cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.17982277.v1
title: Additional file 4 of Universal annotation of the human genome through integration of over a thousand epigenomic datasets

description: Additional file 4. Full-stack states enrichments with repeats, prioritized variants, common variants, structural variants, CTCF, PRC1-PRC2, GWAS catalog variants, sex chromosomes, fine-mapped variants, cancer-associated variants. Excel version of Additional File 1: Figure S28: Full stack states enrichments with RepeatMasker classes of repeats and CG content. Excel version of Additional File 1: Figure S37: Enrichment of all full-stack states for top 1% bases prioritized by variant prioritization scores. Excel version of Additional File 1: Figure S32: Full-stack states enrichments with structural variants. Excel version of Additional File 1: Figure S42: Full-stack states enrichments with variants from GNOMAD stratified by minor allele frequencies, common variants and CG dinucleotides. Excel version of Additional File 1: Figure S14: Full-stack states enrichments with CTCF associated chromatin states. Excel version of Additional File 1: Figure S17B: Neighborhood enrichments of full-stack states with binding sites of PRC1 and PRC2 complexes. Excel version of Additional File 1: Figure S16: Full-stack states enrichments with Polycomb Repressive protein complexes PRC1 and PRC2. Excel version of Additional File 1: Figure S43: Full-stack states enrichments with GWAS catalog variants and sex chromosomes. Excel version of Additional File 1: Figure S44: Full-stack states enrichment values for fine-mapped variants at phenotype associated loci. Excel version of Additional File 1: Figure S47: Full-stack states enrichments with cancer-associated somatic mutations in the non-coding genome.
DOI: 10.6084/m9.figshare.17982271.v1
title: Additional file 3 of Universal annotation of the human genome through integration of over a thousand epigenomic datasets

description: Additional file 3. Summary characterizations of full-stack states. Full characterization of the full-stack states, with detailed comments summarizing states’ characteristics. Emission parameters of the full-stack states. Top 100 highly emitted datasets associated with each full-stack state, colored by the marks associated with the datasets (similar to Fig. 2B). Top 100 highly emitted datasets associated with each full-stack state, colored by the cell groups associated with the datasets (similar to Fig. 2C). Different genome context enrichments with full-stack states: Excel version of Fig. 3A-B, Additional File 1: Figure S18. Excel version of Additional File 1 Figure S31: Enrichment of all full-stack states for ConsHMM states. Excel version of Additional File 1 Figure S30: Full-stack states and maximum-enriched ConsHMM state. Excel version of Additional File 1 Figure S7: Statistically significant tissue—group specificity in full-stack states.
DOI: 10.6084/m9.figshare.17982280
title: Additional file 5 of Universal annotation of the human genome through integration of over a thousand epigenomic datasets

description: Additional file 5 Full stack states’ association with concatenated annotations for multiple cell types. Excel version of Additional File 1: Figure S9: Estimated probabilities of concatenated chromatin states overlapping with full-stack states. Detailed comments about full-stack states’ characteristics through this analysis are provided. Excel version of Additional File 1: Figure S8: Full-stack state maximum enrichments with annotated chromatin states in 127 reference epigenomes. Detailed comments about full-stack states’ characteristics through this analysis are provided. Maximum enrichments of full-stack states and 25-state concatenated annotations for 127 cell/tissue types (reference epigenomes). Median enrichments of full-stack states and 25-state concatenated annotations for 127 cell/tissue types (reference epigenomes).
DOI: 10.6084/m9.figshare.17982280.v1
title: Additional file 5 of Universal annotation of the human genome through integration of over a thousand epigenomic datasets

description: Additional file 5 Full stack states’ association with concatenated annotations for multiple cell types. Excel version of Additional File 1: Figure S9: Estimated probabilities of concatenated chromatin states overlapping with full-stack states. Detailed comments about full-stack states’ characteristics through this analysis are provided. Excel version of Additional File 1: Figure S8: Full-stack state maximum enrichments with annotated chromatin states in 127 reference epigenomes. Detailed comments about full-stack states’ characteristics through this analysis are provided. Maximum enrichments of full-stack states and 25-state concatenated annotations for 127 cell/tissue types (reference epigenomes). Median enrichments of full-stack states and 25-state concatenated annotations for 127 cell/tissue types (reference epigenomes).
DOI: 10.6084/m9.figshare.17982283
title: Additional file 6 of Universal annotation of the human genome through integration of over a thousand epigenomic datasets

