Quantitative and systems biology approaches benefit from the unprecedented depth of next-generation sequencing. A typical experiment yields millions of short reads, which oftentimes carry particular sequence tags. These tags may be: (a) specific to the sequencing platform and library construction method (e.g., adapter sequences); (b) have been introduced by experimental design (e.g., sample barcodes); or (c) constitute some biological signal (e.g., splice leader sequences in nematodes). Our software FLEXBAR enables accurate recognition, sorting and trimming of sequence tags with maximal flexibility, based on exact overlap sequence alignment. The software supports data formats from all current sequencing platforms, including color-space reads. FLEXBAR maintains read pairings and processes separate barcode reads on demand. Our software facilitates the fine-grained adjustment of sequence tag detection parameters and search regions. FLEXBAR is a multi-threaded software and combines speed with precision. Even complex read processing scenarios might be executed with a single command line call. We demonstrate the utility of the software in terms of read mapping applications, library demultiplexing and splice leader detection. FLEXBAR and additional information is available for academic use from the website: http://sourceforge.net/projects/flexbar/.

In animals, RNA binding proteins (RBPs) and microRNAs (miRNAs) post-transcriptionally regulate the expression of virtually all genes by binding to RNA. Recent advances in experimental and computational methods facilitate transcriptome-wide mapping of these interactions. It is thought that the combinatorial action of RBPs and miRNAs on target mRNAs form a post-transcriptional regulatory code. We provide a database that supports the quest for deciphering this regulatory code. Within doRiNA, we are systematically curating, storing and integrating binding site data for RBPs and miRNAs. Users are free to take a target (mRNA) or regulator (RBP and/or miRNA) centric view on the data. We have implemented a database framework with short query response times for complex searches (e.g. asking for all targets of a particular combination of regulators). All search results can be browsed, inspected and analyzed in conjunction with a huge selection of other genome-wide data, because our database is directly linked to a local copy of the UCSC genome browser. At the time of writing, doRiNA encompasses RBP data for the human, mouse and worm genomes. For computational miRNA target site predictions, we provide an update of PicTar predictions.

N6-methyladenosine (m⁶A) is the most abundant internal RNA modification in eukaryotic mRNAs and influences many aspects of RNA processing. miCLIP (m⁶A individual-nucleotide resolution UV crosslinking and immunoprecipitation) is an antibody-based approach to map m⁶A sites with single-nucleotide resolution. However, due to broad antibody reactivity, reliable identification of m⁶A sites from miCLIP data remains challenging. Here, we present miCLIP2 in combination with machine learning to significantly improve m⁶A detection. The optimized miCLIP2 results in high-complexity libraries from less input material. Importantly, we established a robust computational pipeline to tackle the inherent issue of false positives in antibody-based m⁶A detection. The analyses were calibrated with Mettl3 knockout cells to learn the characteristics of m⁶A deposition, including m⁶A sites outside of DRACH motifs. To make our results universally applicable, we trained a machine learning model, m6Aboost, based on the experimental and RNA sequence features. Importantly, m6Aboost allows prediction of genuine m⁶A sites in miCLIP2 data without filtering for DRACH motifs or the need for Mettl3 depletion. Using m6Aboost, we identify thousands of high-confidence m⁶A sites in different murine and human cell lines, which provide a rich resource for future analysis. Collectively, our combined experimental and computational methodology greatly improves m⁶A identification.

Freshwater planaria are a very attractive model system for stem cell biology, tissue homeostasis, and regeneration. The genome of the planarian Schmidtea mediterranea has recently been sequenced and is estimated to contain >20,000 protein-encoding genes. However, the characterization of its transcriptome is far from complete. Furthermore, not a single proteome of the entire phylum has been assayed on a genome-wide level. We devised an efficient sequencing strategy that allowed us to de novo assemble a major fraction of the S. mediterranea transcriptome. We then used independent assays and massive shotgun proteomics to validate the authenticity of transcripts. In total, our de novo assembly yielded 18,619 candidate transcripts with a mean length of 1118 nt after filtering. A total of 17,564 candidate transcripts could be mapped to 15,284 distinct loci on the current genome reference sequence. RACE confirmed complete or almost complete 5′ and 3′ ends for 22/24 transcripts. The frequencies of frame shifts, fusion, and fission events in the assembled transcripts were computationally estimated to be 4.2%–13%, 0%–3.7%, and 2.6%, respectively. Our shotgun proteomics produced 16,135 distinct peptides that validated 4200 transcripts (FDR ≤1%). The catalog of transcripts assembled in this study, together with the identified peptides, dramatically expands and refines planarian gene annotation, demonstrated by validation of several previously unknown transcripts with stem cell-dependent expression patterns. In addition, our robust transcriptome characterization pipeline could be applied to other organisms without genome assembly. All of our data, including homology annotation, are freely available at SmedGD, the S. mediterranea genome database.

RNA editing is a co-transcriptional modification that increases the molecular diversity, alters secondary structure and protein coding sequences by changing the sequence of transcripts. The most common RNA editing modification is the single base substitution (A→I) that is catalyzed by the members of the Adenosine deaminases that act on RNA (ADAR) family. Typically, editing sites are identified as RNA-DNA-differences (RDDs) in a comparison of genome and transcriptome data from next-generation sequencing experiments. However, a method for robust detection of site-specific editing events from replicate RNA-seq data has not been published so far. Even more surprising, condition-specific editing events, which would show up as differences in RNA-RNA comparisons (RRDs) and depend on particular cellular states, are rarely discussed in the literature. We present JACUSA, a versatile one-stop solution to detect single nucleotide variant positions from comparing RNA-DNA and/or RNA-RNA sequencing samples. The performance of JACUSA has been carefully evaluated and compared to other variant callers in an in silico benchmark. JACUSA outperforms other algorithms in terms of the F measure, which combines precision and recall, in all benchmark scenarios. This performance margin is highest for the RNA-RNA comparison scenario. We further validated JACUSA’s performance by testing its ability to detect A→I events using sequencing data from a human cell culture experiment and publicly available RNA-seq data from Drosophila melanogaster heads. To this end, we performed whole genome and RNA sequencing of HEK-293 cells on samples with lowered activity of candidate RNA editing enzymes. JACUSA has a higher recall and comparable precision for detecting true editing sites in RDD comparisons of HEK-293 data. Intriguingly, JACUSA captures most A→I events from RRD comparisons of RNA sequencing data derived from Drosophila and HEK-293 data sets. Our software JACUSA detects single nucleotide variants by comparing data from next-generation sequencing experiments (RNA-DNA or RNA-RNA). In practice, JACUSA shows higher recall and comparable precision in detecting A→I sites from RNA-DNA comparisons, while showing higher precision and recall in RNA-RNA comparisons.