PMC:7354481 / 13096-15193 JSON TXT

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LitCovid-PD-FMA-UBERON

{"project":"LitCovid-PD-FMA-UBERON","denotations":[{"id":"T77","span":{"begin":91,"end":95},"obj":"Body_part"},{"id":"T78","span":{"begin":219,"end":223},"obj":"Body_part"},{"id":"T79","span":{"begin":562,"end":566},"obj":"Body_part"},{"id":"T80","span":{"begin":609,"end":613},"obj":"Body_part"},{"id":"T81","span":{"begin":935,"end":941},"obj":"Body_part"},{"id":"T82","span":{"begin":1648,"end":1652},"obj":"Body_part"}],"attributes":[{"id":"A77","pred":"fma_id","subj":"T77","obj":"http://purl.org/sig/ont/fma/fma7195"},{"id":"A78","pred":"fma_id","subj":"T78","obj":"http://purl.org/sig/ont/fma/fma68646"},{"id":"A79","pred":"fma_id","subj":"T79","obj":"http://purl.org/sig/ont/fma/fma68646"},{"id":"A80","pred":"fma_id","subj":"T80","obj":"http://purl.org/sig/ont/fma/fma68646"},{"id":"A81","pred":"fma_id","subj":"T81","obj":"http://purl.org/sig/ont/fma/fma84116"},{"id":"A82","pred":"fma_id","subj":"T82","obj":"http://purl.org/sig/ont/fma/fma74402"}],"text":"Bioproject data was obtained from PRJNA615032 bioproject trancriptome data, which includes lung biopsies from SARS-CoV-2-infected patients and healthy volunteers as well as mock and SARS-CoV-2-transfected NHEB and A549 cell lines. The data have been deposited with links to BioProject accession number PRJNA615032 in the NCBI BioProject database (https://www.ncbi.nlm.nih.gov/bioproject/). All the selected data were reanalysed at the Rosalind bioinformatics server. Data analysis was performed according to 1.5 fold change between untransfected and transfected cell lines in a data pool calculation for both cell lines at p \u003c 0.05 significance level. Data was analyzed by Rosalind (https://rosalind.onramp.bio/), with a HyperScale architecture developed by OnRamp BioInformatics, Inc. (San Diego, CA, USA). Reads were trimmed using cutadapt [25]. Quality scores were assessed using FastQC [26]. Reads were aligned to the Homo sapiens genome built by GRCh38 using STAR [27]. Individual sample reads were quantified using HTseq [28] and normalized via relative log expression (RLE) using DESeq2 R library [29]. Read distribution percentages, violin plots, identity heatmaps, and sample MDS plots were generated as part of the QC step using RSeQC [30]. DEseq2 was also used to calculate fold changes and p-values and perform optional covariate correction. Clustering of genes for the final heatmap of differentially expressed genes was done using the PAM (partitioning around medoids) method using the fpc R library (https://cran.r-project.org/web/packages/fpc/index.html). Hypergeometric distribution was used to analyze the enrichment of pathways, gene ontology, domain structure, and other ontologies. The topGO R library [31], was used to determine local similarities and dependencies between GO terms in order to perform Elim pruning correction. Several database sources were referenced for enrichment analysis, including Interpro [32], NCBI [33], MSigDB [34,35], REACTOME [36], and WikiPathways [37]. Enrichment was calculated relative to a set of background genes relevant for the experiment."}

