analyses. Several proteins that are conserved in Euryarchaeota and most bacteria are unexpectedly missing in A. pernix, including the entire set of de novo purine biosynthesis enzymes, the GTPase FtsZ (a key component of the bacterial and euryarchaeal cell-division machinery), and the tRNA-specific pseudouridine synthase, previously considered universal. A. pernix is represented in 48 COGs that do not contain any euryarchaeal members. Many of these proteins are TCA cycle and electron transport chain enzymes, reflecting the aerobic lifestyle of A. pernix. Conclusions: Special-purpose databases organized on the basis of phylogenetic analysis and carefully curated with respect to known and predicted protein functions provide for a significant improvement in genome annotation. A differential genome display approach helps in a systematic investigation of common and distinct features of gene repertoires and in some cases reveals unexpected connections that may be indicative of functional similarities between phylogenetically distant organisms and of lateral gene exchange.

Background: The Serratia plymuthica UBCF_13 is a phylloplane associated plant bacterium showing antifungal activity. Whole genome sequence provides information to get more insight about evolutionary study, unique traits in the genome and possibility to explore potential of this microorganism for future study. Here, we report the genome sequence of S. plymuthica UBCF_13 and the comparison with other seventeen strain.

Methods: Continuous short reads were attained from Illumina sequencing runs and reads of 150 bp were merged into a single dataset. A pan-genome based method was used to identify the core-genome of S. plymuthica species and the unique gene in UBCF-13.

Results: Assembled Illumina reads of S. plymuthica strain UBCF_13 genome was produced a 5.46 Mb circular genome sequence. 3315 genes were found to belong to the core-genome sheared by the 18 strains evaluated. The UBCF_13 genome harbors 488 unique genes, where 300 of which only can be found in this strain. The raw and assemble...

THIS RESOURCE IS NO LONGER IN SERVICE, documented on August 20,2019.The COG-database has become a powerful tool in the field of comparative genomics. The construction of this data-base is based on sequence homologies of proteins from different completely sequenced genomes. Highly homologous proteins are assigned to clusters of orthologous groups. The updated collection of orthologous protein sets for prokaryotes and eukaryotes is expected to be a useful platform for functional annotation of newly sequenced genomes, including those of complex eukaryotes, and genome-wide evolutionary studies. The availability of multiple, essentially complete genome sequences of prokaryotes and eukaryotes spurred both the demand and the opportunity for the construction of an evolutionary classification of genes from these genomes. Such a classification system based on orthologous relationships between genes appears to be a natural framework for comparative genomics and should facilitate both functional annotation of genomes and large-scale evolutionary studies. Here is a major update of the previously developed system for delineation of Clusters of Orthologous Groups of proteins (COGs) from the sequenced genomes of prokaryotes and unicellular eukaryotes and the construction of clusters of predicted orthologs for 7 eukaryotic genomes, which we named KOGs after eukaryotic orthologous groups. The COG collection currently consists of 138,458 proteins, which form 4873 COGs and comprise 75% of the 185,505 (predicted) proteins encoded in 66 genomes of unicellular organisms. The eukaryotic orthologous groups (KOGs) include proteins from 7 eukaryotic genomes: three animals (the nematode Caenorhabditis elegans, the fruit fly Drosophila melanogaster and Homo sapiens), one plant, Arabidopsis thaliana, two fungi (Saccharomyces cerevisiae and Schizosaccharomyces pombe), and the intracellular microsporidian parasite Encephalitozoon cuniculi. The current KOG set consists of 4852 clusters of orthologs, which include 59,838 proteins, or approximately 54% of the analyzed eukaryotic 110,655 gene products. Compared to the coverage of the prokaryotic genomes with COGs, a considerably smaller fraction of eukaryotic genes could be included into the KOGs; addition of new eukaryotic genomes is expected to result in substantial increase in the coverage of eukaryotic genomes with KOGs. Examination of the phyletic patterns of KOGs reveals a conserved core represented in all analyzed species and consisting of approximately 20% of the KOG set. This conserved portion of the KOG set is much greater than the ubiquitous portion of the COG set (approximately 1% of the COGs). In part, this difference is probably due to the small number of included eukaryotic genomes, but it could also reflect the relative compactness of eukaryotes as a clade and the greater evolutionary stability of eukaryotic genomes.

COGs stands for Clusters of Orthologous Genes. The database was initially created in 1997 (Tatusov et al., PMID: 9381173) followed by several updates, most recently in 2014 (Galperin et al., PMID: 25428365). The current update includes complete genomes of 1,187 bacteria and 122 archaea that map into 1,234 genera. The new features include ~250 updated COG annotations with corresponding references and PDB links, where available; new COGs for proteins involved in CRISPR-Cas immunity, sporulation, and photosynthesis, and the lists of COGs grouped by pathways and functional systems.

