Resultados totales (Incluyendo duplicados): 34354
Encontrada(s) 3436 página(s)
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/311497
Dataset. 2022

SUPPLEMENTARY FILES OF THE ARTICLE "DETECTION OF EARLY SEEDING OF RICHTER TRANSFORMATION IN CHRONIC LYMPHOCYTIC LEUKEMIA" [DATASET]

  • Nadeu, Ferran
  • Royo, Romina
  • Massoni-Badosa, Ramon
  • Playa-Albinyana, Heribert
  • Garcia-Torre, Beatriz
  • Duran-Ferrer, Martí
  • Dawson, Kevin J.
  • Kulis, Marta
  • Diaz-Navarro, Ander
  • Villamor, Neus
  • Melero, Juan L.
  • Chapaprieta, Vicente
  • Dueso-Barroso, Ana
  • Delgado, Julio
  • Moia, Riccardo
  • Ruiz-Gil, Sara
  • Marchese, Domenica
  • Giró, Ariadna
  • Verdaguer-Dot, Núria
  • Romo, Mónica
  • Clot, Guillem
  • Rozman, María
  • Frigola, Gerard
  • Rivas-Delgado, Alfredo
  • Baumann, Tycho
  • Alcoceba, Miguel
  • González, Marcos
  • Climent, Fina
  • Abrisqueta, Pau
  • Castellví, Josep
  • Bosch, Francesc
  • Aymerich, Marta
  • Enjuanes, Anna
  • Ruiz-Gaspà, Sílvia
  • López-Guillermo, Armando
  • Jares, Pedro
  • Beà, Silvia
  • Capella-Gutiérrez, Salvador
  • Gelpí, Josep Lluis
  • López-Bigas, Nuria
  • Torrents, David
  • Campbell, Peter J.
  • Gut, Ivo
  • Rossi, Davide
  • Gaidano, Gianluca
  • Puente, Xose S.
  • García-Roves, Pablo M.
  • Colomer, Dolors
  • Heyn, Holger
  • Maura, Francesco
  • Martín-Subero, José Ignacio
  • Campo, Elías
List of Supplementary Tables 1. Supplementary Table 1: Metadata and WGS/WES specifications a. Supplementary Table 1a: Metadata b. Supplementary Table 1b: WGS/WES specifications c. Supplementary Table 1c: Sex and age at CLL diagnosis 2. Supplementary Table 2: Immunoglobulin gene rearrangements and oncogenic translocations determined by IgCaller 3. Supplementary Table 3: Mutations (SNV and indels) 4. Supplementary Table 4: Copy number alterations a. Supplementary Table 4a: List of copy number alterations b. Supplementary Table 4b: Candidate driver genes affected by copy number alterations 5. Supplementary Table 5: Structural variants 6. Supplementary Table 6: DNA methylation analyses a. Supplementary Table 6a: Metadata for samples with DNA methylation data b. Supplementary Table 6b: Differentially methylated CpGs between CLL and RT 7. Supplementary Table 7: Bulk ChIP-seq of H3K27ac and transcription factor analysis a. Supplementary Table 7a: Samples and metadata b. Supplementary Table 7b: Number of changes c. Supplementary Table 7c: Richter-specific common changes d. Supplementary Table 7d: Annotated differential expression genes in Richter-specific common regions e. Supplementary Table 7e: Transcription factors (expressed in RT) f. Supplementary Table 7f: Transcription factors (differentially expressed between RT and CLL) 8. Supplementary Table 8: Bulk ATAC-seq analyses a. Supplementary Table 8a: Samples and metadata b. Supplementary Table 8b: Number of changes c. Supplementary Table 8c: Richter-specific common changes d. Supplementary Table 8d: Annotated differential expression genes in Richter-specific common regions 9. Supplementary Table 9: Coding mutations in CLL and RT 10. Supplementary Table 10: CLL and lymphoma driver genes according to previous literature a. Supplementary Table 10a: Driver gene list b. Supplementary Table 10b: Regions considered for driver genes 11. Supplementary Table 11: Bulk