Sequence Data Delivery Pilot (SDDP)
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ABSTRACT:
PROVIDER: PRJNA416034 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:Sequence Data Delivery Pilot (SDDP)
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Project description:This project carries out the pilot CRISPR/Cas9 screens in the K562 background. Its goals are to confirm that positive controls work, and to assess the effects of experimental parameters (listed below) on the sequencing-based fitness readout. We test 1) length of selection 2) biological replicates 3) sampling variation during bottlenecks 4) sampling variation during DNA preparation 5) sequencing depth to inform the setup for the next round of experiments. To do so, we propose to sequence 13 samples (6 timepoints, 2 biological replicates, 2 severe bottlenecks during growth, 2 bottlenecks during DNA preparation, and the screening library itself) on two lanes of HiSeq, using 19bp reads. The sequencing libraries are prepared in our lab.This data is part of a pre-publication release. For information on the proper use of pre-publication data shared by the Wellcome Trust Sanger Institute (including details of any publication moratoria), please see http://www.sanger.ac.uk/datasharing/
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Project description:SEER Remote Access Pilot Test Data (2018)
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Project description:Mar1 deletion and RNA enrichment in Cryptococcus neoformans: pilot data for a high-throughput sequencing course. The goal of this project was to generate pilot data in preparation for a summer course on high-throughput sequencing where participants prepared their own RNA-Seq libraries and analyzed the resulting data. This pilot experiment addressed two questions: 1. Does this experimental system (Cryptococcus neoformans H99 wildtype and mar1 deletion mutant grown in YPD and tissue culture media) provide a good dataset for course participants to analyze. 2. Which rRNA depletion method is best to use in the wetlab component of the course. This data was generated in preparation for the intensive summer course on high-throughput sequencing, funded by NIH grant 5R25EB023928-03 "A hands-on, integrative next-generation sequencing course: design, experiment, and analysis".
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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.
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