cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.13070762.v1
title: Additional file 2 of Genome wide association studies for japonica rice resistance to blast in field and controlled conditions

description: Additional file 2 Table S1. List of accessions used in the study with their geographical origin, taxonomical group, commercial class, group assigned by the STRUCTURE analysis and blast score values.
DOI: 10.6084/m9.figshare.13070762
title: Additional file 2 of Genome wide association studies for japonica rice resistance to blast in field and controlled conditions

description: Additional file 2 Table S1. List of accessions used in the study with their geographical origin, taxonomical group, commercial class, group assigned by the STRUCTURE analysis and blast score values.
DOI: 10.6084/m9.figshare.13070774.v1
title: Additional file 6 of Genome wide association studies for japonica rice resistance to blast in field and controlled conditions

description: Additional file 6 Table S3. Gene models contained in the regions defined by the GWAS analysis, from the Os-Nipponbare-Reference-IRGSP-1.0.
DOI: 10.6084/m9.figshare.13070774
title: Additional file 6 of Genome wide association studies for japonica rice resistance to blast in field and controlled conditions

description: Additional file 6 Table S3. Gene models contained in the regions defined by the GWAS analysis, from the Os-Nipponbare-Reference-IRGSP-1.0.
DOI: 10.6084/m9.figshare.13070765
title: Additional file 3 of Genome wide association studies for japonica rice resistance to blast in field and controlled conditions

description: Additional file 3 Table S2. Statistical analyses of the phenotypic traits: ANOVA and h2 for Field and Growth chamber evaluations.
DOI: 10.6084/m9.figshare.13070765.v1
title: Additional file 3 of Genome wide association studies for japonica rice resistance to blast in field and controlled conditions

description: Additional file 3 Table S2. Statistical analyses of the phenotypic traits: ANOVA and h2 for Field and Growth chamber evaluations.
DOI: 10.6084/m9.figshare.13070777.v1
title: Additional file 7 of Genome wide association studies for japonica rice resistance to blast in field and controlled conditions

description: Additional file 7 Table S4. Colocalization between MTA and known Pi genes. Bold names indicate cloned genes.
DOI: 10.6084/m9.figshare.13070780
title: Additional file 8 of Genome wide association studies for japonica rice resistance to blast in field and controlled conditions

description: Additional file 8 Table S5. A. Salvo x Maratelli linkage map. Linkage Groups (LG), chromosomes, marker names and their position (cM, obtained using the Kosambi mapping function) are reported. B. Distribution of molecular markers in the chromosomes of the Salvo x Maratelli map. C. QTL detected in Salvo x Maratelli segregating population. LOD score, percentage of phenotypic variance explained (R2), additive effect and the confidence interval (CI; Darvasi and Soller,1997) are reported.
DOI: 10.6084/m9.figshare.13070777
title: Additional file 7 of Genome wide association studies for japonica rice resistance to blast in field and controlled conditions

description: Additional file 7 Table S4. Colocalization between MTA and known Pi genes. Bold names indicate cloned genes.
DOI: 10.6084/m9.figshare.13070780.v1
title: Additional file 8 of Genome wide association studies for japonica rice resistance to blast in field and controlled conditions

description: Additional file 8 Table S5. A. Salvo x Maratelli linkage map. Linkage Groups (LG), chromosomes, marker names and their position (cM, obtained using the Kosambi mapping function) are reported. B. Distribution of molecular markers in the chromosomes of the Salvo x Maratelli map. C. QTL detected in Salvo x Maratelli segregating population. LOD score, percentage of phenotypic variance explained (R2), additive effect and the confidence interval (CI; Darvasi and Soller,1997) are reported.
DOI: 10.6084/m9.figshare.13070849
title: Additional file 1 of Genome-wide patterns of differentiation within and among U.S. commercial honey bee stocks

description: Additional file 1 : Supplemental Table 1. DNA sampling method.
DOI: 10.6084/m9.figshare.13070852.v1
title: Additional file 2 of Genome-wide patterns of differentiation within and among U.S. commercial honey bee stocks

description: Additional file 2 : Supplemental Table 2. Average π across the csd locus for all sequenced stocks.
DOI: 10.6084/m9.figshare.13070858.v1
title: Additional file 4 of Genome-wide patterns of differentiation within and among U.S. commercial honey bee stocks

description: Additional file 4 : Supplemental Table 4. A full list of the top 0.1% significant CSS regions.
DOI: 10.6084/m9.figshare.13070858
title: Additional file 4 of Genome-wide patterns of differentiation within and among U.S. commercial honey bee stocks

