Project description:We performed shallow whole genome sequencing (WGS) on circulating free (cf)DNA extracted from plasma or cerebrospinal fluid (CSF), and shallow WGS on the tissue DNA extracted from the biopsy in order to evaluate the correlation between the two biomaterials. After library construction and sequencing (Hiseq3000 or Ion Proton), copy number variations were called with WisecondorX.
Project description:Sequence-based deep learning models have become the state of the art for the analysis of the genomic regulatory code. Particularly for transcriptional enhancers, deep learning models excel at deciphering sequence features and grammar that underlie their spatiotemporal activity. To enable end-to-end enhancer modeling and design, we developed a software and modeling package, called CREsted. It combines preprocessing starting from single-cell ATAC-seq data; modeling with a choice of several architectures for training classification and regression models on either topics or pseudobulk peak heights; sequence design using multiple strategies; and downstream analysis through a collection of tools to locate transcription factor (TF) binding sites, infer the effect of a TF (activating or repressing) on enhancer accessibility, decipher enhancer grammar, and score gene loci. We demonstrate CREsted using a mouse cortex model that we validate using the BICCN collection of in vivo validated mouse brain enhancers. Classical enhancers in immune cells, including the IFN-β enhanceosome are revisited using a PBMC model, and we assess the accuracy of TF binding site predictions with ChIP-seq. Additionally, we use CREsted to compare mesenchymal-like cancer cell states between tumor types; and we investigate different fine-tuning strategies of Borzoi within CREsted, comparing their performance and explainability with CREsted models trained from scratch. Finally, we train a CREsted model on a scATAC-seq atlas of zebrafish development, and use this to design and in vivo validate cell type-specific synthetic enhancers in 3 tissues. For varying datasets we demonstrate that CREsted facilitates efficient training and analyses, enabling scrutinization of the enhancer logic and design of synthetic enhancers across tissues and species. CREsted is available at https://crested.readthedocs.io.
Project description:We evaluated linked-read whole genome sequencing (WGS) for detection of structural chromosomal rearrangements in primary samples of varying DNA quality from 12 patients diagnosed with ALL. Linked-read WGS enabled precise, allele-specific, digital karyotyping at a base-pair resolution for a wide range of structural variants including complex rearrangements, aneuploidy assessment and gene deletions. Additional RNA-sequencing and copy number aberrations (CNA) data from Illumina Infinium arrays were also generated and assessed against the linked-read WGS data. RNA-sequencing data was used to support structural chromosomal rearrangements detected in the linked-read WGS data by detecting expressed fusion genes as a consequence of the rearrangements. Illumina Infinium arrays (450k array and/or SNP array) were used to assess CNA status to further support the findings in the linked-read WGS data. The processed CNA data from the primary ALL patient samples has been deposited to GEO. RNA-sequencing, linked-read WGS data, and raw SNP array data from the primary ALL patient samples will not be deposited because the patient/parent consent does not cover depositing data that may be used for large-scale determination of germline variants in a repository. The ALL samples were collected 10-20 years ago from pediatric patients aged 2-15 years, some whom have deceased. The linked-read WGS data and the RNA-sequencing data sets generated in the study are available upon reasonable request from the corresponding author Jessica.Nordlund@medsci.uu.se.
Project description:We evaluated linked-read whole genome sequencing (WGS) for detection of structural chromosomal rearrangements in primary samples of varying DNA quality from 12 patients diagnosed with ALL. Linked-read WGS enabled precise, allele-specific, digital karyotyping at a base-pair resolution for a wide range of structural variants including complex rearrangements, aneuploidy assessment and gene deletions. Additional RNA-sequencing and copy number aberrations (CNA) data from Illumina Infinium arrays were also generated and assessed against the linked-read WGS data. RNA-sequencing data was used to support structural chromosomal rearrangements detected in the linked-read WGS data by detecting expressed fusion genes as a consequence of the rearrangements. Illumina Infinium arrays (450k array and/or SNP array) were used to assess CNA status to further support the findings in the linked-read WGS data. The processed CNA data from the primary ALL patient samples has been deposited to GEO. RNA-sequencing, linked-read WGS data, and raw SNP array data from the primary ALL patient samples will not be deposited because the patient/parent consent does not cover depositing data that may be used for large-scale determination of germline variants in a repository. The ALL samples were collected 10-20 years ago from pediatric patients aged 2-15 years, some whom have deceased. The linked-read WGS data and the RNA-sequencing data sets generated in the study are available upon reasonable request from the corresponding author Jessica.Nordlund@medsci.uu.se.
Project description:We evaluated linked-read whole genome sequencing (WGS) for detection of structural chromosomal rearrangements in primary samples of varying DNA quality from 12 patients diagnosed with ALL. Linked-read WGS enabled precise, allele-specific, digital karyotyping at a base-pair resolution for a wide range of structural variants including complex rearrangements, aneuploidy assessment and gene deletions. Additional RNA-sequencing and copy number aberrations (CNA) data from Illumina Infinium arrays were also generated and assessed against the linked-read WGS data. RNA-sequencing data was used to support structural chromosomal rearrangements detected in the linked-read WGS data by detecting expressed fusion genes as a consequence of the rearrangements. Illumina Infinium arrays (450k array and/or SNP array) were used to assess CNA status to further support the findings in the linked-read WGS data. The processed CNA data from the primary ALL patient samples has been deposited to GEO. RNA-sequencing, linked-read WGS data, and raw SNP array data from the primary ALL patient samples will not be deposited because the patient/parent consent does not cover depositing data that may be used for large-scale determination of germline variants in a repository. The ALL samples were collected 10-20 years ago from pediatric patients aged 2-15 years, some whom have deceased. The linked-read WGS data and the RNA-sequencing data sets generated in the study are available upon reasonable request from the corresponding author Jessica.Nordlund@medsci.uu.se.
Project description:Whole genome sequencing (WGS) of tongue cancer samples and cell line was performed to identify the fusion gene translocation breakpoint. WGS raw data was aligned to human reference genome (GRCh38.p12) using BWA-MEM (v0.7.17). The BAM files generated were further analysed using SvABA (v1.1.3) tool to identify translocation breakpoints. The translocation breakpoints were annotated using custom scripts, using the reference GENCODE GTF (v30). The fusion breakpoints identified in the SvABA analysis were additionally confirmed using MANTA tool (v1.6.0).
Project description:For this project, we explored the genetic determinants of the heart development condition termed patent foramen ovale (PFO) using quantitative trait loci (QTL) mapping and genomics/transcriptomics analyses. Two mice strains were chosen that exhibit highly divergent phenotypes associated with PFO, 129T2/SvEms and QSi5. In this experiment, we performed whole genome sequencing (WGS) on genomic DNA extracted from liver specimens of 129T2/SvEms or QSi5 mice in order to identify genomic variants that may contribute to the phenotypes associated with PFO.