Project description:Re-analysis of 667 assays (463 samples) from Geuavdis study has been performed to demonstrate the flexibility of the Ballgown package to identify transcript-level eQTLs and identify non-linear artifacts in transcript data. Our package Ballgown is freely available from: https://github.com/alyssafrazee/ballgown. Geuvadis RNA sequencing data set of 465 human lymphoblastoid cell line samples from the CEU, FIN, GBR, TSI and YRI populations from the 1000 Genomes sample collection was created by the Geuvadis consortium (www.geuvadis.org, http://www.geuvadis.org/web/geuvadis/our-rnaseq-project). Original Geuvadis mRNA and small RNA sequencing data, clean data that passed QC and other filters, processed files and analysis results are available under accession E-GEUV-1, E-GEUV-2, E-GEUV-3.
Project description:cytoplasmic extract of Bacillus subtilis wild type control (untreated) separated by non-reducing SDS-PAGE, lane cut into 10 fractions, fraction 1 in-gel-digested
Project description:Missing values in proteomic data sets have real consequences on downstream data analysis and reproducibility. Although several imputation methods exist to handle missing values, no single imputation method is best suited for a diverse range of data sets, and no clear strategy exists for evaluating imputation methods for large-scale DIA-MS data sets, especially at different levels of protein quantification. To navigate through the different imputation strategies available in the literature, we have established a workflow to assess imputation methods on large-scale label-free DIA-MS data sets. We used three DIA-MS data sets with real missing values to evaluate eight different imputation methods with multiple parameters at different levels of protein quantification; dilution series data set, a small pilot data set, and a larger proteomic data set.