Variant View Visualizing Sequence Variants in their Gene Context
Joel A. Ferstay 1*, Cydney B. Nielsen 2, Tamara Munzner 1
University of British Columbia 1, now at AeroInfo Systems/Boeing
Canada* BC Cancer Agency 2 1 Slide 2 Design study Real users, real
tasks, real data Find out what users are doing Support with
visualization tool Reflect and present guidelines Analys t Tool 2
Slide 3 Design Study The Design Process 3 Slide 4 Collaborated with
analysts at the Genome Sciences Centre Study genetic basis of
leukemia Needed help interpreting their data Major problems: What
do we show? How do we show it? 4 Slide 5 Design cycle Intervie w 5
Slide 6 Design cycle Intervie w Data and Tasks 6 Slide 7 Design
cycle Intervie w Data and Tasks Create Data Sketch 7 Slide 8 Design
cycle Intervie w Data and Tasks Create Data Sketch Present Data
Sketch 8 Slide 9 Design cycle Intervie w Data and Tasks Create Data
Sketch Present Data Sketch REPE AT 9 Slide 10 Data sketches
Alternative to paper prototyping Load and show real data Beneficial
when the data is complex 10 [Lloyd and Dykes, InfoVis 2011] Slide
11 Can identify features of interest in data 11 Emphasize some
features over others Slide 12 Can identify design dead ends early
12 Slide 13 Methods and durations Semi-structured interviews 7
months Once per week One hour in duration Presented data sketches 8
deployed over 5 months Implemented with D3 toolkit [Bostock et al.,
InfoVis 2011 ] 13 Design study methodology [Sedlmair et al., 2012]
Slide 14 Problem characterization: Data 14 Slide 15 The data Data
are sequence variants Difference between reference genome and a
given genome 15 Slide 16 The data Data are sequence variants
Difference between reference genome and a given genome 16 Reference
Genome DNA:ATA TGA TCA ACA CTT Sample 1 Genome DNA:ATA TGG TCA ATA
CTT Sample 2 Genome DNA:ATA TGA TGA ACA CCT Slide 17 The data Data
are sequence variants Difference between reference genome and a
given genome 17 Reference Genome DNA:ATA TGA TCA ACA CTT Sample 1
Genome DNA:ATA TGG TCA ATA CTT Sample 2 Genome DNA:ATA TGA TGA ACA
CCT Harmful ? Harmles s? Slide 18 Multi-scale data 18 DNA of the
organism Slide 19 Multi-scale data 19 Genes are functional units
Slide 20 Multi-scale data 20 Exons code for proteins Slide 21
Multi-scale data 21 Regions within proteins perform activities
Slide 22 Related work: Genome scale 22 Slide 23 Ensembl genome
browser 23 Explore genome and variant data [Chen et al., BMC
Genomics 2010] Slide 24 Genome scale shown at the top 24 Slide 25
User data is stacked in horizontal tracks 25 Slide 26 User data is
stacked in horizontal tracks 26 Very flexible framework Slide 27
Problem with the genome scale 27 Features of interest become small
Slide 28 Problem with the genome scale 28 Features of interest
become small Varian t Slide 29 Problem with the genome scale 29
Features of interest become small Varian t Exo n Slide 30 Analysts
must pan and zoom 30 Slide 31 Analysts must pan and zoom 31 Heavy
interaction costs with zooming Slide 32 Analysts must pan and zoom
32 Heavy interaction costs with zooming Varian t Must know where to
look Slide 33 Raw variant data (What they looked at before) 33
Slide 34 Raw data variant-centric 34 Tabular data format Slide 35
Raw data variant-centric 35 Tabular data format Variant row 1 Slide
36 Raw data variant-centric 36 Tabular data format Variant row 2
Slide 37 Raw data variant-centric 37 Tabular data format Variant
row 2 Difficult to reason about variants without their biological
context Slide 38 Multiple biological levels/scales What to show? 38
Slide 39 Many biological levels and scales 39 Slide 40 Only some
levels and scales are beneficial for variant analysis 40 Slide 41
Filter out whole genome; keep genes 41 Slide 42 Filter out non-exon
regions 42 Slide 43 Left with a filtered scope 43 Slide 44 Related
work: Filtered scope 44 Slide 45 The Ensembl Variation Image 45
[Chen et al., BMC Genomics 2010] Slide 46 First filtering step:
per-gene view 46 [Chen et al., BMC Genomics 2010] One gene shown
Slide 47 Second filtering step: partial removal inter-exon regions
47 [Chen et al., BMC Genomics 2010] Partial removal Slide 48
Problem: Extends multiple screens 48 [Chen et al., BMC Genomics
2010] Slide 49 Problem: Extends multiple screens 49 [Chen et al.,
