6377 results for "11+1双色球多少钱一注"

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• Genome and transcriptome sequence data from a rhabdomyosarcoma, alveolar tumor patient

  Genome and transcriptome sequence data from a rhabdomyosarcoma, alveolar patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015335

• Res1_HT29_exp2_MC_03.03.22

  1 sample is pure plasmid DNA and 10 samples are cell pellets for genomic DNA extraction. CRISPR PCR1 and PCR2 indexing - Please use standard Kozuke primers.

  Dataset EGAD00001012231

• Genome and transcriptome sequence data from a infantile fibrosarcoma tumor patient

  Genome and transcriptome sequence data from a infantile fibrosarcoma patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015263

• Genome and transcriptome sequence data from a CNS sarcoma tumor patient

  Genome and transcriptome sequence data from a CNS sarcoma patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015268

• Genome and transcriptome sequence data from a ocular melanoma tumor patient

  Genome and transcriptome sequence data from a ocular Melanoma patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015269

• Genome and transcriptome sequence data from a aggressive fibromatosis tumor patient

  Genome and transcriptome sequence data from a aggressive fibromatosis patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015279

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0163_001

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0163_001 for Triple negative breast cancer patient-derived xenograft SA609X7XB03505

  Dataset EGAD00001011219

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0206_002

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0206_002 for Triple negative breast cancer patient-derived xenograft SA535X10XB03696

  Dataset EGAD00001011235

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0014_001

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0014_001 for Triple negative breast cancer patient-derived xenograft SA604X6XB01979

  Dataset EGAD00001011251

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0015_001

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0015_001 for Triple negative breast cancer patient-derived xenograft SA604X6XB01979

  Dataset EGAD00001011252

• Engineered cartilage: deriving design principles from human developmental: RNA (2025-07-30)

  Engineered cartilage: deriving design principles from human developmental pathways . This dataset contains all the data available for this study on 2025-07-30.

  Dataset EGAD00001015664

• Geographic and age-related variations in mutational processes in colorectal cancer - filtered vcf files (Mutographs)

  Geographic and age-related variations in mutational processes in colorectal cancer - filtered vcf files (Mutographs)

  Dataset EGAD00001015486

• The Genomics of Pilocytic Astrocytoma Formation in Neurofibromatosis Type 1

  Neurofibromatosis type 1 (NF1) inherited cancer predisposition syndrome is one of the most common autosomal dominant tumor predisposition syndromes in which affected individuals develop brain tumors. These low-grade glial neoplasms (pilocytic astrocytomas) typically arise in children younger than 7 years of age and are hypothesized to result from a combination of germline and acquired somatic NF1 tumor suppressor gene mutations. In this study, whole genome sequence analysis was performed on three NF1-associated pilocytic astrocytoma tumors (NF1-PA) and matched normal blood samples to establish the genomic landscape of NF1-PA. These data support the existence of multiple distinct mechanisms (mutation, LOH, and methylation) underlying somatic NF1 inactivation in NF1-PA tumors.

  Study phs000563

• CpG methylation changes associated with hyperglycemia in type 1 diabetes occur at angiogenic glomerular and retinal gene loci.

  Retinal and glomerular microangiopathy, and cardiovascular disease, contribute to the morbidity and mortality observed in adults with type 1 diabetes (T1D). While the level and duration of hyperglycemia are primary factors influencing these complications, the underlying molecular mechanisms behind glucose-induced organ damage remain poorly understood. In this study, blood samples from young T1D patients at diagnosis and three years later in average were analyzed for DNA methylation using whole-genome bisulfite sequencing. Significant DNA methylation changes in critical angiogenic gene loci were associated with HbA1c, a direct hallmark of chronic hyperglycemia. This finding may offer novel epigenetic insights for a better understanding of T1D complications.

  Study EGAS50000000370

• WES analysis in identifying additive genetic factors that may contribute to the occurrence of moyamoya in neurofibromatosis type 1

  Moyamoya is a progressive cerebral vasculopathy, for which genetic susceptibility factors were mainly identified in Asian populations. When associated with other medical conditions, such as neurofibromatosis type 1 (NF1), this vasculopathy is frequently reported as Moyamoya syndrome (MMS). Intriguingly, most cases of MMS-complicated NF1 have been described in Caucasians, inverting the population ratio for moyamoya vasculopathy observed in Asians, despite NF1 prevalence being constant worldwide. This study aims to investigate whether, among Caucasians, additive genetic factors may contribute to the occurrence of MMS in NF1. WES analysis has been carried out on an Italian family with co-occurrence of NF1 and MMS, as well as other cerebral vasculopathies.

