Clinical trials often fail to recruit an adequate number of appropriate patients. Identifying eligible trial participants is resource-intensive when relying on manual review of clinical notes, particularly in critical care settings where the time window is short. Automated review of electronic health records (EHR) may help, but much of the information is in free text rather than a computable form. We applied natural language processing (NLP) to free text EHR data using the CogStack platform to simulate recruitment into the LeoPARDS study, a clinical trial aiming to reduce organ dysfunction in septic shock. We applied an algorithm to identify eligible patients using a moving 1-hour time window, and compared patients identified by our approach with those actually screened and recruited for the trial, for the time period that data were available. We manually reviewed records of a random sample of patients identified by the algorithm but not screened in the original trial. Our method identified 376 patients, including 34 patients with EHR data available who were actually recruited to LeoPARDS in our centre. The sensitivity of CogStack for identifying patients screened was 90% (95% CI 85%, 93%). Of the 203 patients identified by both manual screening and CogStack, the index date matched in 95 (47%) and CogStack was earlier in 94 (47%). In conclusion, analysis of EHR data using NLP could effectively replicate recruitment in a critical care trial, and identify some eligible patients at an earlier stage, potentially improving trial recruitment if implemented in real time.

Motivation: The affordability of DNA sequencing has led to the generation of unprecedented volumes of raw sequencing data. These data must be stored, processed and transmitted, which poses significant challenges. To facilitate this effort, we introduce FaStore, a specialized compressor for FASTQ files. FaStore does not use any reference sequences for compression and permits the user to choose from several lossy modes to improve the overall compression ratio, depending on the specific needs. Results: FaStore in the lossless mode achieves a significant improvement in compression ratio with respect to previously proposed algorithms. We perform an analysis on the effect that the different lossy modes have on variant calling, the most widely used application for clinical decision making, especially important in the era of precision medicine. We show that lossy compression can offer significant compression gains, while preserving the essential genomic information and without affecting the variant calling performance. Availability and implementation: FaStore can be downloaded from https://github.com/refresh-bio/FaStore. Supplementary information: Supplementary data are available at Bioinformatics online.

MOTIVATION: High-coverage sequencing data have significant, yet hard to exploit, redundancy. Most FASTQ compressors cannot efficiently compress the DNA stream of large datasets, since the redundancy between overlapping reads cannot be easily captured in the (relatively small) main memory. More interesting solutions for this problem are disk based, where the better of these two, from Cox et al. (2012), is based on the Burrows-Wheeler transform (BWT) and achieves 0.518 bits per base for a 134.0 Gbp human genome sequencing collection with almost 45-fold coverage. RESULTS: We propose overlapping reads compression with minimizers, a compression algorithm dedicated to sequencing reads (DNA only). Our method makes use of a conceptually simple and easily parallelizable idea of minimizers, to obtain 0.317 bits per base as the compression ratio, allowing to fit the 134.0 Gbp dataset into only 5.31 GB of space. AVAILABILITY AND IMPLEMENTATION: http://sun.aei.polsl.pl/orcom under a free license. CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

SUMMARY: Modern sequencing platforms produce huge amounts of data. Archiving them raises major problems but is crucial for reproducibility of results, one of the most fundamental principles of science. The widely used gzip compressor, used for reduction of storage and transfer costs, is not a perfect solution, so a few specialized FASTQ compressors were proposed recently. Unfortunately, they are often impractical because of slow processing, lack of support for some variants of FASTQ files or instability. We propose DSRC 2 that offers compression ratios comparable with the best existing solutions, while being a few times faster and more flexible. AVAILABILITY AND IMPLEMENTATION: DSRC 2 is freely available at http://sun.aei.polsl.pl/dsrc. The package contains command-line compressor, C and Python libraries for easy integration with existing software and technical documentation with examples of usage. CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.