User Manual


The frequency by which genes are studied highly correlates with the prior knowledge accumulated about these genes. This leads to an imbalance of attention where some genes are highly investigated while others are mostly ignored. Geneshot is a search engine developed to illuminate this gap and promote attention to the under-studied genome. Through a simple web interface, Geneshot enables researchers to enter arbitrary search terms, to receive ranked lists of genes most relevant to the search terms. Returned ranked lists contain genes that have been previously published in association with the search terms, as well as ranked lists of genes predicted to be associated with the terms based on data integration from multiple omics sources. The search results are presented to the user through interactive visualizations. In order to predict associations between genes and search terms, Geneshot performs three analysis steps: First, the search terms are queried against PubMed to identify publications associated with the search terms; Next, the identified PubMed identifiers (PMIDs) are cross-referenced with GeneRIF, or an expanded version of GeneRIF called AutoRIF. AutoRIF was developed specifically for this project and contains ~8 million gene-publication associations. This step ranks genes based on their frequency of association with the PMIDs returned from the PubMed query. Finally, Geneshot utilizes gene-gene similarity metrics from processed RNA-seq data (ARCHS4) or co-occurrence data compiled by processing millions of Pubmed abstracts using the Tagger, to rank genes that are highly similar to the genes identified in the second step. The ranked lists of predicted genes are displayed as interactive tables that can be filtered by druggable genome gene families. In order to evaluate the predictions made by Geneshot, we benchmarked the ability of the gene-gene similarity matrices used by Geneshot: ARCHS4 RNA-seq co-expression, Tagger co-occurrence, and AutoRIF co-mentions, GeneRIF co-mentions, and Enrichr co-occurrence to predict known gene-term associations from published resources converted to gene set libraries. We found that overall the approach is highly predictive while there are unique advantages and disadvantages to each gene-gene similarity matrix in predicting prior knowledge from different sources.


Geneshot is a search engine that accepts any search term to return a list of genes that are mostly associated with the search terms. It can be used to identify novel associations between genes and biological mechanisms and processes. A free text search is redirected to a PubMed search via the NCBI E-utilities API for retrieving publications that match the search terms. The resulting list of publications is cross-referenced with GeneRIF or AutoRIF to convert PMIDs to lists of genes. GeneRIF and AutoRIF enlist associations between publications and genes. Geneshot returns the list of genes ranked by their frequency of mentions within the publications returned by the search terms. In addition, the returned genes from the initial query are supplanted with additional genes based on co-expression, or based on co-occurrence derived from Tagger.
Below we explain how the underlying datasets used by geneshot were generated, and how to make decisions about the various options available for querying geneshot.

Underlying Data Sets

Below we provide a short introduction about the underlying datasets used by Geneshot. These datasets are available from the download section. Here we have a short introduction of the datasets that are being used by Geneshot. The data used by Geneshot is

GeneRIF is a manually curated dataset containing associations of genes with publications. The original data can be found at For Geneshot we focus on human genes. The current version of GeneRIF contains 396,020 gene-publication associations for human genes. The original GeneRIF data provides a timestamp when the association was entered into the GeneRIF database. For Geneshot, this date was replaced with the actual publication date.

Analogous to GeneRIF, we created an alternative dataset called AutoRIF. AutoRIF contains the same type of information as GeneRIF, associations between gene and publications. AutoRIF is automatically generated, and it currently contains 4,908,396 gene-publication associations. Hence, AutoRIF is more comprehensive than GeneRIF. We constructed AutoRIF by querying PubMed with all the human official gene symbols and collected the PMIDs for each query. It is important to note that some gene symbol terms are ambiguous. Since these gene symbols cannot be linked reliably linked to publications, we manually removed them from AutoRIF.

Gene-gene Co-expression Matrix from ARCHS4
Geneshot is using gene-gene co-expression data to make predictions about associations between genes and search terms. The gene-gene co-expression matrix used by Geneshot contains pairwise correlations between all human genes. The correlations were calculated using the Spearman’s correlation formula applied to a subset of samples from the ARCHS4 resource. The correlation is calculated over samples from a diverse set of cell types and cell lines. Before calculating the correlation, we quantile normalize the gene expression profiles and then calculated the Spearman’s correlation. his pairwise gene similarity matrix is used to transitively associate gene sets returned from the original Geneshot query with their most correlated co-expressed genes.

