start-ver=1.4
cd-journal=joma
no-vol=14
cd-vols=
no-issue=1
article-no=
start-page=15139
end-page=
dt-received=
dt-revised=
dt-accepted=
dt-pub-year=2024
dt-pub=20240702
dt-online=
en-article=
kn-article=
en-subject=
kn-subject=
en-title=
kn-title=Genetic background influences mineral accumulation in rice straw and grains under different soil pH conditions
en-subtitle=
kn-subtitle=
en-abstract=
kn-abstract=Mineral element accumulation in plants is influenced by soil conditions and varietal factors. We investigated the dynamic accumulation of 12 elements in straw at the flowering stage and in grains at the mature stage in eight rice varieties with different genetic backgrounds (Japonica, Indica, and admixture) and flowering times (early, middle, and late) grown in soil with various pH levels. In straw, Cd, As, Mn, Zn, Ca, Mg, and Cu accumulation was influenced by both soil pH and varietal factors, whereas P, Mo, and K accumulation was influenced by pH, and Fe and Ni accumulation was affected by varietal factors. In grains, Cd, As, Mn, Cu, Ni, Mo, Ca, and Mg accumulation was influenced by both pH and varietal factors, whereas Zn, Fe, and P accumulation was affected by varietal factors, and K accumulation was not altered. Only As, Mn, Ca and Mg showed similar trends in the straw and grains, whereas the pH responses of Zn, P, K, and Ni differed between them. pH and flowering time had synergistic effects on Cd, Zn, and Mn in straw and on Cd, Ni, Mo, and Mn in grains. Soil pH is a major factor influencing mineral uptake in rice straw and grains, and genetic factors, flowering stage factors, and their interaction with soil pH contribute in a combined manner.
en-copyright=
kn-copyright=

en-aut-name=YamamotoToshio
en-aut-sei=Yamamoto
en-aut-mei=Toshio
kn-aut-name=
kn-aut-sei=
kn-aut-mei=
aut-affil-num=1
ORCID=

en-aut-name=KashiharaKazunari
en-aut-sei=Kashihara
en-aut-mei=Kazunari
kn-aut-name=
kn-aut-sei=
kn-aut-mei=
aut-affil-num=2
ORCID=

en-aut-name=FurutaTomoyuki
en-aut-sei=Furuta
en-aut-mei=Tomoyuki
kn-aut-name=
kn-aut-sei=
kn-aut-mei=
aut-affil-num=3
ORCID=

en-aut-name=ZhangQian
en-aut-sei=Zhang
en-aut-mei=Qian
kn-aut-name=
kn-aut-sei=
kn-aut-mei=
aut-affil-num=4
ORCID=

en-aut-name=YuEn
en-aut-sei=Yu
en-aut-mei=En
kn-aut-name=
kn-aut-sei=
kn-aut-mei=
aut-affil-num=5
ORCID=

en-aut-name=MaJian Feng
en-aut-sei=Ma
en-aut-mei=Jian Feng
kn-aut-name=
kn-aut-sei=
kn-aut-mei=
aut-affil-num=6
ORCID=

affil-num=1
en-affil=Institute of Plant Science and Resources, Okayama University
kn-affil=

affil-num=2
en-affil=Institute of Plant Science and Resources, Okayama University
kn-affil=

affil-num=3
en-affil=Institute of Plant Science and Resources, Okayama University
kn-affil=

affil-num=4
en-affil=Institute of Plant Science and Resources, Okayama University
kn-affil=

affil-num=5
en-affil=Institute of Plant Science and Resources, Okayama University
kn-affil=

affil-num=6
en-affil=Institute of Plant Science and Resources, Okayama University
kn-affil=
END

