BiGR

Adresse postale
UMS INSERM 23/CNRS 3655 AMMICA
114 rue Edouard Vaillant 94800 Villejuif
Structure(s) :
INSERM, CNRS
Unité :
AMMICA, UMS 23 / CNRS 3655
Responsable(s) scientifique
Responsable(s) opérationnel
Certificat(s)
Non renseigné
Type d'infrastructure
Propriétaire
Capacité de stockage
4 000.00 To
Ferme de calcul
1 800 cores
Collection de données
15
Outils bioinformatiques
90
Nombre d'utilisateurs
30
Description des serveurs

- Cluster de calcul dédié à l’analyse de données de séquencage
- Serveur Galaxy
- Serveur d’applications

      - Shiny
      - Rstudio


Condition d'accès

- Serveur Galaxy : Demande de compte auprès de la plate-forme. Ouvert à toute équipe exterieure à Gustave Roussy sur demande (adresse IP).
- Cluster de calcul : accessible exclusivement aux bioinformaticiens du site GR.

Aucun site web renseigné
Visites annuelles :
Non renseignées
Visites uniques :
Non renseignées
Citations :
Non renseigné
Téléchargements :
Non renseigné

DEVA

Description

Detection of variants from NGS data. Pipeline for variant detection adapted to exon capture from clinical DNA samples. Special options for germline or somatic variants are provided. Interface is available in command line or Galaxy system. Output from mutliple samples can be visualized and filtered using web-based interface.

Conditions d'accès

Accès : web server, https://galaxy.gustaveroussy.fr/galaxyprod

Aucun site web renseigné
Visites annuelles :
Non renseignées
Visites uniques :
Non renseignées
Citations :
Non renseigné
Téléchargements :
Non renseigné

VCF2HTML

Description

A visualization and filtering tool for multiple VCF (variant) datasets.

Conditions d'accès

Script documenté sur demande.

Aucun site web renseigné
Visites annuelles :
Non renseignées
Visites uniques :
Non renseignées
Citations :
Non renseigné
Téléchargements :
Non renseigné

SCA

Description

SCA (Similarity Core Analysis) is a computational framework for extracting the core molecular features, such as mRNA or miRNA signatures, common to tumors and corresponding cell lines.

Conditions d'accès

Programme fourni sur demande.

Domaines d'activité
  • Biomédical
  • Biologie
Description des expertises
● Bioanalyses
Conception, définition de design expérimentaux en génomique (microarrays, NGS). Analyse de données génomiques (microarray et NGS)
- Microarray :
- Gene Expression (Agilent, Affymetrix)
- CGH array
- microRNA array
- NGS (DNA-seq):
- Détection de SNP (WES, WGS, TS)
- Détection de variants structuraux (LOH, CNV)
- ChipSeq
- NGS (RNA-seq) :
- Analyse différentielle
- recherche de transcrit de fusion
- recherche de splicing alternatif
- detection de SNP par RNA-seq

 

● Conseil / Support
Elaboration de cahier des charges pour des projets de recherche à composante "bioinformatique" : définition du projet bioinformatique, définition des compétences requises, rédaction de fiche de poste, recrutement de bioinformaticien, estimation du coût global. La plateforme peut se positionner en partenaire du projet ou en prestataire de service.

 

● Formations
Organisation de formations généraliste en interne. Organisation de formations spécifiques, sur demande, dans les locaux des demandeurs (utilisation d’un logiciel, explication de méthodologie bioinformatique, statistiques)

 

● R&D
Développement /Maintenance de pipelines d’analyse.
Développement à facon d’outils d’annotation / de visualisation de données NGS Développement de DB de données génomiques
Participation au developpement de DB en médecine personnalisée

Mots clés:
  • Analyse de données de séquençage NGS
  • Transcriptomique (RNA-seq)
  • Analyse différentielle de l’expression des gènes
  • Analyse de variants
  • Panels (amplicons, captures)
  • Exomes
  • Génomes complets
  • Analyse de la régulation de l’expression des gènes
  • Chip-seq
  • Profils de méthylation
  • Métagénomique, métatranscriptomique
  • Génomique : Puces
  • Biopuces ADN
  • CGH
  • Fonctionelles
  • Biopuces ARN
  • Expression

