Published online 10 October 2018

Nucleic Acids Research, 2019, Vol. 47, No. 2 e8
doi: 10.1093/nar/gky890

Fast and global detection of periodic sequence
repeats in large genomic resources
Hideto Mori1,2,3 , Daniel Evans-Yamamoto1,2,3 , Soh Ishiguro1,2,3 , Masaru Tomita2,3,4 andNozomu Yachie1,2,3,5,6,*
1

Received July 23, 2018; Revised September 18, 2018; Editorial Decision September 19, 2018; Accepted September 20, 2018

ABSTRACTPeriodically repeating DNA and protein elements are
involved in various important biological events including genomic evolution, gene regulation, protein
complex formation, and immunity. Notably, the currently used genome editing tools such as ZFNs, TALENs, and CRISPRs are also all associated with periodically repeating biomolecules of natural organisms. Despite the biological importance of periodically repeating sequences and the expectation thatnew genome editing modules could be discoveredfrom such periodical repeats, no software that globally detects such structured elements in large genomic resources in a high-throughput and unsupervised manner has been developed. We developed
new software, SPADE (Search for Patterned DNA Elements), that exhaustively explores periodic DNA andprotein repeats from large-scale genomic datasets
based on k-mer periodicity evaluation. With a simple
constraint, sequence periodicity, SPADE captured
reported genome-editing-associated sequences andother protein families involving repeating domains
such as tetratricopeptide, ankyrin and WD40 repeats
with better performance than the other software designed for limited sets of repetitive biomolecular sequences, suggesting the high potential of this software to contribute to the discovery of new biologicalevents and new genome editing modules.
INTRODUCTION
Significant roles of repetitive DNA and protein sequences
have been widely reported in both eukaryotes and prokary-otes. Transposable DNA elements are thought to be among
the most important evolutionary driving factors that havebeen expanding within and between species’ genomes via
their copy-and-paste or cut-and-paste mechanisms (1,2).
These repetitive elements induce large-scale genomic rearrangements and transcriptional regulation. Nonmobile
short tandem repeat DNA sequences are also key elements inducing structural genome evolution in prokaryotic
species. Tandem DNA repeats induce mispairing and slippage between repetitive units during DNA replication anddrive genomic contraction and expansion (3). Intramolecular crossover DNA recombination is also promoted between tandem repeat regions of the genome (4). Some ofthese events are known to be reversible and lead to genomic
phase variation, allowing cells and species to rapidly adapt
to changing environments without having to undergo irreversible mutations (5).
Repetitive protein domains often serve as structural binding modules that stably interact with biopolymers. They arewidely involved in protein folding and interactions in various biological processes. Tetratricopeptide repeats (TPRs)(6) and ankyrin (ANK) (7) repeats are large protein repeat families that are conserved from prokaryotes to eukaryotes. The repeat units of TPRs and ANK repeats are
34 and 33 amino acids (aa) long, respectively, and both arecomposed of a helix–turn–helix structure. These repetitive
domains have been reported to mediate interactions withother proteins and RNAs and play important roles in cell
cycle control, transcriptional regulation, translational inhibition, and protein translocation (6,7). WD40 repeat is another large protein repeat family found in both prokaryotic and eukaryotic species, but its functions are particularly well known in eukaryotes (8). A WD40 repeat is composed of seven-bladed ␤-propellers, where each propeller
is around 40 aa long, involving four anti-parallel ␤-sheets,and serves as a scaffold for protein interaction. Accordingly,* To whom correspondence should be addressed. Tel: +81 3 5452 5242; Fax: +81 3 5452 5241; Email: yachie@synbiol.rcast.u-tokyo.ac.jp

C The Author(s) 2018. Published by Oxford University Press on behalf of Nucleic Acids Research.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which
permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

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Synthetic Biology Division, Research Center for Advanced Science and Technology, The University of Tokyo, Tokyo
153-8904, Japan, 2 Institute for Advanced Biosciences, Keio University, Tsuruoka 997-0035, Japan, 3 Systems Biology
Program, Graduate School of Media and Governance, Keio University, Fujisawa 252-0882, Japan, 4 Department ofEnvironment and Information Studies, Keio University, Fujisawa 252-0882, Japan, 5 Department of Biological
Sciences, School of Science, The University of Tokyo, Tokyo 113-0033, Japan and 6 PRESTO, Japan Science andTechnology Agency (JST), Tokyo 153-8904, Japan

