de novo assembly of Ion Torrent Reads

 I am intrigued that this Genome assembly guide includes mention of SOLID and Ion Torrent ... although not much information is given on how to work on them for genome assembly. 

That said, perhaps the SPADES plugin provided with the sequencer solves most of everyone's immediate needs ... Wondering how improve on evaluating the assemblies 

Illumina produces 3k of 8500 bp reads on HiSeq using Moleculo Technology

Keith blogged about how super long read sequencing methods would be a threat to Illumina in Jan 2013. Today, Illumina can now openly acknowledge the shortcomings of their short reads for various applications like

• assembly of complex genomes (polyploid, containing excessive long repeat regions, etc.), 
• accurate transcript assembly, 
• metagenomics of complex communities, 
• and phasing of long haplotype blocks.

the reason?
This latest set of data released on BaseSpace

Read length distribution of synthetic long reads for a D. melanogaster library

The data set, available as a single project in BaseSpace, can be accessed here.

image source: http://blog.basespace.illumina.com/2013/07/22/first-data-set-from-fasttrack-long-reads-early-access-service/

with the integration of Moleculo they have managed to generate ~30 gb of raw sequence data. They have refrained from talking about 'key analysis metrics' that's available in the pdf report. Perhaps it's much easier to let the blogosphere and data scientists dissect the new data themselves.

Am wondering when the 454 versus Illumina Long Reads side-by-side comparison will pop up

UPDATE:

Can't find the 'key analysis metrics' in the pdf report files. Perhaps it's still being uploaded? *shrugs*
so please update me if you see it  otherwise I just have to run something on it

These are the files that I have now

total 512M
 259M Jul 18 01:01 mol-32-2832.fastq.gz
  44K Jul 24  2013 FastTrackLongReads_dmelanogaster_281c.pdf
 149K Jul 24  2013 mol-32-281c-scaffolds.txt
  44K Jul 24  2013 FastTrackLongReads_dmelanogaster_2832.pdf
 151K Jul 24  2013 mol-32-2832-scaffolds.txt
 253M Jul 24  2013 mol-32-281c.fastq.gz

md5sums
6845fc3a4da9f93efc3a52f288e2d7a0  FastTrackLongReads_dmelanogaster_281c.pdf
02f5de4f7e15bbcd96ada6e78f659fdb  FastTrackLongReads_dmelanogaster_2832.pdf
586599bb7fca3c20ba82a82921e8ba3f  mol-32-281c-scaffolds.txt
b25010e9e5e13dc7befc43b5dff8c3d6  mol-32-281c.fastq.gz
6822cfbd3eb2a535a38a5022c1d3c336  mol-32-2832-scaffolds.txt
873f09080cdf59ed37b3676cddcbe26f  mol-32-2832.fastq.gz

I have ran FastQC (FastQC v0.10.1) on both samples the images below are from 281c.
you can download the full HTML report here
https://www.dropbox.com/sh/5unu3zba9u21ywj/JT4HdkzfOP/mol-32-281c_fastqc.zip
https://www.dropbox.com/s/mpxa5wx51iqmiz3/mol-32-2832_fastqc.zip

Reading about the Moleculo sample prep method, it seems like it's just a rather ingenious way to stitch short reads which are barcoded to form a single long contig. if that is the case, then I am not sure if the base quality scores here are meaningful anymore since it's a mini-assembly. Also this takes out any quantitative value of the number of reads I presume. So accurate quantification of long RNA molecules or splice variants isn't possible. Nevertheless it's an interesting development on the Illumina platform. Looking forward to seeing more news about it.