description: Additional file 6. Data of AUROC comparison between full-stack annotation and concatenated and independent annotations in predicting different genomic contexts. Data accompanying Additional File 1: Figure S19-21: AUROC comparison of full-stack annotations and concatenated and independent annotations in predicting external genome contexts. Data accompanying Additional File 1: Figure S34-35: AUROC comparison of full-stack model annotations and 18-state concatenated annotations and 100-state independent annotations in predicting structural variants of type deletions and duplications. Data accompanying Additional File 1: Figure S29: AUROC comparison of the full-stack and concatenated and independent chromatin state annotations at predicting different classes of repeat elements. Data accompanying Additional File 1: Figure S45-46: AUROC comparison of full-stack model annotations and the 100-state independent annotations and 18-state concatenated annotations in predicting fine-mapped variants. Data accompanying Additional File 1: Figure S41: AUROC comparison of the full-stack model annotations and concatenated and independent model annotations at predicting top 1% non-coding bases prioritized by various variant prioritization scores.
DOI: 10.6084/m9.figshare.17982283.v1
title: Additional file 6 of Universal annotation of the human genome through integration of over a thousand epigenomic datasets

description: Additional file 6. Data of AUROC comparison between full-stack annotation and concatenated and independent annotations in predicting different genomic contexts. Data accompanying Additional File 1: Figure S19-21: AUROC comparison of full-stack annotations and concatenated and independent annotations in predicting external genome contexts. Data accompanying Additional File 1: Figure S34-35: AUROC comparison of full-stack model annotations and 18-state concatenated annotations and 100-state independent annotations in predicting structural variants of type deletions and duplications. Data accompanying Additional File 1: Figure S29: AUROC comparison of the full-stack and concatenated and independent chromatin state annotations at predicting different classes of repeat elements. Data accompanying Additional File 1: Figure S45-46: AUROC comparison of full-stack model annotations and the 100-state independent annotations and 18-state concatenated annotations in predicting fine-mapped variants. Data accompanying Additional File 1: Figure S41: AUROC comparison of the full-stack model annotations and concatenated and independent model annotations at predicting top 1% non-coding bases prioritized by various variant prioritization scores.
DOI: 10.6084/m9.figshare.17982286
title: Additional file 7 of Universal annotation of the human genome through integration of over a thousand epigenomic datasets

description: Additional file 7. Summary characteristics of ConsHMM states. Descriptions of select ConsHMM states mentioned in the main text. Excel version of Additional File 1: Figure S30C: Enrichment of all full-stack states for ConsHMM states.
DOI: 10.6084/m9.figshare.17982286.v1
title: Additional file 7 of Universal annotation of the human genome through integration of over a thousand epigenomic datasets

description: Additional file 7. Summary characteristics of ConsHMM states. Descriptions of select ConsHMM states mentioned in the main text. Excel version of Additional File 1: Figure S30C: Enrichment of all full-stack states for ConsHMM states.
DOI: 10.6084/m9.figshare.17982295.v1
title: Additional file 9 of Universal annotation of the human genome through integration of over a thousand epigenomic datasets

description: Additional file 9. Download links for annotation data used throughout the manuscript.
DOI: 10.6084/m9.figshare.17982295
title: Additional file 9 of Universal annotation of the human genome through integration of over a thousand epigenomic datasets

description: Additional file 9. Download links for annotation data used throughout the manuscript.
DOI: 10.15121/1838418
title: Utah FORGE Updated Well, Well Pad, and Seismic Station GPS Coordinates December, 2021

description: This is a CSV spreadsheet containing UTM and Latitude and Longitude coordinates and elevations for Wells 78-32, 78B-32, 56-32, 58-32, 68-32, and 16A(78)-32 and seismic stations BOR1, BOR2, BOR3, FOR1, FOR2, FOR5, FOR6, FOR 7, FOR8, FORK, FORU, FORW, and FORB. These are from a GPS survey conducted by the Utah Geological Survey completed in December, 2021.
DOI: 10.6084/m9.figshare.17982319
title: Additional file 1 of HLA-SPREAD: a natural language processing based resource for curating HLA association from PubMed abstracts

