RNA Sequencing Frequently Asked Questions

View our Sample Submission Guidelines for instructions on preparing and sending samples. Organize your samples in tubes or plates following the order indicated on the sample submission form.

• Tubes: Prepare samples in clearly labeled and well-organized 1.5 mL flip-cap microcentrifuge tubes. Please avoid using Parafilm to seal the tubes. 
  

Unless otherwise instructed, GENEWIZ reserves the right to combine multiple vials of the same sample for extraction and/or library preparation. 

• Plates: For projects with 16 or more samples, prepare samples in clearly labeled, securely sealed 96-well full-skirted PCR v-bottom plates. Arrange the samples vertically by column (i.e. A1, B1, C1, etc.)
  

Ship samples directly to our facility. Use the shipping address listed on the order receipt. 

Please reach out to us by submitting an inquiry.

For a quick tutorial, watch the video below.

For a quick tutorial, watch the video below.

The status of your order, including an estimated date of delivery, can be viewed anytime through your GENEWIZ account. Visit the Order Summary page of your project to find the current order status.

Option #1: Ship samples directly to our facility. Use the shipping address listed on your order receipt.  

Option #2: For double-stranded DNA samples, submit samples into a local GENEWIZ dropbox which are conveniently located throughout the US, Europe and United Kingdom. To locate a dropbox near you, please submit an inquiry. Place your order receipt and samples in a Ziploc bag packaged according to sample submission guidelines specific for your Next Generation Sequencing service. 

Note: Not recommended for RNA and primary sample types. 

During checkout, consult the Order Summary  page to find out the daily cutoff times for your selected dropbox (see example below).

If you miss the dropbox deadline, feel free to ship samples directly to us. 

RNA-Seq is a method for transcriptome profiling that uses next generation sequencing technologies. RNA-Seq provides a comprehensive, quantitative, and unbiased view of RNA sequences within every sample, and is the most powerful tool currently available for analyzing gene expression.

For more information, download our eBook A Guide to RNA-Seq or read our blog Which RNA-Seq Technique Should I Use?

The packages differ in price and turnaround time: 

• Value is the most economical option to help projects come under budget 

• Preferred offers a balance of speed and cost, leveraging our optimized processes for quick results without breaking the bank 

• Express helps meet tight deadlines with industry-leading turnaround times 

• Lightning provides results at lightning speed for select library options 

The package type can be revised after receiving your initial quote.  

Note: Service packages may not be available for all services (e.g. EZ services), projects, or institutions. Please inquire if an expedited solution is required for an exempt service. 

We can accept immunoprecipitated RNA for our ultra-low input RNA-Seq service. Please note that we do not perform the immunoprecipitation step.

Standard RNA-Seq produces a representative snapshot of the transcriptional state averaged across all cells. The caveat with traditional RNA-Seq is the resolution of individual cells and cellular subpopulations are lost. Single-Cell RNA-Seq allows researchers to not only identify cellular subpopulations, but to fully interrogate them at the single-cell level within a heterogeneous sample.

Like Standard RNA-Seq, Ultra-Low Input RNA-Seq provides bulk expression analysis of the entire cell population; however, as the name implies, a very limited amount of starting material is used, as low as 10 pg or a few cells. Single-Cell RNA-Seq requires at least 50,000 cells (1 million is recommended) as an input.

For more information, please read our blog posts:

• Top 3 Factors to Consider for Single-Cell Sequencing
• How Single-Cell Sequencing Works

Generally, Iso-Seq/Kinnex Full-Length RNA sequencing is superior to Illumina approaches when qualitative endpoints are of interest, such as alternative splicing, alternative polyadenylation, genome annotation, and novel transcript detection. For quantitative assessment (e.g. expression of transcript A vs B in one sample, or expression of transcript C in sample 1 vs sample 2), short-read approaches are recommended due to the greater number of reads that can be obtained.

Please submit a quote request to receive accurate pricing information, as the cost depends on the details of the project. You can also contact the team by submitting an inquiry.

For a quick tutorial, watch our step-by-step instructions.

Please download our Technical Specifications Sheet for a summary of our Standard, Strand-Specific, Small, and Ultra-Low Input RNA-Seq services. Visit the following pages for more information about Single-Cell RNA-Seq and Iso-Seq/Kinnex Full-Length RNA.

Standard, Strand-Specific, Single-Cell, Small, and Ultra-Low Input RNA-Seq use short-read sequencing on Illumina® platforms. Iso-Seq/Kinnex Full-Length RNA uses long-read sequencing on the PacBio® platforms.

For more information, visit our NGS Platforms webpage.

The most common starting material is extracted total RNA. However, we have extensive experience performing extractions from a wide variety of materials, including cell pellets, fresh frozen tissue, blood, and FFPE slides. We also accept sorted cells for Ultra-Low Input RNA-Seq. Read our Sample Submission Guidelines for more information.

Since ribosomal RNA (rRNA) makes up most of the total RNA, its removal is necessary for efficient sequencing of other RNA species, such as mRNA, long non-coding RNA (lncRNA), and small RNA.

