cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.12940573
title: Additional file 6 of PTWAS: investigating tissue-relevant causal molecular mechanisms of complex traits using probabilistic TWAS analysis

description: Additional file 6 Table S5: Complete cross-tissue heterogeneity results for all gene-trait pairs.
DOI: 10.6084/m9.figshare.12940570.v1
title: Additional file 5 of PTWAS: investigating tissue-relevant causal molecular mechanisms of complex traits using probabilistic TWAS analysis

description: Additional file 5 Table S4: Complete list of estimation and model validation results for all gene-trait-tissue combinations.
DOI: 10.6084/m9.figshare.12940573.v1
title: Additional file 6 of PTWAS: investigating tissue-relevant causal molecular mechanisms of complex traits using probabilistic TWAS analysis

description: Additional file 6 Table S5: Complete cross-tissue heterogeneity results for all gene-trait pairs.
DOI: 10.6084/m9.figshare.12940561.v1
title: Additional file 2 of PTWAS: investigating tissue-relevant causal molecular mechanisms of complex traits using probabilistic TWAS analysis

description: Additional file 2 Table S1: Descriptions of 114 complex trait datasets used in the PTWAS analysis.
DOI: 10.6084/m9.figshare.12940600.v1
title: Additional file 4 of Optimizing the genetic prediction of the eye and hair color for North Eurasian populations

description: Additional file 4 Table S4. Sample sizes of different phenotypic classes in the regional datasets.
DOI: 10.6084/m9.figshare.12940597
title: Additional file 3 of Optimizing the genetic prediction of the eye and hair color for North Eurasian populations

description: Additional file 3 Table S3. Top SNPs list for North Eurasian pooled dataset for eye color prediction.
DOI: 10.6084/m9.figshare.12940600
title: Additional file 4 of Optimizing the genetic prediction of the eye and hair color for North Eurasian populations

description: Additional file 4 Table S4. Sample sizes of different phenotypic classes in the regional datasets.
DOI: 10.6084/m9.figshare.12940597.v1
title: Additional file 3 of Optimizing the genetic prediction of the eye and hair color for North Eurasian populations

description: Additional file 3 Table S3. Top SNPs list for North Eurasian pooled dataset for eye color prediction.
DOI: 10.6084/m9.figshare.12940603
title: Additional file 5 of Optimizing the genetic prediction of the eye and hair color for North Eurasian populations

description: Additional file 5 Table S5. Best SNPs lists for regional datasets for eye color prediction.
DOI: 10.6084/m9.figshare.12940582
title: Additional file 10 of Optimizing the genetic prediction of the eye and hair color for North Eurasian populations

description: Additional file 10 Table S9. The studied populations.
DOI: 10.6084/m9.figshare.12940585
title: Additional file 11 of Optimizing the genetic prediction of the eye and hair color for North Eurasian populations

description: Additional file 11 Table S10. The analyzed North Eurasian samples.
DOI: 10.6084/m9.figshare.12940591.v1
title: Additional file 1 of Optimizing the genetic prediction of the eye and hair color for North Eurasian populations

description: Additional file 1 Table S1. Performance characteristics of North Eurasian SNPs sets and HIrisPlex-S panel on North Eurasian pooled dataset for eye and hair color prediction.
DOI: 10.6084/m9.figshare.12940609
title: Additional file 7 of Optimizing the genetic prediction of the eye and hair color for North Eurasian populations

description: Additional file 7 Table S7. Frequencies of the five new eye color predictive SNPs in North Eurasian populations.
DOI: 10.6084/m9.figshare.12940612
title: Additional file 8 of Optimizing the genetic prediction of the eye and hair color for North Eurasian populations

description: Additional file 8 Table S8. Top SNPs list for North Eurasian pooled dataset for hair color prediction.
DOI: 10.6084/m9.figshare.12940582.v1
title: Additional file 10 of Optimizing the genetic prediction of the eye and hair color for North Eurasian populations

