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Milletdb: a multi-omics database to accelerate the
research of functional genomics and molecular breeding
of millets
Min Sun
1,†
, Haidong Yan
1,2,3,†
, Aling Zhang
1,†
, Yarong Jin
1
, Chuang Lin
1
, Lin Luo
4
, Bingchao Wu
1
, Yuhang Fan
1
,
Shilin Tian
5,6
, Xiaofang Cao
5
, Zan Wang
7
, Jinchan Luo
1
, Yuchen Yang
1
, Jiyuan Jia
1
, Puding Zhou
1
, Qianzi Tang
8
,
Chris Stephen Jones
9
, Rajeev K. Varshney
10,11
, Rakesh K. Srivastava
10
, Min He
12
, Zheni Xie
1,13
, Xiaoshan Wang
1
,
Guangyan Feng
1
, Gang Nie
1
, Dejun Huang
14
, Xinquan Zhang
1
, Fangjie Zhu
4,
* and Linkai Huang
1,12,
*
1
College of Grassland Science and Technology, Sichuan Agricultural University, Chengdu, China
2
School of Plant and Environmental Sciences, Virginia Tech, Blacksburg, Virginia, USA
3
Department of Genetics, University of Georgia, Athens, Georgia, USA
4
College of Life Sciences, Fujian Agriculture and Forestry University, Fujian, China
5
Novogene Bioinformatics Institute, Beijing, China
6
Department of Ecology, Hubei Key Laboratory of Cell Homeostasis, College of Life Sciences, Wuhan University, Wuhan, China
7
College of Grassland Science and Technology, China Agricultural University, Beijing, China
8
College of Animal Science and Technology, Sichuan Agricultural University, Chengdu, China
9
Feed and Forage Development, International Livestock Research Institute, Nairobi, Kenya
10
Center of Excellence in Genomics and Systems Biology (CEGSB), International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Patancheru, India
11
Murdoch’s Centre for Crop and Food Innovation, Food Futures Institute, Murdoch University, Murdoch, Western Australia, Australia
12
State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Sichuan Agricultural University, Chengdu, Sichuan, China
13
College of Agro-Grassland Science, Nanjing Agricultural University, Nanjing, China
14
Herbivorous Livestock Research Institute, Chongqing Academy of Animal Sciences, Chongqing, China
Received 7 February 2023;
revised 1 July 2023;
accepted 17 July 2023.
*Correspondence (Tel 18783575058; fax
2886080; email huanglinkai@sicau.edu.cn
(LH); Tel 059-86395360; fax 059-86395360;
email fjzhu@fafu.edu.cn (FZ))
†
These authors contributed equally to
this work.
Keywords: millet, database,
functional genomics, multi-omics,
abiotic stress.
Summary
Millets are a class of nutrient-rich coarse cereals with high resistance to abiotic stress; thus, they
guarantee food security for people living in areas with extreme climatic conditions and provide
stress-related genetic resources for other crops. However, no platform is available to provide a
comprehensive and systematic multi-omics analysis for millets, which seriously hinders the
mining of stress-related genes and the molecular breeding of millets. Here, a free, web-
accessible, user-friendly millets multi-omics database platform (Milletdb, http://milletdb.
novogene.com) has been developed. The Milletdb contains six millets and their one related
species genomes, graph-based pan-genomics of pearl millet, and stress-related multi-omics data,
which enable Milletdb to be the most complete millets multi-omics database available. We stored
GWAS (genome-wide association study) results of 20 yield-related trait data obtained under
three environmental conditions [field (no stress), early drought and late drought] for 2 years in
the database, allowing users to identify stress-related genes that support yield improvement.
Milletdb can simplify the functional genomics analysis of millets by providing users with 20
different tools (e.g., ‘Gene mapping’, ‘Co-expression’, ‘KEGG/GO Enrichment’ analysis, etc.). On
the Milletdb platform, a gene PMA1G03779.1 was identified through ‘GWAS’, which has the
potential to modulate yield and respond to different environmental stresses. Using the tools
provided by Milletdb, we found that the stress-related PLATZs TFs (transcription factors) family
expands in 87.5% of millet accessions and contributes to vegetative growth and abiotic stress
responses. Milletdb can effectively serve researchers in the mining of key genes, genome editing
and molecular breeding of millets.
Introduction
Global hunger has been increasing since 2014 (Molotoks
et al., 2021). According to the Food and Agriculture Organization
of the United Nations (FAO, https://www.fao.org/state-of-food-
security-nutrition/en/), approximately 720 to 811 million people
faced hunger in 2020, with those from Asia and Africa being the
worst affected. Global climate change and frequent extreme
weather events are important factors leading to global hunger
(FAO, 2018), with climate-affected cereal production expected to
be 1%–7% by 2060 (Juma and Kelonye, 2016). Consequently,
cultivating crops with high-abiotic stress tolerance is essential to
ensure an adequate food supply in the future (Varshney
et al., 2021a,b).
Millets are a collective name for coarse grains (McSteen and
Kellogg, 2022), with the characteristics of small grains
2348 ª2023 The Authors. Plant Biotechnology Journal published by Society for Experimental Biology and The Association of Applied Biologists and John Wiley & Sons Ltd.
This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and
distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.
Plant Biotechnology Journal (2023) 21, pp. 2348–2357 doi: 10.1111/pbi.14136
and ranking sixth in the world in terms of yield (Shahidi and
Chandrasekara, 2013). In 2018, the world’s total millet produc-
tion was estimated at 31 million tones, with more than 96% of
millet crops grown in regions with poor soil fertility and limited
rainfall in Africa and Asia (Muthamilarasan and Prasad, 2021;
Yousaf et al., 2021). Millets have evolved sophisticated regulatory
mechanisms to improve tolerance to various stresses, in
adaptation to different environmental conditions and become
climate–smart crops (Ceasar and Maharajan, 2022). Conse-
quently, millets can curb food insecurity caused by climate
change (Adebiyi et al., 2018).
Millets include 11 genera, the more famous are pearl millet
(Pennisetum glaucum (L.) R. Br., syn. Cenchrus americanus (L.)
Morrone) and the small millets, finger millet (Eleusine
coracana), foxtail millet (Setaria italica), proso millet (Panicum
miliaceum), barnyard millet (Echinochloa crus-galli), tef (Era-
grostis tef), fonio (Digitaria exilis) and Job’s tears (Coix lacryma-
jobi) (Goron and Raizada, 2015; Muthamilarasan et al., 2019;
Yousaf et al., 2021). Some millets, such as pearl millet, have a
close phylogenetic relationship with other major Poaceae crops
such as sorghum (Sorghum bicolor), maize (Zea mays) and rice
(Oryza sativa), which could enable the easy transfer of its
abiotic-stress resistance genes to these crops (Desai
et al., 2006; Islam et al., 2010; Verma et al., 2007). For
example, a pearl millet glutathione peroxidase (PgGPX) gene
transformed into rice effectively improved both salt tolerance
and drought tolerance (Islam et al., 2015). Thus, millets provide
new insights into understanding plant abiotic stress tolerance
and genetic resources for improving stress tolerance in major
crops.
The broad application of high-throughput sequencing tech-
nologies has enabled the genomes of millets to be deciphered
(Varshney et al., 2017) and a large number of multi-omics data
from millets have been reported. For instance, intensive research
on stress-related genetic resources of millets has produced
enormous data covering transcriptome sequencing (Awan
et al., 2022; Huang et al., 2021;Jiet al., 2021; Sun
et al., 2021;Wuet al., 2021; Zhang et al., 2021), whole-
genome resequencing (Varshney et al., 2017) and phenomics
(Varshney et al., 2017). These offer new opportunities to
innovate knowledge regarding plant abiotic stress tolerance
and to advance the genetic improvement of stress tolerance in
major crops. However, the collection and analysis of these data
are time-consuming, especially for researchers who lack
bioinformatics experience and computing resources, resulting
in data mining of key stress-tolerance genes still being a
challenging task.
To solve the data mining problem, we have developed a
comprehensive and user-friendly millets multi-omics database
(Milletdb, http://milletdb.novogene.com), comprising volumi-
nous data with browsing and analytical tools. The Milletdb
contains genomes of seven genera and 1800 sets of diverse
omics data including graph-based pan-genomics, transcrip-
tomics, epigenomics, variomics and phenomics data. Various
practical tools integrated by Milletdb can help users quickly
identify a single gene from multiple perspectives (homologous
search, blast, traits), build regulatory networks from multiple
levels [TE (transposable element) distribution, TF (transcription
factor) binding sites, expression level, protein level] and
characterize gene sets (sequence characteristics, expression
patterns and functional enrichment). The database platform
can provide effective services for the entire scientific
community in millets’ functional genomics and population
genetic studies.
Results
Database content
Milletdb contains 824 198 entries of genes from 18 genomes viz
11 pearl millet, two elephant grass (Cenchrus purpureus), one
foxtail millet (v 2.0) one proso millet (Pm_0390_v2), one finger
millet (Ragi_PR202_v._2.0), one fonio (DiExil) and one barnyard
millet (ec_v3), with information on 7184 biological pathways (939
pathways related to abiotic stress) and one graph-based pan-
genome with information on 30 050 483 SNPs (single nucleotide
variants), 424 085 SVs (structural variants), and 692 transcrip-
tome data. Approximately 147 of the transcriptome data are
derived from different developmental stages, 527 and 18
are from abiotic and biotic stress, respectively. The database also
holds four histones ChIP–seq data (H3K4me3 and H3K36me3
modification of root and panicle), 400 basic information data
(such as biological status, seed source, etc.) 242 with phenotypic
data, 378 resequencing data, 1 455 924 pearl millet population
SNPs and 124 532 SVs among pearl millet accessions (Figure 1a,
Table S1). Notably, the pearl millet materials for generating stress-
related transcriptome data were uniformly grown and processed.