Formation professionnelle

Aucun site web renseigné
Personnes formées :
120 personnes / an
Temps de formation :
Non renseigné
Pas de nouvelle session prévue

Bioinformatique, NGS et Cancer

Description

Co-organisation Atelier Cancéropole Ile de France « Bioinformatique, NGS et Cancer» (avril 2014 et Nov 2014)

Conditions d'accès

http://www.canceropole-idf.fr/fr/ngs

Aucun site web renseigné
Personnes formées :
Non renseigné
Temps de formation :
Non renseigné
Pas de nouvelle session prévue

Ecole NGS Aviesan Roscoff

Description

(nov 2013, oct 2014): responsabilité de l’atelier RNA-seq (Marc Deloger), intervention atelier CNV (Bastien Job).

Conditions d'accès
Non renseigné
Aucun site web renseigné
Personnes formées :
Non renseigné
Temps de formation :
Non renseigné
Pas de nouvelle session prévue

Formation interne pipeline DEVA detection de variants par NGS

Description
Non renseigné
Conditions d'accès
Non renseigné
Aucun site web renseigné
Personnes formées :
Non renseigné
Temps de formation :
Non renseigné
Pas de nouvelle session prévue

Formation interne pipeline RNASeq

Description
Non renseigné
Conditions d'accès
Non renseigné

Formation universitaire

Aucun site web renseigné
Personnes formées :
20 personnes / an
Temps de formation :
Non renseigné
Pas de nouvelle session prévue

IFSBM

Description
UFR médecine Paris-Sud (mai 2015). Big data en médecine. Acquisition et analyse des données de haut débit dans le but de détecter des variants génomiques (CGH, exome, génome), étudier les profils d’expression ou le statut épigénétique des cellules (expression array, RNA-seq, ChiP-seq, medip-seq).

 

UFR médecine Paris-Sud (janvier 2015) Méthodologie en biologie moléculaire et cellulaire et analyse d’article

Conditions d'accès
Non renseigné
Répartition des utilisateurs
Internationaux
0 %
Nationaux
5 %
Régionaux
0 %
Locaux
95%
Description de la répartition :

La plateforme est soutenue financièrement par des crédits Gustave Roussy

Projets propres de la plateforme

Aucun (ou non renseigné)

Collaborations
Projets d'ANR
- INCA PAIR Mélanome. Partenaires: R. Ballotti (U. Nice), A. de la Fouchardière (Unicancer Lyon), B. Bressac (Gustave Roussy) + PF Bioinformatique (2014-17).

 

- INSERM Plan Cancer. “Molecular signature of radiation-induced thyroid cancers” . Partenaires: S. Chevillard (CEA Fontenay), L. Lacroix (Gustave Roussy), M Schlumberger (Gustave Roussy) + PF Bioinformatique. (2014-2016)

 

- INCA SIRIC MMO “Molecular Medicine in Oncology”. (2012-2017). Projet Gustave Roussy impliquant la plateforme de Bioinformatique et les laboratoires de recherche.

Projets européens et internationaux
Non renseigné
Projets avec des industriels

- 17e FUI (Fond Unique Interministériel): Projet ICE “Interpretation of Clinical Exome”. Partenaires: Sté Integragen, Sté Sogeti High Tech, INSERM, Gustave Roussy. (2014-2017).

Projets de collaboration non financés par un organisme extérieur
Non renseigné
Prestations de service non financés par un organisme extérieur
Non renseigné
Animations

Groupe de travail :
AAP INCA NGS : Volet 1 et volet 2. Participation à l’animation d’un groupe de travail dédié à la mise en place des technologie NGS dans un contexte clinique (en partenariat avec Institut Curie)