Other links

Illumina Long-Read Sequencing Service
Moleculo technology: synthetic long reads for genome phasing, de novo sequencing
CoreGenomics: Genome partitioning: my moleculo-esque idea
Moleculo and Haplotype Phasing - The Next Generation TechnologistNext Generation Technologist
Abstract: Production Of Long (1.5kb – 15.0kb), Accurate, DNA Sequencing Reads Using An Illumina HiSeq2000 To Support De Novo Assembly Of The Blue Catfish Genome (Plant and Animal Genome XXI Conference)
http://www.moleculo.com/ (no info on this page though)
Illumina Announces Phasing Analysis Service for Human Whole-Genome Sequencing - MarketWatch

Illumina Announces Moleculo Long Read Technology and Phasing As Service
First publication using the Long Read Seq (LRseq) The genome sequence of the colonial chordate, Botryllus schlosseri | eLife Contains a diagram explaining the LRSeq protocol. This experiment yielded ~1000 6.3kb fragments

Patent information on the Long Read technology
https://docs.google.com/viewer?url=patentimages.storage.googleapis.com/pdfs/US20130079231.pdf

[pub] SEED: efficient clustering of next-generation sequences.

Bioinformatics. 2011 Sep 15;27(18):2502-9. Epub 2011 Aug 2.

SEED: efficient clustering of next-generation sequences.

Bao E, Jiang T, Kaloshian I, Girke T.

Source

Department of Computer Science and Engineering, University of California, Riverside, CA 92521, USA.

Abstract

MOTIVATION:

Similarity clustering of next-generation sequences (NGS) is an important computational problem to study the population sizes of DNA/RNA molecules and to reduce the redundancies in NGS data. Currently, most sequence clustering algorithms are limited by their speed and scalability, and thus cannot handle data with tens of millions of reads.

RESULTS:

Here, we introduce SEED-an efficient algorithm for clustering very large NGS sets. It joins sequences into clusters that can differ by up to three mismatches and three overhanging residues from their virtual center. It is based on a modified spaced seed method, called block spaced seeds. Its clustering component operates on the hash tables by first identifying virtual center sequences and then finding all their neighboring sequences that meet the similarity parameters. SEED can cluster 100 million short read sequences in <4 10-fold="10-fold" 12-27="12-27" 2-="2-" 21-41="21-41" 60-85="60-85" a="a" able="able" addition="addition" also="also" and="and" area="area" as="as" asis="asis" assembler="assembler" assemblies="assemblies" assembly="assembly" best="best" better="better" by="by" cluster="cluster" clustering="clustering" clusters="clusters" compared="compared" contained="contained" contigs="contigs" data="data" datasets="datasets" demonstrate="demonstrate" discovering="discovering" efficiency="efficiency" fall="fall" for="for" from="from" generating="generating" genome="genome" h="h" in="in" indicated="indicated" into="into" it="it" its="its" larger="larger" linear="linear" longer="longer" memory="memory" most="most" n50="n50" ngs="ngs" non-preprocessed="non-preprocessed" of="of" on="on" organisms.="organisms." other="other" our="our" p="p" performance.="performance." performance="performance" preprocessing="preprocessing" reduce="reduce" requirements="requirements" respectively.="respectively." results="results" rna="rna" s="s" seed="seed" sequences="sequences" showed="showed" similar="similar" small="small" stand-alone="stand-alone" study="study" tests="tests" than="than" the="the" this="this" time="time" to="to" tool="tool" tools="tools" transcriptome="transcriptome" true="true" unsequenced="unsequenced" used="used" using="using" utilities="utilities" values.="values." velvet="velvet" was="was" when="when" while="while" with="with">

AVAILABILITY:

The SEED software can be downloaded for free from this site: http://manuals.bioinformatics.ucr.edu/home/seed.

CONTACT:

thomas.girke@ucr.edu

SUPPLEMENTARY INFORMATION:

Supplementary data are available at Bioinformatics online.

PMID:

 

21810899

 

[PubMed - indexed for MEDLINE] 

PMCID:

 

PMC3167058

 

Free PMC Article

 

[Denovoassembler-users] Ray v2.0.0-rc7 is available online !

