J.Costello_20191003_dataset_rnaseq - samples
Ontology highlight
ABSTRACT:
PROVIDER: EGAD00001005387 | EGA |
REPOSITORIES: EGA
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Project description:113 DNA samples were derived from the tumors of the low grade glioma patients and sequenced using Illumina WES (exome seq) paired-end technology.
Dataset contains 113 BAM files aligned to hg19 using BWA v.0.5.9. After mapping duplicated reads were removed, reads were re-aligned around InDels and read base quality score was re-calibrated.
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Project description:The goal of this study was to conduct an in-depth analysis of the human placental transcriptome. RNA was extracted from 16 placental samples using TRIzol following the manufacturer’s protocol. All samples were spiked with 96 External RNA Controls Consortium (ERCC) ExFold RNA transcripts. Ribosomal RNAs were depleted from samples using Ribo-Zero Gold and sequencing libraries were prepared using Illumina TruSeq Stranded Total RNA Sample Preparation kits. Sequencing was performed on the Illumina Hi-Seq 2500 using a 100bp paired-end protocol. Sequence adapters were trimmed using AdapterRemoval with options --trimns, --minlength 20. Trimmed RNA-Seq reads were aligned to known UCSC hg19 genes and the hg19 genome using Bowtie 2 v2.1.0 and TopHat v2.0.9 with options --library-type=fr-firststrand --mate-inner-dist -20 --mate-std-dev 180. UCSC hg19 reference genome and transcriptome was obtained through Illumina iGenomes (https://support.illumina.com/sequencing/sequencing_software/igenome.html). Aligned RNA-Seq reads were summarised using the summarizeOverlaps algorithm with the UCSC known genes hg19 GTF file using the the options overlapMode=``Union'', ignoreStrand=FALSE, singleEnd=FALSE, fragments=TRUE to generate a table of unique read counts per gene for each sample. All samples were processed in the same way, with all sequencing libraries created in the same batch and sequenced together.
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Project description:Ribba2012 - Low-grade gliomas, tumour growth inhibition model
Using longitudinal mean tumour diameter (MTD) data, this model describe the size evolution of low-grade glioma (LGG) in patients treated with chemotherapy or radiotherapy.
This model is described in the article:
A tumour growth inhibition model for low-grade glioma treated with chemotherapy or radiotherapy
Ribba B, Kaloshi G, Peyre M, Ricard D, Calvez V, Tod M, Cajavec-Bernard B, Idbaih A, Psimaras D, Dainese L, Pallud J, Cartalat-Carel S, Delattre JY, Honnorat J, Grenier E, Ducray F.
Clin. Cancer Res. 2012 Sep; 18(18): 5071-5080
Abstract:
PURPOSE: To develop a tumor growth inhibition model for adult diffuse low-grade gliomas (LGG) able to describe tumor size evolution in patients treated with chemotherapy or radiotherapy.
EXPERIMENTAL DESIGN: Using longitudinal mean tumor diameter (MTD) data from 21 patients treated with first-line procarbazine, 1-(2-chloroethyl)-3-cyclohexyl-l-nitrosourea, and vincristine (PCV) chemotherapy, we formulated a model consisting of a system of differential equations, incorporating tumor-specific and treatment-related parameters that reflect the response of proliferative and quiescent tumor tissue to treatment. The model was then applied to the analysis of longitudinal tumor size data in 24 patients treated with first-line temozolomide (TMZ) chemotherapy and in 25 patients treated with first-line radiotherapy.
RESULTS: The model successfully described the MTD dynamics of LGG before, during, and after PCV chemotherapy. Using the same model structure, we were also able to successfully describe the MTD dynamics in LGG patients treated with TMZ chemotherapy or radiotherapy. Tumor-specific parameters were found to be consistent across the three treatment modalities. The model is robust to sensitivity analysis, and preliminary results suggest that it can predict treatment response on the basis of pretreatment tumor size data.
CONCLUSIONS: Using MTD data, we propose a tumor growth inhibition model able to describe LGG tumor size evolution in patients treated with chemotherapy or radiotherapy. In the future, this model might be used to predict treatment efficacy in LGG patients and could constitute a rational tool to conceive more effective chemotherapy schedules.
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2024-09-02 | BIOMD0000000521 | BioModels
Project description:Purpose: Evaluate p63 regulation of gene expression in human embryonic stem cells and the surface ectoderm Methods: RNA profiles for human embyronic stem cells and surface ectoderm cells with and without p63 using deep sequencing, in duplicate, on Illumina NextSeq 500 sequencer. Quality of reads were checked by fastqc. Reads were aligned to hg19 genome using tophat and FPKM values were generated using Homer. Results: p63 is largely unable to regulate gene expression in pluripotent cells, but acts primarily as a repressor in the surface ectoderm. Conclusion: p63 regulation of gene expression is dependent on the changes the cell undergoes during surface ectoderm commitment
2018-11-06 | GSE121994 | GEO