Circulating RNA analysis

Analysis of circulating DNA has had a major impact in the last couple of years with 100s of publications in the last few years. I've previously posted about work at the Institute on circulating tumour DNA analysis of amplicons and exomes, but what about RNA?

Patterned flowcells: what can we expect?

There was a real buzz in the community when Illumina resurrected the idea of using patterned flowcells for SBS sequencing (Keith Robison covered lots of genomics news back in January, including patterned flowcells ). One of the big problems with clustering is the need to very carefully quantiy samples before loading onto a flowcell. Most labs use qPCR or BioAnalyser but even the best rarely achieve perfect density on every run if they are working with a diverse group of libraries. Jay Flately at a JP Morgan conference said that only 36% of clusters are useable and that the new technology should double that.

Patterned flowcells have been mentioned before in Illumina roadmaps, they'll potentially allow 1Tb or more from a standard HiSeq run and are one way we might see an end to cluster density variability. But a patterned flowcell might also allow improvement of some methods and even interesting new applications to be developed.

We'll have to wait until Illumina release the new flowcells at the end of the year (more likely in January just before AGBT) but for now I thought I'd put down some thoughts I've been having about what we might get and look through one of Illumina's more recent patents.

Mouse models of Human disease constantly need to be improved

I’ve worked on model-organisms for a long time, originally in Plant research but nowadays I'm more likely to run genomic experiments for groups using Mouse models of cancer. It is impossible to do some experiments in Human patients for a variety of reasons, so we use Mouse models instead. Genetically engineered mice (GEMs) are used in many research programs and offer us the ability to tailor a disease phenotype, as our understanding of the driving events in cancer increases we can build GEMs that carry these same driver mutations; we can even turn the specific mutations on at specific time points to try and recapitulate Human disease.



Finding your way around NGS sample prep

I'm often asked which sample prep method a user should consider for their experiments. In my lab we use a lot of Illumina TruSeq kits; we've tried other methods, and do use Rubicon's Thruplex, but Ilumina's end-to-end support is useful in a medium sized core facility. And the kits work!

I wanted to illustrate the fact that Illumina sample preps share many steps in their protocols to demonstrate that once you've mastered one protocol, you can easily move onto another. The map of sample prep below is my first try at that illustration. You can see a higher resolution image here.

Explaining how the kits work always takes time and I have always thought a strength of the Illumina technology is the flexibility given by the core of sample prep: end repair, ligate adapters & PCR. Users can think creatively about what they do to their DNA (or cDNA) before starting or during the prep to come up with novel techniques. Stranded RNA-seq, bisulfite sequencing, RRBS, exome capture are just a few of the methods developed by users of Illumina technology. Hopefully the image above shows how similar things really are with those key steps clearly highlighted as large "interchanges", along with PCR and qPCR & Bioanlayser for QT/QC.

The above image borrows heavily from the London underground maps developed by Harry Beck in 1931. Most of the Illumina protocols are listed and I've included Thruplex on its own network for comparison. I'm aiming to add more detail for some of the different RNA-seq protocols at some point as well as bisulfite sequencing. I'm also thinking about how this might be extended to include details on suggested sequencing depth; lines for Human genome vs CNV-seq or DGE vs splicing.

Let me know what you think.

This work is licensed under a Creative Commons Attribution-ShareAlike 3.0 Unported License.

Targeted RNA-seq methods are here

Illumina and Life Technologies are both launching targeted RNA-seq applications which are likely to become standard tools for many labs; if the price is right.

The ability to target a portion of the genome has revolutionised next-generation sequencing experiments. The analysis of exomes has exploded, custom panels for exome-style pull-down are being used to great effect in 1000’s of samples and amplicon analysis is making it possible to run 10,000’s of samples in a single experiment (we’ve run a HiSeq flowcell with 12288 samples on it, 1536 per lane using Fluidigm – currently unpublished).