---------- Forwarded message ----------
From: Sébastien Boisvert
Date: Thu, May 17, 2012 at 11:02 PM
Subject: [Denovoassembler-users] Ray v2.0.0-rc7 is available online !



Hello !

I am proud to announce the immediate availability of the Ray assembler
version 2.0.0 release candidate 7, code name "Dark Astrocyte of Knowledge".

This version ships with RayPlatform v1.0.2, code name "Timely Gate of
Yields".


Link for download: http://denovoassembler.sourceforge.net/


Changes in Ray

* The CMakeList file was updated.
* GC content for contigs are dumped in XML files.
* New option -one-color-per-file for graph coloring.
* Optimized file system input/output operations.
* Network testing is more verbose.
* Fixed an integer overflow bug in the scaffolder.
* New guide in Documentation/ for software message routing.
* Fixed an integer overflow bug in the profiler.
* Fixed a synchronization bug in the coloring algorithm.
* Increased the sensitivity of the biological profiling algorithms.
* Disabled the plugin for neighbourhoods.
* New plugin to compute gene ontology profiles.
* Added various missing code headers.
* Simplified the plugin creation process.
* Fixed some divisions per 0.
* Fixed a synchronization bug for gene ontology.
* Added simple profile files for sequence abundance, taxonomy profiles
and gene ontology profiles.
* A bug that caused k-mers with >= 65536 coverage to have less coverage
was fixed.
        ---> This was a long-standing bug that caused some issues.
* Added some datatypes.


Changes in RayPlatform

* Command line arguments can be obtained.
* Simplified the plugin creation process.
* Fixed two divisions per 0.
* Added some datatypes.



                        seb


_______________________________________________
Denovoassembler-users mailing list

https://lists.sourceforge.net/lists/listinfo/denovoassembler-users

is 12 million 90 bp transcriptome reads enough for transcriptome assembly?

Posted a pubmed link recently, the authors "report the use of next-generation massively parallel sequencing technologies and de novo transcriptome assembly to gain a comprehensive overview of the H. brasiliensis transcriptome. The sequencing output generated more than 12 million reads with an average length of 90 nt. In total 48,768 unigenes (mean size = 436 bp, median size = 328 bp) were assembled through de novo transcriptome assembly."

Do you think such an assembly truly is useful for research? or would a higher coverage been better? 

Performance of Ray @ Assemblathon 2

Ray is one of the assemblers that I watch closely but sadly lack the time to experiment with. Here's the candid email from Sebastien to the Ray mailing list on Ray's performance on the test data

For those who follow Assemblathon 2, my last run on my testbed (Illumina data from BGI and from Illumina UK for the Bird/Parrot):

(all mate-pairs failed detection because of the many peaks in each library, I will modify Ray to consider that)


Total number of unfiltered Illumina TruSeq v3 sequences: Total: 3 072 136 294, that is ~3 G sequences !


512 compute cores (64 computers * 8 cores/computer = 512)

Typical communication profile for one compute core:

[1,0]:Rank 0: sent 249841326 messages, received 249840303 messages.

Yes, each core sends an average of 250 M messages during the 18 hours !



Peak memory usage per core: 2.2 GiB

Peak memory usage (distributed in a peer-to-peer fashion): 1100 GiB

The peak occurs around 3 hours and goes down to 1.1 GiB per node immediately because the pool of defragmentation groups for k-mers occuring once is freed.


The compute cluster I use has 3 GiB per compute core. So using 2048 compute cores would give me 6144 GiB of distributed memory.