It can avoid errors due to different growth conditions of the
materials. The pan-genome browser in Milletdb displays all SVs,
SNPs, indels and histone modification sites of pearl millet. Thus,
Milletdb contains the largest amount of millet-related data up to
now, solving the dilemma associated with collecting and
interrogating all of these data.
Gene identification
Reverse-genetics strategy
Milletdb shown as follows provides three options when
searching for genes. (A) Gene ID search: where users can
obtain the target gene in ‘Gene’ through gene ID, KO/GO
number, Pfam ID, keywords and corresponding gene ID in
Ensemble and Phytozome databases (Figure 1a,b); (B) Homology
search: whereby users can directly input the gene ID of other
species (e.g., Arabidopsis, rice and maize) in the ‘Homologous
gene search’ bar to identify candidate genes (Figure 1a,b). The
‘Homologous gene search’ supports a two-way search, enabling
users to identify homologous candidate genes across species
that could benefit crop breeding. In addition, the sequence
alignment of protein sequences between homologous genes is
shown; (C) Search by sequence, which allows users to employ
‘Blast’ (basic local alignment search tool) to quickly obtain
specific genes using sequence information (Figure 1a,b). The
hyperlinks provided by ‘Blast’ helps users obtain full information
on target genes.
Forward-genetics strategy
Trait-based search allows users to screen key genes potentially
controlling the target trait via the ‘Significant SNPs & Gene’/
‘Significant SVs & Gene’ part of the ‘GWAS’ (genome-wide
association study) module under ‘Variation’ (Figures 1a and 2).
For example, we identified an SV associated with the vegetative
growth index [GI (kg/ha/d)] through the ‘Significant SVs & Gene’
page in the ‘GWAS’ section of the ‘Variation’ module (Figure 2a,
Figure S1A). Navigating to the results section of this web page
showed that this SV was located within 4.89 kb of the gene
ª2023 The Authors. Plant Biotechnology Journal published by Society for Experimental Biology and The Association of Applied Biologists and John Wiley & Sons Ltd., 21, 2348–2357
Milletdb: a multi-omics database for millets 2349
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PMA1G03779.1, which can be identified from 122 (31%) pearl
millet accessions (Figure 2a, Figure S1B). According to the
‘Individual Alleles’ page, we observed that the GI value of
the accessions (122) with this SV was significantly lower than
that of the accessions (256) without this SV (Figure 2b,
Figure S1B). Further analysis of the potentially associated
gene, PMA1G03779.1, revealed that it is likely to be involved in
the G-protein coupled receptor signalling pathway, which has a
potential role in plant growth (Figure S1C; Colucci et al., 2002).
Utilization of the ‘Heatmap’ feature on the webpage shows that
this gene is not only expressed during the vegetative growth
stage of pearl millet but also in the leaves under high
temperature, drought and salt stress (Figure 2c, Figure S1C).
Through ‘Homologous gene search’, we found that this gene has
no orthologous gene in maize, rice or Arabidopsis and it is
presumed to be a new gene that controls GI (Figure S1D).
The landscape of gene information
Milletdb provides comprehensive information on millet genes,
including their annotation, location, homology and expression
(Figure S2), which is helpful for in-depth functional studies of the
target. The gene information mainly contains three parts: basic
gene information, homologous genes and heat maps. The
basic gene information shows the millet accessions to which
the gene belongs, the annotation information of the gene in five
databases [Swissport (Swiss-Prot Protein Sequence Database), NR
(Non-Redundant Protein Sequence Database), KEGG (Kyoto
Encyclopedia of Genes and Genomes, https://www.kegg.jp/),
GO (Gene Ontology, http://geneontology.org/), Interpro (https://
www.ebi.ac.uk/interpro/)], the sequence information of the gene
(nucleic acid and protein sequence) and the chromosomal
location. Clicking on the location hyperlink opens the pan-
genome browser (the pan-genome browser supports genes from
the reference PI537069 and the others are supported by the
genome browser), which shows the homologous genes and their
structures among pearl millet accessions. The browser also
displays information on SVs and SNPs adjacent to the genes,
enabling users to design markers for exploring the genetic
differences between pearl millet accessions (Figure 1b; Figure S3).
Meanwhile, the homologous genes show those genes with similar
homologous genes among different millets, Arabidopsis, rice and
maize. The heat map shows the expression of genes in three
categories, viz multiple stresses (i.e., heat stress, drought stress
and salt stress), multiple tissues and multiple materials. For
convenience, users can extract the gene information through the
Gene ID hyperlinks in ‘Gene’, ‘Variation’ and ‘Tools’, etc., which
prompts users to obtain information on key genes more
efficiently and comprehensively.
Identification of upstream regulatory elements of genes
Milletdb contains two tools, the ‘Transposable Elements Identi-
fication’ and ‘Motif binding site prediction’ for analysing
regulatory elements adjacent to the genes (Figure 1b). This
facilitates users to find the upstream regulatory elements of the
target gene. ‘Transposable Elements Identification’ can be used to
summarize the TE statistics around a gene set, the distribution of
TEs and the TE information of each gene. ‘Motif binding site
prediction’ provides a convenient method for users to analyse
upstream cis-elements of genes by matching multiple expectation
maximizations for motif elicitation (MEME) motifs. Users can
obtain a list of genes containing the binding sites of the TF after
entering the motif of a target TF and selecting the length of the
upstream sequence of the genes (Figure 1b; Figure S4). Milletdb
also supports directly downloading complete TE information via
the ‘Transposable Elements’ tool. In brief, the Milletdb is helpful
for users to understand the potential regulation of gene
expression in a simplified way.
Gene network construction
Millets have excellent resistance to abiotic stresses through
complex gene regulation networks. Therefore, we developed
modules to enable prediction on the gene regulatory networks.
All the gene information involved in the regulatory pathway can
be quickly retrieved using a keyword or pathway number of the
target gene (Figure 1). The main module ‘Pathway’ contains 422
non-redundant KEGG pathways. The ‘Accessions’ dropdown
menu can be used to select the millet accessions. Moreover, the
‘Map_ID’ contains a hyperlink for displaying detailed pathway
information, while ‘Map Info’ shows the schematic diagram of
the pathway, where the green fill indicates its existence in millet.
The genes involved in a specific pathway are displayed in a table
format at the bottom of the schematic diagram and can be
downloaded in CSV or excel file formats. Furthermore, the ‘Co-
expression’ tool is constructed based on the expression patterns
of genes under various stresses (heat, drought, salt), multiple
materials and multiple tissues (Figure 1, Figure S4). This paves the
way for screening a large number of genes potentially interacting
with a target gene. Also, Milletdb provides ‘PPI’ (protein–protein
interaction) analysis for narrowing down gene sets obtained by
‘Co-expression’.
Analysis of PLATZ genes in millets based on tools
provided by Milletdb
Milletdb provides practical tools for gene sequence analysis and
gene function prediction (Figure 1). For example, users can
extract sequence fragments, coding sequences (CDSs), protein
sequences and upstream or downstream sequences using gene
location or ID through ‘Sequence Fetch’ and ‘Gene Sequence
Extraction’ tools’ (Figure 1a). Subsequently, the polymorphisms
between gene sequences of different millet accessions can be
determined via ‘Gene Synteny Viewer’ and gene sets clustered
according to sequence features using the ‘Phylogenetic Tree’ tool
(Figure 1a, Figure S4). The functional enrichment analysis of the
gene clusters is then conducted using the ‘KEGG/GO Enrichment’
tool, which also supports the online adjustment of pictures
(Figure 1). The ‘Gene Expression’ tool provided by Milletdb allows
batch extraction of expression of the genes under different
catalogues. The distribution of specific gene sets in millet
chromosomes is displayed via the ‘Gene mapping’ tool (Figure 1).
Milletdb also provides the ‘References’ and ‘Primer design’ tools
for searching millet-related publications and primers, respectively
(Figure 1).
A typical case of a user using the web is shown in Figure 3.
Millets are generally cereal crops that are extremely tolerant to
environmental stresses (Muthamilarasan and Prasad, 2021).
PLATZs (plant AT-rich sequence and zinc-binding proteins) are
plant-specific TFs involved in plant responses to abiotic stress (Fu
et al., 2020; Gonz
alez-Morales et al., 2016). However, the
PLATZ gene family in millets has not been reported so far.
Based on the Milletdb platform, we identified that millets
contain 17–59 genes encoding PLATZs depending on the
accession. Of the millet accessions, 87.5% (14/17) contain
more PLATZs than maize, rice and barley (Hordeum vulgare)
(Figure 3a, Figure S4A).
