PMC:7252096 / 113254-114988 JSON TXT

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LitCovid-PD-FMA-UBERON

{"project":"LitCovid-PD-FMA-UBERON","denotations":[{"id":"T112","span":{"begin":0,"end":4},"obj":"Body_part"},{"id":"T113","span":{"begin":116,"end":122},"obj":"Body_part"},{"id":"T114","span":{"begin":276,"end":282},"obj":"Body_part"},{"id":"T115","span":{"begin":317,"end":322},"obj":"Body_part"},{"id":"T116","span":{"begin":323,"end":329},"obj":"Body_part"},{"id":"T117","span":{"begin":884,"end":887},"obj":"Body_part"},{"id":"T118","span":{"begin":1041,"end":1045},"obj":"Body_part"},{"id":"T119","span":{"begin":1050,"end":1054},"obj":"Body_part"},{"id":"T120","span":{"begin":1338,"end":1343},"obj":"Body_part"},{"id":"T121","span":{"begin":1515,"end":1526},"obj":"Body_part"},{"id":"T122","span":{"begin":1653,"end":1658},"obj":"Body_part"},{"id":"T123","span":{"begin":1660,"end":1665},"obj":"Body_part"}],"attributes":[{"id":"A112","pred":"fma_id","subj":"T112","obj":"http://purl.org/sig/ont/fma/fma7195"},{"id":"A113","pred":"fma_id","subj":"T113","obj":"http://purl.org/sig/ont/fma/fma84116"},{"id":"A114","pred":"fma_id","subj":"T114","obj":"http://purl.org/sig/ont/fma/fma84116"},{"id":"A115","pred":"fma_id","subj":"T115","obj":"http://purl.org/sig/ont/fma/fma7490"},{"id":"A116","pred":"fma_id","subj":"T116","obj":"http://purl.org/sig/ont/fma/fma84116"},{"id":"A117","pred":"fma_id","subj":"T117","obj":"http://purl.org/sig/ont/fma/fma67095"},{"id":"A118","pred":"fma_id","subj":"T118","obj":"http://purl.org/sig/ont/fma/fma68646"},{"id":"A119","pred":"fma_id","subj":"T119","obj":"http://purl.org/sig/ont/fma/fma68646"},{"id":"A120","pred":"fma_id","subj":"T120","obj":"http://purl.org/sig/ont/fma/fma68646"},{"id":"A121","pred":"fma_id","subj":"T121","obj":"http://purl.org/sig/ont/fma/fma62499"},{"id":"A122","pred":"fma_id","subj":"T122","obj":"http://purl.org/sig/ont/fma/fma68646"},{"id":"A123","pred":"fma_id","subj":"T123","obj":"http://purl.org/sig/ont/fma/fma68646"}],"text":"Lung from MHV68-Infected WT and IFNγR KO Mice\nLibraries corresponding to 14 mice were aligned to a custom reference genome encompassing both murine (mm10) and herpes virus genes: 84 known genes from MHV68 were retrieved from NCBI (NCBI: txid33708) and added to the mm10 mouse genome. Reads were aligned to the custom joint genome and processed according to the Drop-Seq Computational Protocol v2.0 (https://github.com/broadinstitute/Drop-seq). Barcodes with \u003c 200 unique genes, \u003e 20,000 UMI counts, and \u003e 30% of transcript counts derived from mitochondrially encoded genes were discarded. Data analysis was performed using the Scanpy Package following the common procedure, the expression matrices were normalized using scran’s size factor based approach and log transformed via scanpy’s pp.log1p() function (Lun et al., 2016, Wolf et al., 2018). SoupX was utilized to reduce ambient RNA bias, using default parameters with pCut set to 0.3, and was applied to each sample before merging the count matrices (Young and Behjati, 2020). UMI per cell and cell cycle were regressed out. Highly variable genes were selected by running pp.highly_variable_genes() for each sample separately, returning the top 4,000 variable genes per sample, and genes identified in variable in \u003e 5 samples were retained, yielding 14,305 genes. Next, only Epcam+ cells were considered, principal components (PCs) were calculated using only the selected variable genes, and 6 PCs were used to perform unsupervised Louvain clustering. Type I Pneumocytes were excluded from this analysis based on uniformly negative expression of Ace2, resulting in a final dataset subset of 5,558 cells. Cells were identified as infected if at least one viral read was detected."}

