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)

You may know that the sequencing method of the Ion Torrent PGM is quite similar to the sequencing method of the Roche 454 devices. In both technologies beads that hold the clonally amplified template with appropriate sequencing adaptors are loaded onto a plate with millions of wells. The loading is performed in a way that ensures that most wells are loaded with a single bead (the size of the wells do not allow two beads per well). In a next step dNTPs are flowed over the surface in a predetermined order with only one type of nucleotide at a time. Washing steps occur before the next dNTP is flowed over the surface. The way the incorporation of the nucleotide is measured represents the substantial difference between both technologies:

With the Roche 454 technology an enzymatic cascade follows the polymerization event that finally generates pyrophosphate and light. The light intensity is proportional to the number of nucleotides that were incorporated (if any). The light is detected by the camera of the system.

In contrast, the Ion torrent PGM is measuring pH rather than light to detect incorporation events. A single proton is released for every dNTP incorporated during the flow, which changes the net pH value in the respective well and a ionic sensor measures the pH change.

The Roche system (as well as the first generation of the PGM) cycles the 4 dNTPs in a step-wise fashion. They simply repeat the sequence TACG over and over. With the second generation PGM these 4 base cycles have been changed to 32 base cycles (TACGTACGTCTGAGCATCGATCGATGTACAGC), called the Samba sequence. The sequence starts with the same 4-nucleotide repeats, but after 2 such patterns some nucleotides are repeated in a period shorter than four. According to Bragg et al. this modification was implemented to improve the synchrony of clonal templates which facilitates a more accurate base calling. Unfortunately the Samba sequence is not optimized for read length as the original sequence was. It remains to be seen if Ion Torrent (now owned by Thermo Fisher) will make further modifications in the Samba sequence in order to balance the accuracy and the read length of the system.

We started this series off in January where we reported about the de novo sequencing of the domestic goat Capra hircus.

Today I would like to report about a plant genome, the Cicer arietinum:

According to the GenomeWeb article this de novo genome sequencing approach is only the 3rd one for crop legume plants. For me that is kind of astonishing since breeding and optimisation of crop is already done since years. Maybe this is due to the huge genomes of plants that outperform animal genomes by far. For our chickpea plant with 740 million base pairs we talk about a medium size plant genome. But let’s focus on the sequencing approach for now (Varshney et. al):

What was sequenced?

De novo sequencing of one reference chickpea plant and re-sequencing of 90 cultivated & wild chickpea lines from 10 different countries

Sequencing strategy:

1. De novo genome sequencing on HiSeq 2000 (paired-end module) of 1 genome with 11 shotgun and mate-pair libraries (insert sizes: ~ 170; 500; 800; 2,000; 5,000; 10,000; 20,000 bp) and BAC end sequencing
   Data output: 153.01 Gb; after filtering & correction steps only 87.65 Gb data were used for de novo assembly
2. Re-sequencing of genomes
   • Whole genome re-sequencing on 29 varieties using Illumina 100 bp paired-end sequencing on HiSeq 2000
   • RAD-sequencing of 61 genotypes on HiSeq 2000 (48x ApeKI; 24x HindIII)

According to D. Cook “the sequencing of the chickpea provides genetic information that will help plant breeders develop highly productive chickpea varieties that can better tolerate drought and resist disease — traits that are particularly important in light of the threat of global climate change”. (Davis Enterprise).

Read the complete publication here.

To reveal the secrets of the goat genome the researchers applied a hybrid approach of Illumina shotgun sequencing and whole genome mapping (WGM) using the Argus system from Opgen. As a result, the number of scaffolds could be reduced from 2,090 to 315. This demonstrates that whole-genome mapping for large genomes can be a replacement for traditional genetic maps for de novo assembly (Dong et. al).

This reference genome can now be used for mapping reads of other goats to identify SNPs and other variants that could play a role for breeding, cashmere fiber prodcution or different goat behaviours (Dong et. al).

If you are interested in more information about optical mapping, read our dedicated blog posts: What is optical mapping? and Creating the perfect genome assembly.

Typical answers we get from our customers:

• Easy working with the data
• Data suitable for high quality annotation
• Resolution of structural rearrangements
• High consensus accuracy
• High cost-efficiency

All these requirements can be fulfilled perfectly when combining Roche GS FLX++ and Illumina data. The long Roche FLX++ reads of up to 1100 bp give much longer contigs than Illumina reads only do. For scaffolding and to be able to resolve structural rearrangements we sequence shotgun (SG) and LJD libraries with Illumina technology. The adding of Illumina reads keeps the overall costs at a reasonable level. Furthermore the reads correct the Roche sequencing errors at homopolymer sites and therefore enable us to build a consensus sequence with high accuracy.

The superiority of such a hybrid assembly becomes quickly apparent when looking at the following results of one of our proof of concept studies. In this de novo project, we sequenced a fungal genome of about 30 Mbp and approx. 57% GC content. Using the hybrid strategy we obtained only 10 chromosome-sized scaffolds (see figure below) with up to 8.3 Mbp. Remarkably, the 10 scaffolds represent the majority of genetic information present, given that they make up 99.6% of all scaffold sequence information.

Such results enable easy data handling and definitely are an excellent starting point for annotation and studying of gene content and rearrangements.

Sequencing strategy: SG library with FLX++ (approx. 10-fold coverage), SG and LJD 3 kbp, 8 kbp and 20 kbp on Illumina HiSeq 2000 with 2x 100 bp module.