BMC Genomics 2010] 1 st Screen Slide 50 Problem: Extends multiple
screens 50 [Chen et al., BMC Genomics 2010] 2 nd Screen Slide 51
Problem: Features of interest small 51 [Chen et al., BMC Genomics
2010] Exon regions small Color coding difficult to see Slide 52 Our
Goal: Show attributes necessary for variant analysis 52 Slide 53
Use information-dense visual encoding 53 Slide 54 Use
information-dense visual encoding 54 Slide 55 Use information-dense
visual encoding 55 Slide 56 Use information-dense visual encoding
56 Slide 57 Use information-dense visual encoding 57 Slide 58 Use
information-dense visual encoding 58 Slide 59 Use information-dense
visual encoding 59 Slide 60 The tool: Variant View 60 Slide 61
Variant View 61 Slide 62 Variant View 62 Information-dense single
gene view Slide 63 Variant View 63 Information-dense single gene
view No need for pan and zoom Slide 64 Variant View 64 Sorting
metrics guide gene navigation Slide 65 Variant View 65 Sorting
metrics guide gene navigation Control what shows up here Slide 66
Variant View 66 Peripheral supporting data Slide 67 Related work:
Targeted for variant analysis 67 Slide 68 MuSiC variant
visualization plot 68 [Dees et al., Genome Research 2012] Slide 69
Side-by-side comparison 69 Slide 70 Side-by-side comparison 70
Protein regions can overlap Regions get separate lanes Slide 71
Side-by-side comparison 71 Large bloom of repeated elements: more
salient Many collocated variants Slide 72 Driving biological tasks
72 Slide 73 Task 1: Discover genes Tool originally designed to
discover genes with harmful variants Sorting metrics guide single
gene navigation Uncover new genes affected by variants in leukemia
Want to see if Variant View exposed known genes in leukemia 73
Slide 74 74 Slide 75 Sorting by derived metric revealed known
leukemia genes 75 Slide 76 Highly scored gene by sorting metric 76
Slide 77 Visual inspection reveals collocation of variants 77 Slide
78 Several functional protein regions affected 78 Slide 79 Highly
scored by metric and not known 79 Slide 80 Protein chemical class
change evident 80 Slide 81 In contrast, low scoring gene 81 Slide
82 No collocation of variants 82 Slide 83 Mostly unaffected protein
regions 83 Slide 84 Variant tasks Started with the main task of
discover gene Shared tool with analysts Identified two more tasks!
Patient compare Debug pipeline 84 Slide 85 Task 2: Patient compare
Clinical setting application Compare patient data to known harmful
variants The challenge Similarity is loosely understood rather than
fully characterized What constitutes a match requires visual
inspection 85 Slide 86 Adapted Variant View with minimal changes 86
Slide 87 Navigate through patient data with list 87 Slide 88
Patient data emphasized with arrows 88 Slide 89 Patient has same
harmful L to P mutation 89 Slide 90 These variants probably do not
match 90 Slide 91 Task 3: Debug pipeline Debug data generation
pipeline Remove errors from data before analysis takes place
Analysts originally did not think they needed this support 91 Slide
92 Tool revealed errors in the data 92 The tool exposed artifacts
in the data that slid past at least two rounds of quality metric
filtering this type of problem would not have been caught by our
previous, automated methods. - Analyst 3 Slide 93 Conclusions 93
Designed, implemented, and deployed tool for visual variant impact
assessment Originally designed for Discover Genes task Adapted to
two others with minimal changes Methods What to show Filtering data
scope How to show it Carefully selected visual encodings Goals
Navigation-free main overview at gene level Reveal genes of
interest; accomplished by sorting by new, derived metrics Slide 94
Acknowledgements 94 Our collaborators at the GSC Dr Aly Karsan Rod
Docking Dr Linda Chang Dr Gerben Duns Simon Chang Funding VIVA,
AeroInfo Systems/Boeing Canada, MITACS Slide 95 Questions? 95 Joel
Ferstay: [email protected] Paper Page:
http://www.cs.ubc.ca/labs/imager/tr/2013/ VariantView/ Software
Available as Open Source Slide 96 Future work: Other use contexts
96 Scaling up beyond the current design target ~50 variants at once
Possibly integrate Variant View with MedSavant Tool by Fiume et
al., BioVis Posters 2012 Focus on interactive filtering
