Bio4j: A graph based database for biological Big Data integration

Bio4j: A pioneer graph based
database for the integration of
biological Big Data

www.ohnosequences.com www.bio4j.com

Who am I?
I am Currently working as a Bioinformatics consultant/developer/researcher at
Oh no sequences!

Oh no what !?
We are the R&D group at Era7 Bioinformatics.
we like bioinformatics, cloud computing, NGS, category theory, bacterial
genomics…
well, lots of things.

What about Era7 Bioinformatics?
Era7 Bioinformatics is a Bioinformatics company specialized in sequence analysis,
knowledge management and sequencing data interpretation.
Our area of expertise revolves around biological sequence analysis, particularly
Next Generation Sequencing data management and analysis.


A bit of background…

In Bioinformatics we have highly interconnected overlapping knowledge spread
throughout different DBs


However all this data is in most cases modeled in relational databases.
Sometimes even just as plain CSV files

As the amount and diversity of data grows, domain models
become crazily complicated!


With a relational paradigm, the double implication

Entity  Table

does not go both ways.

You get ‘auxiliary’ tables that have no relationship with the small
piece of reality you are modeling.

You need ‘artificial’ IDs only for connecting entities, (and these are mixed
with IDs that somehow live in reality)

Entity-relationship models are cool but in the end you always have to
deal with ‘raw’ tables plus SQL.

Integrating/incorporating new knowledge into already existing
databases is hard and sometimes even not possible without changing
the domain model


Life in general and biology in particular are probably not 100% like a graph…

but one thing’s sure, they are not a set of tables!


What’s Bio4j?

Bio4j is a bioinformatics graph based DB including most data
available in :

Uniprot KB (SwissProt + Trembl) NCBI Taxonomy

Gene Ontology (GO) RefSeq

UniRef (50,90,100) Enzyme DB


What’s Bio4j?

It provides a completely new and powerful framework
for protein related information querying and
management.

Since it relies on a high-performance graph engine, data
is stored in a way that semantically represents its own
structure


What’s Bio4j?

Bio4j uses Neo4j technology, a "high-performance graph
engine with all the features of a mature and robust
database".

Thanks to both being based on Neo4j DB and the API
provided, Bio4j is also very scalable, allowing anyone
to easily incorporate his own data making the best
out of it.


What’s Bio4j?

Everything in Bio4j is open source !

released under AGPLv3


Bio4j in numbers

The current version (0.8) includes:

Relationships: 717.484.649

Nodes: 92.667.745

Relationship types: 144

Node types: 42

We’re approaching the 1 billion relationships! :)


Let’s dig a bit about Bio4j structure…

Data sources and their relationships:


Bio4j domain model


Bio4j modules

Bio4j includes different data sources but you may not always be interested in having
all of them.

That’s why the importing process is modular and customizable, allowing you to
import just the data you are interested in.


Bio4j modules

You must however keep in mind that you must be coherent when choosing the data
sources you want to have included in your database; that’s to say, you cannot import
for example protein interactions without having first included the proteins! ;)

Here’s a schema showing
the dependencies
for the importing process:


The Graph DB model: representation

Core abstractions:

Nodes

Relationships between nodes

Properties on both


How are things modeled?

Couldn’t be simpler!

Entities Associations / Relationships

Nodes Edges


Some examples of nodes would be:

GO term
Protein
Genome Element

and relationships:

Protein PROTEIN_GO_ANNOTATION

GO te rm


We have developed a tool aimed to be used both as a reference manual and
initial contact for Bio4j domain model: Bio4jExplorer

Bio4jExplorer allows you to:

• Navigate through all nodes and relationships

• Access the javadocs of any node or relationship

• Graphically explore the neighborhood of a node/relationship

• Look up for the indexes that may serve as an entry point for a node

• Check incoming/outgoing relationships of a specific node

• Check start/end nodes of a specific relationship


Entry points and indexing

There are two kinds of entry points for the graph:

Auxiliary relationships going from the reference node, e.g.

- CELLULAR_COMPONENT : leads to the root of GO cellular component
sub-ontology

- MAIN_DATASET : leads to both main datasets: Swiss-Prot and Trembl

Node indexing

There are two types of node indexes:

- Exact: Only exact values are considered hits

- Fulltext: Regular expressions can be used


Retrieving protein info (Bio4j Java API)

//--creating manager and node retriever----
Bio4jManager manager = new Bio4jManager(“/mybio4jdb”);
NodeRetriever nR= new NodeRetriever(manager);

ProteinNode protein = nR.getProteinNodeByAccession(“P12345”);

Getting more related info...

List<InterproNode> interpros = protein.getInterpro();
OrganismNode organism = protein.getOrganism();
List<GoTermNode> goAnnotations = protein.getGOAnnotations();

List<ArticleNode> articles = protein.getArticleCitations();

for (ArticleNode article : articles) {
System.out.println(article.getPubmedId());
}

//Don’t forget to close the manager
manager.shutDown();


Querying Bio4j with Cypher

Getting a keyword by its ID

START k=node:keyword_id_index(keyword_id_index = "KW-0181")
return k.name, k.id

Finding circuits/simple cycles of length 3 where at least one protein is from Swiss-Prot
dataset:

START d=node:dataset_name_index(dataset_name_index = "Swiss-Prot")
MATCH d <-[r:PROTEIN_DATASET]- p,
circuit = (p) -[:PROTEIN_PROTEIN_INTERACTION]-> (p2) -
[:PROTEIN_PROTEIN_INTERACTION]-> (p3) -[:PROTEIN_PROTEIN_INTERACTION]->
(p)
return p.accession, p2.accession, p3.accession

