Frequently Asked Questions

RNA modifications are crucial for regulating numerous aspects of RNA biology, including RNA-protein interactions, splicing, three-dimensional RNA structure, transcript stability, intracellular trafficking, and translation. They offer broad insights into dynamic regulatory mechanisms in cancer, neurological and cardiometabolic disease, cell differentiation, viral infection and other phenotypes relevant to human, animal and plant health.

Unlike DNA methylation, which is heritable, RNA modifications are more dynamic and often reversible. They can change in response to environmental signals, developmental cues, or stress. This flexibility allows cells to fine-tune gene expression rapidly and adapt to changing conditions.

EpiPlex platform enables the localization and quantification of both m6A and inosine in mRNA and other long noncoding RNA species in a simple, 1-day workflow. Unlike other methods, EpiPlex assay can be run with low RNA sample input accommodating even the most precious clinically relevant samples. Designed for use with short-read sequencing, EpiPlex data is analyzed by our accompanying EpiScout software in a simple, push-button analysis of NGS output files.

EpiPlex assay utilizes non-antibody binders for m6A and inosine that have been engineered to be highly specific for the modification of interest. Compared to antibody enrichment, EpiPlex assay demonstrates greater sensitivity and specificity enabling sites of interest to be measured with lower RNA input and sequencing depth requirements. Additionally, EpiPlex assay is available as a complete reagent and software package providing all the necessary reagents, guidance and compute resources to go from RNA to actionable data output.

Traditional chemical methods, like MS (mass spectrometry), are highly sensitive to a broad range of modifications but lack sequence context and require significant material. Other chemical methods are designed to specifically modify bases which are read out as signatures (e.g., SNPs or indels) providing sequence context (e.g., bisulfite sequencing for m5C). However, chemical treatment is often destructive to RNA, exhibits significant off-target signal and typically has high material and sequencing depth requirements.

Mass spectrometry and the EpiPlex assay are highly orthogonal approaches. Mass spectrometry enables global detection and quantification of multiple RNA modifications but requires complete digestion of RNA into single nucleotides. As a result, it loses all information about which transcripts or pathways are being modified. ELISA-based methods face a similar limitation, quantifying global levels of a specific modification (provided a reliable antibody exists), but they also lack gene-specific resolution.

In contrast, the EpiPlex platform enables transcriptome-wide mapping of individual RNA modifications at the gene level in a one-day assay. It uses non-antibody, engineered binders to enrich and sequence modified RNA fragments directly from biological samples, revealing which transcripts are modified and enabling pathway-level insights and differential analysis across conditions. While it does not offer absolute quantification, each sample includes a spike-in control that serves as an internal reference for relative quantification. The kit also includes all necessary bioinformatic tools for alignment, peak calling, and sample comparison. Learn more.

Enrichment-based approaches, like EpiPlex assay, remove unmodified sequences improving sensitivity, particularly for low stoichiometry sites, and reduces sequencing depth requirements. A key drawback of enrichment is the loss of absolute quantification of modification stoichiometry. However, through the use of spike-in standards, such as those included in the EpiPlex kit, relative quantification of modifications between samples can still be obtained.

Yes, each sample includes reagents for a solution control that is unenriched by modifications and provides high quality RNA-seq data. Learn more.

Technical reproducibility and sensitivity to gene expression, as measured by both universal human reference RNA and ERCC controls, is comparable to leading RNA-seq library kits sold by third parties. Learn more.

The spike-in controls serve two purposes: 1) Confirms the assay successfully enriches for modifications and 2) Normalizes the signal to account for technical and biological variation. The detection of the spike-ins are necessary for the EpiScout analysis pipeline to properly function and therefore must be added to your samples.

RNA must be purified and free from contaminating DNA. Purified RNA should be stored in low TE buffer or nuclease-free water.

