Project description:CNVs detection using 13 samples’ pairs of (12 samples’ pairs of histologically confirmed diagnosis of high-grade serous carcinoma and one patient with endometrioid carcinoma and matching normal tissues. Samples have IHC status for p53, WT1, Ki-67, PAX-8, and TNM classification according to WHO Classification of Tumors. Please cite the original atricle: Grebnev, P.A.; Meshkov, I.O.; Ershov, P.V.; Makhotenko, A.V.; Azarian, V.B.; Erokhina, M.V.; Galeta, A.A.; Zakubanskiy, A.V.; Shingalieva, O.S.; Tregubova, A.V.; et al. Benchmarking of Approaches for Gene Copy-Number Variation Analysis and Its Utility for Genetic Aberration Detection in High-Grade Serous Ovarian Carcinomas. Cancers 2024, 16, 3252. https://doi.org/10.3390/cancers16193252
2024-09-24 | GSE244329 | GEO
Project description:Detection of structural variation in RPE-1 cell line
| PRJEB33731 | ENA
Project description:Detection of structural variation in RPE-1 cell line
| PRJEB30027 | ENA
Project description:Haplotype-resolved population-based structural variation detection on linked-read
Project description:State-of-the-art algorithms for m6A detection and quantification via nanopore direct RNA sequencing have been continuously developed, little is known about their capacities and limitations, which makes a comprehensive assessment in urgent need. Therefore, we performed comprehensive benchmarking of 10 computational tools relying on current-based and base-calling “errors” strategies for m6A detection by nanopore sequencing.
Project description:Here, we have collapsed multiple analysis problems into two coherent categories, signal detection and signal estimation and adapted linear-optimal solutions from signal processing theory. Our algorithms for detection (DFilter) and estimation (EFilter) extend naturally to integration of multiple datasets. In benchmarking tests, DFilter outperformed assay-specific algorithms at identifying promoters from histone ChIP-seq, binding sites from transcription factor (TF) ChIP-seq and open chromatin regions from DNase- and FAIRE-seq data. EFilter similarly outperformed an existing method for predicting mRNA levels from histone ChIP-seq data (Spearman correlation: 0.81 - 0.89). We performed H3K4me3 and H3K36me3 ChIP-seq on e11.5 mouse forebrain and used DFilter and EFilter to predict promoters and developmental gene expression, uncovering plausible gene targets for SNPs associated with neurodevelopmental disorders. Generated two histone modifiction ChiP-seq in developing embryo mouse forebrain and using them for making bioligical inferences