LitCovid-PD-UBERON

{"project":"LitCovid-PD-UBERON","denotations":[{"id":"T12","span":{"begin":91,"end":95},"obj":"Body_part"}],"attributes":[{"id":"A12","pred":"uberon_id","subj":"T12","obj":"http://purl.obolibrary.org/obo/UBERON_0002048"}],"text":"Bioproject data was obtained from PRJNA615032 bioproject trancriptome data, which includes lung biopsies from SARS-CoV-2-infected patients and healthy volunteers as well as mock and SARS-CoV-2-transfected NHEB and A549 cell lines. The data have been deposited with links to BioProject accession number PRJNA615032 in the NCBI BioProject database (https://www.ncbi.nlm.nih.gov/bioproject/). All the selected data were reanalysed at the Rosalind bioinformatics server. Data analysis was performed according to 1.5 fold change between untransfected and transfected cell lines in a data pool calculation for both cell lines at p \u003c 0.05 significance level. Data was analyzed by Rosalind (https://rosalind.onramp.bio/), with a HyperScale architecture developed by OnRamp BioInformatics, Inc. (San Diego, CA, USA). Reads were trimmed using cutadapt [25]. Quality scores were assessed using FastQC [26]. Reads were aligned to the Homo sapiens genome built by GRCh38 using STAR [27]. Individual sample reads were quantified using HTseq [28] and normalized via relative log expression (RLE) using DESeq2 R library [29]. Read distribution percentages, violin plots, identity heatmaps, and sample MDS plots were generated as part of the QC step using RSeQC [30]. DEseq2 was also used to calculate fold changes and p-values and perform optional covariate correction. Clustering of genes for the final heatmap of differentially expressed genes was done using the PAM (partitioning around medoids) method using the fpc R library (https://cran.r-project.org/web/packages/fpc/index.html). Hypergeometric distribution was used to analyze the enrichment of pathways, gene ontology, domain structure, and other ontologies. The topGO R library [31], was used to determine local similarities and dependencies between GO terms in order to perform Elim pruning correction. Several database sources were referenced for enrichment analysis, including Interpro [32], NCBI [33], MSigDB [34,35], REACTOME [36], and WikiPathways [37]. Enrichment was calculated relative to a set of background genes relevant for the experiment."}

LitCovid-PD-MONDO

{"project":"LitCovid-PD-MONDO","denotations":[{"id":"T52","span":{"begin":110,"end":118},"obj":"Disease"},{"id":"T53","span":{"begin":182,"end":190},"obj":"Disease"},{"id":"T54","span":{"begin":964,"end":968},"obj":"Disease"},{"id":"T55","span":{"begin":1185,"end":1188},"obj":"Disease"},{"id":"T57","span":{"begin":1449,"end":1452},"obj":"Disease"}],"attributes":[{"id":"A52","pred":"mondo_id","subj":"T52","obj":"http://purl.obolibrary.org/obo/MONDO_0005091"},{"id":"A53","pred":"mondo_id","subj":"T53","obj":"http://purl.obolibrary.org/obo/MONDO_0005091"},{"id":"A54","pred":"mondo_id","subj":"T54","obj":"http://purl.obolibrary.org/obo/MONDO_0010408"},{"id":"A55","pred":"mondo_id","subj":"T55","obj":"http://purl.obolibrary.org/obo/MONDO_0009532"},{"id":"A56","pred":"mondo_id","subj":"T55","obj":"http://purl.obolibrary.org/obo/MONDO_0018881"},{"id":"A57","pred":"mondo_id","subj":"T57","obj":"http://purl.obolibrary.org/obo/MONDO_0018959"}],"text":"Bioproject data was obtained from PRJNA615032 bioproject trancriptome data, which includes lung biopsies from SARS-CoV-2-infected patients and healthy volunteers as well as mock and SARS-CoV-2-transfected NHEB and A549 cell lines. The data have been deposited with links to BioProject accession number PRJNA615032 in the NCBI BioProject database (https://www.ncbi.nlm.nih.gov/bioproject/). All the selected data were reanalysed at the Rosalind bioinformatics server. Data analysis was performed according to 1.5 fold change between untransfected and transfected cell lines in a data pool calculation for both cell lines at p \u003c 0.05 significance level. Data was analyzed by Rosalind (https://rosalind.onramp.bio/), with a HyperScale architecture developed by OnRamp BioInformatics, Inc. (San Diego, CA, USA). Reads were trimmed using cutadapt [25]. Quality scores were assessed using FastQC [26]. Reads were aligned to the Homo sapiens genome built by GRCh38 using STAR [27]. Individual sample reads were quantified using HTseq [28] and normalized via relative log expression (RLE) using DESeq2 R library [29]. Read distribution percentages, violin plots, identity heatmaps, and sample MDS plots were generated as part of the QC step using RSeQC [30]. DEseq2 was also used to calculate fold changes and p-values and perform optional covariate correction. Clustering of genes for the final heatmap of differentially expressed genes was done using the PAM (partitioning around medoids) method using the fpc R library (https://cran.r-project.org/web/packages/fpc/index.html). Hypergeometric distribution was used to analyze the enrichment of pathways, gene ontology, domain structure, and other ontologies. The topGO R library [31], was used to determine local similarities and dependencies between GO terms in order to perform Elim pruning correction. Several database sources were referenced for enrichment analysis, including Interpro [32], NCBI [33], MSigDB [34,35], REACTOME [36], and WikiPathways [37]. Enrichment was calculated relative to a set of background genes relevant for the experiment."}

