LiveKraken--real-time metagenomic classification of illumina data.

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.

Abundance estimation and differential testing on strain level in metagenomics data.

MOTIVATION: Current metagenomics approaches allow analyzing the composition of microbial communities at high resolution. Important changes to the composition are known to even occur on strain level and to go hand in hand with changes in disease or ecological state. However, specific challenges arise for strain level analysis due to highly similar genome sequences present. Only a limited number of tools approach taxa abundance estimation beyond species level and there is a strong need for dedicated tools for strain resolution and differential abundance testing. METHODS: We present DiTASiC ( fferential axa bundance including milarity orrection) as a novel approach for quantification and differential assessment of individual taxa in metagenomics samples. We introduce a generalized linear model for the resolution of shared read counts which cause a significant bias on strain level. Further, we capture abundance estimation uncertainties, which play a crucial role in differential abundance analysis. A novel statistical framework is built, which integrates the abundance variance and infers abundance distributions for differential testing sensitive to strain level. RESULTS: As a result, we obtain highly accurate abundance estimates down to sub-strain level and enable fine-grained resolution of strain clusters. We demonstrate the relevance of read ambiguity resolution and integration of abundance uncertainties for differential analysis. Accurate detections of even small changes are achieved and false-positives are significantly reduced. Superior performance is shown on latest benchmark sets of various complexities and in comparison to existing methods. AVAILABILITY AND IMPLEMENTATION: DiTASiC code is freely available from https://rki_bioinformatics.gitlab.io/ditasic . CONTACT: renardB@rki.de. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

HiLive: real-time mapping of illumina reads while sequencing.

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: renardB@rki.de. Supplementary information: Supplementary data are available at Bioinformatics online.

AI-based prevention of interval cancers in a national mammography screening program.

PURPOSE: To demonstrate that artificial intelligence (AI) can detect and correctly localise retrospectively visible cancers that were missed and diagnosed as interval cancers (false negative (FN) and minimal signs (MS) interval cancers), and to characterise AI performance on non-visible occult and true interval cancers. METHOD: Prior screening mammograms from N = 2,396 women diagnosed with interval breast cancer between March 2006 and May 2018 in north-western Germany were analysed with an AI system, producing a model score for all studies. All included studies previously underwent independent radiological review at a mammography reference centre to confirm interval cancer classification. Model score distributions were visualised with histograms. We computed the proportion and accompanying 95% confidence intervals (CI) of retrospectively visible and true interval cancers detected and correctly localised by AI at different operating points representing recall rates < 3%. Clinicopathological characteristics of retrospectively visible cancers detected by AI and not were compared using the Chi-squared test and binary logistic regression. RESULTS: Following radiological review, 15.6% of the interval cancer cases were categorised as FN, 19.5% MS, 11.4% occult, and 53.4% true interval cancers. At an operating point of 99.0% specificity, AI could detect and correctly localise 27.5% (95% CI: 23.3-32.3%), and 12.2% (95% CI: 9.5-15.5%) of the FN and MS cases on the prior mammogram, respectively. 228 of these retrospectively visible cases were advanced/metastatic at diagnosis; 21.1% (95% CI: 16.3-26.8%) were found by AI on the screening mammogram. Increased likelihood of detection of retrospectively visible cancers with AI was observed for lower-grade carcinomas and those with involved lymph nodes at diagnosis. Among true interval cancers, AI could detect and correctly localise in the screening mammogram where subsequent malignancies would appear in 2.8% (95% CI: 2.0-3.9%) of cases. CONCLUSIONS: AI can support radiologists by detecting a greater number of carcinomas, subsequently decreasing the interval cancer rate and the number of advanced and metastatic cancers.