NGS Expert Blog

The third de novo sequenced genome in our series Whose genome has been sequenced? is the “living fossil” Latimera chalumnae.

The most difficult part for this de novo genome sequencing approach was to get enough starting material. The authors even reported that their first approach was to use the Sanger technology, but is simply was not enough DNA available. Therefore they had to wait until the next generation sequencing techniques were stable enough to risk the sequencing (BioTechniques). Here are the sequencing facts of this study (Amemiya et al.):

What was sequenced?

A blood sample from an adult African coelacanth

De novo sequencing strategy:

1. Libraries: shotgun library 61-fold coverage; 3 kb jumping library – 88-fold coverage, 40 kb fosmid library 1-fold coverage
2. Illumina HiSeq 2000 (paired-end module)
3. De novo genome assembly using the software ALLPATHS-LG
4. RNA sequencing

RNA-Seq sequencing strategy:

1. 4 cDNA libraries (1x mRNA-Seq library, 3x strand specific dUTP libraries from brain, gonad/kidney, gut/liver tissue) were sequenced using a HiSeq
2. Data output: mRNA-Seq library ~ 210M paired-end reads;  dUTP libarires ~ 3-4 Gb of sequence/tissue
3. Assembly was performed using Trinity

The genome sequencing helped to understand the possibility of this prehistoric fish to thrive on dry land and the phenotype that is so similar to 300 million year old fossils (BioTechniques).

Read the complete publication here.

Earlier published genomes: Goat genome (Capra hircus); Chickpea plant (Cicer arietinum)

With today’s press release I am happy to announce that AROS Applied Biotechnolgy A/S  is now a member of the Eurofins group.

Here is a short introduction of our new colleagues from AROS:

• AROS was founded in the year 2000
• AROS started as a spin off of from the Aarhus University Hospital and was the first service provider for Affymetrix in Europe
• AROS is based in Denmark and provides a long term experience in sample preparation, microarray analysis and next generation sequencing (NGS)
• Nowadays AROS has a leading position in NGS service for pharmaceutical research
• AROS is an Illumina reference lab for next generation sequencing
• The main focus in NGS is RNA-Seq and exome sequencing that is accomplished with the exome designs of the leading provider in this area (Illumina TruSeq Exome Enrichment, NimbleGen EZ Capture & Agilent SureSelect)

“AROS is an excellent fit […] with our focus on high-quality next-generation sequencing […]” (Dr. Gilles Martin) and therefore I am confident that this new alliance will help us both in further expanding our experience level in NGS and to benefit from our complementary strength.

I am sure you will hear more about the activities from AROS on our blog and hope you join me in welcoming AROS as a member of Eurofins.

There are many volcanoes and earthquakes in Japan, but it is not always a bad thing, they are also responsible for the many hot springs. Most Japanese people love soaking in a hot spring and they believe that this eliminates fatigue and improves health. Hot springs also had a great contribution to biotechnology via the heat resistant DNA polymerase from Thermus aquaticus (Taq) and its derivatives. Not only PCR, but also Sanger sequencing was accelerated by these heat resistant enzymes as we all know well.

Scientists have started to study the genome/transcriptome world in hot springs with NGS technologies. Murakami et al., peformed 16S-rRNA (Sanger sequencing) and meta-transcriptome analysis from small RNA (GS FLX sequencing) of groundwater (up to 1,000 m depth) from Yunohara hot spring, Japan. Their phylogenetic analysis using 16S rRNA showed the classification of 17 species including archaea and eubacteria.  There are only 2 or 3 dominant species in typical cases of other hot springs, but this one is rich in diversity. Furthermore, they found the very unique group “Archaeal Richmond Mine Acidophilic Nanoorganisms (ARMAN)” which is a small organism/cell with only 200 nm size! Their small RNA analysis identified 64,194 (20,057 nonredundant) cDNA sequences, and they found several novel non coding RNAs which have a very stable secondary structure.

Therefore, hot springs may still be gold mines for useful genes and important biological knowledge of unknown underground ecosystems.

Interested in expression profiling, but you are working with a non-model organism?