start-ver=1.4
cd-journal=joma
no-vol=31
cd-vols=
no-issue=1
article-no=
start-page=dsad027
end-page=
dt-received=
dt-revised=
dt-accepted=
dt-pub-year=2023
dt-pub=20231222
dt-online=
en-article=
kn-article=
en-subject=
kn-subject=
en-title=
kn-title=MCPtaggR: R package for accurate genotype calling in reduced representation sequencing data by eliminating error-prone markers based on genome comparison
en-subtitle=
kn-subtitle=
en-abstract=
kn-abstract=Reduced representation sequencing (RRS) offers cost-effective, high-throughput genotyping platforms such as genotyping-by-sequencing (GBS). RRS reads are typically mapped onto a reference genome. However, mapping reads harbouring mismatches against the reference can potentially result in mismapping and biased mapping, leading to the detection of error-prone markers that provide incorrect genotype information. We established a genotype-calling pipeline named mappable collinear polymorphic tag genotyping (MCPtagg) to achieve accurate genotyping by eliminating error-prone markers. MCPtagg was designed for the RRS-based genotyping of a population derived from a biparental cross. The MCPtagg pipeline filters out error-prone markers prior to genotype calling based on marker collinearity information obtained by comparing the genome sequences of the parents of a population to be genotyped. A performance evaluation on real GBS data from a rice F2 population confirmed its effectiveness. Furthermore, our performance test using a genome assembly that was obtained by genome sequence polishing on an available genome assembly suggests that our pipeline performs well with converted genomes, rather than necessitating de novo assembly. This demonstrates its flexibility and scalability. The R package, MCPtaggR, was developed to provide functions for the pipeline and is available at https://github.com/tomoyukif/MCPtaggR.
en-copyright=
kn-copyright=

en-aut-name=FurutaTomoyuki
en-aut-sei=Furuta
en-aut-mei=Tomoyuki
kn-aut-name=
kn-aut-sei=
kn-aut-mei=
aut-affil-num=1
ORCID=

en-aut-name=YamamotoToshio
en-aut-sei=Yamamoto
en-aut-mei=Toshio
kn-aut-name=
kn-aut-sei=
kn-aut-mei=
aut-affil-num=2
ORCID=

affil-num=1
en-affil=Institute of Plant Science and Resources, Okayama University
kn-affil=

affil-num=2
en-affil=Institute of Plant Science and Resources, Okayama University
kn-affil=
en-keyword=genotyping
kn-keyword=genotyping
en-keyword=genome comparison
kn-keyword=genome comparison
en-keyword=next-generation sequencing
kn-keyword=next-generation sequencing
en-keyword=R package
kn-keyword=R package
END

start-ver=1.4
cd-journal=joma
no-vol=224
cd-vols=
no-issue=2
article-no=
start-page=iyad055
end-page=
dt-received=
dt-revised=
dt-accepted=
dt-pub-year=2023
dt-pub=20230329
dt-online=
en-article=
kn-article=
en-subject=
kn-subject=
en-title=
kn-title=GBScleanR: robust genotyping error correction using a hidden Markov model with error pattern recognition
en-subtitle=
kn-subtitle=
en-abstract=
kn-abstract=Reduced-representation sequencing (RRS) provides cost-effective and time-saving genotyping platforms. Despite the outstanding advantage of RRS in throughput, the obtained genotype data usually contain a large number of errors. Several error correction methods employing the hidden Markov model (HMM) have been developed to overcome these issues. These methods assume that markers have a uniform error rate with no bias in the allele read ratio. However, bias does occur because of uneven amplification of genomic fragments and read mismapping. In this paper, we introduce an error correction tool, GBScleanR, which enables robust and precise error correction for noisy RRS-based genotype data by incorporating marker-specific error rates into the HMM. The results indicate that GBScleanR improves the accuracy by more than 25 percentage points at maximum compared to the existing tools in simulation data sets and achieves the most reliable genotype estimation in real data even with error-prone markers.
en-copyright=
kn-copyright=

en-aut-name=FurutaTomoyuki
en-aut-sei=Furuta
en-aut-mei=Tomoyuki
kn-aut-name=
kn-aut-sei=
kn-aut-mei=
aut-affil-num=1
ORCID=

en-aut-name=YamamotoToshio
en-aut-sei=Yamamoto
en-aut-mei=Toshio
kn-aut-name=
kn-aut-sei=
kn-aut-mei=
aut-affil-num=2
ORCID=

en-aut-name=AshikariMotoyuki
en-aut-sei=Ashikari
en-aut-mei=Motoyuki
kn-aut-name=
kn-aut-sei=
kn-aut-mei=
aut-affil-num=3
ORCID=

