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LitCovid-PD-FMA-UBERON

{"project":"LitCovid-PD-FMA-UBERON","denotations":[{"id":"T14","span":{"begin":269,"end":276},"obj":"Body_part"},{"id":"T15","span":{"begin":461,"end":468},"obj":"Body_part"},{"id":"T16","span":{"begin":864,"end":871},"obj":"Body_part"},{"id":"T17","span":{"begin":1104,"end":1111},"obj":"Body_part"},{"id":"T18","span":{"begin":1185,"end":1192},"obj":"Body_part"},{"id":"T19","span":{"begin":2326,"end":2333},"obj":"Body_part"},{"id":"T20","span":{"begin":2564,"end":2571},"obj":"Body_part"}],"attributes":[{"id":"A14","pred":"fma_id","subj":"T14","obj":"http://purl.org/sig/ont/fma/fma67257"},{"id":"A15","pred":"fma_id","subj":"T15","obj":"http://purl.org/sig/ont/fma/fma67257"},{"id":"A16","pred":"fma_id","subj":"T16","obj":"http://purl.org/sig/ont/fma/fma67257"},{"id":"A17","pred":"fma_id","subj":"T17","obj":"http://purl.org/sig/ont/fma/fma67257"},{"id":"A18","pred":"fma_id","subj":"T18","obj":"http://purl.org/sig/ont/fma/fma67257"},{"id":"A19","pred":"fma_id","subj":"T19","obj":"http://purl.org/sig/ont/fma/fma67257"},{"id":"A20","pred":"fma_id","subj":"T20","obj":"http://purl.org/sig/ont/fma/fma67257"}],"text":"Mass spectrometry data pre-processing\nQuantitative analysis was performed in the R statistical programming language (version 3.6.1, 2019-07-05). Initial quality control analyses, including inter-run clusterings, correlations, principal components analysis, peptide and protein counts and intensities were completed with the R package artMS (version 1.5.3). Based on obvious outliers in intensities, correlations, and clusterings, 2 runs were discarded from the protein abundance data and 2 runs were discarded from the phosphopeptide data (Figure S1). Additionally, the phosphopeptide data were filtered based on feature (i.e., peptide ion) intensity, removing any single feature with intensity less than 214—this decision was made based on apparent lack of correlation between runs for feature intensities below this intensity. Thus, for both phosphopeptides and protein abundance, we had 2 control time points and 6 infected time points, each with 3 biologically distinct replicates, except for infected at 0 and 2 hours in the phosphopeptide data and control at 0 hours and infected at 0 hours in the protein abundance data which only had 2 replicates each.\nStatistical analysis of protein abundance changes between control and infected runs were computed using peptide ion fragment data output from Spectronaut and processed using a pipeline of three functions from the R package MSstats (version 3.19.5) (Choi et al., 2014): function MSstats::SpectronauttoMSstatsFormat with default settings other than setting “removeProtein_with1Feature = TRUE”; function MSstats::dataProcess with default settings other than setting “censoredInt = 0,” “featureSubset = highQuality,” “remove_uninformative_feature_outlier = TRUE,” “clusters=7”; and function MSstats::groupComparison with all default settings.\nPhosphopeptide intensity data were summarized at the peptide ion level along with confident localization of phosphorylation as described in the previous section. Quantification of phosphorylation based on peptide ions were processed using artMS as a wrapper around MSstats, via functions artMS::doSiteConversion and artMS::artmsQuantification with default settings. All peptides containing the same set of phosphorylated sites were grouped and quantified together into phosphorylation site groups. For both phosphopeptide and protein abundance MSstats pipelines, MSstats performs normalization by median equalization, imputation of missing values and median smoothing to combine intensities for multiple peptide ions or fragments into a single intensity for their protein or phosphorylation site group, and statistical tests of differences in intensity between infected and control time points."}

