MD-P001
Ontology highlight
ABSTRACT:
PROVIDER: PRJEB26558 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:Herein, we evaluated the changes in biological functions in soils across global biomes through the identification and quantification of proteins. This knowledge is essential to provide one stepforward in soil microbial ecology in order to decipher the cellular and molecular mechanisms employed by soil microbial communities to adapt to their environment and to explain the potential responses of microbial communities, and their microbially-driven ecosystem services, to global change and land use. Our study aims to provide the most comprehensive assessment on the structure and function of the topsoil metaproteome across global biomes, and hence provide direct identification of the most domimant protein-encoded functions in terrestrial ecosystems.
2023-02-24 | PXD018448 | Pride
Project description:Next-Generation-Sequencing (NGS) technologies have led to important improvement in the detection of new or unrecognized infective agents, related to infectious diseases. In this context, NGS high-throughput technology can be used to achieve a comprehensive and unbiased sequencing of the nucleic acids present in a clinical sample (i.e. tissues). Metagenomic shotgun sequencing has emerged as powerful high-throughput approaches to analyze and survey microbial composition in the field of infectious diseases. By directly sequencing millions of nucleic acid molecules in a sample and matching the sequences to those available in databases, pathogens of an infectious disease can be inferred. Despite the large amount of metagenomic shotgun data produced, there is a lack of a comprehensive and easy-use pipeline for data analysis that avoid annoying and complicated bioinformatics steps. Here we present HOME-BIO, a modular and exhaustive pipeline for analysis of biological entity estimation, specific designed for shotgun sequenced clinical samples. HOME-BIO analysis provides comprehensive taxonomy classification by querying different source database and carry out main steps in metagenomic investigation. HOME-BIO is a powerful tool in the hand of biologist without computational experience, which are focused on metagenomic analysis. Its easy-to-use intrinsic characteristic allows users to simply import raw sequenced reads file and obtain taxonomy profile of their samples.
2021-07-26 | E-MTAB-9189 | biostudies-arrayexpress
Project description:Next-Generation-Sequencing (NGS) technologies have led to important improvement in the detection of new or unrecognized infective agents, related to infectious diseases. In this context, NGS high-throughput technology can be used to achieve a comprehensive and unbiased sequencing of the nucleic acids present in a clinical sample (i.e. tissues). Metagenomic shotgun sequencing has emerged as powerful high-throughput approaches to analyze and survey microbial composition in the field of infectious diseases. By directly sequencing millions of nucleic acid molecules in a sample and matching the sequences to those available in databases, pathogens of an infectious disease can be inferred. Despite the large amount of metagenomic shotgun data produced, there is a lack of a comprehensive and easy-use pipeline for data analysis that avoid annoying and complicated bioinformatics steps. Here we present HOME-BIO, a modular and exhaustive pipeline for analysis of biological entity estimation, specific designed for shotgun sequenced clinical samples. HOME-BIO analysis provides comprehensive taxonomy classification by querying different source database and carry out main steps in metagenomic investigation. HOME-BIO is a powerful tool in the hand of biologist without computational experience, which are focused on metagenomic analysis. Its easy-to-use intrinsic characteristic allows users to simply import raw sequenced reads file and obtain taxonomy profile of their samples.
2021-07-26 | E-MTAB-9191 | biostudies-arrayexpress
Project description:Considering the recent advances in LC and mPSM methodologies, we aimed to provide a systematic comparison and in-depth analysis of the effects that standard and specialized LC setups, as well as different isolation windows, have on the occurrence and assignment of chimeric spectra derived from a complex peptide mixture. To this end, we provide a comprehensive analysis of how these factors influence the chromatographic resolution, isolation interference, and the subsequent identification of MS/MS spectra by a traditional or mPSM algorithm.
2019-05-20 | PXD012635 | Pride
Project description:Lake trout (Salvelinus namaycush) are a top-predator species in the Laurentian Great Lakes that are often used as bioindicators of chemical stressors in the ecosystem. Although many studies are done using these fish to determine concentrations of stressors like legacy persistent, bioaccumulative and toxic chemicals, there are currently no proteomic studies on the biological effects these stressors have on the ecosystem. This lack of proteomic studies on Great Lakes lake trout is because there is currently no complete, comprehensive protein database for this species. In this research, we aimed to use proteomic methods and established protein databases from NCBI and UniProtKB to identify potential proteins in the lake trout species. The current study utilized heart tissue and blood from two separate lake trout. Our previous published work on the lake trout liver revealed 4,194 potential protein hits in the NCBI databases and 3,811 potential protein hits in the UniProtKB databases. In the current study, using the NCBI databases we identified 838 potential protein hits for the heart and 580 potential protein hits for the blood of the first lake trout (biological replicate 1). In the second lake trout (biological replicate 2), using the NCBI databases we identified 1,180 potential protein hits for the heart and 561 potential protein hits for the blood. Similar results were obtained using the UniProtKB databases. This study builds on our previous work by continuing to build the first comprehensive lake trout protein database. Through this investigation, we are also able to make observations as to protein homology through evolutionary relationships.
