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Abstract Background Gaining biological insights into molecular responses to treatments or diseases from omics data can be accomplished by gene set or pathway enrichment methods. A plethora of different tools and algorithms have been developed so far. Among those, the gene set enrichment analysis (GSEA) proved to control both type I and II errors well. In recent years the call for a combined analysis of multiple omics layers became prominent, giving rise to a few multi-omics enrichment tools. Each of these has its own drawbacks and restrictions regarding its universal application. Results Here, we present the multiGSEA package aiding to calculate a combined GSEA-based pathway enrichment on multiple omics layers. The package queries 8 different pathway databases and relies on the robust GSEA algorithm for a single-omics enrichment analysis. In a final step, those scores will be combined to create a robust composite multi-omics pathway enrichment measure. multiGSEA supports 11 different organisms and includes a comprehensive mapping of transcripts, proteins, and metabolite IDs. Conclusions With multiGSEA we introduce a highly versatile tool for multi-omics pathway integration that minimizes previous restrictions in terms of omics layer selection, pathway database availability, organism selection and the mapping of omics feature identifiers. multiGSEA is publicly available under the GPL-3 license at https://github.com/yigbt/multiGSEA and at bioconductor: https://bioconductor.org/packages/multiGSEA .

Abstract 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.

Using retinal organoid systems, organ-like 3D tissues, relies implicitly on their robustness. However, essential key parameters, particularly retinal growth and longer-term culture, are still insufficiently defined. Here, we hypothesize that a previously optimized protocol for high yield of evenly-sized mouse retinal organoids with low variability facilitates assessment of such parameters. We demonstrate that these organoids reliably complete retinogenesis, and can be maintained at least up to 60 days in culture. During this time, the organoids continue to mature on a molecular and (ultra)structural level: They develop photoreceptor outer segments and synapses, transiently maintain its cell composition for about 5–10 days after completing retinogenesis, and subsequently develop pathologic changes – mainly of the inner but also outer retina and reactive gliosis. To test whether this organoid system provides experimental access to the retina during and upon completion of development, we defined and stimulated organoid growth by activating sonic hedgehog signaling, which in patients and mice in vivo with a congenital defect leads to enlarged eyes. Here, a sonic hedgehog signaling activator increased retinal epithelia length in the organoid system when applied during but not after completion of development. This experimentally supports organoid maturation, stability, and experimental reproducibility in this organoid system, and provides a potential enlarged retina pathology model, as well as a protocol for producing larger organoids. Together, our study advances the understanding of retinal growth, maturation, and maintenance, and further optimizes the organoid system for future utilization.

Bacteria lose or gain genetic material and through selection, new variants become fixed in the population. Here we provide the first, genome-wide example of a single bacterial strain's evolution in different deliberately colonized patients and the surprising insight that hosts appear to personalize their microflora. By first obtaining the complete genome sequence of the prototype asymptomatic bacteriuria strain E. coli 83972 and then resequencing its descendants after therapeutic bladder colonization of different patients, we identified 34 mutations, which affected metabolic and virulence-related genes. Further transcriptome and proteome analysis proved that these genome changes altered bacterial gene expression resulting in unique adaptation patterns in each patient. Our results provide evidence that, in addition to stochastic events, adaptive bacterial evolution is driven by individual host environments. Ongoing loss of gene function supports the hypothesis that evolution towards commensalism rather than virulence is favored during asymptomatic bladder colonization.

With few exceptions, current methods for short read mapping make use of simple seed heuristics to speed up the search. Most of the underlying matching models neglect the necessity to allow not only mismatches, but also insertions and deletions. Current evaluations indicate, however, that very different error models apply to the novel high-throughput sequencing methods. While the most frequent error-type in Illumina reads are mismatches, reads produced by 454's GS FLX predominantly contain insertions and deletions (indels). Even though 454 sequencers are able to produce longer reads, the method is frequently applied to small RNA (miRNA and siRNA) sequencing. Fast and accurate matching in particular of short reads with diverse errors is therefore a pressing practical problem. We introduce a matching model for short reads that can, besides mismatches, also cope with indels. It addresses different error models. For example, it can handle the problem of leading and trailing contaminations caused by primers and poly-A tails in transcriptomics or the length-dependent increase of error rates. In these contexts, it thus simplifies the tedious and error-prone trimming step. For efficient searches, our method utilizes index structures in the form of enhanced suffix arrays. In a comparison with current methods for short read mapping, the presented approach shows significantly increased performance not only for 454 reads, but also for Illumina reads. Our approach is implemented in the software segemehl available at http://www.bioinf.uni-leipzig.de/Software/segemehl/.