Access a suite of analysis apps by clicking on the menu (or type command-K to open)

The first step in using the Query App to compute connections with your gene expression data is to assign a name to your query. Results will be stored in your Analysis History after your query is submitted.

Enter an up-regulated gene of interest, hit enter, and type in subsequent genes in the set you would like to query. You may also have down-regulated genes of interest. They can be entered in the box to the right.

Hit submit and the query algorithm will find connections between your genes of interest and perturbagens in CMap that have signatures most similar to your query. Data are generated in approximately 5 minutes and will be stored in your Analysis History.

The L1000 assay directly measures or infers the expression levels of 12,328 genes. By evaluating the current statistical model against a large compendium of RNA-Seq profiles from over 100 tissues from the GTEx consortium, we have identified a subset of 10,174 genes that are either measured or well inferred. This subset is known as the Best INferred Gene (BING) space. The Query App uses BING space to compute similarities between users' gene sets and the gene expression signatures in the CMap database. Each user entry is therefore mapped into one of the three following categories.
Invalid gene: Not a valid HUGO symbol or Entrez ID, and therefore not used in the query.
Valid gene: A valid HUGO symbol or Entrez ID that is also part of BING space, and therefore is used in the query.
Valid but not used in query: A valid HUGO symbol or Entrez ID that is not part of BING space, and therefore is not used in the query.

Click on a perturbagen in this table to see a CLUE Card that contains all of the information available for this perturbagen. You can also select any compound in the table to query connections with all other compounds in Touchstone. Click on Detailed List to view connections in a table, or click Heatmap to see connections in a matrix powered by the Morpheus App.

Filter the Touchstone data table by selecting perturbagen type or perturbational classes of interest.

Average transcriptional impact

Impact is assessed as a transcriptional activity score, which is calculated as a mean value of median replicate correlation and median signature strength of a perturbagen across multiple cell lines and doses. The score describes a perturbagen’s transcriptional activity, relative to all other perturbagens, as derived from its replicate reproducibility and magnitude of differential gene expression.

PCTCCi =  rank( median( CCi ) )N


PCTSSi =  rank( median( SSi ) )N


TASi =  PCTCCi + PCTSSi2


where:

TASi is the transcriptional impact score for the i-th perturbagen

PCTCCi is the percentile, relative to all other perturbagens, of the i-th perturbagen’s median replicate correlation coefficient (CC) across all of its signatures

PCTSSi is the percentile, relative to all other perturbagens, of the i-th perturbagen’s signature strength (SS) across all of its signatures

N is the total number of perturbagens

Signature diversity

Thick black bars signify Transcriptional Activity Scores greater than or equal to 0.5; thinner black bars denote scores less than 0.5. Absence of a bar means no data available. Colored lines (chords) signify similar connectivity scores between cell lines; red for positive connectivity scores of 80-100 (pale to intense color according to the score); blue for negative connectivity. Chords are only shown when TAS scores are > 0.5; thus absence of a chord either means that the perturbagen TAS score is very low, or that no data is available. Chords for individual cell lines can be isolated from the rest of the figure by hovering over the cell line name.

Baseline expression of this gene in each cell line is represented as a z-score (top numbers). Scores were calculated using robust z-score formula:

z-scorei = ( xi - median( X ) )/( MAD( X ) * 1.4826 ),

where:

xi is expression value of a given gene in i-th cell line

X = [ x1, x2 ... xn ] is a vector of expression values for a given gene across n cell lines

MAD( X ) is a median absolute deviation of X

1.4826 is a constant to rescale the score as if the standard deviation of X instead of MAD was used

Median and MAD expression values were calculated using RNA-Seq profiles from a total of 1022 cell lines, comprising data from the Cancer Cell Line Encyclopedia (CCLE; Barretina, et al.) and cell lines nominated by the CMap team. Plots show z-score values only for the core LINCS lines used by CMap in L1000 experiments. Light red or light blue regions indicate positive or negative outlier expression, respectively, of the gene relative to the other lines shown; z-score of a positive outlier in the corresponding cell line is in dark red and a negative outlier is in dark blue.

Summary class connectivity shows a boxplot that summarizes the connectivity of a class. Each data point, shown as a light gray dot, represents the median value of connectivity of one member to the other class members. (This corresponds to the median for each row, excluding the main diagonal, in the heatmap shown below.) The box is the distribution of those data points, where the box boundary represents the interquartile range, the vertical line within the box is the median, and the whiskers reflect the minimum and maximum values of the data (exclusive of extreme outliers, which may appear beyond the whiskers).