  Study EGAS00001003053

• Bibliography Statistics

  EGA Statistics Bibliography Growth Community Archive Distribution Catalogue Here we expose in a yearly and cumulative manner, the publications, citations, publishers journals and impact factor of EGA related studies. Publications citing data stored at the EGA The studies that use EGA datasets are requested to cite the EGA into their bibliography. This fact allows for a faster spreading of the role and purpose of the EGA, being the availability of their studies for testability and reusability purposes. In order to track the studies we use the unique study accession, provided by the EGA when submitting a new study, and comprised of the keyword EGAS followed by 11 digits such as EGAS00000000001. We use the Europe PMC RESTful Web Service in order to query, and index studies where the EGAS accession was used. Below, you can find a chart showing the journal publishers of the studies citing data stored in the EGA being cited: Publications Citing Data Stored in the EGA pieChart('publishers-citations', 'https://stats.ega-archive.org/publishers/citations', 'Publishers') Number of publications citing data stored in the EGA by year Here, you can find a chart showing the number of publications citing data stored in the EGA by year. These publications could have deposit, or re-used the data. The study acession can be found in the publication. The default values are non-cumulative. You can see cumulative values by clicking "Cumulative". Number of Publications Citing Data Stored in the EGA by Year barChart('publications-by-year', 'https://stats.ega-archive.org/publications/year', ['Number of Publications by Year', 'Publications by Year']) Journal Impact Factor of Publications Citing Data Stored in the EGA Here, you can find a chart showing the journal impact factor of publications citing data stored in the EGA. We have used Scimago Journal Rank for this purpose. Low impact treshold was defined between 0.0 and 5.0 excluding; Medium impact treshold was defined between 5.0 and 10.0 excluding; High impact range was established from values equal to or higher than 10.0; Journal Impact Factor of Publications Citing Data Stored in the EGA pieChart('citations-publication-impact-factor', 'https://stats.ega-archive.org/citations/publication/impact/factor', 'Publication Impact Factor') Publications citing a publication containing a EGA study accession The publications, stating a EGA study accession in the bibliography, can be cited for testibility or reusability purposes. Below, you can find a chart showing the publishers of the publications being cited: Publications Citing a Publication Containing a EGA Study Accession pieChart('publications-citations', 'https://stats.ega-archive.org/publications/citations', 'Publishers') Number of publications citing a publication containing a EGA study accession by year Here, you can find a chart showing the number of publications citing a publication containing a EGA study accession by year by year. The values are non-cumulative: Number of published citations by year barChart('citations-year', 'https://stats.ega-archive.org/citations/year', ['Number of Publications', 'Citations by Year'])

  Documentation about/statistics/bibliography

• EGAD00000000007

  WTCCC1 project Rheumatooid arthritis (RA) samples

  Dataset EGAD00000000007

• EGAD00000000009

  WTCCC1 project Type 2 Diabetes (T2D) samples

  Dataset EGAD00000000009

• EGAS00001001311_MalariaGEN_GWAS_summary_statistics_2015

  Summary statistics for Malaria Genomic Epidemiology Network, "A novel locus of resistance to severe malaria in a region of ancient balancing selection", Nature (2015)

  Dataset EGAD00010001081

• Sequencing data for oesophageal and related samples - Normals release 1 (RNA)

  Dataset EGAD00001002258

• johannesburg_20150706_X

  Genotype data and (in binary PLINK format) and imputed data (with merged 1000Gp3 and AGVP reference panel in GEMMA BIMBAM dosage format) for 750 individuals from Respiratory and Meningeal Pathogens Unit in Soweto, South Africa - X chromosome.

  Dataset EGAD00010002580

• johannesburg_20150706_autosomes

  Genotype data and (in binary PLINK format) and imputed data (with merged 1000Gp3 and AGVP reference panel in GEMMA BIMBAM dosage format) for 750 individuals from Respiratory and Meningeal Pathogens Unit in Soweto, South Africa - autosomes.

  Dataset EGAD00010002582

• Variants Derived From Heart Centre Biobank CHD Probands (Canada)

  Short variants identified in 731 CHD probands enrolled through the Heart Centre Biobank Registry (Canada).

  Dataset EGAD50000000838

• Variants Derived From Kids Heart BioBank TOF Probands (Australia)

  Short variants identified in 245 TOF probands enrolled through the Kids Heart BioBank (Australia).

  Dataset EGAD50000000839

• DCC_R26.PRAD-CNSM-Array

  Raw Array data from the PRAD-CA for ICGC DCC Release26

  Dataset EGAD00010001414