LitCovid-PD-UBERON

{"project":"LitCovid-PD-UBERON","denotations":[{"id":"T252","span":{"begin":0,"end":4},"obj":"Body_part"}],"attributes":[{"id":"A252","pred":"uberon_id","subj":"T252","obj":"http://purl.obolibrary.org/obo/UBERON_0002048"}],"text":"Lung from MHV68-Infected WT and IFNγR KO Mice\nLibraries corresponding to 14 mice were aligned to a custom reference genome encompassing both murine (mm10) and herpes virus genes: 84 known genes from MHV68 were retrieved from NCBI (NCBI: txid33708) and added to the mm10 mouse genome. Reads were aligned to the custom joint genome and processed according to the Drop-Seq Computational Protocol v2.0 (https://github.com/broadinstitute/Drop-seq). Barcodes with \u003c 200 unique genes, \u003e 20,000 UMI counts, and \u003e 30% of transcript counts derived from mitochondrially encoded genes were discarded. Data analysis was performed using the Scanpy Package following the common procedure, the expression matrices were normalized using scran’s size factor based approach and log transformed via scanpy’s pp.log1p() function (Lun et al., 2016, Wolf et al., 2018). SoupX was utilized to reduce ambient RNA bias, using default parameters with pCut set to 0.3, and was applied to each sample before merging the count matrices (Young and Behjati, 2020). UMI per cell and cell cycle were regressed out. Highly variable genes were selected by running pp.highly_variable_genes() for each sample separately, returning the top 4,000 variable genes per sample, and genes identified in variable in \u003e 5 samples were retained, yielding 14,305 genes. Next, only Epcam+ cells were considered, principal components (PCs) were calculated using only the selected variable genes, and 6 PCs were used to perform unsupervised Louvain clustering. Type I Pneumocytes were excluded from this analysis based on uniformly negative expression of Ace2, resulting in a final dataset subset of 5,558 cells. Cells were identified as infected if at least one viral read was detected."}

LitCovid-PD-CLO

LitCovid-PD-CHEBI

{"project":"LitCovid-PD-CHEBI","denotations":[{"id":"T21041","span":{"begin":25,"end":27},"obj":"Chemical"}],"attributes":[{"id":"A10184","pred":"chebi_id","subj":"T21041","obj":"http://purl.obolibrary.org/obo/CHEBI_141450"}],"text":"Lung from MHV68-Infected WT and IFNγR KO Mice\nLibraries corresponding to 14 mice were aligned to a custom reference genome encompassing both murine (mm10) and herpes virus genes: 84 known genes from MHV68 were retrieved from NCBI (NCBI: txid33708) and added to the mm10 mouse genome. Reads were aligned to the custom joint genome and processed according to the Drop-Seq Computational Protocol v2.0 (https://github.com/broadinstitute/Drop-seq). Barcodes with \u003c 200 unique genes, \u003e 20,000 UMI counts, and \u003e 30% of transcript counts derived from mitochondrially encoded genes were discarded. Data analysis was performed using the Scanpy Package following the common procedure, the expression matrices were normalized using scran’s size factor based approach and log transformed via scanpy’s pp.log1p() function (Lun et al., 2016, Wolf et al., 2018). SoupX was utilized to reduce ambient RNA bias, using default parameters with pCut set to 0.3, and was applied to each sample before merging the count matrices (Young and Behjati, 2020). UMI per cell and cell cycle were regressed out. Highly variable genes were selected by running pp.highly_variable_genes() for each sample separately, returning the top 4,000 variable genes per sample, and genes identified in variable in \u003e 5 samples were retained, yielding 14,305 genes. Next, only Epcam+ cells were considered, principal components (PCs) were calculated using only the selected variable genes, and 6 PCs were used to perform unsupervised Louvain clustering. Type I Pneumocytes were excluded from this analysis based on uniformly negative expression of Ace2, resulting in a final dataset subset of 5,558 cells. Cells were identified as infected if at least one viral read was detected."}

