High-throughput DNA Sequencing Data Research Articles

High-throughput DNA sequence data compression

The exponential growth of high-throughput DNA sequence data has posed great challenges to genomic data storage, retrieval and transmission. Compression is a critical tool to address these challenges, where many methods have been developed to reduce the storage size of the genomes and sequencing data (reads, quality scores and metadata). However, genomic data are being generated faster than they could be meaningfully analyzed, leaving a large scope for developing novel compression algorithms that could directly facilitate data analysis beyond data transfer and storage. In this article, we categorize and provide a comprehensive review of the existing compression methods specialized for genomic data and present experimental results on compression ratio, memory usage, time for compression and decompression. We further present the remaining challenges and potential directions for future research.

Colonization patterns of soil microbial communities in the Atacama Desert.

BackgroundThe Atacama Desert is one of the driest deserts in the world and its soil, with extremely low moisture, organic carbon content, and oxidizing conditions, is considered to be at the dry limit for life.ResultsAnalyses of high throughput DNA sequence data revealed that bacterial communities from six geographic locations in the hyper-arid core and along a North-South moisture gradient were structurally and phylogenetically distinct (ANOVA test for observed operating taxonomic units at 97% similarity (OTU0.03), P <0.001) and that communities from locations in the hyper-arid zone displayed the lowest levels of diversity. We found bacterial taxa similar to those found in other arid soil communities with an abundance of Rubrobacterales, Actinomycetales, Acidimicrobiales, and a number of families from the Thermoleophilia. The extremely low abundance of Firmicutes indicated that most bacteria in the soil were in the form of vegetative cells. Integrating molecular data with climate and soil geochemistry, we found that air relative humidity (RH) and soil conductivity significantly correlated with microbial communities’ diversity metrics (least squares linear regression for observed OTU0.03 and air RH and soil conductivity, P <0.001; UniFrac PCoA Spearman’s correlation for air RH and soil conductivity, P <0.0001), indicating that water availability and salt content are key factors in shaping the Atacama soil microbiome. Mineralization studies showed communities actively metabolizing in all soil samples, with increased rates in soils from the southern locations.ConclusionsOur results suggest that microorganisms in the driest soils of the Atacama Desert are in a state of stasis for most of the time, but can potentially metabolize if presented with liquid water for a sufficient duration. Over geological time, rare rain events and physicochemical factors potentially played a major role in selecting micro-organisms that are most adapted to extreme desiccating conditions.

Astrogenomics: big data, old problems, old solutions?

The ominous warnings of a data deluge in the life sciences from high-throughput DNA sequencing data are being supplanted by a second deluge, of cliches bemoaning our collective scientific fate unless we address the genomic data tsunami. It is imperative that we explore the many facets of the genome, not just sequence but also transcriptional and epigenetic variability, integrating these observations in order to attain a genuine understanding of how genes function, towards a goal of genomics-based personalized medicine. Determining any individual's genomic properties requires comparison to many others, sifting out the specific from the trends, requiring access to the many in order to yield information relevant to the few. This is the central big data challenge in genomics that still requires some sort of resolution. Is there a practical, feasible way of directly connecting the scientific community to this data universe?

THetA: inferring intra-tumor heterogeneity from high-throughput DNA sequencing data

Tumor samples are typically heterogeneous, containing admixture by normal, non-cancerous cells and one or more subpopulations of cancerous cells. Whole-genome sequencing of a tumor sample yields reads from this mixture, but does not directly reveal the cell of origin for each read. We introduce THetA (Tumor Heterogeneity Analysis), an algorithm that infers the most likely collection of genomes and their proportions in a sample, for the case where copy number aberrations distinguish subpopulations. THetA successfully estimates normal admixture and recovers clonal and subclonal copy number aberrations in real and simulated sequencing data. THetA is available at http://compbio.cs.brown.edu/software/.

A beginners guide to SNP calling from high-throughput DNA-sequencing data

High-throughput DNA sequencing (HTS) is of increasing importance in the life sciences. One of its most prominent applications is the sequencing of whole genomes or targeted regions of the genome such as all exonic regions (i.e., the exome). Here, the objective is the identification of genetic variants such as single nucleotide polymorphisms (SNPs). The extraction of SNPs from the raw genetic sequences involves many processing steps and the application of a diverse set of tools. We review the essential building blocks for a pipeline that calls SNPs from raw HTS data. The pipeline includes quality control, mapping of short reads to the reference genome, visualization and post-processing of the alignment including base quality recalibration. The final steps of the pipeline include the SNP calling procedure along with filtering of SNP candidates. The steps of this pipeline are accompanied by an analysis of a publicly available whole-exome sequencing dataset. To this end, we employ several alignment programs and SNP calling routines for highlighting the fact that the choice of the tools significantly affects the final results.

Efficient storage of high throughput DNA sequencing data using reference-based compression

Data storage costs have become an appreciable proportion of total cost in the creation and analysis of DNA sequence data. Of particular concern is that the rate of increase in DNA sequencing is significantly outstripping the rate of increase in disk storage capacity. In this paper we present a new reference-based compression method that efficiently compresses DNA sequences for storage. Our approach works for resequencing experiments that target well-studied genomes. We align new sequences to a reference genome and then encode the differences between the new sequence and the reference genome for storage. Our compression method is most efficient when we allow controlled loss of data in the saving of quality information and unaligned sequences. With this new compression method we observe exponential efficiency gains as read lengths increase, and the magnitude of this efficiency gain can be controlled by changing the amount of quality information stored. Our compression method is tunable: The storage of quality scores and unaligned sequences may be adjusted for different experiments to conserve information or to minimize storage costs, and provides one opportunity to address the threat that increasing DNA sequence volumes will overcome our ability to store the sequences.

The choice of, and the decision to apply, disease modifying drugs.

<h3>ABSTRACT</h3> Biofilm-forming microbial communities on plastics and textile fibers are of growing interest since they have potential to contribute to disease outbreaks and material biodegradability in the environment. Knowledge on microbial colonization of pollutants in the marine realm is expanding, but metabolic responses during substrate colonization remains poorly understood. Here, we assess the metabolic response in marine microbial communities to three different micropollutants, virgin high-density polyethylene (HDPE) microbeads, polysorbate-20 (Tween), and textile fibers. Intertidal textile fibers, mainly cotton, virgin HDPE, and Tween induced variable levels of microbial growth, respiration, and community assembly in controlled microcosm experiments. RAMAN characterization of the chemical composition of the textile waste fibers and high-throughput DNA sequencing data shows how the increased metabolic stimulation and biodegradation is translated into selection processes ultimately manifested in different communities colonizing the different micropollutant substrates. The composition of the bacterial communities colonizing the substrates were significantly altered by micropollutant substrate type and light conditions. Bacterial taxa, closely related to the well-known hydrocarbonoclastic bacteria <i>Kordiimonas</i> spp. and <i>Alcanivorax</i> spp., were enriched in the presence of textile-waste. The findings demonstrate an increased metabolic response by marine hydrocarbon-degrading bacterial taxa in the presence of microplastics and textile waste, highlighting their biodegradation potential. The metabolic stimulation by the micropollutants was increased in the presence of light, possibly due to photochemical dissolution of the plastic into smaller bioavailable compounds. Our results suggest that the development and increased activity of these unique microbial communities likely play a role in the bioremediation of the relatively long lived textile and microplastic pollutants in marine habitats. <h3>Graphical Abstract</h3>