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.