Publications internes
1.Beke, A. et al. Multilayer intraclonal heterogeneity in chronic myelomonocytic leukemia. Haematologica 105, 112–123 (2020).
2.Lopez, C. K. et al. Ontogenic Changes in Hematopoietic Hierarchy Determine Pediatric Specificity and Disease Phenotype in Fusion Oncogene–Driven Myeloid Leukemia. Cancer Discov 9, 1736–1753 (2019).
3.Heddar, A., Dessen, P., Flatters, D. & Misrahi, M. Novel STAG3 mutations in a Caucasian family with primary ovarian insufficiency. Mol Genet Genomics 294, 1527–1534 (2019).
4.Guillem, F. et al. XPO1 regulates erythroid differentiation and is a new target for the treatment of β-thalassemia. Haematologica (2019) doi:10.3324/haematol.2018.210054.
5.Chapiro, E. et al. Genetic characterization of B-cell prolymphocytic leukemia: a prognostic model involving MYC and TP53. Blood 134, 1821–1831 (2019).
6.Terry, S. et al. AXL Targeting Overcomes Human Lung Cancer Cell Resistance to NK- and CTL-Mediated Cytotoxicity. Cancer Immunol Res 7, 1789–1802 (2019).
7.Lucas, N. et al. Biology and prognostic impact of clonal plasmacytoid dendritic cells in chronic myelomonocytic leukemia. Leukemia 33, 2466–2480 (2019).
8.Chollat-Namy, M. et al. The pharmalogical reactivation of p53 function improves breast tumor cell lysis by granzyme B and NK cells through induction of autophagy. Cell Death Dis 10, (2019).
9.Villepelet, A. et al. Effects of tobacco abuse on major chromosomal instability in human papilloma virus 16-positive oropharyngeal squamous cell carcinoma. International Journal of Oncology 55, 527–535 (2019).
10.Bertucci, F. et al. Author Correction: Genomic characterization of metastatic breast cancers. Nature 572, E7–E7 (2019).
11.Marques da Costa, M. E. et al. In-Vitro and In-Vivo Establishment and Characterization of Bioluminescent Orthotopic Chemotherapy-Resistant Human Osteosarcoma Models in NSG Mice. Cancers (Basel) 11, (2019).
12.Sarasin, A. et al. Familial predisposition to TP53/complex karyotype MDS and leukemia in DNA repair-deficient xeroderma pigmentosum. Blood 133, 2718–2724 (2019).
13.Stoll, G. et al. Metabolic enzymes expressed by cancer cells impact the immune infiltrate. OncoImmunology 8, e1571389 (2019).
14.Roos-Weil, D. et al. A Recurrent Activating Missense Mutation in Waldenström Macroglobulinemia Affects the DNA Binding of the ETS Transcription Factor SPI1 and Enhances Proliferation. Cancer Discov 9, 796–811 (2019).
15.Bertucci, F. et al. Genomic characterization of metastatic breast cancers. Nature 569, 560–564 (2019).
16.Bencheikh, L. et al. Dynamic gene regulation by nuclear colony-stimulating factor 1 receptor in human monocytes and macrophages. Nat Commun 10, (2019).
17.Jonge, M. M. de et al. Frequent Homologous Recombination Deficiency in High-grade Endometrial Carcinomas. Clin Cancer Res 25, 1087–1097 (2019).
18.Selimoglu-Buet, D. et al. A miR-150/TET3 pathway regulates the generation of mouse and human non-classical monocyte subset. Nat Commun 9, (2018).
19.Koeppel, F. et al. Added Value of Whole-Exome and Transcriptome Sequencing for Clinical Molecular Screenings of Advanced Cancer Patients With Solid Tumors. The Cancer Journal 24, 153–162 (2018).
20.Gattolliat, C.-H. et al. Integrative analysis of dysregulated microRNAs and mRNAs in multiple recurrent synchronized renal tumors from patients with von Hippel-Lindau disease. Int J Oncol 53, 1455–1468 (2018).
21.Rivera-Munoz, P. et al. Partial trisomy 21 contributes to T-cell malignancies induced by JAK3-activating mutations in murine models. Blood Adv 2, 1616–1627 (2018).
22.Drubay, D., Gautheret, D. & Michiels, S. A benchmark study of scoring methods for non-coding mutations. Bioinformatics 34, 1635–1641 (2018).
23.Mouly, E. et al. B-cell tumor development in Tet2-deficient mice. Blood Adv 2, 703–714 (2018).
