Oasis 2: improved online analysis of small RNA-seq data

Abstract

Background: Small RNA molecules play important roles in many biological processes and their dysregulation or dysfunction can cause disease. The current method of choice for genome-wide sRNA expression profiling is deep sequencing.

Results: Here we present Oasis 2, which is a new main release of the Oasis web application for the detection, differential expression, and classification of small RNAs in deep sequencing data. Compared to its predecessor Oasis, Oasis 2 features a novel and speed-optimized sRNA detection module that supports the identification of small RNAs in any organism with higher accuracy. Next to the improved detection of small RNAs in a target organism, the software now also recognizes potential cross-species miRNAs and viral and bacterial sRNAs in infected samples. In addition, novel miRNAs can now be queried and visualized interactively, providing essential information for over 700 high-quality miRNA predictions across 14 organisms. Robust biomarker signatures can now be obtained using the novel enhanced classification module.

Conclusions: Oasis 2 enables biologists and medical researchers to rapidly analyze and query small RNA deep sequencing data with improved precision, recall, and speed, in an interactive and user-friendly environment.

Availability and implementation: Oasis 2 is implemented in Java, J2EE, mysql, Python, R, PHP and JavaScript. It is freely available at https://oasis.dzne.de.

Fig. 1

Detection of sRNAs in Oasis 2: The web application allows for the upload of raw or compressed FASTQ files to Oasis 2’s sRNA detection module. After pre-processing (adapter/barcode trimming and length filtering), reads are first aligned to target organism (TO) transcripts that are stored in Oasis-DB (Step 1), including known miRNAs, piRNAs, snoRNAs, snRNAs, rRNAs, and high-stringency predicted miRNAs and their families. Unmapped reads of Step1 are subsequently aligned to the TO’s genome (Step 2) to predict and subsequently store novel miRNAs in Oasis-DB. Unmapped reads from step 2 are mapped to bacterial, archaeal, and viral genomes using Kraken (Step 3) to detect potential pathogenic infections or contaminations. Finally, reads that could not be aligned in steps 1–3 are aligned to all non-target organism (NTO) miRNAs in miRBase (Step 4) to detect potentially orthologous or cross-species miRNAs. In case the user’s data does not correspond to one of the 14 supplied organisms, Oasis 2 aligns the reads only to NTO miRNAs (Step 4), supporting the detection of miRNA expression in any organism

Fig. 2

Pathogen detection performance: To assess the performance of ‘pathogen detection module’, sRNA datasets with defined viral or bacterial infections were analyzed and the F-score (a), recall (b), and precision (c) of the pathogen predictions were measured for the top 10 reported organisms. Overall, the prediction of bacterial (M. abscessus) and viral (HIV, HHV4, HHV5, Gallid_herpesvirus_2) infections resulted in high F-scores, recall, and precision, especially when the top 5 predicted pathogen species are reported. In consequence, Oasis 2 currently reports the top five predicted pathogen species based on their read counts

Fig. 3

Oasis 2′ (QC) outlier detection: To assess the QC of Oasis 2 and its biological relevance, sRNA Psoriasis data (demo dataset) was analyzed. PCA sample distances of psoriasis (green) and control (blue) is shown. (a) PCA of psoriasis and control samples showing a potentially mis-annotated (SRR330866_PP) and an outlier sample (SRR330860_PP). (b) PCA of psoriasis and control samples without misclassified/outlier samples. Removal of these two samples increased the number of significantly (adjusted p-value < 0.1) DE miRNAs from 195 to 256 cases and increased the AUC from 0.9 to 1 in the classification module, providing strong evidence for the utility of Oasis 2’ QC plots