affil-num=1
en-affil=Institute of Plant Science and Resources, Okayama University
kn-affil=

affil-num=2
en-affil=Institute of Plant Science and Resources, Okayama University
kn-affil=

affil-num=3
en-affil=Bioscience and Biotechnology Center, Nagoya University
kn-affil=
en-keyword=reduced-representation sequencing
kn-keyword=reduced-representation sequencing
en-keyword=error correction
kn-keyword=error correction
en-keyword=imputation
kn-keyword=imputation
en-keyword=hidden Markov model
kn-keyword=hidden Markov model
END

start-ver=1.4
cd-journal=joma
no-vol=69
cd-vols=
no-issue=4
article-no=
start-page=633
end-page=639
dt-received=
dt-revised=
dt-accepted=
dt-pub-year=
dt-pub=
dt-online=
en-article=
kn-article=
en-subject=
kn-subject=
en-title=
kn-title=Novel method for evaluation of anaerobic germination in rice and its application to diverse genetic collections
en-subtitle=
kn-subtitle=
en-abstract=
kn-abstract=Direct seeding saves time and labour in the cultivation of rice. However, seedling establishment is often unstable, and yields are lower than in transplanting. Anaerobic germination (AG) is a key trait for improvement of direct seeding of rice. We established a simple and reliable method of evaluating AG in rice breeding. We germinated seeds in distilled water or deoxygenated water and measured coleoptile length several days later; compared the results of each method with survival rate in flooded soil; and used the anoxic water method for QTL analysis and for testing cultivars. Coleoptile elongation in anoxic water and survival rate in flooded soil were significantly correlated (r = 0.879, P < 0.01). A significant QTL, likely to be a major gene (AG1), was found in chromosome segment substitution lines and in a backcrossed F2 population derived from tolerant and sensitive lines. Diverse rice genetic resources were classified into tolerant or sensitive accession groups reflecting their ecotypes. Our study revealed that anoxic water evaluation method saves space and time in a stable environment compared with flooded soil evaluation. It is applicable to QTL analysis and isolation of genes underlying anaerobic germination.
en-copyright=
kn-copyright=

en-aut-name=KuyaNoriyuki
en-aut-sei=Kuya
en-aut-mei=Noriyuki
kn-aut-name=
kn-aut-sei=
kn-aut-mei=
aut-affil-num=1
ORCID=

en-aut-name=SunJian
en-aut-sei=Sun
en-aut-mei=Jian
kn-aut-name=
kn-aut-sei=
kn-aut-mei=
aut-affil-num=2
ORCID=

en-aut-name=IijimaKen
en-aut-sei=Iijima
en-aut-mei=Ken
kn-aut-name=
kn-aut-sei=
kn-aut-mei=
aut-affil-num=3
ORCID=

en-aut-name=VenuprasadRamaiah
en-aut-sei=Venuprasad
en-aut-mei=Ramaiah
kn-aut-name=
kn-aut-sei=
kn-aut-mei=
aut-affil-num=4
ORCID=

en-aut-name=YamamotoToshio
en-aut-sei=Yamamoto
en-aut-mei=Toshio
kn-aut-name=
kn-aut-sei=
kn-aut-mei=
aut-affil-num=5
ORCID=

affil-num=1
en-affil=Institute of Crop Science, National Agriculture and Food Research Organization (NARO)
kn-affil=

affil-num=2
en-affil=Institute of Crop Science, National Agriculture and Food Research Organization (NARO)
kn-affil=

affil-num=3
en-affil=Institute of Crop Science, National Agriculture and Food Research Organization (NARO)
kn-affil=

affil-num=4
en-affil=Africa Rice Center (AfricaRice)
kn-affil=

affil-num=5
en-affil=Institute of Plant Science and Resources, Okayama University
kn-affil=
en-keyword=QTL
kn-keyword=QTL
en-keyword=anaerobic germination
kn-keyword=anaerobic germination
en-keyword=anoxic water
kn-keyword=anoxic water
en-keyword=direct seeding
kn-keyword=direct seeding
en-keyword=genetic resources
kn-keyword=genetic resources
en-keyword=phenotyping method
kn-keyword=phenotyping method
en-keyword=rice
kn-keyword=rice
END