24.Audoux, J. et al. DE-kupl: exhaustive capture of biological variation in RNA-seq data through k-mer decomposition. Genome Biol 18, (2017).
25.Cornelis, G. et al. An endogenous retroviral envelope syncytin and its cognate receptor identified in the viviparous placental Mabuya lizard. Proc Natl Acad Sci U S A 114, E10991–E11000 (2017).
26.Duplomb, L. et al. A constitutive BCL2 down-regulation aggravates the phenotype of PKD1-mutant-induced polycystic kidney disease. Hum Mol Genet 26, 4680–4688 (2017).
27.Bloy, N. et al. Immunogenic stress and death of cancer cells: Contribution of antigenicity vs adjuvanticity to immunosurveillance. Immunological Reviews 280, 165–174 (2017).
28.Buart, S. et al. Transcriptional response to hypoxic stress in melanoma and prognostic potential of GBE1 and BNIP3. Oncotarget 8, 108786–108801 (2017).
29.Harttrampf, A. C. et al. Molecular Screening for Cancer Treatment Optimization (MOSCATO-01) in Pediatric Patients: A Single-Institutional Prospective Molecular Stratification Trial. Clin Cancer Res 23, 6101–6112 (2017).
30.Heidmann, O. et al. HEMO, an ancestral endogenous retroviral envelope protein shed in the blood of pregnant women and expressed in pluripotent stem cells and tumors. Proc Natl Acad Sci U S A 114, E6642–E6651 (2017).
31.Zhang, Y. et al. Engraftment of chronic myelomonocytic leukemia cells in immunocompromised mice supports disease dependency on cytokines. Blood Adv 1, 972–979 (2017).
32.Massard, C. et al. High-Throughput Genomics and Clinical Outcome in Hard-to-Treat Advanced Cancers: Results of the MOSCATO 01 Trial. Cancer Discov 7, 586–595 (2017).
33.Manchev, V. T. et al. Acquired TET2 mutation in one patient with familial platelet disorder with predisposition to AML led to the development of pre‐leukaemic clone resulting in T2‐ALL and AML‐M0. J Cell Mol Med 21, 1237–1242 (2017).
34.Tokonami, N. et al. Endothelin‐1 mediates natriuresis but not polyuria during vitamin D‐induced acute hypercalcaemia. J Physiol 595, 2535–2550 (2017).
35.Stankevicins, L., Barat, A., Dessen, P., Vassetzky, Y. & de Moura Gallo, C. V. The microRNA-205-5p is correlated to metastatic potential of 21T series: A breast cancer progression model. PLoS One 12, (2017).
36.Thirant, C. et al. ETO2-GLIS2 Hijacks Transcriptional Complexes to Drive Cellular Identity and Self-Renewal in Pediatric Acute Megakaryoblastic Leukemia. Cancer Cell 31, 452–465 (2017).
37.De Braekeleer, E., Huret, J.-L., Mossafa, H. & Dessen, P. Cytogenetic Resources and Information. in Cancer Cytogenetics: Methods and Protocols (ed. Wan, T. S. K.) 311–331 (Springer, 2017). doi:10.1007/978-1-4939-6703-2_25.
38.Lefebvre, C. et al. Mutational Profile of Metastatic Breast Cancers: A Retrospective Analysis. PLoS Med 13, (2016).
39.Kempen, L. C. L. van et al. The protein phosphatase 2A regulatory subunit PR70 is a gonosomal melanoma tumor suppressor gene. Science Translational Medicine 8, 369ra177-369ra177 (2016).
40.Zhang, Y. et al. CXCR4/CXCL12 axis counteracts hematopoietic stem cell exhaustion through selective protection against oxidative stress. Sci Rep 6, (2016).
41.Fu, Y. et al. Improving the Performance of Somatic Mutation Identification by Recovering Circulating Tumor DNA Mutations. Cancer Res 76, 5954–5961 (2016).
42.Dmitriev, P. et al. DUX4-induced constitutive DNA damage and oxidative stress contribute to aberrant differentiation of myoblasts from FSHD patients. Free Radical Biology and Medicine 99, 244–258 (2016).
43.Dmitriev, P. et al. Dux4 controls migration of mesenchymal stem cells through the Cxcr4-Sdf1 axis. Oncotarget 7, 65090–65108 (2016).
44.Jovelet, C. et al. Circulating Cell-Free Tumor DNA Analysis of 50 Genes by Next-Generation Sequencing in the Prospective MOSCATO Trial. Clin Cancer Res 22, 2960–2968 (2016).
