The document summarizes an IMGS 2011 bioinformatics workshop. It discusses next-generation sequencing technologies including Roche 454, Illumina/Solexa, and AB SOLiD. It also covers topics like sequence alignments, file formats, tools for analysis including BWA and TopHat, and visualization. The document provides links to video tutorials and resources on sequencing technologies, alignments, and analyzing RNA-seq data.

1. IMGS 2011 Bioinformatics WorkshopDeanna Church, NCBICarol Bult, The Jackson Laboratory

2. IntroSequencing Technology: life in the fast laneAlignments: things to considerFile formats: everything you always wanted to know but were afraid to askTools: Pick the right one for the job at hand

3. CostThroughputGigabasesCost per KbLucinda Fulton, The Genome Center at Washington University

4. Sequencing Technologieshttp://www.geospiza.com/finchtalk/uploaded_images/plates-and-slides-718301.png

5. Sequence “Space”Roche 454 – Flow spaceMeasure pyrophosphate released by a nucleotide when it is added to a growing DNA chainFlow space describes sequence in terms of these base incorporationshttp://www.youtube.com/watch?v=bFNjxKHP8JcAB SOLiD – Color spaceSequencing by DNA ligation via synthetic DNA molecules that contain two nested known bases with a flouorescent dyeEach base sequenced twicehttp://www.youtube.com/watch?v=nlvyF8bFDwM&feature=relatedIllumina/Solexa – Base spaceSingle base extentions of fluorescent-labeled nucleotides with protected 3 ‘ OH groupsSequencing via cycles of base addition/detection followed deprotection of the 3’ OHhttp://www.youtube.com/watch?v=77r5p8IBwJk&feature=relatedGenomeTV – Next Generation Sequencing (lecture)http://www.youtube.com/watch?v=g0vGrNjpyA8&feature=relatedhttp://finchtalk.geospiza.com/2008/03/color-space-flow-space-sequence-space_23.html

6. Optimal global alignmentOptimal local alignmentNeedleman-WunschSmith-WatermanSequences align essentially from end to endSequences align only in small, isolated regionsGlobal and local alignmentsReferencesNeedleman and Wunsch (1970). J. Mol. Biol. 48, 443-453.Smith and Waterman (1981). Nucleic Acids Res 13, 645-656.

8. Word size = 3(configurable) Hashing methodsReferencesWilbur & Lipman (1983), PNAS80, 726-30Lipman & Pearson (1985), Science227, 1435-1441Pearson & Lipman (1988), PNAS85, 2444-2448MVRRLPERTSTPACEQuery sequenceMVRVRRRRLRLPLPEPERERTRTSTSTSTPTPAPACACE

13. http://www.slideshare.net/thomaskeane/eccb-2010-nextgen-sequencing-tutorial

14. http://www.slideshare.net/thomaskeane/eccb-2010-nextgen-sequencing-tutorial

15. Sensitivity vs. SpecificitySensitivity = actual number of true positives (tp) identifiedSpecificity = number of true negatives (tn) identifiedPredictedpositivesnegativespositivesActualnegativesSensitivity= TP/(TP+FN)Specificity=TN/(TN+FP)

16. Richa AgarwalaMHC Alternate locusAlignment to chr6

17. ToolsAlignmentsBLAST: not for NGSBWABowtieMaq…TranscriptomicsTophatCufflinks…Variant callingssahaSNPMosaic…Counting (Chip-Seq, etc)FindPeaksPeakSeq

18. Genome Workbenchhttp://www.ncbi.nlm.nih.gov/projects/gbench/

19. “Standard” File formatsSequence containersFASTAFASTQBAM/SAMAlignmentsBAM/SAMMAFAnnotationBEDGFF/GTF/GFF3WIGVariationVCFGVF

20. FASTQ: Data FormatFASTQText basedEncodes sequence calls and quality scores with ASCII charactersStores minimal information about the sequence read4 lines per sequenceLine 1: begins with @; followed by sequence identifier and optional descriptionLine 2: the sequenceLine 3: begins with the “+” and is followed by sequence identifiers and description (both are optional)Line 4: encoding of quality scores for the sequence in line 2References/Documentationhttp://maq.sourceforge.net/fastq.shtmlCock et al. (2009). Nuc Acids Res 38:1767-1771.

21. FASTQ ExampleFor analysis, it may be necessary to convert to the Sanger form of FASTQ…For example,Illumina stores quality scores ranging from 0-62;Sanger quality scores range from 0-93.Solexa quality scores have to be converted to PHRED quality scores.FASTQ example from: Cock et al. (2009). Nuc Acids Res 38:1767-1771.