description: Additional file 10 Table S9. The studied populations.
DOI: 10.6084/m9.figshare.12940594.v1
title: Additional file 2 of Optimizing the genetic prediction of the eye and hair color for North Eurasian populations

description: Additional file 2 Table S2. The performance of HIrisPlex-S panel on North Eurasian regional datasets.
DOI: 10.6084/m9.figshare.12940585.v1
title: Additional file 11 of Optimizing the genetic prediction of the eye and hair color for North Eurasian populations

description: Additional file 11 Table S10. The analyzed North Eurasian samples.
DOI: 10.6084/m9.figshare.12940603.v1
title: Additional file 5 of Optimizing the genetic prediction of the eye and hair color for North Eurasian populations

description: Additional file 5 Table S5. Best SNPs lists for regional datasets for eye color prediction.
DOI: 10.6084/m9.figshare.12940612.v1
title: Additional file 8 of Optimizing the genetic prediction of the eye and hair color for North Eurasian populations

description: Additional file 8 Table S8. Top SNPs list for North Eurasian pooled dataset for hair color prediction.
DOI: 10.6084/m9.figshare.12940594
title: Additional file 2 of Optimizing the genetic prediction of the eye and hair color for North Eurasian populations

description: Additional file 2 Table S2. The performance of HIrisPlex-S panel on North Eurasian regional datasets.
DOI: 10.6084/m9.figshare.12940606
title: Additional file 6 of Optimizing the genetic prediction of the eye and hair color for North Eurasian populations

description: Additional file 6 Table S6. Summary table of best SNPs lists for the North Eurasian pooled and regional datasets for eye color prediction.
DOI: 10.6084/m9.figshare.12940591
title: Additional file 1 of Optimizing the genetic prediction of the eye and hair color for North Eurasian populations

description: Additional file 1 Table S1. Performance characteristics of North Eurasian SNPs sets and HIrisPlex-S panel on North Eurasian pooled dataset for eye and hair color prediction.
DOI: 10.6084/m9.figshare.12940606.v1
title: Additional file 6 of Optimizing the genetic prediction of the eye and hair color for North Eurasian populations

description: Additional file 6 Table S6. Summary table of best SNPs lists for the North Eurasian pooled and regional datasets for eye color prediction.
DOI: 10.6084/m9.figshare.12940609.v1
title: Additional file 7 of Optimizing the genetic prediction of the eye and hair color for North Eurasian populations

description: Additional file 7 Table S7. Frequencies of the five new eye color predictive SNPs in North Eurasian populations.
DOI: 10.6084/m9.figshare.12940632
title: Additional file 4 of Tracing the genetic history of the ‘Cañaris’ from Ecuador and Peru using uniparental DNA markers

description: Additional file 4: Table S4. MtDNA SNPs data, AMOVA results and mtDNA haplogroup frequencies. a List of mtDNA SNPs according to rCRS among the Peruvian and Ecuadorian populations. The mitochondrial haplogroups were determined using MitoTool program ( http://mitotool.kiz.ac.cn/ ); b AMOVA results for the 10 Peruvian and Ecuadorian populations using the control region mtDNA sequences (n = 379); c Distribution of mtDNA haplogroup frequencies among Ecuadorian and Peruvian populations.
DOI: 10.6084/m9.figshare.12940632.v1
title: Additional file 4 of Tracing the genetic history of the ‘Cañaris’ from Ecuador and Peru using uniparental DNA markers

description: Additional file 4: Table S4. MtDNA SNPs data, AMOVA results and mtDNA haplogroup frequencies. a List of mtDNA SNPs according to rCRS among the Peruvian and Ecuadorian populations. The mitochondrial haplogroups were determined using MitoTool program ( http://mitotool.kiz.ac.cn/ ); b AMOVA results for the 10 Peruvian and Ecuadorian populations using the control region mtDNA sequences (n = 379); c Distribution of mtDNA haplogroup frequencies among Ecuadorian and Peruvian populations.
DOI: 10.6084/m9.figshare.12940623.v1
title: Additional file 1 of Tracing the genetic history of the ‘Cañaris’ from Ecuador and Peru using uniparental DNA markers

