cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.12097659
title: In-silico homology assisted identification of inhibitor of RNA binding against 2019-nCoV N-protein (N terminal domain)

description: The N terminal domain (NTD) of Nucleocapsid protein (N protein) of coronavirus (CoV) binds to the viral (+) sense RNA and results in CoV ribonucleoprotien (CoV RNP) complex, essential for the virus replication. In this study, the RNA-binding N terminal domain (NTD) of the N protein was targeted for the identification of possible inhibitors of RNA binding. Two NTD structures of N proteins were selected (2OFZ and 1SSK, 92% homology) for virtual screening of 56,079 compounds from Asinex and Maybridge library to identify top 15 hits for each of the targets based on ‘docking score’. These top-hits were further screened for MM-GBSA binding free energy, pharmacokinetic properties (QikProp) and drug-likeness (SwissADME) and subjected to molecular dynamics (MD) studies. Two suitable binders (ZINC00003118440 and ZINC0000146942) against the target 2OFZ were identified. ZINC00003118440 is a theophylline derivative under the drug class ‘bronchodilators’ and further screening with approved bronchodilators was also studied to identify their ability to bind to the RNA binding region on the N protein. The other identified top hit is ZINC0000146942, which is a 3,4dihydropyrimidone class molecule. Hence this study suggests two important class of compounds, theophylline and pyrimidone derivaties as possible inhibitors of RNA binding to the N terminal domain of N protein of coronavirus, thus opening new avenues for in vitro validations. Communicated by Ramaswamy H. Sarma
DOI: 10.6084/m9.figshare.12097635
title: Coronavirus (COVID-19) statistics in China

description: A data set on COVID-19 pandemic in China, which covers daily statistics of confirmed cases (new and cumulative), recoveries (new and cumulative) and deaths (new and cumulative) at city level. All data are extracted from Chinese government reports.
DOI: 10.6084/m9.figshare.12097578
title: Additional file 1 of Bridging the gap between low-frequency and very-low-frequency earthquakes

description: Additional file 1. List of matched-filter analysis detections.
DOI: 10.6084/m9.figshare.12097578.v1
title: Additional file 1 of Bridging the gap between low-frequency and very-low-frequency earthquakes

description: Additional file 1. List of matched-filter analysis detections.
DOI: 10.6084/m9.figshare.12097515
title: Additional file 1 of HDACi mediate UNG2 depletion, dysregulated genomic uracil and altered expression of oncoproteins and tumor suppressors in B- and T-cell lines

description: Additional file 1: Table S1. SILAC quantification data.
DOI: 10.6084/m9.figshare.12097515.v1
title: Additional file 1 of HDACi mediate UNG2 depletion, dysregulated genomic uracil and altered expression of oncoproteins and tumor suppressors in B- and T-cell lines

description: Additional file 1: Table S1. SILAC quantification data.
DOI: 10.6084/m9.figshare.12097497
title: Additional file 3 of Temporal expression profiling of DAMPs-related genes revealed the biphasic post-ischemic inflammation in the experimental stroke model

description: Additional file 3: Table S2. DEGs at d1 post-stoke. Table S3. DEGs at d3 post-stoke. Supplementary Table S4. DEGs at d7 post-stoke. Table S5. DEGs at d14 post-stoke. Table S6. DEGs at d28 post-stoke.
DOI: 10.6084/m9.figshare.12097497.v1
title: Additional file 3 of Temporal expression profiling of DAMPs-related genes revealed the biphasic post-ischemic inflammation in the experimental stroke model

description: Additional file 3: Table S2. DEGs at d1 post-stoke. Table S3. DEGs at d3 post-stoke. Supplementary Table S4. DEGs at d7 post-stoke. Table S5. DEGs at d14 post-stoke. Table S6. DEGs at d28 post-stoke.
DOI: 10.6084/m9.figshare.12097404
title: Additional file 1 of Identification of surface proteins in a clinical Staphylococcus haemolyticus isolate by bacterial surface shaving

