Welcome to GenomeView 101!

Transcription

1 Welcome to GenomeView 101! 1. Start your computer 2. Download and extract the example data Suggestion: - Linux, Mac: make new folder in your home directory - For example: /home/tabeel/broade_demo/ - Windows: make new folder on the C drive, i.e. c:/ - For example: c:/broade_demo/ 3. Make sure you have at least Java 6 4. Make sure you have Firefox 20+ (or compatible) browser 5. Remember where you extracted data in step 2 Please ask for help if needed 1

2 Visualizing (comparative) genomics data with Instructor: Thomas Abeel TA s: Abigail Abby McGuire Christopher Chris Desjardins Gustavo Cerqueira Genome Sequencing and Analysis Program 2

3 Housekeeping Format: Introductory chat (5-15 min) Guided practical work Independent exercises Break If you get stuck, ask for help, help your neighbors 3

4 What is GenomeView Genomics visualization platform Reference based visualizations View and explore genomics data (part 1) Share data, results, analyses, (part 2) Annotation editor Fix annotations Add annotations for your regions of interest 4

5 Visual data interpretation Visual language is easier to communicate results and data, because it is more universal Visual encoding allow you to put massive amounts of data together Human eye and brain are superb at recognizing patterns 5

6 Example Mean x and y, variance x and y, correlations, linear regression identical I II III IV x y x y x y x y

7 Example Mean x and y, variance x and y, correlations, linear regression identical I II III IV x y x y x y x y

8 Recognizing patterns 8

9 Recognizing groups 9

10 Reasons for visualization in genomics Sanity check on data and analyses Hypothesis generation and reasoning Provide insights in large-scale data sets Make it easier to develop algorithms Communicate data The appropriate image makes the solution obvious Share data with collaborators Fun 10

11 What do we want to visualize? Genome centric sequencing data ChIP-seq, RNA-seq, WGS, etc Illumina,,454, Solid, etc. Context: Genes annotation Multiple genomes multiple genome alignments Sequencing data: Individual reads Summary coverage, diversity 11

12 Start GenomeView 12

13 GenomeView started Website: Manual: Support: 13

14 THE BASICS 14

15 15

16 16

17 From data to visualization Analyses Alignment Annotation Assembly Variant calling Predictions 17

18 3 example data sets Bacterial genome diversity Reference + annotations VCF files, BAM files, coverage plots Fungal expression Reference + annotation BAM files and coverage plots Bacterial comparative annotation Reference + annotations Whole genome multiple alignment + annotations 18

19 Example data and set up 1. Download and extract the example data Suggestion: - Linux, Mac: make new folder in your home directory - For example: /home/tabeel/broade_demo/ - Windows: make new folder on the C drive, i.e. c:/ - For example: c:/broade_demo/ Please ask for help if needed 19

20 LOADING A REFERENCE + ANNOTATIONS 20

21 Reference sequence GenomeView = reference based visualizations Genome sequence: fasta file >H37RV TTGACCGATGACCCCGGTTCAGGCTTCACCACAGT GTGGAACGCGGTCGTCTCCGAACTT Annotation: gff3 file H37RV CH gene ID=RVBD_2744c Alternative formats: bed, gbk, embl, 21

22 Reference sequence File > Load data Select data in tb directory H37RV_V5.fasta 22

23 Annotation tracks File > Load data Select data in tb directory H37RV_V5.gff3 23

24 Navigation Things to try: - Zoom all the way in - Zoom all the way out - Zoom till you have about 5 kb visible More things to try: - Move to beginning of genome - Move to the end of the genome - Move to position 2 mb - Move to position 4 mb 24

25 Search and goto Navigation > Search > Keyword search Navigation > Goto Things to try: - Search for the gene KATG - Search for the gene RPOB - Move to position 2 mb - Move to position 4 mb - Search for a sequence (make it long enough!) - Search for a motif 25

26 More information about a gene Select a gene = click the gene Information panel (bottom-left) Things to try: - Check out KATG - Check out RPOB 26

27 Track management To re-order: drag tracks up-down Eye = visible/hidden Trash can = unload data Things to try: - Order tracks: Ruler, Gene structure, mrna, CDS, gene, exon - Hide the gene track and the structure track - Get rid of the exon track 27

