Following us on Twitter

The Genomics Core now has two Twitter accounts, you can follow me @CIgenomics (James Hadfield, Head of Genomics) and hear about things I think are interesting, but which you might not necessarily be interested in; and/or you can follow our sequencing queue @CRUKgenomecore which puts out live Tweets directly from the sequencing LIMS.

How does the LIMS Tweet: Some clever work by Rich in Bioinformatics has allowed us to pull out data directly from Genologics Clarity LIMs queue using a script run every 24 hours, and the Twitter API then allows that script to post messages on our behalf. Because of this the Tweets about our queue should happen every day and without manual intervention. Hopefully you'll be able to rely on these to give you a reasonable idea of how long you might have to wait for your sequencing results. Of course we can't predict what will happen with your particular sample so please treat the Tweet as a guide.

Tweets explained: The Tweets have a format that we hope is pretty intuitive, but we've described what all the bits of information mean below...

Thanks especially to Rich Bowers in the Bioinformatics core for pulling all of this together from a vaguely described idea by me.

Improving DNA and RNA quant with plate based fluorimetry

We quantify NGS libraries all the time and qPCR works brilliantly, but nucleic acids need to be handled differently. We don't actually run that much quantifiaction on DNA and RNA as most of our users have already done this; we asked them to do it so we could more efficiently run larger batches of library prep to keep costs down and turnaround times as short as possible. Over the last few years we've been running the Nextera exome preps and DNA quant has become more important than ever before, in fact we started running a secondary quant just to be certain about DNA concentration.

Most of the time DNA and RNA quant works well and we've favoured the fluorescent Qubit assay recommended by Illumina in their protocols. A nanodrop or plate reading spec at 260:280nM measures total nucleic acid and is confounded by ssDNA, RNA, and oligos so can give inaccurate results. We run the Qubit dsDNA BR Assay from Molecular Probes on the PHERAstar fluorescent plate reader (here's their handy protocol). We have only been using 1ul of DNA (Illumina suggest 2) for each sample but we run triplicate assays to get a high-quality quantitation.

Problems with the Qubit assay: Recently some users have reported problems with the accuracy of the QuBit assay on our plate reader and the manager of our Research Instrumentation Core helped us to get to the bottom of the issues and some excellent results. The main problem turned out to be addition of DNA into the working dye solution, it was the DNA coating the outside of the tips that appeared to be making the results so flaky. Changing the protocol to add DNA to the plate first fixed it and the results are looking great.

It ca also be very important to be certain which assay you should use; BR (Broad range) or HS (High Sensitivity). If you are working with low concentration nucleic acids then the HS assay is probably the one to use. For really accurate quant we'd suggest a quick QT check first, then normalisation of samples to about twice what you need; a second triplicate and robust quant will allow you to dilute the samples to the perfect working concentration.

Here are our top tips:

• Add DNA to the measurement plate/tubes before anything else
• Use a repeat pipette to make sure each well gets the same/right amount of dye solution
• Shake the tubes/plate in the dark for at least 10 minutes (quant will be inaccurate if the dye has not intercalated properly, you can check your standard curve replicates to verify if this is an issue)
• The triplicates really are worth the effort - especially if you're doing a Nextera prep

When will my sequencing be done?

Will my sequencing be done before the dying of the sun,
Will wildcats once more roam the land
Will the desert still have sand
Will Norfolk be swallowed by the sea
Do I have time for a cup of tea?
Will rhinos and the manatee
Be urban legend, just like me
Oh, it's done.

Nature reports on "careers in a core lab"

In this weeks issue of Nature a feature by Julie Gould covers what life as a core lab manager is like: Core facilities: Shared support. She interviews several core lab managers/directors from the US and Europe including me. If you've ever fancied a job in a core then I'd recommend the article.

• Stefan Green: DNA-services laboratory at the University of Illinois at Chicago
• Patrick Descombes: Functional Genomics Core at the Nestlé Institute of Health Sciences in Lausanne, Switzerland
• Mónica Morales: Core Facilities and Internal Scientific Services at the Centre for Genomic Regulation, Spain
• Yuriy Alekseyev: Microarray Resource at Boston Universit
• James Hadfield: Cancer Research UK Cambridge Institute Genomics core
• Gerd Prehna: Center for Structural Biology at the University of Illinois

If you have any questions about the realities of running a core and what sort of career move it might be feel free to get i touch. If you are in the CRUK-CI then you've got lots of other core managers who can give you there views as well.

Is your antibody any good

"Doesn't necessarily do what it says on the tin!" 

Probably not is the simple answer, and only if you've verified it is a more comprehensive one. The lack of reproducibility from antibody data in scientific publications is shocking, Nature published a commentary signed by over 100 researchers: Reproducibility: Standardise antibodies used in research, in which they describe the pretty poor state of antibody reproducibility. In this they cite a 2008 BioTechniques article, and a 2012 Nature commentary that discuss the state of affairs with antibodies in particular, and with reproducibility in general. In the BioTechniques paper the authors finish by saying that "for the meantime, however, the responsibility ultimately lies with the researcher or laboratory director to ensure that the antibodies used in their labs are validated for specificity and reproducibility."

