Curso de Genómica - UAT (VHIR) 2012 - Análisis de datos de NGS

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  • 1. Introduction to NGS(Now Generation Sequencing) Data Analysis Alex Sánchez Statistics and Bioinformatics Research Group Statistics department, Universitat de Barelona Statistics and Bioinformatics Unit Vall d’Hebron Institut de Recerca NGS Data analysis
  • 2. Outline• Introduction• Bioinformatics Challenges• NGS data analysis: Some examples and workflows • Metagenomics, De novo sequencing, Variant detection, RNA- seq• Software • Galaxy, Genome viewers• Data formats and quality control NGS Data analysis
  • 3. Introduction NGS Data analysis
  • 4. Why is NGS revolutionary?• NGS has brought high speed not only to genome sequencing and personal medicine,• it has also changed the way we do genome research Got a question on genome organization? SEQUENCE IT !!! Ana Conesa, bioinformatics researcher at Principe Felipe Research Center NGS Data analysis
  • 5. NGS means high sequencing capacity GS FLX 454 HiSeq 2000 5500xl SOLiD (ROCHE) (ILLUMINA) (ABI) GS Junior Ion TORRENT NGS Data analysis
  • 6. NGS Platforms Performance 454 GS Junior 35MB NGS Data analysis
  • 7. 454 Sequencing NGS Data analysis
  • 8. ABI SOLID Sequencing NGS Data analysis
  • 9. Solexa sequencing NGS Data analysis
  • 10. Applications of Next-Generation Sequencing NGS Data analysis
  • 11. Comparison of 2nd NGS NGS Data analysis
  • 12. Some numbersPlatform 454/FLX Solex (Illum a ina)AB S ID OLRead length ~350-400bp 36, 75, or 106 bp 50bpSingle read Yes Yes YesPaired-end Reads Yes Yes YesLong-insert (several Kbp) mate-paired reads Yes Yes NoNumber of reads por instrument run 5.00K >100 M 400MMax Data output 0.5Gbp 20.5 Gbp 20GbpRun time to 1Gb 6 Days > 1 Day >1 DayEase of use (workflow) Difficult Least difficult DifficultBase Calling Flow Space Nucleotide space Color sapceD Applica NA tionsWhole genome sequencing and resequencing Yes Yes Yesde novo sequencing Yes Yes YesTargeted resequencing Yes Yes YesDiscovery of genetic variants ( SNPs, InDels, CNV, ...) Yes Yes YesChromatin Immunopecipitation (ChIP) Yes Yes YesMethylation Analysis Yes Yes YesMetagenomics Yes No NoR Applica NA tions Yes Yes YesWhole Transcriptome Yes Yes YesSmall RNA Yes Yes YesExpression Tags Yes Yes Yes NGS Data analysis
  • 13. Bioinformatics challenges of NGS NGS Data analysis
  • 14. I have my sequences/images. Now what? NGS Data analysis
  • 15. NGS pushes (bio)informatics needs up• Need for computer power • VERY large text files (~10 million lines long) – Can’t do ‘business as usual’ with familiar tools such as Perl/Python. – Impossible memory usage and execution time • Impossible to browse for problems • Need sequence Quality filtering • Need for large amount of CPU power • Informatics groups must manage compute clusters • Challenges in parallelizing existing software or redesign of algorithms to work in a parallel environment• Need for Bioinformatics power!!! • The challenges turns from data generation into data analysis! • How should bioinformatics be structured • Bigger centralized bioinformatics services? (or research groups providing service?) • Distributed model: bioinformaticians must be part of the temas. Interoperability? NGS Data analysis
  • 16. Data management issues• Raw data are large. How long should be kept?• Processed data are manageable for most people – 20 million reads (50bp) ~1Gb• More of an issue for a facility: HiSeq recommends 32 CPU cores, each with 4GB RAM• Certain studies much more data intensive than other – Whole genome sequencing • A 30X coverage genome pair (tumor/normal) ~500 GB • 50 genome pairs ~ 25 TB NGS Data analysis
  • 17. So what?• In NGS we have to process really big amounts of data, which is not trivial in computing terms.• Big NGS projects require supercomputing infrastructures• Or put another way: its not the case that anyone can do everything. – Small facilities must carefully choose their projects to be scaled with their computing capabilities. NGS Data analysis