Number of contigs: 550764
Total length of contigs: 1672750795
Number of contigs >= 500 nt: 501312
Total length of contigs >= 500 nt: 1656776315
Number of scaffolds: 510607
Total length of scaffolds: 1681345451
Number of scaffolds >= 500 nt: 463741
Total length of scaffolds >= 500: 1666464367

k-mer length: 31
Lowest coverage observed: 1
MinimumCoverage: 42
PeakCoverage: 171
RepeatCoverage: 300
Number of k-mers with at least MinimumCoverage: 2453479388 k-mers
Estimated genome length: 1226739694 nucleotides
Percentage of vertices with coverage 1: 83.7771 %
DistributionFile: parrot-Testbed-A2-k31-

20110712.CoverageDistribution.txt

[1,0]: Sequence partitioning: 1 hours, 54 minutes, 47 seconds
[1,0]: K-mer counting: 5 hours, 47 minutes, 20 seconds
[1,0]: Coverage distribution analysis: 30 seconds
[1,0]: Graph construction: 2 hours, 52 minutes, 27 seconds
[1,0]: Edge purge: 57 minutes, 55 seconds
[1,0]: Selection of optimal read markers: 1 hours, 38 minutes, 13 seconds
[1,0]: Detection of assembly seeds: 16 minutes, 7 seconds
[1,0]: Estimation of outer distances for paired reads: 6 minutes, 26 seconds
[1,0]: Bidirectional extension of seeds: 3 hours, 18 minutes, 6 seconds
[1,0]: Merging of redundant contigs: 15 minutes, 45 seconds
[1,0]: Generation of contigs: 1 minutes, 41 seconds
[1,0]: Scaffolding of contigs: 54 minutes, 3 seconds
[1,0]: Total: 18 hours, 3 minutes, 50 seconds


10 largest scaffolds:

257646
266905
268737
272828
281502
294105
294106
296978
333171
397201


# average latency in microseconds (10^-6 seconds) when requesting a reply for a message of 4000 bytes
# Message passing interface rank Name Latency in microseconds
0 r107-n24 138
1 r107-n24 140
2 r107-n24 140
3 r107-n24 140
4 r107-n24 141
5 r107-n24 141
6 r107-n24 140
7 r107-n24 140
8 r107-n25 140
9 r107-n25 139
10 r107-n25 138
11 r107-n25 139
  Sébastien

OpGen Touts Technology's Ability to Improve De Novo Assembly, Correct Errors in Finished Genomes

By Monica Heger
When paired with next-gen sequencing technology, OpGen's Argus optical mapping technology can correct errors in assembled genomes and help close gaps, a company official said last week at a presentation during a one-day conference of BGI users in Rockville, Md.
Trevor Wagner, a senior scientist at OpGen, presented data on how the company has used the Argus platform to find errors in microbial assemblies from the Human Microbiome Project, as well as in finished genomes, and to close introduced gaps in sequenced human genomes.
While the platform has mostly been used for smaller genomes like bacteria and microbes, Wagner said that the company is now also moving into mammalian and plant genomes, using a "hybrid approach" that combines next-gen sequencing with single-molecule restriction maps.
Full article

ZORRO is an hybrid sequencing technology assembler:tested with Solexa 454

Typos in the header aside... you have to love the name!

waiting for the name to become a verb... " I zorroed the NGS reads the other and i had a fantastic assembly!" lol..



Here goes: http://lge.ibi.unicamp.br/zorro/

Overview

ZORRO is an hybrid sequencing technology assembler. It takes 2 sets of pre-assembled contigs and merge them into a more contiguous and consistent assembly. We have already tested Zorro with Illumina Solexa and 454 from some of organisms varying from 3Mb to 100Mb. The main caracteristic of Zorro is the treatment before and after assembly to avoid errors.
The ZORRO project is maintained by Gustavo Lacerda, Ramon Vidal and Marcelo Carazzole and were first used in this Yeast assembly: Genome structure of a Saccharomyces cerevisiae strain widely used in bioethanol production
ZORRO needs to be better documented and has not undergone enough testing. If you want to discuss the pipeline you can join the mailing list: zorro-google group

p.s. the typo is here
"ZORROthe masked assember "

A practical comparison of de novo genome assembly software tools for next-generation sequencing technologies.

