Using the Achilles Portal

Table of Contents

Downloading Data

From any page on the portal, click on the tab called ‘Data’. You’ll then see a list of datasets available. Click the dataset of interest to bring up files that can be downloaded locally, sent to GenomeSpace, launched in GENE-E for visualization, or launched in a PARIS run. See the ‘Tools’ section below for links to details about GenomeSpace, GENE-E, or PARIS.

Genes can be searched by using the search box, and choosing the dataset of interest. Search_dialog

Gene/Reagent Information

If data exists for the gene, a ‘Gene Summary’ page will load. The top part of the page has information and links about the gene and the reagents (for example shRNAs) that map to that gene. This includes quality metrics from the ATARiS algorithm – click on ‘table details’ for more information. The reagent-level and gene-level score(s) for the gene are available for visualization in GENE-E, local download or can be sent to GenomeSpace.



The bottom part of the page contains heatmaps showing the data for that gene (both reagent-level and gene-level data). The heatmaps are interactive and can be sorted, zoomed, labeled with different pre-loaded annotations, etc. Heatmaps run from blue (low values, more dependency/ essentiality) to white to red (high values, less dependency/essentiality). ATARiS gene-level data are median centered and therefore relative within the particular gene that is being viewed.


Running an Analysis

PARIS analyses can be launched from the portal in several places.

1) From the Data Download page

PARIS can be launched with either the reagent-level or gene-level dataset of interest. This sets up a PARIS analysis within GenePattern (you’ll be prompted to either register or login). PARIS will be set up using that file as the ‘Data file’ to be ranked, but will require a ‘Target profile file’ and a ‘Target’ to be uploaded.


2) From the Gene Summary page

For a particular gene, PARIS can also be launched on a particular reagent (such as an individual shRNA), or a gene solution. This sets up a PARIS analysis within GenePattern (you’ll be prompted to either register or login). Any of these parameters can be changed before running, but in this case, the ‘Data’ file is loaded as the entire reagent or gene level dataset (as in #1). Additionally, the ‘Target profile file’ is loaded as the data for that gene (either the group of reagents or the gene solution data for that gene) and the ‘Target’ is set as the reagent or gene solution that was selected.

3) More custom analyses

PARIS can be run using the GenePattern module directly, with files downloaded locally or through GenomeSpace. A tutorial, with examples describing the different ways PARIS can be run is found here.

Data can also be sent to GenomeSpace, to perform other custom analyses in GenePattern or other supported programs.



GenePattern is a powerful genomic analysis platform that provides access to more than 240 tools for gene expression analysis, proteomics, SNP analysis, flow cytometry, RNA-seq analysis, and common data processing tasks. A web-based interface provides easy access to these tools and allows the creation of multi-step analysis pipelines that enable reproducible in silico research.

GenePattern Public Server:

Reich M, Liefeld T, Gould J, Lerner J, Tamayo P, Mesirov JP (2006) GenePattern 2.0 Nature Genetics 38 no. 5 (2006): pp500-501 doi:10.1038/ng0506-500.


ATARiS is a computational method designed to analyze phenotypic readouts from multiple-sample RNAi screens in which each gene is targeted by multiple RNAi reagents. “ATARiS” stands for Analytic Technique for Assessment of RNAi by Similarity. ATARiS was developed by Aviad Tsherniak in the lab of Jill Mesirov.

Shao DD, Tsherniak A, Gopal S, Weir BA, Tamayo P, Stransky N, Schumacher SE, Zack TI, Beroukhim R, Garraway LA, Margolin AA, Root DE, Hahn WC, Mesirov JP. ATARiS: computational quantification of gene suppression phenotypes from multisample RNAi screens. Genome Res. 2013 Apr;23(4):665-78. doi: 10.1101/gr.143586.112.


PARIS is a flexible GenePattern module which uses a mutual information based metric to rank cell line data (shRNA/gene dependencies, genomic features, chemical sensitivities, etc.) based on a classifier feature (one value per cell line). The module can handle both binary and continuous variables as the classifier feature. PARIS was developed by Pablo Tamayo.


GenomeSpace is a cloud-based interoperability framework to support integrative genomics analysis through an easy-to-use Web interface. GenomeSpace provides access to a diverse range of bioinformatics tools, and bridges the gaps between the tools, making it easy to leverage the available analyses and visualizations in each of them. The GenomeSpace project is a collaboration of the Mesirov and Regev laboratories at the Broad Institute; the Chang laboratory at Stanford University; the Ideker laboratory at the University of California, San Diego; the Nekrutenko laboratory at Pennsylvania State University; the Segal laboratory at the Weizmann Institute of Science; and the Haussler and Kent laboratories at the University of California, Santa Cruz.

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