Binsheng Gong

TL;DR: The complete SEQC data sets, comprising >100 billion reads, provide unique resources for evaluating RNA-seq analyses for clinical and regulatory settings, and measurement performance depends on the platform and data analysis pipeline, and variation is large for transcript-level profiling.

TL;DR: RNA-seq outperforms microarray in DEG verification as assessed by quantitative PCR, with the gain mainly due to its improved accuracy for low-abundance transcripts, and classifiers to predict MOAs perform similarly when developed using data from either platform.

TL;DR: An R-based software package (SubpathwayMiner) for flexible pathway identification and provides more flexibility in annotating gene sets and identifying the involved pathways (entire pathways and sub-pathways).

TL;DR: Signature genes of predictive models are reciprocally transferable between microarray andRNA-seq data for model development, and microarray-based models can accurately predict RNA-seq-profiled samples; while RNA-sequencing-based model are less accurate in predicting micro array-Profiled samples and are affected both by the choice of modeling algorithm and the gene mapping complexity.

TL;DR: In this article, a multi-site, cross-platform evaluation of the analytical performance of five industry-leading ctDNA assays is presented, with simulations, synthetic DNA spike-in experiments and proficiency testing on standardized, cell-line-derived reference samples.