description: Additional file 1: Table 1. List of positive and negative N-gram labels (adverbs/verbs) used for semantic analysis. Table 2. Catalogue of susceptible and risk alleles influencing same diseases. Table 3. Number of articles in broad categories. Table 4. HLA gene disease associations. Table 5. List of transplantation, its associated diseases along with pre and post complications information. Table 6. Details of SNPs annotated based on ensemble. Table 7. Geographical entities and their disease associations count. Table 8. HLA alleles and Adverse drug reactions. Table 9. Comparison of ADR drugs present in HLA-SPREAD with AFND and PharmGKB. Table 10. HLA indication as biomarkers.
DOI: 10.6084/m9.figshare.17982319.v1
title: Additional file 1 of HLA-SPREAD: a natural language processing based resource for curating HLA association from PubMed abstracts

description: Additional file 1: Table 1. List of positive and negative N-gram labels (adverbs/verbs) used for semantic analysis. Table 2. Catalogue of susceptible and risk alleles influencing same diseases. Table 3. Number of articles in broad categories. Table 4. HLA gene disease associations. Table 5. List of transplantation, its associated diseases along with pre and post complications information. Table 6. Details of SNPs annotated based on ensemble. Table 7. Geographical entities and their disease associations count. Table 8. HLA alleles and Adverse drug reactions. Table 9. Comparison of ADR drugs present in HLA-SPREAD with AFND and PharmGKB. Table 10. HLA indication as biomarkers.
DOI: 10.6084/m9.figshare.17982373
title: Additional file 1 of Systematic review of performance-enhancing health worker supervision approaches in low- and middle-income countries

description: Additional file 1. Supervision review inventory and taxonomy lists.
DOI: 10.6084/m9.figshare.17982373.v1
title: Additional file 1 of Systematic review of performance-enhancing health worker supervision approaches in low- and middle-income countries

description: Additional file 1. Supervision review inventory and taxonomy lists.
DOI: 10.6084/m9.figshare.17982397
title: Additional file 3 of scDALI: modeling allelic heterogeneity in single cells reveals context-specific genetic regulation

description: Additional file 3: Supplementary Table S1.
DOI: 10.6084/m9.figshare.17982397.v1
title: Additional file 3 of scDALI: modeling allelic heterogeneity in single cells reveals context-specific genetic regulation

description: Additional file 3: Supplementary Table S1.
DOI: 10.6084/m9.figshare.17982389
title: Additional file 1 of NETME: on-the-fly knowledge network construction from biomedical literature

description: Additional file 1. The json files storing all gene1-gene2 pairs used in the first case study having String scores ranging from 500 to 600. The main key of each record is the name ofthe two genes concatenated by "-". The lists of documents, are under the sub-keys "PMID" and "PMC".
DOI: 10.6084/m9.figshare.17982389.v1
title: Additional file 1 of NETME: on-the-fly knowledge network construction from biomedical literature

description: Additional file 1. The json files storing all gene1-gene2 pairs used in the first case study having String scores ranging from 500 to 600. The main key of each record is the name ofthe two genes concatenated by "-". The lists of documents, are under the sub-keys "PMID" and "PMC".
DOI: 10.6084/m9.figshare.17982392
title: Additional file 2 of NETME: on-the-fly knowledge network construction from biomedical literature

description: Additional file 2. The json files storing all gene1-gene2 pairs used in the first case study having String scores ranging from 600 to 700. The main key of each record is the name ofthe two genes concatenated by "-". The lists of documents, are under the sub-keys "PMID" and "PMC".
DOI: 10.6084/m9.figshare.17982392.v1
title: Additional file 2 of NETME: on-the-fly knowledge network construction from biomedical literature

description: Additional file 2. The json files storing all gene1-gene2 pairs used in the first case study having String scores ranging from 600 to 700. The main key of each record is the name ofthe two genes concatenated by "-". The lists of documents, are under the sub-keys "PMID" and "PMC".
DOI: 10.6084/m9.figshare.17982395
title: Additional file 3 of NETME: on-the-fly knowledge network construction from biomedical literature

description: Additional file 3. The json files storing all gene1-gene2 pairs used in the first case study having String scores ranging from 700 to 800. The main key of each record is the name ofthe two genes concatenated by "-". The lists of documents, are under the sub-keys "PMID" and "PMC".
DOI: 10.6084/m9.figshare.17982395.v1
title: Additional file 3 of NETME: on-the-fly knowledge network construction from biomedical literature

description: Additional file 3. The json files storing all gene1-gene2 pairs used in the first case study having String scores ranging from 700 to 800. The main key of each record is the name ofthe two genes concatenated by "-". The lists of documents, are under the sub-keys "PMID" and "PMC".
DOI: 10.6084/m9.figshare.17982404
title: Additional file 5 of NETME: on-the-fly knowledge network construction from biomedical literature