However, we are just beginning to understand the complex relationships of this “social network”, as the Scientific American has called it. The most complex bacterial community within the human body resides inside the gut. In order to obtain a deeper understanding of the bacterial communities of the human gut, there have been several attempts of sequencing the gut microbiomes of larger groups of individuals, such as projects by Arumugam et al., Yatsunenko et al or Schloissnig et al. However, so far, the number of individuals which were analyzed was relatively small (up to several hundreds).

A group of US scientists have now started the “American Gut Project“.  As reported by Genome Web News, this project is planned as a crowd-sourcing study of 10.000 or more individuals in the US. Since this study is part of the “American Food Project”, it will mainly focus on gut microbiome patterns in relation to diet, age and lifestyle. People who would like to participate in this study need to sign up via a website and donate $99. This money will be used to cover a significant part of the cost of the study. In return, participants will receive a taxonomic profile of their gut microbiome.

The analysis itself will be based on 16S sequencing. For part of the samples, additional analyses such as sequencing the complete metagenomes and long term surveys are planned. No doubt, this study will clearly provide us with a huge data set. However, this data set will be highly complex. Also, it still needs to be brought in context with data from other projects.  To my opinion, interpretation of the data still remains the hardest part. Or, as project organizer Jeff Leach has put it in an interview with Genome Web Daily News: “We don’t expect to be able to address some questions, but because of the size of the sample and because of the broad patterns we expect to see in diet and lifestyle, we think some stuff will fall out.”

In a realtively very short time the researchers in the paper were able to isolate the plasmids carring the the NDM-1 gene and sequence 12 plasmids to confirm that the transfer of the NDM-1 (drug resistance gene) amongst other species of bacteria. The implications are serious. Bacteria do not necessarily need to be challenged with a drug or be susceptable for long periods to develop resistance. The resistance can simply be transformed amongst adiverse microbial population. The use of NGS meant that all plasmids could be sequenced in one run to determine the previously unkown, location and the presence of the NDM-1 gene.

Conventional sequencing of over 160Kb of plasmids to try and locate the resisitance marker would have taken a great deal more effort. Thus, NGS is a key tool to track the spread of resisitance genes and hopefully enable researchers to develop prophelactic solutions. Potentially by sequencingf the genomes of all know phages- the natural viral enemy of bacteria. NGS provides another tool to hasten the end to the resistance wars.

For example, over the past years, several approaches have been made to use DNA as a means of storing information. In a study recently published online in Science, scientists developed a strategy to encode and read digital information using DNA Synthesis and  Next Generation Sequencing Systems.

A html document containing more than 50,000 words, 11 JPG images, and a Java Script program was encoded in DNA by synthesizing nearly 55,000 oligonucleotides on high-fidelity microarrays. The information stored in the oligonucleotides library was later “read” by Illumina sequencing.

According to the authors, DNA is a very useful medium for long term storage of information:   DNA is very stable over many years,  allows data storage at very high density and  small volumes. The senior author, Kosuri, told InSequence, they only used some 50 ng of oligonucleotides to store the information of this html document! Kosuri admitted that the study costed several thousand dollars. However, if Next Generation Sequencing continues to develop at the same speed as today, new applications such as using DNA for (long-term) data storage may become a feasible option.

So let us see what is coming next!

But really interesting are now the statements of the spokespersons from the different companies in a recent article from Julia Karow in GenomeWeb. They all agree on the same thing: the data collected in the publication have been true in 2011, but are outdated by now since a lot of effort is put into innovation. Every instrument performs a lot better now. So what is our conclusion? That comparisons for NGS technologies are just a waste of time? For the Sanger institute it means that they invested in 3 new MiSeq’s since the Illumina pipeline is already available. For me, these comparisons are also valuable for all other institutes. Although maybe outdated, they highlight the strength and weaknesses of each technology and help to decide where to invest thousands of dollars. What do you think?

Variants called within projects that aim at analysis of variants definitely need validation to determine the rate at which the mutations have been correctly called and to confirm the specific reported changes. Currently used techniques like Sequenom genotyping and Sanger sequencing provide essential drawbacks, such as the need for manual interpretation or low data throughput. For that reason, Carneiro and his colleagues studied the power of PacBio RS and MiSeq data as a validation tool and compared the results with each other.

They generated amplicons covering 98 variants called in the 1000 Genomes Project and sequenced the PCR products with both instruments, PacBio RS and MiSeq. Using PacBio RS data 96 out of the 98 variants could be correctly genotyped, whereas the MiSeq correctly genotyped only 93 sites. The explanation of the authors is quite simple: The completely random distribution of errors across the reads can overcome the low read accuracy problem if sufficient coverage is applied.

Manual checking of the sites, that were miscalled using the PacBio dataset, revealed, that one of the two miscalls happened due to a reference bias (true variation is hidden). Such bias is introduced by alignment parameters where the gap open penalty is higher than the base mismatch penalty. The high error rate of PacBio RS reads makes these parameters necessary.

However, Carneiro told GenomeWeb, that the researchers are not using a different aligner that was developed at the Broad Institute. This aligner re-aligns the reads using different parameters and therefore reduces the problem to a great extent.

For me the study shows that there is potential for PacBio RS sequencing. Nevertheless, like the variants, also this study result needs to be validated. Furthermore I think that the value of the study needs also to be seen in relation to the sequencing cost for both instruments. While the consumable prices for both techniques are in a similar range, the several fold higher cost for the PacBio RS instrument makes a remarkable difference.