WGS of  Human sample: Nasopharyngeal sample from patient with acute flaccid myelitis: Sample  US/CA/12-5641_NP_v12T00346_AFM_HISEQ1
"The dataset deposited here corresponds to Illumina deep sequencing data generated from Nasopharyngeal and CSF samples from patients with acute flaccid myelitis, respiratory tract infections associated with EV-D68, or encephalitis associated with EV-A71. Total nucleic acid was extracted from 100-400 mL of clinical sample on a robotic Qiagen EZ1 instrument using the Qiagen EZ1 Virus Mini Kit v2.0 (Qiagen, Valencia, CA). Construction of metagenomic NGS libraries from CSF samples:: Amplified cDNA was generated directly from extracted RNA as previously described (Naccache et al CID 2015). Metagenomic NGS libraries were then constructed either from randomly amplified cDNA using a modified TruSeq protocol (Lee et al, PloS one 2012; 7(9): e44954) or random hexamer cDNA synthesis using a Nextera XT protocol (Illumina) with 2 ng of input cDNA. A CSF sample from a patient with EV-A71-associated encephalitis was used as a positive control, and analyzed in parallel with CSF samples from AFM patients. Construction of metagenomic NGS libraries from respiratory samples:: To decrease background from host and microbial flora, extracted RNA from NP/OP samples was treated with DNase, followed by Direct-zol RNA purification (Zymo Research) according to the manufacturer's instructions. After generation of amplified cDNA, metagenomic NGS libraries were constructed using a Nextera XT protocol as previously described (Naccache et al CID 2015). Validation of NGS libraries:: NGS library concentration and average fragment size were determined using the KAPA Library Quantification Kit (Kapa Biosystems, Foster City, CA) and Agilent Bioanalyzer (Agilent Technologies, Santa Clara, CA). NGS was performed on an Illumina MiSeq (150 nt single-end or paired-end reads) or HiSeq 2500 sequencer (100 or 150 nt paired-end reads). Pathogen detection by metagenomic NGS:: Sequencing datasets were analyzed for pathogen presence using the SURPI (""sequence-based ultrarapid pathogen identification"") computational pipeline (Naccache et al Genome research 2014; 24(7): 1180-92.) SURPI first trims low quality regions and adapters using cutadapt, and removes low complexity regions using prinseq, followed by computational subtraction of human host sequences, followed by nucleotide and translated nucleotide (protein) alignment to all microbial sequences in the National Center for Biotechnology Information (NCBI) GenBank database (June 2014) using the nucleotide aligner SNAP (Zaharia et al, arXiv 2011; 1111.5572.) at high stringency (edit distance = 12), allowing for detection of reads ~90% identical to Genbank sequences. The translated nucleotide aligner RAPSearch2 (Zhao et al, Bioinformatics 2012;28(1):125-6) is then used to detect novel pathogens or reads more divergent relative to known sequences by aligning against a viral protein database (June 2014). The sequences deposited here in SRA correspond to the full-length raw reads with human sequences and low quality reads removed and thus represent only the nonhuman fraction of the NGS data. "
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