description: Additional file 1: Table S1. The cell surface shaving and LC-MS/MS analysis results identified 325 proteins with ≥ #2 peptide-spectrum matches (PSMs).
DOI: 10.6084/m9.figshare.12097404.v1
title: Additional file 1 of Identification of surface proteins in a clinical Staphylococcus haemolyticus isolate by bacterial surface shaving

description: Additional file 1: Table S1. The cell surface shaving and LC-MS/MS analysis results identified 325 proteins with ≥ #2 peptide-spectrum matches (PSMs).
DOI: 10.6084/m9.figshare.12097347
title: Additional file 1 of Cytokine response in human leptospirosis with different clinical outcomes: a systematic review

description: Additional file 1. Datasheet of the review on cytokine response in human leptospirosis, 2019. This provides the data extracted from the review.
DOI: 10.6084/m9.figshare.12097347.v1
title: Additional file 1 of Cytokine response in human leptospirosis with different clinical outcomes: a systematic review

description: Additional file 1. Datasheet of the review on cytokine response in human leptospirosis, 2019. This provides the data extracted from the review.
DOI: 10.6084/m9.figshare.12097311
title: Additional file 5 of The DNA hypermethylation phenotype of colorectal cancer liver metastases resembles that of the primary colorectal cancers

description: Additional file 5: Supplementary Table 1.
DOI: 10.6084/m9.figshare.12097311.v1
title: Additional file 5 of The DNA hypermethylation phenotype of colorectal cancer liver metastases resembles that of the primary colorectal cancers

description: Additional file 5: Supplementary Table 1.
DOI: 10.6084/m9.figshare.12097296
title: Additional file 5 of Diversity in Aβ deposit morphology and secondary proteome insolubility across models of Alzheimer-type amyloidosis

description: Additional file 5: Table S4. Excel spreadsheets that list the identity and spectral count data for the SDS-insoluble proteins identified from each model using SAINT score analysis. Each model is tabulated independently. The first tabs labeled “Raw spectra count, Removed duplicates, bait, prey, and inter” are the raw data exported from Scaffold and the intermediate steps of SAINT score calculations. SAINT scores were generated by SAINTexpress as described in Methods. The last 7 tabs are the identified proteins listed according to the genotypes. Any SDS-insoluble data with spectra counts less than 5 were removed, and only proteins with SAINT probability scores equal to or higher than 0.9 were counted as over-represented. This file contains the data used to generate Fig. 6.
DOI: 10.6084/m9.figshare.12097296.v1
title: Additional file 5 of Diversity in Aβ deposit morphology and secondary proteome insolubility across models of Alzheimer-type amyloidosis

description: Additional file 5: Table S4. Excel spreadsheets that list the identity and spectral count data for the SDS-insoluble proteins identified from each model using SAINT score analysis. Each model is tabulated independently. The first tabs labeled “Raw spectra count, Removed duplicates, bait, prey, and inter” are the raw data exported from Scaffold and the intermediate steps of SAINT score calculations. SAINT scores were generated by SAINTexpress as described in Methods. The last 7 tabs are the identified proteins listed according to the genotypes. Any SDS-insoluble data with spectra counts less than 5 were removed, and only proteins with SAINT probability scores equal to or higher than 0.9 were counted as over-represented. This file contains the data used to generate Fig. 6.
DOI: 10.6084/m9.figshare.12097287
title: Additional file 4 of Diversity in Aβ deposit morphology and secondary proteome insolubility across models of Alzheimer-type amyloidosis

description: Additional file 4: Table S3. Excel spreadsheets that list the identity and spectral count data for the SDS-insoluble proteins identified from each model that have the B6/FVB F1 background (See Table 3) using pair-wise analysis combined with SAINT scoring. Each model is tabulated independently. These mice include NTg, Tet.HuAβ, Tet.MoAβ and Tet.Bri42 mice.
DOI: 10.6084/m9.figshare.12097281
title: Additional file 3 of Diversity in Aβ deposit morphology and secondary proteome insolubility across models of Alzheimer-type amyloidosis

