The Virome of Manhattan Project: Virome Data Explorer 🧪 🦠
Viral respiratory infections are an important public health concern, due to their prevalence, transmissibility, and potential to cause serious disease. Disease severity is the product of several factors beyond the presence of the infectious agent, including specific host immune responses, host genetic makeup, and bacterial co-infections.

To understand these interactions within natural infections, we designed a longitudinal cohort study actively surveilling respiratory viruses over the course of 19 months (2016 to 2018) in a diverse cohort in New York City. A cohort of 214 participants was enrolled from various locations in northern Manhattan between October 2016 and April 2018, as part of the Virome of Manhattan Project. The cohort was used to conduct longitudinal sampling of viral respiratory infections among the general population and consisted of children from 2 daycares together with their siblings and one of their parents, teenagers and teachers from a high school, adults working at a medical center, and doctors from an Emergency Department. Duration of enrollment was heterogeneous for the participants, so Day 0 refers to the study start date, not the individual. Demographic and enrollment information for all individuals in the Virome Project and features of the 4,215 samples collected are summarized in (Galanti et al., 2019).

Two nasopharyngeal samples were collected once a week from all available participants by the study coordinators using minitip flocked swabs, irrespective of participant symptoms. Daily reports of 9 respiratory symptoms (fever, chills, muscle pain, watery eyes, runny nose, sneezing, sore throat, cough, chest pain) self-evaluated on a Likert scale (0 = none, 1 = mild, 2 = moderate, 3 = severe) were recorded via a mobile app. The cumulative daily symptom score was defined as the sum of the 9 individual symptom scores (range: 0 to 27) on a given day. Among the 4,215 samples collected, we selected a subset of 847 sequenced samples from 104 participants, with a median number of 7 samples per participant.

RNA-Seq data was processed and analyzed to identify pathogens, host-specific transcriptional signatures, in silico immune cell decomposition, and bacterial coinfections. To facilitate access to the data derived from this study, we have made the results available through an interactive webserver searching for specific queries (e.g., transcript count distribution comparison between negatives and positives or between different symptomatic groups) and plotting longitudinal information of interest (e.g., how expression of genes of interest varies through time with symptomatology, highlighting time points when individuals tested positive for a given virus; see Fig 2)

INSTRUCTIONS: The pie charts and data points contain additional information that may be viewed by hovering on these plots. Users can select multiple options by adding a comma between values. They may also hide datasets from the graphs as needed by clicking on the legend associated with the values you want hidden. We recommend accessing this website using Chrome.

Study participants were asked to rate their various symptoms from 0-3, with 3 being the highest. "All" Symptoms refers to the averaged score of the 9 symptoms per day.
This plot represents the Raw Gene Expression, scaled using (Log(X+1)).
This plot represents the fraction of relative bacterial abundance over total sample reads in a patient over time. There are 6 bacterial strains that are being measured.
The Cohort and Age Metadata plots describe the patients whose samples underwent further analysis.
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"Virome Data Explorer: A web resource to longitudinally explore respiratory viral infections, their interactions with other pathogens and host transcriptomic changes in over 100 people"
Marta Galanti*, Juan Angel Patiño-Galindo*, Ioan Filip*, Haruka Morita^, Angelica Galianese^, Mariam Youssef, Devon Comito, Chanel Ligon, Benjamin Lane, Nelsa Matienzo, Sadiat Ibrahim, Eudosie Tagne, Atinuke Shittu, Oliver Elliott, Tomin Perea-Chamblee, Sanjay Natesan, Daniel Rosenbloom, Jeffrey Shaman†, Raul Rabadan†
* Co-first Authors | ^ Co-second Authors | † Co-senior Authors
publication link

© 2023 The Shaman Group and The Rabadan Lab