The initial FASTQ data from each pool's lane were binned by barcode. The barcode-split reads were trimmed of non-template deriving and potentially error-prone sequence: a randomly incorporated nucleotide (N), the barcode bases, and the sequence corresponding to the random hexamer, leaving 55 (pools 1, 2, and 4), 54 (pool 3), or 90 (pool 5) bases per read. The lowest complexity fraction was identified by sequences with LZW ratios (compressed size/uncompressed size) less than 0.45 [39] . Reads were aligned to the human genome (build hg18) first using BLAT [40] with the ''-fastMap'' flag, and after filtering, the remaining reads were aligned using BLAT without the flag. Paired reads for which at least one of the reads in the pair had at least 80% identity to the database were marked as human and removed from subsequent analyses. After removal of reads identified as human by BLAT, remaining reads were aligned and filtered by mapping to the human transcriptome using nucleotide BLAST (BLASTn version 2.2.21, word size 30, E value 10 23 ). Remaining reads were next aligned to the human genome using BLASTn (word size 30, E value 10 23 ), filtered, and again aligned to the human genome by BLASTn (word size 11, E value 10). After all human filtering, we reanalyzed the distribution of the complexity of reads and observed a relative enrichment of reads with LZW ratios lower than 0.54 (pools1-4) or 0.48 (pool5; different LZW ratio distributions are an inherent property of different read lengths), and those reads were removed from further analysis. To look for reads with viral homology, we searched the non-redundant nucleotide database (nt) using BLASTn (word size 20, E value 10 23 ). Reads that did not map to nt were aligned to the non-redundant protein database (nr) using translated BLAST (BLASTx, word size 4, E value 10).