Joe Meehan

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: 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: This study identified the SNVs that were found in the Korean population but not seen in other populations, and explored the corresponding genes and pathways as well as the associated disease terms and drug terms.

TL;DR: Current available alignment algorithms and their major strategies such as seed-and-extend and q-gram filter are reviewed and the challenges in current alignment algorithms are discussed, including alignment in multiple repeated regions, long reads alignment and alignment facilitated with known genetic variants.