Sharing & Collaboration
Accelerate teamwork by connecting experts and datasets anywhere in the world
Learn how built-in data sharing and collaboration tools accelerate research and enable global teamwork.
Gene Expression: RNA-seq
A complete guide to RNA-seq data analysis
Explore genes, change cut-offs, customize plots, explore pathways, identify targets, and validate gene signatures
Gene Regulation & Anti-Sense: Small RNA-seq
Small RNA-seq data analysis designed for the biologist
Explore miRNAs, change cut-offs, customize plots, explored validated & predicted genes, disease & drug associations
NanoString: Gene, miRNA & Protein Expression
A complete guide to NanoString nCounter data analysis and collaboration
Explore miRNAs, change cut-offs, customize plots, explored validated & predicted genes, disease & drug associations
Histone Mark & Transcription Factor: ChIP-seq
Comprehebnsive ChIP-seq data analysis
Analyze peak overlaps & differential binding, explore results with embedded genome browser and multi-omic systems
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
The study of gene expression provides valuable insights into the nature of diseases and the effect of treatments by quantifying the activity of RNA in a biological sample. RNA-seq is a fast-growing Next Generation Sequencing (NGS) assay for evaluating gene expression, alternative splicing transcripts and fusions.
Scientists working in Oncology, Immunology, Regenerative Medicine, Drug Discovery and other areas of research often conduct experiments between healthy and disease states to identify Differentially expressed genes and biological pathways to discover therapeutic targets. Comparisons between these differential patterns reveal unique gene signatures valuable for drug and diagnostic development.
ROSALIND is a cloud platform that connects researchers to 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 are required. By accepting raw FASTQ sequence data as well as processed counts data, ROSALIND enables powerful downstream analysis and truly insightful visualizations on gene expression datasets. Receive same-day results with every experiment in an interactive experience designed for ease of use 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.
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 RNA-seq data analysis on ROSALIND.
Starting an RNA-seq 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 RNA-seq data analysis, ROSALIND provides scientists with a choice: a) Begin with raw FASTQ files produced by high throughput sequencing, or b) Use processed data files generated by another analysis pipeline. Processed data is imported as normalized or raw counts. This provides flexibility for scientists to utilize the ROSALIND discovery experience to visualize and interpret data regardless of the data source. When analyzing raw FASTQ files, ROSALIND streamlines data analysis using an advanced pipeline for analysis that includes intelligent quality control with automatic contamination detection, identification of Differentially expressed genes and deep pathway interpretation. Visit the technical specifications section to learn more about the ROSALIND RNA-seq data analysis pipeline and available reference materials.
For proper RNA-seq results, an analysis pipeline must adjust for sample preparation and proprietary differences in library preparation kits used in the experiment. Not only is the kit selection important for targeting and capturing the desired transcriptomic elements, the analysis pipeline adjusts and optimizes for the kit’s unique characteristics, such as strandedness, strand direction, any unique molecular identifiers (UMIs) as well as the adapters used. ROSALIND integrates and supports a broad library of sample and library preparation kits, automatically calibrating each analysis with the appropriate details. To learn more about supported kits, visit the technical specifications section. Featured kits and instrument partners are also listed below.
Researchers must be confident in the quality control phase before gathering insights from an RNA-seq 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, contamination, 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 Q30 scores, alignment rates, ribosomal content, duplicate rates, sample correlation, gene coverage, genomic regions and multidimensional scaling (MDS) or principal component analysis (PCA) for all samples. When ROSALIND detects low alignment, non-aligning reads are evaluated for possible contamination. If ribosomal content is higher than expected, ROSALIND generates alerts. With Illumina sequencers, the results are usually good when Q30 values are over 85% and alignment rates are over 80% for the target species. Additionally, duplication rates less than 25% with fewer than 10% of reads trimmed is preferred. 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 FASTQ file for RNA-seq 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.
A typical RNA-seq analysis provides a list of Differentially expressed genes, generally in the form of a massive and obtuse CSV file. Unfortunately, this often results in more questions than answers for scientists. Multiple applications may also need to be used to generate this CSV file. Such applications often have a wide range of complexity with non-standard input/output formats, many of which are command-line tools requiring advanced knowledge in programming — an exercise well beyond the level of most biologists.
ROSALIND moves beyond the CSV file by providing a comprehensive dashboard for differential expression analysis and interpretation of RNA-seq data. Researchers begin with a 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 down regulated with a p-Adjust of 0.05. Further adjustments to achieve a significant set of genes are performed by ROSALIND, if needed. 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 filters, adding covariant corrections, applying gene lists and signatures, and adjusting plot color palettes. The ROSALIND gene expression discovery experience features deep interpretation of top pathways, gene ontology diseases and drug interactions, as 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 every day. 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 protein 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?
Samples 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 email@example.com .
What is considered a Sample?
Any sample that is prepared for processing on an instrument is considered a Sample for ROSALIND. If a Scientist takes two (2) aliquots of an original sample to have replicates and prepares a library for each, this would be considered two (2) Samples on ROSALIND. On the other hand, a Sample may have multiple files associated with it, depending on 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, source and results files are downloadable on ROSALIND. Look for the Download buttons to access publication-ready figures as well as to download all experiment datasets.
Do you have an API for programmatic interfacing?
Yes. We provide API integration for Enterprise customers. This allows production teams to automate the upload, processing and distribution of genomic datasets. API integration also includes Single-Sign-On (SSO) support.
Receive the free ROSALIND Quick Start Guide with your trial