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191 results for “Cell-free DNA”
Cell-free DNA Fragmentomics and Second Malignant Neoplasm Risk in Patients with PTEN Hamartoma Tumor Syndrome
<p><strong>Supplementary dataset for manuscript:</strong></p> <p><strong>Cell-free DNA Fragmentomics and Second Malignant Neoplasm Risk in Patients with PTEN Hamartoma Tumor Syndrome</strong></p> <p>Affiliations:</p> <p><sup>1</sup>Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA</p> <p><sup>2</sup>Cleveland Clinic Lerner College of Medicine of Case Western Reserve University, Cleveland, OH 44195, USA</p> <p><sup>3</sup>Rose Ella Burkhardt Brain Tumor and Neuro-Oncology Center, Cleveland Clinic, Cleveland, OH 44195, USA</p> <p><sup>4</sup>Center for Personalized Genetic Healthcare, Medical Specialties Institute, Cleveland Clinic, Cleveland, OH 44195, USA</p> <p><sup>5</sup>Center for Immunotherapy and Precision Immuno-oncology, Cleveland Clinic, Cleveland, OH 44195, USA</p> <p><sup>6</sup>Taussig Cancer Institute, Cleveland Clinic, Cleveland, OH 44195, USA</p> <p><sup>7</sup>Department of Genetics and Genome Sciences, Case Western Reserve University School of Medicine, Cleveland, OH 44106, USA</p> <p><sup>8</sup>Germline High Risk Cancer Focus Group, Case Comprehensive Cancer Center, Case Western Reserve University, Cleveland, OH 44106, USA</p> <p><strong><em>Correspondence</em></strong>:</p> <p>Darren Liu, MD, MS (<a href="https://orcid.org/0000-0002-6703-1843">https://orcid.org/0000-0002-3693-5145)</a></p> <p>Cleveland Clinic Genomic Medicine Institute</p> <p>9500 Euclid Avenue, Cleveland, OH 44195, USA</p> <p>E-mail: liud5@ccf.org </p>
Genome Sizes of Bacterial Species Detected in Cell-Free DNA of Patients with Acute Leukemia and Sepsis, Including Those Undergoing Bone Marrow Transplantation
<p>Next Generation Sequencing (NGS) analysis of Cell-Free DNA provides valuable insights into a spectrum of pathogenic species (particularly bacterial) in blood. Patients with Sepsis often face problems like delays in treatment regimens (combination or cocktail of antibiotics) due to the long turnaround time (TAT) of classical and standard blood culture procedures. NGS gives results with lower TAT along with high-depth coverage. The use of NGS may be a possible solution to deciding treatment regimens for patients without losing precious time and more accurately possibly saving lives.</p> <p>Our curated dataset is of bacterial species or strains detected along with their genome size in 107 AML patients diagnosed with Sepsis clinically. Cell-free DNA profiles of patients were built and sequencing was done in Illumina (NovaSeq and NextSeq). Bioinformatic analysis was performed using two classification algorithms namely kraken2 and kaiju. For kraken2 based classification reference bacterial index developed by Carlo Ferravante et al (Zenodo 2020) (link: https://zenodo.org/records/4055180) was used, while for kaiju-based classification reference database named "nr_euk" dated "2023-05-10" (link: https://bioinformatics-centre.github.io/kaiju/downloads.html) was used.</p> <p>Genome size annotation is important in metagenomics since for the use of depth of coverage (abundance), genome size is required. In metagenomic classification algorithms like kraken/kraken2 and kaiju output computes reads assigned only and not abundance. In kaiju, the problem is more complicated since the reference database does not have a fasta file but only an index file from which alignment is done. </p> <p>To address the above challenges to compute "depth of coverage" or simply abundance, we build a Genome size annotator tool (https://github.com/patkarlab/Genome-Size-Annotation) which provides genome size for each species detected given its taxid is available. In this tool, the NCBI Datasets tool, NCBI Genome API check tool, and Data Mining from AI search engines like perplexity.ai are used. </p> <p>We have curated two datasets</p> <p>Kraken2 dataset named "FINAL METAGENOMIC DATA MASTERSHEET - kraken_genome_annotation"<br>Kaiju dataset named "FINAL METAGENOMIC DATA MASTERSHEET - kaiju_genome_annotation"</p> <p>*Please note that for kraken2 curated dataset, we used data mining from the AI search engine perplexity.ai while for kaiju we did not use perplexity, ai, and any species whose genome size was not found was labeled "NA"</p>
