Analytical and Translational Genomics

These are guidelines for researchers contemplating the use of Next-Generation Sequencing (NGS) services from the ATG Shared Resource. All researchers are urged to consult with the ATG staff before beginning to prepare or analyze samples. We can assist with experimental design and, if necessary, put you in touch with expert biostatisticians who can help with experimental design. It is very important to consider the experimental design before beginning NGS experiments, which can be quite expensive.

RNA-seq can be successfully performed with very small amounts of RNA. However, the amount required depends on the "read depth" required and whether there will be ribosomal RNA contamination. Ribosomal RNA is 90% of the RNA in cells, so it is necessary to remove or reduce the ribosomal RNA before performing RNA-seq. The two ways to do this are physical removal, through "Ribodepletion", in which biotinylated probes complementary to the ribosomal RNAs are hybridized with the RNA samples, then the complexes are captured and removed. Alternatively, a poly-A-directed library prep method (e.g. Smart-Seq) can be used that avoids sequencing the ribosomal RNA, but that excludes other RNAs that lack polyA tails and that might be of interest (e.g. microRNAs). Please contact the ATG Shared Resource staff to discuss options before beginning the experiments.

The ATG Shared Resource performs full service NGS analysis. However, due to the complexities of NGS library preparation, what ATG can provide depends on the type of experiment. For targeted panel sequencing and exome sequencing, we only need DNA samples and we will produce the libraries, perform the sequencing and the initial analysis. We will provide a quote for the expected cost before beginning the work. For RNA-seq and other approaches, there are many variations in the way libraries can be constructed. We urge users to contact the ATG staff to discuss options before beginning. Besides the complete Affymetrix system, the ATG Shared Resource has a Nanodrop spectrometer for quantifying RNA in small volumes, and an Agilent Bioanalyzer for rapidly analyzing the quality and quantity of RNA or DNA samples.

NGS experiments can be expensive. The total cost for most large experiments (exome sequencing, RNA-seq, etc.) is $500 to $800 per sample, plus a charge for bioinformatics. Some smaller targeted sequencing experiments cost less per sample. Please contact the staff for more information and to get a quote.

Yes! NGS experiments generate large, complex data sets. Bioinformatics analysis is not possible without duplicates. Triplicates are better. Replicates really are necessary to get good results that are meaningful and worth the cost.

The ATG Shared Resource follows strict quality control guidelines and standard operating procedures to insure that data is of the highest quality and meets or exceeds standards set by groups such as the ENCODE consortium. We use standard spike-in controls to monitor internal processes and perform quality control checks at every stage of library production and sequencing. Please contact the ATG staff to see examples of the data that we have successfully produced.

The quality of the starting RNA samples will be confirmed using the Agilent BioAnalyzer or by using real-time PCR. The libraries will be similarly checked before sequencing. Spike-in controls are added at several steps to monitor internal quality control.

NGS assays produce large, complex data sets that contain enormous amounts of information, but can also be difficult to analyze. The ATG Shared Resource provides the first level of analysis, including analysis of quality control parameters, alignment of the reads to the appropriate genome, identifying sequence variants or feature counts, as appropriate, and performing straightforward types of interpretation, such as the production of heatmaps for RNA-seq. However, more complicated types of data analysis, such as correlating results to patient information, should be performed with the input of experts from the Bioinformatics Shared Resource or the Biostatistics Shared Resource. The ATG staff can help set up interactions with the appropriate experts, who should be involved from the beginning to help with experimental design and quality control.

There is a possibility that the complex process involved in generating NGS data will produce a "day effect" or "batch effect", in which the samples processed or analyzed on the same data cluster together. This is a well-known artifact of high-dimensional data analysis, and we include spike-in controls to help us identify and remove these types of data problems.

The ATG Shared Resource has a team of bioinformatics experts who will perform the initial data analysis and who will manage and back-up the data. They can perform most straightforward types of analyses (e.g. gene expression from RNA-seq). However, complex or customized types of analyses will require input from additional experts from the Biostatistics or Bioinformatics Shared Resources. The ATG staff can help set up the required collaborations.

Verification is an important part of each NGS experiment, and the requirements vary depending on the type of experiment. Please contact the ATG staff to discuss options for verifying NGS results.

The facility Director, Scott A. Ness, Ph.D., can provide letters of support and advice about how to describe the ATG Shared Resource and potential NGS experiments in grant applications. Dr. Ness has served on numerous NIH, ACS and DOD study sections and has reviewed many grant applications that include NGS experiments. His own funded grants have NGS experiments in them. He can provide help with writing sections of your grant regarding NGS experiments and can point out potential pitfalls and things to avoid.

The easiest way to criticize an NGS experiment is to describe it as a fishing expedition. Here are some things you should definitely avoid.

1. Do not propose to characterize genes that you have not yet identified. If you have no preliminary data, you don't know what genes or how many genes you will find. However, they will likely number in the hundreds. Simply saying that you will pick some interesting genes to study is a quick way to get a bad score on your grant. If possible, your experiment should test a hypothesis. For example, you might make the hypothesis that certain genes (e.g. apoptosis genes) will get induced. Then you can propose to use NGS assays to test that (and propose real-time PCR as a back-up approach). That way you can test a hypothesis, propose expected outcomes and controls (e.g. genes that should go up and down), which is a much better way of doing an NGS experiment (or any other experiment). Simply going fishing for genes is a bad approach and always draws the ire of the review committee.
2. Simply saying that you will use some software program to analyze the data or group the genes into pathways is also going to get you in trouble. NGS data can be extremely complex, and will require statistical methods for analysis. The pathway data that is known is woefully incomplete. Most genes are not in the pathways, anyway.You will need a well-planned approach for analyzing the data. You should have a way of telling whether the experiment worked or not (e.g. did the expected apoptosis genes get activated?).
3. The NGS experiment should not be merely a paragraph at the end of one of your aims. Whatever you do, absolutely do not add an NGS experiment to the end of a grant application as something that you "will also do". NGS experiments are big, expensive and complicated and they can't be done as an afterthought. Many, many grants have a one-paragraph description of an NGS experiment that the researchers will also do. That is a lightning rod for criticism from the reviewers.
4. If you are looking for genes, you should be looking for them for a reason. Don't just propose to look for regulated genes without proposing to do something with them. Finding the genes that go up and down is not a significant enough goal. You need to be looking for genes with some purpose in mind (e.g. some hypothesis to be tested).