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.

UNLABELLED: We present iFUSE (integrated fusion gene explorer), an online visualization tool that provides a fast and informative view of structural variation data and prioritizes those breaks likely representing fusion genes. This application uses calculated break points to determine fusion genes based on the latest annotation for genomic sequence information, and where relevant the structural variation (SV) events are annotated with predicted RNA and protein sequences. iFUSE takes as input a Complete Genomics (CG) junction file, a FusionMap fusion detection report file or a file already analysed and annotated by the iFUSE application on a previous occasion. RESULTS: We demonstrate the use of iFUSE with case studies from tumour-normal SV detection derived from Complete Genomics whole-genome sequencing results. AVAILABILITY: iFUSE is available as a web service at http://ifuse.erasmusmc.nl.

BACKGROUND: High-throughput experiments, such as with DNA microarrays, typically result in hundreds of genes potentially relevant to the process under study, rendering the interpretation of these experiments problematic. Here, we propose and evaluate an approach to find functional associations between large numbers of genes and other biomedical concepts from free-text literature. For each gene, a profile of related concepts is constructed that summarizes the context in which the gene is mentioned in literature. We assign a weight to each concept in the profile based on a likelihood ratio measure. Gene concept profiles can then be clustered to find related genes and other concepts. RESULTS: The experimental validation was done in two steps. We first applied our method on a controlled test set. After this proved to be successful the datasets from two DNA microarray experiments were analyzed in the same way and the results were evaluated by domain experts. The first dataset was a gene-expression profile that characterizes the cancer cells of a group of acute myeloid leukemia patients. For this group of patients the biological background of the cancer cells is largely unknown. Using our methodology we found an association of these cells to monocytes, which agreed with other experimental evidence. The second data set consisted of differentially expressed genes following androgen receptor stimulation in a prostate cancer cell line. Based on the analysis we put forward a hypothesis about the biological processes induced in these studied cells: secretory lysosomes are involved in the production of prostatic fluid and their development and/or secretion are androgen-regulated processes. CONCLUSION: Our method can be used to analyze DNA microarray datasets based on information explicitly and implicitly available in the literature. We provide a publicly available tool, dubbed Anni, for this purpose.

MOTIVATION: The advent of high-throughput experiments in molecular biology creates a need for methods to efficiently extract and use information for large numbers of genes. Recently, the associative concept space (ACS) has been developed for the representation of information extracted from biomedical literature. The ACS is a Euclidean space in which thesaurus concepts are positioned and the distances between concepts indicates their relatedness. The ACS uses co-occurrence of concepts as a source of information. In this paper we evaluate how well the system can retrieve functionally related genes and we compare its performance with a simple gene co-occurrence method. RESULTS: To assess the performance of the ACS we composed a test set of five groups of functionally related genes. With the ACS good scores were obtained for four of the five groups. When compared to the gene co-occurrence method, the ACS is capable of revealing more functional biological relations and can achieve results with less literature available per gene. Hierarchical clustering was performed on the ACS output, as a potential aid to users, and was found to provide useful clusters. Our results suggest that the algorithm can be of value for researchers studying large numbers of genes. AVAILABILITY: The ACS program is available upon request from the authors.

BACKGROUND: High throughput microarray analyses result in many differentially expressed genes that are potentially responsible for the biological process of interest. In order to identify biological similarities between genes, publications from MEDLINE were identified in which pairs of gene names and combinations of gene name with specific keywords were co-mentioned. RESULTS: MEDLINE search strings for 15,621 known genes and 3,731 keywords were generated and validated. PubMed IDs were retrieved from MEDLINE and relative probability of co-occurrences of all gene-gene and gene-keyword pairs determined. To assess gene clustering according to literature co-publication, 150 genes consisting of 8 sets with known connections (same pathway, same protein complex, or same cellular localization, etc.) were run through the program. Receiver operator characteristics (ROC) analyses showed that most gene sets were clustered much better than expected by random chance. To test grouping of genes from real microarray data, 221 differentially expressed genes from a microarray experiment were analyzed with CoPub Mapper, which resulted in several relevant clusters of genes with biological process and disease keywords. In addition, all genes versus keywords were hierarchical clustered to reveal a complete grouping of published genes based on co-occurrence. CONCLUSION: The CoPub Mapper program allows for quick and versatile querying of co-published genes and keywords and can be successfully used to cluster predefined groups of genes and microarray data.

BACKGROUND: SRS (Sequence Retrieval System) has proven to be a valuable platform for storing, linking, and querying biological databases. Due to the availability of a broad range of different scientific databases in SRS, it has become a useful platform to incorporate and mine microarray data to facilitate the analyses of biological questions and non-hypothesis driven quests. Here we report various solutions and tools for integrating and mining annotated expression data in SRS. RESULTS: We devised an Auto-Upload Tool by which microarray data can be automatically imported into SRS. The dataset can be linked to other databases and user access can be set. The linkage comprehensiveness of microarray platforms to other platforms and biological databases was examined in a network of scientific databases. The stored microarray data can also be made accessible to external programs for further processing. For example, we built an interface to a program called Venn Mapper, which collects its microarray data from SRS, processes the data by creating Venn diagrams, and saves the data for interpretation. CONCLUSION: SRS is a useful database system to store, link and query various scientific datasets, including microarray data. The user-friendly Auto-Upload Tool makes SRS accessible to biologists for linking and mining user-owned databases.

MOTIVATION: To evaluate microarray data, clustering is widely used to group biological samples or genes. However, problems arise when comparing heterologous databases. As the clustering algorithm searches for similarities between experiments, it will most likely first separate the data sets, masking relationships that exist between samples from different databases. RESULTS: We developed a program, Venn Mapper, to calculate the statistical significance of the number of co-occurring differentially expressed genes in any of the two experiments. For proof of principle, we analysed a heterologous data set of 170 microarrays including breast and prostate cancer microarray analyses. Significant overlap was found in an unsupervised analysis between metastasized prostate cancer and metastasized breast cancer and BRCA mutated breast cancer. A comparison between single microarray data and the averaged breast and prostate data sets was also evaluated. This analysis suggests that genes expressed higher in stromal cells are also implicated in metastatic prostate cancer and BRCA mutated breast cancer. The Venn Mapper program identifies overlaps between samples from heterologous data sets and directly extracts the genes responsible for the overlap. From this information novel biological hypotheses may be addressed. AVAILABILITY: Venn Mapper is freely available on http://www.erasmusmc.nl/gatcplatform. SUPPLEMENTARY INFORMATION: http://www.erasmusmc.nl/gatcplatform/vennmapper.html.