  • 18. Computational infrastructure for NGS• There is great variety but a good point to start with: – Computing cluster • Multiple nodes (servers) with multiple cores • High performance storage (TB, PB level) • Fast networks (10Gb ethernet, infiniband) – Enough space and conditions for the equipment ("servers room") – Skilled people (sysadmin, developers) • CNAG, in Barcelona: 36 people, more than 50% of them informaticians NGS Data analysis
  • 19. Alternatives (1): Cloud Computing• Pros – Flexibility. – You pay what you use. – Don´t need to maintain a data center.• Cons – Transfer big datasets over internet is slow. – You pay for consumed bandwidth. That is a problem with big datasets. – Lower performance, specially in disk read/write. – Privacy/security concerns. – More expensive for big and long term projects. NGS Data analysis
  • 20. Alternatives (2): Grid Computing• Pros – Cheaper. – More resources available.• Cons – Heterogeneous environment. – Slow connectivity (specially in Spain). – Much time required to find good resources in the grid. NGS Data analysis
  • 21. In summary?•“NGS” arrived 2007/8•No-one predicted NGS in 2001 (ten years ago)•Therefore we cannot predict what we will come up against•TGS represents specific challenges–Large Data Storage–Technology-aware software–Enables new assays and new science•We would have said the same about NGS….•These are not new problems, but will require new solutions•There is a lag between technology and software…. NGS Data analysis
  • 22. Bioinformatics and bioinformaticians• The term bioinformatician means many things• Some may require a wide range of skills• Others require a depth of specific skills• The best thing we can teach is the ability to learn and adapt • The spirit of adventure • There is a definite skills shortage • There always has been NGS Data analysis
  • 23. Increasing importance of data analysisneeds NGS Data analysis
  • 24. NGS data analysis NGS Data analysis
  • 25. NGS data analysis stages NGS Data analysis
  • 26. Quality control and preprocessing of NGS data NGS Data analysis
  • 27. Data types NGS Data analysis
  • 28. Why QC and preprocessing• Sequencer output: – Reads + quality• Natural questions – Is the quality of my sequenced data OK? – If something is wrong can I fix it?• Problem: HUGE files... How do they look?• Files are flat files and big... tens of Gbs (even hard to browse them) NGS Data analysis
  • 29. Preprocessing sequences improves results NGS Data analysis
  • 30. How is quality measured?• Sequencing systems use to assign quality scores to each peak• Phred scores provide log(10)-transformed error probability values: If p is probability that the base call is wrong the Phred score is Q = .10·log10p – score = 20 corresponds to a 1% error rate – score = 30 corresponds to a 0.1% error rate – score = 40 corresponds to a 0.01% error rate• The base calling (A, T, G or C) is performed based on Phred scores.• Ambiguous positions with Phred scores <= 20 are labeled with N. NGS Data analysis
  • 31. Data formats• FastA format (everybody knows about it) – Header line starts with “>” followed by a sequence ID – Sequence (string of nt).• FastQ format ( – First is the sequence (like Fasta but starting with “@”) – Then “+” and sequence ID (optional) and in the following line are QVs encoded as single byte ASCII codes • Different quality encode variants• Nearly all downstream analysis take FastQ as input sequence NGS Data analysis
  • 32. The fastq format• A FASTQ file normally uses four lines per sequence. – Line 1 begins with a @ character and is followed by a sequence identifier and an optional description (like a FASTA title line). – Line 2 is the raw sequence letters. – Line 3 begins with a + character and isoptionally followed by the same sequence identifier (and any description) again. – Line 4 encodes the quality values for the sequence in Line 2, and must contain the same number of symbols as letters in the sequence. • Different encodings are in use • Sanger format can encode a Phred quality score from 0 to 93 using ASCII 33 to 126@Seq descriptionGATTTGGGGTTCAAAGCAGTATCGATCAAATAGTAAATCCATTTGTTCAACTCACAGTTT+!*((((***+))%%%++)(%%%%).1***-+*))**55CCF>>>>>>CCCCCCC65 NGS Data analysis
