Single Cell Gene Expression
Gene Expression: RNA-seq
A complete guide to RNA-seq data analysis
NanoString: Gene, miRNA & Protein Expression
A complete guide to NanoString nCounter data analysis and collaboration
Gene Regulation & Anti-Sense: Small RNA-seq
Small RNA-seq data analysis designed for the biologist
Histone Mark & Transcription Factor: ChIP-seq
Comprehensive ChIP-seq data analysis
Chromatin Accessibility: ATAC-Seq
Sharing & Collaboration
Accelerate teamwork by connecting experts and datasets anywhere in the world
Knowledge Graph and Search
Explore Gene Expression visually and interactively with deep interpretation.
Seamlessly sift and sort through Differentially expressed genes. Investigate top pathways, change cut-offs and validate gene signatures.
Interpretation from more than 20 leading knowledge bases
Differentially expressed genes
Details for every gene
Advanced platform capabilities inside a simple to use dashboard
Explore your data immediately and stop waiting for results. Seamlessly create new filters to experiment with cut-off values while your interactive plots and interpretation are updated in moments.
Implement covariate corrections and easily understand the trade-offs & benefits
Create unlimited cut-off filters with multiple fold change and p-adjusted parameters
Group by fold change, such as up & down clusters
Sort by fold change, such as alphabetical & pValue
Select, search and create new gene lists and signatures
Choose your favorite color scheme for plot publishing
Create new filters to adjust cut-offs and focus on genes of interest
Experiment with different cut-off values to update plots and explore updated interpretation of enriched genes. Why wait for days when you can explore your data now?
Define a unique filter name
Select an easily identifiable icon color and initial
Set Filter Parameters for up-regulation, down-regulation and pValue
Download and export of filtered gene expression data
Download publication-ready figures with clear explanations for every Scientist.
Every plot and figure is rendered for high-quality and downloadable in multiple formats.
Choose format and download (PNG, SVG and CSV)
Expand current plot to full-size and hide the explanation
Links to industry resources for additional explanation
Focus on genes of interest using Gene Lists and Signatures to rapidly assess every experiment.
Create, collaborate and update gene lists so that you can discover and focus on the most important signatures across oceans of data. Each plot dynamically updates when a new list is selected.
Select or Create New Gene List
Heatmap and Volcano Plot display only the genes from the selected list that pass the current fold change and pValue filter
The informational blue bar indicates how many genes from the selected list are not present in the current filter
Create new lists from selected genes
Add genes and entire pathways to the current list
All plots dynamically update in real-time to showcase changes made
Navigate the most significant pathways and enriched terms with a simple click.
ROSALIND Knowledge Bases provide interpretation based on the gene enrichment for each filter you create. Navigate the details of every term including Pathways, Gene Ontology, Proteins and many others.
Visually explore your results across any pathway or term with one click
Tooltips provide extended information for every gene and sample
Learn more from NCBI on each gene with the bottom bar magnifier
Dive even deeper into pathway interpretation by clicking the knowledge base magnifer
Dive deeper into the pathways and the networks that connect them
Pathways are shown and sorted by significance. Review the number of genes in each term, including totals for up and down regulated genes.
Click on a term to display genes within the current fold change and pVal filter
Click on a gene to display all significant pathways
Sort genes by fold change, alphabetical or pValue significance
Toggle the gene list area into more Interactive plots
Change to any ROSALIND Knowledge Base with one click
Toggle between pValue and pAdj sorting
Download complete set of all pathway interpretation details
Click the golden magnifier to access annotated pathway diagrams
Access rich pathway diagrams colored by gene expression levels
Experience pathway diagrams with detailed descriptions, annonated fold change colors, and gene heatmaps.
Interact with the pathway diagram to see corresponding genes highlight on the left
Interact with the gene list to see corresponding genes highlight in the pathway diagram
Access external references through the pathway magnifier
Download publication-ready pathway diagrams in preferred colors
ROSALIND is a cloud platform that connects researchers from experiment design to quality control, differential expression and pathway exploration in a real-time collaborative environment.
Scientists of every skill level benefit from ROSALIND since no programming or bioinformatics experience are required. By accepting raw RCC files directly off the nCounter instrument, ROSALIND enables powerful downstream analysis and truly insightful visualizations on gene expression datasets. Results are prepared in minutes for every NanoString nCounter Data analysis with an interactive experience designed for ease of use, real-time collaboration and saving valuable time.