RNA-seq analyses a. Supplementary Table 11a: Samples and metadata b. Supplementary Table 11b: Differentially expressed genes between RT and CLL c. Supplementary Table 11c: GSEA using hallmark gene sets d. Supplementary Table 11d: GSEA using curated C2 canonical pathways e. Supplementary Table 11e: Gene Ontology (GO) analysis 12. Supplementary Table 12: SNV identified in 147 CLL samples from the ICGC cohort and in 27 CLL posttreatment samples 13. Supplementary Table 13: Extraction and assignment of genome-wide mutational signatures a. Supplementary Table 13a: Single base substitution signatures extracted by HDP b. Supplementary Table 13b: Assignment of signatures extracted by HDP c. Supplementary Table 13c: Single base substitution signatures extracted by SignatureAnalyzer d. Supplementary Table 13d: Assignment of signatures extracted by SignatureAnalyzer e. Supplementary Table 13e: Single base substitution signatures extracted by SigProfiler f. Supplementary Table 13f: Assignment of signatures extracted by SigProfiler g. Supplementary Table 13g: Single base substitution signatures extracted by sigfit h. Supplementary Table 13h: Assignment of signatures extracted by sigfit i. Supplementary Table 13i. Comparison of SBS-RT with known signatures 14. Supplementary Table 14: Extraction and assignment of mutational signatures leading to clustered mutations a. Supplementary Table 14a: Single base substitution signatures extracted by HDP b. Supplementary Table 14b: Assignment of signatures extracted by HDP c. Supplementary Table 14c: Single base substitution signatures extracted by SignatureAnalyzer d. Supplementary Table 14d: Assignment of signatures extracted by SignatureAnalyzer e. Supplementary Table 14e: Single base substitution signatures extracted by SigProfiler f. Supplementary Table 14f: Assignment of signatures extracted by SigProfiler g. Supplementary Table 14g: Single base substitution signatures extracted by sigfit h. Supplementary Table 14h: Assignment of signatures extracted by sigfit 15. Supplementary Table 15: Fitting of genome-wide mutational signatures a. Supplementary Table 15a: Fitting of mutational signatures per sample (CLL/RT cohort) b. Supplementary Table 15b: Fitting of mutational signatures per sample (147 cases from the ICGCCLL cohort) c. Supplementary Table 15c: Fitting of mutational signatures per sample (27 CLL post-treatment) d. Supplementary Table 15d: Presence of SBS-melphalan in CLL/RT samples (mSigAct) e. Supplementary Table 15e: Fitting of mutational signatures per clone 16. Supplementary Table 16: Characterization of SBS-RT a. Supplementary Table 16a: SBS-RT in coding gene mutations b. Supplementary Table 16b: Activity of mutational processes on specific chromatin states (RTprivate mutations) c. Supplementary Table 16c: Enrichment of SBS-RT on specific chromatin states (RT-specific mutations) d. Supplementary Table 16d: Activity of mutational processes on early/late replication regions (RTprivate mutations) e. Supplementary Table 16e: Enrichment of SBS-RT on early-late replication (RT-private mutations) f. Supplementary Table 16f: Replication strand bias analysis in RT- private mutations g. Supplementary Table 16g: Transcriptional strand bias analysis in RT- private mutations 17. Supplementary Table 17: Subclonal reconstruction from WGS