description: Additional file 3: Table S2. Excel spreadsheets that list the identity and spectral count data for the SDS-insoluble proteins identified from each model that have the B6/C3 background (See Table 3) using pair-wise analysis combined with SAINT scoring. Each model is tabulated independently. These mice include NTg, PrP.HuAβ/PS1, PrP.MoAβ/PS1 and Bri42 Homo mice.
DOI: 10.6084/m9.figshare.12097287.v1
title: Additional file 4 of Diversity in Aβ deposit morphology and secondary proteome insolubility across models of Alzheimer-type amyloidosis

description: Additional file 4: Table S3. Excel spreadsheets that list the identity and spectral count data for the SDS-insoluble proteins identified from each model that have the B6/FVB F1 background (See Table 3) using pair-wise analysis combined with SAINT scoring. Each model is tabulated independently. These mice include NTg, Tet.HuAβ, Tet.MoAβ and Tet.Bri42 mice.
DOI: 10.6084/m9.figshare.12097275
title: Additional file 2 of Diversity in Aβ deposit morphology and secondary proteome insolubility across models of Alzheimer-type amyloidosis

description: Additional file 2: Table S1. Excel spreadsheets for SAINT analysis of spectral count data for the SDS-insoluble proteins identified from young vs old NTg mice.
DOI: 10.6084/m9.figshare.12097281.v1
title: Additional file 3 of Diversity in Aβ deposit morphology and secondary proteome insolubility across models of Alzheimer-type amyloidosis

description: Additional file 3: Table S2. Excel spreadsheets that list the identity and spectral count data for the SDS-insoluble proteins identified from each model that have the B6/C3 background (See Table 3) using pair-wise analysis combined with SAINT scoring. Each model is tabulated independently. These mice include NTg, PrP.HuAβ/PS1, PrP.MoAβ/PS1 and Bri42 Homo mice.
DOI: 10.6084/m9.figshare.12097275.v1
title: Additional file 2 of Diversity in Aβ deposit morphology and secondary proteome insolubility across models of Alzheimer-type amyloidosis

description: Additional file 2: Table S1. Excel spreadsheets for SAINT analysis of spectral count data for the SDS-insoluble proteins identified from young vs old NTg mice.
DOI: 10.6084/m9.figshare.12097260
title: Additional file 2 of Assessing Gammapapillomavirus infections of mucosal epithelia with two broad-spectrum PCR protocols

description: Additional file 2: Table S2. Distribution of HPV types in 21 initial and 23 follow-up anal canal swab samples from 21 individuals. Linear Array HPV Genotyping Test (Roche Diagnostics GmbH, Mannheim, Germany) is based on the reverse-line blot hybridization technique to detect the following 37 different mucosal HPV types distributed in 11 Alpha-PV species (high-risk HPV types depicted in bold): Alpha-1 (HPV42), Alpha-3 (HPV61, HPV62, HPV72, HPV81, HPV83, HPV84, CP6108), Alpha-5 (HPV26, HPV51, HPV69, HPV82, IS39), Alpha-6 (HPV53, HPV56, HPV66), Alpha-7 (HPV18, HPV39, HPV45, HPV59, HPV68, HPV70), Alpha-8 (HPV40), Alpha-9 (HPV16, HPV31, HPV33, HPV35, HPV52, HPV58, HPV67), Alpha-10 (HPV6, HPV11, HPV55), Alpha-11 (HPV73, HPV64), Alpha-13 (HPV54), Alpha-14 (HPV71). RHA Kit Skin (Beta) HPV assay (RHA; Diassay BV, Rijswijk, The Netherlands) detects the following 25 different Beta-PV types by reverse-line blot hybridization technique: Beta-1 (HPV5, HPV8, HPV12, HPV14, HPV19, HPV20, HPV21, HPV24, HPV25, HPV36, HPV47, HPV93), Beta-2 (HPV9, HPV15, HPV17, HPV22, HPV23, HPV37, HPV38, HPV80), Beta-3 (HPV49, HPV75, HPV76), Beta-4 (HPV92), Beta-5 (HPV96). HPV types (or negative results) identified simultaneously by more than one HPV test are depicted in bold. Samples containing possible unknown HPV type(s) are indicated as HPV-X. Pos: Positive; Neg: Negative; N/A: Not Analyzed.
DOI: 10.6084/m9.figshare.12097266
title: Additional file 3 of Assessing Gammapapillomavirus infections of mucosal epithelia with two broad-spectrum PCR protocols

