High-throughput sequencing data (HTS) has been used in detecting not only differential gene expression, but also alternative splicing events, long-range gene interaction events, and different transcription start and termination sites. It has also been used in ribosome profiling for characterizing translation efficiency and the Hi-C method for constructing genomic architecture. There are two major difficulties in analyzing HTS data: the large file size, often in gigabytes, and the allocation of reads to paralogous genes that impacts the accuracy of computed RPKM values, especially for multicellular eukaryotes with many paralogs. This chapter provides a conceptual framework for analyzing HTS data, several methods for read quality control, an innovative approach to compress RNA-Seq data, new ways of allocating reads that map to multiple paralogous genes, and numerical illustrations of applications with RNA-Seq data hosted in NCBI’s SRA database.