ª2023 The Authors. Plant Biotechnology Journal published by Society for Experimental Biology and The Association of Applied Biologists and John Wiley & Sons Ltd., 21, 2348–2357
Min Sun et al.2350
14677652, 2023, 11, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/pbi.14136 by FUJIAN AGRICULTURE & FORESTRY University, Wiley Online Library on [23/10/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License
The ‘Gene Sequence Extraction’ tools were used to extract the
protein sequences of PLATZ genes in millets. According to the
analysis results of the ‘Phylogenetic Tree’ tool, PLATZ genes
are divided into four clades. Of these, two clades (clade 2 and
clade 3) only contain PLATZ genes from millets (Figure 3b,
Figure S4B,C). Further analysis of the protein sequences of the
Genomes
1 pan-genome
11 pearl millet genomes
1 foxtail millet genome
1 proso millet genome
1 finger millet genome
1 fonio genome
1 barnyard millet genome
2 elephant grass genmomes
Variomes
378 pearl millet accessions
GWAS results in 3 environments
Transcriptomes
527 abiotic stress
147 growth and development
18 biotic stress
Epigenomes
4 histone modifications
MongoDB
Gene identification
Gene
Homologous gene search
Blast
Variation
Regulatory elements identification
Pan-JBrowse
Transposable Elements Identification
Motif binding site prediction
Transposable Elements
Phenomes
400 pearl millet accessions basic information
242 pearl millet accessions phenotypic
Gene network construction
Pathway
Co-expression
PPI
Other tools
KEGG/GO Enrichment
Gene mapping
Gene Expression
primer design
References
Sequence analysis
Sequence Fetch
Gene Sequence Extraction
Phylogenetic Tree
Gene Synteny Viewer
Tools for functional genomics
GATGCCT
CATGCCT
AGCACTC
AACGTAG
(a)
Gene identification
1
…
PMA3G00008.1
PMA3G00199.1
PMA3G00275.1
PMA3G01686.1
PMA3G02659.1
PMA3G05858.1
PMA3G06545.1
…
GRMZM5G808366
PF02362
RFRGV...QSYDK
CK High
temperature
1h 24h 1h 24h
PI521612
PI526529
PI537069
PI583800
PI587025
Tifleaf3
0
80
seed
HAI36
T5L
TL
FT
0
2
4
6
0
3
6
9
12
112 bp 1127 bp
1497 bp
1972 bp
ARF
Gene
0
70
D_1h
D_3h
D_5h
D_7h
D_24h
D_48h
D_96h
D_144h
Homologous search
Keyword search
Pfam search
Blast
Genome collinearity
construction
2
Characterization of gene
expression patterns
3Regulatory elements
identification
4
Gene network
construction
5
Pan-JBrowse
Gene Expression / Gene
(b)
H3K36me3
PMA3G
02660.1
PMA3G0
2659.1
PMA6G
01306.1
PMA6G
00389.1
PMA5G
04711.1
PMA2G
04085.1
PMA2G
05174.1
PMA3G
01454.1
Motif binding site prediction
ARF binding site
Co-expression
16.00 99.00
TPM
chrB3 chrB4 chrA3
chr3
Elephant grass
Pearl millet
PMA3G02659.1 map04075
Pathway
Tissue Treat 1h 3h 5h 7h 24h 48h 96h 144h
Leave
CK
H
D
S
Root
CK
H
D
S
PMA1G
06191.1
PMA3G
01454.1
PMA1G
00597.1
AUX1 ARF
IAA
SAUR
TIR1
GX3
H3K36me3
TPM
TPM
Data in Milletdb website
PMA3G02659.1
LTR
ª2023 The Authors. Plant Biotechnology Journal published by Society for Experimental Biology and The Association of Applied Biologists and John Wiley & Sons Ltd., 21, 2348–2357
Milletdb: a multi-omics database for millets 2351
14677652, 2023, 11, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/pbi.14136 by FUJIAN AGRICULTURE & FORESTRY University, Wiley Online Library on [23/10/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License
above two clades found that Cclade 2 contains motifs 1, 2, 3 and
4 and clade 3 contains motifs 12, 3, 4, 7 and 11 structures which
are uniquely present in pearl millet. With the ‘Gene mapping’
tool, we found that these genes are mainly distributed at both
ends of the chromosomes (Figure S4D). The ‘Gene Expression’
tool used to characterize the above genes expression patterns
showed that the gene PMA5G01662.1, containing motifs 1, 2, 3,
4, 5 and 6, was involved in seed germination, seedling growth
and tiller tissue growth of pearl millet and was up-regulated in
response to heat, drought and salt stresses (Figure 3d,
Figure 1 Overview of Milletdb and its application in millets functional genomics. (a) Milletdb data content and functions. The left panel illustrates the
multi-omics data stored in Milletdb and the right panel shows the utilities in Milletdb and their purposes. ‘Gene’ is used to search for target genes based on
gene identifier (ID), KO/GO/Pfam ID and keywords. ‘Homologous gene search’ is used to search for genes of interest across species. The Basic Local
Alignment Search Tool ‘Blast’ is used to retrieve target genes based on sequence similarity. ‘Variation’ is used to search for genes of interest based on
associated traits. ‘Pan-JBrowse’ is used to view homologous genes and TEs (transposable elements), SNPs, SVs and nearby genes. ‘Transposable Elements
Identification’ is used to view TEs near the target gene. ‘Motif binding site prediction’ is used to find genes containing specific motifs. ‘Transposable
Elements’ is used to view and download whole-genome TE information. ‘Pathway’ is used to search for genes involved in a specific pathway. ‘Co-
expression’ searches for gene sets that are co-expressed with the specified genes. ‘PPI’ (protein–protein interaction) searches for proteins that interact with
the specified protein. ‘Sequence Fetch’ is used to extract sequence fragments based on their position. ‘Gene Sequence Extraction’ is used to retrieve coding
sequences (CDS), protein sequences or upstream and downstream sequences based on gene IDs. ‘Phylogenetic Tree’ is used to build a phylogenetic tree of
a specific gene set. ‘Gene Synteny Viewer’ is used to examine collinearity among multiple genes. ‘KEGG/GO Enrichment’ is used for functional enrichment
analysis of specified gene sets. ‘Gene mapping’ is used to display the chromosomal distribution of the specified gene set. ‘Gene expression’ is used to
extract the expression of a gene set. ‘primer design’ is used to design primers. ‘References’ is used to search for references in the literature. (b) Shows the
tools in Milletdb used for the complete analysis process of the auxin response factors (ARFs) gene family. Firstly, the homologous gene ID, keywords, Pfam
and ARF protein sequence information are used to search for ARF genes on the Milletdb platform (1); According to the genome information provided by
Milletdb, genome collinearity analysis is conducted (2); The expression of ARF genes is characterized by ‘Gene expression’ and ‘Gene’ in Milletdb (3);
Histone modified regions are identified based on ‘Pan-JBrowse’ (4); Finally, ‘Motif binding site prediction’, ‘Pathway’, ‘Co-expression’, ‘Pan-JBrowse’, or
‘Transposable Elements Identification’ are used to build the regulatory network (5). CK, control group; D, drought stress; S, salt stress; H, heat stress; seed,
seeds in the ripening stage; HAI36, imbibition after 36 h; T5L, five-leaf stage; TL, tillering stage; FT, flowering stage.
Insertion
394 bp
4.9 kp
No SV SV
Vegetative Growth Index (kg/ha/d)
CK High temperature
1h 24h 1h 24h
PI521612
PI526529
PI537069
PI583800
PI587025
Tifleaf3
(a) (b) (c)
PMA1G03779.1
●
●
●
****
160
120
80
40
No SV SV
Treat 1h 3h 5h 7h 24h 48h 96h 144h
Leave
CK
H
D
S
Root
CK
H
D
S
0.00
23.00
TPM
seed
HAI24_G
HAI0
HAI36_G
HAI48_G
T3L_L
T5L_L
TL_L
TL_M
TL_R
HAI24_R
HAI36_R
HAI48_R
T3L_R
T5L_R
0
5
10
15
20
TPM
Vegetative Growth Index (kg/ha/d)
Chr1 Chr3Chr2 Chr4 Chr7Chr6Chr5
1
2
3
4
5
6
0
- log10 (p value)
169.1 Mb 169.3 Mb
Figure 2 Identification of new genes regulating GI by Milletdb. (a) PAV–GWAS (presence and absence variations genome-wide association study) of SVs
associated with the GI trait based on the graph-based pan-genome. (b) Identification of new genes regulating GI by Milletdb. ****indicates P-values at
significance levels of <0.05. (c) The expression pattern of PMA1G03779.1 under different stresses (heat, drought and salt stress), different growth stages
and different accessions. CK, control group; D, drought stress; S, salt stress; H, heat stress; seed, seeds in ripening stage; HAI0, imbibition; HAI24,
imbibition after 24 h; HAI36, imbibition after 36 h; HAI48, imbibition after 48 h; T3L, three leaf stage; T5L, five-leaf stage; TL, tillering stage; G, germ; R,
root; L, leaf; M, tillering tissue.