description: Additional file 3: Table S3. Alignments of the forward and reverse primer sequences of CUT PCR, Gamma-PV PCR and CP PCR with corresponding regions of L1 and E1 ORFs of 64 selected Gamma-PV types. Lines and characters represent identical and mismatched nucleotides, respectively. Conserved sequences at the 3′ regions of Gamma-PV primers are highlighted in grey. Degenerate nucleotides of primers: i = inosine, y = c/t, r = a/g, w = a/t, h = a/c/t, m = a/c, s = g/c.
DOI: 10.6084/m9.figshare.12097266.v1
title: Additional file 3 of Assessing Gammapapillomavirus infections of mucosal epithelia with two broad-spectrum PCR protocols

description: Additional file 3: Table S3. Alignments of the forward and reverse primer sequences of CUT PCR, Gamma-PV PCR and CP PCR with corresponding regions of L1 and E1 ORFs of 64 selected Gamma-PV types. Lines and characters represent identical and mismatched nucleotides, respectively. Conserved sequences at the 3′ regions of Gamma-PV primers are highlighted in grey. Degenerate nucleotides of primers: i = inosine, y = c/t, r = a/g, w = a/t, h = a/c/t, m = a/c, s = g/c.
DOI: 10.6084/m9.figshare.12097260.v1
title: Additional file 2 of Assessing Gammapapillomavirus infections of mucosal epithelia with two broad-spectrum PCR protocols

description: Additional file 2: Table S2. Distribution of HPV types in 21 initial and 23 follow-up anal canal swab samples from 21 individuals. Linear Array HPV Genotyping Test (Roche Diagnostics GmbH, Mannheim, Germany) is based on the reverse-line blot hybridization technique to detect the following 37 different mucosal HPV types distributed in 11 Alpha-PV species (high-risk HPV types depicted in bold): Alpha-1 (HPV42), Alpha-3 (HPV61, HPV62, HPV72, HPV81, HPV83, HPV84, CP6108), Alpha-5 (HPV26, HPV51, HPV69, HPV82, IS39), Alpha-6 (HPV53, HPV56, HPV66), Alpha-7 (HPV18, HPV39, HPV45, HPV59, HPV68, HPV70), Alpha-8 (HPV40), Alpha-9 (HPV16, HPV31, HPV33, HPV35, HPV52, HPV58, HPV67), Alpha-10 (HPV6, HPV11, HPV55), Alpha-11 (HPV73, HPV64), Alpha-13 (HPV54), Alpha-14 (HPV71). RHA Kit Skin (Beta) HPV assay (RHA; Diassay BV, Rijswijk, The Netherlands) detects the following 25 different Beta-PV types by reverse-line blot hybridization technique: Beta-1 (HPV5, HPV8, HPV12, HPV14, HPV19, HPV20, HPV21, HPV24, HPV25, HPV36, HPV47, HPV93), Beta-2 (HPV9, HPV15, HPV17, HPV22, HPV23, HPV37, HPV38, HPV80), Beta-3 (HPV49, HPV75, HPV76), Beta-4 (HPV92), Beta-5 (HPV96). HPV types (or negative results) identified simultaneously by more than one HPV test are depicted in bold. Samples containing possible unknown HPV type(s) are indicated as HPV-X. Pos: Positive; Neg: Negative; N/A: Not Analyzed.
DOI: 10.6084/m9.figshare.12097254
title: Additional file 1 of Assessing Gammapapillomavirus infections of mucosal epithelia with two broad-spectrum PCR protocols

description: Additional file 1: Table S1. HPV types/putative types detected by Gamma-PV and CUT PCR assays. Novel putative HPV types are depicted in bold. Un: species not included in the current HPV taxonomy. *: The same HPV/putative type was found in different sets of samples. Gamma-X refers to sequences grouped in the genus Gamma-PV from which the prevailing HPV type/s and species could not be determined.
DOI: 10.6084/m9.figshare.12097254.v1
title: Additional file 1 of Assessing Gammapapillomavirus infections of mucosal epithelia with two broad-spectrum PCR protocols