  • 33. Some tools to deal with QC• Use FastQC to see your starting state.• Use Fastx-toolkit to optimize different datasets and then visualize the result with FastQC to prove your success!• Hints: – Trimming, clipping and filtering may improve quality – But beware of removing too many sequences…Go to the tutorial and try the exercises... NGS Data analysis
  • 34. Applications• [1] Metagenomics• [2] De novo sequencing• [3] Amplicon analysis• [4] Variant discovery• [5] Transcriptome analysis• …and more … NGS Data analysis
  • 35. [1] Metagenomics &other community-based “omics”Zoetendal E G et al.Gut 2008;57:1605-1615 NGS Data analysis
  • 36. [1] A metagenomics workflowAAGACGTGGACA GTCCGTCACAACTGA AAGACGTGGACAGATCTGCTCAGGCTAGCATGAAC CATGCGTGCATG GATAGGTGGACCGATATGCATTAGACTTGCAGGGC AGTCGTCAGTCATGGG Short reads (40-150 bps) Assembly Contigs Gene prediction 1 3000 6000 1 3000 6000 1 2000 Homology searching ORFs Proteins, families, functions Functional classification Ontologies Binning Sequences into species Functional profiles
  • 37. [1] Metagenomic ApproachesSMALL-SCALE: 16S rRNA gene profilingThe basic approach is to identify microbes in a complexcommunity by exploiting universal and conserved targets,such as rRNA genesPetrosini.Challenges and limitations: Chimeric sequences caused byPCR amplification and sequencing errors.LARGE-SCALE: Whole Genome Shotgun (WGS)Whole-genome approaches enable to identify andannotate microbial genes and its functions in thecommunity. Challenges and limitations: relatively large amounts of starting material required potential contamination of metagenomic samples with host genetic material high numbers of genes of unknown function. Environmental Shotgun Sequencing (ESS). A primer on metagenomics. PLoS Comput Biol. 2010 Feb 26;6(2):e1000667. NGS Data analysis
  • 38. [1] Comparative MetagenomicsComparing two or more metagenomes is necessary to understand how genomic differencesaffect, and are affected by the abiotic environment.MEGAN can also be used tocompare the OTU compositionof two or more frequency-normalized samples.MG-RAST provides acomparative functional andsequence-based analysis foruploaded samples.Other software based onphylogeneticdata are UniFrac.
  • 39. [1] Some Metagenomics projects"whole-genome shotgun sequencing" was applied to microbial populationsA total of 1.045 billion base pairs of nonredundant sequence were analyzed"whole-genome shotgun sequencing"78 million base pairs of unique DNA sequence were analyzedTo date, 242 metagenomic projects are on going and 103 are completed( NGS Data analysis
  • 40. [2] De novo sequencing NGS Data analysis
  • 41. [3] Amplicon analysisEach amplicon (PCR product) is sequenced individually, allowing for the identification of rare variants and the assignment of haplotype information over the full sequence lengthSome applications: ● Detection of low-frequency (<1%) variants in complex mixtures → rare somatic mutations, viral quasispecies... Ultra-deep amplicon sequencing ● Identification of rare alleles associated with hereditary diseases, heterozygote SNP calling... Ultra-broad amplicon sequencing ● Metabolic profiling of environmental habitats, bacterial taxonomy and phlylogeny 16S rRNA amplicon sequencing NGS Data analysis
  • 42. [3] Example of raw data generation with GS-FLX... NGS Data analysis
  • 43. [3] Data Workflow... Data Processing
  • 44. [3] Final output examples... NT substitution (error) matricesBar plots output example (with circular legend for the AA) AA frequency tables NGS Data analysis
