ncbi refseq genome annotation Search Results


ncbi refseq genome annotation  (DOE Systems Biology Knowledgebase)
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DOE Systems Biology Knowledgebase ncbi refseq genome annotation
Overall process of the model reconstruction, refinement, and subsequent analysis/validation. Kbase, <t>NCBI</t> <t>RefSeq,</t> and ModelSEED database were used to reconstruct the initial model. After standard model curations, a high-quality GSM was ready to use. Later, condition-specific transcriptomics data were incorporated with GSM through E-flux algorithm, which resulted in five contextualized models. These models were used to find metabolic bottlenecks and persistence mechanism. K-mean clustering algorithm applied on the transcriptomics data revealed a global stress response mechanism. Thermodynamics and enzyme cost analysis, along with in vitro experimentation, reveal the regulatory role of pgm in CTL persistence.
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Overall process of the model reconstruction, refinement, and subsequent analysis/validation. Kbase, NCBI RefSeq, and ModelSEED database were used to reconstruct the initial model. After standard model curations, a high-quality GSM was ready to use. Later, condition-specific transcriptomics data were incorporated with GSM through E-flux algorithm, which resulted in five contextualized models. These models were used to find metabolic bottlenecks and persistence mechanism. K-mean clustering algorithm applied on the transcriptomics data revealed a global stress response mechanism. Thermodynamics and enzyme cost analysis, along with in vitro experimentation, reveal the regulatory role of pgm in CTL persistence.

Journal: mSystems

Article Title: Metabolic model guided CRISPRi identifies a central role for phosphoglycerate mutase in Chlamydia trachomatis persistence

doi: 10.1128/msystems.00717-24

Figure Lengend Snippet: Overall process of the model reconstruction, refinement, and subsequent analysis/validation. Kbase, NCBI RefSeq, and ModelSEED database were used to reconstruct the initial model. After standard model curations, a high-quality GSM was ready to use. Later, condition-specific transcriptomics data were incorporated with GSM through E-flux algorithm, which resulted in five contextualized models. These models were used to find metabolic bottlenecks and persistence mechanism. K-mean clustering algorithm applied on the transcriptomics data revealed a global stress response mechanism. Thermodynamics and enzyme cost analysis, along with in vitro experimentation, reveal the regulatory role of pgm in CTL persistence.

Article Snippet: To analyze the metabolism of CTL in different stress conditions, we reconstructed a GSM of CTL using the NCBI RefSeq genome annotation (KBase Genome ID: GCF_000068585.1) from KBase ( ).

Techniques: Biomarker Discovery, In Vitro