LitCovid-PD-CLO

LitCovid-PD-CHEBI

{"project":"LitCovid-PD-CHEBI","denotations":[{"id":"T21","span":{"begin":1449,"end":1452},"obj":"Chemical"}],"attributes":[{"id":"A21","pred":"chebi_id","subj":"T21","obj":"http://purl.obolibrary.org/obo/CHEBI_51135"},{"id":"A22","pred":"chebi_id","subj":"T21","obj":"http://purl.obolibrary.org/obo/CHEBI_60882"}],"text":"Bioproject data was obtained from PRJNA615032 bioproject trancriptome data, which includes lung biopsies from SARS-CoV-2-infected patients and healthy volunteers as well as mock and SARS-CoV-2-transfected NHEB and A549 cell lines. The data have been deposited with links to BioProject accession number PRJNA615032 in the NCBI BioProject database (https://www.ncbi.nlm.nih.gov/bioproject/). All the selected data were reanalysed at the Rosalind bioinformatics server. Data analysis was performed according to 1.5 fold change between untransfected and transfected cell lines in a data pool calculation for both cell lines at p \u003c 0.05 significance level. Data was analyzed by Rosalind (https://rosalind.onramp.bio/), with a HyperScale architecture developed by OnRamp BioInformatics, Inc. (San Diego, CA, USA). Reads were trimmed using cutadapt [25]. Quality scores were assessed using FastQC [26]. Reads were aligned to the Homo sapiens genome built by GRCh38 using STAR [27]. Individual sample reads were quantified using HTseq [28] and normalized via relative log expression (RLE) using DESeq2 R library [29]. Read distribution percentages, violin plots, identity heatmaps, and sample MDS plots were generated as part of the QC step using RSeQC [30]. DEseq2 was also used to calculate fold changes and p-values and perform optional covariate correction. Clustering of genes for the final heatmap of differentially expressed genes was done using the PAM (partitioning around medoids) method using the fpc R library (https://cran.r-project.org/web/packages/fpc/index.html). Hypergeometric distribution was used to analyze the enrichment of pathways, gene ontology, domain structure, and other ontologies. The topGO R library [31], was used to determine local similarities and dependencies between GO terms in order to perform Elim pruning correction. Several database sources were referenced for enrichment analysis, including Interpro [32], NCBI [33], MSigDB [34,35], REACTOME [36], and WikiPathways [37]. Enrichment was calculated relative to a set of background genes relevant for the experiment."}

