Indexcov fast coverage quality control for whole-genome sequencing, bioRxiv, 2017-06-10
AbstractThe BAM1 and CRAM2 formats provide a supplementary linear index that facilitates rapid access to sequence alignments in arbitrary genomic regions. Comparing consecutive entries in a BAM or CRAM index allows one to infer the number of alignment records per genomic region for use as an effective proxy of sequence depth in each genomic region. Based on these properties, we have developed indexcov, an efficient estimator of whole-genome sequencing coverage to rapidly identify samples with aberrant coverage profiles, reveal large scale chromosomal anomalies, recognize potential batch effects, and infer the sex of a sample. Indexcov is available at <jatsext-link xmlnsxlink=httpwww.w3.org1999xlink ext-link-type=uri xlinkhref=httphttpsgithub.combrentpgoleft>httpsgithub.combrentpgoleft<jatsext-link> under the MIT license.
biorxiv genomics 100-200-users 2017Quantifying the impact of rare and ultra-rare coding variation across the phenotypic spectrum, bioRxiv, 2017-06-10
AbstractThere is a limited understanding about the impact of rare protein truncating variants across multiple phenotypes. We explore the impact of this class of variants on 13 quantitative traits and 10 diseases using whole-exome sequencing data from 100,296 individuals. Protein truncating variants in genes intolerant to this class of mutations increased risk of autism, schizophrenia, bipolar disorder, intellectual disability, ADHD. In individuals without these disorders, there was an association with shorter height, lower education, increased hospitalization and reduced age. Gene sets implicated from GWAS did not show a significant protein truncating variants-burden beyond what captured by established Mendelian genes. In conclusion, we provide the most thorough investigation to date of the impact of rare deleterious coding variants on complex traits, suggesting widespread pleiotropic risk.Main abbreviations<jatsdef-list><jatsdef-item>PTV= Protein Truncating Variants<jatsdef-item><jatsdef-item>PI= Protein Truncating Intolerant<jatsdef-item><jatsdef-item>PI-PTV= Protein Truncating Variant in genes that are Intolerant to Protein Truncating Variants<jatsdef-item><jatsdef-list>
biorxiv genetics 100-200-users 2017Highly parallel genome variant engineering with CRISPRCas9 in eukaryotic cells, bioRxiv, 2017-06-09
AbstractDirect measurement of functional effects of DNA sequence variants throughout a genome is a major challenge. We developed a method that uses CRISPRCas9 to engineer many specific variants of interest in parallel in the budding yeast Saccharomyces cerevisiae, and to screen them for functional effects. We used the method to examine the functional consequences of premature termination codons (PTCs) at different locations within all annotated essential genes in yeast. We found that most PTCs were highly deleterious unless they occurred close to the C-terminal end and did not interrupt an annotated protein domain. Surprisingly, we discovered that some putatively essential genes are dispensable, while others have large dispensable regions. This approach can be used to profile the effects of large classes of variants in a high-throughput manner.
biorxiv genomics 0-100-users 2017Integrating long-range connectivity information into de Bruijn graphs, bioRxiv, 2017-06-09
AbstractMotivationThe de Bruijn graph is a simple and efficient data structure that is used in many areas of sequence analysis including genome assembly, read error correction and variant calling. The data structure has a single parameter k, is straightforward to implement and is tractable for large genomes with high sequencing depth. It also enables representation of multiple samples simultaneously to facilitate comparison. However, unlike the string graph, a de Bruijn graph does not retain long range information that is inherent in the read data. For this reason, applications that rely on de Bruijn graphs can produce sub-optimal results given their input.ResultsWe present a novel assembly graph data structure the Linked de Bruijn Graph (LdBG). Constructed by adding annotations on top of a de Bruijn graph, it stores long range connectivity information through the graph. We show that with error-free data it is possible to losslessly store and recover sequence from a Linked de Bruijn graph. With assembly simulations we demonstrate that the LdBG data structure outperforms both the de Bruijn graph and the String Graph Assembler (SGA). Finally we apply the LdBG to Klebsiella pneumoniae short read data to make large (12 kbp) variant calls, which we validate using PacBio sequencing data, and to characterise the genomic context of drug-resistance genes.AvailabilityLinked de Bruijn Graphs and associated algorithms are implemented as part of McCortex, available under the MIT license at <jatsext-link xmlnsxlink=httpwww.w3.org1999xlink ext-link-type=uri xlinkhref=httphttpsgithub.commcveanmccortex>httpsgithub.commcveanmccortex<jatsext-link>.Contactturner.isaac@gmail.com.
biorxiv bioinformatics 0-100-users 2017Genome-wide quantification of the effects of DNA methylation on human gene regulation, bioRxiv, 2017-06-08
AbstractChanges in DNA methylation are important in development and disease, but not all regulatory elements act in a methylation-dependent (MD) manner. Here, we developed mSTARR-seq, a high-throughput approach to quantify the effects of DNA methylation on regulatory element function. We assay MD activity in 14% of the euchromatic human genome, identify 2,143 MD regulatory elements, and predict MD activity using sequence and chromatin state information. We identify transcription factors associated with higher activity in unmethylated or methylated states, including an association between pioneer transcription factors and methylated DNA. Finally, we use mSTARR-seq to predict DNA methylation-gene expression correlations in primary cells. Our findings provide a map of MD regulatory activity across the human genome, facilitating interpretation of the many emerging associations between methylation and trait variation.
biorxiv genomics 0-100-users 2017Non-invasive laminar inference with MEG Comparison of methods and source inversion algorithms, bioRxiv, 2017-06-08
AbstractMagnetoencephalography (MEG) is a direct measure of neuronal current flow; its anatomical resolution is therefore not constrained by physiology but rather by data quality and the models used to explain these data. Recent simulation work has shown that it is possible to distinguish between signals arising in the deep and superficial cortical laminae given accurate knowledge of these surfaces with respect to the MEG sensors. This previous work has focused around a single inversion scheme (multiple sparse priors) and a single global parametric fit metric (free energy). In this paper we use several different source inversion algorithms and both local and global, as well as parametric and non-parametric fit metrics in order to demonstrate the robustness of the discrimination between layers. We find that only algorithms with some sparsity constraint can successfully be used to make laminar discrimination. Importantly, local t-statistics, global cross-validation and free energy all provide robust and mutually corroborating metrics of fit. We show that discrimination accuracy is affected by patch size estimates, cortical surface features, and lead field strength, which suggests several possible future improvements to this technique. This study demonstrates the possibility of determining the laminar origin of MEG sensor activity, and thus directly testing theories of human cognition that involve laminar- and frequency- specific mechanisms. This possibility can now be achieved using recent developments in high precision MEG, most notably the use of subject-specific head-casts, which allow for significant increases in data quality and therefore anatomically precise MEG recordings.
biorxiv neuroscience 0-100-users 2017