metagenomics and cloud_computing_london_january_2014_era7_bioinformatics

http://ohnosequences.com

www.era7bioinformatics.com

A New Cloud Computing System for Massive Analysis of Reads from

Metagenomics Samples

A New Era in Diagnostic Microbiology Pathogen Genomics. Whole Genome Sequencing

15 January 2014. The Royal College of Pathologists.

- A bit of context:

• What is Era7

• What is Oh no sequences! Research group

• Research lines / Research projects

- Clonal cultures versus Metagenomics

- Microbiome

- Microbiome in health and disease

- Metagenomics in a clinical sample

- 16S and shotgun metagenomics

- Metagenomics for detection of viruses

- Metagenomics for detection of bacteria

- The metagenomics bioinformatics challenge:

• High computational cost

• Bining for reducing computation

• Reducing reference database

- MG7

• Cloud computing

• MG7 algorithms and pipeline

• Lowest Common Ancestor assignment

• MG7 uses Graph databases

• MG7 uses NCBI taxonomy tree

A New Cloud Computing System for Massive Analysis of Reads from Metagenomics Samples

MG7 for metagenomics analysis www.era7bioinformatics.com

The Royal College of Pathologists15 January 2014

http://ohnosequences.com www.era7bioinformatics.com

A bit of context



What is Era7 Bioinformatics



• Research driven SME• Open Source• Cloud Computing• Next Generation Sequencing



• Bacterial Genomics projects• Comparative Genomics• Metagenomics• Microbiome• RNA-seq (and Dual RNA-seq)• Cancer Genomics• Big Data management and integration



What is Era7 Oh no sequences!


Research Lines:

• Algorithms for assembly

• Methods for bacterial genome annotation

• New Cloud Computing Architectures

• Graph Databases for Biological data

• Comparative genomics and bacterial

evolution

• Genome Plasticity

• Big Data integration and visualization

• Host Immune System and infection

Software Research

Ptojects• BG7

• Bio4j

• Nextmicro

• Statika

• Nispero

• MG7(All of them are Open Source

AGPLv3 projects)

A New Cloud Computing System for Massive Analysis of Reads from Metagenomics Samples

MG7 for metagenomics analysis www.era7bioinformatics.com



Traditional microbial genome sequencing relies upon clonal cultures,

but the new era of genomics is facing a new challenge: the metagenomics analysis



Microbiome analysis is possible by metagenomics approaches.• Health and Disease • Therapeutic Interventions• Transplant• Immune system



Microbiome in Health and Disease • Inflamatory Bowel Disease• Diabetes• Obesity• Cardiovascular Disease• Colon Cancer



Modifying the Microbiome • Prebiotics• Probiotics• Microbiome Transplant (Clostridium Difficile)



For bacteria, it should be reminded that only a small fraction of the phylogenetic diversity of Bacteria and Archaea is represented by cultivated organisms



Metagenomics has the potential to revolutionize pathogen detection in public health laboratories by allowing the

simultaneous detection of all microorganisms in a clinical sample



Metagenomic analysis after PCR amplification of different gene regions

Shotgun Metagenomics



Metagenomic analysis after PCR amplification of different gene regions:• 16S rRNA• Gyrase• Ribosomal proteins• Elongation Fctors• RNA Polymerase• ……….16S metagenomics tells us about microbial diversity and relative abundance of species and taxa



Shotgun Metagenomics

Shotgun metagenomics is a much more massive approach able to inform about the functional profile of the different genes present in the sample and even to obtain assembled genomes if the sample is not very complex



Thechnology

• 454 in the past

• illumina today (approaches overlaping paired reads)

• Preprocessing steps very important



For viruses:

Unbiased high-throughput sequencing approach is useful for directly detecting pathogenic viruses without advance genetic information.

The use of metagenomics for virus discovery in clinical samples has opened new opportunities for understanding the aetiology of unexplained illness



For Bacteria:

Metagenomics will probably serve to identify new pathogens, and new infections caused by consortiums.

In chronic infections metagenomics will give us information about the relevance of biofilms and other bacterial organizations that would be important in such infections.. Microbiome analysis has been one of the most important applications of metagenomics.



For Bacteria:

As an example, metagenomics for Mycobacterium infections have demonstrated undetected, plural, strains in the same patient



The Bioinformatics challenge

Metagenomics has a high computational cost

1. One approach is to reduce the need of computation

2. The other is to be more efficient





1. Reducing the computation

• Binning (clustering) the reads 16S and Shotgun.

Operational Taxonomic Units (OTUs) in 16S





1. Reducing the computation

• Reducing the size of the reference database: It is frequent to use only the complete bacterial genomes Shotgun





2. The other is to be more efficient: MG7




Cloud computing can solve the problem of massive data analysis providing scalable, real time, on demand computing for metagenomics data analysis.

However, Cloud Computing infrastructure is not easy to manage and publicly available software solutions would be needed to extend the use of cloud for the analysis of huge metagenomics data sets.



MG7

• Based in Cloud Computing (AWS)• Parallel computation• Each read is compared with the complete

database:• No binning, all the reads• All the known sequences (nt database) for

shotgun• NCBI taxonomy• Graph database for analyzing the assignment

results



MG7 Based in Cloud Computing (AWS)

• EC2• S3• SQS• SNS• ……



MG7 Based in Cloud Computing (AWS) parallel

computation

• A Cloud Master machine creates tasks and set Qeues

• A set (hundreds, it could be thousands) of Cloud instances (usually micro cloud EC2 instances) are launched

• After the parallel computation, results are modeled in a graph database. This allows to further analysis




https://github.com/pablopareja/MG7/wiki





https://github.com/pablopareja/MG7/wiki Data Model for the Graph DatabaseNeo4j




MG7 Based in Cloud Computing (AWS)

• Storage , another challenge. AWS Cloud is very useful:

• S3 for inmediate access

• Glacier for archiving .



MG7 Each read is compared with the complete database:

• Direct Assignment Best Blast Hit It can be done by:• E value• Depending on similarity % and length of

the hit

• Lowest Common Ancestor



MG7 Lowest Common Ancestor


First step:

We start from a set of nodes with

an arbitrary length – 4 in this

sample, which are spread through

the taxonomy tree




Second step:

We fetch then the first node from

the set and calculate its whole

ancestor list to the main root of

the taxonomy.




Third step:Now that we have the list, we take the second node of the set and check if it’s contained in it, if not, we keep going up through its ancestors until we find a marked node. Once it has been found, we get rid of the previous elements in the list (if any) so that they are not taken into account for the next iterations in the algorithm.




Fourth step:We keep going trough our node set, and node C also removes some elements of the list…




Fifth step:Finally we reach the last node of our set, but no element is removed from our list as a result.




Here we have our lowest common ancestor!


MG7 All the known sequences (nt database) for

shotgun

Nt database is the largest nucleotide database.It contains nucleotide sequences from all the

organisms.

This is important to detect:

• Unexpected organism• Contamination



MG7

NCBI taxonomy

This Taxonomy is probably the best and mostcomprehensive

A Graph Database is very appropriate to model aTaxonomy tree



Thanks for your attention!

Marina ManriqueEduardo Pareja-

TobesPablo Pareja-Tobes

Raquel TobesEduardo Pareja

[email protected]

metagenomics and cloud_computing_london_january_2014_era7_bioinformatics

Health & Medicine

royal college of pathologists

metagenomics microbiome

metagenomics approaches

metagenomics sampleshttp

metagenomics analysishttp

use of metagenomics

graph databases metagenomics

microbiome analysis