Sources of error per sample preparation method.

<p>(A) Intra-host single nucleotide variants (iSNVs) that were present in multiple samples at greater than 1% of population are shown in a heat map format to visualize patterned diversity acquired during sample preparation. The total number of samples containing iSNVs in greater than 1% of population are summarized in the column “#> 1% of Pop” in green. “Codon” column (second column from right) provides both the nucleotide and protein translation of the site. (B) The detected mean error rates of iSNVs greater than 0. 2% of population (error/site/copy) are stratified by presence in the plasmid (Origin), detection after transcription/reverse transcription (Transc) or preparation, and preparation/sequencer error (Prep). (C) Error profiles are expressed as the number of iSNVs per percent of population obtained from each of the 5 sample preparation methods.</p

Percentage of errors by type of acquired diversity determined during sample preparation.

<p>(A) Percentage of error attributed to synonymous vs. non-synonymous variants. (B) Percentage of errors attributed to transitions vs. transversions. (C) Percentage of errors attributed to insertions or deletions vs. Intra-host single nucleotide variants (iSNV).</p

Total error by sample preparation method.

<p>(A) The mean read depth per position from two DNA controls (PLASMID and P_AMP) and five RNA sample preparation methods (n = 2 of replicate experiments). (B) Errors calculated as error/site/copy (plasmid or transcript) are presented as the average of duplicate experiments as in A. Intra-host single nucleotide variants present in the original plasmid were removed from the calculation of the error (reference positions: 4,697 and 4,725). Student t-tests were performed to demonstrate differences between the mean error per site per copy in the control plasmid (* = <i>p</i><0.05) and each sample preparation method. (C) The error calculated in B is converted to fold change over the control (“PLASMID”). Error bars in all panels represent standard deviation of the mean.</p

Study sample diagram.

<p>Study sample diagram.</p

Development and Evaluation of a Panel of Filovirus Sequence Capture Probes for Pathogen Detection by Next-Generation Sequencing

<div><p>A detailed understanding of the circulating pathogens in a particular geographic location aids in effectively utilizing targeted, rapid diagnostic assays, thus allowing for appropriate therapeutic and containment procedures. This is especially important in regions prevalent for highly pathogenic viruses co-circulating with other endemic pathogens such as the malaria parasite. The importance of biosurveillance is highlighted by the ongoing Ebola virus disease outbreak in West Africa. For example, a more comprehensive assessment of the regional pathogens could have identified the risk of a filovirus disease outbreak earlier and led to an improved diagnostic and response capacity in the region. In this context, being able to rapidly screen a single sample for multiple pathogens in a single tube reaction could improve both diagnostics as well as pathogen surveillance. Here, probes were designed to capture identifying filovirus sequence for the ebolaviruses Sudan, Ebola, Reston, Taï Forest, and Bundibugyo and the Marburg virus variants Musoke, Ci67, and Angola. These probes were combined into a single probe panel, and the captured filovirus sequence was successfully identified using the MiSeq next-generation sequencing platform. This panel was then used to identify the specific filovirus from nonhuman primates experimentally infected with Ebola virus as well as Bundibugyo virus in human sera samples from the Democratic Republic of the Congo, thus demonstrating the utility for pathogen detection using clinical samples. While not as sensitive and rapid as real-time PCR, this panel, along with incorporating additional sequence capture probe panels, could be used for broad pathogen screening and biosurveillance.</p></div

Filoviruses differentiation using a pooled sequence capture probe panel.

1<p>Reads are the total number of reads after trimming and filtering;</p>2<p>% refers to the percentage of the total reads after trimming and filtering that mapped to the respective reference sequence.</p><p>Filoviruses differentiation using a pooled sequence capture probe panel.</p

Overview of the DxSeq technology.

<p>Linear oligonucleotide probes contain complementary sequences that hybridize to the targeted sequence. A polymerase fills in the target sequence, and a ligation reaction captures the sequence within the circularized probe (Circularized ssProbe). Exonucleases remove noncircularized probe and DNA within the reaction, and the captured sequence is amplified by PCR using primers within the probe that bridge the captured sequence.</p

Real-time PCR identification of BDBV in human clinical sera samples.

1<p>For samples not having a corresponding cell supernatant sample, no CPE was observed during viral amplification;</p>2<p>Sera samples were run in duplicate due to limited sample availability.</p><p>Real-time PCR identification of BDBV in human clinical sera samples.</p

Detection of EBOV in challenged NHPs.

<p>Plasma samples from each NHP were assayed for the presence of EBOV by real-time PCR (A) and by plaque assay (B). Samples were run singly due to limited plasma availability. (C) Plasma samples were also assessed using the filovirus SCP panel. The dashed line indicates the signal generated by the EBOV positive control.</p

Screening human clinical sera samples identifies BDBV.

1<p>For samples not having a corresponding cell supernatant sample, no CPE was observed during viral amplification;</p>2<p>Cutoff is equal to the average of the no template controls (NTC) plus 3 times the NTC standard deviation; P  =  positive call, N  =  negative call, ns  =  not sequenced.</p><p>Screening human clinical sera samples identifies BDBV.</p