Project description:Observational, Multicenter, Post-market, Minimal risk, Prospective data collection of PillCam SB3 videos (including PillCam reports) and raw data files and optional collection of Eneteroscopy reports
Project description:This dataset consists of 44 raw MS files, comprising 27 DIA (SWATH) and 15 DDA runs on a TripleTOF 5600 and of two raw mass spectrometry files acquired on a Q Exactive. The composition of the dataset is described in the manuscript by Tsou et al., titled: "DIA-Umpire: comprehensive computational framework for data independent acquisition proteomics", Nature Methods, in press Raw files are deposited here in ProteomeXchange and are associated with the DIA-Umpire processed data. All DIA-Umpire processed results for each sample together with DDA results are deposited in separated folders. Also see the "DataSampleID.xlsx" associated with this Readme file. Internal reference from the Gingras lab ProHits implementation: Project 94, Export version VS2 (Tsou_DIA-Umpire)
Project description:Genome-wide profiling of Copy Number Alterations (CNA) and Loss of Heterozygosity (LOH), gene expression and resequencing of pediatric AML. This study characterizes the CNA and LOH in a representative cross-section through subtypes of pediatric AML. Affymetrix SNP arrays were performed according to the manufacturer's directions on DNA extracted from cryopreserved diagnostic bone marrow or peripheral blood samples. 111 pediatric AML samples were studied using either Affymetrix 100K + 500K 5.0 SNP arrays, 65 of the samples had paired germ line material. The supplemental files 'GSE15730_AML_175_SNP_Nsp_signal.txt' and 'GSE15730_AML_176_SNP_Nsp_signal.txt' contain the raw signals generated by dChip without normalization. The CNA and LOH data can be found in the Supplemental Information of the associated manuscript.
Project description:Genome-wide profiling of Copy Number Alterations (CNA) and Loss of Heterozygosity (LOH), gene expression and resequencing of pediatric AML. This study characterizes the CNA and LOH in a representative cross-section through subtypes of pediatric AML. Affymetrix SNP arrays were performed according to the manufacturer's directions on DNA extracted from cryopreserved diagnostic bone marrow or peripheral blood samples. 111 pediatric AML samples were studied using either Affymetrix 100K + 500K 5.0 SNP arrays, 65 of the samples had paired germ line material. The supplemental files 'GSE15731_AML_175_SNP_Sty_signal.txt' and 'GSE15731_AML_176_SNP_Sty_signal.txt' contain the raw signals generated by dChip without normalization. The CNA and LOH data can be found in the Supplemental Information of the associated manuscript.
Project description:This project explores dietary proteins in human dental calculus through shotgun proteomics. These files are in addition to those accidentally not included in the original publication, Dairying enabled Early Bronze Age Yamnaya steppe expansions . DOI https://doi.org/10.1038/s41586-021-03798-4. Files include raw, mgf, and mzid files from the two Botai individuals: DA092 (Botai 2A), DA089 (CII(3) 30-40), and additional files from Russian sites: DA431, DA431 (Lebyazhinka 5, LEB N-0), Z333 (Khvalynsk 2, KHA2 N-12), and Z444 (Murziha 2, MUR2 N-128) as well as the blanks used in the experiments. It also inlcludes the corresponding files for DA436, as in the previous upload an under-injected sample was included as the MDF.
Project description:Effective analysis of protein samples by mass spectrometry (MS) instrumentation requires careful selection and optimization of a range experimental parameters. As the output from the primary detection device, the ‘raw’ MS data file can be used to gauge the success of a given sample analysis. However, the closed-source nature of the standard raw MS file can complicate effective parsing of the data contained within. To overcome this challenge, the RawQuant tool was developed to enable parsing of raw MS files to yield meta and scan data in an openly readable text format. RawQuant can be commanded to export user-friendly files containing MS1, MS2, and MS3 meta data, as well as matrices of quantification values based on isobaric tagging approaches. In this study, RawQuant is demonstrated through application in a combination of scenarios: 1. Re-analysis of shotgun proteomics data aimed at identification of the human proteome, 2. Re-analysis of experiments utilizing isobaric tagging for whole-proteome quantification, 3. Analysis of a novel bacterial proteome and synthetic peptide mixture for assessing quantification accuracy when using isobaric tags. Together, these analyses successfully demonstrate RawQuant for the efficient parsing and quantification of data from raw MS files acquired in a range common proteomics experiments. In addition, the individual analyses using RawQuant highlights parametric considerations in the different experimental sets, and suggests targetable areas to improve depth of coverage in identification-focused studies, and quantification accuracy when using isobaric tags.