2021-12-30 | PXD023970 | Pride
Project description:General principles for assignment of communities from eDNA: open versus closed taxonomic databases
| PRJEB57749 | ENA
Project description:Senger2008 - Genome-scale metabolic network
of Clostridium acetobutylicum (iCac802)
This model is described in the article:
Genome-scale model for
Clostridium acetobutylicum: Part I. Metabolic network
resolution and analysis.
Senger RS, Papoutsakis ET.
Biotechnol. Bioeng. 2008 Dec; 101(5):
1036-1052
Abstract:
A genome-scale metabolic network reconstruction for
Clostridium acetobutylicum (ATCC 824) was carried out using a
new semi-automated reverse engineering algorithm. The network
consists of 422 intracellular metabolites involved in 552
reactions and includes 80 membrane transport reactions. The
metabolic network illustrates the reliance of clostridia on the
urea cycle, intracellular L-glutamate solute pools, and the
acetylornithine transaminase for amino acid biosynthesis from
the 2-oxoglutarate precursor. The semi-automated reverse
engineering algorithm identified discrepancies in reaction
network databases that are major obstacles for fully automated
network-building algorithms. The proposed semi-automated
approach allowed for the conservation of unique clostridial
metabolic pathways, such as an incomplete TCA cycle. A
thermodynamic analysis was used to determine the physiological
conditions under which proposed pathways (e.g., reverse partial
TCA cycle and reverse arginine biosynthesis pathway) are
feasible. The reconstructed metabolic network was used to
create a genome-scale model that correctly characterized the
butyrate kinase knock-out and the asolventogenic M5 pSOL1
megaplasmid degenerate strains. Systematic gene knock-out
simulations were performed to identify a set of genes encoding
clostridial enzymes essential for growth in silico.
This model is hosted on
BioModels Database
and identified by:
MODEL1507180027.
To cite BioModels Database, please use:
BioModels Database:
An enhanced, curated and annotated resource for published
quantitative kinetic models.
To the extent possible under law, all copyright and related or
neighbouring rights to this encoded model have been dedicated to
the public domain worldwide. Please refer to
CC0
Public Domain Dedication for more information.
2015-07-30 | MODEL1507180027 | BioModels
Project description:The tumor ecosystem of papillary thyroid carcinoma (PTC) is poorly characterized. Using single-cell RNA sequencing, we profiled transcriptomes of 158,577 cells from 11 patients’ paratumors, localized/advanced tumors, initially-treated/recurrent lymph nodes and radioactive iodine (RAI)-refractory distant metastases, covering comprehensive clinical courses of PTC.Our study illuminates a comprehensive landscape of PTC ecosystem that suggests potential prognostic and therapeutic implications. Our work not only adds dimensions to the biological underpinnings of PTC heterogeneity, but also provides a benchmark dataset for this malignancy.
2021-09-19 | GSE184362 | GEO
Project description:<p><strong>BACKGROUND:</strong> The human intestinal microbiome plays a central role in overall health status, especially in early life stages. 16S rRNA amplicon sequencing is used to profile its taxonomic composition; however, multiomic approaches have been proposed as the most accurate methods for study of the complexity of the gut microbiota. In this study, we propose an optimized method for bacterial diversity analysis that we validated and complemented with metabolomics by analyzing fecal samples.</p><p><strong>METHODS:</strong> Forty-eight different analytical combinations regarding (1) 16S rRNA variable region sequencing, (2) a feature selection approach, and (3) taxonomy assignment methods were tested. A total of 18 infant fecal samples grouped depending on the type of feeding were analyzed by the proposed 16S rRNA workflow and by metabolomic analysis.</p><p><strong>RESULTS:</strong> The results showed that the sole use of V4 region sequencing with ASV identification and VSEARCH for taxonomy assignment produced the most accurate results. The application of this workflow showed clear differences between fecal samples according to the type of feeding, which correlated with changes in the fecal metabolic profile.</p><p><strong>CONCLUSION:</strong> A multiomic approach using real fecal samples from 18 infants with different types of feeding demonstrated the effectiveness of the proposed 16S rRNA-amplicon sequencing workflow.</p>
2021-08-18 | MTBLS2942 | MetaboLights
Project description:Mammalian feces can be collected non-invasively during field research and provides valuable information on the ecology and evolution of the host individuals. Undigested food objects, genome/metagenome, steroid hormones, and stable isotopes obtained from fecal samples provide evidence on diet, host/symbiont genetics, and physiological status of the individuals. However, proteins in mammalian feces have hardly been studied, which hampers the molecular investigations into the behavior and physiology of the host individuals. Here, we apply mass spectrometry-based proteomics to fecal samples (n = 10) that were collected from infant, juvenile, and adult captive Japanese macaques (Macaca fuscata) to describe the proteomes of the host, food, and intestinal microbes. The results show that fecal proteomics is a useful method to investigate dietary changes along with breastfeeding and weaning, to reveal the organ/tissue and taxonomy of dietary items, and to estimate physiological status inside intestinal tracts. These types of insights are difficult or impossible to obtain through other molecular approaches. Most mammalian species are facing extinction risk and there is an urgent need to obtain knowledge on their ecology and evolution for better conservation strategy. The fecal proteomics framework we present here is easily applicable to wild settings and other mammalian species, and provides direct evidence of their behavior and physiology.
2021-03-18 | PXD021098 | Pride