The next frontier looks like it could be using the same techniques to target a portion of the transcriptome, again allowing many 1000’s or 10,000’s of samples to be analysed in a single experiment. These technologies are likely to replace real-time PCR for mid- to high-plex studies. Anyone that has tried to run a few 100 TaqMan or SYBR assays on their 96-well qPCR machine will see the potential. And users with BioMark, TLDA, Wafergen, and other high-throughput qPCR systems will see the potential of using just one analysis method (NGS) as their primary data collection tool.

Back from my holidays

Two weeks away from the lab, from papers, from email and from Core Genomics - I’ve just got back from Finland’s wilderness: wood-fired sauna, lakes to swim in, fish to catch and no-one for miles; and all with fantastic 4G connection! Holidays are great and I think cutting yourself off from work is important if you’re truly going to relax and unwind. On my return to work there was the usual mass of email to work through. I thought I’d summarise the things that happened in the world of Genomics that I thought were interesting while I was away.

Illumina's next-gen automation solution

Illumina just bought Advanced Liquid Logic. Never heard of them until now, neither had I but I suspect we'll see some pretty cool devices coming soon.

On the ALL website they have the video below, I could not help but watch at 9sec and see PacMan in action, even PacMan conjoining with his twin! ALL makes disposable digital microfluidic devices allowing cost-effective robotic automation; without the robots. Their technology is based around electrowetting and does not use pumps, valves, pipettes or tubes seen on other liquid handling systems.

Managing your researcher profile in the modern age

I have between 16 and 24 publications in various databases and keeping track of these can be more difficult than I think it should be. I've always used PubMed as my primary search tool and have a link to publications by James Hadfield on my blog. However like most of you I have a name that is not so unique, and other James Hadfield's also pop up in my search results (see the end of this post, and feel free to comment if you're another James Hadfield).

I posted previously about the best way to link to a paper and I'm still suggesting the DOI is the thing to use. It can be found by search engines and aggregators making the collection of commentary a little easier. In the same post I also suggested that a unique identifier for an individual would be a big step forward. Well that was also made available recently and in several different forms, so my new quesiton is which ID should you be using?

Who's looking at my papers (or yours): Before I get onto unique researcher IDs I wanted to come back to the issue of how DOI's and other tools allow aggregators to capture content. The newest "killer-app" for me is Altmetric.

They track how papers are viewed and mentioned; in the news, on blogs, on Twitter, etc. The thing you'll probably be adding to you bookmark immediately after reading this post is their free bookmarklet which will give you a report on any paper you happen to be looking at online. Below is an image of the report for one of the recent papers I was involved with. I'd like to see citations tracked and I'm sure we'll see lots more development from the team!

Altmetric

Which profile managing system to use: There are 5 systems you might use and it is hard to say you can easily choose one. However in writing this post I tried to get all 5 up-to-date. Some talk to each other, which helps - but you can't get away from the fact that no-one has time to waste. I'll probably stick with keeping ORCID and GoogleScholar up-to-dte and then link my ORCID ID to my Scopus and ResearcherID accounts.

ORCID: My ORCID ID 0000-0001-9868-4989. The open access one! Started in 2010 ORCID is "an open, non-profit, community-driven effort to create and maintain a registry of unique researcher identifiers and a transparent method of linking research activities and outputs to these identifiers". The registry gives unique IDS to any registered scientists with data being open access. Organisations can also sign up to allow management of staff research outputs. ORCID stands for Open Researcher and Contributor ID.

Google Scholar: Me. Good listing of citations. Good presentation of citation metrics. Want to know how Google Scholar works, then read this.

Scopus:  My Scopus Author ID: 26662876800. Display of citation numbers and link to citations page. Links to web pages mentioning your work and patents referencing it.

ResearcherID: My ResearcherID: A-1874-2013. ResearcherID is a product from Thomson Reuters and provides a solution to the author ambiguity by assigning a unique identifier to researchers. Only articles from Web of Science with citation data are included in the citation calculations. Good presentation of citation metrics.

