scholarly journals SGID: a comprehensive and interactive database of the silkworm

2019 ◽  
Author(s):  
Zhenglin Zhu ◽  
Zhufen Guan ◽  
Gexin Liu ◽  
Yawang Wang ◽  
Ze Zhang
Keyword(s):  
Tertiary Structure ◽  
Subcellular Location ◽  
Biological Data ◽  
Test Results ◽  
Function Annotation ◽  
Interactive Analysis ◽  
Silkworm Genome ◽  
Genome Scale ◽  
Genome Informatics ◽  
High Depth

AbstractAlthough the domestic silkworm (Bombyx mori) is an important model and economic animal, there is a lack of comprehensive database for this organism. Here, we developed the silkworm genome informatics database, SGID. It aims to bring together all silkworm related biological data and provide an interactive platform for gene inquiry and analysis. The function annotation in SGID is thorough and covers 98% of the silkworm genes. The annotation details include function description, gene ontology, KEGG, pathway, subcellular location, transmembrane topology, protein secondary/tertiary structure, homologous group and transcription factor. SGID provides genome scale visualization of population genetics test results based on high depth resequencing data of 158 silkworm samples. It also provides interactive analysis tools of transcriptomic and epigenomic data from 79 NCBI BioProjects. SGID is freely available at http://sgid.popgenetics.net. This database will be extremely useful to silkworm research in the future.

scholarly journals SGID: a comprehensive and interactive database of the silkworm

Database ◽  
2019 ◽  
Vol 2019 ◽  
Author(s):  
Zhenglin Zhu ◽  
Zhufen Guan ◽  
Gexin Liu ◽  
Yawang Wang ◽  
Ze Zhang
Keyword(s):  
Tertiary Structure ◽  
Subcellular Location ◽  
Biological Data ◽  
Test Results ◽  
Function Annotation ◽  
Interactive Analysis ◽  
Silkworm Genome ◽  
Genome Scale ◽  
Genome Informatics ◽  
High Depth

Abstract Although the domestic silkworm (Bombyx mori) is an important model and economic animal, there is a lack of comprehensive database for this organism. Here, we developed the silkworm genome informatics database (SGID). It aims to bring together all silkworm-related biological data and provide an interactive platform for gene inquiry and analysis. The function annotation in SGID is thorough and covers 98% of the silkworm genes. The annotation details include function description, Gene Ontology, Kyoto Encyclopedia of Genes and Genomes pathway, subcellular location, transmembrane topology, protein secondary/tertiary structure, homologous group and transcription factor. SGID provides genome-scale visualization of population genetics test results based on high-depth resequencing data of 158 silkworm samples. It also provides interactive analysis tools of transcriptomic and epigenomic data from 79 NCBI BioProjects. SGID will be extremely useful to silkworm research in the future.

Implementation for High-Precision and Non-Contact System for the Measurement of Crankshaft and Camshaft

2011 ◽  
Vol 301-303 ◽  
pp. 959-964 ◽  
Author(s):  
Da Lin Cheng ◽  
Yi Wang ◽  
Yong Jie Ren ◽  
Xue You Yang
Keyword(s):  
Standard Deviation ◽  
High Precision ◽  
Measurement System ◽  
General Structure ◽  
Contact System ◽  
Scanning Laser ◽  
Test Results ◽  
Line Measurement ◽  
On Line ◽  
High Depth

A novel crankshaft and camshaft measurement system by optoelectronic scanning of which a flat-crystal was used to generate high depth of parallelism scanning laser was implemented. The general structure and principle were given. Mass of test results showed that the system could achieve high precision. The ranges could achieve ±8μm, standard deviation could achieve 3μm, and easy to operate, work reliably, automatically and on line measurement could be implemented.

scholarly journals Genome-scale phylogenetic function annotation of large and diverse protein families

2011 ◽  
Vol 21 (11) ◽  
pp. 1969-1980 ◽  
Author(s):  
B. E. Engelhardt ◽  
M. I. Jordan ◽  
J. R. Srouji ◽  
S. E. Brenner
Keyword(s):  
Protein Families ◽  
Function Annotation ◽  
Genome Scale

scholarly journals Quantifying cumulative phenotypic and genomic evidence for procedural generation of metabolic network reconstructions

2021 ◽  
Author(s):  
Thomas James Moutinho ◽  
Benjamin C Neubert ◽  
Matthew L Jenior ◽  
Jason A. Papin
Keyword(s):  
Metabolic Network ◽  
Metabolic Networks ◽  
Biological Data ◽  
Supporting Evidence ◽  
Phenotypic Data ◽  
E Coli ◽  
Simultaneous Integration ◽  
Improve Model ◽  
K 12 ◽  
Genome Scale

