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LitCovid-PubTator

LitCovid-PMC-OGER-BB

LitCovid-PD-FMA-UBERON

{"project":"LitCovid-PD-FMA-UBERON","denotations":[{"id":"T97","span":{"begin":1232,"end":1236},"obj":"Body_part"}],"attributes":[{"id":"A97","pred":"fma_id","subj":"T97","obj":"http://purl.org/sig/ont/fma/fma9712"}],"text":"Discussion\nIn this study we provide a profile of computationally predicted immunogenic peptides from 2019-nCoV for functional validation and potential vaccine developments. We are fully aware that an effective vaccine development will require a very thorough investigation of immune correlates to 2019-nCoV. However, due to the emergency and severity of the outbreak as well as the lack of access to samples from infected subjects, such approaches would not serve the urgency. Therefore, computational prediction is instrumental for guiding biologists towards a quick and cost-effective solution to prevent the spread and ultimately help eliminate the infection from the individuals.\nWith a rising global concern of novel coronavirus outbreak, numerous research groups have started to investigate and publish their findings. At the time of preparing this manuscript, we became aware of a similar study conducted in comparing 2019-nCoV proteome with SARS CoV immunogenic peptides 9. Our in silico approach takes the search beyond presenting only common immunogenic peptide between SARS and 2019-nCoV and provides the experimental community with a more comprehensive list including de novo and cross reactive candidates. On the other hand, considering the fact that two studies have been accomplished independently with distinct approaches, this serves to demonstrate a high level of confidence in reproducing the results. Reproducibility of computational prediction is always of high importance and becomes even more significant under urgent scenarios as of this outbreak.\nOur study also suggests the need for further efforts to develop accurate predictive models and algorithms for the characterization of immunogenic peptides.\nIn this study, we provide potential immunogenic peptides from 2019-nCoV for vaccine targets that i) have been characterized immunogenic by previous studies on SARS CoV, ii) have high degree of similarity with immunogenic SARS CoV peptides and iii) are predicted immunogenic by combination of NetMHCpan and iPred/1G4 TCR positional weight matrices. Given the limited time and resources, our work serves as a guide to save time and cost for further experimental validation."}

LitCovid-PD-UBERON

{"project":"LitCovid-PD-UBERON","denotations":[{"id":"T2","span":{"begin":1232,"end":1236},"obj":"Body_part"}],"attributes":[{"id":"A2","pred":"uberon_id","subj":"T2","obj":"http://purl.obolibrary.org/obo/UBERON_0002398"}],"text":"Discussion\nIn this study we provide a profile of computationally predicted immunogenic peptides from 2019-nCoV for functional validation and potential vaccine developments. We are fully aware that an effective vaccine development will require a very thorough investigation of immune correlates to 2019-nCoV. However, due to the emergency and severity of the outbreak as well as the lack of access to samples from infected subjects, such approaches would not serve the urgency. Therefore, computational prediction is instrumental for guiding biologists towards a quick and cost-effective solution to prevent the spread and ultimately help eliminate the infection from the individuals.\nWith a rising global concern of novel coronavirus outbreak, numerous research groups have started to investigate and publish their findings. At the time of preparing this manuscript, we became aware of a similar study conducted in comparing 2019-nCoV proteome with SARS CoV immunogenic peptides 9. Our in silico approach takes the search beyond presenting only common immunogenic peptide between SARS and 2019-nCoV and provides the experimental community with a more comprehensive list including de novo and cross reactive candidates. On the other hand, considering the fact that two studies have been accomplished independently with distinct approaches, this serves to demonstrate a high level of confidence in reproducing the results. Reproducibility of computational prediction is always of high importance and becomes even more significant under urgent scenarios as of this outbreak.\nOur study also suggests the need for further efforts to develop accurate predictive models and algorithms for the characterization of immunogenic peptides.\nIn this study, we provide potential immunogenic peptides from 2019-nCoV for vaccine targets that i) have been characterized immunogenic by previous studies on SARS CoV, ii) have high degree of similarity with immunogenic SARS CoV peptides and iii) are predicted immunogenic by combination of NetMHCpan and iPred/1G4 TCR positional weight matrices. Given the limited time and resources, our work serves as a guide to save time and cost for further experimental validation."}

