This is a question that is often asked when I demo the GenomeQuest platform to potential customers. I always answer that question in three phases.

The first phase goes like this: “It’s really fast, it’s certainly not any slower than BWA/Bowtie or anything else out there.”. Next question is always: “Well, do you have any benchmarks?”.

Which nicely transitions me into the second phase of the answer. This phase is much more rigorous and usually starts with: “Well, it depends. Let me try to explain.”:

• Any good computer scientist can write a mapping algorithm that is really fast. However, that doesn’t mean anything unless it produces the kind of results that are needed. The NGS application you’re working with matters here. For example, finding genetic variation in human disease requires very accurate mapping with mismatches and indels. In contrast, with digital gene expression in maize you can cut a lot of corners. You just need to have a rough idea of the number of alignments on a transcript.
• Then there is the matter of the data and the technology that produced it. Do you have long reads (more than 120 bp)? Will your mapper handle them? Will it also increase the number of mismatches / indels it can use to align a read? Will it significantly slow down execution, or eat up all your memory when reads get longer? Does this mapper also support local alignments when you need them? Will it align in colorspace? In paired end mode? I could go on.
• Next there is the matter of connecting results to the following step in your pipeline. How long does it take to de-duplicate a  1TB alignment file in SAM/BAM format? Or to find those alignments who’s position overlaps with your exome capture experiment, or all dbSNP entries? At GenomeQuest we have a very efficient way of storing and handling alignments (including sequences/annotation). This saves real time, especially when compared to the alignment step itself (it saves a lot of disk space as well by the way).

By the time we get to the third phase of the answer I’m usually much more confident: “Well, how fast do you need our mapping algorithm to be?”.

• Does it really matter how fast the read mapper is, as long as it’s comparable in performance to other algorithms for most common use cases? Does it matter if you have the alignments in 2.5 hours instead of 3? Maybe if you analyze thousands of samples per week it matters, but then other things like reliability and professional software support should matter as well.
• Do these other algorithms scale with the hardware you throw at them? How easy is it to run a read mapping on 64 compute nodes, with 2 CPUs, 8 cores per CPU per node? What about if you double the amount of hardware? Will you go twice as fast? With the GQ- Engine  you will. Want to run on 1024 nodes? That’s possible.
• Are you asking about speed for a single run, or the throughput for a bunch of runs? Last weekend I ran 2000 NGS read databases though our read mapping workflow (low coverage genome sequencing, about 80M reads per database). I started them on Friday afternoon, went for drinks with my friends that evening, had a nice family dinner on Saturday afternoon, and watched a movie with my kid afterwards. The runs were finished before I woke up on Sunday. No hiccups, no failed runs, no logs to monitor, and – best of all – no “one million-file” directories to organize. There were a lot of other customers on the system that weekend, doing their NGS analysis as well.

If we ever meet for a demo, please ask me this question. I love to talk about it.

Henk Heus, Ph.D.
VP Product Management & Services
GenomeQuest Inc.

At GenomeQuest we use an extended version of the GASSST read mapping algorithm (among others). Read about it here in Bioinformatics here: http://bioinformatics.oxfordjournals.org/content/26/20/2534.abstract

So now researchers can go from their ChIP-Seq NGS runs directly to gene-based annotation of the peaks found by their biology. Select regions of interest, or genes of interest, or peaks of a certain class, and drill down to see the actual evidence that backs up the call.

We’re giving away free ChIP-Seq runs to the first 100 people to sign up.

As always, feel free to leave a comment – we read every one.

Richard J. Resnick
VP Software and Services

So what took so long? Until now, it wasn’t necessary. We provided clear business value to pharma for a well-defined use case. An advisory board might have even been a distraction.

So what’s changed? As we build out the sequence data management (SDM) platform, we want to see beyond this year’s application of next generation sequencing (NGS), and make sure we understand where the industry is going.

Dr. Mark Boguski

Mark is a perfect advisor for this initiative. His practical experience at NCBI, Rosetta, and Novartis, and his current vantage point at Harvard Medical School and Beth Israel, places him squarely with a view to the future uses of sequence data in a clinical setting, and with firm grounding in the practical applications of sequence data for the past 20 years.

It’s an honor to have Mark join us as a scientific advisor. We hope to build a diverse advisory team to complement him with skills and experiences that reflect the diversity of talents converging on the digital revolution in biology.

From my view, cloud vendors such as Amazon and others will follow the commodity pricing curve of the instruments and become a way to process larger and larger data sets with fewer and fewer dollars. For us, cloud computing is an opportunity to offer more customers more services at lower costs. However, the winning strategy will require value added software, knowledge and skills to harness those resources for organizations and their end-users.

More on this topic in a future post.