Multimodal Epigenetic Sequencing Analysis (MESA) of Cell-free DNA for Non-invasive Colorectal Cancer Detection
<p>Processed data (feature-by-sample matrices) of non-disruptive bisulfite-free methylation sequencing for cfDNA samples from 4 clinical cohorts (Cohort 1, Cohort 2, Cohort 3, and cfTAPS dataset). Codes used to repeat the results in our paper can be found https://rpubs.com/LiYumei/926228 and https://github.com/ChaorongC/MESA. </p>
Multi-omic brain and behavioral correlates of cell-free fetal DNA methylation in macaque maternal obesity models (NMR datasets, maternal plasma and infant brain)
<p>Maternal obesity during pregnancy is associated with neurodevelopmental disorder (NDD) risk. We utilized integrative multi-omics to examine maternal obesity effects on offspring neurodevelopment in rhesus macaques by comparison to lean controls and two interventions. Differentially methylated regions (DMRs) from longitudinal maternal blood-derived cell-free fetal DNA (cffDNA) significantly overlapped with DMRs from infant brain. The DMRs were enriched for neurodevelopmental functions, methylation-sensitive developmental transcription factor motifs, and human NDD DMRs identified from brain and placenta. Brain and cffDNA methylation levels from a large region overlapping mir-663 correlated with maternal obesity, metabolic and immune markers, and infant behavior. A DUX4 hippocampal co-methylation network correlated with maternal obesity, infant behavior, infant hippocampal lipidomic and metabolomic profiles, and maternal blood measurements of DUX4 cffDNA methylation, cytokines, and metabolites. Ultimately, maternal obesity altered infant brain and behavior, and these differences were detectable in pregnancy through integrative analyses of cffDNA methylation with immune and metabolic factors. </p>
Study for the Prediction of Active Rejection in Organs Using Donor-derived Cell-free DNA Detection
ClinicalTrials.gov study NCT03984747. IPD Sharing: NO. Countries: 1. Publications: 1.
Multi-omic brain and behavioral correlates of cell-free fetal DNA methylation in macaque maternal obesity models (GC-FID dataset infant brains)
Open the record for dataset details and reuse information.
Multi-omic brain and behavioral correlates of cell-free fetal DNA methylation in macaque maternal obesity models (NMR datasets, maternal plasma and infant brain)
Open the record for dataset details and reuse information.
Code and Data for "Genome-wide repeat landscapes in cancer and cell-free DNA" (Annapragada et.al., Science Translational Medicine, 2024)
<h1><strong>Code and Data for "Genome-wide repeat landscapes in cancer and cell-free DNA"</strong></h1> <div> <div> <p>Citation: <br>Annapragada, A.V. Niknafs, N. White, J.R. Bruhm, D.C., Cherry, C., Medina, J.E., Adleff, V., Hruban C., Mathios, D., Foda, Z.H., Phallen, J., Scharpf, R.B., Velculescu, V.E. Genome-wide repeat landscapes in cancer and cell-free DNA. <em>Science Translational Medicine</em>. 2024.</p> <p><a>The code to run the ARTEMIS pipeline and reproduce manuscript figures is publicly available at </a><a href="https://github.com/cancer-genomics/artemis2024">https://github.com/cancer-genomics/artemis2024</a></p> <p>This code also depends on c<a>ode that generates DELFI features for fragmentation-based analysis, which may be found at </a><a href="https://github.com/cancer-genomics/reproduce_lucas_wflow">https://github.com/cancer-genomics/reproduce_lucas_wflow</a> </p> <p>These github repositories have also been archived in this Zenodo as they were on 02/06/2024 (DELFI fragmentation) and 03/11/2024 (ARTEMIS).</p> </div> </div>