LitCovid-PD-MONDO

{"project":"LitCovid-PD-MONDO","denotations":[{"id":"T20","span":{"begin":652,"end":661},"obj":"Disease"},{"id":"T21","span":{"begin":949,"end":953},"obj":"Disease"},{"id":"T22","span":{"begin":1080,"end":1084},"obj":"Disease"},{"id":"T23","span":{"begin":1887,"end":1891},"obj":"Disease"},{"id":"T24","span":{"begin":1949,"end":1953},"obj":"Disease"}],"attributes":[{"id":"A20","pred":"mondo_id","subj":"T20","obj":"http://purl.obolibrary.org/obo/MONDO_0005550"},{"id":"A21","pred":"mondo_id","subj":"T21","obj":"http://purl.obolibrary.org/obo/MONDO_0005091"},{"id":"A22","pred":"mondo_id","subj":"T22","obj":"http://purl.obolibrary.org/obo/MONDO_0005091"},{"id":"A23","pred":"mondo_id","subj":"T23","obj":"http://purl.obolibrary.org/obo/MONDO_0005091"},{"id":"A24","pred":"mondo_id","subj":"T24","obj":"http://purl.obolibrary.org/obo/MONDO_0005091"}],"text":"Discussion\nIn this study we provide a profile of computationally predicted immunogenic peptides from 2019-nCoV for functional validation and potential vaccine developments. We are fully aware that an effective vaccine development will require a very thorough investigation of immune correlates to 2019-nCoV. However, due to the emergency and severity of the outbreak as well as the lack of access to samples from infected subjects, such approaches would not serve the urgency. Therefore, computational prediction is instrumental for guiding biologists towards a quick and cost-effective solution to prevent the spread and ultimately help eliminate the infection from the individuals.\nWith a rising global concern of novel coronavirus outbreak, numerous research groups have started to investigate and publish their findings. At the time of preparing this manuscript, we became aware of a similar study conducted in comparing 2019-nCoV proteome with SARS CoV immunogenic peptides 9. Our in silico approach takes the search beyond presenting only common immunogenic peptide between SARS and 2019-nCoV and provides the experimental community with a more comprehensive list including de novo and cross reactive candidates. On the other hand, considering the fact that two studies have been accomplished independently with distinct approaches, this serves to demonstrate a high level of confidence in reproducing the results. Reproducibility of computational prediction is always of high importance and becomes even more significant under urgent scenarios as of this outbreak.\nOur study also suggests the need for further efforts to develop accurate predictive models and algorithms for the characterization of immunogenic peptides.\nIn this study, we provide potential immunogenic peptides from 2019-nCoV for vaccine targets that i) have been characterized immunogenic by previous studies on SARS CoV, ii) have high degree of similarity with immunogenic SARS CoV peptides and iii) are predicted immunogenic by combination of NetMHCpan and iPred/1G4 TCR positional weight matrices. Given the limited time and resources, our work serves as a guide to save time and cost for further experimental validation."}

