Transcriptomics aims to catalogue the complete set of RNA transcripts produced by the genome, including mRNAs, non-coding RNAs and small RNAs. Transcriptomics provides new biological insight and can be used to determine the structure of genes, their splicing patterns and other post transcriptional modifications, to detect rare and novel transcripts, and to quantify the changing expression levels of each transcript during development and under different disease conditions.
RNA-Seq provides a more comprehensive view of the transcriptome with a single experiment. RNA-Seq enables us to sequence and profile all species of transcripts in your total RNA samples.
RNA-Seq is not necessarily dependent on any prior sequence knowledge. There is no need for design of probes that must be based on prior sequence or secondary structure information. Therefore, transcriptome profiling in any species is possible which makes this method particularly attractive for non-model species.
Additionally, RNA-Seq data can be used to build de novo gene models.
RNA-Seq has increased dynamic range and sensitivity. RNA-Seq enables you to achieve “digital” transcript expression analysis meaning that expression level data are based on each individual transcript that is sequenced and counted. By increasing the sequencing depth, a potentially unlimited dynamic range can be reached making RNA-Seq an ideal tool for detection of rare transcripts.
RNA-Seq provides information of sequence variation in transcripts. RNA-Seq yields a rich data set including information about post transcriptional mutations and their genomic context. Because RNA-Seq data yields information about how exons are connected, it can reveal sequence variations in the transcripts due to alternative splicing events and provide allele-specific or isoform-specific gene expression information. Additionally, RNA-Seq data is useful for gene mapping functions such as describing the length of UTRs and exon boundaries.
We use the Illumina next-generation sequencing platform. Illumina’s industry-leading RNA sequencing methods enable discovery and profiling of RNAs in any organism without prior genome annotation, and allow for the most accurate detection and quantification of rare RNA sequences.
For comprehensive sequencing / analysis projects, you can expect to receive your fully analyzed data within 6-8 weeks from the time we receive your sample.
For sequencing only projects (no library prep or data analysis time), you can expect to receive your raw data in about 4 weeks. Please inquire about a rush service for projects with inflexible deadlines.
You can use any one of the commercially available column based RNA extraction kits that are specifically developed for total RNA extraction, from vendors such as Norgen Biotek, Qiagen or Ambion. If you have multiple samples, make sure to use only one type of extraction kit for all the samples of your project.
Many laboratories have obtained excellent results from total RNA samples extracted using Trizol methods. However, skills, experiences and sometimes sample types may become critical factors in obtaining consistently good sample qualities. According to our statistics, this method has an overall higher failure rate than column-based commercial methods, although the rate varies among different laboratories.
Please transfer your sample to a 1.5ml microcentrifuge tube for shipment (smaller tubes can crack when frozen). Be sure the tube labels match those listed on your sample submission form.
High quality results are dependent on high quality samples. There are several methods you can use to check the total RNA quality before shipping:
- Measure the 260/280nm ratio with a UV spectrophotometer. It should be in the range 1.8-2.0.
- Run your sample on a Bioanalyzer, if you have access. The RNA Integrity Number (RIN) should > 8.
- Alternatively, run your sample on a formaldehyde 1% agarose gel and stain with ethidium bromide. High quality RNA will show a 28S rRNA band at 4.5 kb that should be twice the intensity of the 18S rRNA band at 1.9 kb. Both kb determinations are relative to a RNA 6000 ladder. The mRNA will appear as a smear from 0.5–12 kb.
We use our own version of the standard Illumina RNA-ligation sample preparation protocol.
Sample: We use a combination of Bioanalyzer, spectrophotometer, and gel electrophoresis to determine initial quality of the samples received from the customer and to check the progress of the library preparation at various steps.
Library: We use a combination of gel electrophoresis and Qbit fluorometer to ensure the library is at an acceptable fragment size and concentration.
Sequencing: We check the number of clusters generated per tile for the first cycle of sequencing to make sure that it is within an acceptable density level to be sure chemistry is functioning properly. The total number of raw reads is also an indication of run performance and quality. Additionally, one of the sequencing lanes may be reserved for a quality control sample. The data from this lane must meet certain quality criteria in order for the run to be deemed successful.
Data: There is a standard Illumina base calling and data filtering program that is applied to the raw data to remove low quality reads. Additionally, we apply an LC Sciences developed analysis program to further filter the reads and reduce data to a final set of good quality mappable reads. The percentage of raw reads that are mappable is also a good indicator of run success.
Yes, the Illumina platform is very flexible and the “tunability” of coverage is one of the great advantages of new RNA-technologies. This enables us to achieve just the right amount of coverage that is required for your application. For example: short 35 cycle single-end reads may be adequate for small RNA sequencing or digital expression profiling applications but longer 100 cycle paired-ends reads are preferred for transcriptome sequencing where specific information about sequence and splice variants is desired.
- Illumina base-calling and analysis
- LC Sciences analysis and quality filtering. Processed data is reduced to mappable reads.
- Alignment of RNA-Seq reads to customer specified reference genome
- Identification and construction of splice-junctions
- Report of known transcripts with annotation and abundance
- Report of novel transcript with abundance
- Identification of alternate splicing and report of isoform abundance
- Test for differential expression at gene level and transcript level
- Customer data report – includes a summary of methods and all statistical analysis
Custom Project Level Analysis
Custom project level service by LC Sciences’ experienced bioinformatics team to serve more complex bioinformatics needs such as functional gene information mining or comparative genomic analysis. Please contact us to learn how we can help get you the results you need to keep your research moving ahead.
- We use a variety of tools including both published open-source algorithms and programs developed in-house.
- Raw data is initially filtered for quality with in-house developed software.
- For read mapping and identification, we use TopHat and Cufflinks. TopHat is a fast splice junction mapper that aligns RNA-Seq reads to mammalian-sized genomes using the ultra high-throughput short read aligner Bowtie, and then analyzes the mapping results to identify splice junctions between exons. Cufflinks uses a probabilistic method to perform a statistical deconvolution of the sequence data in order to obtain the relative expression of sequence isoforms.
- Other analyses can be performed by custom developed bioinformatics on an as-needed basis.
- Note – for expression analysis and visualization of your data, we recommend the UCSC Genome Browser.
RNA-Seq yields a rich transcriptome-wide data set including information about post transcriptional mutations and their genomic context. It can reveal sequence variations in the transcripts due to alternative splicing events and provide allele-specific or isoform-specific gene expression information. Microarrays are proven gene expression analysis tools that routinely deliver results rapidly, reproducibly, and cost effectively. So there is a trade-off. While RNA-Seq provides a more comprehensive view of the transcriptome and information about sequence variants, it is more expensive and lower throughput than microarrays, and bias could be introduced during extensive sample preparation steps effecting expression data.
The decision to use one method or the other depends on the specifics of your project. If you are working with a species whose genome is not well known or annotated, and your research is focused on discovery, we would recommend sequencing as the method for transcriptome profiling. If you are working with human samples or a model species whose genome is well annotated, and your goal is to systematically profile and compare the gene expression of many samples in various conditions or disease states, then we would recommend microarray profiling.
In many cases, a combination of the technologies is appropriate as they are quite complementary. Please see our Seq-ArraySM service for more information.