Data for for Detecting cell-of-origin and cancer-specific methylation features of cell-free DNA from Nanopore sequencing
<p>Datasets accompanying the paper https://doi.org/10.1101/2021.10.18.464684</p>
CRAG: De novo characterization of cell-free DNA fragmentation hotspots in plasma whole-genome sequencing
<p><strong>Supplementary dataset for the manuscript:</strong></p> <p><strong>CRAG: De novo characterization of cell-free DNA fragmentation hotspots in plasma whole-genome sequencing</strong></p> <p> Xionghui Zhou1,*, Haizi Zheng1,*, Hailu Fu1,*, Kelsey L. Dillehay McKillip2-3, Susan M. Pinney2,4, Yaping Liu1-2,5-7 #</p> <p>Affiliations:</p> <p>1 Division of Human Genetics, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH 45229</p> <p>2 University of Cincinnati Cancer Center, Cincinnati, OH 45229</p> <p>3 Department of Pathology & Laboratory Medicine, University of Cincinnati College of Medicine, Cincinnati, OH 45229</p> <p>4 Department of Environmental and Public Health Sciences, University of Cincinnati College of Medicine, Cincinnati, OH 45229</p> <p>5 Division of Biomedical Informatics, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH 45229</p> <p>6 Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH 45229</p> <p>7 Department of Electrical Engineering and Computing Sciences, University of Cincinnati College of Engineering and Applied Science, Cincinnati, OH 45229</p> <p>* These authors contributed equally</p> <p># Email: lyping1986@gmail.com</p>
Fig. 1 in Rapid screening of glycosyltransferases in plants using a linear DNA expression template based cell-free transcription-translation system
Fig. 1. Rapid screening of plant glycosyltransferases using the LET-based-TX-TL system. We can either use long primers which contain a promoter, a ribosome binding site, and a terminator to generate expressible linear DNAs or use short primers to amplify the targeted gene fragments and then ligate them with a promoter, a ribosome binding site, a terminator, and a backbone; then another pair of primers is used to generate expressible linear DNAs. Afterward, combine TX-TL extracts, buffers, and expressible linear DNAs to start protein expression. Then this TX-TL mixture is directly added with substrates (such as quercetin) to start glycosylation reactions. Finally, UPLC-MS is used to analyze the reaction mixture to examine whether targeted products (such as isoquercitrin) are generated.
Fig. 2 in Rapid screening of glycosyltransferases in plants using a linear DNA expression template based cell-free transcription-translation system
Fig. 2. UPLC-MS analysis of isoquercitrin converted from quercetin by AtUGTs expressed in TX-TL. (A) A glycosylation reaction catalyzed by a UDP-glucose glycosyltransferase. (B) The chromatogram of the blank sample, which only has 50% methanol solvent. (C) The chromatogram of the negative group, which has TX-TL, quercetin, UDPglucose but no additional DNA. (D) The chromatogram of the quercetin standard. (E) The chromatogram of the isoquercitrin standard. (F–O) Chromatograms of products from the catalysis of quercetin by different AtUGTs (the final concentrations of the linear DNAs used for each AtUGTs are listed below in brackets): (F) AT1G07250 (32.25 nM), (G) AT1G07260 (29.24 nM), (H) AT2G36790 (20.64 nM), (I) AT2G15480 (29.98 nM), (J) AT2G15490 (29.50 nM), (K) AT3G16520 (25.03 nM), (L) AT3G21750 (23.7 nM), (M) AT3G46660 (24.14 nM), (N) AT4G15280 (24.47 nM), and (O) AT4G34138 (22.52 nM), see Supplementary Fig. S4 for the duplicate group. The y axis indicates ion signal abundance relative to the highest signal in each chromatogram (%). The numbers in the top right of each chromatogram (for instance 3.38e7 in [E]) indicate the ion counts represented by "100" relative abundance. The retention time of the isoquercitrin standard is 4.0 min, and the red arrow in each figure indicates the retention time of the product isoquercitrin. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)
Fig. 4 in Rapid screening of glycosyltransferases in plants using a linear DNA expression template based cell-free transcription-translation system
