Download as PDF, PPTX
Uploaded byAdrian Baez-Ortega
IonGAP - an Integrated Genome Assembly Platform for Ion Torrent Data
The document outlines the development of IonGap, an integrated software platform for the de novo assembly of genomic data derived from Ion Torrent sequencing. It includes a comprehensive analysis of available genome assemblers, concluding that the Mira assembler is most suitable for long-read assembly, while explaining various stages of genome processing and analysis. Future work includes options for cloud-based assembly and enhanced parallel processing for large genomes.
More Related Content
Similar to IonGAP - an Integrated Genome Assembly Platform for Ion Torrent Data
IonGAP - an Integrated Genome Assembly Platform for Ion Torrent Data
- 1. Author Adrián Báez Ortega Supervisors MarcosColebrook Santamaría José Luis Roda García Date 17/07/2014 IonGAP
- 2. Contents 1. Introduction 2. Objectiveof 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 IonGAP 1
- 3. DNA Genomics Genome Proteins GenesDouble helix BiomedicineLife Introduction IonGAP 2
- 4. Genome sequencing Genome de novo assembly Adaptedfrom: http://en.wikipedia.org/wiki/Genomic_library#mediaviewer/File:Whole_genome_shotgun_sequencing_versus_Hierarchical_shotgun_sequencing.png Introduction IonGAP 3
- 5. Introduction Genomics Instituto Universitario de Enfermedades Tropicalesy Salud Pública de Canarias Computer Science Escuela Técnica Superior de Ingeniería InformáticaBioinformatics IonGAP 4
- 6. Objective of theproject 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. IonGAP 5
- 7. Most sequencing technologies: Paired-end shortreads IUETSPC’s sequencing technology: 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 IonGAP 6
- 8.
- 9. Genome assembly • Genomeassembler – Overlap-layout-consensus (OLC) assemblers – De Bruijn graph (DBG) assemblers State of the art IonGAP 8
- 10. Genome assembly • Genomeassembler – Overlap-layout-consensus (OLC) assemblers – De Bruijn graph (DBG) assemblers Adapted from: http://gcat.davidson.edu/phast State of the art IonGAP 9
- 11. Genome assembly • Genomeassembler – Overlap-layout-consensus (OLC) assemblers – De Bruijn graph (DBG) assemblers State of the art IonGAP 1 0
- 12.
- 13. Data preprocessing • Removingadapters • Quality control State of the art IonGAP 12
- 14. Data preprocessing • Qualitycontrol State of the art IonGAP 13
- 15. Genome finishing • Scaffolding •Correction of assembly errors – Discrepancies with reads or reference genome – Repeat correction State of the art IonGAP 14
- 16. Genome finishing • Scaffolding •Correction of assembly errors – Discrepancies with reads or reference genome – Repeat correction State of the art IonGAP 15
- 17. Genome finishing • Scaffolding •Correction of assembly errors – Discrepancies with reads or reference genome – Repeat correction State of the art IonGAP 16
- 18. The genome assembler IonGAP17 Data preprocessing Genome assembly Genome finishing Genome analysis
- 19. The genome assembler Dataset Streptococcus agalactiae (686,800 reads) IonGAP 18 Source: http://ngm.nationalgeographic.com/wallpaper/img/2013/01/08-streptococcus_1600.jpg
- 20. The genome assembler Comparativestudy of assemblers • OLC assemblers – MIRA – Celera Assembler – SGA IonGAP 19 • DBG assemblers – ABySS – Ray – Velvet – SparseAssembler – Minia
- 21. Results • Number ofcontigs ≥ 500 bp • N50 length Conclusions • MIRA is the most suitable assembler • DBG is not indicated for long-read assembly The genome assembler IonGAP 20
- 22. Results • Number ofcontigs ≥ 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 IonGAP 21
- 23. Results • Number ofcontigs ≥ 500 bp • N50 length Conclusions • MIRA is the most suitable assembler • DBG is not indicated for long-read assembly The genome assembler IonGAP 22
- 24. Results • Number ofcontigs ≥ 500 bp • N50 length Conclusions • MIRA is the most suitable assembler • DBG is not indicated for long-read assembly 1 The genome assembler IonGAP 23
- 25. Results • Number ofcontigs ≥ 500 bp • N50 length Conclusions • MIRA is the most suitable assembler • DBG is not indicated for long-read assembly The genome assembler IonGAP 24
- 26. MIRA assembler The genomeassembler IonGAP 25 1 Automatic editing Data preprocessing Fast read comparison Smith-Waterman alignment Contig assembly Finished project
- 27. 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 IonGAP 26 Minimum size of a contig for being considered as "large"
- 28. A genome assemblyand analysis pipeline IonGAP 27 Data preprocessing Genome assembly Genome finishing Genome analysis