A very elegant way for this purpose is to (1) generate long cDNA contigs with NGS technologies that serve as a reference transcriptome and (2)  perform expression profiling by mapping Illumina HiSeq 2000 derived short reads of each sample back onto the reference. As only one read is generated per transcript, down and up regulated genes easily can be identified by counting the sequence hits.

This approach was used by Mutasa-Göttgens et al., 2012  in order to analyze targets involved in bolting and flowering in sugar beet. Understanding the regulation of the vernalization-induced bolting and the change towards the reproductive phase is of high importance because bolting and flowering cause considerably reduced sugar content.

To generate the reference transcriptome of the shoot apex, a normalised random primed cDNA library was prepared and sequenced on Illumina HiSeq 2000 with single read module and 100 bp read length. De novo assembly yielded at total of 225’000 unique transcripts, 53’000 of which represent large transcripts (>500 bp and up to >8’700 bp). For quantitative comparison we prepared for the research group a digital gene expression (DGE) library from samples which were subjected to vernalization and / or phytohormone treatment. The libraries were sequenced on Illumina HiSeq 2000 and reads were mapped onto the transcriptome reference sequence.

Bioinformatics analysis identified (amongst others) a potential regulator of vernalization, and therefore an interesting breeding target for the sugar beet crop.

In my opinion, this study is an excellent example of how to combine the strength of different available RNA-Seq libraries most effectively. The normalized random primed library allows unbiased site-directed sequencing. Furthermore the normalization process levels high and low expressed transcripts, which allow identification of low expressed genes accurately and facilitate de novo assembly with short read technology considerably. The DGE library in contrast produces only one tag per transcript, thus allowing much deeper resolution than the mRNA-Seq approach from Illumina, which generates reads that cover the whole transcript.

In the meanwhile, with new NGS libraries available, one would rather use a 3’-fragment library instead of the DGE library. While displaying similar costs, this library type offers longer sequence information (100 bp versus 17bp) and in consequence higher mapping accuracies and reduced numbers of non-mappable reads.

You will find more information regarding this combined approach including the 3’-fragment library for read counting in the following Application Note.

ALEXA-Seq, Apopto-Seq, AutoMeDip-Seq… its all about NGS.

It seems, that the scientific community is more and more using NGS technologies. Interestingly NGS techniques are not only utilized for the (de novo) analysis of complete genomes and transcriptomes. Recently I found a nice summary of currently published NGS acronyms at James Blog. The author states that the majority of NGS related publications are still dealing with Chip-Seg and (m)RNA-Seq. However, due to the creativity of scientists the list is not limited to those two and contains more than 40 acronyms at the moment. This illustrates that NGS can be used for a wide variety of different approaches just limited by your creativity.

What can NGS do for you and your research?

Dear James, please keep your list up-to-date…

RNA-Seq is replacing more and more the microarray analysis for quantitative comparison of expression levels in different samples. Several studies have compared independently both techniques with respect to accuracy and reproducibility. All of these have reported the superiority of RNA-Seq (Richard et al., 2010 and Sultan et al., 2008).

 Microarray systems rely on hybridization. Background hybridization levels or signal saturation constrict accurate quantification of the expression pattern of low and high abundance transcripts, respectively (e.g. Gautier et al., 2004 and Mortazavi et al., 2008).  Provided that sufficient sequencing depth is applied, RNA-Seq does not show these kinds of limitations. This was demonstrated by e.g. Wang et al., 2008 who compared RNA-Seq read counts to published qRT–PCR measurements for hundreds of genes. They have observed almost linear relationship across five orders of magnitude.

The added value of RNA-Seq is that it enables the identification of new splicing variants and new transcription initiation sites. In addition, the quantification of particular exon and splicing isoform expression at a genome-wide scale is possible. Using RNA-Seq significant differences in gene isoform expression level and transcriptions start sites were found recently between normal and Alzheimer’s disease brain tissue (Twine et al., 2011). This result provided a big step forward in Alzheimer’s disease research.

Given the better accuracy and the add-on values in my opinion RNA-Seq will become an essential technique for researchers analyzing genome-wide gene regulation in any species. This essentially being true where reference sequences are available. Further decrease in costs of next generations sequencing will even facilitate this development.

Have you made first experiences with RNA-Seq in terms of expression profiling that you want to share with other blog visitors?