Biology subjects

Galas, D. J.

Publications and source records attributed to Galas, D. J..

7 recordsLinked to original sources

bioRxiv2020

Allele Frequency Mismatches and Apparent Mismappings in UK Biobank SNP Data

We report here some anomalies discovered in the minor allele frequencies (MAFs) and some likely mismappings found in our analyses of UK Biobank dataset (UKB) and several other databases. We compared the MAFs present in the UKB to those measured in two other UK studies, ALSPAC and TwinsUK, and found a large set of SNPs for which the UKB MAFs are inconsistent. Additionally, even after accounting for population structure effects and other possible causes of spurious correlations, we found many SNPs that appear to be in interchromosomal linkage. Analyzing these interchromosomal linkages carefully, we found that they are all associated with identical sequences on different chromosomes, implying that these SNPs are simply mismapped. Some (but certainly not all) of the MAF disagreements appear to be the result of these mismappings. Our results, including lists of SNPs with inconsistent MAFs and/or apparent interchromosomal linkage, are freely available to download at: http://kunertgraf.com/data/biobank.html

genomics↗

bioRxiv2019

Towards an information theory of quantitative genetics

Quantitative genetics has evolved dramatically in the past century, and the proliferation of genetic data, in quantity as well as type, enables the characterization of complex interactions and mechanisms beyond the scope of its theoretical foundations. In this paper, we argue that revisiting the framework for analysis is important and we begin to lay the foundations of an alternative formulation of quantitative genetics based on information theory. Information theory can provide sensitive and unbiased measures of statistical dependencies among variables, and it provides a natural mathematical language for an alternative view of quantitative genetics. In previous work we examined the information content of discrete functions and applied this approach and methods to the analysis of genetic data. In this paper we present a framework built around a set of relationships that both unifies the information measures for the discrete functions and uses them to express key quantitative genetic relationships. Information theory measures of variable interdependency are used to identify significant interactions, and a general approach is described for inferring functional relationships within genotype and phenotype data. We present information-based measures of the genetic quantities: penetrance, heritability and degrees of statistical epistasis. Our scope here includes the consideration of both two- and three-variable dependencies and independently segregating variants, which captures additive effects, genetic interactions, and two phenotype pleiotropy. This formalism and the theoretical approach naturally applies to higher multi-variable interactions and complex dependencies, and can be adapted to account for population structure, linkage and non-randomly segregating markers. This paper thus focuses on presenting the initial groundwork for a full formulation of quantitative genetics based on information theory.

genetics↗

bioRxiv2019

Computational Inference Software for Tetrad Assembly from Randomly Arrayed Yeast Colonies

Here, we describe an information-theory-based method and associated software for computationally identifying sister spores derived from the same meiotic tetrad. The method exploits specific DNA sequence features of tetrads that result from meiotic centromere and allele segregation patterns. Because the method uses only the genomic sequence, it alleviates the need for tetrad-specific barcodes or other genetic modifications to the strains. Using this method, strains derived from randomly arrayed spores can be efficiently grouped back into tetrads.

bioinformatics↗

bioRxiv2019

Phospho-sRNA-seq reveals extracellular mRNA/lncRNA fragments as potential biomarkers in human plasma

Extracellular RNAs (exRNAs) in biofluids have attracted great interest as potential biomarkers. Whereas extracellular microRNAs (miRNAs) in blood plasma are extensively characterized, extracellular messenger RNAs (mRNA) and long noncoding RNAs (lncRNA) are less well-studied. We report that plasma contains fragmented mRNAs and lncRNAs that are largely missed by standard small RNA-seq protocols due to lack of 5 phosphate or presence of 3 phosphate. These fragments were revealed using a modified protocol ("phospho-sRNA-seq") incorporating RNA treatment with T4-polynucleotide kinase, which we compared with standard small RNA-seq for sequencing synthetic RNAs with varied 5 and 3 ends, as well as human plasma exRNA. Analyzing phospho-sRNA-seq data using a custom, high-stringency bioinformatic pipeline, we identified mRNA/lncRNA transcriptome fingerprints in plasma, including tissue-specific gene sets. In a longitudinal study of bone marrow transplant patients, bone marrow-and liver-enriched exRNA genes tracked with bone marrow recovery and liver injury, respectively, providing proof-of-concept validation as a biomarker approach. By enabling access to an unexplored realm of mRNA and lncRNA fragments, phospho-sRNA-seq opens up new possibilities for plasma transcriptomic biomarker development.