- 29. aagttttggaaccattcgaaacagcacagctctaaaacttaccgattagaacatcatcta aggtaatcgttttggaaccattcgaaacagcacagctctaaaactatcgctcaagcattc gtatttgttttggagttttggaaccattcgaaacagcacagctctaaaacaacatttaac tcataactatcatttagagtgttttggaaccattcgaaacagcacagctctaaaactaag taacaagacagacttgaaactgttaagttttggaaccattcgaaacagcacagctctaaa acttaccgattagaacatcatctaaggtaatcgttttggaaccattcgaaacagcacagc tctaaaactatcgctcaagcattcgtatttgttttggagttttggaaccattcgaaacag cacagctctaaaacatttccagtaagttcaaatttaacaaatgtgttttggaaccattcg aaacagcacagctctaaaacagttttaacattaaatcacgtcttaaataagttttggaac cattcgaaacagcacagctctaaaactaccgcaataagatcaccaatgttgtttgagttt tggaaccattcgaaacagcacagctctaaaacgctattagtggaaacttttgaacgttat gtgttttggaaccattcgaaacagcacagctctaaaacgaacaagatgtagatatgaaat taacatttgttttggaaccattcgaaacagcacagctctaaaacctccaagtgctttaaa gtcatttattttttgttttggaaccattcgaaacagcacagctctaaaacccatcatcaa cctgaatgactccacatttcgttttggaaccattcgaaacagcacagctctaaaacgacc cttatcaaacccaagcagaagtaactgttttggaaccattcgaaacagcacagctctaaa acgatggtcgagcacttagaaaaccaataaaagttttggaaccattcgaaacagcacagc tctaaaacgcttgtttcgctgtcgctcttgtttgacgggttttggaaccattcgaaacag cacagctctaaaacaagcacaagaagcaactgttagaagacatagttttggaaccattcg aaacagcacagctctaaaacacagctgaagagttagaaaaggctaatgttgttttggaac cattcgaaacagcacagctctaaaacacatgacctgctgaacctgtccaccatatcgttt tggaaccattcgaaacagcacagctctaaaactctgagatgagaacatatacttattctt ttgttttggaaccattcgaaacagcacagctctaaaactctgagatgagaacatatactt attcttttgttttggaaccattcgaaacagcacagctctaaaacctcgtagaaaattttc ttttgagctttcgtaatcgcgccattcgtctcagcaggacttcagtttcgatgattcctt gttattactgtgcttttactaatattataccatattttcgcctatcaagaaataatcctt atcaataacatattgcggtaaatcatagagtcttctaggttctagaaagagtactgactt ttgcattaaattgatgtattcacataattttataacttcatctttggtaagataagctcc gctattaacaaaaaccaagagattctttttcgttaaataatggtaaacttgtataatttc aaaacatttttcaaagatagtgtcgctctgtgtctcaattttgactcccagtgccttaat gagttctaaaatcgtaatttcatcgtattctaaatcaagctcattctctagacactcaaa gene cas2 inference abinitio 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 IonGAP 28
- 30. A genome assemblyand analysis pipeline IonGAP 29 Genome assembly Data preprocessing Genome finishing Genome analysis
- 31. Data preprocessing • Comparativestudy 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 IonGAP 30
- 32. A genome assemblyand analysis pipeline Data preprocessing Mauve Assembly Metrics IonGAP 31
- 33. Data preprocessing Conclusion Read preprocessinghas 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 IonGAP 32
- 34. A genome assemblyand analysis pipeline IonGAP 33 Data preprocessing Genome finishing Genome assembly Genome analysis
- 35. Genome finishing • Scaffolding –Impossible: no mate-pair reads • Correction of assembly errors – Simplifier: selective elimination of redundant sequences A genome assembly and analysis pipeline IonGAP 34
- 36. Genome finishing Simplifier • Onlyeliminates 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 IonGAP 35
- 37. A genome assemblyand analysis pipeline IonGAP 36 Data preprocessing Genome analysis Genome assembly Genome finishing
- 38. Genome analysis • Qualityanalysis 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 IonGAP 37
- 39. Genome analysis • Taxonomicclassification (BLAST) • Genome annotation (Prokka) A genome assembly and analysis pipeline IonGAP 38
- 40. Genome analysis • Genomeannotation (Prokka) UGENE genome viewer A genome assembly and analysis pipeline IonGAP 39
- 41. Genome analysis If referencesequence provided: • Genome alignment and coverage analysis (MUMmer, Circos, BLAST, Circoletto, Mauve, genoPlotR) A genome assembly and analysis pipeline IonGAP 40
- 42. Generated by Circos, BLAST andCircoletto A genome assembly and analysis pipeline IonGAP 41
- 43. Genome analysis If referencesequence provided: • Contig reordering (Mauve) A genome assembly and analysis pipeline IonGAP 42 Mauve genome viewer
- 44. Genome analysis If referencesequence provided: • Contig reordering (Mauve) A genome assembly and analysis pipeline IonGAP 43 Mauve genome viewer
- 45. Functioning and implementation •Web user interface • Input Web form • Two independent modules (daemons) – Assembly module – Analysis module • User notification via email IonGAP Web service IonGAP 44
- 46. Functioning and implementation •Hosting: ETSII’s Computing Center – Virtual machine (Ubuntu 12.04) – Dual core 64 bits processor – 17 GB RAM IonGAP Web service IonGAP 45
- 47.
- 48.
- 49. Web service demo IonGAP| an integrated Genome Assembly Platform for Ion Torrent data IonGAP Web service IonGAP 48 (http://193.145.101.223/)
- 50. Genome assembly withIonGAP Trypanosoma cruzi • Extremely repetitive genome • Data explosion • Data filtering: 900 MB = 1,500,000 reads IonGAP Web service IonGAP 49
- 51. Parallel assembly oflarge genomes Parallel genome assembly • Parallel computing: Computer cluster • Contrail – Parallel assembly on Hadoop • ETSII’s Computing Center – Cluster of 108 computers – Hadoop installation IonGAP 50
- 52. Parallel assembly oflarge genomes Parallel assembly with Contrail IonGAP 51
- 53. Parallel assembly withContrail 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 IonGAP 52
- 54. • IonGAP solvesthe 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 IonGAP 53
- 55. Future work • Newoptions and features • Cloud assembly with Amazon Web Services • Parallel OLC assembly on Hadoop • High performance computing – ITER’s Teide HPC – September 2014 Conclusions IonGAP 54
- 56. Conclusions Multidisciplinary work isthe way to tackle the new science of the 21st century IonGAP 55 Genomics Instituto Universitario de Enfermedades Tropicales y Salud Pública de Canarias Computer Science Escuela Técnica Superior de Ingeniería Informática Bioinformatics
- 57.