ª2023 The Authors. Plant Biotechnology Journal published by Society for Experimental Biology and The Association of Applied Biologists and John Wiley & Sons Ltd., 21, 2348–2357
Min Sun et al.2352
14677652, 2023, 11, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/pbi.14136 by FUJIAN AGRICULTURE & FORESTRY University, Wiley Online Library on [23/10/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License
Figure S4E). The gene PMA6G00675.1, which only contains
motifs 3, 4 and 5, plays a role in flowering and high-temperature
stress (Figure 3d). The protein sequence of gene PMA2G00809.1,
which regulates the flowering in pearl millet and participates in
the response to high-temperature stress, contains the motifs 12,
3, 4, 7 and 11 (Figure 3d). The above results suggest that motifs
(a)
(b)
0.00%
0.01%
0.02%
0.03%
0.04%
0.05%
0.06%
0.07%
Motif 1
Motif 2
Motif 3
Motif 4
Motif 5
Motif 6
Motif 7
Motif 8
Motif 9
Motif 10
Clade 3
Clade 2
Unique to millets
Not unique to millets
Unique to millets
Not unique to millets
Motif 11
Motif 12
(c)
(d)
HAI24_G HAI24_R HAI36_R PI587025
CK-24h H-24h
PMA5G01662.1
PMA6G00675.1 FW_F CK-PI537069-24h H-PI537069-24h
PMA2G00809.1 FW_F CK-PI526529-24h H-PI526529-24h
CK_1hR D_1hR S_1hRHAI48_G T5LF_R TL_M
Multi-material
Genes with PLATZ
binding sites
18
410 2242
(e)
(f) Term Pvalue Pvalue Pathway
Stress
2-oxoglutarate-
dependent dioxygenase
activity
Cysteine and methionine
metabolism
L-ascorbic acid binding Arginine and proline
metabolism
regulation of endocytosis Pyruvate metabolism
Growth and
development
malate transport Glutathione metabolism
calmodulin binding p value 0.050
Drought
0.00 2.00
TPM
PI537069
PI250656
PmiG
PI587025
Zma
Yugu1
PI521612
PI583800
Tifleaf3
AN00000390
PI343841
PI526529
PI186338
PI527388
Niatia
STB08_2
Osa
Hvu
PR202
12
12
21 0
1
0
246
1
Heat
Salt
2213
0
2240
144
241
Number of PLATZs / total gene number
PMF3G01048.1
PMK3G00891.1
PMB3G01075.1
PMH3G00784.1
PMH3G00786.1
PME3G00987.1
PMI3G00828.1
PMC3G00986.1
Pgl_GLEAN_10012651
PMD3G01061.1
PMA3G00960.1
CH09.2432.mRNA1
XM_004976595.4
AH09.2122.mRNA1
BH09.2293.mRNA1
PM16G18320
Zm00001d002489_P001
Os04t0591100-01
PM15G20190
HORVU2Hr1G106020.2
HU200 063281
gene-PR202_gb14522
gene-PR202_gb14525
HU200_033032
gene-PR202_gb09432
PMC3G07309.1
PMF3G07188.1
PMD3G07577.1
PME3G07566.1
PMA3G07064.1
PMC3G07363.1
PMB3G06907.1
PMH3G04378.1
PMI3G04410.1
PMJ3G04485.1
PMK3G04612.1
XM_004953457.3
HORVU6Hr1G067060.1
AH07.3063.mRNA1
CH07.2969.mRNA1
PM11G29540
BH07.2887.mRNA1
HU200_003938
HU200_049807
Os02t0692700-01
PM12G23880
Zm00001d051511_P001
PMI5G02703.1
PMJ5G02671.1
PMH5G02575.1
PMF5G03227.1
PMB5G03335.1
PMC5G03185.1
PMA5G03156.1
PMD5G03207.1
PME5G03210.1
Pgl_GLEAN_10029079
CH01.3381.mRNA1
PMK5G02649.1
XM_004983503.4
PM01G47160
PM02G19230
Zm00001d030032_P002
BH01.3138.mRNA1
HU200_018775
HU200_041986
Zm00001d047025_P001
HORVU1Hr1G050480.1
Os10t0574700-01
AH06.2345.mRNA1
HU200 042734
BH06.2144.mRNA1
CH06.2362.mRNA1
PM10G16510
XM_004965788.4
PM09G18900
HORVU7Hr1G093120.4
gene-PR202_ga14908
gene-PR202_gb27943
Os06t0624900-01
PMH5G03675.1
PMJ5G03753.1
PMI5G03830.1
PMK5G03758.1
PMA5G04507.1
Pgl_GLEAN_10011418
PMC5G04612.1
PME5G04675.1
PMF5G04327.1
PMA6G02016.1
PMJ0G00767.1
PMD6G02070.1
PMH6G01559.1
PMI6G01434.1
PMK6G01540.1
PMJ6G01588.1
PMD2G03769.1
PME2G03060.1
Pgl_GLEAN_10019320
PMC2G04413.1
PMB6G02282.1
PMC6G02118.1
PME6G02094.1
PMK6G01541.1
PMI6G01435.1
PMJ6G01589.1
PMA6G02017.1
PMH6G01560.1
PMD6G02071.1
XM_004968661.1
PMB6G02281.1
PMC6G02116.1
PME6G02092.1
PMF6G02065.1
Pgl_GLEAN_10022276
Pgl_GLEAN_10022277
PMB2G03528.1
1
.
7
6
0
2
0
G
6
F
M
P
1
A
N
R
m
.
2
6
9
1
.
4
0
H
A
1
A
N
R
m
.
4
2
3
2
.
4
0
H
C
1
A
N
R
m
.
2
7
9
1
.
4
0
H
B
Zm00001d009292_P001
BH01.3767.mRNA1
CH01.4016.mRNA1
CH04.850.mRNA1
AH04.875.mRNA1
AH01.3415.mRNA1
PMB4G01939.1
PMF4G01876.1
PMI4G01409.1
PMJ4G00848.1
PMD4G02145.1
PME4G02073.1
PMK4G01396.1
PMC4G01407.1
PMH4G01311.1
XM_004968818.1
HU200_017003
HU200_050435
HU200_050198
gene-PR202_ga27910
HORVU3Hr1G110200.1
PMH4G02387.1
PMJ4G02364.1
PMK4G02290.1
PMB4G03460.1
PMC4G03153.1
PMA4G03395.1
PME4G03258.1
PMD4G03346.1
Pgl_GLEAN_10023676
XM 004974164.3
gene-PR202_gb02351
gene-PR202_ga21813
HU200_011805
HU200_062151
PM13G25980
PM14G20770
HU200_061859
HU200_065769
AH08.2394.mRNA1
BH08.2566.mRNA1
HORVU7Hr1G059900.2
Zm00001d031925_P001
Os08t0560300-01
Os11t0428700-00
★
★
★
★
★
★
★
★
★
★
★
★
PMB2G01019.1
Pgl_GLEAN_10017587cc
PMI2G00795.1
Pgl_GLEAN_10017585
PMC2G00536.1
PMA2G00811.1
PMI2G00798.1
AH07.682.mRNA1
CH01.1534.mRNA1
PM08G15090
XM_004954621.1
PM11G00160
PM11G00170
CH01.1539.mRNA1
HU200_006190
HU200_040506
Zm00001d015868_P001
gene-PR202_ga06698
gene-PR202_ga06762
█PMJ5G02045.1
█PMJ5G02046.1
█PMK5G02038.1
█PMH5G01984.1
█PMF5G02412.1
█PMD5G02382.1
█PME5G02400.1
█PMA5G02333.1
█PMB5G02481.1
█Pgl_GLEAN_10034907
█PMI5G02090.1
█PM01G17430
█PM02G23970
█PM01G17420
█BH01.3684.mRNA1
█CH01.3972.mRNA1
█AH01.3384.mRNA1
█HU200_018217
█
█Zm00001d047250_P001
█CH01.5075.mRNA1
█Contig192.109.mRNA1
█AH01.4469.mRNA1
█HU200_025360
█PMF5G01118.1
█PMI5G01000.1
█PMH5G00900.1
█PME5G01141.1
█PMA5G01064.1
█PMB5G01118.1
█PMK5G00967.1
█PMC5G01089.1
█PMD5G01120.1
█
█Os09t0116050-00
█CH02.1381.mRNA1
PMD3G07403.1
PMI0G01001.1
PMK3G04441.1
PMJ3G04298.1
PMH3G04237.1
PMF3G06936.1
PME3G07301.1
Pgl_GLEAN_10017334
PMA3G06873.1
PMB3G06725.1
PMI3G04271.1
AH07.2893.mRNA1
CH07.2783.mRNA1
BH07.2716.mRNA1
PM12G22590
PM11G31170
XM_012845452.1
Zm00001d051376_P001
HU200_008616
HU200_007239
PM12G22730
Os02t0661400-00
gene-PR202_gb09280
▲
▲
▲
▲
▲
▲
▲
▲
▲
▲
▲
▲
▲
▲
▲
▲
▲
▲
▲
▲
▲
PMI5G03547.1
PMK5G03448.1
PMH5G03380.1
PMF5G04106.1
PMJ5G03483.1
PMB5G04470.1
1
.
7
52
40
G5
C
M
P
1
.4
6
24
0
G
5
D
MP
1
.
1
9
2
4
0
G
5
E
M
P
1
.