description: Additional file 4 : Supplemental Table 4. A full list of the top 0.1% significant CSS regions.
DOI: 10.6084/m9.figshare.13070849.v1
title: Additional file 1 of Genome-wide patterns of differentiation within and among U.S. commercial honey bee stocks

description: Additional file 1 : Supplemental Table 1. DNA sampling method.
DOI: 10.6084/m9.figshare.13070852
title: Additional file 2 of Genome-wide patterns of differentiation within and among U.S. commercial honey bee stocks

description: Additional file 2 : Supplemental Table 2. Average π across the csd locus for all sequenced stocks.
DOI: 10.6084/m9.figshare.13070861
title: Additional file 5 of Genome-wide patterns of differentiation within and among U.S. commercial honey bee stocks

description: Additional file 5 : Supplemental File 1. R code used to find overlapping SNPs for PCA analysis.
DOI: 10.6084/m9.figshare.13070855.v1
title: Additional file 3 of Genome-wide patterns of differentiation within and among U.S. commercial honey bee stocks

description: Additional file 3 : Supplemental Table 3. Stock-Specific SNPs and FST values for the top 1% of FST.
DOI: 10.6084/m9.figshare.13070855
title: Additional file 3 of Genome-wide patterns of differentiation within and among U.S. commercial honey bee stocks

description: Additional file 3 : Supplemental Table 3. Stock-Specific SNPs and FST values for the top 1% of FST.
DOI: 10.6084/m9.figshare.13070861.v1
title: Additional file 5 of Genome-wide patterns of differentiation within and among U.S. commercial honey bee stocks

description: Additional file 5 : Supplemental File 1. R code used to find overlapping SNPs for PCA analysis.
DOI: 10.6084/m9.figshare.12623096
title: Additional file 2 of Magnitude and predictors of common mental disorder among people with HIV/AIDS in Ethiopia: a systematic review and meta-analysis

description: Additional file 2. Sample data extraction format.
DOI: 10.6084/m9.figshare.12623096.v1
title: Additional file 2 of Magnitude and predictors of common mental disorder among people with HIV/AIDS in Ethiopia: a systematic review and meta-analysis

description: Additional file 2. Sample data extraction format.
DOI: 10.6084/m9.figshare.13070923
title: Additional file 10 of Development and comparison of RNA-sequencing pipelines for more accurate SNP identification: practical example of functional SNP detection associated with feed efficiency in Nellore beef cattle

description: Additional file 10.
DOI: 10.6084/m9.figshare.13070923.v1
title: Additional file 10 of Development and comparison of RNA-sequencing pipelines for more accurate SNP identification: practical example of functional SNP detection associated with feed efficiency in Nellore beef cattle

description: Additional file 10.
DOI: 10.6084/m9.figshare.13070953
title: Additional file 9 of Development and comparison of RNA-sequencing pipelines for more accurate SNP identification: practical example of functional SNP detection associated with feed efficiency in Nellore beef cattle

description: Additional file 9.
DOI: 10.6084/m9.figshare.13070947.v1
title: Additional file 7 of Development and comparison of RNA-sequencing pipelines for more accurate SNP identification: practical example of functional SNP detection associated with feed efficiency in Nellore beef cattle

description: Additional file 7.
DOI: 10.6084/m9.figshare.13070944
title: Additional file 6 of Development and comparison of RNA-sequencing pipelines for more accurate SNP identification: practical example of functional SNP detection associated with feed efficiency in Nellore beef cattle

description: Additional file 6.
DOI: 10.6084/m9.figshare.13070944.v1
title: Additional file 6 of Development and comparison of RNA-sequencing pipelines for more accurate SNP identification: practical example of functional SNP detection associated with feed efficiency in Nellore beef cattle

description: Additional file 6.
DOI: 10.6084/m9.figshare.13070950.v1
title: Additional file 8 of Development and comparison of RNA-sequencing pipelines for more accurate SNP identification: practical example of functional SNP detection associated with feed efficiency in Nellore beef cattle

description: Additional file 8.
DOI: 10.6084/m9.figshare.13070947
title: Additional file 7 of Development and comparison of RNA-sequencing pipelines for more accurate SNP identification: practical example of functional SNP detection associated with feed efficiency in Nellore beef cattle

description: Additional file 7.
DOI: 10.6084/m9.figshare.13070953.v1
title: Additional file 9 of Development and comparison of RNA-sequencing pipelines for more accurate SNP identification: practical example of functional SNP detection associated with feed efficiency in Nellore beef cattle

