Margaret Woodhouse
MaizeGDB
Gale and Devos, 1998, PNAS

What is a pan-genome?
A pan-genome represents the full complement
of diversity within a clade, or the union of all
genes or SNPs across a representaAve selecAon
of genomes

• A single reference genome cannot represent
the diversity within a given species
• Advances in sequencing technology and
lowered costs has made generaAng a pan-
genome a feasible goal for many genome
research groups
Why a pan-genome?

The first pan-genome: S. agalactiae
• Microbial pan-genome of
Streptococcus agalac.ae
• Released in 2005
• Comprised of eight S.
agalac.ae genomes (8x
coverage; ~1.8Mb in size)
• Whole-genome alignments
were done using the
MUMmer alignment program
• Gene sequence similarity was
determined by translated
protein sequence similarity
TeTelin et al 2005 PNAS doi: 10.1073/pnas.0506758102
Reference lacking sequence similarity
to other accessions (white space)
Reference sharing sequence similarity in the same
syntenic posiAon across all accessions
Reference sharing sequence similarity in some accessions but not in the same syntenic posiAon

A recent pan-genome: rice
• a pan-genome dataset of the O.
sa.va–O. rufipogon species
complex (430Mb in size)
• deep sequencing (115X) of 66
divergent accessions into conAgs
• ConAgs were anchored to the
reference genome using the
MUMmer alignment program
• Predicted genes in conAgs were
aligned against reference genome
with BLASTN to determine gene
presence-absence in conAgs
Zhou et al 2018 Nature GeneAcs doi: 10.1038/s41588-018-0041-z

A future pan-genome: maize
Nested Association Population (NAM)
• 25 diverse inbred lines, plus
the reference line B73
• All 25 complete genomes
are currently being
sequenced, assembled, and
annotated by the NAM
Sequencing ConsorAum
• Each genome is
approximately 2 gigabases
in size
• Whole-genome alignment
to create the pan-genome
D. Hufnagel, ISU, unpublished

Species with existing pan-genomes
• Human pan-genome
• Dozens of microbial pan-genomes
• Many crop plant pan-genomes (though mostly
re-sequencing efforts)
– pepper
– soybean
– rice
– wheat
– maize

Usually the comparisons done to create a pan-
genome include
• whole-genome alignment
• or alignment of short reads to a reference
sequence.
Methods of creating a pan-genome

Features of a pan-genome (genes as unit)
• Core genome (C): Made up of genes present in all accessions in the pan-
genome
• “Dispensible” genome (D): The genes that are present in some, but not
all, accessions in the pan-genome
• Orphan genes (O): Lineage-specific genes; found only within a parAcular
accession
Accession 1
Accession 2
Accession 3
O C C
C
C
C
C
D
D
D
D
Pan-genome C C D D O
D

Types of pan-genomes
1. Reference-based: All accessions are mapped to a single reference
Ø pros: fast and easy; can be represented as a fasta file (consensus sequence)
Ø cons: does not represent genes that are present in other accessions but
missing in the reference
Accession 1
Accession 2
Reference
O C C
C
C
C
C
D
D
D
D
Pan-genome C C D D
D
X

2. All-against-all: The pan-genome includes the total diversity of all
accessions studied
Ø pros: contains the total diversity among all accessions
Ø cons: can’t be easily represented as a fasta file; very slow; eats CPU
Types of pan-genomes
Accession 1
Accession 2
Accession 3
O C C
C
C
C
C
D
D
D
D
Pan-genome C C D D O
D

Pan-genome formats
• Consensus fasta file: all variaAon across accessions are collapsed
into a consensus sequence
• Used for either SNP diversity or whole-genome alignment against a
reference sequence
Ø pros: blastable/alignable
Ø cons: Hard to represent large regions of variaAon; difficult to
represent an all-against-all pan-genome
sorghum 5’ AGTAAGACCTGATGCAT 3’
setaria 5’ AGCAAGACCTGATGCAT 3’
rice 5’ AGCAAGACCTGATGCAT 3’
consensus 5’ AGCAAGACCTGATGCAT 3’
pineapple 5’ AGAATGACCTGATTCCT 3’