LitCovid-PD-GO-BP

{"project":"LitCovid-PD-GO-BP","denotations":[{"id":"T7","span":{"begin":512,"end":522},"obj":"http://purl.obolibrary.org/obo/GO_0006351"},{"id":"T8","span":{"begin":1050,"end":1060},"obj":"http://purl.obolibrary.org/obo/GO_0007049"}],"text":"Lung from MHV68-Infected WT and IFNγR KO Mice\nLibraries corresponding to 14 mice were aligned to a custom reference genome encompassing both murine (mm10) and herpes virus genes: 84 known genes from MHV68 were retrieved from NCBI (NCBI: txid33708) and added to the mm10 mouse genome. Reads were aligned to the custom joint genome and processed according to the Drop-Seq Computational Protocol v2.0 (https://github.com/broadinstitute/Drop-seq). Barcodes with \u003c 200 unique genes, \u003e 20,000 UMI counts, and \u003e 30% of transcript counts derived from mitochondrially encoded genes were discarded. Data analysis was performed using the Scanpy Package following the common procedure, the expression matrices were normalized using scran’s size factor based approach and log transformed via scanpy’s pp.log1p() function (Lun et al., 2016, Wolf et al., 2018). SoupX was utilized to reduce ambient RNA bias, using default parameters with pCut set to 0.3, and was applied to each sample before merging the count matrices (Young and Behjati, 2020). UMI per cell and cell cycle were regressed out. Highly variable genes were selected by running pp.highly_variable_genes() for each sample separately, returning the top 4,000 variable genes per sample, and genes identified in variable in \u003e 5 samples were retained, yielding 14,305 genes. Next, only Epcam+ cells were considered, principal components (PCs) were calculated using only the selected variable genes, and 6 PCs were used to perform unsupervised Louvain clustering. Type I Pneumocytes were excluded from this analysis based on uniformly negative expression of Ace2, resulting in a final dataset subset of 5,558 cells. Cells were identified as infected if at least one viral read was detected."}

LitCovid-sentences

{"project":"LitCovid-sentences","denotations":[{"id":"T811","span":{"begin":0,"end":45},"obj":"Sentence"},{"id":"T812","span":{"begin":46,"end":178},"obj":"Sentence"},{"id":"T813","span":{"begin":179,"end":283},"obj":"Sentence"},{"id":"T814","span":{"begin":284,"end":443},"obj":"Sentence"},{"id":"T815","span":{"begin":444,"end":588},"obj":"Sentence"},{"id":"T816","span":{"begin":589,"end":846},"obj":"Sentence"},{"id":"T817","span":{"begin":847,"end":1032},"obj":"Sentence"},{"id":"T818","span":{"begin":1033,"end":1080},"obj":"Sentence"},{"id":"T819","span":{"begin":1081,"end":1319},"obj":"Sentence"},{"id":"T820","span":{"begin":1320,"end":1507},"obj":"Sentence"},{"id":"T821","span":{"begin":1508,"end":1659},"obj":"Sentence"},{"id":"T822","span":{"begin":1660,"end":1734},"obj":"Sentence"}],"namespaces":[{"prefix":"_base","uri":"http://pubannotation.org/ontology/tao.owl#"}],"text":"Lung from MHV68-Infected WT and IFNγR KO Mice\nLibraries corresponding to 14 mice were aligned to a custom reference genome encompassing both murine (mm10) and herpes virus genes: 84 known genes from MHV68 were retrieved from NCBI (NCBI: txid33708) and added to the mm10 mouse genome. Reads were aligned to the custom joint genome and processed according to the Drop-Seq Computational Protocol v2.0 (https://github.com/broadinstitute/Drop-seq). Barcodes with \u003c 200 unique genes, \u003e 20,000 UMI counts, and \u003e 30% of transcript counts derived from mitochondrially encoded genes were discarded. Data analysis was performed using the Scanpy Package following the common procedure, the expression matrices were normalized using scran’s size factor based approach and log transformed via scanpy’s pp.log1p() function (Lun et al., 2016, Wolf et al., 2018). SoupX was utilized to reduce ambient RNA bias, using default parameters with pCut set to 0.3, and was applied to each sample before merging the count matrices (Young and Behjati, 2020). UMI per cell and cell cycle were regressed out. Highly variable genes were selected by running pp.highly_variable_genes() for each sample separately, returning the top 4,000 variable genes per sample, and genes identified in variable in \u003e 5 samples were retained, yielding 14,305 genes. Next, only Epcam+ cells were considered, principal components (PCs) were calculated using only the selected variable genes, and 6 PCs were used to perform unsupervised Louvain clustering. Type I Pneumocytes were excluded from this analysis based on uniformly negative expression of Ace2, resulting in a final dataset subset of 5,558 cells. Cells were identified as infected if at least one viral read was detected."}