description: Additional file 9.
DOI: 10.6084/m9.figshare.13070950
title: Additional file 8 of Development and comparison of RNA-sequencing pipelines for more accurate SNP identification: practical example of functional SNP detection associated with feed efficiency in Nellore beef cattle

description: Additional file 8.
DOI: 10.6084/m9.figshare.13070963.v1
title: Additional file 2 of De novo missense variants disrupting protein–protein interactions affect risk for autism through gene co-expression and protein networks in neuronal cell types

description: Additional file 2: Table 1. Interaction-disruption predictions of dnMis variants (.xlsx). 524 dnMisvariant–PPI pairs identified in ASD probands and 94 pairs identified in unaffected siblings are listed. Foreach interaction disruption, we show the protein that harbors the dnMis variant and the amino acid changecaused by the variant, the interaction partner of the protein with dnMis variant, and the subject who carriesthe dnMis variant.
DOI: 10.6084/m9.figshare.13070975
title: Additional file 6 of De novo missense variants disrupting protein–protein interactions affect risk for autism through gene co-expression and protein networks in neuronal cell types

description: Additional file 6: Table 5. Gene ontology (GO) enrichment results of CTS-DAWN genes (.xlsx). GOterms enriched for CTS-DAWN genes (significant after multiple testing correction) are listed.
DOI: 10.6084/m9.figshare.13070969.v1
title: Additional file 4 of De novo missense variants disrupting protein–protein interactions affect risk for autism through gene co-expression and protein networks in neuronal cell types

description: Additional file 4: Table 3. Cell-type-specific (CTS) DAWN genes (.xlsx). 421, 413, and 281 significantCTS-DAWN genes identified in ExN, InN, and NPC are listed, respectively. In each cell type, CTS-DAWNgenes are ordered by their FDR values and are indicated whether they have been previously implicated inASD (in TADA/SFARI gene list, “1” for yes and “0” for no).
DOI: 10.6084/m9.figshare.13070966
title: Additional file 3 of De novo missense variants disrupting protein–protein interactions affect risk for autism through gene co-expression and protein networks in neuronal cell types

description: Additional file 3: Table 2. The ASD disrupted network (.xlsx). 526 nodes (proteins) and 507 edges(protein–protein interactions) composing the ASD disrupted network are listed. Nodes are ordered first bywhether the protein is a previously implicated ASD protein (in TADA/SFARI gene list, “1” for yes and “0”for no) and then by their degrees in the disrupted network.
DOI: 10.6084/m9.figshare.13070972
title: Additional file 5 of De novo missense variants disrupting protein–protein interactions affect risk for autism through gene co-expression and protein networks in neuronal cell types

description: Additional file 5: Table 4. Diagnostic scores of ASD probands carrying CTS-DAWN gene variants(.xlsx). IQ score, social responsiveness scale, and age of walking of probands carrying a PTV or missensevariant on CTS-DAWN genes are listed.
DOI: 10.6084/m9.figshare.13070978
title: Additional file 7 of De novo missense variants disrupting protein–protein interactions affect risk for autism through gene co-expression and protein networks in neuronal cell types

description: Additional file 7: Table 6. CTSu-TADA gene co-expression in GER and NC (.xlsx). GER- and NC-enrichedCTSu genes together with the corresponding co-expressed GER/NC-TADA genes are listed.
DOI: 10.6084/m9.figshare.13070963
title: Additional file 2 of De novo missense variants disrupting protein–protein interactions affect risk for autism through gene co-expression and protein networks in neuronal cell types

description: Additional file 2: Table 1. Interaction-disruption predictions of dnMis variants (.xlsx). 524 dnMisvariant–PPI pairs identified in ASD probands and 94 pairs identified in unaffected siblings are listed. Foreach interaction disruption, we show the protein that harbors the dnMis variant and the amino acid changecaused by the variant, the interaction partner of the protein with dnMis variant, and the subject who carriesthe dnMis variant.
DOI: 10.6084/m9.figshare.13070966.v1
title: Additional file 3 of De novo missense variants disrupting protein–protein interactions affect risk for autism through gene co-expression and protein networks in neuronal cell types

description: Additional file 3: Table 2. The ASD disrupted network (.xlsx). 526 nodes (proteins) and 507 edges(protein–protein interactions) composing the ASD disrupted network are listed. Nodes are ordered first bywhether the protein is a previously implicated ASD protein (in TADA/SFARI gene list, “1” for yes and “0”for no) and then by their degrees in the disrupted network.
DOI: 10.6084/m9.figshare.13070975.v1
title: Additional file 6 of De novo missense variants disrupting protein–protein interactions affect risk for autism through gene co-expression and protein networks in neuronal cell types