45.Scourzic, L. et al. DNMT3AR882H mutant and Tet2 inactivation cooperate in the deregulation of DNA methylation control to induce lymphoid malignancies in mice. Leukemia 30, 1388–1398 (2016).
46.Modjtahedi, N., Tokatlidis, K., Dessen, P. & Kroemer, G. Mitochondrial Proteins Containing Coiled-Coil-Helix-Coiled-Coil-Helix (CHCH) Domains in Health and Disease. Trends in Biochemical Sciences 41, 245–260 (2016).
47.Cabagnols, X. et al. Presence of atypical thrombopoietin receptor (MPL) mutations in triple-negative essential thrombocythemia patients. Blood 127, 333–342 (2016).
48.Dib, C. et al. Correction of the FSHD myoblast differentiation defect by fusion with healthy myoblasts. Journal of Cellular Physiology 231, 62–71 (2016).
49.Li, J., Drubay, D., Michiels, S. & Gautheret, D. Mining the coding and non-coding genome for cancer drivers. Cancer Letters 369, 307–315 (2015).
50.Li, J. et al. A Dual Model for Prioritizing Cancer Mutations in the Non-coding Genome Based on Germline and Somatic Events. PLoS Comput Biol 11, (2015).
51.Pujals, A. et al. Constitutive autophagy contributes to resistance to TP53-mediated apoptosis in Epstein-Barr virus-positive latency III B-cell lymphoproliferations. Autophagy 11, 2275–2287 (2015).
52.Rambow, F. et al. New Functional Signatures for Understanding Melanoma Biology from Tumor Cell Lineage-Specific Analysis. Cell Rep 13, 840–853 (2015).
53.Nanbakhsh, A. et al. miR-181a modulates acute myeloid leukemia susceptibility to natural killer cells. Oncoimmunology 4, (2015).
54.Saliba, J. et al. Germline duplication of ATG2B and GSKIP predisposes to familial myeloid malignancies. Nat Genet 47, 1131–1140 (2015).
55.Bach, A.-S. et al. Nuclear cathepsin D enhances TRPS1 transcriptional repressor function to regulate cell cycle progression and transformation in human breast cancer cells. Oncotarget 6, 28084–28103 (2015).
56.Vignot, S. et al. Comparative analysis of primary tumour and matched metastases in colorectal cancer patients: Evaluation of concordance between genomic and transcriptional profiles. European Journal of Cancer 51, 791–799 (2015).
57.Lazar, V. et al. A simplified interventional mapping system (SIMS) for the selection of combinations of targeted treatments in non-small cell lung cancer. Oncotarget 6, 14139–14152 (2015).
58.Djenidi, F. et al. CD8+CD103+ Tumor–Infiltrating Lymphocytes Are Tumor-Specific Tissue-Resident Memory T Cells and a Prognostic Factor for Survival in Lung Cancer Patients. The Journal of Immunology 194, 3475–3486 (2015).
59.Lee, W. et al. Identifying and Assessing Interesting Subgroups in a Heterogeneous Population. Biomed Res Int 2015, (2015).
60.Couvé, S. et al. Genetic evidence of a precisely tuned dysregulation in the hypoxia signaling pathway during oncogenesis. Cancer Res 74, 6554–6564 (2014).
61.Damm, F. et al. Acquired Initiating Mutations in Early Hematopoietic Cells of CLL Patients. Cancer Discov 4, 1088–1101 (2014).
62.David, M. et al. Suppressor of cytokine signaling 1 modulates invasion and metastatic potential of colorectal cancer cells. Mol Oncol 8, 942–955 (2014).
63.Albiges, L. et al. Chk1 as a new therapeutic target in triple-negative breast cancer. The Breast 23, 250–258 (2014).
64.Noman, M. Z. et al. PD-L1 is a novel direct target of HIF-1α, and its blockade under hypoxia enhanced MDSC-mediated T cell activation. J Exp Med 211, 781–790 (2014).
65.Lazar, V. et al. Sorafenib plus dacarbazine in solid tumors: a phase I study with dynamic contrast-enhanced ultrasonography and genomic analysis of sequential tumor biopsy samples. Invest New Drugs 32, 312–322 (2014).
66.Domingues, M. J. et al. β-Catenin Inhibitor ICAT Modulates the Invasive Motility of Melanoma Cells. Cancer Res 74, 1983–1995 (2014).