description: Additional file 1: Table S1. Y-STR data and AMOVA results. (a) List of 17 Y-STRs among Peruvian and Ecuadorian populations; (b) AMOVA results for the 10 Peruvian and Ecuadorian populations using 15 STR haplotypes of Y chromosome (n = 376).
DOI: 10.6084/m9.figshare.12940623
title: Additional file 1 of Tracing the genetic history of the ‘Cañaris’ from Ecuador and Peru using uniparental DNA markers

description: Additional file 1: Table S1. Y-STR data and AMOVA results. (a) List of 17 Y-STRs among Peruvian and Ecuadorian populations; (b) AMOVA results for the 10 Peruvian and Ecuadorian populations using 15 STR haplotypes of Y chromosome (n = 376).
DOI: 10.6084/m9.figshare.12940651.v1
title: Additional file 2 of STARR-seq identifies active, chromatin-masked, and dormant enhancers in pluripotent mouse embryonic stem cells

description: Additional file 2: Table S1. Public data.
DOI: 10.6084/m9.figshare.12940651
title: Additional file 2 of STARR-seq identifies active, chromatin-masked, and dormant enhancers in pluripotent mouse embryonic stem cells

description: Additional file 2: Table S1. Public data.
DOI: 10.6084/m9.figshare.12940657
title: Additional file 4 of STARR-seq identifies active, chromatin-masked, and dormant enhancers in pluripotent mouse embryonic stem cells

description: Additional file 4: Table S3. STARRseq 2iL SL ESCs.
DOI: 10.6084/m9.figshare.12940657.v1
title: Additional file 4 of STARR-seq identifies active, chromatin-masked, and dormant enhancers in pluripotent mouse embryonic stem cells

description: Additional file 4: Table S3. STARRseq 2iL SL ESCs.
DOI: 10.6084/m9.figshare.12940660.v1
title: Additional file 5 of STARR-seq identifies active, chromatin-masked, and dormant enhancers in pluripotent mouse embryonic stem cells

description: Additional file 5: Table S4. Zic3 WT KO RNAseq.
DOI: 10.6084/m9.figshare.12940666.v1
title: Additional file 7 of STARR-seq identifies active, chromatin-masked, and dormant enhancers in pluripotent mouse embryonic stem cells

description: Additional file 7: Table S6. Cistrome ChIPseq enrichment.
DOI: 10.6084/m9.figshare.12940660
title: Additional file 5 of STARR-seq identifies active, chromatin-masked, and dormant enhancers in pluripotent mouse embryonic stem cells

description: Additional file 5: Table S4. Zic3 WT KO RNAseq.
DOI: 10.6084/m9.figshare.12940671
title: Additional file 1 of Assessing mental well-being in a Sinhala speaking Sri Lankan population: validation of the WHO-5 well-being index

description: Additional file 1.
DOI: 10.6084/m9.figshare.12940654
title: Additional file 3 of STARR-seq identifies active, chromatin-masked, and dormant enhancers in pluripotent mouse embryonic stem cells

description: Additional file 3: Table S2. Primers and loci.
DOI: 10.6084/m9.figshare.12940663.v1
title: Additional file 6 of STARR-seq identifies active, chromatin-masked, and dormant enhancers in pluripotent mouse embryonic stem cells

description: Additional file 6: Table S5 Trp53 WT KO RNAseq.
DOI: 10.6084/m9.figshare.12940666
title: Additional file 7 of STARR-seq identifies active, chromatin-masked, and dormant enhancers in pluripotent mouse embryonic stem cells

description: Additional file 7: Table S6. Cistrome ChIPseq enrichment.
DOI: 10.6084/m9.figshare.12940654.v1
title: Additional file 3 of STARR-seq identifies active, chromatin-masked, and dormant enhancers in pluripotent mouse embryonic stem cells

description: Additional file 3: Table S2. Primers and loci.
DOI: 10.6084/m9.figshare.12940663
title: Additional file 6 of STARR-seq identifies active, chromatin-masked, and dormant enhancers in pluripotent mouse embryonic stem cells

