Long noncoding RNAs (lncRNAs) are noncoding RNAs with a length greater than 200 nucleotides. Studies have shown that they play an important role in many life activities. Dozens of lncRNAs have been characterized to some extent, and they are reported to be related to the development of diseases in a variety of cells. However, the biological functions of most lncRNAs are currently still unclear. Therefore, accurately identifying and predicting lncRNAs would be helpful for research on their biological functions. Due to the disadvantages of high cost and high resource-intensiveness of experimental methods, scientists have developed numerous computational methods to identify and predict lncRNAs in recent years. In this paper, we systematically summarize the machine learning-based lncRNAs prediction tools from several perspectives, and discuss the challenges and prospects for the future work.

Targeted 'omics' research for seaweeds, utilizing various computational and informatics frameworks, has the potential to rapidly develop our understanding of biological processes at the molecular level and contribute to solutions for the most pressing environmental and social issues of our time. Here, a systematic review into the current status of seaweed omics research was undertaken to evaluate the biological diversity of seaweed species investigated (red, green and brown phyla), the levels to which the work was undertaken (from full genome to transcripts, proteins or metabolites) and the field of research to which it has contributed. We report that from 1994 to 2021 the majority of seaweed omics research has been performed on the red seaweeds (45% of total studies), with more than half of these studies based upon two genera Pyropia and Gracilaria. A smaller number of studies examined brown seaweed (key genera Saccharina and Sargassum) and green seaweed (primarily Ulva). Overall, seaweed omics research is most highly associated with the field of evolution (46% of total studies), followed by the fields of ecology, natural products and their biosynthesis, omics methodology and seaweed-microbe interactions. Synthesis and specific outcomes derived from omics studies in the red seaweeds are provided. Together, these studies have provided a broad-scale interrogation of seaweeds, facilitating our ability to answer fundamental queries and develop applied outcomes. Crucial to the next steps will be establishing analytical tools and databases that can be more broadly utilized by practitioners and researchers across the globe because of their shared interest in the key seaweed genera.

Heart formation in the zebrafish involves a rapid, complex series of morphogenetic events in three-dimensional space that spans cardiac lineage specification through to chamber formation and maturation. This process is tightly orchestrated by a cardiac gene regulatory network (GRN), which ensures the precise spatio-temporal deployment of genes critical for heart formation. Alterations of the timing or spatial localisation of gene expression can have a significant impact in cardiac ontogeny and may lead to heart malformations. Hence, a better understanding of the cellular and molecular basis of congenital heart disease relies on understanding the behaviour of cardiac GRNs with precise spatiotemporal resolution. Here, we review the recent technical advances that have expanded our capacity to interrogate the cardiac GRN in zebrafish. In particular, we focus on studies utilising high-throughput technologies to systematically dissect gene expression patterns, both temporally and spatially during heart development.