description: Additional file 6: Table 5. Gene ontology (GO) enrichment results of CTS-DAWN genes (.xlsx). GOterms enriched for CTS-DAWN genes (significant after multiple testing correction) are listed.
DOI: 10.6084/m9.figshare.13070978.v1
title: Additional file 7 of De novo missense variants disrupting protein–protein interactions affect risk for autism through gene co-expression and protein networks in neuronal cell types

description: Additional file 7: Table 6. CTSu-TADA gene co-expression in GER and NC (.xlsx). GER- and NC-enrichedCTSu genes together with the corresponding co-expressed GER/NC-TADA genes are listed.
DOI: 10.6084/m9.figshare.13070972.v1
title: Additional file 5 of De novo missense variants disrupting protein–protein interactions affect risk for autism through gene co-expression and protein networks in neuronal cell types

description: Additional file 5: Table 4. Diagnostic scores of ASD probands carrying CTS-DAWN gene variants(.xlsx). IQ score, social responsiveness scale, and age of walking of probands carrying a PTV or missensevariant on CTS-DAWN genes are listed.
DOI: 10.6084/m9.figshare.13070969
title: Additional file 4 of De novo missense variants disrupting protein–protein interactions affect risk for autism through gene co-expression and protein networks in neuronal cell types

description: Additional file 4: Table 3. Cell-type-specific (CTS) DAWN genes (.xlsx). 421, 413, and 281 significantCTS-DAWN genes identified in ExN, InN, and NPC are listed, respectively. In each cell type, CTS-DAWNgenes are ordered by their FDR values and are indicated whether they have been previously implicated inASD (in TADA/SFARI gene list, “1” for yes and “0” for no).
DOI: 10.6084/m9.figshare.13070980
title: Additional file 1 of Aging-related tumor associated fibroblasts changes could worsen the prognosis of GBM patients

description: Additional file 1: Table S1. Proportions of sex and lobes in different age groups.
DOI: 10.6084/m9.figshare.13070980.v1
title: Additional file 1 of Aging-related tumor associated fibroblasts changes could worsen the prognosis of GBM patients

description: Additional file 1: Table S1. Proportions of sex and lobes in different age groups.
DOI: 10.6084/m9.figshare.13070986
title: Additional file 3 of Aging-related tumor associated fibroblasts changes could worsen the prognosis of GBM patients

description: Additional file 3: Table S2. Differentially expressed genes.
DOI: 10.6084/m9.figshare.13070986.v1
title: Additional file 3 of Aging-related tumor associated fibroblasts changes could worsen the prognosis of GBM patients

description: Additional file 3: Table S2. Differentially expressed genes.
DOI: 10.6084/m9.figshare.13071044
title: Additional file 17 of Identification of African swine fever virus-like elements in the soft tick genome provides insights into the virus’ evolution

description: Additional file 17: Table S11. O. porcinus piRNA ping-pong signature analysed by PingPongPro v1.0 with default parameters and -b option (creates additional browser track files, which are suitable for display in common genome browsers). The closer the Score-value (1 minus the FDR-value: estimated fraction of signatures that have the same combination of properties, but that are not true ping-pong signatures) which is calculated from adenine bias at pos. 10 of the piRNA, stack height - e.g. the number of reads that make up the stack and independence of local coverage is to 1, the more likely is a true ping-pong signature.
DOI: 10.6084/m9.figshare.13071038
title: Additional file 15 of Identification of African swine fever virus-like elements in the soft tick genome provides insights into the virus’ evolution

description: Additional file 15: Table S9. Results of small RNA sequencing and mapping against ASFV and ASFLI-elements. Small RNA was sequenced from Ornithodoros porcinus and Ornithodoros moubata nymphal stage ticks. After deduplication using BBMap, 22 nt siRNA and 28-29 nt piRNA fractions were extracted and mapped against ASFV and ASFLI-elements using Bowtie.
DOI: 10.6084/m9.figshare.13071041.v1
title: Additional file 16 of Identification of African swine fever virus-like elements in the soft tick genome provides insights into the virus’ evolution

description: Additional file 16: Table S10. O. moubata piRNA ping-pong signature analysed by PingPongPro v1.0 with default parameters and -b option (creates additional browser track files, which are suitable for display in common genome browsers). The closer the Score-value (1 minus the FDR-value: estimated fraction of signatures that have the same combination of properties, but that are not true ping-pong signatures) which is calculated from adenine bias at pos. 10 of the piRNA, stack height - e.g. the number of reads that make up the stack and independence of local coverage is to 1, the more likely is a true ping-pong signature.
DOI: 10.6084/m9.figshare.13071044.v1
title: Additional file 17 of Identification of African swine fever virus-like elements in the soft tick genome provides insights into the virus’ evolution

