Michael Schatz:Assembling Crop Genomes With SMS

PDF of the presentation on Feb 22, 2013 AGBT, Marco Island, FL

http://schatzlab.cshl.edu/presentations/2013-02-20.AGBT.Assembling%20Crop%20Genomes.pdf

if you need an intro

"In a talk during the evening session, Mike Schatz, an assistant professor at Cold Spring Harbor Laboratory, spoke about “Assembling Crop Genomes with Single Molecule Sequencing.” Crops are important to sequence — 15 crops represent 90% of the world’s food, Schatz said — but are notoriously difficult to study because of their large genome size, high repeat content, and higher ploidy. Along with Sergey Koren and Adam Phillippy, he has built a pipeline to create hybrid genome assemblies using PacBio long reads combined with shorter-read sequence — either CCS reads from PacBio or data from another sequencing platform. In an example he offered of a rice strain, an attempted genome assembly using just Illumina reads yielded an N50 contig of 16Kb, but adding PacBio long reads to that boosted the N50 contig to 25Kb. Ultimately, Schatz said, he expects that as PacBio's readlength improves, this kind of approach could routinely generate megabase-size contigs or even pull plant chromosomes into single contigs.

For more information on Mike Schatz’s work using SMRT Sequencing, check out this case studydescribing an automated pipeline for genome finishing with PacBio long reads."

source: http://blog.pacificbiosciences.com/2013/02/notes-from-agbt-long-read-sequence-data.html

He includes a snippet of code to answer this question from twitter

'What's the longest single contig from a de Bruijn assembler without PE or a jumping library?'

$ perl -e 'print ">random\n"; @D=split //,"ACGT"; \for (1...100000000){print $D[int(rand(4))];} \print "\n"’ | fold > random.fa$ wgsim –r 0 -e 0 -N 50000000 -1 100 -2 1 \random.fa random.reads.fq /dev/null$ SOAPdenovo-63mer all –s random.cfg -K 63 -o random.63$ getlengths random.63.contig           1 99999990

OICR's Genomics Pathway-Sequencing Project Highlights Genomic Data's Steady March Into Clinics

OICR's Genomics Pathway-Sequencing Project Highlights Genomic Data's Steady March Into Clinics

The Ontario Institute for Cancer Research has kicked off a project that promises to address some key informatics challenges related to clinical sequencing.

Lincoln Stein, platform leader of informatics and biocomputing at the OICR, described the project in a presentation at the fourth Summit on Translational Bioinformatics in San Francisco this week.

Stein said that the project, conducted in partnership with Canada's Princess Margaret Hospital and dubbed the Genomics Pathway Sequencing project, or GPS, will sequence genes in normal and tumor samples excised from patients.

.....

“The biggest challenge,” Stein said, “is trying to keep the amount of information in this report to a minimum without keeping potentially important information back [from physicians].”

....

For each individual, OICR researchers will sequence about 1,000 cancer-related genes in control and tumor samples and attempt to identify mutations that are of immediate relevance to the patient’s cancer care, or that are targeted by drugs that are currently being tested in trials.

...

The genes in the study — which include the usual suspects such as KRAS, p53, and B-Raf — were selected based on suggestions from oncologists in the Toronto area as well as data stored in knowledgebases at the Memorial Sloan Kettering Cancer Center, the Wellcome Trust Sanger Institute, and the National Cancer Institute.

To sequence patient samples, OICR will use Pacific Biosystems' single-molecule sequencing as a first step to discover novel mutations. It will then sequence known mutations on Sequenom's MassArray platform. The sequence variants will be confirmed using a Sanger sequencer housed at the clinical sequencing lab at Princess Margaret hospital.

Stein explained that the OICR selected the PacBio system because “it allows us to do very long reads at high coverage … currently 1,000-base-pair reads in a circular consensus, which gives high accuracy for targeted genes.”

In addition, he said, the platform has a rapid turnaround time, taking only “15 minutes per run to completely analyze … a single typical gene.”

Kevin: fascinating! Would their's become the de facto standard for clinical sequencing?

A comparison of single molecule and amplification based sequencing of cancer transcriptomes.

1. PLoS One. 2011 Mar 1;6(3):e17305.

A comparison of single molecule and amplification based sequencing of cancer transcriptomes.
Sam LT, Lipson D, Raz T, Cao X, Thompson J, Milos PM, Robinson D, Chinnaiyan AM, 
Kumar-Sinha C, Maher CA.

Michigan Center for Translational Pathology, University of Michigan, Ann Arbor,
Michigan, United States of America.

The second wave of next generation sequencing technologies, referred to as
single-molecule sequencing (SMS), carries the promise of profiling samples
directly without employing polymerase chain reaction steps used by
amplification-based sequencing (AS) methods. To examine the merits of both
technologies, we examine mRNA sequencing results from single-molecule and
amplification-based sequencing in a set of human cancer cell lines and tissues.
We observe a characteristic coverage bias towards high abundance transcripts in
amplification-based sequencing. A larger fraction of AS reads cover highly
expressed genes, such as those associated with translational processes and
housekeeping genes, resulting in relatively lower coverage of genes at low and
mid-level abundance. In contrast, the coverage of high abundance transcripts
plateaus off using SMS. Consequently, SMS is able to sequence lower- abundance
transcripts more thoroughly, including some that are undetected by AS methods;
however, these include many more mapping artifacts. A better understanding of the
technical and analytical factors introducing platform specific biases in high
throughput transcriptome sequencing applications will be critical in cross
platform meta-analytic studies.


PMID: 21390249 [PubMed - in process]