Seed germination rate is a critical factor in agricultural productivity. Traditional approaches to germination assessment necessitate human scrutiny, introducing subjectivity and diminishing operational efficiency. While fully supervised deep learning approaches offer objectivity, reproducibility and efficiency, they require large-scale and high-quality labeled datasets, which are often challenging to obtain. To address this limitation, this study introduce a Semi-Supervised Germination Detection (SSGD) method built upon the Soft Teacher framework. SSGD employs a Faster R-CNN detector with ResNet50-FPN feature extraction network in both the teacher model and the student model. To ensure higher learning stability, the parameters of the teacher model are dynamically improved by an exponential moving average (EMA) updating mechanism. This study conducted a comprehensive evaluation of SSGD on the publicly available Pennisetum glaucum (PG), Secale cereale (SC), and Zea mays (ZM) datasets. Remarkably, with only 10% of labeled data, SSGD achieved mAP50 scores of 0.954, 0.928, and 0.961 on PG, SC, and ZM, respectively, surpassing fully supervised methods trained on 100% labeled data, including YOLOv3, FCOS, Cascade R-CNN and Faster R-CNN. Moreover, SSGD consistently outperformed the Faster R-CNN baseline across various annotation ratios (1%, 10%, 20% and 30%). Notably, even when the PG dataset’s labeling rate dropped to only 1%, SSGD maintained a high mAP50 of 0.94, exceeding the baseline by 5 percentage points. These findings underscore SSGD’s strong adaptability to limited data and further emphasize the effectiveness of semi-supervised learning in seed germination detection.

Accurately predicting phenotypes from genotypes holds great promise to improve health management in humans and animals, and breeding efficiency in animals and plants. Although many prediction methods have been developed, the optimal method differs across datasets due to multiple factors, including species, environments, populations and traits of interest. Studies have demonstrated that the number of genes underlying a trait and its heritability are the two key factors that determine which method fits the trait the best. In many cases, however, these two factors are unknown for the traits of interest. We developed a cloud computing platform for Mining the Maximum Accuracy of Predicting phenotypes from genotypes (MMAP) using unsupervised learning on publicly available real data and simulated data. MMAP provides a user interface to upload input data, manage projects and analyses and download the output results. The platform is free for the public to conduct computations for predicting phenotypes and genetic merit using the best prediction method optimized from many available ones, including Ridge Regression, gBLUP, compressed BLUP, Bayesian LASSO, Bayes A, B, Cpi and many more. Users can also use the platform to conduct data analyses with any methods of their choice. It is expected that extensive usage of MMAP would enrich the training data, which in turn results in continual improvement of the identification of the best method for use with particular traits. AVAILABILITY AND IMPLEMENTATION: The MMAP user manual, tutorials and example datasets are available at http://zzlab.net/MMAP. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BACKGROUND: Linkage disequilibrium (LD) analysis is broadly utilized in genetics to understand the evolutionary and demographic history and helps geneticists identify genes associated with interested inherited traits, such as diseases. There are some tools for linkage disequilibrium analysis either in a local or online way; however, there has been no such tool supporting both graphical user interface (GUI) and parallel computing. RESULTS: We developed a GUI software called LDkit for LD analysis, which supports parallel computing. The LDkit supports both variant call format (VCF) and PLINK 'ped + map' format. At the same time, users could also just analyze a subset of individuals from the whole population. The LDkit reads the data by block and then paralleled the computation process by monitoring the usage of processes. Assessment on the Human 1000 genome data showed that when paralleled with 32 threads, the running time was reduced to less than 6 minutes from ~77 minutes using the chromosome 22 dataset with 1,103,547 SNPs and 2504 individuals. CONCLUSIONS: The software LDkit can be effectively used to calculate and plot LD decay, LD block, and linkage disequilibrium analysis between a site and a given region. Most importantly, both graphical user interface (GUI) and stand-alone packages are available for users' convenience. LDkit was written in JAVA language under cross-platform support.

In functional genomics, small interfering RNA (siRNA) can be used to knockdown gene expression. Usually, a target gene has numerous potential siRNAs, but their efficiencies of gene silencing often varies. Thus, for a successful RNA interference (RNAi), selecting the most effective siRNA is a critical step. Despite various computational algorithms have been developed, the efficacy prediction accuracy is not so satisfactory. In this paper, to explore the effect of different motifs on gene silencing and further improve the prediction accuracy, we developed a new powerful predictor by using a deep learning algorithm - Convolutional Neural Network (CNN). The comparison results showed that the Pearson Correlation Coefficient (PCC) of our model is 0.717, which is 13.81%, 16.78% and 5.91% higher than Biopredsi, i-Score, ThermoComposition21 and DSIR. In addition, the area under the ROC curve (AUC) of our model is 0.894, which is 10.10%, 12.59% and 7.07% higher than those four algorithms. The results show that our model is stable and efficient to predict siRNA silencing efficacy.