28 DIY annotations GFF3 is simple text based file format 9 columns, tab-delimited Important ones: 1 sequence ID 3 type 4 and 5, start and end coordinate Useful ones 6 score 7 strand 9 attributes: semi-colon separated key=value pairs 'Useless' ones 2 source 8 phase, only used for CDS 28

29 Making an annotation file by hand 9 columns, tab-delimited Important ones: 1 sequence ID 2 source 3 type 4 and 5, start and end 6 score 7 strand 8 phase, only used for CDS Things to do: - Make annotation file with several annotations - Make annotations with different types - Add annotations to existing tracks (CDS, mrna, ) - Add two locations that are connected - Try the color key in column 9 9 attributes: semi-colon separated key=value pairs Name -> displayed ID -> links multiple locations together 29

30 Get rid of everything File > Unload all data Load reference and annotation for the crypto data set Reference = H99.fa and start over Annotation 1 = CNA2_FINAL_CALLGENES_2.gff3 Annotation 2 = CNA2_MISC_RNA.gff3 30

31 WORKING WITH RNA-SEQDATA 31

32 Read alignments 32

33 Coverage plot 33

34 Preparing read data Get your reads aligned Sort and index your reads Memory, speed, network access Create coverage plots Sometimes summary information is sufficient 34

35 Working on CLI Command-line interface Go to your data directory (remember?) Windows: cd c:\broade *nix, Mac: cd ~/broade See what s there Windows: dir *nix, Mac: ls 35

36 Making sure Let s test we have Java 6+ java version Result: java version "1.7.0_21" Java(TM) SE Runtime Environment (build 1.7.0_21- b11) Java HotSpot(TM) Client VM (build b01, mixed mode, sharing) 36

37 Sorting bam file Open console/terminal On the command-line, go to folder where you downloaded all files Run: java -Xmx500m jar SortSam.jar I=crypto/plus.bam O=crypto/plus.sorted.bam SO=coordinate Note: - Multi-whitespace is for illustration, single space is sufficient - Instruction is single line - Expected runtime <2 min 37

38 Indexing bam file Open console/terminal On the command-line, go to folder where you downloaded all files Run: java Xmx500m jar BuildBamIndex.jar I=crypto/plus.sorted.bam Note: - Multi-whitespace is for illustration, single space is sufficient - Instruction is single line - Expected runtime <1 min 38

39 Sorting and indexing java -Xmx500m jar SortSam.jar I=crypto/plus.bam O=crypto/plus.sorted.bam SO=coordinate java Xmx500m jar BuildBamIndex.jar I=crypto/plus.sorted.bam More to do: Process the minus files as well and also load it up 39

40 Sequence read mappings Green-blue = reads Orange- cyan = reads Purple = connector for -paired-end (thin) reads -spliced (thick) reads 40

41 Read detail information Read name Cigar string Read sequence Mate/pair information 41

42 Creating coverage plots Open console/terminal On the command-line, go to folder where you downloaded all files Run: java Xmx500m jar bam2tdf.jar crypto/plus.sorted.bam Note: - Multi-whitespace is for illustration, single space is sufficient - Instruction is single line 42

43 Generating coverage plot java Xmx500m jar bam2tdf.jar crypto/plus.sorted.bam More to do: Process the minus files as well and also load it up 43

44 Exercises Search for anti-sense transcripts Zoom around a look for highly expressed genes Zoom around and look for genes with no expression Find a gene with unexpressed exons Share with the group when you find something 44

45 Strand-specific pile-up plots 45

46 WORKING WITH WGS AND VARIANT CALLS 46

47 Switching back to TB Load reference and annotation for the tb data set Reference = H37RV_V5.fasta Annotation = H37RV_V5.gff3 Switch to TB chromosome 47

48 VCF files: SNPs and other variants VCF file is Variant Call Format Output of variant callers: GATK, Pilon, samtools, etc. TAB delimited with headers Exercise: Compare output of two variant callers Look for support in read data 48

49 Loading VCF TB reference and annotation loaded File > Load data Data, in folder tb/variants KZN1.vcf KZN1_gatk.vcf 49

50 Variants Single Substitution (SNP): green, single bp deletion: yellow, single bp insertion: blue, single bp Multi base insertion: blue with wings deletion: yellow block green block with wings Things to do: - Find one of each variant types 50