description: Additional file 6: Table S5 Trp53 WT KO RNAseq.
DOI: 10.6084/m9.figshare.12940671.v1
title: Additional file 1 of Assessing mental well-being in a Sinhala speaking Sri Lankan population: validation of the WHO-5 well-being index

description: Additional file 1.
DOI: 10.6084/m9.figshare.12940732.v1
title: Additional file 3 of A vast resource of allelic expression data spanning human tissues

description: Additional file 3: Table S2: Sample-threshold and allelic imbalance statistics for haplotype-level AE data. Table where rows are each of the 49 GTEx tissues where eQTLs were called and columns list the number of genes with haplotype-level AE data at minimum number of sample thresholds from 1 to 300 (minXXX). For example, min1 lists the number of genes that have AE data from at least 1 sample. The table has three sheets, the first (all_data) presents statistics generated using all haplotype-level AE data, the second (sig_imb_fdr05), counting only cases with significant allelic imbalance (binomial test versus 50/50, gene-level FDR
DOI: 10.6084/m9.figshare.12940732
title: Additional file 3 of A vast resource of allelic expression data spanning human tissues

description: Additional file 3: Table S2: Sample-threshold and allelic imbalance statistics for haplotype-level AE data. Table where rows are each of the 49 GTEx tissues where eQTLs were called and columns list the number of genes with haplotype-level AE data at minimum number of sample thresholds from 1 to 300 (minXXX). For example, min1 lists the number of genes that have AE data from at least 1 sample. The table has three sheets, the first (all_data) presents statistics generated using all haplotype-level AE data, the second (sig_imb_fdr05), counting only cases with significant allelic imbalance (binomial test versus 50/50, gene-level FDR
DOI: 10.6084/m9.figshare.12940729.v1
title: Additional file 2 of A vast resource of allelic expression data spanning human tissues

description: Additional file 2: Table S1: Tissue-level summary statistics for haplotype-level AE data. Table listing sample size, number of expressed genes (defined as genes with &gt; = 0.1 TPM in at least 1 individual), number of genes with phASER data (defined as genes with &gt; = 8 reads in at least 1 individual), median number of samples per gene with phASER data, and if the tissue was used for GTEx v8 eQTL mapping.
DOI: 10.6084/m9.figshare.12940729
title: Additional file 2 of A vast resource of allelic expression data spanning human tissues

description: Additional file 2: Table S1: Tissue-level summary statistics for haplotype-level AE data. Table listing sample size, number of expressed genes (defined as genes with &gt; = 0.1 TPM in at least 1 individual), number of genes with phASER data (defined as genes with &gt; = 8 reads in at least 1 individual), median number of samples per gene with phASER data, and if the tissue was used for GTEx v8 eQTL mapping.
DOI: 10.6084/m9.figshare.12940761
title: Additional file 1 of Repeated measures of Heparin-binding protein (HBP) and procalcitonin during septic shock: biomarker kinetics and association with cardiovascular organ dysfunction

description: Additional file 1: Figure S1. Procalcitonin and NA doses for all 24 patients during the first 72 hours of ICU stay. NA values are truncated at 0.5 μg/kg/min. Deaths within 72 hours are marked with a “+” (n = 2). Patient id 12 only had one HBP measurement in ICU prior to death, marked with an “o”. Figure S2. Plasma HBP and NA doses for patient number 10 during the first 72 hours of ICU stay. This patient’s graph is shown separately because his high HBP levels were truncated in Fig. 2. The patient died due to refractory septic shock at 59 hours from ICU admission marked with an “+”. Figure S3. The central black line represents the marginal predicted SVRI based on a linear mixed-effects model including HBP levels, NA dose time and CI as co-variates among 13 patients with non-invasive monitoring. Caps represent 95% confidence intervals for the marginal prediction. Scattered dots are all the actual SVRI and HBP values with a separate colour for each patient. HBP is presented on the log scale on the x-axis and samples with 0 ng/mL of HBP are equalled to the next sample minimum (4.72 ng/mL).
DOI: 10.6084/m9.figshare.12940761.v1
title: Additional file 1 of Repeated measures of Heparin-binding protein (HBP) and procalcitonin during septic shock: biomarker kinetics and association with cardiovascular organ dysfunction

