Prime-InDel version 1: A web tool providing InDel markers for targeted regions narrowing

Lei, Y.; Myo Thu, K.; Zhou, Z.; Xu, L.; Wang, C.; Liu, T.; Meng, L.; Yang, D.; Faruquee, M.; Liu, D.; Zhao, Y.; Zhao, X.; Chen, H.; Li, Z.; Wang, W.; Xu, J.; Zheng, T.

doi:10.64898/2026.01.29.702688

bioRxiv · 10.64898/2026.01.29.702688

Prime-InDel version 1: A web tool providing InDel markers for targeted regions narrowing

Lei, Y.·Myo Thu, K.·Zhou, Z.·Xu, L.·Wang, C.·Liu, T.·Meng, L.·Yang, D.·Faruquee, M.·Liu, D.·Zhao, Y.·Zhao, X.·Chen, H.·Li, Z.·Wang, W.·Xu, J.·Zheng, T.

View original source ↗Export metadata ↓

Abstract

While high-density SNP genotyping enables genome-wide assays in rice (Oryza sativa L.) and other crops, PCR-based markers--particularly those derived from insertion-deletion (InDel) variations--remain crucial for fine-mapping. Currently, readily available primer information for high-density InDel rice markers is still limited. We present the first version of PrimeInDel (https://rfgb.rmbreeding.cn/search/variation/primeIndel), an online tool integrating three sets of InDel markers: an 8K-set and a 316K-set from the 3K Rice Genomes (3K-RG) project, and a 22K-set compiled from published literatures. In user cases, primers from PrimeInDel proved highly effective, narrowing a cold-tolerance QTL (qSR2) from 2.7 Mb to 200 kb and a heading-date QTL (qDeh1.1) from 239.5 kb to 83.6 kb. Our work provides the community with a robust, PCR-based InDel primer, accessible via an online data set, which will facilitate germplasm genotyping and gene identification in rice breeding.

Explore related subjects

plant biology Preprint

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

Lei, Y., Myo Thu, K., Zhou, Z., Xu, L., Wang, C., Liu, T., Meng, L., Yang, D., Faruquee, M., Liu, D., Zhao, Y., Zhao, X., Chen, H., Li, Z., Wang, W., Xu, J., Zheng, T.. 2026-01-30. Prime-InDel version 1: A web tool providing InDel markers for targeted regions narrowing. https://doi.org/10.64898/2026.01.29.702688

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

bioRxiv2026

MpILR1 Hydrolyzes Jasmonate-Amino Acid Conjugates to Activate dn-iso-OPDA Signaling in Marchantia polymorpha.

Jasmonates are essential phytohormones that coordinate defense responses and developmental programs across land plants. In angiosperms, the active jasmonate ligand jasmonoyl-L-isoleucine (JA-Ile), is produced through GH3-mediated conjugation of jasmonic acid to isoleucine and JA-Ile homeostasis is further shaped by ILR1/ILL-family amidohydrolases. In contrast, the primary bioactive jasmonate ligand in bryophytes, dinor-12-oxo-phytodienoic acid (dn-iso-OPDA), is inactivated through conjugation with amino acids, raising the question of whether these conjugates constitute a reversible hormone reservoir or an irreversible catabolic end point. Although the ILR1-like family has been characterized extensively for its role in auxin and jasmonate homeostasis in angiosperms, its function in bryophytes remains basically unexplored. Here we show that MpILR1, the sole Marchantia ortholog of the ILR1/ILL family, hydrolyzes a specific subset of dn-iso-OPDA-amino acid conjugates in vivo. Loss-of-function Mpilr1 mutants exhibit enhanced accumulation of dn-iso-OPDA conjugated to hydrophobic amino acids (Val, Leu and Ile) but not to hydrophilic residues (His, Glu and Gln), demonstrating substrate-selective hydrolysis. MpILR1 hydrolytic activity is required for full dn-iso-OPDA-mediated responses, including transcriptional activation and defense against gastropod herbivory. These findings establish MpILR1 as a key positive regulator of jasmonate signaling in Marchantia polymorpha and suggest that hormone conjugation/deconjugation is an ancient regulatory mechanism evolved during plant terrestrialization.

plant biology↗

bioRxiv2026

Drought-Spec-Net: Early Tomato Drought Detection and Potential Yield-Impact Assessment Using Vis NIR Data

Drought stress significantly reduces tomato (Solanum lycopersicum L.) productivity, and early detection is critical to minimize yield losses through timely interventions. In this study, we developed Drought-Spec-Net, a hybrid 1D convolutional neural network that integrates local and global spectral feature extraction to detect early drought stress from visible and near infrared (Vis NIR) spectra data of tomato seedlings. The model was trained on 378 samples using an 80:20 train test split, with 20% of the training set reserved for validation. DroughtSpecNet outperformed the evaluated baseline and state of the art models, achieving 97% accuracy, 95% precision, 98% recall, and an F1 score of 97%. To improve the agronomic interpretation of the model outputs, predicted drought probabilities were converted into a literature-informed potential yield impact indicator using a maximum impact level of 60%. On the test set (76 samples), mapped potential yield-impact values ranged from 0% to 60%, with an average reduction of 12.97%. We also conducted an initial experiment using our greenhouse RGB dataset, collected daily from drought treated and well-watered tomato plants at West Virginia State University (WVSU). From this dataset, 44 images were selected for ilastik-based canopy segmentation, producing plant-level drought severity indices (DSI) with a mean of 0.28, median of 0.14, and range of 0.01 to 0.91. Additionally, we trained and fine-tuned a large language model (LLM) based on PLLaMA7BInstruct, called AgriLLaMA, for automated agronomic report generation from Drought-Spec-Net outputs. The generated reports summarize predicted stress levels, mapped potential yield impacts, and preliminary management considerations. This integrated approach not only improves early drought stress detection but also delivers quantitative and interpretable estimates of potential productivity losses, providing a complete framework connecting physiological stress detection to actionable agricultural outcomes.

plant biology↗

bioRxiv2026

BSA101: Unlocking Historical Mutant Collections with BSA-Seq

Forward genetics is a powerful approach for gene discovery, but identifying causal mutations becomes difficult when mutants are maintained in heterogeneous populations with uncertain pedigrees. This is exemplified by classical tasselseed (ts) mutants, which have long served as a genetic model for studying sex determination and carpel suppression. Decades of repeated outcrossing to diverse inbred lines have created substantial genetic heterogeneity, limiting the effectiveness of conventional bulked-segregant analysis sequencing (BSA-Seq). To address this, we developed a BSA-Seq framework that integrates flexible experimental designs, multiple reference genomes, and complementary statistical methods tailored for genetically heterogeneous populations. Applying this framework revealed that reference genome selection is critical for mapping success and that Euclidean distance raised to the fourth power (ED4) outperformed homozygosity mapping (HM). Furthermore, the framework enables simultaneous mapping of multiple mutations within a single population, eliminating the need for additional mapping populations. Applying this framework to 26 ts mutant stocks from the Maize Genetics Cooperation Stock Center, we successfully mapped 24 mutants to genomic intervals containing known ts genes, while the remaining mutants mapped to distinct genomic intervals, defining novel candidate regions underlying carpel suppression. Together, these results demonstrate that historical mutant collections represent an underutilized resource for gene discovery and establish a generalizable mapping strategy for unlocking their genetic potential across diverse species.

plant biology↗