Gene-gene Co-occurrence Matrix from Tagger
The gene-gene co-occurrence matrix Tagger data contains pairwise gene-gene similarity based on the co-occurrence of genes in publication abstracts. The matrix contains the counts of how often two genes co-occur in the same query list.

Gene-gene Co-occurrence Matrix from Enrichr user-submitted lists
Enrichr is a leading tool for enrichment analysis. It processes thousands of queries from experiments involving gene expression analysis. Each list contains unique information about the composition of real user queries and can shed light into gene-gene dependencies across a wide variety of experimental conditions.

Submitting Queries to Geneshot

The query box of Geneshot contains two free text input fields where users can enter multiple search terms in combination. The top filed is for terms that will be converted to associated with genes. For example, if we want to identify genes that are most relevant to the search terms “liver fibrosis”. The bottom free text input field is for terms that the user wishes to exclude from the search. For example, if we want o exclude all matches of “liver fibrosis” that also mention the term “cancer”.

On the right side of the search box is a switch that provides the user with a choice between querying with GeneRIF or AutoRIF.

The input field labeled "top associated genes to make predictions" sets the number of top genes from the query to use for generating the predicted lists. This value can be changed later after the query completes.

The query time varies from instantly returned results to about 30 seconds. The time for the query to complete is mostly dependent on the number of associated publications found for the search terms. A very general search term such as "cancer" or “diabetes” will result in a longer wait time than a more specific search term such as "hair loss". Below the free text input fields, several sample queries are provided. Clicking on these terms triggers a query.

Data Visualization of the Query Results

The scatterplot becomes visible once the query results are returned to the user. The plot shows the genes that are associated with the search terms. Each point represents a gene. The x-axis is the number of publications that match the search term and the associated gene (either by GeneRIF or AutoRIF). The y-axis displays the normalized fraction of publications relative to the total number of publications the gene is associated with regardless of the search term. For instance, the gene FGF7 is associated with 19 publications that are also matching the search term "wound healing" (x-axis). The normalized fraction (y-axis) is 0.146. This means that from all the publications that mention FGF7, 14.6% of them also mention "wound healing".

When a gene in the scatterplot is selected by clicking on it, additional information about the gene is retrieved from our server. On the right of the scatterplot, a histogram is displaying the association of the gene with the search terms over time. The number of publications for the selected gene that do not match the search term is displayed as pink bars, while the number of publications matching the search term and the gene is displayed as blue bars. In the example below, we can see that the gene MGMT was not associated with Glioblastoma until around 2005. In the year 2017, about 50% of the publications that mentioned MGMT also mentioned Glioblastoma. This plot helps in identifying association trends for genes and terms over time.
Cumulative Distribution Plot
This plot contains the same information as the histogram, but it visualizes the data differently. The axis is the total number of publications and the publications that match the search term.

Result Tables

Associated Gene Table
The associated gene table contains the same information that is displayed in the scatter plot. The table will appear once the query results are returned. The number of genes returned by the query can vary, depending on how many genes are associated with the search terms. On top of the table, there are six buttons labeled: “Kinases”, “Dark kinases”, “GCPRs”, “Dark GCPRs”, “Ion channels”, and “Dark ion channels”. These buttons serve as filters. When clicked, the table displays only the genes that belong in the respective gene family category. A gene is considered dark based on its inclusion in lists published by the NIH for the Illuminating the Druggable Genome (IDG) NIH Commons Fund program. These lists are taken from the IDG Knowledge Management Center (KMC) RFA slider above the table enables users to set the threshold for how many genes to include for predicting additional genes. In this example, the top 50 associated genes are used to perform the prediction.

Predicted Gene Tables
Predicting additional genes that should be associated with the search terms is performed using the gene-gene co-expression matrix from ARCHS4 and the gene-gene co-occurrence matrix from Tagger. The results table can be viewed by selecting the corresponding tab. The prediction tables list the top 200 genes most associated with the gene sets from the associated genes table on the left. When hovering over the score of a gene, a popup will show the top 10 genes that caused it to be predicted to be associated with the search term.