51 Bookmarking places in the genome You can create features and new tracks Edit > Feature from coordinates Edit > Feature from selection Select sequence in structure view Drag-mouse while pressing Shift Things to do: - Find regions where KZN1 and KZN1_gatk disagree - Mark those regions with bookmarks - There are 4 more KZN data sets, feel free to check them out 51

52 Exploring underlying data Variant calls are made from read-alignments Coverage plots may help to identify deletions and copy-number variants Load up read data and coverage: tb/coverage/kzn1.bam.tdf tb/kzn1.bam 52

53 Configuring visualizations File > Configuration > Short reads Turn down display depth to ~ 20 Turn off draw connection between pairs Before After 53

54 Quality, indelsand mismatches Red: Deletion in read Yellow: mismatch Black: insertion in read, hover tooltip contains details Brightness of color corresponds to mapping quality Read color: forward vs. reverse, sense vs. anti-sense 54

55 Alignment quality 55

56 Spotting variation: SNP 56

57 Spotting variation: large deletion 57

58 Spotting variation: single insertion 58

59 Spotting variation: single deletion 59

60 Spotting variation: large insertion 60

61 Collapsed tandem:

62 Exercise: Sanity checking and hypothesis generation Find a SNP, confirm it's present in the read data Find an indel, confirm it's present in the read data Find spurious SNP call Find collapsed repeat Find other interesting features Mark locations to share with the group 62

63 Piling on more data Wiggle tracks: text based format to represent value data track type=wiggle_0 fixedstep chrom=h37rv start=1 step=

64 Converting wiggle data to TDF Open console/terminal On the command-line, go to folder where you downloaded all files Run: java Xmx500m jar wig2tdf.jar tb/pilonclippedalignments.wig Note: - Multi-whitespace is for illustration, single space is sufficient - Instruction is single line - Expected runtime < 2 min 64

65 Converting wig to TDF java Xmx500m jar wig2tdf.jar tb/pilonclippedalignments.wig Things to do: - Convert both wig files - Load both wig files into GenomeView - Explore the neighborhood of large insertions and deletions - Check-out the region around position 80,000, any idea what may be going on? - Example near 3,795,601 65

66 Examining multiple strains at once Load up remainder of VCF files, you can load up more bam files and coverage files Compare region around 3,795,601 What may be happening in 3,244,871 Check for mutation in drug resistance genes rpob and katg. What s happening across the strains around position 3,594,331 Zoom around, explore and share other interesting things with the group. 66

67 Other interesting places (multi) (KZN2) (multi) 67

68 COMPARATIVE ANNOTATIONS 68

69 Loading a GV instance File > Unload all data and start over: Things to do: - (Read and) dismiss warning - Zoom in all the way - Zoom out all the way - Have a look around and organize your tracks 69

70 What s what? Zoom-out: conservation Middle-zoom: block conservation and orientation + annotations Zoom-in: nucleotide conservation Keep all regions visible: makes it easier to keep track of where each species goes 70

71 Places to check out Well conserved region: 62,000-92,000 Garbled regions: 1,703,200-1, ,830,100 Truncated gene: ThiF Species specific gene: FSDG_02363 Split gene?: FSDG_01659 Annotation error?: FSDG_01650 Weird annotation: FSDG_01799 Tandem copy evolution: 12,000 71

72 Things to try Find a conserved region/gene Find some possible annotation errors Find some strain specific genes Find garbled region, i.e. region with lots of partial genes Share interesting regions with the group! 72

73 WRAP-UP 73

74 What did we do? Loaded reference + annotations Made our own annotations Prepared and explored read data and coverage plots Explored variant calls and used primary data to verify Explore comparative annotations and sanity checked whole genome alignments 74

75 Summary GenomeView = interactive tool to explore sequencing data Many data types supported Manual for GenomeView 75

76 Support and feedback Ideas and feedback: Problems: 76

77 BroadE: GenomeView for sharing data Coming soon: November 19 Advanced data preparation Using GV to make data and analysis results available to collaborators and colleagues Setting up data on a (web) server Setting up sessions/instances with configurations Sharing those sessions and data Making sure your data is access controlled Best-practices for preparing online data 77

78 Questions, ideas, feedback 78