Post on 27-Aug-2014
description
Author
Adrián Báez Ortega
Supervisors
Marcos Colebrook SantamaríaJosé Luis Roda García
Date
17/07/2014
IonGAP
Contents1. Introduction
2. Objective of the project
3. State of the art
4. The genome assembler
5. A genome assembly and analysis pipeline
6. IonGAP Web service
7. Parallel assembly of large genomes
8. Conclusions
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DNA
Genomics
Genome Proteins
GenesDouble helix
Biomedicine Life
Introduction
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Genomesequencing
Genomede novo assembly
Adapted from:http://en.wikipedia.org/wiki/Genomic_library#mediaviewer/File:Whole_genome_shotgun_sequencing_versus_Hierarchical_shotgun_sequencing.png
Introduction
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Introduction
Genomics
Instituto Universitario de Enfermedades Tropicales y Salud Pública de Canarias
ComputerScience
Escuela Técnica Superior de
Ingeniería InformáticaB
ioin
form
ati
cs
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Objective of the project
The development of an easy-to-use integrated software
platform that offers an optimally configured processing and
de novo assembly of genomic data obtained by Ion Torrent
sequencing, also complemented with several result analysis
stages.
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Most sequencingtechnologies:
Paired-end short reads
IUETSPC’s sequencingtechnology:
Single-end long reads
DNA DNA
5’ 3’ 5’ 3’
Gap25-250 bp 25-250 bp 200-400 bp
Genome sequencing
Genome fragments FASTQ file
State of the art
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Source:http://gcat.davidson.edu/phast/img/contig.png
Genome assembly
State of the art
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Genome assembly
• Genome assembler
– Overlap-layout-consensus (OLC) assemblers
– De Bruijn graph (DBG) assemblers
State of the art
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Genome assembly
• Genome assembler
– Overlap-layout-consensus (OLC) assemblers
– De Bruijn graph (DBG) assemblers
Adapted from:http://gcat.davidson.edu/phast
State of the art
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Genome assembly
• Genome assembler
– Overlap-layout-consensus (OLC) assemblers
– De Bruijn graph (DBG) assemblers
State of the art
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Source:http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2874646
State of the art
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Data preprocessing
• Removing adapters
• Quality control
State of the art
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Data preprocessing
• Quality control
State of the art
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Genome finishing
• Scaffolding
• Correction of assembly errors
– Discrepancies with reads or reference genome
– Repeat correction
State of the art
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Genome finishing
• Scaffolding
• Correction of assembly errors
– Discrepancies with reads or reference genome
– Repeat correction
State of the art
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Genome finishing
• Scaffolding
• Correction of assembly errors
– Discrepancies with reads or reference genome
– Repeat correction
State of the art
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The genome assembler
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Data preprocessing
Genomeassembly
Genome finishing
Genome analysis
The genome assembler
Data set
Streptococcus
agalactiae
(686,800 reads)
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Source:http://ngm.nationalgeographic.com/wallpaper/img/2013/01/08-streptococcus_1600.jpg
The genome assembler
Comparative study of assemblers
• OLC assemblers
– MIRA
– Celera Assembler
– SGA
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• DBG assemblers
– ABySS
– Ray
– Velvet
– SparseAssembler
– Minia
Results
• Number of contigs ≥ 500 bp
• N50 length
Conclusions
• MIRA is the most suitable assembler
• DBG is not indicated for long-read assembly
The genome assembler
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Results
• Number of contigs ≥ 500 bp
• N50 length
Conclusions
• MIRA is the most suitable assembler
• DBG is not indicated for long-read assembly
50% of the genome is in contigs larger than N50
Source:http://schatzlab.cshl.edu/teaching/2012/CSHL.Sequencing/Whole%20Genome%20Assembly%20and%20Alignment.pdf
The genome assembler
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Results
• Number of contigs ≥ 500 bp
• N50 length
Conclusions
• MIRA is the most suitable assembler
• DBG is not indicated for long-read assembly
The genome assembler
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Results
• Number of contigs ≥ 500 bp
• N50 length
Conclusions
• MIRA is the most suitable assembler
• DBG is not indicated for long-read assembly1
The genome assembler
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Results
• Number of contigs ≥ 500 bp
• N50 length
Conclusions
• MIRA is the most suitable assembler
• DBG is not indicated for long-read assembly
The genome assembler
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MIRA assembler
The genome assembler
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1Automatic
editing
Data preprocessing
Fast readcomparison
Smith-Watermanalignment
Contig assembly
Finishedproject
Assembly parameter optimization
• Number of assembly iterations
• Uniform read distribution
• Separation of long repeats in different contigs
• Maximum number of times a contig can be rebuilt during an iteration
• Minimum number of reads per contig
Conclusion
The assembler is set by default in its optimal configuration
• Minimum size of a contig for being considered as "large"
• Minimum read length
• Minimum repeat length
• Minimum overlap length
• Minimum overlap score
The genome assembler
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Minimum size of a contig for being considered as "large"
A genome assembly and analysis pipeline
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Data preprocessing
Genomeassembly
Genome finishing
Genome analysis
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gene cas2
inference ab initio prediction:Prodigal:2.60
inference similar to AA sequence:UniProtKB:G3ECR3
locus_tag Sagalactiae_00003
product CRISPR-associated endoribonuclease Cas2
protein_id gnl|Prokka|Sagalactiae_00003
Contig name Subject name Score % Identity
Sagalactiae_c8 Streptococcus agalactiae 2603V/R strain 2603V/R 16S ribosomal RNA, complete sequence 2846 100.00
Sagalactiae_c8 Streptococcus agalactiae ATCC 13813 strain JCM 5671 16S ribosomal RNA, complete sequence 2772 100.00
Sagalactiae_c10 Streptococcus agalactiae 2603V/R strain 2603V/R 16S ribosomal RNA, complete sequence 2846 100.00
Sagalactiae_c10 Streptococcus agalactiae ATCC 13813 strain JCM 5671 16S ribosomal RNA, complete sequence 2772 100.00
A genome assembly and analysis pipeline
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A genome assembly and analysis pipeline
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Genome assembly
Data preprocessing
Genome finishing
Genome analysis
Data preprocessing
• Comparative study of trimmers
(PRINSEQ, ERNE-filter, Trimmomatic)
– Removing adapters → 5’ trimming
– Discarding useless reads → Minimum length
– Removing low-quality regions
• Internal quality control of MIRA
– Sliding window trimming
Maximum length
Sliding window trimming
Window length
Quality threshold
A genome assembly and analysis pipeline
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A genome assembly and analysis pipeline
Data preprocessing
Mauve Assembly Metrics
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Data preprocessing
Conclusion
Read preprocessing has negative effects on the assembly
• An extensive evaluation of read trimming effects on Illumina NGS data analysis
(Del Fabbro C, Scalabrin S, Morgante M, Giorgi FM. PLoS ONE 2013):
"For high quality values, trimmed datasets produce slightly more fragmented assemblies, probably due to a more stringent trimming that reflects also on lower computational needs."
