Integration and analysis of multi-type high-throughput data for biomolecular knowledge discovery Dr. Erik Bongcam-Rudloff SGBC-SLU Uppsala, Sweden

Biologists modus operandi Observing a phenomenon that is in some way interesting or puzzling. Making a guess as to the explanation of the phenomenon. Making a guess as to the explanation of the phenomenon. Devising a test to show how likely this explanation is to be true or false. Devising a test to show how likely this explanation is to be true or false. Carrying out the test, and, on the basis of the results, deciding whether the explanation is a good one or not. In the latter case, a new explanation will (with luck) 'spring to mind' as a result of the first test. Carrying out the test, and, on the basis of the results, deciding whether the explanation is a good one or not. In the latter case, a new explanation will (with luck) 'spring to mind' as a result of the first test. http : // http : //

The Observed phenomenon

Selection of test times

But was is the real event?

Sometimes you could be lucky Positive “Positive” results are used “negative” rejected Why? Only positive results are publishable “Positive” results are used “negative” rejected Why? Only positive results are publishable

Next Generation techniques

New challenges 1 TB data

Gbases produced at Sanger

World NGS Map

But this is wonderful! Or? Sequence without knowledge connected to it is worth: 0 The deluge of data produced by these hordes of machines worldwide demand automatic workflows Complete new systems to shuffle data around Storage of never used amounts Machines with gigantic amounts of RAM

COSTS

PROBLEMS NOmenclature Publishing culture Moving target development Old ways of work and resistance to changes in culture

Publishing culture as example We get tax payers money, we pay publishers to publish, the publishers sell the articles and obtain the copy rights To connect knowledge to sequences we need automatic methods, workflows, text mining. Most of this is limited by close database systems. Only available is PubMed. But PubMed has only short abstracts. NO information about conditions, M&M etc We need to change this culture

The BLAST analogy... By far the most used tool by biologists Not possible if databases were not Open Access and freely searchable Imagine if Nucleotide and Protein databases followed the life science publishing model

BLAST

BLAST

BLAST

BLAST

BLAST

Human centric What about all other areas of the Life Sciences? Most genes are named by sequence similarity, but are the functions the same?

Microbiome A microbiome is the totality of microbes, their genetic elements (genomes), and environmental interactions in a particular environment. microbesent.microbesent.

Fat and lean Metabolic effects of transplanting gut microbiota from lean donors to subjects with metabolic syndrome. A. Vrieze et al, EASD abstracts, 24 September The result was: Lean donor faecal infusion improves hepatic and peripheral insulin resistance as well as fasting lipid levels in obese individuals with the metabolic syndrome

Genome sizes

How many species? Several orders of magnitude: Some estimates: 3-50 million species of arthropods million species of nematodes Only a portion of bacterias have being identified, 99% of bacterias cannot be cultured. “Once the diversity of the microbial worldis catalogued, it will make astronomy to look like a pitiful science” Julian Davies, Professor Emeritus. UBC

New research strategies MicrobialLivestockPlants

Typical Sources of Metagenomics Soil samples Sea water samples Air samples Medical samples Farm animal samples Ancient bones Human microbiome

Ion Proton: "Personal Genome Machine". LIFE TECHNOLOGIES CORPORATION Real tests of transcriptome sequencing on the Proton. Using 500 ng of input poly-A RNA, it was possible to generate 50 million reads from a melanoma cancer sample. Joe Boland of the National Cancer Institute according to Genomeweb.

Oxford Nanopore

High technology everywhere!

New applications Only imagination will put the limits of what its possible to be done using Next Generation Technologies!

The big challenge: Open Access, Open source, collaborative networks Data sharing Common language Tool systems to glue all together!!

SeqAhead COST Action BM1006: Next Generation Sequencing Data Analysis Network COST Action 25 countries

ALLBIO 10 partners 8 countries FP7 project Broadening the Bioinformatics Infrastructure to unicellular, animal, and plant science

THANKS!! Como 2012