  • 45. [4] Variant discoveryYour aligner decides the type/amount of variants you can identifyNaive SNP calling Reads countingStatistic support SNP calling Maximum likelihood, BayesianQuality score recalibration Recalibrate quality score from whole alignmentLocal realignment around indels Realign readsKnown variants (limited species) dbSNP NGS Data analysis
  • 46. [4] Example: Exome Variant Analysis NGS Data analysis
  • 47. [4] Genotype calling tools NGS Data analysis
  • 48. [4] GATK pipeline NGS Data analysis
  • 49. [4] NGS Data analysis
  • 50. [4] Many ongoing sequencing projects NGS Data analysis
  • 51. [5] Transcriptome Analysis using NGS RNA-Seq, or "Whole Transcriptome Shotgun Sequencing" ("WTSS") refers to use of HTS technologies to sequence cDNA in order to get information about a samples RNA content.  Reads produced by sequencing  Aligned to a reference genome to build transcriptome mappings. NGS Data analysis
  • 52. [5] Applications (1)  Whole transcriptome analysismRNA AAAA Fragmentation  Detects expression of known and novel mRNAs RT  Identification of alternative splicing events cDNA library  Detects expressed SNPs or mutations  Identifies allele specific sequencing expression patterns NGS Data analysis
  • 53. [5] Applications (2) Differential expression 1.Reads are mapped to the reference genome or transcriptome 2.Mapped reads are assembled into expression summaries (tables of counts, showing how may reads are in coding region, exon, gene or junction); 3.The data are normalized; 4.Statistical testing of differential expression (DE) is performed, producing a list of genes with P-values and fold changes. NGS Data analysis
  • 54. [5] RNA Seq data analysis - Mapping•Main Issues: –Number of allowed mismatches End up with a list of # of reads per transcript –Number of multihits –Mates expected distance These will be our (discrete) response variable –Considering exon junctions NGS Data analysis
  • 55. [5] RNA Seq data analysis -Normalization• Two main sources of bias – Influence of length: Counts are proportional to the transcript length times the mRNA expression level. – Influence of sequencing depth: The higher sequencing depth, the higher counts.• How to deal with this – Normalize (correct) gene counts to minimize biases. – Use statistical models that take into account length and sequencing depth NGS Data analysis
  • 56. [5] RNA Seq - Differential expression methods• Fishers exact test or similar approaches.• Use Generalized Linear Models and model counts using – Poisson distribution. – Negative binomial distribution.• Transform count data to use existing approaches for microarray data.• … NGS Data analysis
  • 57. [5] Advantages of RNA-seq Unlike hybridization approaches does not require existing genomic sequence  Expected to replace microarrays for transcriptomic studies Very low background noise  Reads can be unabmiguously mapped Resolution up to 1 bp High-throughput quantitative measurement of transcript abundance  Better than Sanger sequencing of cDNA or EST libraries Cost decreasing all the time  Lower than traditional sequencing Can reveal sequence variations (SNPs) Automated pipelines available NGS Data analysis
  • 58. Software for NGS preprocessing and analysis NGS Data analysis
  • 59. Which software for NGS (data) analysis?• Answer is not straightforward.• Many possible classifications – Biological domains • SNP discovery, Genomics, ChIP-Seq, De-novo assembly, … – Bioinformatics methods • Mapping, Assembly, Alignment, Seq-QC,… – Technology • Illumina, 454, ABI SOLID, Helicos, … – Operating system • Linux, Mac OS X, Windows, … – License type • GPLv3, GPL, Commercial, Free for academic use,… – Language • C++, Perl, Java, C, Phyton – Interface • Web Based, Integrated solutions, command line tools, pipelines,… NGS Data analysis