1
6
1
4
0
G
5
A
M
P
PMH5G03379.1
CH06.2613.mRNA1
PM09G20940
BH06.2399.mRNA1
PM10G18400
HU200_008471
HU200_059136
HU200_042962
AH06.2598.mRNA1
Zm00001d046958_P001
HORVU7Hr1G108680.3
Pgl_GLEAN_10010064
Os02t0172800-01
Os06t0666100-01
gene-PR202_ga14685
gene-PR202_gb28152
PM11G16350
PMI2G00587.1
PMH2G00402.1
PMJ2G00605.1
PMF0G00872.1
PMF2G00855.1
PMB2G00738.1
PMC2G00239.1
PMA2G00519.1
PMD2G00654.1
Pgl_GLEAN_10027860
XM_004951702.2
BH07.641.mRNA1
CH07.519.mRNA1
PMB2G00735.1
PMK2G00429.1
PME2G00538.1
AH07.515.mRNA1
Zm00001d015394_P001
HU200_000222
HU200_040320
PM12G04180
gene-PR202_ga06595
gene-PR202_gb06348
PMK0G01135.1
PMK2G00549.1
Pgl_GLEAN_10016705
PMA2G00663.1
PMH2G00511.1
PME2G00681.1
PMI2G00691.1
PMF2G01050.1
PMJ2G00715.1
PMB2G00882.1
PMD2G00815.1
AH07.612.mRNA1
gene-PR202_gb06433
XM_022823704.1
BH07.736.mRNA1
CH01.1620.mRNA1
PM11G00780
PM12G04970
HU200_000321
HU200_040426
Os02t0183000-00
PMC2G00393.1
Zm00001d015560_P001
HORVU6Hr1G028520.1
PMC2G00394.1
HORVU6Hr1G031700.6
BH05.1313.mRNA1
CH09.1601.mRNA1
HU200_036395
CH08.2739.mRNA1
PMD2G00979.1
PMK2G00647.1
PMI2G00793.1
PMI2G00794.1
PMI2G00796.1
PMH2G00617.1
PMC2G00534.1
PMA2G00810.1
Pgl_GLEAN_10017586
PMJ2G00814.1
PMA2G00809.1
PMB2G01024.1
AH07.679.mRNA1
BH07.810.mRNA1
AH07.676.mRNA1
BH07.808.mRNA1
CH01.1538.mRNA1
CH01.1536.mRNA1
CH04.987.mRNA1
HU200_006191
HU200_040507
PMA2G00800.1
PMA2G00805.1
PMC2G00527.1
PMF2G01182.1
PMH2G00610.1
PMH2G00615.1
PMJ2G00812.1
PMI2G00785.1
PMI2G00791.1
PMK2G00640.1
PMK2G00645.1
PMC2G00533.1
PMB2G01022.1
PMD2G00974.1
PMD2G00971.1
Pgl_GLEAN_10000107
Pgl_GLEAN_10017589
Zm00001d029437_P001
PMJ5G00958.1
HU200_007262
PMA6G06493.1
PME6G06727.1
PMC6G06457.1
PMK6G04457.1
PME3G03373.1
PMF3G05633.1
PMA3G05642.1
PMJ6G04367.1
PMK7G04725.1
PMB6G07190.1
PMF6G06822.1
PMH6G04210.1
PMH3G03335.1
PMJ3G03418.1
PMA5G01662.1
PM10G12710
PM13G06400
PM16G00120
PMD5G04637.1
PMF4G06486.1
PMA6G00675.1
BH07.811.mRNA1
CH01.1535.mRNA1
AH07.678.mRNA1
CH06.2097.mRNA1
gene-PR202_ga06686
Zm00001d026047_P002
Zm00001d028594_P001
Pgl_GLEAN_10020016
HU200_017773
Zm00001d017682_P001
AH01.2803.mRNA1
CH08.2376.mRNA1
PM08G14990
Os01t0517800-01
Os01t0518000-01
gene-PR202_gb12739
PMF4G04035.1
Clade 1
Clade 4
Clade 3
Clade 2
(n = 17, Ntotal = 21, 80.95%)
(n = 3, Ntotal = 4, 75.00%)
(n = 20, Ntotal = 24, 83.33%)
(n = 27, Ntotal = 36, 75.00%)
(n = 17, Ntotal = 21, 80.95%)
(n = 12, Ntotal = 19, 63.16%)
(n = 12, Ntotal = 17, 70.59%)
ª2023 The Authors. Plant Biotechnology Journal published by Society for Experimental Biology and The Association of Applied Biologists and John Wiley & Sons Ltd., 21, 2348–2357
Milletdb: a multi-omics database for millets 2353
14677652, 2023, 11, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/pbi.14136 by FUJIAN AGRICULTURE & FORESTRY University, Wiley Online Library on [23/10/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License
1, 2 and 6 may be important in plant vegetative growth and
multiple abiotic stress responses, although subsequent functional
analysis is still needed. Based on ‘Co-expression’ and ‘Motif
binding site prediction’, 165 genes and 18 genes were identified
as potentially interacting with PMA5G01662.1 at levels of stress
(heat, drought and salinity) and growth, respectively (Figure 3e,
Figure S4F,G).
The ‘KEGG/GO Enrichment’ tool showed that 165 genes were
enriched in the pathway or terms related to stress, such as the
‘Cysteine and methionine metabolism’ pathway (Romero
et al., 2014), ‘Arginine and proline metabolism pathway’ (Dar
et al., 2016), ‘Pyruvate metabolism’ pathway (Kato-
Noguchi, 2006), ‘2-oxoglutarate-dependent dioxygenase activ-
ity’ term (Vigani et al., 2013), ‘L-ascorbic acid binding’ term
(Gallie, 2013), ‘regulation of endocytosis’ term (Fan et al., 2015)
(Figure 3f, Figure S4H). Eighteen genes were enriched in the
‘Glutathione metabolism’ pathway (May et al., 1998) and
‘calmodulin binding’ term (Bouch
eet al., 2005), which are
associated with plant growth and development (Figure 3f). In
summary, the Milletdb platform contains a large amount of data
and practical tools to meet the needs of researchers to quickly
mine key genes, identify interacting genes and build regulatory
networks using forward or reverse genetics strategies.
Discussion
In summary, Milletdb is the most comprehensive millet database
produced so far. It comprises and visualizes a large amount of
multi-omics data for users to extend their studies from individual
genes to the level of millet genetic networks. Milletdb provides
plenty of candidate stress-related genes orthologous to genes of
other major crops, which may help breeders easily identify
important genes that improve crop yields and positively respond
to stress. The interface is user-friendly and bilingual and provides
operation manuals (http://milletdb.novogene.com/home;http://
milletdb.novogene.com/document) for all tools. Moreover, the
database is an open platform where users can extract and share
data through the contact information provided on the contact
page (http://milletdb.novogene.com/contact). It will be continu-
ously updated to provide long-term support for scientists working
on millets and developing stress-tolerant crops.
Materials and methods
Genomics and pan-genome data
We collected whole genome sequence data from eleven pearl
millet accessions, including PI186338 (SAMN28616529), PI250
656 (SAMN28613898), PI343841 (SAMN28616536), PI521612
(SAMN20372179), PI526529 (SAMN20372180), PI527388 (SA
MN28614406), PI537069 (SAMN20372178), PI587025 (SAMN20
372182), PI583800 (SAMN20372181), Tifleaf 3 (SAMN20
372183), Tifleaf 3 (SAMN20372183) (Yan et al., 2023) and PmiG
(Varshney et al., 2017) from NCBI (https://www.ncbi.nlm.nih.gov/
assembly). Genome sequence information of other millet
accessions and related species, including foxtail millet (Setaria_i-
talica_v2.0) (Bennetzen et al., 2012), proso millet (Pm_0390_v2)
(Zou et al., 2019), finger millet (Ragi_PR202_v._2.0) (Hatakeyama
et al., 2017), fonio (DiExil) (Wang et al., 2021), barnyard millet
(ec_v3) (Wu et al., 2022) and elephant grass (Yan et al., 2021;
Zhang et al., 2022), was downloaded from NCBI. The software
package EggNOG v5 (http://eggnog5.embl.de/#/app/home)
(Huerta-Cepas et al., 2018) was used for functional annotation.
TE annotation is done by the software DeepTE (Yan et al., 2020).
The OrthoMCL10 (v2.0.9) (http://orthomcl.org/orthomcl/) and
MUMmer (v4.0.0) (Delcher et al., 2003) software packages were
used to identify the gene atlas of the eleven genomes (core,
dispensable and private genes), generate a genetic variation atlas
and construct a graph-based pearl millet pan-genome.
Resequencing data
The 378 whole-genome resequencing data of pearl millet were
derived from SRP063925 (Varshney et al., 2017) and mapped to
the graph-based pan-genome using vg tools (Garrison et al.,
2018). Thereafter, PAV–GWAS (presence and absence variations
genome-wide association study) and SNP–GWAS were performed
using GEMMA (v0.94.1) (Zhou and Stephens, 2012) and the
results were made available in Milletdb.
RNA-Seq data
We collected a total of 192 transcriptome data of accession
Tifleaf 3 grown under abiotic stress conditions, including heat
(Huang et al., 2021; Sun et al., 2021), drought (Ji et al., 2021;
Zhang et al., 2021) and salt (Awan et al., 2022) (Table S1). The
13-day-old pearl millet seedlings were grouped into the normal
culture group (CK), heat treatment group (40 °C/35 °C), drought
treatment group (20% PEG) and salt treatment group (100 mM/
L). The treatments were performed simultaneously, and fresh
leaves and roots were collected at 1, 3, 5, 7, 24, 48, 96 and 144 h
(h) after the treatments. The raw data was filtered by fastq
(Version 0.11.9, Default setting) using the default parameters
(Andrews, 2014) and Kallisto (v0.46.2, b 100) (Bray et al., 2016)
was used to assess expression levels. Moreover, we collected
transcriptome data of PI537069, PI521612, PI587025, PI583800,
PI526529 and Tifleaf 3 from normal culture and high-
temperature treatment (45 °C/40 °C).