LitCovid-sentences

{"project":"LitCovid-sentences","denotations":[{"id":"T89","span":{"begin":0,"end":230},"obj":"Sentence"},{"id":"T90","span":{"begin":231,"end":389},"obj":"Sentence"},{"id":"T91","span":{"begin":390,"end":466},"obj":"Sentence"},{"id":"T92","span":{"begin":467,"end":651},"obj":"Sentence"},{"id":"T93","span":{"begin":652,"end":807},"obj":"Sentence"},{"id":"T94","span":{"begin":808,"end":847},"obj":"Sentence"},{"id":"T95","span":{"begin":848,"end":895},"obj":"Sentence"},{"id":"T96","span":{"begin":896,"end":974},"obj":"Sentence"},{"id":"T97","span":{"begin":975,"end":1109},"obj":"Sentence"},{"id":"T98","span":{"begin":1110,"end":1250},"obj":"Sentence"},{"id":"T99","span":{"begin":1251,"end":1353},"obj":"Sentence"},{"id":"T100","span":{"begin":1354,"end":1571},"obj":"Sentence"},{"id":"T101","span":{"begin":1572,"end":1702},"obj":"Sentence"},{"id":"T102","span":{"begin":1703,"end":1848},"obj":"Sentence"},{"id":"T103","span":{"begin":1849,"end":2004},"obj":"Sentence"},{"id":"T104","span":{"begin":2005,"end":2097},"obj":"Sentence"}],"namespaces":[{"prefix":"_base","uri":"http://pubannotation.org/ontology/tao.owl#"}],"text":"Bioproject data was obtained from PRJNA615032 bioproject trancriptome data, which includes lung biopsies from SARS-CoV-2-infected patients and healthy volunteers as well as mock and SARS-CoV-2-transfected NHEB and A549 cell lines. The data have been deposited with links to BioProject accession number PRJNA615032 in the NCBI BioProject database (https://www.ncbi.nlm.nih.gov/bioproject/). All the selected data were reanalysed at the Rosalind bioinformatics server. Data analysis was performed according to 1.5 fold change between untransfected and transfected cell lines in a data pool calculation for both cell lines at p \u003c 0.05 significance level. Data was analyzed by Rosalind (https://rosalind.onramp.bio/), with a HyperScale architecture developed by OnRamp BioInformatics, Inc. (San Diego, CA, USA). Reads were trimmed using cutadapt [25]. Quality scores were assessed using FastQC [26]. Reads were aligned to the Homo sapiens genome built by GRCh38 using STAR [27]. Individual sample reads were quantified using HTseq [28] and normalized via relative log expression (RLE) using DESeq2 R library [29]. Read distribution percentages, violin plots, identity heatmaps, and sample MDS plots were generated as part of the QC step using RSeQC [30]. DEseq2 was also used to calculate fold changes and p-values and perform optional covariate correction. Clustering of genes for the final heatmap of differentially expressed genes was done using the PAM (partitioning around medoids) method using the fpc R library (https://cran.r-project.org/web/packages/fpc/index.html). Hypergeometric distribution was used to analyze the enrichment of pathways, gene ontology, domain structure, and other ontologies. The topGO R library [31], was used to determine local similarities and dependencies between GO terms in order to perform Elim pruning correction. Several database sources were referenced for enrichment analysis, including Interpro [32], NCBI [33], MSigDB [34,35], REACTOME [36], and WikiPathways [37]. Enrichment was calculated relative to a set of background genes relevant for the experiment."}

2_test

{"project":"2_test","denotations":[{"id":"32512929-25977294-144200714","span":{"begin":843,"end":845},"obj":"25977294"},{"id":"32512929-23104886-144200715","span":{"begin":1028,"end":1030},"obj":"23104886"},{"id":"32512929-25260700-144200716","span":{"begin":1105,"end":1107},"obj":"25260700"},{"id":"32512929-25516281-144200717","span":{"begin":1246,"end":1248},"obj":"25516281"},{"id":"32512929-22743226-144200718","span":{"begin":1724,"end":1726},"obj":"22743226"},{"id":"32512929-30398656-144200719","span":{"begin":1946,"end":1948},"obj":"30398656"},{"id":"32512929-19854944-144200720","span":{"begin":1959,"end":1961},"obj":"19854944"},{"id":"32512929-29145629-144200721","span":{"begin":2000,"end":2002},"obj":"29145629"}],"text":"Bioproject data was obtained from PRJNA615032 bioproject trancriptome data, which includes lung biopsies from SARS-CoV-2-infected patients and healthy volunteers as well as mock and SARS-CoV-2-transfected NHEB and A549 cell lines. The data have been deposited with links to BioProject accession number PRJNA615032 in the NCBI BioProject database (https://www.ncbi.nlm.nih.gov/bioproject/). All the selected data were reanalysed at the Rosalind bioinformatics server. Data analysis was performed according to 1.5 fold change between untransfected and transfected cell lines in a data pool calculation for both cell lines at p \u003c 0.05 significance level. Data was analyzed by Rosalind (https://rosalind.onramp.bio/), with a HyperScale architecture developed by OnRamp BioInformatics, Inc. (San Diego, CA, USA). Reads were trimmed using cutadapt [25]. Quality scores were assessed using FastQC [26]. Reads were aligned to the Homo sapiens genome built by GRCh38 using STAR [27]. Individual sample reads were quantified using HTseq [28] and normalized via relative log expression (RLE) using DESeq2 R library [29]. Read distribution percentages, violin plots, identity heatmaps, and sample MDS plots were generated as part of the QC step using RSeQC [30]. DEseq2 was also used to calculate fold changes and p-values and perform optional covariate correction. Clustering of genes for the final heatmap of differentially expressed genes was done using the PAM (partitioning around medoids) method using the fpc R library (https://cran.r-project.org/web/packages/fpc/index.html). Hypergeometric distribution was used to analyze the enrichment of pathways, gene ontology, domain structure, and other ontologies. The topGO R library [31], was used to determine local similarities and dependencies between GO terms in order to perform Elim pruning correction. Several database sources were referenced for enrichment analysis, including Interpro [32], NCBI [33], MSigDB [34,35], REACTOME [36], and WikiPathways [37]. Enrichment was calculated relative to a set of background genes relevant for the experiment."}