description: Additional file 1: Table S1. HPV types/putative types detected by Gamma-PV and CUT PCR assays. Novel putative HPV types are depicted in bold. Un: species not included in the current HPV taxonomy. *: The same HPV/putative type was found in different sets of samples. Gamma-X refers to sequences grouped in the genus Gamma-PV from which the prevailing HPV type/s and species could not be determined.
DOI: 10.6084/m9.figshare.12097233
title: Additional file 9 of Using multiple reference genomes to identify and resolve annotation inconsistencies

description: Additional file 9. Formatted input containing PH207 candidate and simulated split-genes for classification via the M2f_Classify.R script.
DOI: 10.6084/m9.figshare.12097233.v1
title: Additional file 9 of Using multiple reference genomes to identify and resolve annotation inconsistencies

description: Additional file 9. Formatted input containing PH207 candidate and simulated split-genes for classification via the M2f_Classify.R script.
DOI: 10.6084/m9.figshare.12097236
title: Additional file 1 of The utility of multi-stack alignment and 3D longitudinal image registration to assess bone remodeling in rheumatoid arthritis patients from second generation HR-pQCT scans

description: Additional file 1.
DOI: 10.6084/m9.figshare.12097227
title: Additional file 8 of Using multiple reference genomes to identify and resolve annotation inconsistencies

description: Additional file 8. Formatted input containing W22 candidate and simulated split-genes for classification via the M2f_Classify.R script.
DOI: 10.6084/m9.figshare.12097236.v1
title: Additional file 1 of The utility of multi-stack alignment and 3D longitudinal image registration to assess bone remodeling in rheumatoid arthritis patients from second generation HR-pQCT scans

description: Additional file 1.
DOI: 10.6084/m9.figshare.12097218
title: Additional file 7 of Using multiple reference genomes to identify and resolve annotation inconsistencies

description: Additional file 7. Formatted input containing B73 candidate and simulated split-genes for classification via the M2f_Classify.R script.
DOI: 10.6084/m9.figshare.12097218.v1
title: Additional file 7 of Using multiple reference genomes to identify and resolve annotation inconsistencies

description: Additional file 7. Formatted input containing B73 candidate and simulated split-genes for classification via the M2f_Classify.R script.
DOI: 10.6084/m9.figshare.12097215
title: Additional file 6 of Using multiple reference genomes to identify and resolve annotation inconsistencies

description: Additional file 6. Expression matrix for reads mapped to PH207. Sample names have format Genotype-Tissue-Rep and correspond to the names as defined in Table S3.
DOI: 10.6084/m9.figshare.12097227.v1
title: Additional file 8 of Using multiple reference genomes to identify and resolve annotation inconsistencies

description: Additional file 8. Formatted input containing W22 candidate and simulated split-genes for classification via the M2f_Classify.R script.
DOI: 10.6084/m9.figshare.12097209
title: Additional file 5 of Using multiple reference genomes to identify and resolve annotation inconsistencies

description: Additional file 5. Expression matrix for reads mapped to W22. Sample names have format Genotype-Tissue-Rep and correspond to the names as defined in Table S3.
DOI: 10.6084/m9.figshare.12097206
title: Additional file 4 of Using multiple reference genomes to identify and resolve annotation inconsistencies

description: Additional file 4. Expression matrix for reads mapped to B73. Sample names have format Genotype-Tissue-Rep and correspond to the names as defined in Table S3.
DOI: 10.6084/m9.figshare.12097215.v1
title: Additional file 6 of Using multiple reference genomes to identify and resolve annotation inconsistencies

description: Additional file 6. Expression matrix for reads mapped to PH207. Sample names have format Genotype-Tissue-Rep and correspond to the names as defined in Table S3.
DOI: 10.6084/m9.figshare.12097206.v1
title: Additional file 4 of Using multiple reference genomes to identify and resolve annotation inconsistencies

description: Additional file 4. Expression matrix for reads mapped to B73. Sample names have format Genotype-Tissue-Rep and correspond to the names as defined in Table S3.
DOI: 10.6084/m9.figshare.12097209.v1
title: Additional file 5 of Using multiple reference genomes to identify and resolve annotation inconsistencies

