SUMMARY: Sophisticated approaches for the in silico prediction of toxicity are required to support the risk assessment of chemicals. The number of chemicals on the global chemical market and the speed of chemical innovation stand in massive contrast to the capacity for regularizing chemical use. We recently proved our ready-to-use application deepFPlearn as a suitable approach for this task. Here, we present its extension deepFPlearn+ incorporating (i) a graph neural network to feed our AI with a more sophisticated molecular structure representation and (ii) alternative train-test splitting strategies that involve scaffold structures and the molecular weights of chemicals. We show that the GNNs outperform the previous model substantially and that our models can generalize on unseen data even with a more robust and challenging test set. Therefore, we highly recommend the application of deepFPlearn+ on the chemical inventory to prioritize chemicals for experimental testing or any chemical subset of interest in monitoring studies. AVAILABILITY AND IMPLEMENTATION: The software is compatible with python 3.6 or higher, and the source code can be found on our GitHub repository: https://github.com/yigbt/deepFPlearn. The data underlying this article are available in Zenodo, and can be accessed with the link below: https://zenodo.org/record/8146252. Detailed installation guides via Docker, Singularity, and Conda are provided within the repository for operability across all operating systems.

Many chemicals are present in our environment, and all living species are exposed to them. However, numerous chemicals pose risks, such as developing severe diseases, if they occur at the wrong time in the wrong place. For the majority of the chemicals, these risks are not known. Chemical risk assessment and subsequent regulation of use require efficient and systematic strategies. Lab-based methods-even if high throughput-are too slow to keep up with the pace of chemical innovation. Existing computational approaches are designed for specific chemical classes or sub-problems but not usable on a large scale. Further, the application range of these approaches is limited by the low amount of available labeled training data. We present the ready-to-use and stand-alone program deepFPlearn that predicts the association between chemical structures and effects on the gene/pathway level using a combined deep learning approach. deepFPlearn uses a deep autoencoder for feature reduction before training a deep feed-forward neural network to predict the target association. We received good prediction qualities and showed that our feature compression preserves relevant chemical structural information. Using a vast chemical inventory (unlabeled data) as input for the autoencoder did not reduce our prediction quality but allowed capturing a much more comprehensive range of chemical structures. We predict meaningful-experimentally verified-associations of chemicals and effects on unseen data. deepFPlearn classifies hundreds of thousands of chemicals in seconds. We provide deepFPlearn as an open-source and flexible tool that can be easily retrained and customized to different application settings at https://github.com/yigbt/deepFPlearn.

BACKGROUND: A lack of reproducibility has been repeatedly criticized in computational research. High throughput sequencing (HTS) data analysis is a complex multi-step process. For most of the steps a range of bioinformatic tools is available and for most tools manifold parameters need to be set. Due to this complexity, HTS data analysis is particularly prone to reproducibility and consistency issues. We have defined four criteria that in our opinion ensure a minimal degree of reproducible research for HTS data analysis. A series of workflow management systems is available for assisting complex multi-step data analyses. However, to the best of our knowledge, none of the currently available work flow management systems satisfies all four criteria for reproducible HTS analysis. RESULTS: Here we present uap, a workflow management system dedicated to robust, consistent, and reproducible HTS data analysis. uap is optimized for the application to omics data, but can be easily extended to other complex analyses. It is available under the GNU GPL v3 license at https://github.com/yigbt/uap. CONCLUSIONS: uap is a freely available tool that enables researchers to easily adhere to reproducible research principles for HTS data analyses.

BACKGROUND: snoReport uses RNA secondary structure prediction combined with machine learning as the basis to identify the two main classes of small nucleolar RNAs, the box H/ACA snoRNAs and the box C/D snoRNAs. Here, we present snoReport 2.0, which substantially improves and extends in the original method by: extracting new features for both box C/D and H/ACA box snoRNAs; developing a more sophisticated technique in the SVM training phase with recent data from vertebrate organisms and a careful choice of the SVM parameters C and γ; and using updated versions of tools and databases used for the construction of the original version of snoReport. To validate the new version and to demonstrate its improved performance, we tested snoReport 2.0 in different organisms. RESULTS: Results of the training and test phases of boxes H/ACA and C/D snoRNAs, in both versions of snoReport, are discussed. Validation on real data was performed to evaluate the predictions of snoReport 2.0. Our program was applied to a set of previously annotated sequences, some of them experimentally confirmed, of humans, nematodes, drosophilids, platypus, chickens and leishmania. We significantly improved the predictions for vertebrates, since the training phase used information of these organisms, but H/ACA box snoRNAs identification was improved for the other ones. CONCLUSION: We presented snoReport 2.0, to predict H/ACA box and C/D box snoRNAs, an efficient method to find true positives and avoid false positives in vertebrate organisms. H/ACA box snoRNA classifier showed an F-score of 93 % (an improvement of 10 % regarding the previous version), while C/D box snoRNA classifier, an F-Score of 94 % (improvement of 14 %). Besides, both classifiers exhibited performance measures above 90 %. These results show that snoReport 2.0 avoid false positives and false negatives, allowing to predict snoRNAs with high quality. In the validation phase, snoReport 2.0 predicted 67.43 % of vertebrate organisms for both classes. For Nematodes and Drosophilids, 69 % and 76.67 %, for H/ACA box snoRNAs were predicted, respectively, showing that snoReport 2.0 is good to identify snoRNAs in vertebrates and also H/ACA box snoRNAs in invertebrates organisms.