Genome-scale metabolic network reconstructions (GENREs) are valuable tools for understanding microbial community metabolism. The process of automatically generating GENREs includes identifying metabolic reactions supported by sufficient genomic evidence to generate a draft metabolic network. The draft GENRE is then gapfilled with additional reactions in order to recapitulate specific growth phenotypes as indicated with associated experimental data. Previous methods have implemented absolute mapping thresholds for the reactions automatically included in draft GENREs; however, there is growing evidence that integrating annotation evidence in a continuous form can improve model accuracy. There is a need for flexibility in the structure of GENREs to better account for uncertainty in biological data, unknown regulatory mechanisms, and context specificity associated with data inputs. To address this issue, we present a novel method that provides a framework for quantifying combined genomic, biochemical, and phenotypic evidence for each biochemical reaction during automated GENRE construction. Our method, Constraint-based Analysis Yielding reaction Usage across metabolic Networks (CANYUNs), generates accurate GENREs with a quantitative metric for the cumulative evidence for each reaction included in the network. The structure of a CANYUN GENRE allows for the simultaneous integration of three data inputs while maintaining all supporting evidence for biochemical reactions that may be active in an organism. CANYUNs is designed to maximize the utility of experimental and annotation datasets and to ultimately assist in the curation of the reference datasets used for the automatic reconstruction of metabolic networks. We validated CANYUNs by generating an E. coli K-12 model and compared it to the manually curated reconstruction iML1515. Finally, we demonstrated the use of CANYUNs to build a model by generating an E. coli Nissle CANYUN GENRE using novel phenotypic data that we collected. This method may address key challenges for the procedural construction of metabolic networks by leveraging uncertainty and redundancy in biological data.

scholarly journals Insights into telomeric G-quadruplex DNA recognition by HMGB1 protein

2019 ◽  
Vol 47 (18) ◽  
pp. 9950-9966 ◽  
Author(s):  
Jussara Amato ◽  
Linda Cerofolini ◽  
Diego Brancaccio ◽  
Stefano Giuntini ◽  
Nunzia Iaccarino ◽  
...  
Keyword(s):  
Biological Effects ◽  
Dna Recognition ◽  
Subcellular Location ◽  
Biological Data ◽  
Telomere Maintenance ◽  
Hmgb1 Protein ◽  
Bent Dna ◽  
Quadruplex Dna ◽  
G Quadruplex ◽  
Negative Tumor

Abstract HMGB1 is a ubiquitous non-histone protein, which biological effects depend on its expression and subcellular location. Inside the nucleus, HMGB1 is engaged in many DNA events such as DNA repair, transcription and telomere maintenance. HMGB1 has been reported to bind preferentially to bent DNA as well as to noncanonical DNA structures like 4-way junctions and, more recently, to G-quadruplexes. These are four-stranded conformations of nucleic acids involved in important cellular processes, including telomere maintenance. In this frame, G-quadruplex recognition by specific proteins represents a key event to modulate physiological or pathological pathways. Herein, to get insights into the telomeric G-quadruplex DNA recognition by HMGB1, we performed detailed biophysical studies complemented with biological analyses. The obtained results provided information about the molecular determinants for the interaction and showed that the structural variability of human telomeric G-quadruplex DNA may have significant implications in HMGB1 recognition. The biological data identified HMGB1 as a telomere-associated protein in both telomerase-positive and -negative tumor cells and showed that HMGB1 gene silencing in such cells induces telomere DNA damage foci. Altogether, these findings provide a deeper understanding of telomeric G-quadruplex recognition by HMGB1 and suggest that this protein could actually represent a new target for cancer therapy.

scholarly journals Comparison of Different Label-Free Techniques for the Semi-Absolute Quantification of Protein Abundance

Proteomes ◽  
2022 ◽  
Vol 10 (1) ◽  
pp. 2
Author(s):  
Aarón Millán-Oropeza ◽  
Mélisande Blein-Nicolas ◽  
Véronique Monnet ◽  
Michel Zivy ◽  
Céline Henry
Keyword(s):  
Shotgun Proteomics ◽  
Absolute Quantification ◽  
Biological Data ◽  
Protein Quantification ◽  
Protein Abundance ◽  
Label Free ◽  
Absolute Abundance ◽  
Yeast Saccharomyces Cerevisiae ◽  
Genome Scale ◽  
Free Quantification

In proteomics, it is essential to quantify proteins in absolute terms if we wish to compare results among studies and integrate high-throughput biological data into genome-scale metabolic models. While labeling target peptides with stable isotopes allow protein abundance to be accurately quantified, the utility of this technique is constrained by the low number of quantifiable proteins that it yields. Recently, label-free shotgun proteomics has become the “gold standard” for carrying out global assessments of biological samples containing thousands of proteins. However, this tool must be further improved if we wish to accurately quantify absolute levels of proteins. Here, we used different label-free quantification techniques to estimate absolute protein abundance in the model yeast Saccharomyces cerevisiae. More specifically, we evaluated the performance of seven different quantification methods, based either on spectral counting (SC) or extracted-ion chromatogram (XIC), which were applied to samples from five different proteome backgrounds. We also compared the accuracy and reproducibility of two strategies for transforming relative abundance into absolute abundance: a UPS2-based strategy and the total protein approach (TPA). This study mentions technical challenges related to UPS2 use and proposes ways of addressing them, including utilizing a smaller, more highly optimized amount of UPS2. Overall, three SC-based methods (PAI, SAF, and NSAF) yielded the best results because they struck a good balance between experimental performance and protein quantification.