To identify clusters of orthologous genes (COGs) that correlate with nutrient limitation in the modern ocean, we examined the Ocean Microbial Reference Catalog v2 (OM-RGC.v2) from the Tara Oceans Project. The OM-RGC.v2 includes relative gene abundances of all COGs (n = 4,787) in 139 Tara Oceans metagenomic samples, along with metadata information including phosphate, oxygen, and nitrate/nitrite concentrations. (Nitrate/nitrite values were reported together for OM-RGC v2.) Iron concentrations for Tara Oceans samples were not available and were thus estimated using the PISCES2 model based on iron concentration model predictions for Tara Oceans sampling locations as described in Table S1 of Caputi et al., 2019. Iron concentrations were predicted for surface and the deep chlorophyll maximum (DCM) only; iron concentrations for samples from the mesopelagic zone were not available under the PISCES2 model. All other metadata for Tara Oceans samples were directly obtained from Salazar et al., 2019.Estimation of correlations between COGs and metadata information was performed using regression models. Compound poisson linear models were fitted in bulk using the MaAsLin2 software package (v. 1.18.0). Separate models were fit for each COG to analyze the effect of metadata variables on individual COG abundances. While the main focus was to investigate correlation with nutrient abundance, environmental metadata was included in the model to control for as many potential confounding effects as the data allowed. The following predictors were included in the final model (based on variables available from the Tara Oceans dataset): the size fraction at which the sample was taken, mean temperature, depth, salinity, mean oxygen concentration, PO4 concentration, NO2 + NO3 concentration, iron concentration, and absolute latitude. Of these, the following predictors were log-transformed to allow greater model fit: depth, PO4 concentration, NO2 + NO3 concentration. To the same end, the iron concentration was transformed by taking the square root, and the absolute value of the latitude was taken. Otherwise, no transformations or normalization was performed. No abundance cutoff was applied, but COGs present in less than one-third of the Tara Oceans samples were discarded in order to ensure that the COGs identified by the statistical model were meaningful.

Master tally sheet of the total curation process of generating a new list of COGs representative of gene/protein families involved in tRNA modifications as per published gene-/protein-modification pairs curated from the literature. Original COG Pathway list (via the COG Database, June 2022) contained 59 COGs; the final list (see other Object, namely 4-S3) totalled 89 COGs, 52 retained from the original list and 37 were added to contribute to the new list. Of the original 59, 7 were removed.

This dataset contains the predicted prices of the asset Cogs over the next 16 years. This data is calculated initially using a default 5 percent annual growth rate, and after page load, it features a sliding scale component where the user can then further adjust the growth rate to their own positive or negative projections. The maximum positive adjustable growth rate is 100 percent, and the minimum adjustable growth rate is -100 percent.

Dataset

This dataset was created by Ayoub Hammal

Contents

The towns of Connecticut (CT) Parcels and Computer-Assisted Mass Appraisal (CAMA) data for 2022 are part of a zipped file containing two items: CT parcels in geodatabases organized by COGs and associated CAMA files.

The parcel information includes 169 out of 169 town organized with geodatabases for each of the 9 Council of Governments. Most of the parcel data sets can be linked to the CAMA data which has attribute information (e.g. value of house, number of bedrooms) about real property. The parcel features for each town are in shape files, feature classes, or within a geodatabase. Most parcels are organized by town and COG and placed within a geodatabases.

The CAMA data sets have information about real property within the towns of CT. It may be linked to the parcels using a join process within a GIS package like ArcGIS Pro or QGIS. 154 out of 169 towns have complete CAMA information. Of the remaining 15 towns, four have no information and the remaining have some limited information mixed into the parcel attribute tables. These files were gathered from the CT towns by the COGs and then submitted to CT OPM. Town data is organized by COG. Attribute names, primary key, secondary key, naming conventions, and file formats are not fully consistent but some cleaning and reorganization was conducted to improve quality. This file was created on 03/08/2023 from data collected in 2021-2022.

Cogs-Excluding-Depreciation-and-Amortization Time Series for Amazon.com Inc. Amazon.com, Inc. engages in the retail sale of consumer products, advertising, and subscriptions service through online and physical stores in North America and internationally. The company operates through three segments: North America, International, and Amazon Web Services (AWS). It also manufactures and sells electronic devices, including Kindle, fire tablets, fire TVs, echo, ring, blink, and eero; and develops and produces media content. In addition, the company offers programs that enable sellers to sell their products in its stores; and programs that allow authors, independent publishers, musicians, filmmakers, Twitch streamers, skill and app developers, and others to publish and sell content. Further, it provides compute, storage, database, analytics, machine learning, and other services, as well as advertising services through programs, such as sponsored ads, display, and video advertising. Additionally, the company offers Amazon Prime, a membership program. The company's products offered through its stores include merchandise and content purchased for resale and products offered by third-party sellers. It also provides AgentCore services, such as AgentCore Runtime, AgentCore Memory, AgentCore Observability, AgentCore Identity, AgentCore Gateway, AgentCore Browser, and AgentCore Code Interpreter. It serves consumers, sellers, developers, enterprises, content creators, advertisers, and employees. Amazon.com, Inc. was incorporated in 1994 and is headquartered in Seattle, Washington.