ROSALIND enables scientists and researchers to analyze and interpret differential gene expression without the need for bioinformatics or programming skills. All that is required is basic background in biology and a current subscription or active trial. No downloads of nSolver software are required since ROSALIND operates in a browser and performs the identical statistical calculations that previously would have been performed in nSolver.
Biological questions can also be explored independently, or in conjunction with, uploaded experiment data as ROSALIND automates the import of public data from the National Center for Biotechnology Information (NCBI) Short Read Archive (SRA) and Gene Expression Omnibus (GEO).
ROSALIND simplifies data analysis and works like a data hub interconnecting every stage of data interpretation. The ROSALIND Gene Expression discovery experience enables visual exploration and self-investigation of experiment results to give researchers the freedom to adjust cut-offs, add comparisons, apply covariate corrections, and even find patterns across multiple datasets, without the need for bioinformatic expertise. There are five easy steps to performing nCounter data analysis on ROSALIND.
Starting a NanoString data analysis begins with creating a new experiment and capturing the experiment design. ROSALIND walks through the key aspects of an experiment in a guided experience to record biological objectives, sample attributes and analysis parameters. These details become the basis of the experiment discovery dashboard. Researchers who publish papers and work with NCBI public data know the importance of natively supporting NCBI data models. ROSALIND fully supports the NCBI BioProject and BioSample models for metadata assignment and sample attribute descriptions. ROSALIND also enables scientists to create custom attributes to describe biological behaviors in terms relevant to the experiment. Setup of comparisons is simplified by describing and annotating samples using these familiar terms. This methodology minimizes the risk of differential expression errors when selecting samples for comparison.
For the NanoString nCounter Analysis System, ROSALIND provides scientists with a choice: a) Begin with raw RCC files produced on the nCounter instrument, or b) Use normalized data that has been processed and exported by nSolver. This provides flexibility for scientists to utilize the ROSALIND discovery experience to visualize and interpret data regardless of the data source. When analyzing raw RCC files, ROSALIND streamlines data analysis using a specialized pipeline that follows the NanoString guidelines for Advanced Analysis, includes intelligent quality control with automatic anomaly detection, Cell Type Profiler, identification of differentially expressed genes and deep pathway interpretation. Visit the technical specifications section to learn more about the ROSALIND NanoString data analysis pipeline and available reference materials.
For optimal NanoString results, an analysis pipeline must consider the panels used, genes detected and control levels before performing normalization or calculating fold changes. ROSALIND integrates and supports all gene expression and miRNA panels, including those with custom content, such as the COVID-19 Panel Plus spike-in kit. Each panel is automatically detected with the target species. In the case of custom panels, ROSALIND provides a broad list of available species and attempts to match those with the highest levels of corresponding genes.
Researchers must be confident in the quality control phase before gathering insights from an experiment, otherwise the results of the analysis should not be trusted. Biology’s mysteries are elusive and complex. Time should not be lost chasing corrective measures for outliers, swapped samples and the many other errors that can occur in the course of a well-designed experiment.
Some of the most important Quality Control metrics to verify are Imaging Quality, Binding Density, Limit of Detection, Control Linearity, Selection of Housekeeping Genes, Sample Correlation and multidimensional scaling (MDS) or principal component analysis (PCA) for all samples. When ROSALIND detects low imaging quality, binding density or limit of detection, an alert is provided and the values are highlighted in the QC. Researchers can eliminate offending samples and the deleterious effects on results by identifying the sample as an outlier and move confidently into the discovery and exploration phase of results interpretation.
ROSALIND Quality Control Intelligence identifies potential data quality issues and triages the data before presenting the results. This eliminates the needs for researchers to be experts in Sequencing quality control issues. Learn how researchers gain confidence in their results through Quality Control Intelligence.
After a researcher has reviewed the quality control phase the interactive presentation of results is ready to begin. The next step is to unlock the experiment. ROSALIND calculates the quantity of Analysis Units (“AU”) required to unlock the results. This is generally 1 AU per single-sample RCC file for NanoString experiments, however this may differ based on counts files or other experiment parameters. Account balances and quick links for acquiring more AU are directly accessible from the unlock screen. To learn more about Analysis Units, check out the Q&A in the section below, or visit the ROSALIND Store.