The transcriptome data for pearl millet seed germination were
also obtained from a previous study (Wu et al., 2021) (Table S1).
We conducted transcriptome sequencing using different tissues
of accession Tifleaf3 under normal culture conditions at different
developmental stages: root and leaf samples at the three-leaf and
five-leaf stages; roots, stems, leaves and tiller tissues samples at
the tillering stage; panicles, leaves and stem samples at the
Figure 3 The Milletdb platform is used to identify the PLATZ gene family. (a) The proportion of PLATZ gene family members among all genes in millets,
maize, rice and barley. (b) The phylogenetic tree of PLATZ genes. (c) MEME motif structure of PLATZ genes in Clade2 and Clade3. The solid line and dotted
line represent the conservative motif and alternative motif, respectively. (d) Expression pattern of genes PMA6G00675.1,PMA2G00809.1 and
PMA5G01662.1. (e) Venn diagram of gene sets interacting with PMA5G01662.1. (f) Functional enrichment of genes interacting with PMA5G01662.1. The
solid line and dotted line represent the conservative motif and alterable motif, respectively. The black triangle and star represent the pearl millet-specific
protein structure. CK, control group; D, drought stress; S, salt stress; H, heat stress; HAI24, imbibition after 24 h; HAI36, imbibition after 36 h; HAI48,
imbibition after 48 h; T5L, five-leaf stage; TL, tillering stage; FW, flowering stage; R, root; M, tillering tissue; F, spike.
ª2023 The Authors. Plant Biotechnology Journal published by Society for Experimental Biology and The Association of Applied Biologists and John Wiley & Sons Ltd., 21, 2348–2357
Min Sun et al.2354
14677652, 2023, 11, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/pbi.14136 by FUJIAN AGRICULTURE & FORESTRY University, Wiley Online Library on [23/10/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License
heading, flowering and dough stage. Fastq (Version 0.11.9)
(Andrews, 2014) and Kallisto (Bray et al., 2016) were used to
analyse the transcriptome data.
Raw transcriptomic data of other millets was sourced from the
SRA database (Bandyopadhyay et al., 2020; Bennetzen et al.,
2012; Cannarozzi et al., 2014; Fang et al., 2019; Guo et al.,
2017; Hatakeyama et al., 2017; Jin et al., 2021; Lai et al., 2021;
Pan et al., 2020; Qin et al., 2020; Ramadoss, 2014; Shen et al.,
2020; Sun et al., 2022; Wang et al., 2020,2021; Watson-
Lazowski et al., 2019;Wuet al., 2022; Yan et al., 2021,2022,
2023;Yuet al., 2020; Yuan et al., 2021,2022; Zhang et al.,
2022; Zou et al., 2019) (Table S1). Fastq (Version 0.11.9) was
used for raw data filtering. Kallisto was used to align with the
reference genome (AN00000390, Yugu1 and PR202) and to
calculate the count value.
Co-expression analysis
We grouped the transcriptome data into five catalogues: heat
stress, drought stress, salt stress, multi-tissue (growth and
development) and multi-accession (PI537069, PI521612,
PI587025, PI583800, PI526529 and Tifleaf3). After that, the
Pearson correlation coefficient was used to analyse the correla-
tion between gene expression within each catalogue based on
the transcript per million (TPM) value of the genes using Hmisc (Jr
and Dupont, 2015) software. We obtained the correlation index
and P-values between genes within each class and saved the
correlation results in Milletdb.
ChIP–seq data
Spikelets and mature roots of pearl millet grown under normal
field conditions were collected separately and treated according
to the method described previously (Wedel and Siegel, 2017).
Briefly, the samples were homogenized in liquid nitrogen and
digested with micrococcal nuclease (MNase) for 8 min to achieve
chromatin shearing. Following this, anti-H3K4me3 (Millipore, 05-
745R) and anti-H3K36me3 (Abcam, ab9050) were added to the
immunoprecipitated sheared chromatin, whose fragments were
then captured using protein A/G magnetic beads (Thermo, cat
88 802). All libraries were prepared with the VAHTS universal
DNA library prep kit for Illumina Library Systems (Vazyme)
according to the manufacturer’s instructions. The obtained library
was then sequenced using Illumina Hiseq-Xten and Fastp
software (Chen et al., 2018) was used to filter reads. Alignment
was performed using Bowtie 2.4.5 (Langmead and Salz-
berg, 2012) with PI537069 as the reference genome sequence.
The software MACS 3.0.0a7 (Feng et al., 2012) was used to call
peaks (Table S1).
Pearl millet accession details
We collected basic information on 400 pearl millet accessions
(Varshney et al., 2017), of which 242 materials contained
phenotypic data while growing under three conditions (field,
early drought stress and late drought stress) (Varshney
et al., 2017). The obtained information was then deposited in
Milletdb.
References
We searched the Web of Science website (http://webofknowledge.
com) for pearl millet-related articles and exported the search results
in batches. The Citavi software (Com, 2006) was used to generate
hyperlinks to the articles.
Data integration
The genomic, transcriptomic, epigenomics, phenotypic, co-
expression analysis, SNP–GWAS and PAV–GWAS data of millets
were stored in MongoDB.
Database construction
Milletdb is implemented by the Linux-operating system and
Nginx. Ant Design and Django were used for interactive front-end
and back-end queries. Genomic and pan-genomic features were
displayed by JBrowse (Buels et al., 2016) and its plugins. The
variation in the pearl millet population is displayed via Ant Design
Charts (https://charts.ant.design/), igv (Thorvaldsdottir et al.,
2012) and ant-design/icons (https://ant.design/components/icon-
cn/). BLAST 2.2.31+(Tatusova and Madden, 1999) was used for
‘Blast’ construction. SYNVISIO (https://github.com/kiranbandi/
synvisio) was used to visualize results based on inter-genome
alignments (blastp). The ‘Sequence Fetch’, ‘Gene Sequence
Extraction’, ‘Transposable Elements Identification’, ‘Transcription
Factor identification’ and ‘Co-expression’ tools were built using
Python 3.9.
Acknowledgements
This work was supported by the Modern Agricultural Industry
System Sichuan Forage Innovation Team (SCCXTD-2021-16), the
earmarked fund for CARS (CARS-34), the Sichuan Province
Research Grant (2021YFYZ0013) and the National Natural
Science Foundation of China (Nos. 31771866 and 32071867).
We thank John Pablo Mendieta (Department of Genetics,
University of Georgia, Athens, GA, 30602, USA) for providing
valuable suggestions for the language improvement of the
manuscript.
Author contributions
L.H., H.Y. and M.S. designed and managed the project. M.S.,
A.Z., Y.J., C.L., L.L., R.K.S., R.K.V. and B.W. participated in
material collecting and processing. S.T., M.S, Y.F., H.Y. and X.C.
performed bioinformatics analyses. L.H., H.Y. and M.S. wrote the
manuscript. F.Z., L.H., X.Z., X.W., D.H., G.N., G.F., Z.X., M.H.,
R.K.S., R.K.V., C.S.J., Q.T., P.Z., J.J., Y.Y., Z.W. and J.L. revised the
article.
Competing interests
The authors declare no competing interests.
References
Adebiyi, J.A., Obadina, A.O., Adebo, O.A. and Kayitesi, E. (2018) Fermented
and malted millet products in Africa: Expedition from traditional/ethnic foods
to industrial value-added products. Crit. Rev. Food Sci. Nutr. 58, 463–474.
Andrews, S. (2014) FastQC A quality control tool for high throughput sequence
data.
Awan, S.A., Khan, I., Tariq, R., Rizwan, M., Wang, X., Zhang, X. and Huang, L.
(2022) Genome-wide expression and physiological profiling of pearl millet
genotype reveal the biological pathways and various gene clusters underlying
salt resistance. Front. Plant Sci. 13, 849618.
Bandyopadhyay, T., Swarbreck, S.M., Jaiswal, V., Gupta, R., Bentley, A.R.,
Griffiths, H. and Prasad, M. (2020) Grain number and genotype drive
nitrogen-dependent yield response in the C4 model Setaria italica (L.) P.
Beauv. https://doi.org/10.1101/2020.03.23.003004
ª2023 The Authors. Plant Biotechnology Journal published by Society for Experimental Biology and The Association of Applied Biologists and John Wiley & Sons Ltd., 21, 2348–2357
Milletdb: a multi-omics database for millets 2355
14677652, 2023, 11, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/pbi.14136 by FUJIAN AGRICULTURE & FORESTRY University, Wiley Online Library on [23/10/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License
Bennetzen, J.L., Schmutz, J., Wang, H., Percifield, R., Hawkins, J., Pontaroli,
A.C., Estep, M. et al. (2012) Reference genome sequence of the model plant
Setaria. Nature Biotechnology 30(6), 555–561.
Bouch
e, N., Yellin, A., Snedden, W.A. and Fromm, H. (2005) Plant-specific
calmodulin-binding proteins. Annu. Rev. Plant Biol. 56, 435–466.