a. Supplementary Table 17a: MCMC sampler details and tolerated errors b. Supplementary Table 17b: Clusters identified c. Supplementary Table 17c: Abundance of clusters in each time point 18. Supplementary Table 18: High-coverage, UMI-based NGS analysis a. Supplementary Table 18a: Metadata b. Supplementary Table 18b: Targeted mutations c. Supplementary Table 18c: Design of the amplicon-based NGS panel d. Supplementary Table 18d: Results 19. Supplementary Table 19: Fitting of clustered mutational signatures a. Supplementary Table 19a: Kataegis identified in the ICGC-CLL cohort b. Supplementary Table 19b: Kataegis identified in the initial CLL (#1) and RT subclones c. Supplementary Table 19c: Fitting of mutational signatures in kataegis 20. Supplementary Table 20: Single-cell DNA-seq a. Supplementary Table 20a: Samples and metadata b. Supplementary Table 20b: Studied genes c. Supplementary Table 20c: Mutations identified by scDNA-seq (from Tapestri Insights) d. Supplementary Table 20d: Allele dropout and doublet rates e. Supplementary Table 20e: Count matrices (based on infSCITE) 21. Supplementary Table 21: Characterization of immunoglobulin heavy chain gene rearrangements using high-coverage NGS a. Supplementary Table 21a: Samples and summary (Lymphotrack, DNA-based) b. Supplementary Table 21b: IGH subclones identified in case 3495 at time point 1 (Lymphotrack) c. Supplementary Table 21c: IGH subclones identified in case 3495 at time point 2 (Lymphotrack) d. Supplementary Table 21d: IGH subclones identified in case 12 at time point 1 (Lymphotrack) e. Supplementary Table 21e: Samples and results (RNA-based) 22. Supplementary Table 22: Single-cell RNA-seq: metadata, QC, clusters, and marker genes a. Supplementary Table 22a: Samples and metadata b. Supplementary Table 22b: Marker genes for clusters of case 12 c. Supplementary Table 22c: Marker genes for clusters of case 19 d. Supplementary Table 22d: Marker genes for clusters of case 63 e. Supplementary Table 22e: Marker genes for clusters of case 365 f. Supplementary Table 22f: Marker genes for clusters of case 3299 g. Supplementary Table 22g: Number of cells per time point and cluster 23. Supplementary Table 23: Single-cell RNA-seq: patient-specific DEA and GSEA a. Supplementary Table 23a: DEA for case 12 (RT vs CLL) b. Supplementary Table 23b: DEA for case 19 (RT vs CLL) c. Supplementary Table 23c: DEA for case 63 (RT vs CLL) d. Supplementary Table 23d: DEA for case 365 (RT vs CLL) e. Supplementary Table 23e: DEA for case 3299 (RT vs CLL) f. Supplementary Table 23f: GSEA for case 12 (RT vs CLL) g. Supplementary Table 23g: GSEA for case 19 (RT vs CLL) h. Supplementary Table 23h: GSEA for case 63 (RT vs CLL) i. Supplementary Table 23i: GSEA for case 365 (RT vs CLL) j. Supplementary Table 23j: GSEA for case 3299 (RT vs CLL) 24. Supplementary Table 24: Respirometry assays in intact CLL and RT cells a. Supplementary Table 24a: Samples and metadata b. Supplementary Table 24b: Measurements c. Supplementary Table 24c: Summary 25. Supplementary Table 25: BCR signaling and cell growth assays in CLL and RT cells a. Supplementary Table 25a: Samples and metadata b. Supplementary Table 25b: Mean fluorescent ratio Indo-1(violet)/Indo-1(blue) c. Supplementary Table 25c: Flow cytometry gating strategy and proliferation results, Peer reviewed