description: Additional file 1: Figure S1. Procalcitonin and NA doses for all 24 patients during the first 72 hours of ICU stay. NA values are truncated at 0.5 μg/kg/min. Deaths within 72 hours are marked with a “+” (n = 2). Patient id 12 only had one HBP measurement in ICU prior to death, marked with an “o”. Figure S2. Plasma HBP and NA doses for patient number 10 during the first 72 hours of ICU stay. This patient’s graph is shown separately because his high HBP levels were truncated in Fig. 2. The patient died due to refractory septic shock at 59 hours from ICU admission marked with an “+”. Figure S3. The central black line represents the marginal predicted SVRI based on a linear mixed-effects model including HBP levels, NA dose time and CI as co-variates among 13 patients with non-invasive monitoring. Caps represent 95% confidence intervals for the marginal prediction. Scattered dots are all the actual SVRI and HBP values with a separate colour for each patient. HBP is presented on the log scale on the x-axis and samples with 0 ng/mL of HBP are equalled to the next sample minimum (4.72 ng/mL).
DOI: 10.6084/m9.figshare.12940804
title: Additional file 4 of Estimating the quality of eukaryotic genomes recovered from metagenomic analysis with EukCC

description: Additional file 4: Table S3. Benchmark information, contamination from outside clade. (CSV 1 kb)
DOI: 10.6084/m9.figshare.12940804.v1
title: Additional file 4 of Estimating the quality of eukaryotic genomes recovered from metagenomic analysis with EukCC

description: Additional file 4: Table S3. Benchmark information, contamination from outside clade. (CSV 1 kb)
DOI: 10.6084/m9.figshare.12940798.v1
title: Additional file 2 of Estimating the quality of eukaryotic genomes recovered from metagenomic analysis with EukCC

description: Additional file 2: Table S1. Table of Genomes included in EukCC database. (CSV 17 kb)
DOI: 10.6084/m9.figshare.12940801
title: Additional file 3 of Estimating the quality of eukaryotic genomes recovered from metagenomic analysis with EukCC

description: Additional file 3: Table S2. Benchmark information, contamination from same clade. (CSV 1 kb)
DOI: 10.6084/m9.figshare.12940807
title: Additional file 5 of Estimating the quality of eukaryotic genomes recovered from metagenomic analysis with EukCC

description: Additional file 5: Table S4. Cluster completeness contribution. (CSV 121 bytes)
DOI: 10.6084/m9.figshare.12940810
title: Additional file 6 of Estimating the quality of eukaryotic genomes recovered from metagenomic analysis with EukCC

description: Additional file 6: Table S5. Recovered MAGs metadata. (CSV 526 bytes)
DOI: 10.6084/m9.figshare.12940798
title: Additional file 2 of Estimating the quality of eukaryotic genomes recovered from metagenomic analysis with EukCC

description: Additional file 2: Table S1. Table of Genomes included in EukCC database. (CSV 17 kb)
DOI: 10.6084/m9.figshare.12940810.v1
title: Additional file 6 of Estimating the quality of eukaryotic genomes recovered from metagenomic analysis with EukCC

description: Additional file 6: Table S5. Recovered MAGs metadata. (CSV 526 bytes)
DOI: 10.6084/m9.figshare.12940807.v1
title: Additional file 5 of Estimating the quality of eukaryotic genomes recovered from metagenomic analysis with EukCC

description: Additional file 5: Table S4. Cluster completeness contribution. (CSV 121 bytes)
DOI: 10.6084/m9.figshare.12940840.v1
title: Additional file 4 of Species delimitation in the cyanolichen genus Rostania