• MIRA user manual (Chevreux B):
"For heavens' sake: do NOT try to clip or trim by quality yourself. Do NOT try to remove standard sequencing adaptors yourself. Just leave the data alone!"
A genome assembly and analysis pipeline
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A genome assembly and analysis pipeline
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Data preprocessing
Genomefinishing
Genome assembly
Genome analysis
Genome finishing
• Scaffolding
– Impossible: no mate-pair reads
• Correction of assembly errors
– Simplifier: selective elimination of redundant sequences
A genome assembly and analysis pipeline
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Genome finishing
Simplifier
• Only eliminates complete redundant contigs
• Time expensive
• Natural repeats in genome → Risky
Conclusion
It is better to leave postprocessing in the user's hands
A genome assembly and analysis pipeline
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A genome assembly and analysis pipeline
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Data preprocessing
Genomeanalysis
Genome assembly
Genome finishing
Genome analysis
• Quality analysis of reads and contigs (FastQC)
• Taxonomic classification (BLAST)
• Genome annotation (Prokka)
If reference sequence provided:
• Genome alignment and coverage analysis (MUMmer, Circos, BLAST, Circoletto, Mauve, genoPlotR)
• Contig reordering (Mauve)
A genome assembly and analysis pipeline
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Genome analysis
• Taxonomic classification (BLAST)
• Genome annotation (Prokka)
A genome assembly and analysis pipeline
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Genome analysis
• Genome annotation (Prokka)
UGENE genome viewer
A genome assembly and analysis pipeline
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Genome analysis
If reference sequence provided:
• Genome alignment and coverage analysis (MUMmer, Circos, BLAST, Circoletto, Mauve, genoPlotR)
A genome assembly and analysis pipeline
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Generated byCircos, BLAST and Circoletto
A genome assembly and analysis pipeline
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Genome analysis
If reference sequence provided:
• Contig reordering (Mauve)
A genome assembly and analysis pipeline
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Mauve genome viewer
Genome analysis
If reference sequence provided:
• Contig reordering (Mauve)
A genome assembly and analysis pipeline
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Mauve genome viewer
Functioning and implementation
• Web user interface
• Input Web form
• Two independent modules (daemons)
– Assembly module
– Analysis module
• User notification via email
IonGAP Web service
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Functioning and implementation
• Hosting: ETSII’s Computing Center
– Virtual machine (Ubuntu 12.04)
– Dual core 64 bits processor
– 17 GB RAM
IonGAP Web service
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IonGAP Web service
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IonGAP Web service
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Web service demo
IonGAP | an integrated Genome Assembly Platform
for Ion Torrent data
IonGAP Web service
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(http://193.145.101.223/)
Genome assembly with IonGAP
Trypanosoma cruzi
• Extremely repetitive genome
• Data explosion
• Data filtering: 900 MB = 1,500,000 reads
IonGAP Web service
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Parallel assembly of large genomes
Parallel genome assembly
• Parallel computing: Computer cluster
• Contrail
– Parallel assembly on Hadoop
• ETSII’s Computing Center
– Cluster of 108 computers
– Hadoop installation
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Parallel assembly of large genomes
Parallel assembly with Contrail
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Parallel assembly with Contrail
Conclusions
• Good performance
– Parallel computing is the future of assembly
• Bad results
– Contrail uses DBG → Not suitable for long reads
Parallel assembly of large genomes
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• IonGAP solves the need for an automated tool for the assembly and preliminary analysis of Ion Torrent data suffered by IUETSPC
• Availability to the scientific community is directed to stimulate low-cost genome research and development of other customized solutions
• The S. agalactiae genome has been successfully
assembled, and a manuscript is been prepared for publication in a scientific journal
Conclusions
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Future work
• New options and features
• Cloud assembly with Amazon Web Services
• Parallel OLC assembly on Hadoop
• High performance computing
– ITER’s Teide HPC – September 2014
Conclusions
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Conclusions
Multidisciplinary work is the way to tackle the new science of the 21st century
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Genomics
Instituto Universitario de Enfermedades Tropicales y Salud Pública de Canarias
ComputerScience
Escuela Técnica Superior de
Ingeniería Informática
Bio
info
rma
tics
Many thanksfor yourattention
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