Proyecto: //
DOI: http://hdl.handle.net/10261/311497
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/311497
HANDLE: http://hdl.handle.net/10261/311497
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/311497
PMID: http://hdl.handle.net/10261/311497
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/311497
Ver en: http://hdl.handle.net/10261/311497
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/311497

Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/311500
Dataset. 2022

GENETIC ASSESSMENT AND CLIMATE MODELLING OF THE IBERIAN SPECIALIST BUTTERFLY EUCHLOE BAZAE (LEPIDOPTERA: PIERIDAE) [DATASET]

  • Hinojosa, Joan Carles
  • Escuer, Paula
  • Minguet-Parramona, Carla
  • Romo, Helena
  • Munguira, Miguel L.
  • Dincă, Vlad
  • Olivares, Javier
  • Talavera, Gerard
  • Vila, Roger
Ecological Niche Modelling output and input., PID2019-107078GB-I00 funded by MCIN/AEI/10.13039/501100011033. 2017-SGR-991 funded by Generalitat de Catalunya. BES-2017-080641 funded by MCIN/AEI/10.13039/501100011033 and by “ESF Investing in your future”., Peer reviewed

DOI: http://hdl.handle.net/10261/311500
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/311500
HANDLE: http://hdl.handle.net/10261/311500
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/311500
PMID: http://hdl.handle.net/10261/311500
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/311500
Ver en: http://hdl.handle.net/10261/311500
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/311500

Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/311502
Dataset. 2022

DATA ON MARINE AREA-BASED MANAGEMENT TOOLS TO ASSESS THEIR CONTRIBUTION TO THE UN SUSTAINABLE DEVELOPMENT GOALS [DATASET]

  • Gissi, Elena
  • Maes, Frank
  • Kyriazi, Zacharoula
  • Ruiz-Frau, Ana
  • Frazão Santos, Catarina
  • Neumann, Barbara
  • Quintela, Adriano
  • Alves, Fátima L.
  • Borg, Simone
  • Chen, Wenting
  • Fernandes, Maria da Luz
  • Hadjimichael, Maria
  • Manea, Elisabetta
  • Marques, Márcia
  • Platjouw, Froukje Maria
  • Portman, Michelle E.
  • Sousa, Lisa P.
  • Bolognini, Luca
  • Flannery, Wesley
  • Grati, Fabio
  • Pita, Cristina
  • Văidianu, Natașa
  • Stojanov, Robert
  • Tatenhove, Jan van
  • Micheli, Fiorenza
  • Hornidge, Anna-Katharina
  • Unger, Sebastian
Dataset: Review of Area-based management tools (ABMTs) and related legal sources from International and Regional Agreements. -- All the web links of Tab. 7 were accessed on March 9, 2020., Peer reviewed

Proyecto: //
DOI: http://hdl.handle.net/10261/311502
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/311502
HANDLE: http://hdl.handle.net/10261/311502
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/311502
PMID: http://hdl.handle.net/10261/311502
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/311502
Ver en: http://hdl.handle.net/10261/311502
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/311502

Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/311503
Dataset. 2022

DATASHEET_1_DEVELOPMENT OF A STANDARDIZED AND VALIDATED FLOW CYTOMETRY APPROACH FOR MONITORING OF INNATE MYELOID IMMUNE CELLS IN HUMAN BLOOD.ZIP

  • Pan, Kyra van der
  • Bruin Versteeg, Sandra de
  • Damasceno, Daniela
  • Hernández-Delgado, Alejandro
  • Sluijs-Gelling, Alita J. van der
  • Bossche, Wouter B. L.van den
  • Laat, Inge F. de
  • Díez, Paula
  • Naber, Brigitta A. E.
  • Diks, Annieck M.
  • Berkowska, Magdalena A.
  • Mooij, Bas de
  • Groenland, R. J.
  • Bie, Fenna J. de
  • Khatri, Indu
  • Kassem, Sara
  • Jager, Anniek L. de
  • Louis, Alesha
  • Almeida, Julia
  • Gaans-van den Brink, Jacqueline A. M. van
  • Barkoff, Alex-Mikael
  • He, Qiushui
  • Ferwerda, Gerben
  • Versteegen, Pauline
  • Berbers, Guy A. M.
  • Orfao, Alberto
  • Dongen, J. J. M. van
  • Teodosio, Cristina
Innate myeloid cell (IMC) populations form an essential part of innate immunity. Flow cytometric (FCM) monitoring of IMCs in peripheral blood (PB) has great clinical potential for disease monitoring due to their role in maintenance of tissue homeostasis and ability to sense micro-environmental changes, such as inflammatory processes and tissue damage. However, the lack of standardized and validated approaches has hampered broad clinical implementation. For accurate identification and separation of IMC populations, 62 antibodies against 44 different proteins were evaluated. In multiple rounds of EuroFlow-based design-testing-evaluation-redesign, finally 16 antibodies were selected for their non-redundancy and separation power. Accordingly, two antibody combinations were designed for fast, sensitive, and reproducible FCM monitoring of IMC populations in PB in clinical settings (11-color; 13 antibodies) and translational research (14-color; 16 antibodies). Performance of pre-analytical and analytical variables among different instruments, together with optimized post-analytical data analysis and reference values were assessed. Overall, 265 blood samples were used for design and validation of the antibody combinations and in vitro functional assays, as well as for assessing the impact of sample preparation procedures and conditions. The two (11- and 14-color) antibody combinations allowed for robust and sensitive detection of 19 and 23 IMC populations, respectively. Highly reproducible identification and enumeration of IMC populations was achieved, independently of anticoagulant, type of FCM instrument and center, particularly when database/software-guided automated (vs. manual “expert-based”) gating was used. Whereas no significant changes were observed in identification of IMC populations for up to 24h delayed sample processing, a significant impact was observed in their absolute counts after >12h delay. Therefore, accurate identification and quantitation of IMC populations requires sample processing on the same day. Significantly different counts were observed in PB for multiple IMC populations according to age and sex. Consequently, PB samples from 116 healthy donors (8-69 years) were used for collecting age and sex related reference values for all IMC populations. In summary, the two antibody combinations and FCM approach allow for rapid, standardized, automated and reproducible identification of 19 and 23 IMC populations in PB, suited for monitoring of innate immune responses in clinical and translational research settings., Peer reviewed