LitCovid-PD-CLO

{"project":"LitCovid-PD-CLO","denotations":[{"id":"T18","span":{"begin":257,"end":264},"obj":"http://purl.obolibrary.org/obo/PR_000018263"},{"id":"T19","span":{"begin":547,"end":549},"obj":"http://purl.obolibrary.org/obo/CLO_0050050"},{"id":"T20","span":{"begin":628,"end":635},"obj":"http://purl.obolibrary.org/obo/PR_000018263"},{"id":"T21","span":{"begin":1265,"end":1272},"obj":"http://purl.obolibrary.org/obo/PR_000018263"},{"id":"T22","span":{"begin":1335,"end":1336},"obj":"http://purl.obolibrary.org/obo/CLO_0001020"},{"id":"T23","span":{"begin":1853,"end":1860},"obj":"http://purl.obolibrary.org/obo/PR_000018263"},{"id":"T24","span":{"begin":2005,"end":2012},"obj":"http://purl.obolibrary.org/obo/PR_000018263"},{"id":"T25","span":{"begin":2048,"end":2049},"obj":"http://purl.obolibrary.org/obo/CLO_0001020"},{"id":"T26","span":{"begin":2170,"end":2178},"obj":"http://purl.obolibrary.org/obo/PR_000018263"},{"id":"T27","span":{"begin":2504,"end":2511},"obj":"http://purl.obolibrary.org/obo/PR_000018263"},{"id":"T28","span":{"begin":2535,"end":2536},"obj":"http://purl.obolibrary.org/obo/CLO_0001020"},{"id":"T29","span":{"begin":2619,"end":2624},"obj":"http://purl.obolibrary.org/obo/UBERON_0000473"}],"text":"Mass spectrometry data pre-processing\nQuantitative analysis was performed in the R statistical programming language (version 3.6.1, 2019-07-05). Initial quality control analyses, including inter-run clusterings, correlations, principal components analysis, peptide and protein counts and intensities were completed with the R package artMS (version 1.5.3). Based on obvious outliers in intensities, correlations, and clusterings, 2 runs were discarded from the protein abundance data and 2 runs were discarded from the phosphopeptide data (Figure S1). Additionally, the phosphopeptide data were filtered based on feature (i.e., peptide ion) intensity, removing any single feature with intensity less than 214—this decision was made based on apparent lack of correlation between runs for feature intensities below this intensity. Thus, for both phosphopeptides and protein abundance, we had 2 control time points and 6 infected time points, each with 3 biologically distinct replicates, except for infected at 0 and 2 hours in the phosphopeptide data and control at 0 hours and infected at 0 hours in the protein abundance data which only had 2 replicates each.\nStatistical analysis of protein abundance changes between control and infected runs were computed using peptide ion fragment data output from Spectronaut and processed using a pipeline of three functions from the R package MSstats (version 3.19.5) (Choi et al., 2014): function MSstats::SpectronauttoMSstatsFormat with default settings other than setting “removeProtein_with1Feature = TRUE”; function MSstats::dataProcess with default settings other than setting “censoredInt = 0,” “featureSubset = highQuality,” “remove_uninformative_feature_outlier = TRUE,” “clusters=7”; and function MSstats::groupComparison with all default settings.\nPhosphopeptide intensity data were summarized at the peptide ion level along with confident localization of phosphorylation as described in the previous section. Quantification of phosphorylation based on peptide ions were processed using artMS as a wrapper around MSstats, via functions artMS::doSiteConversion and artMS::artmsQuantification with default settings. All peptides containing the same set of phosphorylated sites were grouped and quantified together into phosphorylation site groups. For both phosphopeptide and protein abundance MSstats pipelines, MSstats performs normalization by median equalization, imputation of missing values and median smoothing to combine intensities for multiple peptide ions or fragments into a single intensity for their protein or phosphorylation site group, and statistical tests of differences in intensity between infected and control time points."}

