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• PAGE: The Charles Bronfman Institute for Personalized Medicine (IPM) BioMe BioBank

  The Institute for Personalized Medicine (IPM) BioMe Biobank is a consented, EMR-linked medical care setting biorepository of the Mount Sinai Medical Center (MSMC) drawing from a population of over 70,000 inpatients and 800,000 outpatient visits annually. MSMC serves diverse local communities of upper Manhattan, including Central Harlem (86% African American), East Harlem (88% Hispanic Latino), and Upper East Side (88% Caucasian/white) with broad health disparities. IPM BioMe Biobank populations include 28% African American, 38% Hispanic Latino predominantly of Caribbean origin, 23% Caucasian/White. IPM BioMe Biobank disease burden is reflective of health disparities with broad public health impact. Biobank operations are fully integrated in clinical care processes, including direct recruitment from clinical sites waiting areas and phlebotomy stations by dedicated Biobank recruiters independent of clinical care providers, prior to or following a clinician standard of care visit. Recruitment currently occurs at a broad spectrum of over 30 clinical care sites. This study is part of the Population Architecture using Genomics and Epidemiology (PAGE) study (phs000356).

  Study phs000925

• Multimodal Epigenetic Sequencing Analysis (MESA) of Cell-free DNA for Non-invasive Cancer Detection

  Multimodal characterization of cell-free DNA (cfDNA) in the blood can enable the sensitive and non-invasive detection of human cancers but remains technically challenging and costly. Here, we developed Multimodal Epigenetic Sequencing Analysis (MESA), a flexible and sensitive method of capturing and integrating multimodal epigenetic information of cfDNA using a single experimental assay, i.e., non-disruptive bisulfite-free methylation sequencing, such as Enzymatic Methyl-seq (EM-seq) and TET-assisted pyridine borane sequencing (TAPS). MESA can simultaneously infer cfDNA methylation, nucleosome occupancy, nucleosome fuzziness, and fragmentation profile for regions surrounding the promoters and polyadenylation sites (PASs). MESA’s integrated analysis of multimodal epigenetic features significantly improved the performance of cancer detection models compared to the usage of any single modality alone. MESA captures additional and highly complementary epigenetic information from cfDNA without additional experimental assays, highlighting the importance and clinical prospect of using multimodal epigenetic features for non-invasive cancer detection

  Study EGAS00001006462

• Genetic vulnerability of knockout cancer lines (2019-04-01)

  Genome-wide CRISPR/Cas9 library screen was performed in isogenic cell lines. Three biological replicates were used. Cells were harvested at initiation of screen and at specific time points following cell culture. Using this approach we aim to identify genes specifically important in cell survival in engineered cell lines. Please perform 72, instead of 36, PCR reactions as the screen performed at 200x coverage. . This dataset contains all the data available for this study on 2019-04-01.

  Dataset EGAD00001004881

• Single cell transcriptomics of hESC-derived midbrain dopaminergic neurons generated by a new human development-based protocol

  Single cell RNA-seq from D0,D11,D16,D21,D28 of dopamingeric differentiation from hESCs cell lines H9 and HS980 using current protocols. Different time points along the differentiation for each cell line were multiplexed using BD™ Single-Cell Multiplexing Kit for use with the 10x Chromium™ Single Cell 3’ Reagent Kit v2.

  Dataset EGAD00001008834

• Sequencing of cell-free DNA from breast cancer patients

  To assess the utility of high coverage whole genome sequencing analysis of cell-free DNA to detect tumour-specific genomic changes

  Study EGAS00001004960

• Whole blood RNAseq from a large ALS case-control study at Univ of Michigan

  This dataset consists of 694 bulk RNA-seq data (sorted aligned BAM files) from whole blood of ALS participants meeting the Gold Coast definition of ALS during their clinical visit (n=422) and healthy controls (n=272) at the University of Michigan (UM). Samples were collected as part of the UM Pranger ALS Clinic between 2011-2021, and prepared either using poly-A selection of rRNA depletion (see Experiments). RNAseq was performed on the NovSeq 6000 S4 at the UM Advanced Genomics Core, resulting in 150bp paired-end reads. Raw reads were trimmed using Cutadapt (v2.3) and aligned to hg38 using STAR (v2.5.3a). Age provided is the age at blood sample collection date.

  Dataset EGAD50000001488

• Whole-transcriptome characterization of cell-free RNA (cfRNA) in cancer and non-cancer patients

  Whole-transcriptome characterization of cfRNA in cancer (stage III breast [n=46], lung [n=30]) and non-cancer (n=89) participants from the Circulating Cell-free Genome Atlas (NCT02889978). Dataset includes collapsed BAM files for plasma cfRNA from each patient, as well as collapsed BAM files for RNA from matched tumor tissue (when available).

  Dataset EGAD00001006484

• The effect of blood tube and time delay on the genome-wide methylation pattern of cfDNA

  In this study, we evaluated the effect of preservation agens on the effect of the methylation pattern of cell-free DNA. The methylation pattern was assessed with cell-free reduced representation sequencing (cf-RRBS). All 45 samples were sequenced on a NovaSeq6000, and samples are provided as raw fastq files.

  Dataset EGAD00001006007

• ECCO-GEN Valve Methylation Dataset (non-diseased)

  The valve methylation dataset consists of 12 bam files of human non-diseased valve tissue samples that are free from calcification (6 aortic and 6 mitral valves - matched; 10 males: 2 females; age range 42 – 64 years, mean age 52.2 years, SD 9.9682). Donor hearts are free from cardiovascular and valvular complications.

  Dataset EGAD00001006303

• Cell-free DNA methylation profiling of sepsis patients

  This data set contains paired-end NGS files (in FASTQ format) generated from a methylation profiling experiment of cell-free DNA derived from the plasma of sepsis patients and healthy controls. Methylation profiling was performed using the TET-assisted pyridine borane sequencing (TAPS) protocol.

  Dataset EGAD50000001505