scholarly journals ReCodLiver0.9: Overcoming challenges in genome-scale metabolic reconstruction of a non-model species

2020 ◽  
Author(s):  
Eileen Marie Hanna ◽  
Xiaokang Zhang ◽  
Marta Eide ◽  
Shirin Fallahi ◽  
Tomasz Furmanek ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Gadus Morhua ◽  
Metabolic Response ◽  
Atlantic Cod ◽  
Biological Data ◽  
Environmental Toxicants ◽  
Expression Data ◽  
Model Species ◽  
Genome Scale

AbstractThe availability of genome sequences, annotations and knowledge of the biochemistry underlying metabolic transformations has led to the generation of metabolic network reconstructions for a wide range of organisms in bacteria, archaea, and eukaryotes. When modeled using mathematical representations, a reconstruction can simulate underlying genotype-phenotype relationships. Accordingly, genome-scale models (GEMs) can be used to predict the response of organisms to genetic and environmental variations. A bottom-up reconstruction procedure typically starts by generating a draft model from existing annotation data on a target organism. For model species, this part of the process can be straightforward, due to the abundant organism-specific biochemical data. However, the process becomes complicated for non-model less-annotated species. In this paper, we present a draft liver reconstruction, ReCodLiver0.9, of Atlantic cod (Gadus morhua), a non-model teleost fish, as a practicable guide for cases with comparably few resources. Although the reconstruction is considered a draft version, we show that it already has utility in elucidating metabolic response mechanisms to environmental toxicants by mapping gene expression data of exposure experiments to the resulting model.Author summaryGenome-scale metabolic models (GEMs) are constructed based upon reconstructed networks that are carried out by an organism. The underlying biochemical knowledge in such networks can be transformed into mathematical models that could serve as a platform to answer biological questions. The availability of high-throughput biological data, including genomics, proteomics, and metabolomics data, supports the generation of such models for a large number of organisms. Nevertheless, challenges arise for non-model species which are typically less annotated. In this paper, we discuss these challenges and possible solutions in the context of generation of a draft liver reconstruction of Atlantic cod (Gadus morhua). We also show how experimental data, here gene expression data, can be mapped to the resulting model to understand the metabolic response of cod liver to environmental toxicants.

scholarly journals LocateP: Genome-scale subcellular-location predictor for bacterial proteins

2008 ◽  
Vol 9 (1) ◽  
pp. 173 ◽  
Author(s):  
Miaomiao Zhou ◽  
Jos Boekhorst ◽  
Christof Francke ◽  
Roland J Siezen
Keyword(s):  
Subcellular Location ◽  
Bacterial Proteins ◽  
Genome Scale

scholarly journals LncRNA NR038975, A Serum-Based Biomarker, Promotes Gastric Tumorigenesis by Interacting With NF90/NF45 Complex

2021 ◽  
Vol 11 ◽  
Author(s):  
Sisi Wei ◽  
Suli Dai ◽  
Cong Zhang ◽  
Ruinian Zhao ◽  
Zitong Zhao ◽  
...  
Keyword(s):  
Diagnostic Marker ◽  
Subcellular Location ◽  
Potential Therapeutic Target ◽  
Interactive Analysis ◽  
Genetic Deficiency ◽  
Rna Immunoprecipitation ◽  
Full Length Sequence ◽  
The Stability ◽  
Serum Exosomes ◽  
Independent Cohort

Gastric cancer (GC) is one of the deadliest cancers, and long noncoding RNAs (lncRNAs) have been reported to be the important regulators during the occurrence and development of GC. The present study identified a novel and functional lncRNA in GC, named NR038975, which was confirmed to be markedly upregulated in the Gene Expression Profiling Interactive Analysis (GEPIA) dataset and our independent cohort of GC tissues. We firstly characterized the full-length sequence and subcellular location of NR038975 in GC cells. Our data demonstrated that upregulated NR038975 expression was significantly related to lymph node metastasis and TNM stage. In addition, knockdown of NR038975 inhibited GC cell proliferation, migration, invasion, and clonogenicity and vice versa. Mechanistically, RNA pull-down and mass spectrometry assays identified the NR038975-binding proteins and NF90/NF45 complex, and the binding was also confirmed by RNA immunoprecipitation and confocal experiments. We further demonstrated that genetic deficiency of NR038975 abrogated the interaction between NF45 and NF90. Moreover, NF90 increased the stability of NR038975. Thus, NR038975-NF90/NF45 will be an important combinational target of GC. Finally, we detected NR038975 in serum exosomes and serum of GC patients. Our results indicated that NR038975 was a biomarker for gastric tumorigenesis. The current study demonstrated that NR038975 is a novel lncRNA that is clinically and functionally engaged in GC progression and might be a novel diagnostic marker and potential therapeutic target.

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