We study the problem of mass screening of heterogeneous populations under limited testing budget. Mass screening is an essential tool that arises in various settings, e.g., the COVID-19 pandemic. The objective of mass screening is to classify the entire population as positive or negative for a disease as efficiently and accurately as possible. Under limited budget, testing facilities need to allocate a portion of the budget to target sub-populations (i.e., proactive screening) while reserving the remaining budget to screen for symptomatic cases (i.e., reactive screening). This paper addresses this decision problem by taking advantage of accessible population-level risk information to identify the optimal set of sub-populations for proactive/reactive screening. The framework also incorporates two widely used testing schemes: Individual and Dorfman group testing. By leveraging the special structure of the resulting bilinear optimization problem, we identify key structural properties, which in turn enable us to develop efficient solution schemes. Furthermore, we extend the model to accommodate customized testing schemes across different sub-populations and introduce a highly efficient heuristic solution algorithm for the generalized model. We conduct a comprehensive case study on COVID-19 in the US, utilizing geographically-based data. Numerical results demonstrate a significant improvement of up to 52% in total misclassifications compared to conventional screening strategies. In addition, our case study offers valuable managerial insights regarding the allocation of proactive/reactive measures and budget across diverse geographic regions.

OBJECTIVE: In response to COVID-19, the informatics community united to aggregate as much clinical data as possible to characterize this new disease and reduce its impact through collaborative analytics. The National COVID Cohort Collaborative (N3C) is now the largest publicly available HIPAA limited dataset in US history with over 6.4 million patients and is a testament to a partnership of over 100 organizations. MATERIALS AND METHODS: We developed a pipeline for ingesting, harmonizing, and centralizing data from 56 contributing data partners using 4 federated Common Data Models. N3C data quality (DQ) review involves both automated and manual procedures. In the process, several DQ heuristics were discovered in our centralized context, both within the pipeline and during downstream project-based analysis. Feedback to the sites led to many local and centralized DQ improvements. RESULTS: Beyond well-recognized DQ findings, we discovered 15 heuristics relating to source Common Data Model conformance, demographics, COVID tests, conditions, encounters, measurements, observations, coding completeness, and fitness for use. Of 56 sites, 37 sites (66%) demonstrated issues through these heuristics. These 37 sites demonstrated improvement after receiving feedback. DISCUSSION: We encountered site-to-site differences in DQ which would have been challenging to discover using federated checks alone. We have demonstrated that centralized DQ benchmarking reveals unique opportunities for DQ improvement that will support improved research analytics locally and in aggregate. CONCLUSION: By combining rapid, continual assessment of DQ with a large volume of multisite data, it is possible to support more nuanced scientific questions with the scale and rigor that they require.

Motivation: The data generation capabilities of high throughput sequencing (HTS) instruments have exponentially increased over the last few years, while the cost of sequencing has dramatically decreased allowing this technology to become widely used in biomedical studies. For small labs and individual researchers, however, storage and transfer of large amounts of HTS data present a significant challenge. The recent trends in increased sequencing quality and genome coverage can be used to reconsider HTS data storage strategies. Results: We present Broom, a stand-alone application designed to select and store only high-quality sequencing reads at extremely high compression rates. Written in C++, the application accepts single and paired-end reads in FASTQ and FASTA formats and decompresses data in FASTA format. Availability and implementation: C++ code available at https://scsb.utmb.edu/labgroups/fofanov/broom.asp. Supplementary information: Supplementary data are available at Bioinformatics online.

Motivation: Identification of complex relationships among members of microbial communities is key to understand and control the microbiota. Co-exclusion is arguably one of the most important patterns reflecting micro-organisms' intolerance to each other's presence. Knowing these relations opens an opportunity to manipulate microbiotas, personalize anti-microbial and probiotic treatments as well as guide microbiota transplantation. The co-exclusion pattern however, cannot be appropriately described by a linear function nor its strength be estimated using covariance or (negative) Pearson and Spearman correlation coefficients. This manuscript proposes a way to quantify the strength and evaluate the statistical significance of co-exclusion patterns between two, three or more variables describing a microbiota and allows one to extend analysis beyond micro-organism abundance by including other microbiome associated measurements such as, pH, temperature etc., as well as estimate the expected numbers of false positive co-exclusion patterns in a co-exclusion network. Results: The implemented computational pipeline (CoEx) tested against 2380 microbial profiles (samples) from The Human Microbiome Project resulted in body-site specific pairwise co-exclusion patterns. Availability and implementation: C++ source code for calculation of the score and P-value for two, three and four dimensional co-exclusion patterns as well as source code and executable files for the CoEx pipeline are available at https://scsb.utmb.edu/labgroups/fofanov/co-exclusion_in_microbial_communities.asp. Supplementary information: Supplementary data are available at Bioinformatics online.