e8 Nucleic Acids Research, 2019, Vol. 47, No. 2

double-stranded DNA cleavage activity from the type II
CRISPR system has been widely used for targeted gene disruption and targeted fragment knock-in in various organisms including mammals. Similar to ZFNs and TALENs,
nuclease-deficient Cas9 (dCas9) or mutant Cas9 nickase(nCas9) fused to effector proteins such as transcription factors, deaminases, and fluorescent proteins have been usedfor various applications such as gene silencing (25), activation (26), base editing (27), and chromosomal labeling (28).
Since periodically repeating DNA and protein sequences
have diverse and important roles in biology, a simple andoptimistic hypothesis could be proposed that new genome
editing modules can be discovered from other periodic repeats in large-scale genomic resources. However, there isno universal software that captures various types of periodic repeats from large-scale genomic datasets in an unsupervised manner (Table 1). For example, RepeatMasker is
one of the most commonly used tools to detect interspersed
DNA repeats and low-complexity DNA sequences (29).
However, this software screens only DNA sequences against
a database of reported elements and does not evaluate repeat periodicity. Previous software programs developed forde novo searches of repetitive biomolecular sequences also
have certain limitations. Tandem Repeat Finder is one of thefirst types of software to screen tandem and low-complexity
DNA repeats without prior knowledge (30), but is incapableof capturing highly degenerated or interspaced DNA sequences or protein repeats. RECON (31) and RepeatScout(32) also screen only DNA sequences, focus only on interspersed repeats regardless of periodicity, and exclude tandem or low-complexity repeats. PRAP captures both tandem and interspersed repeats, but screens only DNA sequences (33). Although the recently developed software
XSTREAM (34) and T-REKS (35) search for both tandem
and highly degenerate repeats from DNA and protein sequences, both are ineffective at capturing interspersed orinterspaced repeats including CRISPRs. With the recent interest in genome editing, several software packages suchas CRISPRFinder (36), CRISPRDetect (37) and AnnoTALE (38) have been developed to capture genome-editingassociated sequences. However, such specialized software
does not have the potential to discover novel genome editing
modules.
The previously developed software focuses on limited
types of repeat sequences for specific biological targets, but
it seems that any software that combines the abilities of theprevious software packages for any type of repetitive sequence would give an ambiguous and large set of sequences,which would require substantial effort for further curationand validation. However, none of the above-mentioned software screens repetitive sequences based on sequence periodicity that commonly appears in many significant biological processes. This could be a strong constraint in screening
to obtain a set of biomolecular sequences with high potential for expanding our biological knowledge and developing
new biotechnologies. Accordingly, we have been motivatedto develop simple and fast software called SPADE (Search
for Patterned DNA Elements) that globally captures such
periodically repetitive biomolecular sequences in large genomic datasets mainly based on a simple evaluation of kmer periodicity.

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WD40 proteins coordinate multi-protein complex formation and underlie diverse biological functions such as signaltransduction, transcriptional regulation, cell cycle control,
chemotaxis, autophagy and apoptosis (8,9).
The structural code for proteins in general remains
largely unclear and there have been major challenges in engineering these repeat protein modules to develop synthetic
binding reagents for biomedical and nanotechnology applications (10–12). Most protein repeat sequences are ‘imperfect’ or ‘degenerated,’ where each repetitive unit contains variable amino acid residues and the degrees of repeat
imperfectness vary widely. Some of these variable residues
determine binding to specific biomolecules and deciphering this code for DNA binding has been extremely beneficial in the development of genome editing technologies(13,14). Several transcriptional regulators involve tandem
protein repeats with specific periodicities to wrap aroundthe double-stranded DNA helix. Cys2His2 zinc fingers(C2H2 ZNFs) are the most common DNA-binding motif
found in eukaryotic transcription factors (15). C2H2 ZNFs
represent periodic protein repeats that make tandem contact to targeting DNA sequence. The repeat unit size ranges
from 28 to 30 aa and the variable amino acid residue pattern in each unit defines its binding to a specific DNA
triplet (16). Similarly, transcription activator-like effectors(TALEs) of the type III secretion system encoded in theplant pathogenic bacteria of the Xanthomonas genus also
have repeating domains (17). They are virulence proteinsthat bind to the host plant genomic DNA and hijack its gene
expression system. The periodicity of the repeat unit ranges
from 33 to 35 aa, where the combination of two variableamino acid residues at the 12th and 13th positions of the repeat sequence has a one-to-one relationship with a specificmononucleotide. By fusing DNA cleavage domains such as
FokI endonuclease to C2H2 ZNFs and TALEs, the genome
editing tools zinc finger nucleases (ZFNs) and TALE nucleases (TALENs), respectively, have been developed, both ofwhich enable highly specific targeted DNA cleavage. Other
effector proteins have also been fused to C2H2 ZNFs andTALEs to regulate gene expression and chromosomal structures in various organisms (18,19).
The CRISPR–Cas systems have become the most widelyused genome editing technologies in recent years (20). As
indicated by their name, clustered regularly interspaced
short palindromic repeats (CRISPRs) are widely encodedin prokaryotic genomes (21). The unique characteristics ofthese CRISPRs and CRISPR-associated (Cas) proteins inbacterial and archaeal immunity have been rapidly identified recently (22). In the immunization process, a fragmentof defined length from invading phage or plasmid DNA
is incorporated into the 5 end of a CRISPR locus with aconstant motif sequence. Accordingly, the periodic interspaced repeats of CRISPRs have been derived by continuous cycles of this immunization process. In the immunity
process, an RNA originating from the immunized DNA
is transcribed and processed and guides Cas protein(s) to
its complementary sequence of exogenous DNA for cleavage and degradation. Harnessing different Cas proteins andRNAs involved in the immunization/immunity processesof different CRISPR-type families, various genome editing technologies have been established (20,23,24). Cas9 with

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Nucleic Acids Research, 2019, Vol. 47, No. 2 e8

Table 1. Comparison of different software tools for capturing repetitive biomolecular sequences
Protein or DNA

Repeat type

MethodDNA

Protein

TandemDegenerate Interspersed Periodicity

RepeatMasker
CRISPRFinder
CRISPRDetect
AnnoTALE
Tandem Repeat Finder
RECON
RepeatScout
PRAP
XSTREAM
T-REKS
SPADE

