Correction: Correction: A Novel Rhabdovirus Associated with Acute Hemorrhagic Fever in Central Africa.

[This corrects the article DOI: 10.1371/journal.ppat.1002924.]

Time course of the development of cytopathic effects by Lone Star virus in human (HeLa) and monkey (Vero) cell cultures.

<p>CPE is shown at 24, 48, 72, 96, and 120 hours post-inoculation (hpi). Uninfected controls at 120 hpi are also shown.</p

Amino acid phylogenetic analysis of the four LSV protein sequences relative to those from representative phleboviruses and Gouleako virus.

<p>For the RdRp, glycoprotein, and N protein, Gouleako virus is included as an outgroup to the phleboviruses (tan). Gouleako virus, the closest known bunyavirus relative to phleboviruses, is a member of a proposed new genus in the family <i>Bunyaviridae </i><a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0062083#pone.0062083-Marklewitz1" target="_blank">[36]</a>. Also shown color-coded are the Uukuniemi (blue), Bhanja (red), and SFTS (green) clades of known tick-borne phleboviruses. GenBank accession numbers are reported in the text.</p

Deep sequencing reads matching to viral sequences.

*<p>Viral reads were identified by comparison to viral nucleotide and amino acid databases using the SNAP <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0062083#pone.0062083-Zaharia1" target="_blank">[29]</a> and RAPSearch <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0062083#pone.0062083-Ye1" target="_blank">[30]</a> aligners, respectively. Viral hits were confirmed to be true by BLASTn alignment to LSV or the closest viral genus or species in GenBank using an E-value cutoff of 1×10⁻⁸. Out of 15,134,328 total reads, 40,489 reads (0.27%) were identified as viral by SNAP and/or RAPSearch. Out of these 40,489 viral hits, 37,322 (92.2%) corresponded to LSV. The actual number of LSV reads in the dataset is 142,941 (0.94% of the total reads); thus, only 26.1% (37,322 of 142,941) of the actual number of LSV reads was detected.</p

Amino acid pairwise identity of LSV relative to other representative bunyaviruses.

<p>The amino acid identities are shown for the four LSV proteins (RdRp, G, N, and NSs). A sliding window of 50 bp was used. GenBank accession numbers are reported in the text.</p

Identification and assembly of the LSV genome by unbiased deep sequencing.

<p>(<b>A</b>) Using a rapid computational pipeline, reads identified as bunyaviruses by SNAP nucleotide alignment (orange) or RAPSearch amino acid alignment (dark red) were mapped to the assembled LSV genome. The coverage (y-axis) achieved at each position along the genome (x-axis) is plotted on a logarithmic scale. (<b>B</b>) <i>De novo</i> assembly of the LSV genome using the PRICE assembler (3 rounds of 15 cycles each) and LSV seed sequences (“S”) identified from (A). (<b>C</b>) The genome structure of LSV. Boxes represent open reading frames (ORFs) corresponding to the RdRp, G, N, and NSs proteins, flanked by noncoding regions, which are indicated by lines. Coding directions are indicated by arrows. (<b>D</b>) Mapping of the actual deep sequencing reads derived from LSV to the final assembled genome. The coverage (y-axis) achieved at each position along the genome (x-axis) is plotted on a logarithmic scale. GenBank accession numbers are reported in the text. Abbreviations: kb, kilobases; bp, base pairs.</p

Artificial intelligence enables comprehensive genome interpretation and nomination of candidate diagnoses for rare genetic diseases.

BACKGROUND: Clinical interpretation of genetic variants in the context of the patient's phenotype is becoming the largest component of cost and time expenditure for genome-based diagnosis of rare genetic diseases. Artificial intelligence (AI) holds promise to greatly simplify and speed genome interpretation by integrating predictive methods with the growing knowledge of genetic disease. Here we assess the diagnostic performance of Fabric GEM, a new, AI-based, clinical decision support tool for expediting genome interpretation. METHODS: We benchmarked GEM in a retrospective cohort of 119 probands, mostly NICU infants, diagnosed with rare genetic diseases, who received whole-genome or whole-exome sequencing (WGS, WES). We replicated our analyses in a separate cohort of 60 cases collected from five academic medical centers. For comparison, we also analyzed these cases with current state-of-the-art variant prioritization tools. Included in the comparisons were trio, duo, and singleton cases. Variants underpinning diagnoses spanned diverse modes of inheritance and types, including structural variants (SVs). Patient phenotypes were extracted from clinical notes by two means: manually and using an automated clinical natural language processing (CNLP) tool. Finally, 14 previously unsolved cases were reanalyzed. RESULTS: GEM ranked over 90% of the causal genes among the top or second candidate and prioritized for review a median of 3 candidate genes per case, using either manually curated or CNLP-derived phenotype descriptions. Ranking of trios and duos was unchanged when analyzed as singletons. In 17 of 20 cases with diagnostic SVs, GEM identified the causal SVs as the top candidate and in 19/20 within the top five, irrespective of whether SV calls were provided or inferred ab initio by GEM using its own internal SV detection algorithm. GEM showed similar performance in absence of parental genotypes. Analysis of 14 previously unsolved cases resulted in a novel finding for one case, candidates ultimately not advanced upon manual review for 3 cases, and no new findings for 10 cases. CONCLUSIONS: GEM enabled diagnostic interpretation inclusive of all variant types through automated nomination of a very short list of candidate genes and disorders for final review and reporting. In combination with deep phenotyping by CNLP, GEM enables substantial automation of genetic disease diagnosis, potentially decreasing cost and expediting case review