To date, NanoString nCounter data has been analyzed using nSolver’s powerful desktop solution to import, normalize and calculate differentially expressed genes. ROSALIND enables a new, unique approach that is cloud-based and empowers scientists to collaborate and visually explore their NanoString nCounter data while providing the peace of mind that the settings and methods are approved by NanoString.
ROSALIND extends nSolver by providing an online, simplified experience for differential expression analysis and interpretation of NanoString nCounter data. This experience begins with a discovery dashboard that shows the list of significant differentially expressed genes determined by a calculated cut-off filter. Default settings for the filter begin with a fold change of 1.5 upregulated and 1.5 downregulated with a p-Adjust of 0.05. Further adjustments to achieve a significant set of genes are performed by ROSALIND, if desired by the scientist. Researchers may also create an unlimited set of their own customized filters using fold changes and p-Value parameters. Convenient on-screen controls are easily accessible for modifying these filters, adding covariant corrections, applying gene lists and signatures, and adjusting plot color palettes. In the ROSALIND gene expression experience, scientists discover insights from deep interpretation of pathways, gene ontology, diseases and drug interactions that are presented through rich interactive plots that fill the screen and respond to interactions from the scientist, showing customizable heatmaps, volcano and MA plots as well as box and bar plots.
New comparisons and meta-analysis may be added at any time. Comparisons are created using BioProject attributes. Meta-analyses created can be cross experiments and multi-omic. Each of these perspectives are available within minutes of setup, reducing internal bioinformatic workload and enabling scientists to react fluidly by focusing directly on the science of the experiment.
The discovery process rarely ends with a single point of view from a single researcher opinion. ROSALIND Spaces enables true scientist-to-scientist collaboration through virtual data rooms where scientists and collaborators can come together on related datasets anywhere in the world to interactively explore shared experiments much like working with Google Docs. Researchers access a consistent version of the data, without the need to transfer unwieldy files or reinterpret origin files. All changes are interactive, instantly available, and viewable everywhere in the world (as authorized by the organization) with real-time activity feeds and historical reports. Spaces participants can add experiments, explore pathways, change cut-offs, add meta-analyses and add new comparisons all within the shared collaborative environment.
Spaces are virtual meeting rooms where scientists meet with niche experts, clients and supporting teams to maximize the discovery value of every experiment and prepare for the next one.
I am not a bioinformatician. Can I really perform my own analysis?
Absolutely and other scientists just like you run their own analyses on ROSALIND everyday. To learn more how to get started, check out the ROSALIND Quick Start Guide here.
What types of Gene Expression experiments are supported?
The ROSALIND Gene Expression discovery experience supports RNA-seq, NanoString gene and miRNA panels, and Micro-Array (via counts).
What types of input files are supported?
For Gene Expression experiments, FASTQ files and count files are supported. Compressed FASTQs will have faster upload times. Supported file types: .FASTQ, .FASTQ.GZ, .CSV, .TXT, .RCC (NanoString only)
What is an Analysis Unit and how is it used on ROSALIND?
Sample that are processed on ROSALIND require an Analysis Unit to unlock the ROSALIND discovery experience. Analysis Units are already included in most subscriptions on ROSALIND. Additional Analysis Units may be purchased in packs of 10 or 50 from the ROSALIND Store. Analysis Units do not expire. A current subscription is required to utilize Analysis Units. Enterprise Subscriptions provide additional flexibility for high-volume environments. Please contact sales to learn more Sales@OnRamp.Bio
What is considered a Sample?
Any sample that is prepared for processing on an instrument is considered a Sample for ROSALIND. If a Scientists decides to take two (2) aliquots of the original sample to have replicates and prepares library for each, this would be considered two (2) Samples on ROSALIND. On the other hand, a Sample may have mutliple files associated with it, depending how sequencing is performed. A single sample may be single-end, paired-end and also multi-lane and will still be considered as one (1) Sample.
Can I download my results and plots?
Yes. All plots, diagrams, unprocessed and final processed data files are downloadable on ROSALIND. Look for the Download buttons to access publication-ready figures as well as to download all experiment datasets.
Now available free of charge to all NanoString nCounter users!