Supervised
Supervised
Supervised
Supervised
De novo
De novo
De novo
De novo
De novo
De novo
De novo

Yes
n.a.
n.a.
n.a.
Yes
Yes
Yes
Yes
Yes
Yes
Yes

No
n.a.
n.a.
n.a.
No
No
No
No
Yes
Yes
Yes

Yes
n.a.
n.a.
n.a.
Yes
No
No
Yes
Yes
Yes
Yes

Yes
n.a.
n.a.
n.a.
No
Yes
Yes
Yes
Yes
Yes
Yes

MATERIALS AND METHODSGenomic resources
The GenBank files for the 7006 complete prokaryotic
genomes (downloaded on 31 March 2017) and the humanreference genome version GRCh38.p10 were downloaded
from the NCBI RefSeq genomes FTP server (ftp://ftp.ncbi.
nlm.nih.gov/genomes/refseq/).
SPADE screening phase 1: detection of highly repetitive regions
In SPADE, each entry sequence is first scanned by a sliding
window of a given size w to roughly detect highly repetitive regions (HRRs). Let Wi and ki be the sliding windowat sequence position i of the entry sequence and its leftmost k-mer sequence, respectively. At every sliding window
position i, the number of ki within Wi (ni ) is cumulatively
counted for every position in the left-most ki sequence region. In the same sliding window, 1 is also counted for every position of the other ki sequence regions. Let ci be theresulting cumulative k-mer score at position i of the entry sequence. After scanning the query sequence with the sliding
window, cumulative k-mer peak areas (CKPAs) for which
the peak heights are s or more are extracted. From the sequence regions for all of the CKPAs (c > 0), the broadest possible regions consisting of multiple gaps of size g orless were extracted as highly repetitive regions (HRRs). We
adopted w = 1000, k = 10, s = 20, and g = 300 for nucleotide
sequence and w = 300, k = 3, s = 6, and g = 50 for protein
sequence as default parameters of the software.
SPADE screening phase 2: evaluation of periodicity
Let h be the size of a given HRR. For each HRR surrounding region of size h ± m, SPADE generates a position-period
matrix (PPM) using a similar sliding window of size h. Let
Wi and ki be the sliding window at sequence position i andits left-most k-mer sequence, respectively. When multiple ki
sequence regions are detected in Wi , the number of ki regions (ni ) is cumulatively counted for all of the corresponding row–column cells of the first two k-mer regions, where
row represents distance between two identical k-mers andcolumn represents sequence position. When ni > 2, fromthe second ki sequence regions, this procedure is iteratively

Yes
n.a.
n.a.
n.a.
No
Yes
Yes
Yes
No
No
Yes

No
n.a.
n.a.
n.a.
No
No
No
No
No
No
Yes

Comments
CRISPR only
CRISPR only
TALE onlyThis study

repeated except that the number added to each cell is 1. After scanning by the sliding window, the highest peak period
d in the column sum distribution of the resulting PPM is
identified. All values in a sub-PPM of rows from [d × 0.8]
to [d × 1.2] are then added up and divided by that from 1 tohalf the column size of the PPM to produce the periodicity
score. HRRs with periodicity scores of p and more are redefined as periodic repeat regions (PRRs). We set m = 1000
and P = 0.5 for nucleotide sequence and m = 300 and P =0.3 for protein sequence as default parameters.
SPADE screening phase 3: identification of repetitive motifs
From each PRR with sequence period d, the k-mer sequencethat has contributed the most to the sequence periodicity is
extracted as kseed . When multiple k-mer sequences are extracted as the k-mers contributing the most, the left-mostk-mer in the PRR is selected as kseed . Starting from all of
the kseed sequences found in the PRR, SPADE obtains sequence fragments of size d. The extracted sequences arethen aligned by multiple sequence alignment using MAFFT
version 7.22 (39) to identify their consensus sequence motif. For each sequence position of the alignment result, theinformation content of appearing letters (b, bit) and thefrequency of alignment gaps (f) are calculated using the
Python WebLogo 3.6.0 package (40). After removing positions with f of more than q from the alignment result, letter
consistency l of every position is calculated by b × f. The
positions of the alignment result are then treated as circularsince they are for periodic repeats and punctuated by removing the longest continuous nonconsensus region (l < u) of
more than r letters. When this punctuation does not happen,
the sequence alignment result is linearized as it was before.
To map the repeat motif to the PRR sequence, a representative sequence is obtained by taking the most frequent letterin each position of the alignment result. When the representative sequence is shorter than k-mer, the identical sequence
regions are scanned in the PRR and annotated as repeat
units. Otherwise, the representative sequence is mapped using BLAST+ version 2.6 (41) with the blastn-short (for nucleotide) or blastp-short (for protein) option and alignment
length threshold of 50% to the query length or E-value of
0.01 or less. The hit regions in the PRR are then used to construct a sequence logo profile using Python WebLogo 3.6.0
package (40). From the sequence logo profile, a repeat motif

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Softwaree8 Nucleic Acids Research, 2019, Vol. 47, No. 2

sequence is generated by the most frequent letters, where ahighest letter frequency of less than 60% is masked with ‘*’.
q = 0.5, u = 0.8, and r = 5 were set as default parameters of
the software.
Protein secondary structure prediction

