SummaryCircular RNAs (circRNAs) are abundant and evolutionarily conserved RNAs of largely unknown function. Here, we show that a subset of circRNAs is translated in vivo. By performing ribosome footprinting from fly heads, we demonstrate that a group of circRNAs is associated with translating ribosomes. Many of these ribo-circRNAs use the start codon of the hosting mRNA, are bound by membrane-associated ribosomes, and have evolutionarily conserved termination codons. In addition, we found that a circRNA generated from the muscleblind locus encodes a protein, which we detected in fly head extracts by mass spectrometry. Next, by performing in vivo and in vitro translation assays, we show that UTRs of ribo-circRNAs (cUTRs) allow cap-independent translation. Moreover, we found that starvation and FOXO likely regulate the translation of a circMbl isoform. Altogether, our study provides strong evidence for translation of circRNAs, revealing the existence of an unexplored layer of gene activity.
Here we describe a new integrative approach for accurate annotation and quantification of circRNAs named Short Read circRNA Pipeline (SRCP). Our strategy involves two steps: annotation of validated circRNAs followed by a quantification step. We show that SRCP is more sensitive than other individual pipelines and allows for more comprehensive quantification of a larger number of differentially expressed circRNAs. To facilitate the use of SRCP, we generate a comprehensive collection of validated circRNAs in five different organisms, including humans. We then utilize our approach and identify a subset of circRNAs bound to the miRNA-effector protein AGO2 in human brain samples.
Identification and quantification of circular RNAs (circRNAs) depends strongly on the utilized computational pipeline. Here we describe an integrative approach for accurate annotation and quantification of circRNAs. First, we utilize several circRNA-identification pipelines to annotate circRNAs in a given organism. Second, we build a short sequence index that is used to search the unaligned RNA-seq reads. Our approach allows full annotation of circRNAs with fewer false positives and negatives than any individual pipeline or combination of them. Moreover, our approach is more sensitive than any individual pipeline and allows more accurate quantification and larger number of differentially expressed circRNAs. BACKGROUND:Circular RNAs (circRNAs) are abundant RNAs generated through circularization of specific exons by a process called backsplicing [1][2][3][4]. As covalently closed circles, circRNAs are generally more stable than linear RNA transcripts. This is likely due to the lack of free ends that can be targeted by exonucleases. circRNAs have been found in bacteria, archaea, and most eukaryotes [5]. They are highly expressed in metazoans, particularly in the central nervous system (CNS) [1,[6][7][8]. Interestingly, circRNAs accumulate in the CNS as animals age in flies, worms, and mice [9, 10]. When first discovered, circRNAs were thought to be a byproduct of splicing; however, multiple studies in the past five years have clearly shown that at least some of these RNAs are functional. Two pioneering works showed that circRNAs can bind to and likely modulate miRNA function [11, 12]. Recent studies showed that these molecules can also regulate the activity of RNA binding proteins[13] and ribosome biogenesis [14] and that a subset of them encode proteins [15][16][17]. Their functionality has also been demonstrated in vivo [15, 18, 19], and there is evidence that circRNAs are disease biomarkers [20].Many computational pipelines exist for de novo discovery and quantification of circRNAs from RNA-seq data [21][22][23] . These include Acfs [24], DCC [25], segemehl [26], CIRCexplorer [27], KNIFE [28], MapSplice2 [29], circRNA_finder [30], CIRI [31], and find_circ [11]. The pipelines differ in sensitivity, precision, runtime, and storage requirements [21] as shown in several independent studies [21, 32, 33]. Analysis of a large number of datasets allowed researchers to generate different circRNA databases [34]. 4One of the most popular circRNAs databases is known as circBASE [35], which contains annotations and information on many, but not all, circRNAs.Since many circRNAs have been already discovered, the very timeconsuming [21] process of de novo identification of circRNAs for every RNAseq library is redundant. Moreover, most circRNAs are identified by only subsets or only one pipeline, making it difficult to determine whether these are real circRNAs or sequencing or annotation artifacts. Therefore, relying on the results of only one pipeline for circRNA annotation and quantification is highlyGenerally, circRNA detecti...