LitCovid-PD-CHEBI

LitCovid-PD-GO-BP

{"project":"LitCovid-PD-GO-BP","denotations":[{"id":"T1","span":{"begin":1892,"end":1904},"obj":"http://purl.obolibrary.org/obo/GO_0051179"},{"id":"T2","span":{"begin":1908,"end":1923},"obj":"http://purl.obolibrary.org/obo/GO_0016310"},{"id":"T3","span":{"begin":1980,"end":1995},"obj":"http://purl.obolibrary.org/obo/GO_0016310"},{"id":"T4","span":{"begin":2269,"end":2284},"obj":"http://purl.obolibrary.org/obo/GO_0016310"},{"id":"T5","span":{"begin":2564,"end":2590},"obj":"http://purl.obolibrary.org/obo/GO_0006468"},{"id":"T6","span":{"begin":2575,"end":2590},"obj":"http://purl.obolibrary.org/obo/GO_0016310"}],"text":"Mass spectrometry data pre-processing\nQuantitative analysis was performed in the R statistical programming language (version 3.6.1, 2019-07-05). Initial quality control analyses, including inter-run clusterings, correlations, principal components analysis, peptide and protein counts and intensities were completed with the R package artMS (version 1.5.3). Based on obvious outliers in intensities, correlations, and clusterings, 2 runs were discarded from the protein abundance data and 2 runs were discarded from the phosphopeptide data (Figure S1). Additionally, the phosphopeptide data were filtered based on feature (i.e., peptide ion) intensity, removing any single feature with intensity less than 214—this decision was made based on apparent lack of correlation between runs for feature intensities below this intensity. Thus, for both phosphopeptides and protein abundance, we had 2 control time points and 6 infected time points, each with 3 biologically distinct replicates, except for infected at 0 and 2 hours in the phosphopeptide data and control at 0 hours and infected at 0 hours in the protein abundance data which only had 2 replicates each.\nStatistical analysis of protein abundance changes between control and infected runs were computed using peptide ion fragment data output from Spectronaut and processed using a pipeline of three functions from the R package MSstats (version 3.19.5) (Choi et al., 2014): function MSstats::SpectronauttoMSstatsFormat with default settings other than setting “removeProtein_with1Feature = TRUE”; function MSstats::dataProcess with default settings other than setting “censoredInt = 0,” “featureSubset = highQuality,” “remove_uninformative_feature_outlier = TRUE,” “clusters=7”; and function MSstats::groupComparison with all default settings.\nPhosphopeptide intensity data were summarized at the peptide ion level along with confident localization of phosphorylation as described in the previous section. Quantification of phosphorylation based on peptide ions were processed using artMS as a wrapper around MSstats, via functions artMS::doSiteConversion and artMS::artmsQuantification with default settings. All peptides containing the same set of phosphorylated sites were grouped and quantified together into phosphorylation site groups. For both phosphopeptide and protein abundance MSstats pipelines, MSstats performs normalization by median equalization, imputation of missing values and median smoothing to combine intensities for multiple peptide ions or fragments into a single intensity for their protein or phosphorylation site group, and statistical tests of differences in intensity between infected and control time points."}