description: Additional file 5. The json files storing all gene1-gene2 pairs used in the first case study having String scores greater than 900. The main key of each record is the name ofthe two genes concatenated by "-". The lists of documents, are under the sub-keys "PMID" and "PMC".
DOI: 10.6084/m9.figshare.17982404.v1
title: Additional file 5 of NETME: on-the-fly knowledge network construction from biomedical literature

description: Additional file 5. The json files storing all gene1-gene2 pairs used in the first case study having String scores greater than 900. The main key of each record is the name ofthe two genes concatenated by "-". The lists of documents, are under the sub-keys "PMID" and "PMC".
DOI: 10.6084/m9.figshare.17982398
title: Additional file 4 of NETME: on-the-fly knowledge network construction from biomedical literature

description: Additional file 4. The json files storing all gene1-gene2 pairs used in the first case study having String scores ranging from 800 to 900. The main key of each record is the name ofthe two genes concatenated by "-". The lists of documents, are under the sub-keys "PMID" and "PMC".
DOI: 10.6084/m9.figshare.17982398.v1
title: Additional file 4 of NETME: on-the-fly knowledge network construction from biomedical literature

description: Additional file 4. The json files storing all gene1-gene2 pairs used in the first case study having String scores ranging from 800 to 900. The main key of each record is the name ofthe two genes concatenated by "-". The lists of documents, are under the sub-keys "PMID" and "PMC".
DOI: 10.6084/m9.figshare.17982412
title: Additional file 1 of Mutational landscape and in silico structure models of SARS-CoV-2 spike receptor binding domain reveal key molecular determinants for virus-host interaction

description: Additional file 1: Table S1. List of clusterwise interfacial interactions between wild type or mutant SARS CoV-2 RBD protein and human or mouse ACE2.
DOI: 10.6084/m9.figshare.17982412.v1
title: Additional file 1 of Mutational landscape and in silico structure models of SARS-CoV-2 spike receptor binding domain reveal key molecular determinants for virus-host interaction

description: Additional file 1: Table S1. List of clusterwise interfacial interactions between wild type or mutant SARS CoV-2 RBD protein and human or mouse ACE2.
DOI: 10.6084/m9.figshare.17982410
title: Additional file 6 of NETME: on-the-fly knowledge network construction from biomedical literature

description: Additional file 6. The json files storing all BSG-Disease available in DisGenNET. The lists of documents, are under the sub-keys "PMCID".
DOI: 10.6084/m9.figshare.17982410.v1
title: Additional file 6 of NETME: on-the-fly knowledge network construction from biomedical literature

description: Additional file 6. The json files storing all BSG-Disease available in DisGenNET. The lists of documents, are under the sub-keys "PMCID".
DOI: 10.6084/m9.figshare.17982421
title: Additional file 1 of Integrative profiling of Epstein–Barr virus transcriptome using a multiplatform approach

description: Additional file 1. Detailed descriptive statistics of the sequencing datasets. (A) Oxford Nanopore; B (Illumina).
DOI: 10.6084/m9.figshare.17982421.v1
title: Additional file 1 of Integrative profiling of Epstein–Barr virus transcriptome using a multiplatform approach

description: Additional file 1. Detailed descriptive statistics of the sequencing datasets. (A) Oxford Nanopore; B (Illumina).
DOI: 10.6084/m9.figshare.17982430
title: Additional file 2 of Integrative profiling of Epstein–Barr virus transcriptome using a multiplatform approach

description: Additional file 2. TSSs of EBV. TSSs were detected using the LoRTIA software. The -150–0 nt region upstream from the TSS was analyzed for GC, CAAT and TATA boxes. Distances from the TSS are given in nucleotides. The ±10 nts region of the TSS was also retrieved for sequence analysis. The TSS positions from previous CAGE-Seq [26] and PacBio [26] analyses were matched with our results. The sample number in which a given TSS was detected is shown. TSS coordinates are according to the genome of strain Akata (NCBI accession: KC207813.1).
DOI: 10.6084/m9.figshare.17982436
title: Additional file 3 of Integrative profiling of Epstein–Barr virus transcriptome using a multiplatform approach