<p>The goal of Google&#8217;s Flood Forecasting Initiative is to provide timely and actionable flood warnings to everyone, globally. Until recently, Google provided operational flood warnings only for specific partner countries, namely India, Bangladesh, Sri Lanka, Colombia, and Brazil. In 2021 our flood alerting system sent out around 115 million flood notifications, reaching over 23 million people in the affected local areas. In all of the regions mentioned above, our operational model relies on partnerships with local governments to provide real-time measurements of observed discharge or water level (Nevo et al. 2021). However, relying on real-time measurement data makes it harder to scale to new regions as a) this data does not exist everywhere, and b) even if it exists, it requires significant per-country time and resource investment.</p> <p>Building on research results from the last few years (e.g., Kratzert et al. 2019a, Kratzert et al. 2019b, Klotz et al. 2021), we built a global rainfall-runoff model that does not rely on real-time measurements in the operational context but only uses globally available forcing data and globally available catchment attributes. It can therefore be deployed everywhere, including in ungauged basins. Following Kratzert et al. (2019a), our rainfall-runoff model is based on the Long Short-Term Memory network (LSTM) and is trained on thousands of hydrologically diverse basins from all around the world. To forecast river discharge for any given river on Earth, the model uses time series data from various meteorological forcing products (IMERG, CPC, ERA5-Land, ECMWF&#8217;s IFS), as well as static catchment characteristics.</p> <p>This new model allows us to scale to new regions more quickly. As of January 2023, we now provide operational flood warnings to hundreds of sites across 48 countries worldwide, with hundreds of more sites being rolled out in the coming months. Besides our previous channels of communicating flood warnings (e.g. Google Search, Google Maps, Google Alerts, and direct communications with NGOs and governments), we also released FloodHub (g.co/floodhub), a new interactive portal that allows for easy access to all operational forecasts.</p> <p>Here, we present more information about the modeling methodology shifts, the challenges we faced and finally showcase the latest advancements made.</p> <p>&#160;</p> <p>References:</p> <p>Klotz, D., et al. (2022). Uncertainty estimation with deep learning for rainfall&#8211;runoff modeling. <em>Hydrology and Earth System Sciences</em>, <em>26</em>(6), 1673-1693.</p> <p>Kratzert, F., et al. (2019a). Towards learning universal, regional, and local hydrological behaviors via machine learning applied to large-sample datasets. <em>Hydrology and Earth System Sciences</em>, <em>23</em>(12), 5089-5110.</p> <p>Kratzert, F., et al. (2019b). Toward improved predictions in ungauged basins: Exploiting the power of machine learning. <em>Water Resources Research</em>, <em>55</em>(12), 11344-11354.</p> <p>Nevo, S., et al., (2021). Flood forecasting with machine learning models in an operational framework. <em>Hydrology and Earth System Sciences Discussions</em>, pp.1-31.</p>
<p>Deep learning has become the de facto standard for streamflow simulation. While there are examples of deep learning based streamflow <em>forecast</em> models (e.g., 1-5), the majority of the development and research has been done with <em>hindcast</em> models. The primary challenge in using deep learning models for forecasting (e.g., flood forecasting) is that the meteorological input data are drawn from different distributions in hindcast vs. forecast. The (relatively small) amount of research that has been done on deep learning streamflow forecasting has largely used an encoder-decoder approach to account for forecast distribution shifts. This is, for example, what Google&#8217;s operational flood forecasting model uses [4].&#160;</p> <p>In this work we show that the encoder-decoder approach results in artifacts in forecast trajectories that are not detectable with standard hydrological metrics, but which can cause forecasts to have incorrect trends (e.g., rising when they should be falling and vice-versa).&#160; We solve this problem using regularized embeddings, which remove forecast artifacts without harming overall accuracy.&#160;</p> <p>Perhaps more importantly, input embeddings allow for training models on spatially and/or temporally incomplete meteorological inputs, meaning that a single model can be trained using input data that does not exist everywhere or does not exist during the entire training or forecast period. This allows models to learn from a significantly larger training data set, which is important for high-accuracy predictions. It also allows large (e.g., global) models to learn from local weather data. We demonstrate how and why this is critical for state-of-the-art global-scale streamflow forecasting.&#160;</p> <p>&#160;</p> <ul> <li aria-level="1">Franken, Tim, et al. <em>An operational framework for data driven low flow forecasts in Flanders</em>. No. EGU22-6191. Copernicus Meetings, 2022.</li> <li aria-level="1">Kao, I-Feng, et al. "Exploring a Long Short-Term Memory based Encoder-Decoder framework for multi-step-ahead flood forecasting." <em>Journal of Hydrology</em> 583 (2020): 124631.</li> <li aria-level="1">Liu, Darong, et al. "Streamflow prediction using deep learning neural network: case study of Yangtze River." <em>IEEE access</em> 8 (2020): 90069-90086.</li> <li aria-level="1">Nevo, Sella, et al. "Flood forecasting with machine learning models in an operational framework." <em>Hydrology and Earth System Sciences</em> 26.15 (2022): 4013-4032.</li> <li aria-level="1">Girihagama, Lakshika, et al. "Streamflow modelling and forecasting for Canadian watersheds using LSTM networks with attention mechanism." <em>Neural Computing and Applications</em> 34.22 (2022): 19995-20015.</li> </ul> <p>&#160;</p>
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