audit_sample_missing_publication

audit_file_set_missing_publication

audit_input_file_sets_derived_from

Andrew Allen

Design, prediction, and prioritization of systematic perturbations of the human genome

BlueSTARR is a neural network trained on STARR-seq data that takes genomic sequence as input and predicts STARR-seq activity as output.  This version of BlueSTARR was trained on a STARR-seq experiment done in K562 cells, and takes a 300 bp sequence as input.  Variant predictions are made by running the model twice (once for the reference allele and once for the alternate allele) and computing the predicted log2 fold change in STARR-seq activity for the change from reference to alternate.

Bill Majoros, Duke

virtual Homo sapiens (female) K562 cell line

BlueSTARR-K562 v0.1.0 neural network predicting regulatory elements

audit_external_reference_files

ENCODE

ENCODE Project, NHGRI

Accession

Aliases

Alternate Accessions

Assay Term

Assemblies

Assessed Genes

Associated Phenotypes

Award

Controlled Access

Data Use Limitation Summaries

Description

Donors

Taxa

File Set Type

File Type

Measured Mechanism

Input File Sets

Measurement Sets

Model Name

Prediction Objects

Preferred Assay Title

Protocols

Simplified Sample Summary

Samples

Sample Institutional Certificates

Sample Summary

Prediction Scope

Selection Criteria

Sequencing Library Types

Genes

Status

Summary

Assay Targeted Genes

Transcriptome Annotations

UUID

Object Type

Classification

Sample

File Format

Collections

Data Use Limitation

Audit category: NOT COMPLIANT

Audit category: WARNING

Audit Category: Internal Action

Externally Hosted

input_for

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