molecular biology↗

bioRxiv2018

Extracting Reproducible Time-Resolved Resting State Networks using Dynamic Mode Decomposition

Resting state networks (RSNs) extracted from functional magnetic resonance imaging (fMRI) scans are believed to reflect the intrinsic organization and network structure of brain regions. Most traditional methods for computing RSNs typically assume these functional networks are static throughout the duration of a scan lasting 5-15 minutes. However, they are known to vary on timescales ranging from seconds to years; in addition, the dynamic properties of RSNs are affected in a wide variety of neurological disorders. Recently, there has been a proliferation of methods for characterizing RSN dynamics, yet it remains a challenge to extract reproducible time-resolved networks. In this paper, we develop a novel method based on dynamic mode decomposition (DMD) to extract networks from short windows of noisy, high-dimensional fMRI data, allowing RSNs from single scans to be resolved robustly at a temporal resolution of seconds. We demonstrate this method on data from 120 individuals from the Human Connectome Project and show that unsupervised clustering of DMD modes discovers RSNs at both the group (gDMD) and the single subject (sDMD) levels. The gDMD modes closely resemble canonical RSNs. Compared to established methods, sDMD modes capture individualized RSN structure that both better resembles the population RSN and better captures subject-level variation. We further leverage this time-resolved sDMD analysis to infer occupancy and transitions among RSNs with high reproducibility. This automated DMD-based method is a powerful tool to characterize spatial and temporal structures of RSNs in individual subjects.

neuroscience↗

bioRxiv2017

Isolation of nucleic acids from low biomass samples: detection and removal of sRNA contaminants

Sequencing-based analyses of low-biomass samples are known to be prone to misinterpretation due to the potential presence of contaminating molecules derived from laboratory reagents and environments. Due to its inherent instability, contamination with RNA is usually considered to be unlikely. Here we report the presence of small RNA (sRNA) contaminants in widely used microRNA extraction kits and means for their depletion. Sequencing of sRNAs extracted from human plasma samples was performed and significant levels of non-human (exogenous) sequences were detected. The source of the most abundant of these sequences could be traced to the microRNA extraction columns by qPCR-based analysis of laboratory reagents. The presence of artefactual sequences originating from the confirmed contaminants were furthermore replicated in a range of published datasets. To avoid artefacts in future experiments, several protocols for the removal of the contaminants were elaborated, minimal amounts of starting material for artefact-free analyses were defined, and the reduction of contaminant levels for identification of bona fide sequences using ultra-clean extraction kits was confirmed. In conclusion, this is the first report of the presence of RNA molecules as contaminants in laboratory reagents. The described protocols should be applied in the future to avoid confounding sRNA studies.

molecular biology↗

bioRxiv2017

Accuracy, Reproducibility And Bias Of Next Generation Sequencing For Quantitative Small RNA Profiling: A Multiple Protocol Study Across Multiple Laboratories

Small RNA-seq is increasingly being used for profiling of small RNAs. Quantitative characteristics of long RNA-seq have been extensively described, but small RNA-seq involves fundamentally different methods for library preparation, with distinct protocols and technical variations that have not been fully and systematically studied. We report here the results of a study using common references (synthetic RNA pools of defined composition, as well as plasma-derived RNA) to evaluate the accuracy, reproducibility and bias of small RNA-seq library preparation for five distinct protocols and across nine different laboratories. We observed protocol-specific and sequence-specific bias, which was ameliorated using adapters for ligation with randomized end-nucleotides, and computational correction factors. Despite this technical bias, relative quantification using small RNA-seq was remarkably accurate and reproducible, even across multiple laboratories using different methods. These results provide strong evidence for the feasibility of reproducible cross-laboratory small RNA-seq studies, even those involving analysis of data generated using different protocols.

genomics↗