Bray, N.L., Pimentel, H., Melsted, P. and Pachter, L. (2016) Near-optimal
probabilistic RNA-seq quantification. Nat. Biotechnol. 34, 525–527.
Buels, R., Yao, E., Diesh, C.M., Hayes, R.D., Munoz-Torres, M., Helt, G.,
Goodstein, D.M. et al. (2016) JBrowse: a dynamic web platform for genome
visualization and analysis. Genome Biol. 17, 66.
Cannarozzi, G., Plaza-W€
uthrich, S., Esfeld, K., Larti, S., Wilson, Y.S., Girma, D.,
de Castro, E. et al. (2014) Genome and transcriptome sequencing identifies
breeding targets in the orphan crop tef (Eragrostis tef). BMC Genomics 15(1).
https://doi.org/10.1186/1471-2164-15-581
Ceasar, S.A. and Maharajan, T. (2022) The role of millets in attaining United
Nation’s sustainable developmental goals. Plants People Planet,4, 345–349.
Chen, S., Zhou, Y., Chen, Y. and Gu, J. (2018) fastp: an ultra-fast all-in-one
FASTQ preprocessor. Bioinformatics (Oxford, England),34, i884–i890.
Colucci, G., Apone, F., Alyeshmerni, N., Chalmers, D. and Chrispeels, M.J.
(2002) GCR1, the putative Arabidopsis G protein-coupled receptor gene is
cell cycle-regulated, and its overexpression abolishes seed dormancy and
shortens time to flowering. Proc. Natl. Acad. Sci. USA,99, 4736–4741.
Com, I. (2006) Citavi –literaturverwaltung und wissensorganisation.
Dar, M.I., Naikoo, M.I., Rehman, F., Naushin, F. and Khan, F.A. (2016) Proline
accumulation in plants: roles in stress tolerance and plant development. In
Osmolytes and Plants Acclimation to Changing Environment: Emerging Omics
Technologies(Iqbal, N., Nazar, R. and Khan, N.A., eds), pp. 155–166. New
Delhi: Springer India.
Delcher, A.L., Salzberg, S.L. and Phillippy, A.M. (2003) Using MUMmer to
identify similar regions in large sequence sets. Curr. Protoc. Bioinform. (29).
https://doi.org/10.1002/0471250953.bi1003s00
Desai, M.K., Mishra, R.N., Verma, D., Nair, S., Sopory, S.K. and Reddy, M.K.
(2006) Structural and functional analysis of a salt stress inducible gene
encoding voltage dependent anion channel (VDAC) from pearl millet
(Pennisetum glaucum). Plant Physiol. Biochem. 44, 483–493.
Fan, L., Li, R., Pan, J., Ding, Z. and Lin, J. (2015) Endocytosis and its regulation in
plants. Trends Plant Sci. 20, 388–397.
Fang, J., Zhang, Y., Liu, T., Yan, B., Li, J. and Dong, L. (2019) Target-Site and
Metabolic Resistance Mechanisms to Penoxsulam in Barnyardgrass
(Echinochloa crus-galli (L.) P. Beauv). Journal of Agricultural and Food
Chemistry 67(29), 8085–8095.
FAO, F. (2018) Food and agriculture organization of the United Nations. Rome.
http://faostat.fao.org.
Feng, J., Liu, T., Qin, B., Zhang, Y. and Liu, X.S. (2012) Identifying ChIP-seq
enrichment using MACS. Nat. Protoc. 7, 1728–1740.
Fu, Y., Cheng, M., Li, M., Guo, X., Wu, Y. and Wang, J. (2020) Identification
and characterization of PLATZ transcription factors in wheat. Int. J. Mol. Sci.
21, 8934.
Gallie, D.R. (2013) The role of L-ascorbic acid recycling in responding to
environmental stress and in promoting plant growth. J. Exp. Bot. 64, 433–443.
Garrison, E., Sir
en, J., Novak, A.M., Hickey, G., Eizenga, J.M., Dawson, E.T.,
Jones, W. et al. (2018) Variation graph toolkit improves read mapping by
representing genetic variation in the reference. Nat. Biotechnol. 36, 875–879.
Gonz
alez-Morales, S.I., Ch
avez-Montes, R.A., Hayano-Kanashiro, C., Alejo-
Jacuinde, G., Rico-Cambron, T.Y., de Folter, S. and Herrera-Estrella, L. (2016)
Regulatory network analysis reveals novel regulators of seed desiccation
tolerance in Arabidopsis thaliana. Proc. Natl. Acad. Sci. USA,113, E5232–
E5241.
Goron, T.L. and Raizada, M.N. (2015) Genetic diversity and genomic resources
available for the small millet crops to accelerate a New Green Revolution.
Front. Plant Sci. 6, 157.
Guo, L., Qiu, J., Ye, C., Jin, G., Mao, L., Zhang, H., Yang, X. et al. (2017)
Echinochloa crus-galli genome analysis provides insight into its adaptation
and invasiveness as a weed. Nature Communications 8(1). https://doi.org/10.
1038/s41467-017-01067-5
Hatakeyama, M., Aluri, S., Balachadran, M.T., Sivarajan, S.R., Patrignani, A.,
Gr€
uter, S., Poveda, L. et al. (2017) Multiple hybrid de novo genome assembly
of finger millet, an orphan allotetraploid crop. DNA Research 25(1), 39–47.
Huang, D., Sun, M., Zhang, A., Chen, J., Zhang, J., Lin, C., Zhang, H. et al.
(2021) Transcriptional changes in pearl millet leaves under heat stress. Genes,
12, 1716.
Huerta-Cepas, J., Szklarczyk, D., Heller, D., Hern
andez-Plaza, A., Forslund, S.K.,
Cook, H., Mende, D.R. et al. (2018) eggNOG 5.0: a hierarchical, functionally
and phylogenetically annotated orthology resource based on 5090 organisms
and 2502 viruses. Nucleic Acids Research 47(D1), D309–D314.
Islam, S.M.T., Tammi, R.S., Singla-Pareek, S.L. and Seraj, Z.I. (2010) Enhanced
salinity tolerance and improved yield properties in Bangladeshi rice Binnatoa
through Agrobacterium-mediated transformation of PgNHX1 from
Pennisetum glaucum.Acta Physiol. Plant,32,657–663.
Islam, T., Manna, M. and Reddy, M.K. (2015) Glutathione peroxidase of
Pennisetum glaucum (PgGPx) is a functional Cd2+dependent peroxiredoxin
that enhances tolerance against salinity and drought stress. PloS One,10,
e0143344.
Ji, Y., Lu, X., Zhang, H., Luo, D., Zhang, A., Sun, M., Wu, Q. et al. (2021)
Transcriptome reveals the dynamic response mechanism of pearl millet roots
under drought stress. Genes,12, 1988.
Jin, F., Liu, J., Wu, E., Yang, P., Gao, J., Gao, X. and Feng, B. (2021) Leaf
transcriptome analysis of broomcorn millet uncovers key genes and pathways
in response to Sporisorium destruens.International Journal of Molecular
Sciences 22(17), 9542.
Jr, F.H. and Dupont, C. (2015) Hmisc: Harrell Miscellaneous. R package version
3.17-1. Accessed on, 12.
Juma, S.G. and Kelonye, F. (2016) Projected rainfall and temperature changes
over Bungoma county in western Kenya by the year 2050 based precis
modeling system. Ethiop. J. Env. Stud. Manag. 9, 625.
Kato-Noguchi, H. (2006) Pyruvate metabolism in rice coleoptiles under
anaerobiosis. Plant Growth Regul. 50,41–46.
Lai, X., Bendix, C., Zhang, Y., Schnable, J.C. and Harmon, F.G. (2021) 72-h
diurnal RNA-seq analysis of fully expanded third leaves from maize, sorghum,
and foxtail millet at 3-h resolution. BMC Research Notes 14(1). https://doi.
org/10.1186/s13104-020-05431-5
Langmead, B. and Salzberg, S.L. (2012) Fast gapped-read alignment with
Bowtie 2. Nat. Methods,9, 357–359.
May, M.J., Vernoux, T., Leaver, C., Montagu, M.V. and Inze, D. (1998)
Glutathione homeostasis in plants: implications for environmental sensing
and plant development. J. Exp. Bot. 49, 649–667.
Molotoks, A., Smith, P. and Dawson, T.P. (2021) Impacts of land use, population,
and climate change on global food security. Food Energy Secur. 10, 153.
Muthamilarasan, M. and Prasad, M. (2021) Small millets for enduring food
security amidst pandemics. Trends Plant Sci. 26,33–40.
Muthamilarasan, M., Singh, N.K. and Prasad, M. (2019) Multi-omics
approaches for strategic improvement of stress tolerance in underutilized
crop species: a climate change perspective. Adv. Genet. 103,1–38.
Pan, J., Li, Z., Dai, S., Ding, H., Wang, Q., Li, X., Ding, G. et al. (2020) Integrative
analyses of transcriptomics and metabolomics upon seed germination of
foxtail millet in response to salinity. Scientific Reports 10(1). https://doi.org/
10.1038/s41598-020-70520-1
Qin, L., Chen, E., Li, F., Yu, X., Liu, Z., Yang, Y., Wang, R. et al. (2020) Genome-
wide gene expression profiles analysis reveal novel insights into drought stress
in foxtail millet (Setaria italica L.). International Journal of Molecular Sciences
21(22), 8520.
