Next-Generation Sequencing Reveals HIV-1-Mediated Suppression of T Cell Activation and RNA Processing and Regulation of Noncoding RNA Expression in a CD4(+) T Cell Line

Chang, Stewart T.; Sova, Pavel; Peng, Xinxia; Weiss, Jeffrey; Law, G. Lynn; Palermo, Robert E.; Katze, Michael G.

Read mapping and transcript quantification. We mapped the 75-nt reads to human ribosomal sequences using the short-read aligner software Bowtie to remove potential rRNA sequences (40) . We then mapped the remaining unmapped reads to the HIV genome (GenBank accession no. K02013) using the gapped aligner software TopHat, which predicts HIV splicing junctions and maps intron-spanning reads to known splicing junctions (41) . To quantify transcript expression, we mapped all reads that remained unmapped to the human reference genome (hg19, build GRCh37, downloaded from UCSC genome browser, http://genome.ucsc .edu) using the gapped aligner software TopHat. RefSeq transcript annotations were supplied to facilitate the mapping of reads spanning known splicing junctions. On the basis of these human genome mapping results, we then estimated the levels of expression at both the transcript and locus levels for RefSeq-annotated genes using the transcript assembly software Cufflinks (42) . Read sequences that mapped to more than one genomic location were excluded from expression quantification. For visualization, BAM files were generated using TopHat and SAMtools (43) and displayed using the UCSC Genome Browser.