• For Standard and Strand-Specific RNA-Seq, you can select either poly-A selection or rRNA depletion methods. Poly-A selection is sufficient for studying mRNA in eukaryotes. Analysis of lncRNA or bacterial transcripts requires rRNA depletion.
• For Ultra-Low Input RNA-Seq, the default is to use poly-A selection. However, if your project requires analysis of lncRNA in addition to mRNA, please make a comment on the quote request form, and we can discuss the available options. 

Due to the anticipated poor quality and integrity of FFPE samples, we recommend using a library preparation with rRNA depletion or a targeted RNA exome library preparation (if applicable).

We recommend using a library preparation with rRNA depletion and globin depletion to improve the detection of low-expression transcripts.

Yes, within your GENEWIZ account, select our Small RNA-Seq service. Please note that our library preparation for Small RNA-Seq uses kits that specifically recognize the 5’ and 3’ ends of RNA after processing by DICER. A different library preparation method is used for Standard RNA-Seq projects for analysis of mRNA and lncRNA.

Yes, if enough input material is provided and the total RNA preparation contains small RNAs. Since Standard RNA-Seq and Small RNA-Seq use different library preparation methods, the total RNA sample must be split.

ERCC stands for the External RNA Controls Consortium. The ERCC is a group of researchers and organizations that have developed a set of synthetic RNA molecules to standardize RNA quantification in gene expression profiling. The use of ERCC spike-ins can help standardize RNA quantification across different experiments. Using the read counts of the ERCC controls, researchers can determine the sensitivity (i.e., the limit of detection), dynamic range, linearity, and accuracy of an RNA-Seq experiment. They can also control technical variations between runs. 

When requested, GENEWIZ will use ERCC Spike-in Mix. The ERCC spike-in mix contains 92 transcripts of known concentration. The set is organized into four subgroups, each containing 23 controls that span six logs of dynamic range in concentration. Unless otherwise requested, spike-in across samples will be performed in a checkerboard pattern (assuming at least 1 sample between). 

We do not recommend utilizing ERCC spike-in on samples of low concentration.

Libraries usually undergo PCR amplification to boost the number of low-abundance transcripts in the sample, increasing the chances they’ll be read during sequencing. However, PCR does not copy DNA with perfect fidelity; it’s usually biased, as some molecules in a library are amplified more efficiently than others. PCR can also introduce errors, or mutations, when making copies. As a result, the sequencing data may not accurately represent the original population of RNA molecules. In other words, the relative abundance of reads differs from that of the input RNA. 

UMIs can correct the bias and errors caused by PCR amplification. By tagging the original cDNA molecule with a UMI, all its PCR copies will carry the same barcode. We recommend using UMIs with deep sequencing (>50 million reads/sample) or samples with low-input for library preparation. 

The number of reads required depends upon the genome size, the number of known genes, and transcripts. Generally, we recommend 5-10 million reads per sample for small genomes (e.g. bacteria) and 20-30 million reads per sample for large genomes (e.g. human, mouse). Medium genomes often depend on the project, but we would generally recommend between 15-20 million reads per sample. For de novo transcriptome assembly projects, we recommend 100 million reads per sample.

We provide raw data as FASTQ files for all projects. We also have advanced bioinformatics capabilities to provide optional data analysis services, including:

• Standard RNA Analysis Package: mapping, differential gene expression, alternative splicing, and gene ontology analysis
• Gene fusion discovery
• SNP/INDEL detection
• Novel transcript discovery
• De novo transcriptome assembly for sequences that do not have a reference
• Custom analysis

View our demo bioinformatics report.

GENEWIZ can perform gene ontology analysis on the species in the following link. If the species is not listed, we reserve the right to remove gene ontology analysis from the project at the client’s discretion.

Yes, multiple comparative analyses can be performed. Depending on the complexity and number of comparisons, additional charges may apply and would be communicated prior to the work being performed.

We use the Twist UMI system by default. UMIs are sequenced in-line and each pair of reads is structured as 5bp UMI + 2bp spacer + subsequent read sequence:

For UMI extraction together with quality/adapter trimming, we recommend using the fastp tool found here: https://github.com/OpenGene/fastp. Please download and install the tool before proceeding. Detailed instructions on how to process your data with UMIs can be found on the git page under session ‘unique molecular identifier (UMI) processing’ and the corresponding example.

UMI extraction will be enabled with the -U or --umi option in the command line.

For UMI deduplication, it can be performed in a one-line command together with the previous step by enabling deduplication in fastp (specifically -D or --dedup).

After trimming and deduplication, the resulting reads can be aligned to your reference genome. You can also proceed with downstream analysis including hit count calculation as normal.

The raw reads we provide can also be processed with other UMI extraction and deduplication tools such as umis tool and umi-tools, depending on your preference. The processing order will be different compared to fastp, as the deduplication step will need to be performed after UMI extraction, trimming, and alignment. More details can be found in the user instructions for the umis tool (https://github.com/vals/umis) and umi-tools (https://umi-tools.readthedocs.io/en/latest/index.html).