Additional file 11: Figure S9. of Tools and best practices for data processing in allelic expression analysis

QC measures improve the power to detect biologically relevant allelic expression at genes that have eQTLs (eGenes), where individuals that are heterozygous for the top eQTL SNP (eSNP) are expected to have more allelic expression than homozygous individuals (extended). a QC measures increase the significance of the difference between heterozygous and homozygous individuals within eGenes. b QC measures reduce the variance of allelic expression between individuals within eGenes. (TIFF 2856 kb

Additional file 13: Table S3. of Tools and best practices for data processing in allelic expression analysis

Summary of QC problems for AE data, proposed solutions, and potential drawbacks. (XLSX 31 kb

Additional file 1: Figure S1. of Tools and best practices for data processing in allelic expression analysis

Allelic expression signal from a population of monoclonal versus polyclonal cells. In the latter, standard RNA-sequencing will show allelic imbalance only when the two alleles are systematically differentially expressed, e.g., due to a regulatory variant or imprinting. (TIFF 3238 kb

Additional file 12: Figure S10. of Tools and best practices for data processing in allelic expression analysis

Complete workflow for AE analysis illustrating appropriate quality control measures and filters. (TIFF 782 kb

Definitions of COMBREX functional status symbols and fractions of microbial genes in COMBREX in each status category.

<p>Experimentally characterized proteins are <i>green</i>. (Those in the <i>green</i> set that have been manually curated by the GSDB are also marked with a gold “G.”) Proteins with functional predictions but no experimental evidence are <i>blue</i>. Proteins with no available functional predictions are <i>black</i>.</p

Schematic overview of the computational and experimental contributions of COMBREX and its users, and the interrelationships of these contributions.

<p>Data and results specific to COMBREX are shown in boxes. External data imported into COMBREX are also shown, with arrows indicating entry points into the cycle. Methodology employed by COMBREX and its users is shown in blue type, as it is used to generate data. Not shown are two critical contributions to COMBREX: genome and cluster data imported from NCBI RefSeq and ProtClustDB, respectively, and NIH funding, which enables the grants that COMBREX issues to experimental laboratories.</p