LitCovid-PubTator

{"project":"LitCovid-PubTator","denotations":[{"id":"226","span":{"begin":964,"end":968},"obj":"Gene"},{"id":"227","span":{"begin":130,"end":138},"obj":"Species"},{"id":"228","span":{"begin":182,"end":192},"obj":"Species"},{"id":"229","span":{"begin":922,"end":934},"obj":"Species"},{"id":"230","span":{"begin":110,"end":129},"obj":"Disease"},{"id":"231","span":{"begin":214,"end":218},"obj":"CellLine"}],"attributes":[{"id":"A226","pred":"tao:has_database_id","subj":"226","obj":"Gene:6770"},{"id":"A227","pred":"tao:has_database_id","subj":"227","obj":"Tax:9606"},{"id":"A228","pred":"tao:has_database_id","subj":"228","obj":"Tax:2697049"},{"id":"A229","pred":"tao:has_database_id","subj":"229","obj":"Tax:9606"},{"id":"A230","pred":"tao:has_database_id","subj":"230","obj":"MESH:C000657245"},{"id":"A231","pred":"tao:has_database_id","subj":"231","obj":"CVCL:0023"}],"namespaces":[{"prefix":"Tax","uri":"https://www.ncbi.nlm.nih.gov/taxonomy/"},{"prefix":"MESH","uri":"https://id.nlm.nih.gov/mesh/"},{"prefix":"Gene","uri":"https://www.ncbi.nlm.nih.gov/gene/"},{"prefix":"CVCL","uri":"https://web.expasy.org/cellosaurus/CVCL_"}],"text":"Bioproject data was obtained from PRJNA615032 bioproject trancriptome data, which includes lung biopsies from SARS-CoV-2-infected patients and healthy volunteers as well as mock and SARS-CoV-2-transfected NHEB and A549 cell lines. The data have been deposited with links to BioProject accession number PRJNA615032 in the NCBI BioProject database (https://www.ncbi.nlm.nih.gov/bioproject/). All the selected data were reanalysed at the Rosalind bioinformatics server. Data analysis was performed according to 1.5 fold change between untransfected and transfected cell lines in a data pool calculation for both cell lines at p \u003c 0.05 significance level. Data was analyzed by Rosalind (https://rosalind.onramp.bio/), with a HyperScale architecture developed by OnRamp BioInformatics, Inc. (San Diego, CA, USA). Reads were trimmed using cutadapt [25]. Quality scores were assessed using FastQC [26]. Reads were aligned to the Homo sapiens genome built by GRCh38 using STAR [27]. Individual sample reads were quantified using HTseq [28] and normalized via relative log expression (RLE) using DESeq2 R library [29]. Read distribution percentages, violin plots, identity heatmaps, and sample MDS plots were generated as part of the QC step using RSeQC [30]. DEseq2 was also used to calculate fold changes and p-values and perform optional covariate correction. Clustering of genes for the final heatmap of differentially expressed genes was done using the PAM (partitioning around medoids) method using the fpc R library (https://cran.r-project.org/web/packages/fpc/index.html). Hypergeometric distribution was used to analyze the enrichment of pathways, gene ontology, domain structure, and other ontologies. The topGO R library [31], was used to determine local similarities and dependencies between GO terms in order to perform Elim pruning correction. Several database sources were referenced for enrichment analysis, including Interpro [32], NCBI [33], MSigDB [34,35], REACTOME [36], and WikiPathways [37]. Enrichment was calculated relative to a set of background genes relevant for the experiment."}

PMC:7354481 / 13096-15193 JSONTXT

Annnotations TAB JSON ListView MergeView

PMC:7354481 / 13096-15193 JSON TXT