ResearchGate: Me. Good presentation of citation metrics. Founded by 2 doctors and a computer scientist, Research Gate has been billed as "social networking for scientists". 

Why bother? Given all this information is correctly assigned the the correct authors, at the correct institution and the correct funders then we should be able to get into some very interesting meta-analysis.

Who's collaborative and who's not? Do some institutions or grant funders punch above their weight? If so why, is there something cultural that can be translated to others? Does the journal you publish in impact the coverage your work gets over time? Many other questions can also be asked, although some may not want to see the answers!

My papers in a little bit more detail: Each has a link to its DOI, PbMed and Altmetric stats.

Mohammad Murtaza et al, Non-invasive analysis of acquired resistance to cancer therapy by sequencing of plasma DNA, in Nature 2013 May 2;497(7447):108-12. PMID:23563269 with 8 citations* at the time of writing. Altmetric stats.

Saad Idris et al, The role of high-throughput technologies in clinical cancer genomics, in Expert Rev Mol Diagn. 2013 Mar;13(2):167-81. PMID:23477557 with 2 citations* at the time or writing. Altmetric stats.

Tim Forshew et al, Noninvasive identification and monitoring of cancer mutations by targeted deep sequencing of plasma DNA, in Sci Transl Med. 2012 May 30;4(136):136ra68. PMID:22649089 with 31 citations* at the time or writing. Altmetric stats.

Christina Curtis et al: The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups, in Nature 486 (7403), 346-352. PMID with 229 citations* at the time or writing. Altmetric stats.

Kelly Holmes et al, Transducin-like enhancer protein 1 mediates estrogen receptor binding and transcriptional activity in breast cancer cells, in Proc Natl Acad Sci U S A. 2012 Feb 21;109(8):2748-53. PMID:21536917 with 15 citations* at the time or writing. Altmetric stats.

Sarah Aldridge & James Hadfield, Introduction to miRNA profiling technologies and cross-platform comparison, in Methods Mol Biol. 2012;822:19-31. PMID:22144189 with 5 citations* at the time or writing. Altmetric stats.

Charlie Massie et al: The androgen receptor fuels prostate cancer by regulating central metabolism and biosynthesis, in EMBO J. 2011 May 20;30(13):2719-33. PMID:21602788 with 56 citations* at the time or writing. Altmetric stats.

Andy Lynch et al: The cost of reducing starting RNA quantity for Illumina BeadArrays: a bead-level dilution experiment, in BMC Genomics. 2010 Oct 6;11:540. PMID:20925945 with 2 citations* at the time or writing. Altmetric stats.

Anna Git et al: Systematic comparison of microarray profiling, real-time PCR, and next-generation sequencing technologies for measuring differential microRNA expression, in RNA. 2010 May;16(5):991-1006. PMID:20360395 with 132 citations* at the time or writing. Altmetric stats.

Christina Curtis et al: The pitfalls of platform comparison: DNA copy number array technologies assessed, in BMC Genomics. 2009 Dec 8;10:588. PMID:19995423 with 57 citations* at the time or writing. Altmetric stats.

Dominic Schmidt et al: ChIP-seq: using high-throughput sequencing to discover protein-DNA interactions, in Methods. 2009 Jul;48(3):240-8. PMID:19275939 with citations* at the time or writing. Altmetric stats.

Partha Das et al: Piwi and piRNAs act upstream of an endogenous siRNA pathway to suppress Tc3 transposon mobility in the Caenorhabditis elegans germline, in Molecular cell 31 (1), 79-90. PMID:18571451with 131 citations* at the time or writing. Altmetric stats.

Phil Smith et al: STS markers for the wheat yellow rust resistance gene Yr5 suggest a NBS-LRR-type resistance gene cluster, in Genome 2007 Mar;50(3):259-65. PMID:17502899 with 7 citations* at the time or writing. Altmetric stats.