description: Additional file 4. Substitution model parameters and partitions according to the best scheme calculated in PartitionFinder. The models for each codon-position partition of the protein coding genes were estimated separately. Appropriate molecular clocks were calculated in MrBayes for all codon-positions in each gene combined.
DOI: 10.6084/m9.figshare.12940837.v1
title: Additional file 3 of Species delimitation in the cyanolichen genus Rostania

description: Additional file 3. A-D Results of 16 species delimitation analyses (× 2 runs) with BPP with (A10) and without (A11) guide tree, with 8 or 13 as the maximum number of species and under four prior combinations of theta and tau. In addition, with two prior combinations of theta and tau, the sensitivity of the results to additional analysis assumptions and algorithms settings were evaluated. Values in cells are posterior probabilities.
DOI: 10.6084/m9.figshare.12940837
title: Additional file 3 of Species delimitation in the cyanolichen genus Rostania

description: Additional file 3. A-D Results of 16 species delimitation analyses (× 2 runs) with BPP with (A10) and without (A11) guide tree, with 8 or 13 as the maximum number of species and under four prior combinations of theta and tau. In addition, with two prior combinations of theta and tau, the sensitivity of the results to additional analysis assumptions and algorithms settings were evaluated. Values in cells are posterior probabilities.
DOI: 10.6084/m9.figshare.12940840
title: Additional file 4 of Species delimitation in the cyanolichen genus Rostania

description: Additional file 4. Substitution model parameters and partitions according to the best scheme calculated in PartitionFinder. The models for each codon-position partition of the protein coding genes were estimated separately. Appropriate molecular clocks were calculated in MrBayes for all codon-positions in each gene combined.
DOI: 10.6084/m9.figshare.12940831
title: Additional file 1 of Species delimitation in the cyanolichen genus Rostania

description: Additional file 1. Specimens and sequences utilized in this study including newly produced sequences (in bold), from reference [20] (21 sequences) and from reference [17] (1 sequence). Each specimen has a unique DNA extraction code. The analyses included four genes, mtSSU = small subunit of the mitochondrial rDNA, MCM7 = replication licensing factor mini-chromosome maintenance complex component 7, β-tub = β-tubulin gene. In addition, analysis of RPB2 included two parts of the gene - RPB2 5–7 = RPB2 protein-coding gene part 5–7 and RPB2 7–11 = RPB2 protein-coding gene part 7–11.
DOI: 10.6084/m9.figshare.12940831.v1
title: Additional file 1 of Species delimitation in the cyanolichen genus Rostania

description: Additional file 1. Specimens and sequences utilized in this study including newly produced sequences (in bold), from reference [20] (21 sequences) and from reference [17] (1 sequence). Each specimen has a unique DNA extraction code. The analyses included four genes, mtSSU = small subunit of the mitochondrial rDNA, MCM7 = replication licensing factor mini-chromosome maintenance complex component 7, β-tub = β-tubulin gene. In addition, analysis of RPB2 included two parts of the gene - RPB2 5–7 = RPB2 protein-coding gene part 5–7 and RPB2 7–11 = RPB2 protein-coding gene part 7–11.
DOI: 10.6084/m9.figshare.12940968
title: Additional file 1 of A genome-wide identification of the BLH gene family reveals BLH1 involved in cotton fiber development

description: Additional file 1: Supplementary Table 1. Comparative analysis of GhBLH genes in CRI_V1.0 and CRI_V3.0.
DOI: 10.6084/m9.figshare.12940968.v1
title: Additional file 1 of A genome-wide identification of the BLH gene family reveals BLH1 involved in cotton fiber development

description: Additional file 1: Supplementary Table 1. Comparative analysis of GhBLH genes in CRI_V1.0 and CRI_V3.0.
DOI: 10.6084/m9.figshare.12941068.v1
title: Additional file 2 of Preliminary study of the production of metabolites from in vitro cultures of C. ensiformis

description: Additional file 2.
DOI: 10.6084/m9.figshare.12941068
title: Additional file 2 of Preliminary study of the production of metabolites from in vitro cultures of C. ensiformis

description: Additional file 2.
DOI: 10.6084/m9.figshare.12940712.v2
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