67.Al Nakouzi, N. et al. Targeting CDC25C, PLK1 and CHEK1 to overcome Docetaxel resistance induced by loss of LZTS1 in prostate cancer. Oncotarget 5, 667–678 (2014).
68.Romero, A. I. et al. Regulation of CD4+NKG2D+ Th1 Cells in Patients with Metastatic Melanoma Treated with Sorafenib: Role of IL-15Rα and NKG2D Triggering. Cancer Res 74, 68–80 (2014).
69.Dmitriev, P. et al. Simultaneous miRNA and mRNA transcriptome profiling of human myoblasts reveals a novel set of myogenic differentiation-associated miRNAs and their target genes. BMC Genomics 14, 265 (2013).
70.Bluteau, O. et al. Developmental changes in human megakaryopoiesis. Journal of Thrombosis and Haemostasis 11, 1730–1741 (2013).
71.Bernheim, A., Toujani, S. & Dessen, P. Du caryotype au séquençage GWS dans le lymphome de Burkitt. Annales de Pathologie 32, S46–S47 (2012).
72.Thiollier, C. et al. Characterization of novel genomic alterations and therapeutic approaches using acute megakaryoblastic leukemia xenograft models. J Exp Med 209, 2017–2031 (2012).
73.Julien, S. et al. Characterization of a Large Panel of Patient-Derived Tumor Xenografts Representing the Clinical Heterogeneity of Human Colorectal Cancer. Clin Cancer Res 18, 5314–5328 (2012).
74.Arnedos, M. et al. Array CGH and PIK3CA/AKT1 mutations to drive patients to specific targeted agents: A clinical experience in 108 patients with metastatic breast cancer. European Journal of Cancer 48, 2293–2299 (2012).
75.Poaty, H. et al. [Cytogenomic studies of hydatiform moles and gestational choriocarcinoma]. Bull Cancer 99, 827–843 (2012).
76.Poaty, H. et al. Genome-Wide High-Resolution aCGH Analysis of Gestational Choriocarcinomas. PLoS One 7, (2012).
77.Dupressoir, A. et al. A pair of co-opted retroviral envelope syncytin genes is required for formation of the two-layered murine placental syncytiotrophoblast. Proc Natl Acad Sci U S A 108, E1164–E1173 (2011).
78.Vernochet, C. et al. A syncytin-like endogenous retrovirus envelope gene of the guinea pig specifically expressed in the placenta junctional zone and conserved in Caviomorpha. Placenta 32, 885–892 (2011).
79.Gattolliat, C.-H. et al. Expression of miR-487b and miR-410 encoded by 14q32.31 locus is a prognostic marker in neuroblastoma. Br J Cancer 105, 1352–1361 (2011).
80.Friboulet, L. et al. Molecular Characteristics of ERCC1-Negative versus ERCC1-Positive Tumors in Resected NSCLC. Clin Cancer Res 17, 5562–5572 (2011).
81.Itzhar, N. et al. Chromosomal Minimal Critical Regions in Therapy-Related Leukemia Appear Different from Those of De Novo Leukemia by High-Resolution aCGH. PLoS One 6, (2011).
82.Peyre, M. et al. Portrait of Ependymoma Recurrence in Children: Biomarkers of Tumor Progression Identified by Dual-Color Microarray-Based Gene Expression Analysis. PLoS One 5, (2010).
83.Sarasin, A. & Dessen, P. DNA repair pathways and human metastatic malignant melanoma. Curr. Mol. Med. 10, 413–418 (2010).
84.Delhommeau, F. et al. Mutation in TET2 in Myeloid Cancers. http://dx.doi.org/10.1056/NEJMoa0810069 https://www.nejm.org/doi/10.1056/NEJMoa0810069?url_ver=Z39.88-2003&rfr_i... (2009) doi:10.1056/NEJMoa0810069.
85.Toujani, S. et al. High Resolution Genome-Wide Analysis of Chromosomal Alterations in Burkitt’s Lymphoma. PLoS One 4, (2009).
86.Criollo, A., Dessen, P. & Kroemer, G. DRAM: A phylogenetically ancient regulator of autophagy. Cell Cycle 8, 2319–2323 (2009).
87.Bénard, J. et al. MYCN‐non‐amplified metastatic neuroblastoma with good prognosis and spontaneous regression: A molecular portrait of stage 4S. Mol Oncol 2, 261–271 (2008).
 
Publications externes
Publications avec le laboratoire d'hébergement
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