Proyecto: //
DOI: http://hdl.handle.net/10261/311503
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/311503
HANDLE: http://hdl.handle.net/10261/311503
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/311503
PMID: http://hdl.handle.net/10261/311503
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/311503
Ver en: http://hdl.handle.net/10261/311503
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oai:digital.csic.es:10261/311503

Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/311504
Dataset. 2022

THE ATLAS OF MITOCHONDRIAL GENETIC DIVERSITY FOR WESTERN PALEARCTIC BUTTERFLIES [DATASET]

  • Dapporto, Leonardo
[Usage notes] The dataset of the Atlas of mitochondrial genetic diversity for Western Palearctic butterflies (COI sequences and metadata) is available in the iodatabase R package available at https://github.com/leondap/iodatabase. Here the R script with a few supplementaty files is available in order to replicate the figures composing the Atlas, [Motivation] Butterflies represent a model in biology and a flagship group for invertebrate conservation. We provide four new resources for the Western-Palearctic butterflies: 1) An updated checklist comprising 552 species; 2) a curated dataset of 32,129 mitochondrial COI sequences for 532 species, including a de novo reference library for the Maghreb (Morocco and northern Algeria and Tunisia) and Macaronesia (Azores, Madeira and Canary Islands); 3) seven indexes of intraspecific genetic variation (IGV): observed and expected number of haplotypes, haplotype and nucleotide diversity, two fixation indexes, and maximum p-distance; 4) species-level maps illustrating the distribution of COI variability and haplotype networks. The updated checklist will be fundamental for any application dealing with butterfly diversity in Western Palearctic. IGV indexes provide measures for genetic polymorphism and spatial structure and represent proxies for dispersal capacity. These resources will facilitate comparative studies of macrogenetics, will foster integrative taxonomy, and will aid conservation strategies., [Main types of variables contained] A complete species checklist in table format, 32,129 mitochondrial DNA-barcodes provided with metadata (species membership, WGS84 coordinates, sequence length), and a book in PDF format including the IGV atlas and indexes., [Spatial location and grain] The checklist encompasses Europe up to Urals in the east, north Macaronesia (Azores, Madeira and Canary Islands), as well as the Maghreb (Morocco and northern Algeria and Tunisia). DNA-barcodes have been retained in the geographic interval of -31.3–67.5 degrees of longitude and 27.5–71.2 degrees of latitude., [Time period and grain] DNA-barcodes originate from studies published between 1998-2022 and from de novo sequencing of 2,608 specimens done between 2007-2022., [Major taxa and level of measurement] Butterflies (Lepidoptera, Papilionoidea), analysed from individual to species level., [Software format] Data and functions to manage the dataset are provided in the iodatabase R package (https://github.com/leondap/iodatabase) and in Dryad., Università degli Studi di Firenze ., Peer reviewed