LitCovid-PubTator

{"project":"LitCovid-PubTator","denotations":[{"id":"2305","span":{"begin":918,"end":926},"obj":"Disease"},{"id":"2306","span":{"begin":997,"end":1005},"obj":"Disease"},{"id":"2307","span":{"begin":1077,"end":1085},"obj":"Disease"},{"id":"2309","span":{"begin":1231,"end":1239},"obj":"Disease"},{"id":"2313","span":{"begin":1853,"end":1860},"obj":"Chemical"},{"id":"2314","span":{"begin":2170,"end":2178},"obj":"Chemical"},{"id":"2315","span":{"begin":2661,"end":2669},"obj":"Disease"}],"attributes":[{"id":"A2305","pred":"tao:has_database_id","subj":"2305","obj":"MESH:D007239"},{"id":"A2306","pred":"tao:has_database_id","subj":"2306","obj":"MESH:D007239"},{"id":"A2307","pred":"tao:has_database_id","subj":"2307","obj":"MESH:D007239"},{"id":"A2309","pred":"tao:has_database_id","subj":"2309","obj":"MESH:D007239"},{"id":"A2313","pred":"tao:has_database_id","subj":"2313","obj":"MESH:D010455"},{"id":"A2314","pred":"tao:has_database_id","subj":"2314","obj":"MESH:D010455"},{"id":"A2315","pred":"tao:has_database_id","subj":"2315","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":"Mass spectrometry data pre-processing\nQuantitative analysis was performed in the R statistical programming language (version 3.6.1, 2019-07-05). Initial quality control analyses, including inter-run clusterings, correlations, principal components analysis, peptide and protein counts and intensities were completed with the R package artMS (version 1.5.3). Based on obvious outliers in intensities, correlations, and clusterings, 2 runs were discarded from the protein abundance data and 2 runs were discarded from the phosphopeptide data (Figure S1). Additionally, the phosphopeptide data were filtered based on feature (i.e., peptide ion) intensity, removing any single feature with intensity less than 214—this decision was made based on apparent lack of correlation between runs for feature intensities below this intensity. Thus, for both phosphopeptides and protein abundance, we had 2 control time points and 6 infected time points, each with 3 biologically distinct replicates, except for infected at 0 and 2 hours in the phosphopeptide data and control at 0 hours and infected at 0 hours in the protein abundance data which only had 2 replicates each.\nStatistical analysis of protein abundance changes between control and infected runs were computed using peptide ion fragment data output from Spectronaut and processed using a pipeline of three functions from the R package MSstats (version 3.19.5) (Choi et al., 2014): function MSstats::SpectronauttoMSstatsFormat with default settings other than setting “removeProtein_with1Feature = TRUE”; function MSstats::dataProcess with default settings other than setting “censoredInt = 0,” “featureSubset = highQuality,” “remove_uninformative_feature_outlier = TRUE,” “clusters=7”; and function MSstats::groupComparison with all default settings.\nPhosphopeptide intensity data were summarized at the peptide ion level along with confident localization of phosphorylation as described in the previous section. Quantification of phosphorylation based on peptide ions were processed using artMS as a wrapper around MSstats, via functions artMS::doSiteConversion and artMS::artmsQuantification with default settings. All peptides containing the same set of phosphorylated sites were grouped and quantified together into phosphorylation site groups. For both phosphopeptide and protein abundance MSstats pipelines, MSstats performs normalization by median equalization, imputation of missing values and median smoothing to combine intensities for multiple peptide ions or fragments into a single intensity for their protein or phosphorylation site group, and statistical tests of differences in intensity between infected and control time points."}

