Biology subjects

Glusman, G.

Publications and source records attributed to Glusman, G..

9 recordsLinked to original sources

bioRxiv2018

Scale-invariant geometric data analysis (SIGDA) provides robust, detailed visualizations of human ancestry specific to individuals and populations

Scale invariance is a common property of physical laws and a key concept in perspective drawing, which aims to provide a meaningful two-dimensional representation of a more complex, three-dimensional scene. Here we describe Scale Invariant Geometric Data Analysis (SIGDA), a new, general exploratory data analysis (EDA) method based on normalization of data to scale invariance. We discuss similarities and differences between SIGDA and two widely-used EDA methods, Correspondence Analysis (CA) and Principal Components Analysis (PCA). We then illustrate SIGDAs ability to analyze and visualize population structure relationships within the data that inspired its development: genetic marker data, in which context PCA is considered a standard method. We show that SIGDA provides significant advantages over PCA of the same data, including: (a) robust detection and separation of a larger number of population axes, leading to (b) better separation of annotated populations; (c) separation of an independent allele frequency axis interpretable as a proxy for allele age, (d) visualization of marker flow between populations (population history), and (d) robust detection and visualization of relationships between closely-related individuals and among family groups. Although this illustration focuses on a specific task, SIGDA is a general-purpose EDA method and derives its advantages from its novel approach to fundamental issues in data analysis, rather than clever sampling or other task-specific methodology.\n\nOne Sentence SummaryWe illustrate the advantages of Scale Invariant Geometric Data Analysis (SIGDA), a new exploratory data analysis method similar to PCA, by applying SIGDA to derive detailed, robust visualizations of the complex history of human population structure from a large sample of single nucleotide variants.

bioinformatics↗

bioRxiv2018

Fast and simple comparison of semi-structured data, with emphasis on electronic health records

We present a locality-sensitive hashing strategy for summarizing semi-structured data (e.g., in JSON or XML formats) into data fingerprints: highly compressed representations which cannot recreate details in the data, yet simplify and greatly accelerate the comparison and clustering of semi-structured data by preserving similarity relationships. Computation on data fingerprints is fast: in one example involving complex simulated medical records, the average time to encode one record was 0.53 seconds, and the average pairwise comparison time was 3.75 microseconds. Both processes are trivially parallelizable.\n\nApplications include detection of duplicates, clustering and classification of semi-structured data, which support larger goals including summarizing large and complex data sets, quality assessment, and data mining. We illustrate use cases with three analyses of electronic health records (EHRs): (1) pairwise comparison of patient records, (2) analysis of cohort structure, and (3) evaluation of methods for generating simulated patient data.

bioinformatics↗

bioRxiv2018

Reproducible big data science: A case study in continuous FAIRness

Big biomedical data create exciting opportunities for discovery, but make it difficult to capture analyses and outputs in forms that are findable, accessible, interoperable, and reusable (FAIR). In response, we describe tools that make it easy to capture, and assign identifiers to, data and code throughout the data lifecycle. We illustrate the use of these tools via a case study involving a multi-step analysis that creates an atlas of putative transcription factor binding sites from terabytes of ENCODE DNase I hypersensitive sites sequencing data. We show how the tools automate routine but complex tasks, capture analysis algorithms in understandable and reusable forms, and harness fast networks and powerful cloud computers to process data rapidly, all without sacrificing usability or reproducibility--thus ensuring that big data are not hard-to-(re)use data. We compare and contrast our approach with other approaches to big data analysis and reproducibility.

bioinformatics↗

bioRxiv2018

BDQC: a general-purpose analytics tool for domain-blind validation of Big Data

Translational biomedical research is generating exponentially more data: thousands of whole-genome sequences (WGS) are now available; brain data are doubling every two years. Analyses of Big Data, including imaging, genomic, phenotypic, and clinical data, present qualitatively new challenges as well as opportunities. Among the challenges is a proliferation in ways analyses can fail, due largely to the increasing length and complexity of processing pipelines. Anomalies in input data, runtime resource exhaustion or node failure in a distributed computation can all cause pipeline hiccups that are not necessarily obvious in the output. Flaws that can taint results may persist undetected in complex pipelines, a danger amplified by the fact that research is often concurrent with the development of the software on which it depends. On the positive side, the huge sample sizes increase statistical power, which in turn can shed new insight and motivate innovative analytic approaches. We have developed a framework for Big Data Quality Control (BDQC) including an extensible set of heuristic and statistical analyses that identify deviations in data without regard to its meaning (domain-blind analyses). BDQC takes advantage of large sample sizes to classify the samples, estimate distributions and identify outliers. Such outliers may be symptoms of technology failure (e.g., truncated output of one step of a pipeline for a single genome) or may reveal unsuspected \" signal\" in the data (e.g., evidence of aneuploidy in a genome). We have applied the framework to validate real-world WGS analysis pipelines. BDQC successfully identified data outliers representing various failure classes, including genome analyses missing a whole chromosome or part thereof, hidden among thousands of intermediary output files. These failures could then be resolved by reanalyzing the affected samples. BDQC both identified hidden flaws as well as yielded new insights into the data. BDQC is designed to complement quality software development practices. There are multiple benefits from the application of BDQC at all pipeline stages. By verifying input correctness, it can help avoid expensive computations on flawed data. Analysis of intermediary and final results facilitates recovery from aberrant termination of processes. All these computationally inexpensive verifications reduce cryptic analytical artifacts that could seriously preclude clinical-grade genome interpretation. BDQC is available at https://github.com/ini-bdds/bdqc.

bioinformatics↗

bioRxiv2018

Atlas of Transcription Factor Binding Sites from ENCODE DNase Hypersensitivity Data Across 27 Tissue Types