description: Additional file 5. Expression matrix for reads mapped to W22. Sample names have format Genotype-Tissue-Rep and correspond to the names as defined in Table S3.
DOI: 10.6084/m9.figshare.12097200
title: Additional file 3 of Using multiple reference genomes to identify and resolve annotation inconsistencies

description: Additional file 3. One-to-many homologs identified in the syntenic homology pipeline. A filtered subset of these entries are the split-gene candidates analyzed in this study.
DOI: 10.6084/m9.figshare.12097191
title: Additional file 2 of Using multiple reference genomes to identify and resolve annotation inconsistencies

description: Additional file 2. One-to-one homologs identified in the syntenic homology pipeline.
DOI: 10.6084/m9.figshare.12097200.v1
title: Additional file 3 of Using multiple reference genomes to identify and resolve annotation inconsistencies

description: Additional file 3. One-to-many homologs identified in the syntenic homology pipeline. A filtered subset of these entries are the split-gene candidates analyzed in this study.
DOI: 10.6084/m9.figshare.12097191.v1
title: Additional file 2 of Using multiple reference genomes to identify and resolve annotation inconsistencies

description: Additional file 2. One-to-one homologs identified in the syntenic homology pipeline.
DOI: 10.6084/m9.figshare.12097185
title: Additional file 12 of Using multiple reference genomes to identify and resolve annotation inconsistencies

description: Additional file 12. Annotation Edit Distance (AED) scores for B73 and PH207 annotated gene models.
DOI: 10.6084/m9.figshare.12097179
title: Additional file 11 of Using multiple reference genomes to identify and resolve annotation inconsistencies

description: Additional file 11. IsoSeq cDNA count data. See B73 IsoSeq analysis in Methods for procedure used to generate counts.
DOI: 10.6084/m9.figshare.12097176
title: Additional file 10 of Using multiple reference genomes to identify and resolve annotation inconsistencies

description: Additional file 10. Supported annotations according to our M2f procedure.
DOI: 10.6084/m9.figshare.12097179.v1
title: Additional file 11 of Using multiple reference genomes to identify and resolve annotation inconsistencies

description: Additional file 11. IsoSeq cDNA count data. See B73 IsoSeq analysis in Methods for procedure used to generate counts.
DOI: 10.6084/m9.figshare.12097176.v1
title: Additional file 10 of Using multiple reference genomes to identify and resolve annotation inconsistencies

description: Additional file 10. Supported annotations according to our M2f procedure.
DOI: 10.6084/m9.figshare.12097185.v1
title: Additional file 12 of Using multiple reference genomes to identify and resolve annotation inconsistencies

description: Additional file 12. Annotation Edit Distance (AED) scores for B73 and PH207 annotated gene models.
DOI: 10.6084/m9.figshare.12097143
title: Additional file 1 of Enhanced diagnostic accuracy for neurocognitive disorders: a revised cut-off approach for the Montreal Cognitive Assessment

description: Additional file 1: Supplementary Table 1. Diagnoses in the patient sample. Supplementary Figure 1. ROC curves. Supplementary Figure 2. Positive and negative predictive values. Excel file for automatic calculation of demographically corrected z-score for the German version of the MocA. MoCA z-score calculation
DOI: 10.6084/m9.figshare.12097143.v1
title: Additional file 1 of Enhanced diagnostic accuracy for neurocognitive disorders: a revised cut-off approach for the Montreal Cognitive Assessment

description: Additional file 1: Supplementary Table 1. Diagnoses in the patient sample. Supplementary Figure 1. ROC curves. Supplementary Figure 2. Positive and negative predictive values. Excel file for automatic calculation of demographically corrected z-score for the German version of the MocA. MoCA z-score calculation
DOI: 10.6084/m9.figshare.12097074
title: Additional file 2 of Comprehensive routine diagnostic screening to identify predictive mutations, gene amplifications, and microsatellite instability in FFPE tumor material

description: Additional file 2:. This files contains supplementary Table 1–8.