Yams (Dioscorea spp.) are a highly important class of horticultural crops, serving as a staple food for millions of people in Africa and contributing significantly to food security. They are also widely cultivated in East Asia as medicinal herbs, bringing substantial economic incomes. Diverse omics data play a pivotal role in advancing yam research and breeding. However, these data are often scattered, lacking in systematic organization and analysis, which underscores the need for centralized and comprehensive data management. In view of this, we gathered extensive omics data and developed the Yam Omics Database (YamOmics; https://biotec.njau.edu.cn/yamdb ). The database currently offers a vast and diverse range of omics data, covering genomic, transcriptomic and plastomic data from 41 distinct yam species, along with detailed records of genomic variants from 935 germplasms, and gene expression profiles from 191 samples. Additionally, the database features thorough annotations, encompassing aspects like genome synteny, ortholog groups, signaling pathways, gene families and protein interactions. To support yam basic biology and breeding research, it is also equipped with a suite of user-friendly online tools, including PCR primer design, CRISPR design, expression analysis, enrichment analysis, and phylogenetic inference among Dioscorea accessions tools.

The deconvolution of the mixture contributors is essential but always presents challenges in crime scene investigation, especially when encountering mixtures with multiple contributors. In recent years, RNA-based genotyping has shown great advantages in assigning a body fluid to a specific mixture contributor, whereas transcriptome sequencing has been seldom applied for mixture deconvolution. In this study, we investigated the feasibility of bulk transcriptomes data for deconvoluting biological mixtures, and described a novel approach, utilizing bulk transcriptomes data of multi-body-fluid mixtures to identify body fluid donors. Computational deconvolution methods were introduced here to infer body fluid proportions from bulk transcriptomes data, and SNPs corresponding to a specific body fluid donor could be separated from differentially expressed genes with the knowledge of body fluid composition. The described method was tested on both in-silico and real mixtures. After a quantitative evaluation, robust regression-based deconvolution methods with unnormalized expression profiles were finally applied to body fluid deconvolution tasks, and body fluid compositions of mixtures could be determined with high accuracy according to the deconvolution results. Finally, the corresponding SNPs were successfully extracted from both in-silico and real mixtures, producing high likelihood ratios and strong evidentiary weight for individually identifying body fluid donors. Altogether, our proof-of-concept study established the utility of bulk transcriptomes data to identify mixture contributors, providing new avenues for mixture deconvolution.

SUMMARY: Fungi form a large and heterogeneous group of eukaryotic organisms with diverse ecological niches. The high importance of fungi contrasts with our limited understanding of fungal lifestyle and adaptability to environment. Over the last decade, the high-throughput sequencing technology produced tremendous RNA-sequencing (RNA-seq) data. However, there is no comprehensive database for mycologists to conveniently explore fungal gene expression and alternative splicing. Here, we have developed FungiExp, an online database including 35 821 curated RNA-seq samples derived from 220 fungal species, together with gene expression and alternative splicing profiles. It allows users to query and visualize gene expression and alternative splicing in the collected RNA-seq samples. Furthermore, FungiExp contains several online analysis tools, such as differential/specific, co-expression network and cross-species gene expression conservation analysis. Through these tools, users can obtain new insights by re-analyzing public RNA-seq data or upload personal data to co-analyze with public RNA-seq data. AVAILABILITY AND IMPLEMENTATION: The FungiExp is freely available at https://bioinfo.njau.edu.cn/fungiExp. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BACKGROUND: Piwi-interacting RNAs (piRNAs) are a class of small non-coding RNA primarily expressed in germ cells that can silence transposons at the post-transcriptional level. Accurate prediction of piRNAs remains a significant challenge. RESULTS: We developed a program for piRNA annotation (Piano) using piRNA-transposon interaction information. We downloaded 13,848 Drosophila piRNAs and 261,500 Drosophila transposons. The piRNAs were aligned to transposons with a maximum of three mismatches. Then, piRNA-transposon interactions were predicted by RNAplex. Triplet elements combining structure and sequence information were extracted from piRNA-transposon matching/pairing duplexes. A support vector machine (SVM) was used on these triplet elements to classify real and pseudo piRNAs, achieving 95.3 ± 0.33% accuracy and 96.0 ± 0.5% sensitivity. The SVM classifier can be used to correctly predict human, mouse and rat piRNAs, with overall accuracy of 90.6%. We used Piano to predict piRNAs for the rice stem borer, Chilo suppressalis, an important rice insect pest that causes huge yield loss. As a result, 82,639 piRNAs were predicted in C. suppressalis. CONCLUSIONS: Piano demonstrates excellent piRNA prediction performance by using both structure and sequence features of transposon-piRNAs interactions. Piano is freely available to the academic community at http://ento.njau.edu.cn/Piano.html .