Fig. 4. UPLC-MS analysis of isoquercitrin converted from quercetin by ArUGTs using heterologous expression. (A) The SDS-PAGE gel of heterologous expression of ArUGTs AR14572, AR11662, and AR43718 protein. The theoretical molecular weights of AR14572 protein, AR11662 protein, and AR43718 protein are 80.1 kDa, 79.8 kDa, and 80.4 kDa, respectively. (B) The chromatogram of isoquercitrin standard. (C–E) Chromatograms of products from the catalysis of quercetin by different ArUGTs using heterologous expression: (C) AR14572, (D) AR11662, and (E) AR43718, see Supplementary Fig. S6 for the duplicate group. The y axis indicates ion signal abundance relative to the highest signal in each chromatogram (%). The numbers in the top right of each chromatogram (for instance 4.56e6 in [B]) indicate the ion counts represented by "100" relative abundance. The retention time of the isoquercitrin standard is 3.93 min, and the red arrow in each figure indicates the retention time of the product isoquercitrin. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)
Fig. 5. A in Rapid screening of glycosyltransferases in plants using a linear DNA expression template based cell-free transcription-translation system
Fig. 5. A phylogenetic tree based on protein sequences of six ArUGTs: AR06047, AR06981, AR07558, AR11662, AR14572, and AR43718. The phylogenetic tree is constructed using MEGA. Numbers at the forks are bootstrap values from 100 replicates.
Fig. 3 in Rapid screening of glycosyltransferases in plants using a linear DNA expression template based cell-free transcription-translation system
Fig. 3. UPLC-MS analysis of isoquercitrin converted from quercetin by ArUGTs expressed in TX-TL. (A) A glycosylation reaction catalyzed by a UDP-glucose glycosyltransferase. (B) The chromatogram of the blank sample is 50% methanol solvent. (C) The chromatogram of the negative group, which has TXTL, quercetin, UDP-glucose but no additional DNA. (D) The chromatogram of quercetin standard. (E) The chromatogram of isoquercitrin standard. (F–K) Chromatograms of products from the catalysis of quercetin by different ArUGTs (the final concentrations of the linear DNAs used for each ArUGTs are 30 nM): (F) AR14572, (G) AR11662, (H) AR43718, (I) AR06047, (J) AR06981, and (K) AR07558, see Supplementary Fig. S5 for the duplicate group. The y axis indicates ion signal abundance relative to the highest signal in each chromatogram (%). The numbers in the top right of each chromatogram (for instance 2.21e7 in [E]) indicate the ion counts represented by "100" relative abundance. The retention time of the isoquercitrin standard is 4.08 min, and the red arrow in each figure indicates the retention time of the product isoquercitrin. The numbers under the protein names are the peak intensities of the product. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)
Longitudinal Changes in Donor-Derived Cell-Free DNA With Tocilizumab Treatment for Chronic Antibody-Mediated Rejection
ClinicalTrials.gov study NCT03859388. IPD Sharing: NO. Countries: 1. Publications: 6.
Cell-free DNA Methylation Markers for Cancer Screening
ClinicalTrials.gov study NCT03096938. IPD Sharing: NO. Countries: 1. Publications: 1.
Circulating Cell-free DNA in Metastatic Melanoma Patient: Mutational Analyses in Consecutive Measurement Before and After Chemotherapy
ClinicalTrials.gov study NCT02133222. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Cell-free Fetal DNA Circulating in the Maternal Plasma as a Marker for Morbidly Adherent Placenta
ClinicalTrials.gov study NCT02784886. IPD Sharing: NO. Countries: 1. Publications: 4.
Circulating Cell-free DNA as a Predictive Biomarker for Hepatocelluar Carcinoma.
ClinicalTrials.gov study NCT02036216. IPD Sharing: Not stated. Countries: 2. Publications: 3.
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Allen Brain Atlas
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DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.