    A practical comparison of de novo genome assembly software tools for next-generation sequencing technologies.
    Zhang W, Chen J, Yang Y, Tang Y, Shang J, Shen B.
    PLoS One. 2011 Mar 14;6(3):e17915.
    PMID: 21423806 [PubMed - in process]

Should be interesting..

de novo assembly of Illumina CEO genome in 11.5 h - new ver of Ray

Kevin:You can't ignore an email with that subject header.. but 512 compute cores? Shall have a chat with my HPC vendor.. 
Also am waiting for public release of Cortex http://sourceforge.net/projects/cortexassembler/
Strange that courses that teach the software are available but the software ain't ... 
http://www.ebi.ac.uk/training/onsite/NGS_120510.html


Velvet and Curtain seems promising for de novo assembly as well.

Ray 1.3.0 is now available online.
http://sourceforge.net/projects/denovoassembler/files/Ray-1.3.0.tar.bz2

The most important change is the correction of a major bug that caused
parallel infinite loop on the human genome.

This, along concepts incorporated in Ray 1.2.4, allowed Ray to assemble
the genome of Illumina's CEO in 11.5 hours using 512 compute cores (see
below for the link).

What's new?

1.3.0

2011-03-22

   * Vertices with less than 1 of coverage are ignored during the
computation of seeds and during the computation of extensions.
   * Computation of library outer distances relies on the virtual
communicator.
   * Expiry positions are used to toss away reads that are out-of-range
   * When only one choice is given during the extension and some reads
are in-range, then the sole choice is picked up.
   * Fixed a bug for empty reads.
   * A read is not added in the active set if it is marked on a
repeated vertex and its mate was not encountered yet.
   * Grouped messages in the extension of seeds.
   * Reads marked on repeated vertices are cached during the extension.
   * Paths are cached in the computation of fusions.
   * Fixed an infinite loop in the extension of seeds.
   * When fetching read markers for a vertex, send a list of mates to
meet if the vertex is repeated in order to reduce the communication.
   * Updated the Instruction Manual
   * Added a version of the logo without text.


I fixed a bug that caused an infinite loop. Now Ray can assemble large
genomes. See my blog post for more detail about that.
http://dskernel.blogspot.com/2011/03/de-novo-assembly-of-illumina-ceo-genome.html


Version 1.2.4 of Ray incorporated also new concepts that I will present
at RECOMB-Seq 2011.

The talk is available online:
http://boisvert.info/dropbox/recomb-seq-2011-talk.pdf


Sébastien Boisvert

Pending release of Contrail, Hadoop de novo assembler?

Jermdemo on Twitter

Just noticed the source code for Contrail, the first Hadoop based de-novo assembler, has been uploaded http://bit.ly/96pSbw 26 days ago


Oh the suspense!

de novo assembly of large genomes

Here's a informative post by Ewan Birney on the velvet user list about de novo assembly of large genomes.

Velvet's algorithms in theory work for any size. However, the engineering aspects
of Velvet, in particular memory consumption, means it's unable to handle read sets
of a particular size. This of course depends on how big a real memory machine
you have.

I know we have "routinely" (ie, for multiple strains) done Drosophila sized genomes
(~120MB) on a 125GB machine.

I've heard of Velvet being used into the 200-300MB region, but rarely further. Memory
size is not just about the size of the genome but also how error prone you reads
are (though sheer size is important).


Beyond this there are a variety of strategies:

  "Raw" de Bruijn graphs, without a tremendously aggressive use of read pairs can
be made using Cortex (unpublished, from Mario Cacamo and Zam Iqbal) or ABySS (published,
well understood, from the BC genome centre).

   Curtain (unpublished, but available, from Matthias Haimel at EBI) can do a
smart partition of the reads given an initial de Bruijn graph, run Velvet on the paritions
and thus provide an improved more read-pair aware graph. This can be iterated and in
at least some cases, the Curtain approach gets close to what Velvet can produce alone
(in the scenarios where Velvet can be run on a single memory machine to understand
Curtain's performance)


   SOAP de novo from the BGI is responsible for a number of the published assemblies
(eg, Panda, YH) although like many assemblers, tuning it seems quite hard, and I would
definitely be asking the BGI guys for advice.

