Targeted sequencing of genes recurrently mutated in AML part2
Ontology highlight
ABSTRACT:
PROVIDER: EGAS00001000570 | EGA |
REPOSITORIES: EGA
Similar Datasets
Project description:Genomic libraries will be generated from total genomic DNA derived from 4000 samples with Acute Myeloid Leukaemia. Libraries will be enriched for a selected panel of genes using a bespoke pulldown protocol. 64 Samples will be individually barcoded and subjected to up to one lanes of Illumina HiSeq. Paired reads will be mapped to build 37 of the human reference genome to facilitate the characterisation of known gene mutations in cancer as well as the validation of potentially novel variants identified by prior exome sequencing.
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Project description:Genomic libraries will be generated from total
genomic DNA derived from 200+ patients with childhood Transient
Myeloproliferative Disorder (TMD) and or Acute Megakaryocytic
Leukemia (AMKL) as well some matched constitutional samples (n <
50 ). Libraries will be enriched for a selected panel of genes using a bespoke pulldown protocol. 96 Samples will be individually barcoded and subjected to up to two lanes of Illumina HiSeq. Paired reads will be mapped to build 37 of the human reference genome to facilitate the characterisation of known gene mutations in cancer as well as the validation of potentially novel variants identified by prior exome sequencing.
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Project description:Genomic libraries will be generated from total genomic DNA derived from 200+ patients with childhood Transient Myeloproliferative Disorder (TMD) and or Acute Megakaryocytic Leukemia (AMKL) as well some matched constitutional samples (n < 50). Libraries will be enriched for a selected panel of genes using a bespoke pulldown protocol. 96 Samples will be individually barcoded and subjected to up to two lanes of Illumina HiSeq. Paired reads will be mapped to build 37 of the human reference genome to facilitate the characterisation of known gene mutations in cancer as well as the validation of potentially novel variants identified by prior exome sequencing.
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Project description:We used a 48.48 Fluidigm Access Array approach (1070 Amplicons spanning 62 genes: 157kB coverage) of genomic DNA from over 100 samples to genotype individuals with transient abnormal myelopoiesis (TAM) and acute myeloid leukemia of Down syndrome (ML-DS). Individually barcoded patient samples were subjected to paired-end 150bp sequencing (Illumina MiSeq), including paired GATA1 variant negative controls (including Post treatment) and 8 known reference GATA1- controls. Samples were mapped to build 37 of the human reference genome and subject to bioinformatic analysis (Varscan, Pindel, GATK) to facilitate the characterisation of known gene mutations in cancer as well as the identification / validation of potentially novel variants.
2019-07-17 | E-MTAB-7729 | biostudies-arrayexpress
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2020-04-03 | GSE147989 | GEO
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2016-05-17 | E-ERAD-37 | biostudies-arrayexpress
Project description:The paper describes a model of acute myeloid leukaemia.
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This model is described in the article:
Optimal control of acute myeloid leukaemia
Jesse A. Sharp, Alexander P Browning, Tarunendu Mapder, Kevin Burrage, Matthew J Simpson
Journal of Theoretical Biology 470 (2019) 30–42
Abstract:
Acute myeloid leukaemia (AML) is a blood cancer affecting haematopoietic stem cells. AML is routinely treated with chemotherapy, and so it is of great interest to develop optimal chemotherapy treatment strategies. In this work, we incorporate an immune response into a stem cell model of AML, since we find that previous models lacking an immune response are inappropriate for deriving optimal control strategies. Using optimal control theory, we produce continuous controls and bang-bang controls, corre- sponding to a range of objectives and parameter choices. Through example calculations, we provide a practical approach to applying optimal control using Pontryagin’s Maximum Principle. In particular, we describe and explore factors that have a profound influence on numerical convergence. We find that the convergence behaviour is sensitive to the method of control updating, the nature of the control, and to the relative weighting of terms in the objective function. All codes we use to implement optimal control are made available.
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2024-09-02 | BIOMD0000000798 | BioModels