LitCovid-PD-CLO

{"project":"LitCovid-PD-CLO","denotations":[{"id":"T214","span":{"begin":36,"end":37},"obj":"http://purl.obolibrary.org/obo/CLO_0001020"},{"id":"T215","span":{"begin":87,"end":95},"obj":"http://purl.obolibrary.org/obo/PR_000018263"},{"id":"T216","span":{"begin":243,"end":244},"obj":"http://purl.obolibrary.org/obo/CLO_0001020"},{"id":"T217","span":{"begin":516,"end":528},"obj":"http://purl.obolibrary.org/obo/OBI_0000968"},{"id":"T218","span":{"begin":560,"end":561},"obj":"http://purl.obolibrary.org/obo/CLO_0001020"},{"id":"T219","span":{"begin":689,"end":690},"obj":"http://purl.obolibrary.org/obo/CLO_0001020"},{"id":"T220","span":{"begin":886,"end":887},"obj":"http://purl.obolibrary.org/obo/CLO_0001020"},{"id":"T221","span":{"begin":970,"end":978},"obj":"http://purl.obolibrary.org/obo/PR_000018263"},{"id":"T222","span":{"begin":1064,"end":1071},"obj":"http://purl.obolibrary.org/obo/PR_000018263"},{"id":"T223","span":{"begin":1144,"end":1145},"obj":"http://purl.obolibrary.org/obo/CLO_0001020"},{"id":"T224","span":{"begin":1366,"end":1367},"obj":"http://purl.obolibrary.org/obo/CLO_0001020"},{"id":"T225","span":{"begin":1718,"end":1726},"obj":"http://purl.obolibrary.org/obo/PR_000018263"},{"id":"T226","span":{"begin":1776,"end":1784},"obj":"http://purl.obolibrary.org/obo/PR_000018263"},{"id":"T227","span":{"begin":1958,"end":1966},"obj":"http://purl.obolibrary.org/obo/PR_000018263"},{"id":"T228","span":{"begin":2133,"end":2134},"obj":"http://purl.obolibrary.org/obo/CLO_0001020"}],"text":"Discussion\nIn this study we provide a profile of computationally predicted immunogenic peptides from 2019-nCoV for functional validation and potential vaccine developments. We are fully aware that an effective vaccine development will require a very thorough investigation of immune correlates to 2019-nCoV. However, due to the emergency and severity of the outbreak as well as the lack of access to samples from infected subjects, such approaches would not serve the urgency. Therefore, computational prediction is instrumental for guiding biologists towards a quick and cost-effective solution to prevent the spread and ultimately help eliminate the infection from the individuals.\nWith a rising global concern of novel coronavirus outbreak, numerous research groups have started to investigate and publish their findings. At the time of preparing this manuscript, we became aware of a similar study conducted in comparing 2019-nCoV proteome with SARS CoV immunogenic peptides 9. Our in silico approach takes the search beyond presenting only common immunogenic peptide between SARS and 2019-nCoV and provides the experimental community with a more comprehensive list including de novo and cross reactive candidates. On the other hand, considering the fact that two studies have been accomplished independently with distinct approaches, this serves to demonstrate a high level of confidence in reproducing the results. Reproducibility of computational prediction is always of high importance and becomes even more significant under urgent scenarios as of this outbreak.\nOur study also suggests the need for further efforts to develop accurate predictive models and algorithms for the characterization of immunogenic peptides.\nIn this study, we provide potential immunogenic peptides from 2019-nCoV for vaccine targets that i) have been characterized immunogenic by previous studies on SARS CoV, ii) have high degree of similarity with immunogenic SARS CoV peptides and iii) are predicted immunogenic by combination of NetMHCpan and iPred/1G4 TCR positional weight matrices. Given the limited time and resources, our work serves as a guide to save time and cost for further experimental validation."}

LitCovid-PD-CHEBI

{"project":"LitCovid-PD-CHEBI","denotations":[{"id":"T118","span":{"begin":87,"end":95},"obj":"Chemical"},{"id":"T119","span":{"begin":587,"end":595},"obj":"Chemical"},{"id":"T120","span":{"begin":970,"end":978},"obj":"Chemical"},{"id":"T121","span":{"begin":1064,"end":1071},"obj":"Chemical"},{"id":"T122","span":{"begin":1718,"end":1726},"obj":"Chemical"},{"id":"T123","span":{"begin":1776,"end":1784},"obj":"Chemical"},{"id":"T124","span":{"begin":1958,"end":1966},"obj":"Chemical"}],"attributes":[{"id":"A118","pred":"chebi_id","subj":"T118","obj":"http://purl.obolibrary.org/obo/CHEBI_16670"},{"id":"A119","pred":"chebi_id","subj":"T119","obj":"http://purl.obolibrary.org/obo/CHEBI_75958"},{"id":"A120","pred":"chebi_id","subj":"T120","obj":"http://purl.obolibrary.org/obo/CHEBI_16670"},{"id":"A121","pred":"chebi_id","subj":"T121","obj":"http://purl.obolibrary.org/obo/CHEBI_16670"},{"id":"A122","pred":"chebi_id","subj":"T122","obj":"http://purl.obolibrary.org/obo/CHEBI_16670"},{"id":"A123","pred":"chebi_id","subj":"T123","obj":"http://purl.obolibrary.org/obo/CHEBI_16670"},{"id":"A124","pred":"chebi_id","subj":"T124","obj":"http://purl.obolibrary.org/obo/CHEBI_16670"}],"text":"Discussion\nIn this study we provide a profile of computationally predicted immunogenic peptides from 2019-nCoV for functional validation and potential vaccine developments. We are fully aware that an effective vaccine development will require a very thorough investigation of immune correlates to 2019-nCoV. However, due to the emergency and severity of the outbreak as well as the lack of access to samples from infected subjects, such approaches would not serve the urgency. Therefore, computational prediction is instrumental for guiding biologists towards a quick and cost-effective solution to prevent the spread and ultimately help eliminate the infection from the individuals.\nWith a rising global concern of novel coronavirus outbreak, numerous research groups have started to investigate and publish their findings. At the time of preparing this manuscript, we became aware of a similar study conducted in comparing 2019-nCoV proteome with SARS CoV immunogenic peptides 9. Our in silico approach takes the search beyond presenting only common immunogenic peptide between SARS and 2019-nCoV and provides the experimental community with a more comprehensive list including de novo and cross reactive candidates. On the other hand, considering the fact that two studies have been accomplished independently with distinct approaches, this serves to demonstrate a high level of confidence in reproducing the results. Reproducibility of computational prediction is always of high importance and becomes even more significant under urgent scenarios as of this outbreak.\nOur study also suggests the need for further efforts to develop accurate predictive models and algorithms for the characterization of immunogenic peptides.\nIn this study, we provide potential immunogenic peptides from 2019-nCoV for vaccine targets that i) have been characterized immunogenic by previous studies on SARS CoV, ii) have high degree of similarity with immunogenic SARS CoV peptides and iii) are predicted immunogenic by combination of NetMHCpan and iPred/1G4 TCR positional weight matrices. Given the limited time and resources, our work serves as a guide to save time and cost for further experimental validation."}