   A new version of ALLPATHS (from the Broad crew) looks extremely interesting, but
is not quite released yet.

In all above the cases I know of successes, but also quite a few failures, and untangling
data quality/algorithm/choice of parameters/running bugs is really complex. So - whereas
assemblies < 100MB are "routine", currently assemblies 100MB-500MB are "challenging" and
>500MB are theoretically doable, and have been done by specific groups, but I think still
are at the leading edge of development and one should not be confident of success for
"any particular genome".


Thanks Ewan for letting me reproduce his post here


Velvet-users mailing list
http://listserver.ebi.ac.uk/mailman/listinfo/velvet-users


Cortex seems very promising for de novo assembly of human reads using reasonable amounts of ram ( 128 Gb ) based on the mailing list. I know I be watching out for it on Sourceforge!

Installing SOLiD™ System de novo Accessory Tools 2.0 with Velvet and MUMmer

howto install on CentOS 5.4 

 wget http://solidsoftwaretools.com/gf/project/denovo/ #just to keep a record
 wget http://www.ebi.ac.uk/~zerbino/velvet/velvet_0.7.55.tgz
 wget http://downloads.sourceforge.net/project/mummer/mummer/3.22/MUMmer3.22.tar.gz
 tar zxvf denovo2.tgz
 cp velvet_0.7.55.tgz denovo2 #you can use mv if you don’t mind downloading again
 cp MUMmer3.22.tar.gz denovo2
 cd denovo2
 tar zxvf velvet_0.7.55.tgz
 tar zxvf MUMmer3.22.tar.gz
 cd MUMmer3.22/src/kurtz #this was the part where I deviated from instructions
 gmake mummer #Might be redundant but running gmake at root dir gave no binary
 gmake |tee gmake-install.log

Next step:
download the example data to run through the pipeline
http://solidsoftwaretools.com/gf/project/ecoli50x50/
http://download.solidsoftwaretools.com/denovo/ecoli_600x_F3.csfasta.gz
http://download.solidsoftwaretools.com/denovo/ecoli_600x_F3.qual.gz
http://download.solidsoftwaretools.com/denovo/ecoli_600x_R3.csfasta.gz
http://download.solidsoftwaretools.com/denovo/ecoli_600x_R3.qual.gz

Description
This is a 50X50 Mate pair library from DH10B produced by SOLiD™ system.The set includes .csfasta and .qual files for F3 and R3. The insert size of the library is 1300bp and it is about 600X coverage of the DH10B genome. The results from MP library in the de novo documents are generated from this dataset.


YMMV

pitfalls for SAET for de novo assembly

Spotted in manual for

SOLiD™ System de novo Accessory Tools 2.0

Usage of pre-assembly error correction: This is an optional tool which was
demonstrated to increase contigs length in de novo assembly by factor of 2 to 3. Do not use this tool if coverage is less than 20x. Overcorrection and under-correction are equally bad for de novo assembly; therefore use balanced number of local and global rounds of error correction. For example, the pipeline will use 1 global and 3 local rounds if reads are 25bp long, and 2 global and 5 local rounds if reads are 50bp long.

Is it just me? I would think it is trivial to implement the correction tool to correct only when the coverage is > 20x. Not sure why you would need human intervention.

Coral Transcriptomics-a budget NGS approach?

Was surprised I didn't blog about this earlier.
Dr Mikhail Matz is a researcher in the field of coral genomics. His approach to doing de novo transcriptomics for an organism whose genome is unavailable.


his compute cluster is basically
"two Dell PowerEdge 1900 servers joined together with ROCKS clustering software v5.0. Each server had: two Intel Quad Core E5345 (2.33 Ghz, 1333 Mhz FSB, 2x4MB L2 Cache) CPU’s and 16 GB of 667 Mhz DDR2 RAM. The cluster had a combined total of 580 GB disk space."