There is intense interest in mapping the tissue-specific binding sites of transcription factors in the human genome to reconstruct gene regulatory networks and predict functions for non-coding genetic variation. DNase-seq footprinting provides a means to predict genome-wide binding sites for hundreds of transcription factors (TFs) simultaneously. However, despite the public availability of DNase-seq data for hundreds of samples, there is neither a unified analytical workflow nor a publicly accessible database providing the locations of footprints across all available samples. Here, we implemented a workflow for uniform processing of footprints using two state-of-the-art footprinting algorithms: Wellington and HINT. Our workflow scans the footprints generated by these algorithms for 1,530 sequence motifs to predict binding sites for 1,515 human transcription factors. We applied our workflow to detect footprints in 192 DNase-seq experiments from ENCODE spanning 27 human tissues. This collection of footprints describes an expansive landscape of potential TF occupancy. At thresholds optimized through machine learning, we report high-quality footprints covering 9.8% of the human genome. These footprints were enriched for true positive TF binding sites as defined by ChIP-seq peaks, as well as for genetic variants associated with changes in gene expression. Integrating our footprint atlas with summary statistics from genome-wide association studies revealed that risk for neuropsychiatric traits was enriched specifically at highly-scoring footprints in human brain, while risk for immune traits was enriched specifically at highly-scoring footprints in human lymphoblasts. Our cloud-based workflow is available at github.com/globusgenomics/genomics-footprint and a database with all footprints and TF binding site predictions are publicly available at http://data.nemoarchive.org/other/grant/sament/sament/footprint_atlas.

bioinformatics↗

bioRxiv2017

Genotype fingerprints enable fast and private comparison of genetic testing results for research and direct-to-consumer applications

As genetic testing expands out of the research laboratory into medical practice as well as the direct-to-consumer market, the efficiency with which the resulting genotype data can be compared between individuals is of increasing importance.\n\nWe present a method for summarizing personal genotypes, yielding genotype fingerprints that can be derived from any single nucleotide polymorphism (SNP)-based assay and readily compared to estimate relatedness. The resulting fingerprints remain comparable as chip designs evolve to higher marker densities. We demonstrate that they support applications including distinguishing genotypes of closely related individuals by relationship type, distinguishing closely related individuals from individuals from the same background population, identification of individuals in known background populations, and de novo identification of subpopulations within a large cohort in a high-throughput manner.\n\nAn important feature of genotype fingerprints is that, while fingerprints do not preserve anonymity, they summarize individual marker data in a way that prevents phenotype prediction. Genotype fingerprints are therefore well-suited to public sharing for ancestry determination purposes, without revealing personal health risk status.

bioinformatics↗

bioRxiv2017

Whole genome sequence and comparative analysis of Borrelia burgdorferi MM1

Lyme disease is caused by spirochaetes of the Borrelia burgdorferi sensu lato genospecies. Complete genome assemblies are available for fewer than ten strains of Borrelia burgdorferi sensu stricto, the primary cause of Lyme disease in North America. MM1 is a sensu stricto strain originally isolated in the midwestern United States. Aside from a small number of genes, the complete genome sequence of this strain has not been reported. Here we present the complete genome sequence of MM1 in relation to other sensu stricto strains and in terms of its Multi Locus Sequence Typing. Our results indicate that MM1 is a new sequence type which contains a conserved main chromosome and 15 plasmids. Our results include the first contiguous 28.5 kb assembly of lp28-8, a linear plasmid carrying the vls antigenic variation system, from a Borrelia burgdorferi sensu stricto strain.

genomics↗

bioRxiv2017

Novel metrics for quantifying bacterial genome composition skews

BackgroundBacterial genomes have characteristic compositional skews, which are differences in nucleotide frequency between the leading and lagging DNA strands across a segment of a genome. It is thought that these strand asymmetries arise as a result of mutational biases and selective constraints, particularly for energy efficiency. Analysis of compositional skews in a diverse set of bacteria provides a comparative context in which mutational and selective environmental constraints can be studied. These analyses typically require finished and well-annotated genomic sequences.\n\nResultsWe present three novel metrics for examining genome composition skews; all three metrics can be computed for unfinished or partially-annotated genomes. The first two metrics, (dot-skew and cross-skew) depend on sequence and gene annotation of a single genome, while the third metric (residual skew) highlights unusual genomes by subtracting a GC content-based model of a library of genome sequences. We applied these metrics to all 7738 available bacterial genomes, including partial drafts, and identified outlier species. A number of these outliers (i.e., Borrelia, Ehrlichia, Kinetoplastibacterium, and Phytoplasma) display similar skew patterns despite only distant phylogenetic relationship. While unrelated, some of the outlier bacterial species share lifestyle characteristics, in particular intracellularity and biosynthetic dependence on their hosts.\n\nConclusionsOur novel metrics appear to reflect the effects of biosynthetic constraints and adaptations to life within one or more hosts on genome composition. We provide results for each analyzed genome, software and interactive visualizations at http://db.systemsbiology.net/gestalt/skew_metrics.

genomics↗

bioRxiv2017

Ultrafast comparison of personal genomes

We present an ultra-fast method for comparing personal genomes. We transform the standard genome representation (lists of variants relative to a reference) into genome fingerprints that can be readily compared across sequencing technologies and reference versions. Because of their reduced size, computation on the genome fingerprints is fast and requires little memory. This enables scaling up a variety of important genome analyses, including quantifying relatedness, recognizing duplicative sequenced genomes in a set, population reconstruction, and many others. The original genome representation cannot be reconstructed from its fingerprint; the method thus has significant implications for privacy-preserving genome analytics.

bioinformatics↗