  • 60. Which software for NGS (data) analysis?• Answer is not straightforward.• Many possible classifications – Biological domains • SNP discovery, Genomics, ChIP-Seq, De-novo assembly, … – Bioinformatics methods • Mapping, Assembly, Alignment, Seq-QC,… – Technology • Illumina, 454, ABI SOLID, Helicos, … – Operating system • Linux, Mac OS X, Windows, … – License type • GPLv3, GPL, Commercial, Free for academic use,… – Language • C++, Perl, Java, C, Phyton – Interface • Web Based, Integrated solutions, command line tools, pipelines,… NGS Data analysis
  • 61. Some popular tools and places NGS Data analysis
  • 62. Site 62
  • 63. Obtain data from many data sources including the UCSC Table Browser, Prepare data for further BioMart, WormBase, analysis by rearranging or your own data. or cutting data columns, Analyze data by finding filtering data and many overlapping regions, other actions. determining statistics, phylogenetic analysis and much more 63
  • 64. User Register contains links to Shows the history the downloading, of analysis steps,pre-procession and displays data and result viewing analysis tools menus and data inputs NGS Data analysis 64
  • 65. Click Get Data 65
  • 66. Get Data from DatabaseNGS Data analysis 66
  • 67. Upload File File Format Upload or paste file 67
  • 68. NGS Data analysis 68
  • 69. FASTQ file manipulation: format conversation, summary statistics, trimming reads, filtering reads by quality score…
  • 70. Input: sanger FASTQOutput: SAM format
  • 71. Downstream analysis: SAM -> BAMNGS Data analysis
  • 72. Co py rig ht Op en He lix. No us e or re pr List saved histories and od uct shared histories. ion Work on a current history, wit ho create new, share workflow ut ex pr es s wri tte n co ns enNGS Data analysis t2 7
  • 73. Creates a workflow, allows user to repeat analysis using different datasets.NGS Data analysis
  • 74. DATA VISUALIZATION NGS Data analysis
  • 75. Why is visualization important?make large amounts of data more interpretableglean patterns from the datasanity check / visual debuggingmore… NGS Data analysis
  • 76. History of Genome Visualization 1800s 1900s 2000s time NGS Data analysis
  • 77. What is a “Genome Browser”linear representation of a genomeposition-based annotations, each called a track continuous annotations: e.g. conservation interval annotations: e.g. gene, read alignment point annotations: e.g. SNPsuser specifies a subsection of genome to look at NGS Data analysis
  • 78. Server-side model (e.g. UCSC, Ensembl, Gbrowse) serve• central data rstore• rendersimages• sends to client client• requestsimages• displaysimages NGS Data analysis
  • 79. Client-side model (e.g. Savant, IGV) serve• stores data r client HTS• local HTS machinestore• rendersimages• displaysimages
  • 80. Rough comparison of Genome Browsers UCSC Ensembl GBrowse Savant IGVModel Server Server Server Client ClientInteractiveHTS supportDatabase oftracksPlugins No support Some support Good support NGS Data analysis
  • 81. Limitations of most genomebrowsersdo not support multiple genomes simultaneouslydo not capture 3-dimensional conformationdo not capture spatial or temporal informationdo not integrate well with analyticscannot be customized The SAVANT GENOME BROWSER has been created to overcome these limitations NGS Data analysis
  • 82. Integrative Genomics Viewer (IGV)he Integrative Genomics Viewer (IGV) is a high-performance visualization toolfor interactive exploration of large, integrated datasets. It supports a wide varietyof data types including sequence alignments, microarrays, and genomicannotations.
  • 83. Acknowledgements Grupo de investigación en Estadística y Bioinformática del departamento de Estadística de la Universidad de Barcelona. All the members at the Unitat d’Estadística i Bioinformàtica del VHIR (Vall d’Hebron Institut de Recerca) Unitat de Serveis Científico Tècnics (UCTS) del VHIR (Vall d’Hebron Institut de Recerca) People whose materials have been borrowed or who have contributed with their work  Manel Comabella, Rosa Prieto, Paqui Gallego, Javier Santoyo, Ana Conesa, Thomas Girke and Silvia Cardona.… NGS Data analysis
  • 84. Gracias por la atención y la paciencia NGS Data analysis