BG7, a new system for bacterial genome annotation designed for NGS data

BG7A new system for bacterial genome annotation designed for NGS data

www.ohnosequences.com www.era7bioinformatics.com

annotation designed for NGS data

Motivation

Features

How it works?

Comparisons

Motivation

The need of a system specially designed for NGS data annotation with a pipeline unbiased by existing annotation systems designed for Sanger sequences

The need of a versatile system able to annotate genes even in the

step of preliminary assembly of the genome


Upcoming features

step of preliminary assembly of the genome

Special focus is given to the detection of “unexpected proteins” without orthologous in close genomes (horizontally acquired genes, phage genes, plasmid genes…)

A fast, automated and scalable process to face the challenge of analyzing the huge amount of genomes that are being sequenced with NGS technologies

Motivation

Features

How it works?

Comparisons

Features

1. A new approach

2. It’s tolerant to NGS errors


Upcoming features3. It’s based on cloud computing

4. It uses bio4j

Motivation

Features

How it works?

Comparisons

Features: Approach

ORF prediction

is based on


Upcoming features

is based on

protein similarity

Motivation

Features

How it works?

Comparisons

Features: Approach

Use as much information as you can (not just start/stop signals)

TGGATGTGGCTCAGGACGAACGCTGGCGGCGTGCTTAACACATGCAAGTCGAACGGAAAGGCTGA


Upcoming features


A B C D E

Motivation

Features

How it works?

Comparisons

Features: Approach

Standard

Sequence Sequence

BG7


Upcoming features ORF prediction(Glimmer)

Function prediction(Blast)

Protein searching(Blast)

CDS prediction

RNA searching(Blast)

Motivation

Features

How it works?

Comparisons

Features: NGS errors

Issue Technology

Genomes in several contigs All

Sequencing errors in start/stop codons Illumina substitutions

454 indels


Upcoming features

454 indels

Frameshifts 454 indels

Horizontal gene transfer None

BG7 system is tolerant to all these issues

Motivation

Features

How it works?

Comparisons

Features: Cloud computing

AWS (Amazon Web Services)

Completely Scalable On demand


Upcoming features

CheapFast

1 genome in ~2 hours 100 genomes in ~2 hours once you’ve got the reference proteins

Useful in tracking outbreaks

Motivation

Features

How it works?

Comparisons

Features: bio4j

It uses


Upcoming features

www.bio4j.com

Much richer annotations

Motivation

Features

How it works?

Comparisons

How it works?


Upcoming features

1• Expert Manual Selection of reference sequences

2• Protein search• Blast

3

• CDS definition• HSPs merge

• Extension of the similarity region searching for start/stop signals


4

• Solving conflicts• Solving duplicates

• Solving overlaps

5•RNA search• Blast

6

• Incorporation of RNA genes• Definition of RNA genes

• Conflicts with protein coding genes previously annotated are solved

A B C

Motivation

Features

How it works?

Comparisons

Step 2: Protein search with tBlastn

Input contigs (aa)


Upcoming features

Reference Proteins (aa)

are searched in the contigs sequences

Motivation

Features

How it works?

Comparisons

Step 3: CDS definitionMerging HSPs

Input contigs (aa)

Several HSPs


Upcoming features

Input contigs (aa)

Protein

Motivation

Features

How it works?

Comparisons

Step 3: CDS definitionMerging HSPs

Input contigs (aa)

Several HSPs


Upcoming features

Input contigs (aa)

Protein

We merge the HSPs to form a single similarity region

Motivation

Features

How it works?

Comparisons

Step 3: CDS definitionSearch for start/stop signals


Upcoming features

We then search for start/stop signals upstream and downstream the region with high similarity with the protein

Motivation

Features

How it works?

Comparisons

Step 3: CDS definition

Although we don’t find an start/stop codon for a given CDS we keep it

We just mark it accordingly


Upcoming features

We just mark it accordingly

Motivation

Features

How it works?

Comparisons

Step 4: Solving conflictsDuplicates


Upcoming features

Motivation

Features

How it works?

Comparisons

Step 4: Solving conflictsDuplicates


Upcoming features

Motivation

Features

How it works?

Comparisons

Step 4: Solving conflictsOverlapping CDS


Upcoming features

Motivation

Features

How it works?

Comparisons

Step 5: RNA searchBlastn

Input contigs (nt)


Upcoming features

Reference RNAs (nt) are searched in the contigs

Motivation

Features

How it works?

Comparisons

Step 6: Incorporation of RNA genesDefinition of RNA genes

Input contigs (nt)


Upcoming features

Motivation

Features

How it works?

Comparisons

Step 6: Incorporation of RNA genesConflicts with protein coding genes are solved


Upcoming features

If in a particular region we find a protein coding gene and a RNA gene. RNA gene is selected over the protein coding one

Motivation

Features

How it works?

Comparisons

Finally


Upcoming features


A B C D E

Motivation

Features

How it works?

Comparisons

Comparisons

We’ve compared the NCBI annotations for

Escherichia coli str. K-12 substr. MG1655(Refseq ID NC_000913)


Upcoming featuresWith BG7 annotations

Motivation

Features

How it works?

Comparisons

Comparisons

Feature NCBI BG7

The results we got were:


Upcoming features

Feature NCBI BG7

Protein coding genes 4145 43701

49512

RNA 175 156

1 Selected genes2 All detected genes: Selected + dismissed

Motivation

Features

How it works?

Comparisons

Comparisons


Upcoming features

Motivation

Features

How it works?

Comparisons

Comparisons

Conclusions

Even in a not advantageous situation (not a NGS project and a very well annotated genome)


Upcoming features

(not a NGS project and a very well annotated genome)

We got in one round annotation step

- ~95% of the NCBI protein coding genes- ~89% of the NCBI RNA genes- 419 new proteins detected

Motivation

Features

How it works?

Comparisons

Upcoming features

Improvements now focused on:

- Overlapping solving phase


Upcoming features- Detection of very small proteins

And any new need we find using it

Motivation

Features

How it works?

Comparisons

Thanks:

Oh no sequences! team

Raquel Tobes: Bioinformatician, main advisor

Pablo Pareja: Main developer


Upcoming features

Pablo Pareja: Main developer

Eduardo Pareja: Scientific advisor

Eduardo Pareja-Tobes: Mathematician, advisor

Carmen Torrecillas: Junior Bioinformatician

Marina Manrique: Bioinformatician

Thanks for your attention!


Thanks for your attention!

Health & Medicine

BG7, a new system for bacterial genome annotation designed for NGS data