Evaluation of performance for detecting CRISPRs
From the entire periodic DNA repeats detected by SPADE,
we extracted CRISPR candidates with interspace sizes of
25–60 bp and repeating periods of 58–81 bp. The interspace size parameters and the minimum threshold for the
repeating period (interspace size plus repetitive sequencesize) were set with reference to the CRISPRFinder screens
for CRISPR candidates with interspace size being 25–60 bp
and repetitive sequence size being 23–55 bp, but our maximum threshold for the repeating period was defined empirically based on the reported RefSeq CRISPRs. Region
overlap agreement (ROA) between two given regions wascalculated by dividing the size of the overlapping region by
the combined size of the two regions. Recall and precisionof the recapturing RefSeq CRISPRs were evaluated for each
ROA threshold.
Evaluation of performance for detecting tandem protein repeats
From the 7006 prokaryotic genome resources, we screened
the positive reference set (PRS) proteins for TALE, TPR,
ANK repeat and WD40 repeat families using HMMER
version 3.1 with the Pfam domain signatures of PF03377,
PF00515, PF00023 and PF00400, respectively. The PRS
proteins for the C2H2 ZNF family were screened from thehuman reference genome version GRCh38.p10 using the
Pfam domain signature of PF00096. Every PRS protein
was required to contain three or more of the corresponding Pfam domain copies mapped with an E-value of lessthan 1.0e–10, and we obtained 331, 26 289, 4428, 2672 and4084 PRS proteins for TALE, TPR, ANK repeats, WD40
repeats, and C2H2 ZNF, respectively (Supplementary Table S4). A total of 100 000 randomly picked prokaryotic
proteins and the entire human proteome were screened for
Pfam-A domain families version 31.0. Among those that do
not have more than one copy of any Pfam domain with an
E-value of less than 1.0e–10, we randomly selected 10 000
prokaryotic proteins and 10 000 human proteins as negativereference sets ProNRS10K and HuNRS10K. The performance of the software programs SPADE, XSTREAM and
T-REKS was estimated using the recall of PRS proteins andthe false positive rate (FPR) in ProNRS10K (for prokaryotic protein repeats) or HuNRS10K (for human protein repeats). The positive likelihood ratio (PLR) was calculatedby dividing recall by FPR. Each software was used with its
default parameters. Similar analysis was also performed by
restricting the detected repeat unit sizes to within the rangeof expected sizes for different repeat families (34 ± 5 aa, 34± 5 aa, 33 ± 5 aa, 42 ± 5 aa, and 28 ± 5 aa for TALE, TPR,
ANK repeats, WD40 repeats, and C2H2 ZNF, respectively).
Note that, owing to the size filtering, FPRs varied for different repeat families, even when the same negative reference
set was used. These measurements were also repeated with
PRS proteins prepared using different criteria.
RESULTS
Overview of SPADE
We implemented SPADE to efficiently screen periodicallyrepeating sequences as follows (Supplementary Figure S1).
The software first automatically extracts multiple sequence
entries from an input file (GenBank or FASTA format) andidentifies the sequence type (DNA or protein) for each entry. Each entry sequence is scanned by a sliding window
to count k-mers and highly repetitive regions are extracted.
The sequence periodicity of each highly repetitive region isthen evaluated based on a position-period matrix that cumulatively plots the distance between the same neighboring k-mers and their sequence positions (see ‘Materials andMethods’). From each periodic sequence region, the periodic sequence units are queried for a multiple alignment to
identify repetitive motifs. A representative motif sequenceis then aligned back to the entry sequence to annotate theperiodically repeating units. Finally, the annotations for thedetected periodic repeats are added to the input informationand output in the GenBank format with options to visualize k-mer density, position-period matrix, repetitive unitloci with neighboring genes, and motif sequence logo foreach periodic repeat.
Periodic repeats in a CRISPR-encoding genome
Using SPADE, we exhaustively searched for periodic DNA
and protein sequences in the 7006 complete prokaryotic
genomes that were available in the NCBI RefSeq database.
The default parameter set was used for the entire analysis of this study. In the Streptococcus thermophilus LMD9 genome, 7 periodic DNA repeats and 27 periodic protein repeats were detected, including 2 previously annotated
CRISPR loci (Figure 1A, Supplementary Table S1). The repeat periods of the annotated CRISPRs were both 66 bp
and their detected repeat motif sequences were identical to
the reported motifs (Figure 1B). We also found an unannotated interspaced repeat region containing four repeats witha period of 72 bp, in which the repeat motif and interspacesequences were all 36 bp long (Figure 1C). While type II-A
Cas genes were found in the neighboring regions of the reported CRISPRs, a type III-A Cas gene cluster was foundin the adjacent region of the unannotated repeat, suggestinga functional type III-A CRISPR system in this genome.
The other periodic DNA repeats were all short tandem
repeats with a period size of 1–7 bp that were commonly
found in prokaryotic genomes (Figure 1D). Among the27 periodic protein repeats, 24 were short tandem repeats
with periodicity of 10 aa or less. The other three included

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For each visualized protein repeat motif sequence, the confidence score for ␣-helix structure or ␤-sheet structure wascalculated using PSIPRED version 3.3 (42). For each PRR
detected by SPADE, PSIPRED was initially used to predict
all possible secondary structure motifs with the confidence
score at each amino acid residue position. We then calculated the average confidence score for each motif at every
position in the repeat sequence unit.

