Motivation: Next generation sequencing (NGS) has provided researchers with a powerful tool to characterize metagenomic and clinical samples in research and diagnostic settings. NGS allows an open view into samples useful for pathogen detection in an unbiased fashion and without prior hypothesis about possible causative agents. However, NGS datasets for pathogen detection come with different obstacles, such as a very unfavorable ratio of pathogen to host reads. Alongside often appearing false positives and irrelevant organisms, such as contaminants, tools are often challenged by samples with low pathogen loads and might not report organisms present below a certain threshold. Furthermore, some metagenomic profiling tools are only focused on one particular set of pathogens, for example bacteria. Results: We present PAIPline, a bioinformatics pipeline specifically designed to address problems associated with detecting pathogens in diagnostic samples. PAIPline particularly focuses on userfriendliness and encapsulates all necessary steps from preprocessing to resolution of ambiguous reads and filtering up to visualization in a single tool. In contrast to existing tools, PAIPline is more specific while maintaining sensitivity. This is shown in a comparative evaluation where PAIPline was benchmarked along other well-known metagenomic profiling tools on previously published well-characterized datasets. Additionally, as part of an international cooperation project, PAIPline was applied to an outbreak sample of hemorrhagic fevers of then unknown etiology. The presented results show that PAIPline can serve as a robust, reliable, user-friendly, adaptable and generalizable stand-alone software for diagnostics from NGS samples and as a stepping stone for further downstream analyses. Availability and implementation: PAIPline is freely available under https://gitlab.com/rki_bioinformatics/paipline.

Motivation: In next-generation sequencing, re-identification of individuals and other privacy-breaching strategies can be applied even for anonymized data. This also holds true for applications in which human DNA is acquired as a by-product, e.g. for viral or metagenomic samples from a human host. Conventional data protection strategies including cryptography and post-hoc filtering are only appropriate for the final and processed sequencing data. This can result in an insufficient level of data protection and a considerable time delay in the further analysis workflow. Results: We present PriLive, a novel tool for the automated removal of sensitive data while the sequencing machine is running. Thereby, human sequence information can be detected and removed before being completely produced. This facilitates the compliance with strict data protection regulations. The unique characteristic to cause almost no time delay for further analyses is also a clear benefit for applications other than data protection. Especially if the sequencing data are dominated by known background signals, PriLive considerably accelerates consequent analyses by having only fractions of input data. Besides these conceptual advantages, PriLive achieves filtering results at least as accurate as conventional post-hoc filtering tools. Availability and implementation: PriLive is open-source software available at https://gitlab.com/rki_bioinformatics/PriLive. Supplementary information: Supplementary data are available at Bioinformatics online.

Motivation: In metagenomics, Kraken is one of the most widely used tools due to its robustness and speed. Yet, the overall turnaround time of metagenomic analysis is hampered by the sequential paradigm of wet and dry lab. In urgent experiments, it can be crucial to gain a timely insight into a dataset. Results: Here, we present LiveKraken, a real-time read classification tool based on the core algorithm of Kraken. LiveKraken uses streams of raw data from Illumina sequencers to classify reads taxonomically. This way, we are able to produce results identical to those of Kraken the moment the sequencer finishes. We are furthermore able to provide comparable results in early stages of a sequencing run, allowing saving up to a week of sequencing time on an Illumina HiSeq in High Throughput Mode. While the number of classified reads grows over time, false classifications appear in negligible numbers and proportions of identified taxa are only affected to a minor extent. Availability and implementation: LiveKraken is available at https://gitlab.com/rki_bioinformatics/LiveKraken. Supplementary information: Supplementary data are available at Bioinformatics online.

Motivation: Next Generation Sequencing is increasingly used in time critical, clinical applications. While read mapping algorithms have always been optimized for speed, they follow a sequential paradigm and only start after finishing of the sequencing run and conversion of files. Since Illumina machines write intermediate output results, HiLive performs read mapping while still sequencing and thereby drastically reduces crucial overall sample analysis time, e.g. in precision medicine. Methods: We present HiLive as a novel real time read mapper that implements a k-mer based alignment strategy. HiLive continuously reads intermediate BCL files produced by Illumina sequencers and then extends initial k-mer matches by increasingly produced data from the sequencer. Results: We applied HiLive on real human transcriptome data to show that final read alignments are reported within few minutes after the end of a full Illumina HiSeq 1500 run, while already the necessary conversion to FASTQ files as the standard input to current read mapping methods takes roughly five times as long. Further, we show on simulated and real data that HiLive has comparable accuracy to recent read mappers. Availability and Implementation: HiLive and its source code are freely available from https://gitlab.com/SimonHTausch/HiLive . Contact: [email protected]. Supplementary information: Supplementary data are available at Bioinformatics online.