description: Additional file 3. TESs of EBV. TESs were detected using the LoRTIA software. The -50–0 nt region upstream from the TES was analyzed for PASs. Distances from the TES are in nucleotides. The ±50 nts region of the TES was also retrieved for sequence analysis. The TES positions from previous PE-seq experiment was matched with our results. The sample number in which a given TES was detected is shown. TES coordinates are according to the genome of strain Akata (NCBI accession: KC207813.1).
DOI: 10.6084/m9.figshare.17982436.v1
title: Additional file 3 of Integrative profiling of Epstein–Barr virus transcriptome using a multiplatform approach

description: Additional file 3. TESs of EBV. TESs were detected using the LoRTIA software. The -50–0 nt region upstream from the TES was analyzed for PASs. Distances from the TES are in nucleotides. The ±50 nts region of the TES was also retrieved for sequence analysis. The TES positions from previous PE-seq experiment was matched with our results. The sample number in which a given TES was detected is shown. TES coordinates are according to the genome of strain Akata (NCBI accession: KC207813.1).
DOI: 10.6084/m9.figshare.17982430.v1
title: Additional file 2 of Integrative profiling of Epstein–Barr virus transcriptome using a multiplatform approach

description: Additional file 2. TSSs of EBV. TSSs were detected using the LoRTIA software. The -150–0 nt region upstream from the TSS was analyzed for GC, CAAT and TATA boxes. Distances from the TSS are given in nucleotides. The ±10 nts region of the TSS was also retrieved for sequence analysis. The TSS positions from previous CAGE-Seq [26] and PacBio [26] analyses were matched with our results. The sample number in which a given TSS was detected is shown. TSS coordinates are according to the genome of strain Akata (NCBI accession: KC207813.1).
DOI: 10.6084/m9.figshare.17982445
title: Additional file 4 of Integrative profiling of Epstein–Barr virus transcriptome using a multiplatform approach

description: Additional file 4. Transcripts of EBV. The transcripts were annotated using the LoRTIA software. The transcript coordinates are according to the genome of strain Akata (NCBI accession: KC207813.1). Transcript abundance in each sample is also shown.
DOI: 10.6084/m9.figshare.17982445.v1
title: Additional file 4 of Integrative profiling of Epstein–Barr virus transcriptome using a multiplatform approach

description: Additional file 4. Transcripts of EBV. The transcripts were annotated using the LoRTIA software. The transcript coordinates are according to the genome of strain Akata (NCBI accession: KC207813.1). Transcript abundance in each sample is also shown.
DOI: 10.6084/m9.figshare.17982451
title: Additional file 5 of Integrative profiling of Epstein–Barr virus transcriptome using a multiplatform approach

description: Additional file 5. The list of the transcripts determined in our study and the comparison of our data with those of published by O’Grady and colleagues.
DOI: 10.6084/m9.figshare.17982451.v1
title: Additional file 5 of Integrative profiling of Epstein–Barr virus transcriptome using a multiplatform approach

description: Additional file 5. The list of the transcripts determined in our study and the comparison of our data with those of published by O’Grady and colleagues.
DOI: 10.6084/m9.figshare.17982457
title: Additional file 7 of Integrative profiling of Epstein–Barr virus transcriptome using a multiplatform approach

description: Additional file 7. The introns of EBV. The introns were detected using the LoRTIA software. Read counts per sample are shown. Splice junction coordinates are according to the genome of strain Akata (NCBI accession: KC207813.1).
DOI: 10.6084/m9.figshare.17982457.v1
title: Additional file 7 of Integrative profiling of Epstein–Barr virus transcriptome using a multiplatform approach

description: Additional file 7. The introns of EBV. The introns were detected using the LoRTIA software. Read counts per sample are shown. Splice junction coordinates are according to the genome of strain Akata (NCBI accession: KC207813.1).
DOI: 10.6084/m9.figshare.17982454
title: Additional file 6 of Integrative profiling of Epstein–Barr virus transcriptome using a multiplatform approach

description: Additional file 6. Putative transcripts of EBV. Unique very long reads were named putative transcripts, because of their uncertain TSS position. Transcript coordinates are according to the genome of strain Akata (NCBI accession: KC207813.1).
DOI: 10.6084/m9.figshare.17982454.v1
title: Additional file 6 of Integrative profiling of Epstein–Barr virus transcriptome using a multiplatform approach

description: Additional file 6. Putative transcripts of EBV. Unique very long reads were named putative transcripts, because of their uncertain TSS position. Transcript coordinates are according to the genome of strain Akata (NCBI accession: KC207813.1).