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Nucleic Acids Research, 2019, Vol. 47, No. 2 e8

a peptidoglycan-binding protein (three repeats with a 17aa period) and a subtilisin-like serine protease (three repeats with a 32-aa period), both of which were annotatedto involve protein repeats, and a nucleotide exchange factor(four repeats with a 14-aa period), which was annotated to
involve two ␣-helices.
Performance in detecting CRISPRs
We then measured the performance of SPADE in detecting
CRISPRs, the annotation criteria of which are standardizedin the NCBI prokaryotic genome annotation pipeline (43).
From the entire 161 465 periodic DNA repeats detected inthe 7006 prokaryotic genomes, we obtained 8168 genomic
regions with a repeat period size and interspace size of 58–
81 and 25–60 bp, respectively (Supplementary Table S2).
These parameters were partly derived from CRISPRFinder,
the most commonly used tool for CRISPR annotations
in recent genomic resources (36,44,45), and partly defined

empirically based on the reported CRISPRs (see ‘Materials and Methods’). We confirmed that the distribution of
period-interspace size combinations for the defined parameter space had good agreement with that for the 6354 reported CRISPRs in the RefSeq database (Figure 1D and
E). We then compared the performance of SPADE withthe commonly used CRISPR annotation software tools
CRISPRFinder and CRISPRDetect in capturing RefSeq
CRISPRs. In the same genomic datasets, 8033 regions weredetected by CRISPRFinder and 5924 regions were detectedby CRISPRDetect (Supplementary Table S2). Precisionand recall were decreased along with region overlap agreement (ROA) with reported RefSeq CRISPR regions for allof the software tools but recalls by SPADE were the highest
for ROA of up to 98% where the recalls by CRISPRDetect
were markedly lower than those by the other tools (Figure1F). On the other hand, CRISPRDetect outperformed pre-Downloaded from https://academic.oup.com/nar/article/47/2/e8/5124599 by guest on 25 January 2024

Figure 1. CRISPRs detected by SPADE. (A) Circular genome map of the S. thermophilus LMD-9 genome. From the outer side, it represents genes encodedon the sense and antisense strands of the genome, cumulative k-mer counts with the annotations of periodic DNA and protein repeats, and the previously
reported CRISPR loci. (B) Previously reported CRISPRs detected by SPADE. Each periodic repeat region is visualized along with cumulative k-mer count,neighboring genes, positions of repeat unit sequences and position-period matrix of the surrounding genomic region, and its motif sequence is represented
by sequence logo. Each periodic repeat unit is represented by a gradient box where color indicates relative position in each repeat unit sequence. (C) A
novel CRISPR found in the S. thermophilus LMD-9 genome. (D) Period-interspace size distribution of the entire periodic DNA repeats captured in the7006 RefSeq prokaryotic genomes. Magenta bars in the probability density distributions represent CRISPRs reported in the RefSeq dataset. Dashed whiteline box represents DNA repeats further screened as CRISPR candidates. (E) Enlarged view of the dashed white line box in (D) and distribution of the
RefSeq CRISPRs in the same area. (F) Precision and recall in predicting RefSeq CRISPRs by SPADE, CRISPRFinder and CRISPRDetect along with
region overlap agreement (ROA) thresholds. (G) Venn diagram for DNA repeats detected by SPADE, CRISPRFinder and CRISPRDetect with ROA of≥50% and their agreement with RefSeq CRISPRs.

e8 Nucleic Acids Research, 2019, Vol. 47, No. 2

Periodic repeats in a TALE-encoding genome
In the Xanthomonas oryzae pv. oryzae (Xoo) PXO83
genome encoding TALE genes and TALE pseudogenes(38), 49 DNA repeats and 194 protein repeats were detectedby SPADE (Figure 2A and B, Supplementary Table S3).
All of the reported TALEs were recaptured with a repeating period of 34 aa and variable residues at the 12th and13th amino acid residues and two ␣-helices in each repeat
unit, which were all consistent with the reported features of
TALE. We also detected an annotated large CRISPR locus
where a highly constant motif of 31 bp was repeated periodically 86 times each with an interspace sequence of around
34 bp (Figure 2C).
Among the other 46 periodic DNA repeats, 40 were shorttandem repeats with a period of 10 bp or less, including
25 heptamer repeats that were previously suggested to contribute to phase variation in the Xanthomonas genus (46).
Three short tandem DNA repeats were found in intergenic
regions, one with a period of 12 bp and two with a period of 14 bp. Another short tandem DNA repeat region
was found in the middle of an ABC transporter-encoding
gene with a period of 16 bp, which is relatively prime to3, the protein coding frame size (Figure 3A), and anotherlonger sequence with a period of 60 bp was also found to
encode a hypothetical gene in less than half of its region(Figure 3B). Furthermore, we found a large periodic DNA
region from the genomic position of 3 559 997 to 3 563 142(3144 bp long) with an average period of ∼787 bp (Supplementary Figure S2). Following a transposase-encoding
gene, this region involved three different hypothetical genes,
each of which was in a different repeat unit. Interestingly,
all of the three repeats partially overlapping with proteincoding regions were found to be widely conserved in the
Xanthomonas genus with different numbers of repeats, but
the coding gene architecture had markedly diverged evolutionarily (Figure 3C–E), indicating that phase variations ofprotein-coding patterns for these regions rapidly occurred
after speciation by genomic contraction and expansion via
the repetitive sequences.

