1.
FIND MEANING IN COMPLEXITY
© Copyright 2014 by Pacific Biosciences of California, Inc. All rights reserved.For Research Use Only. Not for use in diagnostic procedures.
Jason Chin (@infoecho) / Sept. 20 2014, GRC Workshop, Cambridge,
UK
Learning Genomic Structures From De
Novo Assembly and Long-read Mapping
de novol

2.
Cost per Genome Dilemma
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Sequencing cost is down for sure, but getting a de novo human genome that has the
same scientific standard as the initial work does NOT follow Moore’s law.
PacBio® CHM1: 4378 kb
from just single random fragment
library
HGP, N50 ~100kb
NCBI-34
Contig N50 29Mb
HuRef: 107kb
BGI YH: 7.4kb
KB1: 5.5kb
NA12878: 24kb
CHM1: 144kb
RP11: 127kb
According to the NHGRI
website, the definition of
“sequencing a genome”
changed in 2008.
The 1000 Genomes Project
starts in 2008, too.

3.
Question Asked!!
• Since the 1000 Genomes
Project, we have learned a lot
of about point mutations. Can
we go beyond that?
• What if we have 50, 100 or
more human assemblies so we
can address all genetic
variations as much as
possible?
• Will one day all human genome
sequencing be done in de novo
fashion?
– If so, how can we get ready
for that as bioinformatists?
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Evan Eichler , In Future Opportunities
for Genome Sequencing and Beyond,
July 28-29, 2014

4.
Where We Are Now
• One PacBio® human data set is publicly available, more are likely to
come
• Multiple groups have successfully assembled the public CHM1 data
set independently with new algorithms from raw data
• With new alignment/assembly tools from Gene Myers:
one can assemble a genome in ~ 20,000 CPU-hours. (20X faster
than 400,000+ CPU-hours from previous effort.)
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New Assembly Statistics done
With Daligner:
#Seqs
5,058
Mean
562,695
Max
27,292,514
n50
5,265,098
Total
2,846,115,586
http://dazzlerblog.wordpress.com

5.
What Can We Learn from High-contiguity
De Novo Human Assemblies?
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6.
What Can We Learn from High-contiguity Human
Assemblies?
• Low-hanging Fruits
– Calling SNPs (assembly not needed, but it helps)
– Calling structure variants with whole-genome alignment
approaches
– Inferring repeats by coverage analysis
• Assembly graph can provide information for understanding
more complicated polymorphisms
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7.
Call SNPs / Example: HLA-B
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8.
Call Structure Variation By Whole-genome Alignment
• Whole-genome alignments ( ~ 1 hr in a 32-core machine)
– With multi-threaded Mummer
– Clustering the hits with Mgaps and identified “gaps” in the alignments,
convert to bed format for visualization
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Structure Variants Called in Chromosome 1

9.
Distribution of The Structure Variation Sizes
• Number of insertions/deletions: 13796 SV calls (for insertion or deletion >
100 bp against hg19)
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10.
PacBio® vs. Short-read Alignment View for SV in the MHC region
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318bp insertion

11.
Assembly Graph
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Each edge is associated with a sequence.
Every path is a candidate of a model of part
of the genome.
From Gene Myers’ ISMB 2014 Keynote talk

12.
Dissect a Contig from a String Graph
The autonomy of a contig from a string graph layout
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A contig: a linear non-branching path
Each node: the begin (5’) or end (3’)
of a read
Each edge: a continuous sub-
sequence from one read
Ek:
(V1,
V2,
Read,
Range)
=
(
00099576_1:B,
00101043_0:B,
00101043_0,
1991-­‐0
)
Read
1:
00099576_1,
Read
2:
00101043_0
In practice, we might just encode the paths in a contig rather than each single
edge:
C
=
(Ek,
Ek+1,
Ek+2,
Ek+2)
=
(Pj
Pj+1)
V1 V2 V3 V4 V5
Ek Ek+1 Ek+2 Ek+3
V1 V3 V5
Pj Pj+1
C =
=