LitCovid-sentences

{"project":"LitCovid-sentences","denotations":[{"id":"T185","span":{"begin":0,"end":10},"obj":"Sentence"},{"id":"T186","span":{"begin":11,"end":172},"obj":"Sentence"},{"id":"T187","span":{"begin":173,"end":307},"obj":"Sentence"},{"id":"T188","span":{"begin":308,"end":476},"obj":"Sentence"},{"id":"T189","span":{"begin":477,"end":683},"obj":"Sentence"},{"id":"T190","span":{"begin":684,"end":824},"obj":"Sentence"},{"id":"T191","span":{"begin":825,"end":981},"obj":"Sentence"},{"id":"T192","span":{"begin":982,"end":1218},"obj":"Sentence"},{"id":"T193","span":{"begin":1219,"end":1420},"obj":"Sentence"},{"id":"T194","span":{"begin":1421,"end":1571},"obj":"Sentence"},{"id":"T195","span":{"begin":1572,"end":1727},"obj":"Sentence"},{"id":"T196","span":{"begin":1728,"end":2075},"obj":"Sentence"},{"id":"T197","span":{"begin":2076,"end":2199},"obj":"Sentence"}],"namespaces":[{"prefix":"_base","uri":"http://pubannotation.org/ontology/tao.owl#"}],"text":"Discussion\nIn this study we provide a profile of computationally predicted immunogenic peptides from 2019-nCoV for functional validation and potential vaccine developments. We are fully aware that an effective vaccine development will require a very thorough investigation of immune correlates to 2019-nCoV. However, due to the emergency and severity of the outbreak as well as the lack of access to samples from infected subjects, such approaches would not serve the urgency. Therefore, computational prediction is instrumental for guiding biologists towards a quick and cost-effective solution to prevent the spread and ultimately help eliminate the infection from the individuals.\nWith a rising global concern of novel coronavirus outbreak, numerous research groups have started to investigate and publish their findings. At the time of preparing this manuscript, we became aware of a similar study conducted in comparing 2019-nCoV proteome with SARS CoV immunogenic peptides 9. Our in silico approach takes the search beyond presenting only common immunogenic peptide between SARS and 2019-nCoV and provides the experimental community with a more comprehensive list including de novo and cross reactive candidates. On the other hand, considering the fact that two studies have been accomplished independently with distinct approaches, this serves to demonstrate a high level of confidence in reproducing the results. Reproducibility of computational prediction is always of high importance and becomes even more significant under urgent scenarios as of this outbreak.\nOur study also suggests the need for further efforts to develop accurate predictive models and algorithms for the characterization of immunogenic peptides.\nIn this study, we provide potential immunogenic peptides from 2019-nCoV for vaccine targets that i) have been characterized immunogenic by previous studies on SARS CoV, ii) have high degree of similarity with immunogenic SARS CoV peptides and iii) are predicted immunogenic by combination of NetMHCpan and iPred/1G4 TCR positional weight matrices. Given the limited time and resources, our work serves as a guide to save time and cost for further experimental validation."}

PMC:7111504 / 16552-18751 JSONTXT

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PMC:7111504 / 16552-18751 JSON TXT