BACKGROUND: The FASTA file format, used to store polymeric sequence data, has become a bioinformatics file standard used for decades. The relatively large files require additional files, beyond the scope of the original format, to identify sequences and to provide random access. Multiple compressors have been developed to archive FASTA files back and forth, but these lack direct access to targeted content or metadata of the archive. Moreover, these solutions are not directly backwards compatible to FASTA files, resulting in limited software integration. RESULTS: We designed a linux based toolkit that virtualises the content of DNA, RNA and protein FASTA archives into the filesystem by using filesystem in userspace. This guarantees in-sync virtualised metadata files and offers fast random-access decompression using bit encodings plus Zstandard (zstd). The toolkit, FASTAFS, can track all its system-wide running instances, allows file integrity verification and can provide, instantly, scriptable access to sequence files and is easy to use and deploy. The file compression ratios were comparable but not superior to other state of the art archival tools, despite the innovative random access feature implemented in FASTAFS. CONCLUSIONS: FASTAFS is a user-friendly and easy to deploy backwards compatible generic purpose solution to store and access compressed FASTA files, since it offers file system access to FASTA files as well as in-sync metadata files through file virtualisation. Using virtual filesystems as in-between layer offers format conversion without the need to rewrite code into different programming languages while preserving compatibility.

UNLABELLED: A new generation of tools that identify fusion genes in RNA-seq data is limited in either sensitivity and or specificity. To allow further downstream analysis and to estimate performance, predicted fusion genes from different tools have to be compared. However, the transcriptomic context complicates genomic location-based matching. FusionMatcher (FuMa) is a program that reports identical fusion genes based on gene-name annotations. FuMa automatically compares and summarizes all combinations of two or more datasets in a single run, without additional programming necessary. FuMa uses one gene annotation, avoiding mismatches caused by tool-specific gene annotations. FuMa matches 10% more fusion genes compared with exact gene matching due to overlapping genes and accepts intermediate output files that allow a stepwise analysis of corresponding tools. AVAILABILITY AND IMPLEMENTATION: The code is available at: https://github.com/ErasmusMC-Bioinformatics/fuma and available for Galaxy in the tool sheds and directly accessible at https://bioinf-galaxian.erasmusmc.nl/galaxy/ CONTACT: [email protected] or [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: Recent discoveries show that most types of small non-coding RNAs (sncRNAs) such as miRNAs, snoRNAs and tRNAs get further processed into putatively active smaller RNA species. Their roles, genetic profiles and underlying processing mechanisms are only partially understood. To find their quantities and characteristics, a proper annotation is essential. Here, we present FlaiMapper, a method that extracts and annotates the locations of sncRNA-derived RNAs (sncdRNAs). These sncdRNAs are often detected in sequencing data and observed as fragments of their precursor sncRNA. Using small RNA-seq read alignments, FlaiMapper is able to annotate fragments primarily by peak detection on the start and end position densities followed by filtering and a reconstruction process. RESULTS: To assess performance of FlaiMapper, we used independent publicly available small RNA-seq data. We were able to detect fragments representing putative sncdRNAs from nearly all types of sncRNA, including 97.8% of the annotated miRNAs in miRBase that have supporting reads. Comparison of FlaiMapper-predicted boundaries of miRNAs with miRBase entries demonstrated that 89% of the start and 54% of the end positions are identical. Additional benchmarking showed that FlaiMapper is superior in performance compared with existing software. Further analysis indicated a variety of characteristics in the fragments, including sequence motifs and relations with RNA interacting factors. These characteristics set a good basis for further research on sncdRNAs. AVAILABILITY AND IMPLEMENTATION: The platform independent GPL licensed Python 2.7 code is available at: https://github.com/yhoogstrate/flaimapper.