MOTIVATION: Although small nucleolar RNAs form an important class of non-coding RNAs, no comprehensive annotation efforts have been undertaken, presumably because the task is complicated by both the large number of distinct small nucleolar RNA families and their relatively rapid pace of sequence evolution. RESULTS: With snoStrip we present an automatic annotation pipeline developed specifically for comparative genomics of small nucleolar RNAs. It makes use of sequence conservation, canonical box motifs as well as secondary structure and predicts putative targets. AVAILABILITY AND IMPLEMENTATION: The snoStrip web service and the download version is available at http://snostrip.bioinf.uni-leipzig.de/

MOTIVATION: Small nucleolar RNAs are an abundant class of non-coding RNAs that guide chemical modifications of rRNAs, snRNAs and some mRNAs. In the case of many 'orphan' snoRNAs, the targeted nucleotides remain unknown, however. The box H/ACA subclass determines uridine residues that are to be converted into pseudouridines via specific complementary binding in a well-defined secondary structure configuration that is outside the scope of common RNA (co-)folding algorithms. RESULTS: RNAsnoop implements a dynamic programming algorithm that computes thermodynamically optimal H/ACA-RNA interactions in an efficient scanning variant. Complemented by an support vector machine (SVM)-based machine learning approach to distinguish true binding sites from spurious solutions and a system to evaluate comparative information, it presents an efficient and reliable tool for the prediction of H/ACA snoRNA target sites. We apply RNAsnoop to identify the snoRNAs that are responsible for several of the remaining 'orphan' pseudouridine modifications in human rRNAs, and we assign a target to one of the five orphan H/ACA snoRNAs in Drosophila. AVAILABILITY: The C source code of RNAsnoop is freely available at http://www.tbi.univie.ac.at/ -htafer/RNAsnoop

Abstract MicroRNA-offset-RNAs (moRNAs) were recently detected as highly abundant class of small RNAs in a basal chordate. Using short read sequencing data, we show here that moRNAs are also produced from human microRNA precursors, albeit at quite low expression levels. The expression levels of moRNAs are unrelated to those of the associated microRNAs. Surprisingly, microRNA precursors that also show moRNAs are typically evolutionarily old, comprising more than half of the microRNA families that were present in early Bilateria, while evidence for moRNAs was found only for a relative small fraction of microRNA families of recent origin. Contact: [email protected] Supplementary information: Supplementary data are available at Bioinformatics online and in machine-readable form at http://www.bioinf.uni-leipzig.de/Publications/SUPPLEMENTS/09-015/

UNLABELLED: Unlike tRNAs and microRNAs, both classes of snoRNAs, which direct two distinct types of chemical modifications of uracil residues, have proved to be surprisingly difficult to find in genomic sequences. Most computational approaches so far have explicitly used the fact that snoRNAs predominantly target ribosomal RNAs and spliceosomal RNAs. The target is specified by a short stretch of sequence complementarity between the snoRNA and its target. This sequence complementarity to known targets crucially contributes to sensitivity and specificity of snoRNA gene finding algorithms. The discovery of 'orphan' snoRNAs, which either have no known target, or which target ordinary protein-coding mRNAs, however, begs the question whether this class of 'housekeeping' non-coding RNAs is much more widespread and might have a diverse set of regulatory functions. In order to approach this question, we present here a combination of RNA secondary structure prediction and machine learning that is designed to recognize the two major classes of snoRNAs, box C/D and box H/ACA snoRNAs, among ncRNA candidate sequences. The snoReport approach deliberately avoids any usage of target information. We find that the combination of the conserved sequence boxes and secondary structure constraints as a pre-filter with SVM classifiers based on a small set of structural descriptors are sufficient for a reliable identification of snoRNAs. Tests of snoReport on data from several recent experimental surveys show that the approach is feasible; the application to a dataset from a large-scale comparative genomics survey for ncRNAs suggests that there are likely hundreds of previously undescribed 'orphan' snoRNAs still hidden in the human genome. AVAILABILITY: The snoReport software is implemented in ANSI C. The source code is available under the GNU Public License at http://www.bioinf.uni-leipzig.de/Software/snoReport.