OncoSig: a new algorithm for the accurate and systematic de novo prediction of Oncoprotein-centric Signaling networks
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PROVIDER: PRJNA418958 | ENA |
REPOSITORIES: ENA
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Project description:We introduce a new algorithm, OncoSig, to address the deficiencies of canonical pathways through the accurate, context-specific prediction of proteins representing both known and novel functional members of Oncoprotein-centric Signaling networks. OncoSig performs systematic, de novo elucidation of Protein-Centric molecular interaction maps (PC-maps), which include upstream modulators, cognate binding partners, and downstream effectors. The reconstructed KRAS PC-Map in lung adenocarcinoma (LUAD) demonstrates the recapitulation of known biology and, critically, the discovery of novel KRAS effectors that elicit synthetic lethality in LUAD organoid cultures. To illustrate the generalizability of OncoSig, the methodology is applied to elucidate the PC-maps of ten recurrently mutated oncoproteins and oncopathways in LUAD as well as KRAS PC-Maps across three tumor contexts. OncoSig thus reveals tumorigenic architecture to an unprecedented degree and provides a unique mechanistic perspective on how the flow of information in cells is dysregulated in support of tumor homeostasis. To validate predicted KRAS effectors in primary tumor organoid cultures, this study includes a pooled RNAi screening approach (shRNA-seq) in primary lung tumors from a mouse model driven by mutant Kras and p53 loss.
2020-11-11 | GSE107042 | GEO
Project description:Transcriptional enhancers play critical roles in regulation of gene expression, but their identification has remained a challenge. Recently, it was shown that enhancers in the mammalian genome are associated with characteristic histone modification patterns, which have been increasingly exploited for enhancer identification. However, only a limited number of histone modifications have previously been investigated for this purpose, leaving the questions answered whether there exist an optimal set of histone modifications that could improve the enhancer prediction. Here, we address this issue by exploring a rich dataset produced by the human Epigenome Roadmap Project. Specifically, we examined genome-wide profiles of 24 histone modifications in human embryonic stem cells and fibroblasts, and developed a Random-Forest based algorithm to integrate histone modification profiles for identification of enhancers.As a training set, we used histone modification profiles at genome-wide binding sites of p300 in the two cell types identified using ChIP-seq. We show that this algorithm not only leads to more accurate and precise prediction of enhancers than previous methods, but also helps identify an optimal set of three chromatin marks for enhancer prediction.
2012-05-10 | GSE37858 | GEO
Project description:Transcriptional enhancers play critical roles in regulation of gene expression, but their identification has remained a challenge. Recently, it was shown that enhancers in the mammalian genome are associated with characteristic histone modification patterns, which have been increasingly exploited for enhancer identification. However, only a limited number of histone modifications have previously been investigated for this purpose, leaving the questions answered whether there exist an optimal set of histone modifications that could improve the enhancer prediction. Here, we address this issue by exploring a rich dataset produced by the human Epigenome Roadmap Project. Specifically, we examined genome-wide profiles of 24 histone modifications in human embryonic stem cells and fibroblasts, and developed a Random-Forest based algorithm to integrate histone modification profiles for identification of enhancers.As a training set, we used histone modification profiles at genome-wide binding sites of p300 in the two cell types identified using ChIP-seq. We show that this algorithm not only leads to more accurate and precise prediction of enhancers than previous methods, but also helps identify an optimal set of three chromatin marks for enhancer prediction. ChIP-Seq Analysis of p300 in hESC H1 and IMR90 cells. Sequencing was done on the Illumina Genome Analyzer II platform for the H1 data and Illumina HiSeq for IMR90.Data was mapped to hg18 using Bowtie.
2012-05-09 | E-GEOD-37858 | biostudies-arrayexpress
Project description:Aminoglycosides resistance mechanisms inference algorithm
| PRJEB29576 | ENA
Project description:Chronological age prediction from DNA methylation sheds light on human aging, indicates poor health and predicts lifespan. Previous studies developed methylation clocks based on linear regression models on methylation array data. While accurate, these models are limited to fixed-rate changes in methylation levels across age. Moreover, the high cost of methylation arrays, compared to targeted-PCR sequencing, hinders widespread utility of such predictors. We present an AI-based alternative termed GP-age, which uses a non-parametric approach based on Gaussian Process Regression of a large cohort of ~12K blood methylomes. Given a new blood sample, methylation levels are compared to the cohort samples, which are then weighted to predict the query age. Using only 30 CpG sites, our approach outperforms state-of-the-art methylation clocks that use hundreds of sites, with a median error of 2.1 years (on held-out data). Our model was also applied to sequencing-based data yielding highly accurate predictions. Overall, we provide an accessible alternative to current array-based methylation clocks, with future applications in aging research, forensic profiling, and monitoring epigenetic processes in transplantation medicine and cancer.
2023-01-20 | GSE207605 | GEO
Project description:Accurate ethnicity prediction from placental DNA methylation data
| PRJNA529078 | ENA
Project description:Highly accurate Skin-Specific methylome analysis algorithm as a platform to screen and validate therapeutics for healthy aging
| PRJNA636644 | ENA
Project description:Purpose Aromatase inhibitors (AIs) have an established role in breast cancer treatment. Response rates are only 50-70% in the neoadjuvant setting and lower in advanced disease. Accurate biomarkers are urgently needed to predict response in these settings and to determine which individuals will benefit from adjuvant AI therapy. Participants and Methods Pre- and on-treatment (after 2 weeks and 3 months) biopsies were obtained from 89 post-menopausal women with ER+ breast cancer receiving neoadjuvant letrozole for transcript profiling. Dynamic clinical response was assessed by three-dimensional ultrasound measurements. Results The molecular response to letrozole was characterised and a four gene classifier of clinical response was established (accuracy of 96%) based upon the level of two genes prior to treatment (one associated with immune signalling, IL6ST and the other with apoptosis, NGFRAP1) and two proliferation genes (ASPM, MCM4) at 2 weeks of therapy. The four gene signature was found to be 91% accurate in a blinded, completely independent validation dataset of patients treated with anastrozole. Matched 2 week on-treatment biopsies improved predictive power over pre-treatment biopsies alone. This signature also significantly predicted recurrence free survival (p=0.029) and breast cancer specific survival (p=0.009). We demonstrate that the test can also be performed using quantitative PCR or immunohistochemistry. Conclusion A four gene predictive model of clinical response to AIs by two weeks has been generated and validated. Deregulated immune and apoptotic responses before treatment and a failure to reduce proliferation by 2 weeks are functional characteristics of breast tumours that do not respond to AIs. 25 Pre treatment, 25 two week and 25 three month on-treatment primary breast tumour samples from the same patients. Data was analysed with two previously generated datasets, GSE55374 which was also processed on Illumina HT-12v4 BeadChips (GPL10558) and GSE20181 on Affymetrix U133A GeneChips (GPL96)
2015-07-06 | E-GEOD-59515 | biostudies-arrayexpress
Project description:Highly accurate Skin-Specific methylome analysis algorithm as a platform to screen and validate therapeutics for healthy aging [DS2]
| PRJNA636519 | ENA
Project description:Highly accurate Skin-Specific methylome analysis algorithm as a platform to screen and validate therapeutics for healthy aging [DS5]
| PRJNA636520 | ENA