EpiPlex assay supports both total and polyA selected RNA as inputs. If using total RNA, a ribosomal RNA (rRNA) depletion step will be necessary. For more information on the effect of RNA input quantity on total or poly(A)-enriched RNA, please refer to our RNA input technical note.

EpiPlex assay works with degraded samples such as RNA isolated from FFPE treated tissue. Depending on the length of the RNA, a modification to the workflow may be required. Please contact support@alidabio.com for more information on running FFPE RNA through the EpiPlex workflow.

The total RNA workflow will capture both mature messenger RNA (mRNA) and immature messenger RNA (pre-mRNA), mitochondrial RNA (mtRNA) and long non–coding RNA (lncRNA). Ribosomal RNA (rRNA) can also be captured but should be depleted as part of the workflow and not make it on the sequencer in substantial amounts.

If rRNA is depleted post-library completion, we require the use of the SEQuoia RiboDepletion Kit from Bio-rad. Ribosomal RNA depleted at the beginning of the workflow is compatible with a number of depletion kits and may be used in the EpiPlex assay with the Poly(A)-enriched RNA workflow.

We suggest sequencing the enriched and solution control libraries to 25 million paired-end reads each. Depending on the complexity of your libraries and genes/sites in question, you may be able to sequence more or less than this suggested target. Please refer to our technical note for more information.

We recommend a 200 or 300 cycle kit (100 or 150 cycle paired end). A shorter read, 100 cycle (50 cycle paired end) kit can be used to produce reliable data, but will reduce inosine resolution since this relies on A to G variant calls in sequencing.

EpiPlex libraries have been validated to be compatible with NextSeq 1000/2000, NovaSeq 6000 and NovaSeq X from Illumina, DNBSEQ-T7 from Complete Genomics, and Aviti from Element Biosciences. Other short-read sequencers that accept Illumina-style adapters may work but have not been validated by AlidaBio. Use of sequencers other than those from Illumina may require libraries to be converted for compatibility prior to sequencing.

Yes, but as optimal loading concentrations often vary between library types, providing a designated lane for EpiPlex libraries is suggested to maximize throughput.

No, EpiPlex UDI primer kits are specific to the EpiPlex Library configuration. UDI kits from other vendors are not compatible with EpiPlex libraries. EpiPlex index primer sequences and barcodes for the 24 UDI set and 96 UDI set are freely available if you prefer to have them synthesized by a third party vendor.

EpiScout analysis software can be accessed in two ways:

DNAnexus cloud portal (recommended): Create a free DNAnexus account, then email your username along with proof of kit purchase to AlidaBio Support. We’ll enable your access within 48 hours. The cloud interface offers guided setup and is best for most users. Please note that while pipeline access is included with kit purchase, sample analysis requires purchase of a separate EpiScout Compute Token.

Docker image (advanced users): If you prefer to run the pipeline on your own compute infrastructure, you can download the Docker image. This option requires command-line experience. Hardware and installation details are provided in the EpiScout User Guide.

The EpiScout pipeline requires three inputs:

1. Demultiplexed FASTQ sequence files generated from EpiPlex libraries.
2. A completed EpiScout sample sheet, and either DNAnexus access or a local Docker setup. 
3. A properly formatted reference genome – we currently support human, mouse, and drosophila reference genomes. If you require a custom genome, please contact bioinfo-support@alidabio.com 

The EpiScout analysis pipeline currently supports these reference genomes—Human (GRCh38.p14), mouse (GRCm39), and drosophila (dm6).

EpiScout analysis software performs comprehensive analysis of RNA modifications and gene expression from EpiPlex libraries. It includes:

• Peak calling for m6A and inosine using a custom ML-based algorithm
• Relative quantification using spike-in controls for cross-sample comparisons
• RNA-seq outputs (TPM and count tables) from solution control libraries
• Differential analysis of modification abundance across conditions (via AlidaDiff)
• Visualization files (.bam, .bed, .bigWig) for genome browsers
• Summary reports of quality metrics, motif enrichment, and metagene profiles
• Base-resolution inosine A-to-I variant calls in .bed format
• Downsampled outputs, controlling for differences in sequencing depth

All outputs support downstream analysis and are accessible via DNAnexus or local Docker.

