How to Analyze Small RNA with ROSALIND

Empowering miRNA Data Analysis & Interpretation

WHY STUDY SMALL RNA EXPRESSION

Small RNA sequencing (SmallRNA-Seq) is a type of RNA sequencing based on the use of NGS technologies to characterize small noncoding RNA molecules. Small RNA-Seq is commonly used to evaluate and discover new forms of small RNA and to predict their possible functions. By using this technique, it is possible to discriminate small RNAs from the larger RNA family to better understand their functions in the cell and in gene expression. Small RNA-Seq can analyze thousands of small RNA molecules with a high throughput and specificity.

Small RNAs are noncoding RNA molecules between 20 and 200 nucleotide in length that include several different classes of noncoding RNAs, depending on their sizes and functions: miRNA, snRNA, snoRNA, scRNA, piRNA, and siRNA. Their functions range from RNAi, RNA processing and modification, gene silencing, epigenetics modifications, protein stability and transport.

The miRNA is the most commonly analyzed class of small RNA and their RNAi functions are often studied by scientists working in Oncology, Immunology, Regenerative Medicine, Drug Discovery, and other areas of research in order to understand their impact on gene regulation. For this reason, ROSALIND offers basic analysis pipelines for all smallRNAs and expanded options for analysis and interpretation of miRNAs.

OVERVIEW

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.

FIVE STEPS TO SUCCESS WITH SMALL RNA-SEQ DATA ANALYSIS

ROSALIND simplifies data analysis and works like a data hub interconnecting every stage of data interpretation. The ROSALIND small RNA 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.

1. EXPERIMENT DESIGN

Starting a small 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. The setup of comparisons is simplified by describing and annotating samples using these familiar terms. This methodology minimizes the risk of differential accessibility errors when selecting samples for comparison.

For small 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 miRNA as well as predicted and validated miRNA targets, drugs and disease associated. Visit the technical specifications section to learn more about the ROSALIND small RNA-seq data analysis pipeline and available reference materials.

For proper smallRNA-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 miRNA differential expression, but the analysis pipeline also adjusts and optimizes for the kit’s unique characteristics, such as strandedness, strand direction, any unique molecular identifiers (UMIs) or trimming requirement 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.

2. SMALL RNA-SEQ QUALITY CONTROL

Researchers must be confident in the quality control phase before gathering insights from a smallRNA-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, % of bases trimmed, length before and after trimming, sample correlation, smallRNA classed distribution and multidimensional scaling (MDS) or principal component analysis (PCA) for all samples.

ROSALIND Quality Control Intelligence identifies potential data quality issues and triages the data before presenting the results. This eliminates the need for researchers to be experts in Sequencing quality control issues. Learn how researchers gain confidence in their results through Quality Control Intelligence.

3. UNLOCKING RESULTS

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 small 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.

4. ANALYSIS & DISCOVERY

A typical small RNA-seq analysis provides a list of differentially expressed miRNA, 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 and interpretation of smallRNA-seq data. Researchers begin with a list of significant differentially expressed miRNA 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 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 smallRNA experience features deep interpretation of predicted and validated targets, diseases and drug associated, 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 may be added at any time. 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.

5. COLLABORATION & DATA SHARING

The discovery process rarely ends with a single point of view from a single researcher’s 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 unwieldly 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.