Cogs-Excluding-Depreciation-and-Amortization Time Series for EOG Resources Inc. EOG Resources, Inc., together with its subsidiaries, explores for, develops, produces, and markets crude oil, natural gas liquids, and natural gas in producing basins in the United States, the Republic of Trinidad and Tobago, and internationally. The company was formerly known as Enron Oil & Gas Company. EOG Resources, Inc. was incorporated in 1985 and is headquartered in Houston, Texas.

250 Global exporters importers export import shipment records of Cogs toy with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

Cogs-Excluding-Depreciation-and-Amortization Time Series for Quest Diagnostics Incorporated. Quest Diagnostics Incorporated provides diagnostic testing and services in the United States and internationally. The company develops and delivers diagnostic information services, such as routine, non-routine and advanced clinical testing, anatomic pathology testing, and other diagnostic information services. It also provides diagnostic information services primarily under the Quest Diagnostics brand, as well as under the AmeriPath, Dermpath Diagnostics, ExamOne, and Quanum brands to physicians, hospitals, patients and consumers, health plans, government agencies, employers, emerging retail healthcare providers, pharmaceutical companies and insurers, commercial clinical laboratories, and accountable care organizations through a network of laboratories, patient service centers, phlebotomists in physician offices, call centers and mobile phlebotomists, nurses, and other health and wellness professionals. In addition, the company offers risk assessment services for the life insurance industry; and healthcare organizations and clinicians information technology solutions. Quest Diagnostics Incorporated was founded in 1967 and is headquartered in Secaucus, New Jersey.

Worksheet includes the mapping of both tRNA modification-relevant and -irrelevant K numbers to their respective overlapping COGs. Representative sequences of Object 4-S2 informed overlap at the sequence-level, maintaining the theme of data being generated and curated corresponding to support provided by published data. Additional tabs include the same data with expanded names as well as other KEGG K number and representative sequence entry-sourced data (e.g., EC numbers).

This data release reformats data fields from the STATSGO soil characteristics dataset (Schwarz and Alexander, 1995) as cloud-optimized GeoTIFFs (COGs). The COG format allows standard software tools to efficiently access the datasets over an internet connection. Please refer to the documentation of the source archive (Schwarz and Alexander, 1995) for additional details on the underlying dataset. This data release includes COGs for the KF-factor (KFFACT) and soil thickness (THICK) data fields. KF-factors are defined as the saturated hydraulic conductivity of the fine soil (< 2mm) fraction in units of inches per hour. The soil thickness dataset has units of inches. Each COG raster spans the continental US at a nominal 30 meter resolution. The spatial reference is EPSG:5069. Each COG uses a float32 precision, and the NoData value (NaN) indicates raster pixels not covered by the original STATSGO dataset. The COGs also include non-physical values of -0.1, which were used by the source STATSGO archive to mark large water bodies. The COG format uses compression internally to reduce file size. As such, reading large portions of a COG into memory can require much more RAM than the nominal file size. The COGs in this dataset will require ~60GB of memory to read in full. This dataset can be reproduced by running the included rasterize_statsgo.py Python script. Please refer to the script for documentation and usage instructions. Disclaimer: Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government. References: Schwarz, G.E. and Alexander, R.B., 1995, Soils data for the Conterminous United States Derived from the NRCS State Soil Geographic (STATSGO) Data Base. [Original title: State Soil Geographic (STATSGO) Data Base for the Conterminous United States.]: U.S. Geological Survey data release, https://doi.org/10.5066/P94JAULO.

Absence-presence data of tRNA modification relevant COGs and KEGG Orthology K numbers across the genomes shared between the original exports of Object 4-S3 and 4-S4. Normalization completed based upon tax ID overlap between the two lists.

Statistics of DEG of coYP versus coGS.

Cogs-Excluding-Depreciation-and-Amortization Time Series for GeneDx Holdings Corp.. GeneDx Holdings Corp., a genomics company, provides genetic testing services. It primarily offers pediatric and rare disease diagnostics with a focus on whole exome and genome sequencing, as well as data and information services. The company also develops an AI-based platform for NGS analysis, interpretation, and clinical reporting for rare disease, hereditary risk, and cancer testing. The company is headquartered in Stamford, Connecticut.

Councils of Government (COG) boundaries for the state of Oklahoma.