Metabolomics,Unknown,Transcriptomics,Genomics,Proteomics

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Strand-seq of human embryonic stem cells and differentiated cells in ATRX wt and ATRX ko cells

ABSTRACT: Briefly, human embryonic stem cells (hESCs) were collected after BrdU pulse and resuspended in nuclei staining buffer (100 mM Tris-HCl pH 7.4, 150 mM NaCl, 1 mM CaCl2, 0.5 mM MgCl2, 0.1% NP-40, and 2% bovine serum albumin [Sigma-Aldrich] supplemented with 10 μg/mL Hoechst 33258 [Enzo] and 10 μg/mL propidium iodide [Sigma-Aldrich]. Single nuclei were sorted into 5 μL ProFreeze-CDM freeze medium [Lonza] + 7.5% dimethyl sulfoxide in 96-well skirted PCR plates [4titude], based on low propidium iodide (G1 phase) and low Hoechst (BrdU-induced quenching) fluorescence using an Influx cell sorter [BD Biosciences]. Strand-seq libraries were prepared using an Agilent Bravo liquid handling platform as described before (Sanders et al., 2017). For each experiment, 96 libraries were pooled and 250-450 bp-sized fragments were isolated and purified. DNA quality and concentrations were assessed using the High Sensitivity dsDNA kit [Agilent] on the Agilent 2100 Bio-Analyzer and on a Qubit 2.0 Fluorometer [Life Technologies] resp. Single-end sequencing reads from Strand-seq libraries were generated using the HiSeq 2500 or the NextSeq 500 sequencing platform [Illumina; up to 77 bp]. Indexed reads were aligned to the human reference genome (GRCh38) using Bowtie2 68. Only non-duplicate reads with a mapping quality greater than or equal to 10 were further analyzed with the 69 and AneuFinder R-based package; (https://github.com/ataudt/aneufinder). Strand-seq libraries were prefiltered to avoid errors arising from low-quality data. For this, we excluded libraries with less than 25 reads/Mb, >10% background reads, no template strand inheritance, uneven coverage (high number of copy number segments) or libraries from cells in second cell division. Libraries passing these quality criteria served as input for further analysis. AneuFinder was used to locate and classify template strand switch and copy number change breakpoints. In short, following GC corrections and blacklisting of artefact-prone regions, libraries were analyzed using the edivisive copy number calling algorithm with variable width bins (binsize: 100 kb; step size: 40kb) and breakpoint refinement (R = 10, confint = 0.99; other settings as default). Copy numbers for both the Watson (negative) and Crick (positive) strand were called and breakpoints were defined as changes in copy number state. As BAIT and AneuFinder also detect stable chromosomal rearrangements (e.g., inversions), template switching events that occurred at the exact same locations were excluded from the analysis. Computationally localized SCE or copy number alteration events were further manually verified by visual inspection of chromosome ideograms obtained from AneuFinder and BAIT. Aneuploidy, structural and heterogeneity scores were calculated as previously described.

INSTRUMENT(S): Illumina HiSeq 2500

ORGANISM(S): Homo sapiens

SUBMITTER: Antonio Maffia

PROVIDER: E-MTAB-12582 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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Project description:Current methods for detection of copy number aberrations (CNA) from whole-exome sequencing (WES) data are based on the read counts of the captured exons only. However, accurate CNA determination is complicated by the non-uniform read depth and uneven distribution of exons. Therefore, we developed ENCODER (ENhanced COpy number Detection from Exome Reads), which eludes these problems. By exploiting the ‘off-target’ sequence reads, it allows for creation of robust copy number profiles from WES. The accuracy of ENCODER compares to approaches specifically designed for copy number detection, and outperforms current exon-based WES methods, particularly in samples of low quality. Current methods for detection of copy number aberrations (CNA) from whole-exome sequencing (WES) data are based on the read counts of the captured exons only. However, accurate CNA determination is complicated by the non-uniform read depth and uneven distribution of exons. Therefore, we developed ENCODER (ENhanced COpy number Detection from Exome Reads), which eludes these problems. By exploiting the ‘off-target’ sequence reads, it allows for creation of robust copy number profiles from WES. The accuracy of ENCODER compares to approaches specifically designed for copy number detection, and outperforms current exon-based WES methods, particularly in samples of low quality. Current methods for detection of copy number aberrations (CNA) from whole-exome sequencing (WES) data are based on the read counts of the captured exons only. However, accurate CNA determination is complicated by the non-uniform read depth and uneven distribution of exons. Therefore, we developed ENCODER (ENhanced COpy number Detection from Exome Reads), which eludes these problems. By exploiting the ‘off-target’ sequence reads, it allows for creation of robust copy number profiles from WES. The accuracy of ENCODER compares to approaches specifically designed for copy number detection, and outperforms current exon-based WES methods, particularly in samples of low quality. DNA copy number profiles generated with a new tool, ENCODER, were compared to DNA copy number profiles from SNP6, NimbleGen and low-coverage Whole Genome Sequencing.

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Strand-seq of human embryonic stem cells and differentiated cells in ATRX wt and ATRX ko cells

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