EpiScout analysis software calculates fold-enrichment (FE) by comparing read pileups in the enriched library against the matched solution control. These FE values are then scaled using internal spike-in standards (Lambda Model System, LMS) to normalize for technical variability.

A Hidden Markov Model (HMM)-based algorithm analyzes the FE signal and labels regions as peaks when the data are more likely in the peak state than background. Candidate peaks are then filtered using a random forest model, and annotated with gene features and inspected for known molecular patterns (e.g., DRACH for m6A and A-to-G variant calls for inosine).

The result: high-confidence, modification-specific peaks that account for both expression level and technical variability.

The EpiScout analysis software does not currently support running user-provided peak-callers as plug-ins. Intermediate data output by the pipeline could be used as input for 3rd party peakcallers by expert users, however this approach won’t benefit from scaling and normalization by the EpiPlex internal controls. 

Peaks are evaluated using an ensemble machine learning approach, combining supervised Hidden Markov Model (HMM) and random forest algorithms. Training and validation datasets are expertly curated using public data sources, internally generated datasets containing precise modification levels, and a diverse set of IVT false-positive controls. 

The result is a collection of q-scores generated by bayesian inference (q_bayes), an HMM posterior probability (q_hmm), and a random forest score (random_forest_q). The latter score takes into account the former features as well as other peak features, resulting in precise peak filtering criteria.

Peak signal intensity is measured as the ratio of pileup counts between the enriched and solution control samples, scaled by our internal spike-in controls.

The EpiPlex platform  provides single-base resolution for inosine, which is read out as an A to G mutation in sequencing. These sites are included as .bed files. The resolution for m6A modified regions or peaks is about ~100-300 nt, and this can be further narrowed down through identification of known motifs (e.g. DRACH) under the peak. EpiPlex peaks typically contain around 3-4 DRACH motifs.

The EpiPlex assay includes spike-in controls with every sample, which allows sample to sample and batch to batch comparison. EpiScout analysis pipeline utilizes the spike-in controls to correct for experiment-to-experiment pulldown differences in the peak signal. After correction, the software can compare two peak’s fold-enrichments directly, resulting in “differential plots”.

We denote a “region” as an intersection of many “peaks”. This is useful for measuring differences in enrichment, as a peak’s boundaries are not necessarily shared between two samples. A region’s borders are less well defined than a peak, and are generally more broad, as they incorporate many individual peaks.

The AlidaDiff module is a novel differential analysis algorithm inspired by the DESeq2 algorithm used for RNAseq type data. To utilize this function, fill out the “group” columns in the intake sample sheet, labeled as “group_[conditionA_v_conditionB]”. The samples you would like to compare will automatically be grouped by the software, outputting volcano plots and p-values evaluating the probability of a significant difference between modified regions in those samples.

RNA-Seq is included with the EpiPlex assay through the paired “solution control” sample(s) produced in the workflow. EpiScout analysis software provides raw RNA-seq data generated by the subread module featureCounts in paired-end mode. The raw counts matrix can be imported into your favorite differential expression module such as DESeq2, edgeR, or Limma.

We provide a raw gene_counts.tsv file and a TPM.tsv file, which includes gene-level RNA-seq counts normalized to samples’ total depth. 

No. EpiScout  analysis software is designed specifically for data generated with EpiPlex libraries and does not support MeRIP analysis.

To receive pricing information, please submit this Request for Quote form. A member of our Sales team will be in touch with you within one business day.

The EpiScout compute token is available in three sizes: 1 sample (2 libraries), 8 samples (16 libraries), and 24 samples (48 libraries). Select the token size that matches the number of libraries you plan to analyze.

Our support team would be happy to assist with setup, troubleshooting, or access requests. Please reach out to bioinfo-support@alidabio.com.