Handling ridiculous amounts of data with probabilistic data structures

C. Titus Brown

Michigan State University

Computer Science / Microbiology

Resources

http://www.slideshare.net/c.titus.brown/

Webinar: http://oreillynet.com/pub/e/1784

Source: github.com/ctb/N-grams (this talk): khmer-ngramDNA (the real cheese): khmer

khmer is implemented in C++ with a Python wrapper, which has been awesome for

scripting, testing, and general development. (But man, does C++ suck…)

Lincoln Stein

Sequencing capacity is outscalingMoore’s Law.

Hat tip to Narayan Desai / ANL

We don’t have enough resources or people to analyze data.

Data generation vs data analysis

It now costs about $10,000 to generate a 200 GB sequencing data set (DNA) in about a week.

(Think: resequencing human; sequencing expressed genes; sequencing metagenomes, etc.)

…x1000 sequencers

Many useful analyses do not scale linearly in RAM or CPU with the amount of data.

The challenge?

Massive (and increasing) data generation capacity, operating at a boutique level, with

algorithms that are wholly incapable of scaling to the data volume.

Note: cloud computing isn’t a solution to a sustained scaling problem!!

(See: Moore’s Law slide)

Awesomeness

Easy stuff like Google Search

Life’s too short to tackle the easy problems – come to academia!

A brief intro to shotgun assembly

It was the best of times, it was the wor

, it was the worst of times, it was the

isdom, it was the age of foolishness

mes, it was the age of wisdom, it was th

It was the best of times, it was the worst of times, it was the age of wisdom, it was the age of foolishness

…but for 2 bn fragments.

Not subdivisible; not easy to distribute; memory intensive.

Define a hash function (word => num)

Storing words in a Bloom filter>>>x = BloomFilter([1001, 1003, 1005])

>>> 'oogaboog' in x

False

>>>x.add('oogaboog')

>>> 'oogaboog' in x

True

Storing words in a Bloom filter

Storing text in a Bloom filter

Storing and retrieving text

Sequence assembly

Repetitive strings are the devil

Note, it’s a probabilistic data structure

Assembling DNA sequence

• Can’t directly assemble with Bloom filter approach (false connections, and also lacking many convenient graph properties)

• But we can use the data structure to grok graph properties and eliminate/break up data:– Eliminate small graphs (no false negatives!)– Disconnected partitions (parts -> map reduce)– Local graph complexity reduction & error/artifact trimming

…and then feed into other programs.

This is a data reducing prefilter

Right, but does it work??

• Can assemble ~200 GB of metagenome DNA on a single 4xlarge EC2 node (68 GB of RAM) in 1 week ($500).

…compare with not at allon a 512 GB RAM machine.

• Error/repeat trimming on a tricky worm genome: reduction from

– 170 GB resident / 60 hrs

– 54 GB resident / 13 hrs

How good is this graph representation?

• V. low false positive rates at ~2 bytes/k-mer;– Nearly exact human genome graph in ~5 GB.– Estimate we eventually need to store/traverse 50 billion k-

mers (soil metagenome)

• Good failure mode: it’s all connected, Jim! (No loss of connections => good prefilter)

• Did I mention it’s constant memory? And independent of word size?

• …only works for de Bruijn graphs

Thoughts for the future

• Unless your algorithm scales sub-linearly as you distribute it across multiple nodes (hah!), oryour problem size has an upper bound, cloud computing isn’t a long-term solution in bioinformatics

• Synopsis data structures & algorithms (which incl. probabilistic data structures) are a neat approach to parsing problem structure.

• Scalable in-memory local graph exploration enables many other tricks, including near-optimal multinodegraph distribution.

Groxel view of knot-like region / ArendHintze

Acknowledgements:

The k-mer gang:

• Adina Howe

• Jason Pell

• RosangelaCanino-Koning

• Qingpeng Zhang

• ArendHintze

Collaborators:

• Jim Tiedje (Il padrino)

• Janet Jansson, Rachel Mackelprang, Regina Lamendella, Susannah Tringe, and many others (JGI)

• Charles Ofria (MSU)Funding: USDA NIFA; MSU, startup and iCER; DOE; BEACON/NSF

STC; Amazon Education.