Tools used are
- Blast executables from NCBI, including blast, blastcl3, and blastclust
- Washington University blast (Wu-blast)
- ESTate sequence clustering software
- Perl

He admits that the assembled transcriptome might be incomplete (~40,000 contigs with five-fold average sequencing see Figure 2 for the size distribution of the assembled contigs

But it is "good enough" to use as a reference transcriptome to align SOLiD reads accurately and to generate the coverage that 454 can't give for the same amount of grant money.

the results are published in BMC Genomics

Not sure if you have heard of just in time inventory. But I think "good enough" science takes a bit of dare to spend that money to ask those what-ifs.

Ray-0.0.3: a NEW MPI-based parallel genome assembler

The Ray Project Team gives you a 100% parallel MPI-based assembler called Ray. Ray is NOW available at http://sourceforge.net/projects/denovoassembler/files/. It supports Illumina paired-end reads. It is 100% parallel, and it is a single executable (no pesky perl scripts!). The source code is licensed with the GPL-v3.

Try it, and give us your comments, bugs, suggestions, and concerns on our mailing list.
http://lists.sourceforge.net/lists/l...ssembler-users

Ray-0.0.3: a NEW MPI-based parallel genome assembler
http://sourceforge.net/mailarchive/f...ssembler-users

***
The Ray Project Team
http://denovoassembler.SourceForge.net/

Tips for de novo bacterial genome assembly

Found this handy article for  de novo bacterial genome assembly.

In Summary,

1. Filter reads
2. Use VelvetOptimiser Script
3. Use Bowtie to map back reads to finished assembly to validate

Illumina assembly using velvet

UC Davis has a wiki on this.
Covers Single End and Paired End

The sequence and de novo assembly of the giant panda genome

The sequence and de novo assembly of the giant panda genome

 Using next-generation sequencing technology alone, we have successfully generated and assembled a draft sequence of the giant panda genome. The assembled contigs (2.25 gigabases (Gb)) cover approximately 94% of the whole genome, and the remaining gaps (0.05 Gb) seem to contain carnivore-specific repeats and tandem repeats. Comparisons with the dog and human showed that the panda genome has a lower divergence rate. The assessment of panda genes potentially underlying some of its unique traits indicated that its bamboo diet might be more dependent on its gut microbiome than its own genetic composition. We also identified more than 2.7 million heterozygous single nucleotide polymorphisms in the diploid genome. Our data and analyses provide a foundation for promoting mammalian genetic research, and demonstrate the feasibility for using next-generation sequencing technologies for accurate, cost-effective and rapid de novo assembly of large eukaryotic genomes.

 

SOAPdenovo uses the de Bruijn graph algorithm⁷ and applies a stepwise strategy to make it feasible to assemble the panda genome using a supercomputer (32 cores and 512 Gb random access memory (RAM))

Amazing... 

See full paper

Cloudburst Contrail using cloud computing to speed up your NGS data analysis

Been having problems of all sorts trying to do de novo assembly of transcriptome data on my cluster. It might be possible that not enough RAM is the problem.. apparently at BGI they have 512 GB ram beasts.

I think it might be worthwhile to explore computing algorithm changes rather than hardware upgrades.
after all there comes a point when the cost far exceeds the "worthiness" of an experiment.

Contrail: Assembly of Large Genomes using Cloud Computing

[excerpt .... Preliminary results show Contrail’s contigs are of similar size and quality to those generated by Velvet when applied to small (bacterial) genomes, but provides vastly superior scaling capabilities when applied to large genomes....]

CloudBurst: Highly Sensitive Short Read Mapping with MapReduce
[excerpt ...CloudBurst's running time scales linearly with the number of reads mapped, and with near linear speedup as the number of processors increases. In a 24-processor core configuration, CloudBurst is up to 30 times faster than RMAP executing on a single core...]