Except for TALEs, ∼88% (156 out of 178) of protein repeats were composed of short tandem repeats with a repeat unit size of 10 aa or less (Supplementary Table S3).
The other repetitive proteins included three chemotaxisassociated proteins with different periods of 27, 46 and 90
aa, a DNA topoisomerase I, a TolB-like protein known to
involve non-WD40 ␤-propellers, and transporters and a hypothetical protein involving six repeats with a large unit sizeof 215 aa. Notably, another type III secretion system effector protein of the Xanthomonas host infection process wasfound to have repetitive peptide units, suggesting anotherfunction of pathogenic periodic protein structure in hijacking the host plant system (Supplementary Figure S3).
Performance in detecting TALEs and C2H2 ZFNs
As C2H2 ZNFs are the most widely used transcription
factors in the human genome, we also examined whether
SPADE can capture human C2H2 ZNFs. When a C2H2
ZNF encoded on human chromosome 7p22.1 was scannedby SPADE, 20 degenerative repeats of ∼28 aa were detected with two cysteine and two histidine residues conserved at specific positions, like typically reported C2H2
ZNFs (Figure 4). We then assessed the performance of
SPADE in detecting TALEs and C2H2 ZNFs. Using theprotein domain search software HMMER, we obtained
positive reference sets (PRSs) for TALE and human C2H2
ZNF from the prokaryotic genomic dataset and the humanproteome, respectively, so each PRS protein contained threeor more of the corresponding Pfam motifs (see ‘Materials
and Methods’). We also prepared 10 000 prokaryotic proteins and 10 000 human proteins that did not have any Pfam
motif more than once as negative reference sets (NRSs)
ProNRS10K and HuNRS10K, respectively (see ‘Materials
and Methods’). Using SPADE, repetitive sequences of any
period were detected in 328 out of 331 TALE PRS proteins (99.1%) and 3079 out of 4084 human C2H2 ZNF PRS
proteins (75.4%), while 192 ProNRS10K proteins (1.9%)
and 1269 HuNRS10K proteins (12.7%) were positive (Figure 5A, Supplementary Table S4). When the detected positives were filtered by maximum repeat unit size per protein(maxRUSPP) to be within ±5 aa from the expected average
repeat unit size (34 aa for TALE and 28 aa for C2H2 ZNF),
the recall of TALE PRS stayed the same (99.1%) and therecall of human C2H2 ZNF PRS was 58.9%, while the false
positive rate (FPR) of TALE estimated using ProNRS10K
and the FPR of C2H2 ZNF estimated using HuNRS10K
were greatly decreased to 0.03% and 0.21%, respectively(Figure 5B and C). This simple size limitation improved
positive likelihood ratios (PLRs) of the prediction from 51.6
to 3303.1 (64.0-fold) for TALE and from 5.9 to 280.7 for human C2H2 ZNF (47.2-fold).
Comparison with other software capturing tandem protein repeats
SPADE successfully detected the other degenerate tandem protein repeats widely spread in prokaryotes, including TPRs, ANK repeats, and WD40 repeats (Figure 5D–
F). The secondary structure prediction of these degenerated repeats also properly captured their reported structural

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cision of capturing RefSeq CRISPRs while the precisions of
SPADE and CRISPRFinder were similar (Figure 1F).
At 50% ROA, SPADE, CRISPRFinder and CRISPRDetect captured 6181, 6093 and 5548 RefSeq CRISPR regions,
respectively, where 5420 were captured by all of the software tools (Figure 1G). We defined 5548 RefSeq CRISPR
regions captured by CRISPRDetect with a minimized false
positive rate as a high confidence gold standard CRISPR
set. Amongst this set, 5520 and 5445 regions were captured by SPADE and CRISPRFinder, respectively. Given
that precisions and recalls of CRISPRFinder were higher
than those by SPADE for ROA of more than 98%, we concluded that SPADE was slightly better than CRISPRFinder
at roughly capturing CRISPRs, but not at the single-baseresolution. In sum, although SPADE was not specificallydesigned for CRISPR annotation, its performance for capturing CRISPRs with simple size thresholds was at least
comparable to the most commonly used CRISPR prediction software tools.

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Tal AS3

TalAS3TalAD5CRISPR

TalAG4

TalAE4TalAO3
TalAQ3
TalAP3*TalAI3
TalAD5
TalAE4TalAO3
TalAQ3
TalAP3

TalAR3

Xanthomonas
oryzae PXO83(5.0M bp)

TalAA5
TalBA2Bits

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TalAB5

TalAC5TalAG4

TalBJ2C

TalCA1
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TalAF4 TalBJ2TalAB5 *TalAI4

TalAR3
TalAA5 TalCA1
TalBA2 TalAN3*TalAI4

Bits

k-mer count (a.u.)
Sense strand ORFs
Antisense strand ORFs
Non-periodic DNA repeats
Periodic DNA repeats
Reported CRISPR
Matched with protein repeats
Reported TAL effectors

Figure 2. TALEs and a CRISPR detected in the Xoo PXO83 genome. (A) Example (TalAS3) of TALEs detected by SPADE. For details, see Figure 1B.
The heat map under the repeat motif sequence logo represents confidence scores for ␣-helical structure at each amino acid residue. (B) Circular map
representation of the Xoo PXO83 genome along with position-period matrices for all of the TALE genes annotated in the RefSeq database. ‘*’ denote theones annotated as TALE pseudogenes by AnnoTALE. For details, Figure 1A. (C) CRISPR locus of the Xoo PXO83 genome detected by SPADE.

