Big Data Challenges in Biology and the Sheffield Bioinformatics Hub

Dr. Roy ChaudhuriDepartment Of Molecular Biology and Biotechnology

Marta MiloBiomedical Science

Roy ChaudhuriMolecular Biology

and BiotechnologyEran ElhaikAnimal and Plant Sciences

James BradfordOncology

Winston HideSITran

(from August)

Ian SudberyMolecular Biology and Biotechnology(from December)

What is Big Data?

Small data: 1

Big data: 1

Biological Big Data – Imaging Data

Biological Big Data – Sequence Data

Sanger Dideoxy Sequencing

Dye-terminator Sequencing

Read lengths ~800bp

phiX174 genome - 1977

Escherichia coli K-124.6m base pairs

E.coli K-12 genome - 1997Ordered sequencing approach

Shotgun Sequencing

Human genome ~3 billion base pairs

119 volumes, 4.75pt Courier

2007: Next Generation Sequencinga.k.a. Massively parallel sequencing

Read lengths 50-300bp (initially 37bp)

De novo Genome Assembly

Genomic DNA

Gap ContigContig

De Bruijn graph assembly

“It was the best of times,it was the worst of times,it was the age of wisdomit was the age of foolishness”

Break up into fixed length chunks called k-mers

• As read lengths increase, the de Bruijn graph becomes simpler.

• Resolving bubbles is one of the key functions of assembly software. The process uses additional information such as coverage levels and paired reads.

• If a bubble cannot be resolved, it results in a break in the assembly.

• Memory is the limiting factor. De novo assembly of large and complex genomes can require >1TB

Resequencing: mapping to a reference genome

Variant detection• Efficient Burrows-Wheeler transformed genome indexes• Memory is less of an issue than de novo assembly• Embarrassingly parallelisable task – number of cores important• Deep coverage required – issues with storage and disk I/O

Transcriptome sequencing• RNA sequencing to understand gene expression• Requires splice-aware mapping to reference genome• It can be challenging to resolve alternative transcripts

De novo transcriptome assembly

• De novo transcriptome assembly is a complex problem

• Many reads could belong to multiple transcripts

• Transcripts present at different levels, so use coverage to distinguish overlapping transcripts

Metagenome assembly of complex populations

• Sargasso sea• Soil metagenomes• Human microbiota eg. gut, skin, oral cavity etc. “the second human genome”, linked with non-infectious conditions such as obesity and cancer

Single Molecule Real Time (SMRT) SequencingRead lengths up to 30kb

50kb reads “easily obtained”Promise of direct DNA, RNA and protein sequencing, and detection of epigenetic factors such as methylation

Min-ION Grid-ION

• Genome sequencing technologies are developing at a rate that exceeds Moore’s Law

• The limiting factor is our ability to analyse the data (this is known as “the Bioinformatics Gap”)

• This may be as bad as it gets, improved read lengths and sequence quality may mean that less coverage will be required for variant calling, and de novo assembly will become trivial or unnecessary

• In the long run, it may be simpler to store DNA and resequence, rather than store the data

• But there is no shortage of DNA to sequence, and there will be a need for real time analysis software as sequencing becomes routine and ubiquitous

• Increased emphasis on understanding genome function rather than structure

The future