LitCovid-sentences

{"project":"LitCovid-sentences","denotations":[{"id":"T888","span":{"begin":0,"end":37},"obj":"Sentence"},{"id":"T889","span":{"begin":38,"end":144},"obj":"Sentence"},{"id":"T890","span":{"begin":145,"end":356},"obj":"Sentence"},{"id":"T891","span":{"begin":357,"end":551},"obj":"Sentence"},{"id":"T892","span":{"begin":552,"end":828},"obj":"Sentence"},{"id":"T893","span":{"begin":829,"end":1160},"obj":"Sentence"},{"id":"T894","span":{"begin":1161,"end":1799},"obj":"Sentence"},{"id":"T895","span":{"begin":1800,"end":1961},"obj":"Sentence"},{"id":"T896","span":{"begin":1962,"end":2165},"obj":"Sentence"},{"id":"T897","span":{"begin":2166,"end":2297},"obj":"Sentence"},{"id":"T898","span":{"begin":2298,"end":2694},"obj":"Sentence"}],"namespaces":[{"prefix":"_base","uri":"http://pubannotation.org/ontology/tao.owl#"}],"text":"Mass spectrometry data pre-processing\nQuantitative analysis was performed in the R statistical programming language (version 3.6.1, 2019-07-05). Initial quality control analyses, including inter-run clusterings, correlations, principal components analysis, peptide and protein counts and intensities were completed with the R package artMS (version 1.5.3). Based on obvious outliers in intensities, correlations, and clusterings, 2 runs were discarded from the protein abundance data and 2 runs were discarded from the phosphopeptide data (Figure S1). Additionally, the phosphopeptide data were filtered based on feature (i.e., peptide ion) intensity, removing any single feature with intensity less than 214—this decision was made based on apparent lack of correlation between runs for feature intensities below this intensity. Thus, for both phosphopeptides and protein abundance, we had 2 control time points and 6 infected time points, each with 3 biologically distinct replicates, except for infected at 0 and 2 hours in the phosphopeptide data and control at 0 hours and infected at 0 hours in the protein abundance data which only had 2 replicates each.\nStatistical analysis of protein abundance changes between control and infected runs were computed using peptide ion fragment data output from Spectronaut and processed using a pipeline of three functions from the R package MSstats (version 3.19.5) (Choi et al., 2014): function MSstats::SpectronauttoMSstatsFormat with default settings other than setting “removeProtein_with1Feature = TRUE”; function MSstats::dataProcess with default settings other than setting “censoredInt = 0,” “featureSubset = highQuality,” “remove_uninformative_feature_outlier = TRUE,” “clusters=7”; and function MSstats::groupComparison with all default settings.\nPhosphopeptide intensity data were summarized at the peptide ion level along with confident localization of phosphorylation as described in the previous section. Quantification of phosphorylation based on peptide ions were processed using artMS as a wrapper around MSstats, via functions artMS::doSiteConversion and artMS::artmsQuantification with default settings. All peptides containing the same set of phosphorylated sites were grouped and quantified together into phosphorylation site groups. For both phosphopeptide and protein abundance MSstats pipelines, MSstats performs normalization by median equalization, imputation of missing values and median smoothing to combine intensities for multiple peptide ions or fragments into a single intensity for their protein or phosphorylation site group, and statistical tests of differences in intensity between infected and control time points."}

2_test

{"project":"2_test","denotations":[{"id":"32645325-24794931-20773114","span":{"begin":1423,"end":1427},"obj":"24794931"}],"text":"Mass spectrometry data pre-processing\nQuantitative analysis was performed in the R statistical programming language (version 3.6.1, 2019-07-05). Initial quality control analyses, including inter-run clusterings, correlations, principal components analysis, peptide and protein counts and intensities were completed with the R package artMS (version 1.5.3). Based on obvious outliers in intensities, correlations, and clusterings, 2 runs were discarded from the protein abundance data and 2 runs were discarded from the phosphopeptide data (Figure S1). Additionally, the phosphopeptide data were filtered based on feature (i.e., peptide ion) intensity, removing any single feature with intensity less than 214—this decision was made based on apparent lack of correlation between runs for feature intensities below this intensity. Thus, for both phosphopeptides and protein abundance, we had 2 control time points and 6 infected time points, each with 3 biologically distinct replicates, except for infected at 0 and 2 hours in the phosphopeptide data and control at 0 hours and infected at 0 hours in the protein abundance data which only had 2 replicates each.\nStatistical analysis of protein abundance changes between control and infected runs were computed using peptide ion fragment data output from Spectronaut and processed using a pipeline of three functions from the R package MSstats (version 3.19.5) (Choi et al., 2014): function MSstats::SpectronauttoMSstatsFormat with default settings other than setting “removeProtein_with1Feature = TRUE”; function MSstats::dataProcess with default settings other than setting “censoredInt = 0,” “featureSubset = highQuality,” “remove_uninformative_feature_outlier = TRUE,” “clusters=7”; and function MSstats::groupComparison with all default settings.\nPhosphopeptide intensity data were summarized at the peptide ion level along with confident localization of phosphorylation as described in the previous section. Quantification of phosphorylation based on peptide ions were processed using artMS as a wrapper around MSstats, via functions artMS::doSiteConversion and artMS::artmsQuantification with default settings. All peptides containing the same set of phosphorylated sites were grouped and quantified together into phosphorylation site groups. For both phosphopeptide and protein abundance MSstats pipelines, MSstats performs normalization by median equalization, imputation of missing values and median smoothing to combine intensities for multiple peptide ions or fragments into a single intensity for their protein or phosphorylation site group, and statistical tests of differences in intensity between infected and control time points."}