motifs. In each of the repeat sequence motifs identified fora TPR and an ANK repeat, both of which have been reported to have helix–turn–helix structures, we observed two
␣-helical loops (Figure 5D and E). Four ␤-strands were also
captured in a repeat sequence motif of WD40, consistent
with its ␤-propeller structure (Figure 5F). As XSTREAM(34) and T-REKS (35) have been widely used to explore
tandem protein repeats in an unsupervised manner in recent studies (47,48), we next performed a benchmark comparison of SPADE, XSTREAM, and T-REKS in detecting TPRs, ANK repeats, and WD40 repeats, in addition to
TALEs and human C2H2 ZNFs. For TPRs, ANK repeats,
and WD40 repeats, PRSs were prepared as described above
for TALE. ProNRS10K and HuNRS10K were again usedas NRSs for detecting repeats in prokaryotic and humanprotein families, respectively.
T-REKS performed the best in recall for detecting
repetitive sequences regardless of repeat unit size, except
for WD40, in which SPADE performed the best (Figure 5A, Supplementary Table S4). However, T-REKS also
demonstrated the highest FPRs in both ProNRS10K and
HuNRS10K datasets. When the overall prediction performance was estimated by PLR, SPADE performed the bestin every repeat type (between 1.02-fold and 3.39-fold compared with the second-best software XSTREAM for all repeat types). We also found that the maxRUSPPs detectedby SPADE were distributed with peaks at 34, 33 and 42 aa
for TPR, ANK repeats, and WD40 repeats, respectively, allof which were the reported typical unit sizes for these protein repeats (Figure 5B). This was not the case for all of therepeats detected by XSTREAM and T-REKS. XSTREAM

captured wider ranges of repeat unit sizes for every repeat
type and T-REKS tended to capture shorter tandem repeats for the subpopulation of positive reference proteinsfor TPRs, ANK repeats, and WD40 repeats. Filtering thedetected positives by maxRUSPP to be within ±5 aa fromthe expected average repeat sizes, the recall performance of
SPADE was the best for all repeat types, whereas the FPRsof the three software packages were all minimized to below
0.005 in all of the repeat types (Figure 5C, Supplementary
Table S4). (Note that the performances could not be compared using PLR as many FPRs for different protein families were zero.) These observations were maintained when
the positive reference protein sets were prepared differently(Supplementary Figure S4).
DISCUSSION
No software program have been developed that can universally screen for periodic DNA and protein repeats; theonly available software tools are those that screen for reported motifs or certain types of periodic repeats. Nevertheless, the performance of SPADE capturing CRISPRs wason par with the commonly used CRISPR prediction software tools and outperformed XSTREAM and T-REKS in
the sensitivity for capturing various tandem protein repeats,regardless of the degree of consensus in the repeat unit motifs. SPADE also captured TALEs and ZNFs in a highly
specific and unsupervised manner, indicating its potential tocontribute to the discovery of new genome editing modules
from large genomic and/or metagenomic resources. Thisis supported by the fact that we found that a non-TALE

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C

PXO83 PXO86 PXO99 KACC MAFFE

PXO83 PXO86 PXO99 KACC MAFF
MAFF KACC PXO99 PXO86 PXO83

Bits

MAFF KACC PXO99 PXO86 PXO83

ABC transporter

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50 bp

D

PXO83 PXO86 PXO99 KACC MAFF

Coding ORF
Pseudogene

Bits

O83
MAFF KACC PXO99 PXO86 PXO83

Hypothetical protein

DNA alignment result
Aligned protein pairregions

200 bpFigure 3. Comparative analysis of the repetitive DNA regions determined to overlap with protein-coding regions in the Xanthomonas genus. (A) An ABC
transporter-coding region involving a DNA repeat of 16-bp period. Line box denotes the genomic regions used for the comparative genomic analysis
presented in (C). (B) A hypothetical protein-coding region overlapped with a DNA repeat of 60-bp period. Line box denotes the genomic regions used forthe comparative genomic analysis presented in (D). (C–E) Sequence alignment analysis of the repetitive DNA sequences and their protein-coding patterns
amongst the Xoo PXO83, PXO86, PXO99, KACC10331, and MAFF311018 genomes. With protein/pseudogene-coding structures under the label of eachgenome, the within- and between-genome alignment results are represented by light blue lines for DNA and orange boxes for proteins, respectively. (C)For an ABC transporter-coding region in the Xoo PXO83 genome. (D) For a hypothetical protein-coding region overlapping with a DNA repeat of 60-bp
period in the Xoo PXO83 genome. (E) For a large DNA repeat region with a repeat unit size of 787 that overlaps with three different hypothetical genes in
the Xoo PXO83 genome.