Antibodies sold as being specific for a protein are oftentimes not, they can be very promiscuous in what else they bind and sometimes don't even bind the targeted protein. To make sure you are not affected by poor choices of antibodies make sure you run some validation studies before diving into your ChIP-seq experiments!

Not doing this risks wasting money (a lot according to the Nature article -see figure below). But more importantly you might waste your time, or even worse publish something that is erroneous. Hopefully you've already validated that your MCF7 cells are actually MCF7s with the BioRepository, so why not do the same with your antibody before starting your next experiment?

Figure from Bradbury and Plückthun Nature 2015.

Use your local support team

We have a half-day workshop on Thursday for NGS newbies, the focus of which is library prep for next-generation sequencing. We organise seminars from commercial providers of new technologies throughout the year; but this is a semi-annual event where local users get a chance to present their work, and new users get to hear about what's possible with NGS.

This year we have presentations about RNA-seq, ChIP-seq, Exome-seq, FFPE genomes, DNA methylation, targeted resequencing and a talk on the UoC 10,000 Genomes Project; and afterwards we'll wrap up with beer and pizza. These days require lots of organisation (thanks to Fatimah for organising this years event) but, for the new users especially, turn out to be well worth the effort.

Making use of your local support teams: We also make sure we keep a good relationship with our local technical support teams and run a series of commercial presentations throughout the year. This works out to be much easier to organise as they do the prep work! While we're here in the Genomics Core to help our local users, we get lots of queries from people outside the Cambridge Institute, and this is one way we've found to increase the support we can offer.

Every other month we have Illumina come in to present on a specific library prep, or talk about recent updates. Sandra (Field Application Specialist), and Carla (Marketing Technology Specialist) generally talk for 30 minutes followed by Q&A, and then spend some time with users on a one-to-one basis troubleshooting their problems.

We also try to arrange a training session once per quarter with Thermo. We've been using their ABI 7900 qPCR instruments for eight years and buy in quite a lot of their SYBR and TaqMan master-mixes. Ever since we started working with them we've run "An introduction to qPCR" course for new users. The last one was run by Emma and everyone said it was a great introductory session.

What's in it for them: Neither Illumina or Thermo would do this for free if there was nothing in it for them. They get to interact directly with potential new customers, and get feedback on how their technologies are working in the real world. Some of these conversations might end up as research collaborations. Some of the contacts might end up as new sales contracts too (I know why they are really here)!

What's in it for us: These talks have been reasonably well attended and increase the support we can offer (albeit indirectly), and the feedback from users has been almost universally positive. I'd encourage you to get in touch with your local sales or technical rep and ask if they can help you too. They might even supply doughnuts!

PS: Thanks very much to Carla and Sandra at Illumina for the seminars over the past 12 months. And to Emma for the most recent qPCR training.

PPS: If you missed the registration link to the event on Thursday, send us message via a comment below!

How many reads do I need to sequence?

A common question we're asked is "how many reads should I use to sequence a sample?" I'm going to focus on genomes, exomes and amplicomes in this post and introduce the Lander-Waterman equation [1]. Other apps are more complex because the number is very much 'how long is a piece of string' for RNA-seq, ChIP-seq and other counting applications - it depends on the complexity of your sample and the sensitivity you'd like to get, but is also affected by the number of replicates you have.

The Lander-Waterman equation

Lander-Waterman: Almost everyone doing NGS is using this equation, even if they are not aware of it. Anyone under 27 was born after it was published (1988), but it is an equation that is good to understand if you are sequencing. Basically it allows you to estimate how many reads of a specific length you need to sequence your genome.

The general equation is C = LN/G where: C = redundancy of coverage, G is the haploid genome size, L is the sequence read length, and N is the number of sequence reads. It can be rearranged to N = CG/L allowing you to compute the number of reads to sequence a genome, exome or amplicome (amplicon-panel) to a desired coverage (this is what we typically discuss when designing experiments).

In the examples below paired-end reads of 125bp from each end of a fragment are used, but these are converted to single 250bp reads for simplicity.

• Human genome (3Gb) 30x coverage = 360M reads.
• Human exome (150Mb) 50x coverage = 30M reads.
• Human amplicome (30x250bp amplicons 0.075Gb) 1000x coverage = 0.3M reads.
  

[1] Lander, E. S. & Waterman, S. Genomic Mapping by Fingerprinting Random Clones : A Mathematical Analysis. Genomics 239, 231–239 (1988).  

 

Eric Lander founded both the Whitehead and Broad Institutes. Michael S. Waterman is one of the founders of computational biology and gave his name to another important algorithm: Smith-Waterman alignment, he also wrote Computational Genome Analysis with our Director Simon Tavare while at the University of Southern California