Ramadoss, A. (2014). Effects of drought on Eleusine coracana (L.) Gaertn.
(finger millet) and identification of microsatellite markers. University of
Alberta Libraries. https://doi.org/10.7939/R3HX16015
Romero, L.C., Aroca, M.
A., Laureano-Mar
ın, A.M., Moreno, I., Garc
ıa, I. and
Gotor, C. (2014) Cysteine and cysteine-related signaling pathways in
Arabidopsis thaliana.Mol. Plant,7, 264–276.
Shahidi, F. and Chandrasekara, A. (2013) Millet grain phenolics and their role in
disease risk reduction and health promotion: a review. J. Funct. Foods,5,
570–581.
Shen, J., Guo, M., Wang, Y., Yuan, X., Dong, S., Song, X. and Guo, P. (2020) An
investigation into the beneficial effects and molecular mechanisms of humic
acid on foxtail millet under drought conditions. PLoS One 15(6), e0234029.
Sun, M., Lin, C., Zhang, A., Wang, X., Yan, H., Khan, I., Wu, B. et al. (2021)
Transcriptome sequencing revealed the molecular mechanism of response of
pearl millet root to heat stress. J. Agro. Crop. Sci. 207, 768–773.
ª2023 The Authors. Plant Biotechnology Journal published by Society for Experimental Biology and The Association of Applied Biologists and John Wiley & Sons Ltd., 21, 2348–2357
Min Sun et al.2356
14677652, 2023, 11, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/pbi.14136 by FUJIAN AGRICULTURE & FORESTRY University, Wiley Online Library on [23/10/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License
Sun, L., Liu, L., Wang, Y., Feng, Y., Yang, W., Wang, D., Gao, S. et al. (2022)
Integration of Metabolomics and Transcriptomics for Investigating the
Tolerance of Foxtail Millet (Setaria italica) to Atrazine Stress. Frontiers in
Plant Science 13.https://doi.org/10.3389/fpls.2022.890550
Tatusova, T.A. and Madden, T.L. (1999) BLAST 2 sequences, a new tool for
comparing protein and nucleotide sequences. Fems Microbiol. Lett. 174,
247–250.
Thorvaldsdottir, H., Robinson, J.T. and Mesirov, J.P. (2012) Integrative
Genomics Viewer (IGV): high-performance genomics data visualization and
exploration. Briefings in Bioinformatics 14(2), 178–192.
Varshney, R.K., Shi, C., Thudi, M., Mariac, C., Wallace, J., Qi, P., Zhang, H. et al.
(2017) Pearl millet genome sequence provides a resource to improve
agronomic traits in arid environments. Nat. Biotechnol. 35, 969–976.
Varshney, R.K., Bohra, A., Roorkiwal, M., Barmukh, R., Cowling, W.A.,
Chitikineni, A., Lam, H.M. et al. (2021a) Fast-forward breeding for a food-
secure world. Trends Genet. 37, 1124–1136.
Varshney, R.K., Bohra, A., Yu, J., Graner, A., Zhang, Q. and Sorrells, M.E.
(2021b) Designing future crops: genomics-assisted breeding comes of age.
Trends Plant Sci. 26, 631–649.
Verma, D., Singla-Pareek, S.L., Rajagopal, D., Reddy, M.K. and Sopory, S.K.
(2007) Functional validation of a novel isoform of Na+/H+antiporter from
Pennisetum glaucum for enhancing salinity tolerance in rice. J. Biosci. 32,
621–628.
Vigani, G., Morandini, P. and Murgia, I. (2013) Searching iron sensors in plants
by exploring the link among 2’-OG-dependent dioxygenases, the iron
deficiency response and metabolic adjustments occurring under
iron deficiency. Front. Plant Sci. 4, 169.
Wang, X., Chen, S., Ma, X., Yssel, A.E.J., Chaluvadi, S.R., Johnson, M.S.,
Gangashetty, P. et al. (2021) Genome sequence and genetic diversity analysis
of an under-domesticated orphan crop, white fonio (Digitaria exilis).
GigaScience 10(3). https://doi.org/10.1093/gigascience/giab01
Wang, T., Song, H., Li, P., Wei, Y., Hu, N., Chen, Z., Wang, W. et al. (2020)
Transcriptome analysis provides insights into grain filling in foxtail millet
(Setaria italica L.). International Journal of Molecular Sciences 21(14), 5031.
Watson-Lazowski, A., Papanicolaou, A., Koller, F. and Ghannoum, O. (2019)
The transcriptomic responses of C
4
grasses to subambient CO
2
and low light
are largely species specific and only refined by photosynthetic subtype. The
Plant Journal 101(5), 1170–1184. Portico.
Wedel, C. and Siegel, T.N. (2017) Genome-wide analysis of chromatin
structures in Trypanosoma brucei using high-resolution MNase-ChIP-seq.
Exp. Parasitol. 180,2–12.
Wu, B., Sun, M., Zhang, H., Yang, D., Lin, C., Khan, I., Wang, X. et al.
(2021) Transcriptome analysis revealed the regulation of gibberellin and
the establishment of photosynthetic system promote rapid seed
germination and early growth of seedling in pearl millet. Biotechnol.
Biofuels,14, 94.
Wu, D., Shen, E., Jiang, B., Feng, Y., Tang, W., Lao, S., Jia, L. et al. (2022)
Genomic insights into the evolution of Echinochloa species as weed and
orphan crop. Nat. Commun. 13, 689.
Yan, H., Bombarely, A. and Li, S. (2020) DeepTE: a computational method for
de novo classification of transposons with convolutional neural network.
Bioinformatics (Oxford, England),36, 4269–4275.
Yan, Q., Li, J., Lu, L., Yi, X., Yao, N., Lai, Z. and Zhang, J. (2022) Comparative
transcriptome study of the elongating internode in elephant grass (Cenchrus
purpureus) seedlings in response to exogenous gibberellin applications.
Industrial Crops and Products 178, 114653.
Yan, Q., Wu, F., Xu, P., Sun, Z., Li, J., Gao, L., Lu, L. et al. (2021) The elephant
grass (Cenchrus purpureus) genome provides insights into anthocyanidin
accumulation and fast growth. Mol. Ecol. Resour. 21, 526–542.
Yan, H., Sun, M., Zhang, Z., Jin, Y., Zhang, A., Lin, C., Wu, B. et al. (2023)
Pangenomic analysis identifies structural variation associated with heat
tolerance in pearl millet. Nat. Genet. 55, 507–518.
Yousaf, L., Hou, D., Liaqat, H. and Shen, Q. (2021) Millet: a review of its
nutritional and functional changes during processing. Food Res. Int. (Ottawa,
Ont.) 142, 110197.
Yu, A., Zhao, J., Wang, Z., Cheng, K., Zhang, P., Tian, G., Liu, X. et al. (2020)
Transcriptome and metabolite analysis reveal the drought tolerance of foxtail
millet significantly correlated with phenylpropanoids-related pathways during
germination process under PEG stress. BMC Plant Biology 20(1). https://doi.
org/10.1186/s12870-020-02483-4
Yuan, Y., Liu, C., Gao, Y., Ma, Q., Yang, Q. and Feng, B. (2021) Proso millet
(Panicum miliaceum L.): A potential crop to meet demand scenario for
sustainable saline agriculture. Journal of Environmental Management 296,
113216.
Yuan, Y., Liu, L., Gao, Y., Yang, Q., Dong, K., Liu, T. and Feng, B. (2022)
Comparative analysis of drought-responsive physiological and transcriptome
in broomcorn millet (Panicum miliaceum L.) genotypes with contrasting
drought tolerance. Industrial Crops and Products 177, 114498.
Zhang, A., Ji, Y., Sun, M., Lin, C., Zhou, P., Ren, J., Luo, D. et al. (2021)
Research on the drought tolerance mechanism of Pennisetum glaucum (L.) in
the root during the seedling stage. BMC Genom. 22, 568.
Zhang, S., Xia, Z., Li, C., Wang, X., Lu, X., Zhang, W., Ma, H. et al. (2022)
Chromosome-scale genome assembly provides insights into speciation of
allotetraploid and massive biomass accumulation of elephant grass
(Pennisetum purpureum Schum.). Mol. Ecol. Resour. 22, 2363–2378.
Zhou, X. and Stephens, M. (2012) Genome-wide efficient mixed-model analysis
for association studies. Nat. Genet. 44, 821–824.
Zou, C., Li, L., Miki, D., Li, D., Tang, Q., Xiao, L., Rajput, S. et al. (2019) The
genome of broomcorn millet. Nat. Commun. 10, 436.
Supporting information
Additional supporting information may be found online in the
Supporting Information section at the end of the article.
Table S1 Data used in Milletdb.
Material S1 Database usage example.
Figure S1 An example of mining new genes potentially
regulating GI.
Figure S2 Gene information in Milletdb.
Figure S3 The JBrowse window in Milletdb.
Figure S4 An example of analysing the PLATZ TF family in
Milletdb.
ª2023 The Authors. Plant Biotechnology Journal published by Society for Experimental Biology and The Association of Applied Biologists and John Wiley & Sons Ltd., 21, 2348–2357
Milletdb: a multi-omics database for millets 2357
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