Steve Marquadt et al: Additional targets of the Arabidopsis autonomous pathway members, FCA and FY, in J Exp Bot. 2006;57(13):3379-86. PMID:16940039 with X 34 citations* at the time or writing. Altmetric stats.

Raka Mitra et al: A Ca2+/calmodulin-dependent protein kinase required for symbiotic nodule development: Gene identification by transcript-based cloning, in PNAS 2004 Mar 30;101(13):4701-5. PMID:15070781 with 289 citations* at the time or writing. Altmetric stats.

Robert Koebner and James Hadfield: Large-scale mutagenesis directed at specific chromosomes in wheat, in Genome. 2001 Feb;44(1):45-9. PMID:11269355 with 2 citations* at the time or writing. Altmetric stats.

Barbara Jennings et al, A differential PCR assay for the detection of c-erbB 2 amplification used in a prospective study of breast cancer, in Mol Pathol. 1997 Oct;50(5):254-6. PMID:9497915 with 14 citations* at the time of writing. Altmetric stats.

*****************************
There is a J Hadfield working on plants in Australia (here, here, here, here, here &; here), at least one other J Hadfield working in clinical sciences (here & here), and possibly one other in basic research (here).


(*according to Google Scholar)  

Ethanomics a great blogger who really seems to know about ChIP-seq

I was pointed to Ethan Ford's blog by a colleague and thought I'd recommend you take a look. Ethan is a post-doc in Ryal Lister's lab at The University of Western Australia.

The post that got me interested was one about homemade AMPure beads. The protocol was not written by Ethan, but modified by Brant Faircloth & Travis Glenn (from the methods section in: Cost-effective, high-throughput DNA sequencing libraries for multiplexed target capture) for a SeqCap workshop, referencing.

They also wrote a protocol to adapt SureSelect to work with Illumina TruSeq and Nextera libraries.

Nadin Rohland from Harvard Medical School, who was the lead author on the above paper, was also one of the researchers that discovered Africa has two species of elephant not one. And if anyone is interested in a genetics/philosophy story for kids this one about African elephants is great, it raises all sorts of questions for young kids (and adults) about prejudice, discrimination, and violence. The genetics is not so clear.

Ethans blog has some great resources for ChIP-seq afficionado's on his protocols page:

Native ChIP protocol

NGS qPCR quantitation protocol

MeDIP-seq protocol with TruSeq adapters

ChIP-Seq library construction using the Illumina TruSeq adapters

ChIP (MNase fragmentation) protocol

ChIP (fragmentation with Covaris) protocol

ChIP (fragmentation in 0.1% SDS – tip sonicator) protocol

ChIP (fragmentation in 1% SDS – tip sonicator) protocol

Quick ChIP (MNase fragmentation) protocol

ChIP Primer Design

Antigen production protocol

Antibody purification protocol

Immunoprecipitation of chromatin associated protein complexes

The GenomeWeb effect

Dan Kobolt wrote a pair of articles about why he suggests you start blogging. In the first he talks about why you should, and perhaps why you would not, start blogging. I'd certainly encourage people to start, it's fun, free and the feedback can be great. Many people leave comments on this blog and I get emails from readers about blogs they'd like to see written. I'm also seeing blogs get +1's now, although I'm not really up to speed with that particular social networking.

The traffic I get to my blog is a real inspiration to keep writing. I regularly meet people who read my blog at conferences and other meetings, although no-one's bought me a beer because they liked it so much! I do keep an eye on my stats and occasionally get a massive spike in readers. Usually this is because GenomeWeb has covered one of my blog posts and the numbers of readers can reach over 1000 a day. I'm sure other bloggers see the same effect on their sites too.

I call this spike "the GenomeWeb effect".

The GenomeWeb effct in action on Core Genomics

Thanks GenomeWeb, I know you can't make them stay but at least you're sending them my way occasionally.