13.
Assembly String Graph of CHM1 Genome
• Largest connect component: 31998 nodes, 39399 edges, ~36.5%
(~1Gbp) of the human genome (total: 87572 nodes, 94530 edges)
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Centromere?
Casey Bergman:
“it almost looks like an
electron micrograph of
the nucleus”
#convergence

14.
Polymorphism Structure vs. Local Assembly Graph
Structure
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SNPs
SNPs SNPs
SVsSVs
Diploid Genome
Segmental Duplication
Similar String Graph

15.
Identify Contigs: A New Proposal
SNPs
SNPs SNPs
SVs
SVs
Associated
contig 1
Associated
contig 2
Primary
contig
1 full length contig + 2 associated contigs
Keep the long-range information
while maintaining the relations of
the alternative alleles.

16.
Contig 4076 Alignment Around DPY19L2 Locus
Same contig

17.
Contig Graph and Segmental Duplication
Contig 4076, one primary contig, 3 associate contigs, aligned to Chr7 and Chr12

18.
Coting 4076 Alignment to Chr7
Same contig
SV calls from
CHM1 asm
SV calls from
GRC38

19.
Local Neighborhood Subgraph of Contig 4076
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20.
Examining an Assembly Graph at Contig Level Around
1q21
• Contig graph, 1q21, contig 4108, another potential segmental
duplication?
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21.
Another Intriguing Case
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• Contig 4006 mapped to chr 9
The aligned region changes a lot in GRC38.

22.
Contig Coverage Analysis
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18.5 X
2 * 18.5 X
3 * 18.5 X
High coverage long contigs
40 contigs > 100kbp
> 2.5 * 18.5 X
Poor assemblies,
alignment artifacts,
or sequence errors?
High repeat elements

23.
Checking the Complexity of the High-coverage Contigs
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Contig 4006, 687kb, 53x coverage
Contig 4235, 453k, 59x coverage
Contig 3842, 235k, 54x coverage
Warning: These contigs may not be 100% correctly assembled due to
some nasty repeats. However, the local graphs give hints about the
true genome structures.

24.
How does the High-coverage Contig Look?
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>2000X in this region

25.
How does The High-coverage Contig Look?
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High-coverage
Region
Alpha satellites?

26.
For Research Use Only. Not for use in diagnostic procedures.
Extreme Repeats
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27.
Identify Centromere Alpha-satellite Structure
• Most of the nasty contig graphs are around the centromere.
Currently, it remains hard to get long contigs around those very long
tandem repeats.
• However, we can still learn many useful things from long-read data
• Tool In Development: α-Centauri for identifying different high-order
repeat structures (https://github.com/volkansevim/alpha-CENTAURI,
Volkan Sevim, Ali Bashir & Karen Miga )
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28.
Centromere Alpha Satellites Have Non-trivial High-order
Repeat Structure
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Karen Miga

29.
Example: A Read Reconstructs a 24-mer HOR
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Align monomer to each other to
identify near identical mon0mers
Identify HOR with the monomer
IDs and positions
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30.
Many Other Open Topics
• Low-coverage assembly: cost vs. quality analysis
• Phasing for haplotypes
• Crowd-sourcing infrastructure for examining / annotating / correcting
genome assemblies
• Evaluation about SNPs calling with short reads on better assembly
• Large-scale comparative genomes with de novo assemblies
• Assembly-graph data format
• Visualization Techniques
• Combining other data types, e.g. optical mapping
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It is a very exciting time. We still need more tools to harvest
information to generate new knowledge.

31.
For Research Use Only. Not for use in diagnostic procedures. Pacific Biosciences, the Pacific Biosciences logo, PacBio, SMRT, SMRTbell and Iso-Seq
are trademarks of Pacific Biosciences in the United States and/or other countries. All other trademarks are the sole property of their respective owners.
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