Connectivity between members of class is a standard heat map of the connectivity scores, summarized across cell lines, between members of the class, where dark red represents the highest positive scores and deep blue the highest negative scores. Individual scores are revealed to the left below the map by hovering over each cell of the map.

Class inter-cell line connectivity is a plot of the median (black line) and Q25-Q75 connectivity scores (blue area around black line) for each cell line as well as the summary scores across cell lines. In some cases perturbations have not been tested in every cell line; the absence of data is indicated by a “0” for that cell line. The example shown reveals that these estrogen agonists show the strongest connectivity to each other in MCF7, a human breast cancer cell line that expresses the estrogen receptor.

Profile status

Colored portion of top bar indicates the Broad assays in which this compound has been profiled.

L1000 cell/dose coverage

For compounds profiled by L1000, cell lines and dose range for which signatures are available are indicated by dark gray bars (lighter gray bar indicates no data is available for that cell line/dose combination). A bar displayed one row above the 10 uM row indicates that doses higher than 10uM were tested. The 6 rows correspond to 6 canonical doses: 20 nM, 100 nM, 500 nM, 1 uM, 2.5 uM, and 10 uM. (In some cases non-canonical doses were tested; these are rounded to the nearest canonical dose for the purpose of this display. For example, if the dose tested was 3.33uM, the 2.5uM bar is shown in dark gray here.)

Careers with CMap@Broad

The decoding of the human genome sparked a revolution in how we understand human health and disease. The Broad Institute of MIT and Harvard was founded in 2004 to fulfill the promise of this revolution: to reveal the fundamental basis of disease and catalyze rational treatments. Key to the realization of the Broad's mission is the Connectivity Map (CMap) project.

Who we are and what we do

Our goals, methods and progress

Connectivity Map's Big Hairy Audacious Goal is to build a comprehensive lookup table describing the functions of all human genes and the effects on them of a broad range of small-molecule and genetic perturbations to accelerate both the understanding of human disease and the discovery of novel therapeutics. Our approach is to generate perturbational signatures of cellular states and build pattern-matching algorithms to identify relationships between them. Based at the Broad Institute campus in the dynamic biomedical hub of Kendall Square, the CMap group consists of ~30 scientists and scores of collaborators across the biomedical community who work together to apply cutting edge genomic technologies to propel therapeutic discovery.

It's a great time to join

You will join the CMap group at a critical juncture of its development. Successful proof of principle of the core technologies used in CMap has created a surge of interest in leveraging the resource's datasets, algorithms and software products for therapeutic discovery in academia and within pharmaceutical companies.

Are you a candidate?

We are looking for:

  • Associate Computational Biologists. Candidates with Bachelors or Masters in Bioinformatics, Computer Science, Engineering, Statistics, Physics or any quantitative discipline
  • Post-docs in Computational Biology. Recent graduates from doctoral programs in Bioinformatics, Computational Biology or Systems Biology
  • Visual Designers. Candidates with Bachelors or Masters in a design field (e.g., graphic design, information design, architecture, etc.) and work experience in a related industry (web design, data visualization, UI/UX)

Necessary experience

Candidates should have outstanding academic records; a demonstrated history of developing algorithms; demonstrated proficiency with statistical and programmatic tools needed for the exploration of high-dimensionality datasets; be innovative and analytical thinkers with strong communication skills; and work well in an inter-disciplinary team.

Significant experience with at least some of the following is required: Analytical programming using MATLAB, Python, R or other appropriate language, Computations methods, including algorithm development, data analysis and statistics; Implementing algorithms for search, information retrieval and machine learning. A background in biology is favorable, but not necessary, and well-qualified applicants will be considered from backgrounds including Engineering, Computational Biology, Statistics and Software Engineering.

What does the job entail?

Characteristic duties: Work directly with wet-lab biologists to identify and develop data-analysis strategies; Write algorithms and deploy computational tools for the exploration of high-dimensionality datasets; Conceive, implement and test statistical models; work with wet-lab researchers to translate these models into testable experiments; analyze data from experiments Explore novel data representation modes with emphasis on integrating diverse data types. Help implement analysis methodologies into software tools for publication and distribution to the genomics research community.

To apply, email aravind@broadinstitute.org or apply via the Broad careers website www.broadinstitute.org/careers.