22. SAM (Sequence Alignment/Map)It may not be necessary to align reads from scratch…you can instead use existing alignments in SAM formatSAM is the output of aligners that map reads to a reference genomeTab delimited w/ header section and alignment sectionHeader sections begin with @ (are optional)Alignment section has 11 mandatory fieldsBAM is the binary format of SAMhttp://samtools.sourceforge.net/

23. Mandatory Alignment Fieldshttp://samtools.sourceforge.net/SAM1.pdf

24. Alignment ExamplesAlignments in SAM formathttp://samtools.sourceforge.net/SAM1.pdf

25. Valid BED fileschr1 86114265 86116346 nsv433165chr2 1841774 1846089 nsv433166chr16 2950446 2955264 nsv433167chr17 14350387 14351933 nsv433168chr17 32831694 32832761 nsv433169chr17 32831694 32832761 nsv433170chr18 61880550 61881930 nsv433171chr1 16759829 16778548 chr1:21667704 270866 -chr1 16763194 16784844 chr1:146691804 407277 +chr1 16763194 16784844 chr1:144004664 408925 -chr1 16763194 16779513 chr1:142857141 291416 -chr1 16763194 16779513 chr1:143522082 293473 -chr1 16763194 16778548 chr1:146844175 284555 -chr1 16763194 16778548 chr1:147006260 284948 -chr1 16763411 16784844 chr1:144747517 405362 +

26. Mouse chrX: 35,000,000-36,000000

27. Mouse chrX: 35,000,000-36,000000XMGSCv3Build 36

28. NC_000086.6

29. GRCh37hg19Zv7danRer5MGSCv37mm8NCBIM37

30. Assemblies with the same name aren’t always the samechr21:8,913,216-9,246,964

31. Assemblies with the same name aren’t always the sameZv7 chr21:8,913,216-9,246,964 X Mouse Build 36 chrX

32. GRCh37hg19GCA_000001405.1

33. Tutorial Web Sitehttp://www.ncbi.nlm.nih.gov/staff/church/GenomeAnalysis/index.shtmlThis site will be accessible after the meeting. Check back for updates and new tutorials.

35. RNA Seq WorkflowConvert data to FASTQUpload files to GalaxyQuality Control Throw out low quality sequence reads, etc.Map reads to a reference genomeMany algorithms availableTrade off between speed and sensitivityData summarizationAssociating alignments with genome annotationsCountsData VisualizationStatistical Analysis

36. Typical RNA_Seq Project Work Flow Tissue SampleTotal RNAmRNAcDNA Sequencing FASTQ fileQCTopHatCufflinksGene/Transcript/Exon ExpressionVisualizationStatistical AnalysisJAX Computational Sciences Service

37. TopHathttp://tophat.cbcb.umd.edu/TopHat is a good tool for aligning RNA Seq data compared to other aligners (Maq, BWA) because it takes splicing into account during the alignment process.Figure from: Trapnell et al. (2010). Nature Biotechnology 28:511-515.Trapnell et al. (2009). Bioinformatics 25:1105-1111.

38. TopHat is built on the Bowtie alignment algorithm.Trapnell C et al. Bioinformatics 2009;25:1105-1111

39. Cufflinkshttp://cufflinks.cbcb.umd.edu/ Assembles transcripts,

40. Estimates their abundances, and

41. Tests for differential expression and regulation in RNA-Seq samples Trapnell et al. (2010). Nature Biotechnology 28:511-515.

42. GalaxySee Tutorial 1 http://main.g2.bx.psu.edu/Build and share data and analysis workflowsNo programming experience requiredStrong and growing development and user community

43. Short Read Archivehttp://trace.ncbi.nlm.nih.gov/Traces/sra/sra.cgi?Short Read Archive Handbookhttp://www.ncbi.nlm.nih.gov/books/NBK47528/

44. Aspera Connecthttp://www.asperasoft.com/en/products/client_software_2/aspera_connect_8High performance file transfer for getting data from the Short Read Archive

45. SRA Toolkithttp://trace.ncbi.nlm.nih.gov/Traces/sra/sra.cgi?view=software

46. Galaxy on the CloudCreate an Amazon Web Services AWS accountSign up for Amazon Elastic Compute Cloude (EC2) andAmazon Simple Storage Service (S3 service)Use the AWS Management Console to start a master EC2 instanceUse the Galaxy Cloud web interface to manage the clusterStep by step instructions are here:https://bitbucket.org/galaxy/galaxy-central/wiki/cloudScreencast to demonstrate the sign up process is here:https://bitbucket.org/galaxy/galaxy-central/wiki/cloudAfgan E., Baker D., Coraor N., Chapman B., Nekrutenko A., Taylor J. (2010) BMC Bioinformatics. 11:2010.

47. Why Go to the Cloud?Files and Compute needs are much greater for next gen sequence data Amazon cloud provides a scalable, cost-effective solutionAfgan E., Baker D., Coraor N., Chapman B., Nekrutenko A., Taylor J. (2010) BMC Bioinformatics. 11:2010.

48. Some Tips You’ll need a credit card to activate the service You’ll need to be near a phone so that you can verify your identity during the sign up processThere is a time lag between signing up for AWS and getting access

49. HistoryDialog/Parameter SelectionToolsLet’s Get Started!