Proyecto: //
DOI: http://hdl.handle.net/10261/311504
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/311504
HANDLE: http://hdl.handle.net/10261/311504
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/311504
PMID: http://hdl.handle.net/10261/311504
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/311504
Ver en: http://hdl.handle.net/10261/311504
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/311504

Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/311506
Dataset. 2022

THE ATLAS OF MITOCHONDRIAL GENETIC DIVERSITY FOR WESTERN PALEARCTIC BUTTERFLIES [SOFTWARE]

  • Dapporto, Leonardo
The dataset of the Atlas of mitochondrial genetic diversity for Western Palearctic butterflies (COI sequences and metadata) is available in the iodatabase R package available at https://github.com/leondap/iodatabase.-- Here the R script with a few supplementaty files is available in order to replicate the figures composing the Atlas., [Motivation] Butterflies represent a model in biology and a flagship group for invertebrate conservation. We provide four new resources for the Western-Palearctic butterflies: 1) An updated checklist comprising 552 species; 2) a curated dataset of 32,129 mitochondrial COI sequences for 532 species, including a de novo reference library for the Maghreb (Morocco and northern Algeria and Tunisia) and Macaronesia (Azores, Madeira and Canary Islands); 3) seven indexes of intraspecific genetic variation (IGV): observed and expected number of haplotypes, haplotype and nucleotide diversity, two fixation indexes, and maximum p-distance; 4) species-level maps illustrating the distribution of COI variability and haplotype networks. The updated checklist will be fundamental for any application dealing with butterfly diversity in Western Palearctic. IGV indexes provide measures for genetic polymorphism and spatial structure and represent proxies for dispersal capacity. These resources will facilitate comparative studies of macrogenetics, will foster integrative taxonomy, and will aid conservation strategies., [Main types of variables contained] A complete species checklist in table format, 32,129 mitochondrial DNA-barcodes provided with metadata (species membership, WGS84 coordinates, sequence length), and a book in PDF format including the IGV atlas and indexes., [Spatial location and grain] The checklist encompasses Europe up to Urals in the east, north Macaronesia (Azores, Madeira and Canary Islands), as well as the Maghreb (Morocco and northern Algeria and Tunisia). DNA-barcodes have been retained in the geographic interval of -31.3–67.5 degrees of longitude and 27.5–71.2 degrees of latitude., [Time period and grain] DNA-barcodes originate from studies published between 1998-2022 and from de novo sequencing of 2,608 specimens done between 2007-2022., [Major taxa and level of measurement] Butterflies (Lepidoptera, Papilionoidea), analysed from individual to species level., [Software format] Data and functions to manage the dataset are provided in the iodatabase R package (https://github.com/leondap/iodatabase) and in Dryad., Funding provided by: Università degli Studi di Firenze. Crossref Funder Registry ID: http://dx.doi.org/10.13039/501100004434, Peer reviewed

Proyecto: //
DOI: http://hdl.handle.net/10261/311506
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/311506
HANDLE: http://hdl.handle.net/10261/311506
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/311506
PMID: http://hdl.handle.net/10261/311506
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/311506
Ver en: http://hdl.handle.net/10261/311506
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/311506

Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/311509
Dataset. 2022

HMG20A-PHF14 MODEL-1

  • Reyes, José C.
  • Guerrero-Martínez, José A.
Material For modeling AlphaFold2_multimer was used. AlphaFold2 was downloaded from https://github.com/deepmind/alphafold/releases. Full length amino acid sequences of human PHF14 and HMG20A were used: >NP_001291433.1 high mobility group protein 20A isoform a [Homo sapiens] >NP_001007158.1 PHD finger protein 14 isoform 1 [Homo sapiens] To perform PHF14-HMG20A complex models we used Alphafold_multimers (v2.1.1) with --db_preset=reduced_dbs, --model_preset=multimer --max_template_date=2021-11-01, and default parameters. Computation was performed in the CESGA Supercomputing Center. RCSB PDB (https://www.rcsb.org/3d-view) or Swiss PDB viewer (DeepView) were used for structure viewing. Procedure Full lenth amino-acid protein sequences were obtained form NCBI Protein. Then, to perform PHF14-HMG20A complex models we used Alphafold_multimers (v2.1.1) with --db_preset=reduced_dbs, --model_preset=multimer --max_template_date=2021-11-01, and default parameters. A PAE plot (Predicted Aligned Error Plot) is uploaded. PAE plot "reports AlphaFold’s expected position error at residue x, when the predicted and true structures are aligned on residue y. This is useful for assessing confidence in global features, especially domain packing. For residues x and y drawn from two different domains, a consistently low PAE at (x, y) suggests AlphaFold is confident about the relative domain positions" (https://deepmind.com/research/publications/2021/enabling-high-accuracy-protein-structure-prediction-at-the-proteome-scale). A distogram (Matrix of distances between different parts of the proteins) is also uploaded., Structural modeling by using the AlphaFold2_multimer software, indicated that HMG20A forms a complex with PHF14 through the establishment of a two-stranded alpha-helical coiled-coil structure, Peer reviewed

