PNNGS, a multi-convolutional parallel neural network for genomic selection.

Genomic selection (GS) can accomplish breeding faster than phenotypic selection. Improving prediction accuracy is the key to promoting GS. To improve the GS prediction accuracy and stability, we introduce parallel convolution to deep learning for GS and call it a parallel neural network for genomic selection (PNNGS).

RASSF1A promotes ATM signaling and methylation is a synthetic lethal marker for ATR inhibitors.

The mechanism of RASSF1A in DNA damage repair remains to be further clarified for applying to synthetic lethal strategy. Eight esophageal cancer cell lines, 181 cases of esophageal dysplasia and 1066 cases of primary esophageal squamous cell carcinoma (ESCC) were employed. Methylation-specific PCR, the CRISPR/Cas9 technique, immunoprecipitation assay and a xenograft mouse model were used.

Dynamic transcriptome and network-based analysis of yellow leaf mutant Ginkgo biloba.

Background: Golden leaf in autumn is a prominent feature of deciduous tree species like Ginkgo biloba L., a landscape tree widely cultivated worldwide. However, little was known about the molecular mechanisms of leaf yellowing, especially its dynamic regulatory network.

Temporal Dynamic Transcriptome Landscape Reveals Regulatory Network During the Early Differentiation of Female Strobilus Buds in .

Reproductive bud differentiation is one of the most critical events for the reproductive success of seed plants. Yet, our understanding of genetic basis remains limited for the development of the reproductive organ of gymnosperms, namely, unisexual strobilus or cone, leaving its regulatory network largely unknown for strobilus bud differentiation. In this study, we analyzed the temporal dynamic landscapes of genes, long non-coding RNAs (lncRNAs), and microRNAs (miRNAs) during the early differentiation of female strobilus buds in based on the whole transcriptome sequencing.

TOD-CUP: a gene expression rank-based majority vote algorithm for tissue origin diagnosis of cancers of unknown primary.

Gene expression profiling holds great potential as a new approach to histological diagnosis and precision medicine of cancers of unknown primary (CUP). Batch effects and different data types greatly decrease the predictive performance of biomarker-based algorithms, and few methods have been widely applied to identify tissue origin of CUP up to now. To address this problem and assist in more precise diagnosis, we have developed a gene expression rank-based majority vote algorithm for tissue origin diagnosis of CUP (TOD-CUP) of most common cancer types.

Characteristics of plant circular RNAs.

Circular RNA (circRNA) is a kind of covalently closed single-stranded RNA molecules that have been proved to play important roles in transcriptional regulation of genes in diverse species. With the rapid development of bioinformatics tools, a huge number (95143) of circRNAs have been identified from different plant species, providing an opportunity for uncovering the overall characteristics of plant circRNAs. Here, based on publicly available circRNAs, we comprehensively analyzed characteristics of plant circRNAs with the help of various bioinformatics tools as well as in-house scripts and workflows, including the percentage of coding genes generating circRNAs, the frequency of alternative splicing events of circRNAs, the non-canonical splicing signals of circRNAs and the networks involving circRNAs, miRNAs and mRNAs.