description: Additional file 17: Table S11. O. porcinus piRNA ping-pong signature analysed by PingPongPro v1.0 with default parameters and -b option (creates additional browser track files, which are suitable for display in common genome browsers). The closer the Score-value (1 minus the FDR-value: estimated fraction of signatures that have the same combination of properties, but that are not true ping-pong signatures) which is calculated from adenine bias at pos. 10 of the piRNA, stack height - e.g. the number of reads that make up the stack and independence of local coverage is to 1, the more likely is a true ping-pong signature.
DOI: 10.6084/m9.figshare.13071023.v1
title: Additional file 10 of Identification of African swine fever virus-like elements in the soft tick genome provides insights into the virus’ evolution

description: Additional file 10: Table S8. qRT-PCR results of Ornithodoros ticks experimentally infected with different ASFV-genotype isolate. Shown are ASFV-P72 transcript-specific Cq-values of third nymphal stage ticks fed with defibrinated pig blood, containing either 1 x 104 HAU/ml or 1 x 106 HAU/ml ASFV-ken.rie1 (GT X) (A-D), 1 x 105 HAU/ml ASFV-Ken06.bus (GT IX) (E-F) or 1 x 104 HAU/ml ASFV-Sardinia (GT I) (G-H). Due to the limited number of field ticks available and feeding under artificial conditions, fifteen Ornithodoros porcinus ticks were collected in each of three experiments and ten in two experiments while for the laboratory-reared Ornithodoros moubata, twenty-five individuals were collected in each of three experiments and fifteen in one experiment. All samples were stored at – 80 °C until RNA-extraction and ASFV transcript-specific qRT-PCR analysis as described in the ‘Material and methods’ section.
DOI: 10.6084/m9.figshare.13071050
title: Additional file 19 of Identification of African swine fever virus-like elements in the soft tick genome provides insights into the virus’ evolution

description: Additional file 19: Table S12.Primer and probe sequences used in this study.
DOI: 10.6084/m9.figshare.13071053.v1
title: Additional file 1 of Identification of African swine fever virus-like elements in the soft tick genome provides insights into the virus’ evolution

description: Additional file 1: Table S1. Overview of NGS experiments conducted in this study and data obtained from the literature. Ornithodoros moubata cell line names have been abbreviated by removal of “/CTVM” and Ornithodoros porcinus and Ornithodoros moubata have been abbreviated for convenience.
DOI: 10.6084/m9.figshare.13071023
title: Additional file 10 of Identification of African swine fever virus-like elements in the soft tick genome provides insights into the virus’ evolution

description: Additional file 10: Table S8. qRT-PCR results of Ornithodoros ticks experimentally infected with different ASFV-genotype isolate. Shown are ASFV-P72 transcript-specific Cq-values of third nymphal stage ticks fed with defibrinated pig blood, containing either 1 x 104 HAU/ml or 1 x 106 HAU/ml ASFV-ken.rie1 (GT X) (A-D), 1 x 105 HAU/ml ASFV-Ken06.bus (GT IX) (E-F) or 1 x 104 HAU/ml ASFV-Sardinia (GT I) (G-H). Due to the limited number of field ticks available and feeding under artificial conditions, fifteen Ornithodoros porcinus ticks were collected in each of three experiments and ten in two experiments while for the laboratory-reared Ornithodoros moubata, twenty-five individuals were collected in each of three experiments and fifteen in one experiment. All samples were stored at – 80 °C until RNA-extraction and ASFV transcript-specific qRT-PCR analysis as described in the ‘Material and methods’ section.
DOI: 10.6084/m9.figshare.13071041
title: Additional file 16 of Identification of African swine fever virus-like elements in the soft tick genome provides insights into the virus’ evolution

description: Additional file 16: Table S10. O. moubata piRNA ping-pong signature analysed by PingPongPro v1.0 with default parameters and -b option (creates additional browser track files, which are suitable for display in common genome browsers). The closer the Score-value (1 minus the FDR-value: estimated fraction of signatures that have the same combination of properties, but that are not true ping-pong signatures) which is calculated from adenine bias at pos. 10 of the piRNA, stack height - e.g. the number of reads that make up the stack and independence of local coverage is to 1, the more likely is a true ping-pong signature.