Proyecto: //
DOI: http://hdl.handle.net/10261/311509
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/311509
HANDLE: http://hdl.handle.net/10261/311509
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/311509
PMID: http://hdl.handle.net/10261/311509
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/311509
Ver en: http://hdl.handle.net/10261/311509
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/311509

Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/311516
Dataset. 2022

SUPPLEMENTARY INFORMATION FROM A CULTURAL EVOLUTIONARY THEORY THAT EXPLAINS BOTH GRADUAL AND PUNCTUATED CHANGE

  • Vidiella, Blai
  • Carrignon, Simon
  • Bentley, R. Alexander
  • O’Brien, Michael J.
  • Valverde, Sergi
Cumulative cultural evolution (CCE) occurs among humans who may be presented with many similar options from which to choose, as well as many social influences and diverse environments. It is unknown what general principles underlie the wide range of CCE dynamics and whether they can all be explained by the same unified paradigm. Here, we present a scalable evolutionary model of discrete choice with social learning, based on a few behavioural science assumptions. This paradigm connects the degree of transparency in social learning to the human tendency to imitate others. Computer simulations and quantitative analysis show the interaction of three primary factors—information transparency, popularity bias and population size—drives the pace of CCE. The model predicts a stable rate of evolutionary change for modest degrees of popularity bias. As popularity bias grows, the transition from gradual to punctuated change occurs, with maladaptive sub-populations arising on their own. When the popularity bias gets too severe, CCE stops. This provides a consistent framework for explaining the rich and complex adaptive dynamics taking place in the real-world, such as modern digital media., Peer reviewed

Proyecto: //
DOI: http://hdl.handle.net/10261/311516
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/311516
HANDLE: http://hdl.handle.net/10261/311516
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/311516
PMID: http://hdl.handle.net/10261/311516
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/311516
Ver en: http://hdl.handle.net/10261/311516
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/311516

Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/311517
Dataset. 2022

CRYPTO WHITE PAPERS DOCUMENTS

  • Valverde, Sergi
  • Durán Nebreda, Salva
This is the database used in the script 'information_density_whitepapers'., Peer reviewed

Proyecto: //
DOI: http://hdl.handle.net/10261/311517
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/311517
HANDLE: http://hdl.handle.net/10261/311517
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/311517
PMID: http://hdl.handle.net/10261/311517
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/311517
Ver en: http://hdl.handle.net/10261/311517
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/311517

Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/311518
Dataset. 2022

OPTICS PUBLICATIONS DATASET

  • Durán Nebreda, Salva
  • Valverde, Sergi
This dataset includes the publication date of all the collected articles in the field of optics, as well as their file name and DOI., AGAUR 2019 BP 00206. Grant PID2020-117822GB-I00 funded by MCIN/AEI/10.13039/501100011033 and, as appropriate, by “ERDF A way of making Europe”, by the “European Union” or by the “European Union NextGenerationEU/PRTR”., Peer reviewed

DOI: http://hdl.handle.net/10261/311518
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/311518
HANDLE: http://hdl.handle.net/10261/311518
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/311518
PMID: http://hdl.handle.net/10261/311518
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/311518
Ver en: http://hdl.handle.net/10261/311518
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/311518

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