Check this blog post for more info and our Bio4j Cypher cheetsheet


Mining Bio4j data

Finding topological patterns in Protein-Protein
Interaction networks


A graph traversal language

Get protein by its accession number and return its full name

gremlin> g.idx('protein_accession_index')[['protein_accession_index':'P12345']].full_name
==> Aspartate aminotransferase, mitochondrial

Get proteins (accessions) associated to an interpro motif (limited to 4 results)
gremlin>
g.idx('interpro_id_index')[['interpro_id_index':'IPR023306']].inE('PROTEIN_INTERPRO').outV.
accession[0..3]
==> E2GK26
==> G3PMS4
==> G3Q865
==> G3PIL8

Check our Bio4j Gremlin cheetsheet


REST Server

You can also query/navigate through Bio4j with the Neo4j REST API !

The default representation is json, both for responses and or data sent with
POST/PUT requests

Get protein by its accession number: (Q9UR66)

http://server_url:7474/db/data/index/node/protein_accession_index/
protein_accession_index/Q9UR66

Get outgoing relationships for protein Q9UR66

http://server_url:7474/db/data/node/Q9UR66_node_id/relationships/o
ut


Visualizations (1)  REST Server Data Browser

Navigate through Bio4j data in real time !


Visualizations (2)  Bio4j GO Tools


Visualizations (3)  Bio4j + Gephi

Get really cool graph visualizations using Bio4j and Gephi visualization and
exploration platform


Bio4j + Cloud

We use AWS (Amazon Web Services) everywhere we can around Bio4j, giving
us the following benefits:

Interoperability and data distribution

Releases are available as public EBS Snapshots, giving AWS users the
opportunity of creating and attaching to their instances Bio4j DB 100% ready
volumes in just a few seconds.

CloudFormation templates:

- Basic Bio4j DB Instance

- Bio4j REST Server Instance

Backup and Storage using S3 (Simple Storage Service)

We use S3 both for backup (indirectly through the EBS snapshots) and
storage (directly storing RefSeq sequences as independent S3 files)


Why would I use Bio4j ?

Massive access to protein/genome/taxonomy… related information

Integration of your own DBs/resources around common information

Development of services tailored to your needs built around Bio4j

Networks analysis

Visualizations

Besides many others I cannot think of myself…
If you have something in mind for which Bio4j might be useful, please let us know so we
can all see how it could help you meet your needs! ;)


OK, but why starting all this?
Were you so bored…?!

It all started somehow around our need for massive access to protein GO
(Gene Ontology) annotations.

At that point I had to develop my own MySQL DB based on the official
GO SQL database, and problems started from the beginning:

I got crazy ‘deciphering’ how to extract Uniprot protein annotations
from GO official tables schema

Uniprot and GO official protein annotations were not always consistent

Populating my own DB took really long due to all the joins and
subqueries needed in order to get and store the protein annotations.

Soon enough we also had the need of having massive access to basic
protein information.


These processes had to be automated for our (specifically designed for NGS data)
bacterial genome annotation system BG7 (PLOS ONE 2012 in Press)

Uniprot web services available were too limited:

- Slow

- Number of queries limitation

- Too little information available

So I downloaded the whole Uniprot DB in XML format
(Swiss-Prot + Trembl)

and started to have some fun with it !


We got used to having massive direct access to all this protein related
information…

So why not adding other resources we needed quite often in most
projects and which now were becoming a sort of bottleneck
compared to all those already included in Bio4j ?

Then we incorporated:
- Isoform sequences

- Protein interactions and features

- Uniref 50, 90, and 100

- RefSeq

- NCBI Taxonomy

- Enzyme Expasy DB


Bio4j + MG7 + 48 Blast XML files (~1GB each)

Some numbers:

• 157 639 502 nodes

• 742 615 705 relationships

• 632 832 045 properties

• 148 relationship types

• 44 node types

And it works just fine!


MG7 domain model


What’s MG7?

MG7 provides the possibility of choosing different parameters to fix the
thresholds for filtering the BLAST hits:

i. E-value
ii. Identity and query coverage

It allows exporting the results of the analysis to different data formats like:
• XML
• CSV
• Gexf (Graph exchange XML format)

As well as provides to the user with Heat maps and graph visualizations whilst
including an user-friendly interface that allows to access to the alignment
responsible for each functional or taxonomical read assignation and that displays
the frequencies in the taxonomical tree --> MG7Viewer


Heat-map Viz


Finding the lowest common ancestor of a set of NCBI
taxonomy nodes with Bio4j


Future directions

Improvements in modules

Integration of even more massive data

Application to Cancer genomics

Gene flux tool (New tool for bacterial comparative genomics)

Pathways tool Data from Metacyc is going to be included in Bio4j. This data
would allow to dissect the metabolic pathways in which a genome element,
organism or community (metagenomic samples) is involved.
.
Data visualization, network analysis and much more…


Community

Bio4j has a fast growing internet presence:

- Twitter: check @bio4j for updates

- Blog: go to http://blog.bio4j.com

- Mail-list: ask any question you may have in our list.

- LinkedIn: check the Bio4j group

- Github issues: don’t be shy! open a new issue if you think
something’s going wrong.


That’s it !

Thanks for
your time ;)


Bio4j: A graph based database for biological Big Data integration

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