The data from both tables can be downloaded in a variety of formats. The gene set within each table can be directly submitted to Enrichr for further analysis.

Gene Function Prediction

The gene-gene similarity matrices can be used to predict novel gene functions. On the gene function page a gene symbol can be entered into the search field. Geneshot includes a set of gene set libraries. By using functional prediction by association the input gene can be predicted to be a member of gene sets. In gene set libraries each gene set represents a group of genes that share a common property, such as a membership in a biological pathway. The performance of the prediction varies from gene and similarity matrix selected. Literature based methods such as Tagger and AutoRIF are capable in retrieving known associations well. But they are also able to identify novel insights. Data driven approaches such as gene co-expression perform well in retrieving existing knowledge at the same time as being unbiased from prior knowledge. The query returns a table with ranked gene sets. If the gene was known to be a member of the gene set the row will be blue. As an internal benchmark Geneshot displays a ROC curve to show where the true positive gene sets fell in the prediction ranking. This plot can only be generated when there is sufficient prior knowledge about the gene of interest.

AUC plot
The AUC plot shows all gene sets ordered by similarity score to a given gene. If a gene is peviously annotated with properties from the gene set library the prediction is considered a true positive. Ideally all previously known gene sets of which a gene is a member should be recovered in the prediction step. The quality of the retrieval can be expressed by the AUC. High AUCs can suggest that the co-expression or co-occurrence is a good metric to infer the biological properties of the gene. If there are no prior annotations of the gene in the geneset no AUC can be computed. Generally, literature based similarity matrices perform well here since they encode the known memberships directly. More datadriven similarity matrices such as gene co-expression are less biased to priviously known gene properties, but have to retrieve known properties denovo. Hovering with the mouse over the AUC plot will show the property names.

Gene Set Augmentation

The gene set augmentation page lets users upload a set of genes. Geneshot can compute the group similarity of the gene set to all genes in the given gene similarity matrix. Gene shot will identify the top 200 genes that ranked by their similarity score. In the case of the gene correlation matrix the average correlation to the input genes is calculated. In the case of co-occurrence matrices the average sum of log2 transformed similarity scores is computed. The values in the co-occurrence matrix are the odds ratio of observed and expected overlap for two genes. These values can be very large. As such a single pair of genes could dominat the final average similarity score. By log transforming the values first a gene set member candidate must be similar to multiple elements in the user provided gene set.
The novelty of genes is also reported here by returning the number of publications per gene. A representation of the distribution of novelty is displayed in a barplot. Genes are grouped into four quantiles of novelty based on the publication count of genes in AutoRIF. A gene is rare if it has 7 or fewer publications. An gene is uncommon when it has between 8 and 25 publications. Common genes have 26 to 87 publications. All genes with at least 88 publications are considered very common. These bins divide the genes into equisized bins.

Terms of Use

Please acknowledge Geneshot in your publications by citing the following reference:

Geneshot: search engine for ranking genes from arbitrary text queries
Alexander Lachmann Brian M Schilder Megan L Wojciechowicz Denis Torre Maxim V Kuleshov Alexandra B Keenan Avi Ma’ayan
Nucleic Acids Research, gkz393,

The Geneshot source code is available from GitHub under the Apache License 2.0. Commercial users should contact Mount Sinai Innovation Partners at for licensing.

GitHub Repository
The Geneshot source code is available on GitHub at

Geneshot is not to be used for treating or diagnosing human subjects. Geneshot or any documents available from this server are provided as is without any warranty of any kind, either express, implied, or statutory, including, but not limited to, any implied warranties of merchantability, fitness for particular purpose and freedom from infringement, or that Geneshot or any documents available from this server will be error free. The Ma'ayan Laboratory makes no representations that the use of Geneshot or any documents available from this server will not infringe any patent or proprietary rights of third parties. In no event will the Ma'ayan Laboratory or any of its members be liable for any damages, including but not limited to direct, indirect, special or consequential damages, arising out of, resulting from, or in any way connected with the use of Geneshot or documents available from this server.