LitCovid-PubTator

{"project":"LitCovid-PubTator","denotations":[{"id":"2775","span":{"begin":10,"end":27},"obj":"Disease"},{"id":"2783","span":{"begin":1331,"end":1336},"obj":"Gene"},{"id":"2784","span":{"begin":1602,"end":1606},"obj":"Gene"},{"id":"2785","span":{"begin":76,"end":80},"obj":"Species"},{"id":"2786","span":{"begin":141,"end":147},"obj":"Species"},{"id":"2787","span":{"begin":199,"end":204},"obj":"Species"},{"id":"2788","span":{"begin":270,"end":275},"obj":"Species"},{"id":"2789","span":{"begin":1685,"end":1693},"obj":"Disease"}],"attributes":[{"id":"A2775","pred":"tao:has_database_id","subj":"2775","obj":"MESH:D007239"},{"id":"A2783","pred":"tao:has_database_id","subj":"2783","obj":"Gene:4072"},{"id":"A2784","pred":"tao:has_database_id","subj":"2784","obj":"Gene:59272"},{"id":"A2785","pred":"tao:has_database_id","subj":"2785","obj":"Tax:10090"},{"id":"A2786","pred":"tao:has_database_id","subj":"2786","obj":"Tax:10090"},{"id":"A2787","pred":"tao:has_database_id","subj":"2787","obj":"Tax:1440122"},{"id":"A2788","pred":"tao:has_database_id","subj":"2788","obj":"Tax:10090"},{"id":"A2789","pred":"tao:has_database_id","subj":"2789","obj":"MESH:D007239"}],"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":"Lung from MHV68-Infected WT and IFNγR KO Mice\nLibraries corresponding to 14 mice were aligned to a custom reference genome encompassing both murine (mm10) and herpes virus genes: 84 known genes from MHV68 were retrieved from NCBI (NCBI: txid33708) and added to the mm10 mouse genome. Reads were aligned to the custom joint genome and processed according to the Drop-Seq Computational Protocol v2.0 (https://github.com/broadinstitute/Drop-seq). Barcodes with \u003c 200 unique genes, \u003e 20,000 UMI counts, and \u003e 30% of transcript counts derived from mitochondrially encoded genes were discarded. Data analysis was performed using the Scanpy Package following the common procedure, the expression matrices were normalized using scran’s size factor based approach and log transformed via scanpy’s pp.log1p() function (Lun et al., 2016, Wolf et al., 2018). SoupX was utilized to reduce ambient RNA bias, using default parameters with pCut set to 0.3, and was applied to each sample before merging the count matrices (Young and Behjati, 2020). UMI per cell and cell cycle were regressed out. Highly variable genes were selected by running pp.highly_variable_genes() for each sample separately, returning the top 4,000 variable genes per sample, and genes identified in variable in \u003e 5 samples were retained, yielding 14,305 genes. Next, only Epcam+ cells were considered, principal components (PCs) were calculated using only the selected variable genes, and 6 PCs were used to perform unsupervised Louvain clustering. Type I Pneumocytes were excluded from this analysis based on uniformly negative expression of Ace2, resulting in a final dataset subset of 5,558 cells. Cells were identified as infected if at least one viral read was detected."}

2_test

{"project":"2_test","denotations":[{"id":"32413319-27909575-20790583","span":{"begin":821,"end":825},"obj":"27909575"},{"id":"32413319-29409532-20790584","span":{"begin":840,"end":844},"obj":"29409532"}],"text":"Lung from MHV68-Infected WT and IFNγR KO Mice\nLibraries corresponding to 14 mice were aligned to a custom reference genome encompassing both murine (mm10) and herpes virus genes: 84 known genes from MHV68 were retrieved from NCBI (NCBI: txid33708) and added to the mm10 mouse genome. Reads were aligned to the custom joint genome and processed according to the Drop-Seq Computational Protocol v2.0 (https://github.com/broadinstitute/Drop-seq). Barcodes with \u003c 200 unique genes, \u003e 20,000 UMI counts, and \u003e 30% of transcript counts derived from mitochondrially encoded genes were discarded. Data analysis was performed using the Scanpy Package following the common procedure, the expression matrices were normalized using scran’s size factor based approach and log transformed via scanpy’s pp.log1p() function (Lun et al., 2016, Wolf et al., 2018). SoupX was utilized to reduce ambient RNA bias, using default parameters with pCut set to 0.3, and was applied to each sample before merging the count matrices (Young and Behjati, 2020). UMI per cell and cell cycle were regressed out. Highly variable genes were selected by running pp.highly_variable_genes() for each sample separately, returning the top 4,000 variable genes per sample, and genes identified in variable in \u003e 5 samples were retained, yielding 14,305 genes. Next, only Epcam+ cells were considered, principal components (PCs) were calculated using only the selected variable genes, and 6 PCs were used to perform unsupervised Louvain clustering. Type I Pneumocytes were excluded from this analysis based on uniformly negative expression of Ace2, resulting in a final dataset subset of 5,558 cells. Cells were identified as infected if at least one viral read was detected."}

PMC:7252096 / 113254-114988 JSONTXT

Annnotations TAB JSON ListView MergeView

PMC:7252096 / 113254-114988 JSON TXT