• Graphical format: variaAon represented as a graph with nodes and
edges; shared sequence similarity is collapsed into a single node
• Can be used for all types of pan-genomes
Ø pros: can represent large regions of diversity beTer than a fasta file
Ø cons: not blastable/alignable
Pan-genome formats
Accession 1
Accession 2
Accession 3
CCTG ATGCGA TCTGAC ACTC
ATTC
GGAC ATGCGA
ATGCGA
TCTGAC
TCTGAC
GGTC
GGAG
ATGCGA TCTGAC
ATTC
ACTC GGAC
GGTC
GGAG
CCTG
Pan-genome

Levels of pan-genomes
• Species-specific: e.g. culAvars within a species (most common)
• Genus-specific: closely related species within a genus
• Family-specific: the species within a family (e.g. Grasses)
à Higher-order clade pan-genomes are possible, but complexity increases as
you go up in clade since number of shared genes declines:
Sorghum
Setaria
Peach
Pineapple
Rice
Dicots
Monocots
Tomato
Species # syntenic orthologs in Sorghum
Setaria 31142
Rice 28406
Pineapple 16147
Peach 5317
Tomato 1196 Asterids
Rosids
Grasses

Challenges of creating whole-genome pan-
genomes for complex genomes
• Plants such as maize and wheat have undergone recent
polyploidy events, have large inversions and translocaRons
relaAve to outgroups, and contain a great number of repeat
elements
• These genomes also contain a large number of duplicated
genes in cis and trans, and have simultaneously undergone a
great deal of gene deleRon relaAve to outgroups
• Therefore, creaAng pan-genomes to represent the extent of
the diversity in these species is daunAng

Example: two different maize inbred lines compared
to the reference maize line B73
hTps://genomevoluAon.org/r/14hzw
W22
Mo17
B73
Brown: blast hit from
B73 to Mo17
Orange: blast hit from
B73 to W22
Gene models
DNA sequence in chromosomal
coordinates (chr, start, stop)
Tandem array
W22 culAvar

Red circles: genes with no syntenic ortholog to B73
Tandem array
hTps://genomevoluAon.org/r/14hzw
W22 culAvar
W22
Mo17
B73
Brown: blast hit from
B73 to Mo17
Orange: blast hit from
B73 to W22
Gene models
Example: two different maize inbred lines compared
to the reference maize line B73

The role of biological databases
in curating pan-genomes
• Biological databases will need to address
nomenclature rules for pan-genes
• AgBioData is in a unique posiAon to direct and
standardize naming convenAons for pan-genes at
this early stage in pan-genome development
• Our understanding of the complexity of pan-
genomes can help inform us as to the best
nomenclature convenAons to implement

• Storing pan-genomic data in a public database
is also a concern
• What pan-genome formats would we store?
How would we connect funcAonal data
(expression data, mapping and marker data,
SNP data) to the pan-genome in a useful
manner?
The role of biological databases
in curating pan-genomes

• MaizeGDB will
soon be hosAng
these 25 genomes
and is working on
the most efficient
way to do so
Case study: the maize Nested Association
Mapping (NAM) founder lines
D. Hufnagel, ISU, unpublished

Some approaches MaizeGDB is
considering:
Gene model pages:
1. Storing all gene models with
an ortholog in either B73 or at
least one other NAM line* in
a single gene model page;
2. These pages will also contain
mapping and funcAonal data
associated with the NAM loci
as well as the B73 gene model
3. Orphan NAM gene models
will have their own page
Case study: the maize Nested Association
Mapping (NAM) founder lines
*as determined by the NAM Sequencing ConsorAum

Nomenclature:
• NAM gene models will be given a pan-ID (i.e.
Z0123456).
• All NAM gene models that share orthology will
share a single pan-ID.
Case study: the maize Nested Association
Mapping (NAM) founder lines

• There are various ways of represenAng a pan-
genome
• Pan-genomes can be very complex in certain
species
• Biological databases will need to determine
Ø How to assign pan-IDs
Ø What sorts of pan-genome formats to host
Ø The most efficient way to host pan-genomes
Summary