Bits

ZNF768

5

10 15

20 25

0
10
Confidence for-helix structure

Figure 4. Periodic features of human ZNF768 detected by SPADE.

type III secretion system protein of Xanthomonas host infection machinery had periodic repeats like TALEs and
ZFNs (Supplementary Figure S3). We also captured bacterial homologs of pentatricopeptide repeats (PPRs) that are
involved in translational regulation in plants (Supplementary Figure S5). As the binding code of PPR to RNA has re-cently been deciphered, it has been suggested as a potential
programmable RNA editing and regulating module (49).
The majority of the periodic repeats detected in the 7006
prokaryotic genomes still need further investigation. We detected many short tandem DNA and proteins repeats. In
particular, tandem heptamer DNA repeats were the mostabundant in intergenic regions of a wide range of prokaryotic species (Figure 1D). However, there has been no clear
clue about the function of this globally existing prime number periodicity in genomic DNA. We also found various interspaced repeats that had clear sequence periodicities withno CRISPR annotation or neighboring Cas gene. They included many tRNA operons in various prokaryotes, as reported previously (Supplementary Figure S6), but the others remain to be explored. Genomic expansion and contraction have been thought to occur at the tandem repeat
sequences, leading to phase variation. Even after excluding corresponding protein repeats, the repeat periods ofboth tandem and interspaced DNA repeats showed particular abundance for these in multiples of three. Furthermore,some genes were encoded in part of a repeat unit of a large
tandem repeat region (Supplementary Figure S2). As seenin the Xanthomonas genus (Figure 3E), these findings suggest the roles of tandem repeats in de novo gene birth or gene
death. We also found many tandem DNA repeats within(or partially within) protein-coding regions, some of which

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TPR (WP011032057.1)

5

E

10 15

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Confidence for-helix structure

ANK (WP015178228.1)

5

10 15

20 25

30

0
10
Confidence for-helix structure

WD40 (WP011319391.1)

F

Bits

FPR

RecallC

HuNRS10K

ProNRS10K

Bits

maxRUSPP

maxRUSPP

B

5

10 15

20 25

30 35 40

0
10Confidence for-strand structure

Figure 5. Comparison of the performance of SPADE, XSTREAM, and T-REKS to detect tandem degenerate protein repeats. (A) Recalls, false positive
rates (FPRs), and positive likelihood ratios (PLRs) of SPADE, XSTREAM, and T-REKS in capturing TALEs, ZNFs, TPRs, ANK repeats, and WD40
repeats. (B) Distribution of maximum repeat unit sizes per protein (maxRUSPPs) detected by each software for each protein family. Each bold black bar
and each gray box denote the reported typical repeat unit size for the corresponding protein category and the ±5 aa range from the reported repeat unit
size, respectively. (C) Recalls and FPRs of the different software after filtering maxRUSPPs of the detected positives to be within ±5 aa from the expected
repeat unit size. From the maxRUSPP distributions of ProNRS10K (negative control for prokaryotic protein repeat families) and HuNRS10K (negative
control for ZNFs), FPRs for different protein repeats of different expected repeat unit sizes were estimated. (D–F) Example protein repeats detected by
SPADE for TPR (D), ANK repeat (E), and WD40 repeat (F). The heat map under each repeat motif sequence logo represents the confidence scores for␣-helical structure (red heat map) or ␤-sheet structure (blue heat map) at each amino acid residue position.

were indicated to have contributed to functional phase variation of protein-coding patterns (Figure 3A–D).
The default parameter set of SPADE robustly captured
most of the important biological sequences tested in thisstudy with higher precision than did the other software. We
further analyzed various kinds of simulated repeats using
SPADE and confirmed that the default k-mer parametersfor DNAs and proteins performed the best and precisely
captured the periodicities of various degenerate tandem andinterspaced repeats with up to ∼10% and ∼30% sequence
noise for DNA and proteins, respectively, regardless of therepeat unit size and interspace size (Supplementary Figure
S7).
SPADE was implemented using Python and can be executed with MAFFT and BLAST on MacOS X and Linux
operating systems, and on Windows Subsystem for Linux(WSL) of Windows 10. It automatically detects the input
file and sequence types and outputs results in the GenBank file format, which can be further input into other software programs, with various visualizations as represented
in the figures. Accordingly, here we propose that SPADE
is fast and user-friendly software based on a simple algo-

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XSTREAM
T-REKSBits

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e8 Nucleic Acids Research, 2019, Vol. 47, No. 2

rithm to globally capture periodic biomolecular sequences.
Although we mainly focused on measuring the performanceof this software predominantly using prokaryotic genomes
in this study, further wide-ranging investigations of these
periodically repeating sequences together with screening ofeukaryotic and metagenomic resources could lead to thediscovery of new biological events and genome editing tools.
DATA AVAILABILITYSPADE is open-source software under GNU General Public License v3.0, which is available at https://github.com/
yachielab/SPADE.

Supplementary Data are available at NAR Online.
ACKNOWLEDGEMENTS
We thank the members of the Yachie laboratory at The University of Tokyo and Keio University for useful comments
and discussions throughout the course of this study. We also
sincerely appreciate Hirotada Mori for sharing his laboratory space at the Nara Institute of Science and Technology(NAIST).
FUNDINGNew Energy and Industrial Technology Development Organization (NEDO) Genome Editing Program; Japan Society for the Promotion of Science (JSPS) 18K19777(to N.Y.); Japan Science and Technology Agency (JST)PRESTO program 10814 (to N.Y.); Japan Agency for Medical Research and Development (AMED) PRIME program 17gm6110007 (to N.Y.); The Naito Foundation (toN.Y.); The Nakajima Foundation (to N.Y.); The Takeda
Foundation (to N.Y.); SECOM Science and Technology
Foundation (to N.Y.); TTCK fellowships (to H.M., D.E.Y., S.I.); Mori Memorial Foundation (to H.M.); Yamagishi
Student Project Support Program (to D.E.-Y.) of Keio University; JSPS DC1 Fellowship (to S.I.). Funding for open
access charge: Research Budget.
Conflict of interest statement. None declared.
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