Transcriptome-wide analyses of piRNA binding sites suggest distinct mechanisms regulate piRNA binding and silencing in C. elegans.

PIWI-interacting RNAs (piRNAs) protect genome integrity by silencing transposon mRNAs and some endogenous mRNAs in various animals. However, C. elegans piRNAs only trigger gene silencing at select predicted targeting sites, suggesting additional cellular mechanisms regulate piRNA silencing. To gain insight into possible mechanisms, we compared the transcriptome-wide predicted piRNA targeting sites to the in vivo piRNA binding sites. Surprisingly, while sequence-based predicted piRNA targeting sites are enriched in 3' UTRs, we found that C. elegans piRNAs preferentially bind to coding regions (CDS) of target mRNAs, leading to preferential production of secondary silencing small RNAs in the CDS. However, our analyses suggest that this CDS binding preference cannot be explained by the action of antisilencing Argonaute CSR-1. Instead, our analyses imply that CSR-1 protects mRNAs from piRNA silencing through two distinct mechanisms-by inhibiting piRNA binding across the entire CSR-1 targeted transcript, and by inhibiting secondary silencing small RNA production locally at CSR-1 bound sites. Together, our work identifies the CDS as the critical region that is uniquely competent for piRNA binding in C. elegans. We speculate the CDS binding preference may have evolved to allow the piRNA pathway to maintain robust recognition of RNA targets in spite of genetic drift. Together, our analyses revealed that distinct mechanisms are responsible for restricting piRNA binding and silencing to achieve proper transcriptome surveillance.

CLASH Analyst: A Web Server to Identify In Vivo RNA-RNA Interactions from CLASH Data.

Non-coding RNAs, such as miRNAs and piRNAs, play critical roles in gene regulation through base-pairing interactions with their target molecules. The recent development of the crosslinking, ligation, and sequencing of hybrids (CLASH) method has allowed scientists to map transcriptome-wide RNA-RNA interactions by identifying chimeric reads consisting of fragments from regulatory RNAs and their targets. However, analyzing CLASH data requires scientists to use advanced bioinformatics, and currently available tools are limited for users with little bioinformatic experience. In addition, many published CLASH studies do not show the full scope of RNA-RNA interactions that were captured, highlighting the importance of reanalyzing published data. Here, we present CLASH Analyst, a web server that can analyze raw CLASH data within a fully customizable and easy-to-use interface. CLASH Analyst accepts raw CLASH data as input and identifies the RNA chimeras containing the regulatory and target RNAs according to the user's interest. Detailed annotation of the captured RNA-RNA interactions is then presented for the user to visualize within the server or download for further analysis. We demonstrate that CLASH Analyst can identify miRNA- and piRNA-targeting sites reported from published CLASH data and should be applicable to analyze other RNA-RNA interactions. CLASH Analyst is freely available for academic use.

Identifying piRNA targets on mRNAs in C. elegans using a deep multi-head attention network.

BACKGROUND: Piwi-interacting RNAs (piRNAs) are the small non-coding RNAs (ncRNAs) that silence genomic transposable elements. And researchers found out that piRNA also regulates various endogenous transcripts. However, there is no systematic understanding of the piRNA binding patterns and how piRNA targets genes. While various prediction methods have been developed for other similar ncRNAs (e.g., miRNAs), piRNA holds distinctive characteristics and requires its own computational model for binding target prediction. RESULTS: Recently, transcriptome-wide piRNA binding events in C. elegans were probed by PRG-1 CLASH experiments. Based on the probed piRNA-messenger RNAs (mRNAs) binding pairs, in this research, we devised the first deep learning architecture based on multi-head attention to computationally identify piRNA targeting mRNA sites. In the devised deep network, the given piRNA and mRNA segment sequences are first one-hot encoded and undergo a combined operation of convolution and squeezing-extraction to unravel motif patterns. And we incorporate a novel multi-head attention sub-network to extract the hidden piRNA binding rules that can simulate the biological piRNA target recognition process. Finally, the true piRNA-mRNA binding pairs are identified by a deep fully connected sub-network. Our model obtains a supreme discriminatory power of AUC [Formula: see text] 93.3% on an independent test set and successfully extracts the verified binding pattern of a synthetic piRNA. These results demonstrated that the devised model achieves high prediction performance and suggests testable potential biological piRNA binding rules. CONCLUSIONS: In this research, we developed the first deep learning method to identify piRNA targeting sites on C. elegans mRNAs. And the developed deep learning method is demonstrated to be of high accuracy and can provide biological insights into piRNA-mRNA binding patterns. The piRNA binding target identification network can be downloaded from http://cosbi2.ee.ncku.edu.tw/data_download/piRNA_mRNA_binding .

Cancer DEIso: An integrative analysis platform for investigating differentially expressed gene-level and isoform-level human cancer markers.

Transcript isoforms regulated by alternative splicing can substantially impact carcinogenesis, leading to a need to obtain clues for both gene differential expression and malfunctions of isoform distributions in cancer studies. The Cancer Genome Atlas (TCGA) project was launched in 2008 to collect cancer-related genome mutation raw data from the population. While many repositories tried to add insights into the raw data in TCGA, no existing database provides both comprehensive gene-level and isoform-level cancer stage marker investigation and survival analysis. We constructed Cancer DEIso to facilitate in-depth analyses for both gene-level and isoform-level human cancer studies. Patient RNA-seq data, sample sheets, patient clinical data, and human genome datasets were collected and processed in Cancer DEIso. And four functions to search differentially expressed genes/isoforms between cancer stages were implemented: (i) Search potential gene/isoform markers for a specified cancer type and its two stages; (ii) Search potentially induced cancer types and stages for a gene/isoform; (iii) Expression survival analysis on a given gene/isoform for some cancer; (iv) Gene/isoform stage expression comparison visualization. As an example, we demonstrate that Cancer DEIso can indicate potential colorectal cancer isoform diagnostic markers that are not easily detected when only gene-level expressions are considered. Cancer DEIso is available at http://cosbi4.ee.ncku.edu.tw/DEIso/.

A tool for analyzing and visualizing ribo-seq data at the isoform level.

BACKGROUND: Translational regulation is one important aspect of gene expression regulation. Dysregulation of translation results in abnormal cell physiology and leads to diseases. Ribosome profiling (RP), also called ribo-seq, is a powerful experimental technique to study translational regulation. It can capture a snapshot of translation by deep sequencing of ribosome-protected mRNA fragments. Many ribosome profiling data processing tools have been developed. However, almost all tools analyze ribosome profiling data at the gene level. Since different isoforms of a gene may produce different proteins with distinct biological functions, it is advantageous to analyze ribosome profiling data at the isoform level. To meet this need, previously we developed a pipeline to analyze 610 public human ribosome profiling data at the isoform level and constructed HRPDviewer database. RESULTS: To allow other researchers to use our pipeline as well, here we implement our pipeline as an easy-to-use software tool called RPiso. Compared to Ribomap (a widely used tool which provides isoform-level ribosome profiling analyses), our RPiso (1) estimates isoform abundance more accurately, (2) supports analyses on more species, and (3) provides a web-based viewer for interactively visualizing ribosome profiling data on the selected mRNA isoforms. CONCLUSIONS: In this study, we developed RPiso software tool ( http://cosbi7.ee.ncku.edu.tw/RPiso/ ) to provide isoform-level ribosome profiling analyses. RPiso is very easy to install and execute. RPiso also provides a web-based viewer for interactively visualizing ribosome profiling data on the selected mRNA isoforms. We believe that RPiso is a useful tool for researchers to analyze and visualize their own ribosome profiling data at the isoform level.