Bacterial adaptation through gene loss and acquisition are the fundamental mechanisms that determine the structure of the core and pan genomes. One of the challenges in identifying homologous genes required for the description of core and pan genomes is the absence of appropriate alignment scoring rules and clustering thresholds. In this work, we present new methods to establish appropriate alignment scoring rules and clustering thresholds.

More than 70 million people have been infected with the human immunodeficiency virus (HIV). There is no cure for HIV and modern treatment is only effective at delaying the onset of acquired immunodeficiency syndrome. Incorporated HIV serves as a reservoir for constant release of virions. Knowing the locations and quantity of HIV in the reservoirs can help guide development of complete treatment. Patient/Organ-specific HIV genome reconstruction allows to significantly improve detection of sequences originating from HIV. In this paper, we present a novel personalized medicine approach based on the reconstruction of patient/organ-specific HIV genome sequences in latent reservoirs.

The storage, manipulation, and especially internet transfer of large amounts of data produced by High-Throughput Sequencing (HTS) instruments present major obstacles utilizing the full potential of this promising technology. The current standard is based on storing all data, which are produced in text (FASTQ and FASTA) and often stored in binary (SRA and BAM) formats. To date, significant effort has been devoted to efficiently compressing these cumbersome sequencing data sets in their existing formats. However, given the substantial improvements in the quality of HTS data, we believe that if one can afford to exclude low quality data and read headers, new much more compressed data formats can be used to reduce size of HTS data files by at least two orders of magnitude. Here we present several examples of file formats specifically designed to store only high quality sequencing reads in space efficient text and binary form. The basic principles used to decrease file size include storage of only one copy of a sequence when reads are present in multiple copies; alphabetical sorting of all reads and storage of only the differences (suffixes) between consecutive reads; and optimization of the number of bits/bytes required to store the information in binary formats. While file size reduction depends on properties of the sequencing data, the size of the resulting files can be as low as 0.1 %-5% of the original FASTQ, SRA, or BAM files. The greatest advantage of the proposed formats however, is based on its time and memory efficiency. The time required to convert reads from FASTQ/FAST A files into the proposed formats is up to 10 times faster than gzip and SRA. The conversion of files in the proposed formats back to FAST A is limited only by the time required to read the file from the hard drive. We present the source code of the C++ object (class) implemented to store, sort, and perform I/O operations with equal length subsequences; and two executable LINUX command line applications (CoCo and CoCo-PIus) able to work with all types of sequencing data including paired-end and flexible size reads. Source code, Linux executables, as well as user manual can be downloaded from http://bgl.utmb.edu/publications/34cocoplus.

BACKGROUND: The emergence of Next Generation Sequencing technologies has made it possible for individual investigators to generate gigabases of sequencing data per week. Effective analysis and manipulation of these data is limited due to large file sizes, so even simple tasks such as data filtration and quality assessment have to be performed in several steps. This requires (potentially problematic) interaction between the investigator and a bioinformatics/computational service provider. Furthermore, such services are often performed using specialized computational facilities. RESULTS: We present a Windows-based application, Slim-Filter designed to interactively examine the statistical properties of sequencing reads produced by Illumina Genome Analyzer and to perform a broad spectrum of data manipulation tasks including: filtration of low quality and low complexity reads; filtration of reads containing undesired subsequences (such as parts of adapters and PCR primers used during the sample and sequencing libraries preparation steps); excluding duplicated reads (while keeping each read's copy number information in a specialized data format); and sorting reads by copy numbers allowing for easy access and manual editing of the resulting files. Slim-Filter is organized as a sequence of windows summarizing the statistical properties of the reads. Each data manipulation step has roll-back abilities, allowing for return to previous steps of the data analysis process. Slim-Filter is written in C++ and is compatible with fasta, fastq, and specialized AS file formats presented in this manuscript. Setup files and a user's manual are available for download at the supplementary web site ( https://www.bioinfo.uh.edu/Slim_Filter/). CONCLUSION: The presented Windows-based application has been developed with the goal of providing individual investigators with integrated sequencing reads analysis, curation, and manipulation capabilities.