Improving genetic diagnosis in Mendelian disease with transcriptome sequencing, bioRxiv, 2016-09-09
AbstractExome and whole-genome sequencing are becoming increasingly routine approaches in Mendelian disease diagnosis. Despite their success, the current diagnostic rate for genomic analyses across a variety of rare diseases is approximately 25-50%. Here, we explore the utility of transcriptome sequencing (RNA-seq) as a complementary diagnostic tool in a cohort of 50 patients with genetically undiagnosed rare muscle disorders. We describe an integrated approach to analyze patient muscle RNA-seq, leveraging an analysis framework focused on the detection of transcript-level changes that are unique to the patient compared to over 180 control skeletal muscle samples. We demonstrate the power of RNA-seq to validate candidate splice-disrupting mutations and to identify splice-altering variants in both exonic and deep intronic regions, yielding an overall diagnosis rate of 35%. We also report the discovery of a highly recurrent de novo intronic mutation in COL6A1 that results in a dominantly acting splice-gain event, disrupting the critical glycine repeat motif of the triple helical domain. We identify this pathogenic variant in a total of 27 genetically unsolved patients in an external collagen VI-like dystrophy cohort, thus explaining approximately 25% of patients clinically suggestive of collagen VI dystrophy in whom prior genetic analysis is negative. Overall, this study represents a large systematic application of transcriptome sequencing to rare disease diagnosis and highlights its utility for the detection and interpretation of variants missed by current standard diagnostic approaches.One Sentence SummaryTranscriptome sequencing improves the diagnostic rate for Mendelian disease in patients for whom genetic analysis has not returned a diagnosis.
biorxiv genomics 100-200-users 2016Power Analysis of Single Cell RNA-Sequencing Experiments, bioRxiv, 2016-09-09
AbstractHigh-throughput single cell RNA sequencing (scRNA-seq) has become an established and powerful method to investigate transcriptomic cell-to-cell variation, and has revealed new cell types, and new insights into developmental process and stochasticity in gene expression. There are now several published scRNA-seq protocols, which all sequence transcriptomes from a minute amount of starting material. Therefore, a key question is how these methods compare in terms of sensitivity of detection of mRNA molecules, and accuracy of quantification of gene expression. Here, we assessed the sensitivity and accuracy of many published data sets based on standardized spike-ins with a uniform raw data processing pipeline. We developed a flexible and fast UMI counting tool (<jatsext-link xmlnsxlink=httpwww.w3.org1999xlink ext-link-type=uri xlinkhref=httpsgithub.comvalsumis>httpsgithub.comvalsumis<jatsext-link>) which is compatible with all UMI based protocols. This allowed us to relate these parameters to sequencing depth, and discuss the trade offs between the different methods. To confirm our results, we performed experiments on cells from the same population using three different protocols. We also investigated the effect of RNA degradation on spike-in molecules, and the average efficiency of scRNA-seq on spike-in molecules versus endogenous RNAs.
biorxiv genomics 100-200-users 2016Using high-resolution variant frequencies to empower clinical genome interpretation, bioRxiv, 2016-09-03
ABSTRACTWhole exome and genome sequencing have transformed the discovery of genetic variants that cause human Mendelian disease, but discriminating pathogenic from benign variants remains a daunting challenge. Rarity is recognised as a necessary, although not sufficient, criterion for pathogenicity, but frequency cutoffs used in Mendelian analysis are often arbitrary and overly lenient. Recent very large reference datasets, such as the Exome Aggregation Consortium (ExAC), provide an unprecedented opportunity to obtain robust frequency estimates even for very rare variants. Here we present a statistical framework for the frequency-based filtering of candidate disease-causing variants, accounting for disease prevalence, genetic and allelic heterogeneity, inheritance mode, penetrance, and sampling variance in reference datasets. Using the example of cardiomyopathy, we show that our approach reduces by two-thirds the number of candidate variants under consideration in the average exome, and identifies 43 variants previously reported as pathogenic that can now be reclassified. We present precomputed allele frequency cutoffs for all variants in the ExAC dataset.
biorxiv genomics 100-200-users 2016Direct determination of diploid genome sequences, bioRxiv, 2016-08-20
ABSTRACTDetermining the genome sequence of an organism is challenging, yet fundamental to understanding its biology. Over the past decade, thousands of human genomes have been sequenced, contributing deeply to biomedical research. In the vast majority of cases, these have been analyzed by aligning sequence reads to a single reference genome, biasing the resulting analyses and, in general, failing to capture sequences novel to a given genome.Some de novo assemblies have been constructed, free of reference bias, but nearly all were constructed by merging homologous loci into single ‘consensus’ sequences, generally absent from nature. These assemblies do not correctly represent the diploid biology of an individual. In exactly two cases, true diploid de novo assemblies have been made, at great expense. One was generated using Sanger sequencing and one using thousands of clone pools.Here we demonstrate a straightforward and low-cost method for creating true diploid de novo assemblies. We make a single library from ~1 ng of high molecular weight DNA, using the 10x Genomics microfluidic platform to partition the genome. We applied this technique to seven human samples, generating low-cost HiSeq X data, then assembled these using a new ‘pushbutton’ algorithm, Supernova. Each computation took two days on a single server. Each yielded contigs longer than 100 kb, phase blocks longer than 2.5 Mb, and scaffolds longer than 15 Mb. Our method provides a scalable capability for determining the actual diploid genome sequence in a sample, opening the door to new approaches in genomic biology and medicine.
biorxiv genomics 0-100-users 2016DNA damage is a major cause of sequencing errors, directly confounding variant identification, bioRxiv, 2016-08-20
AbstractPervasive mutations in somatic cells generate a heterogeneous genomic population within an organism and may result in serious medical conditions. While cancer is the most studied disease associated with somatic variations, recent advances in single cell and ultra deep sequencing indicate that a number of phenotypes and pathologies are impacted by cell specific variants. Currently, the accurate identification of low allelic frequency somatic variants relies on a combination of deep sequencing coverage and multiple evidences of the presence of variants. However, in this study we show that false positive variants can account for more than 70% of identified somatic variations, rendering conventional detection methods inadequate for accurate determination of low allelic variants. Interestingly, these false positive variants primarily originate from mutagenic DNA damage which directly confounds determination of genuine somatic mutations. Furthermore, we developed and validated a simple metric to measure mutagenic DNA damage, and demonstrated that mutagenic DNA damage is the leading cause of sequencing errors in widely used resources including the 1000 Genomes Project and The Cancer Genome Atlas.
biorxiv genomics 0-100-users 2016Highly parallel direct RNA sequencing on an array of nanopores, bioRxiv, 2016-08-13
AbstractRibonucleic acid sequencing can allow us to monitor the RNAs present in a sample. This enables us to detect the presence and nucleotide sequence of viruses, or to build a picture of how active transcriptional processes are changing – information that is useful for understanding the status and function of a sample. Oxford Nanopore Technologies’ sequencing technology is capable of electronically analysing a sample’s DNA directly, and in real-time. In this manuscript we demonstrate the ability of an array of nanopores to sequence RNA directly, and we apply it to a range of biological situations. Nanopore technology is the only available sequencing technology that can sequence RNA directly, rather than depending on reverse transcription and PCR. There are several potential advantages of this approach over other RNA-seq strategies, including the absence of amplification and reverse transcription biases, the ability to detect nucleotide analogues and the ability to generate full-length, strand-specific RNA sequences. Direct RNA sequencing is a completely new way of analysing the sequence of RNA samples and it will improve the ease and speed of RNA analysis, while yielding richer biological information.
biorxiv genomics 100-200-users 2016