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Speed, T. P.

Publications and source records attributed to Speed, T. P..

7 recordsLinked to original sources
bioRxiv2018

Using long-read sequencing to detect imprinted DNA methylation

Systematic variation in the methylation of cytosines at CpG sites plays a critical role in early development of humans and other mammals. Of particular interest are regions of differential methylation between parental alleles, as these often dictate monoallelic gene expression, resulting in parent of origin specific control of the embryonic transcriptome and subsequent development, in a phenomenon known as genomic imprinting. Using long-read nanopore sequencing we show that, with an average genomic coverage of approximately ten, it is possible to determine both the level of methylation of CpG sites and the haplotype from which each read arises. The long-read property is exploited to characterise, using novel methods, both methylation and haplotype for reads that have reduced basecalling precision compared to Sanger sequencing. We validate the analysis both through comparison of nanopore-derived methylation patterns with those from Reduced Representation Bisulfite Sequencing data and through comparison with previously reported data. Our analysis successfully identifies known imprinting control regions as well as some novel differentially methylated regions which, due to their proximity to hitherto unknown monoallelically expressed genes, may represent new imprinting control regions.

genomics↗
bioRxiv2018

MSCypher: an integrated database searching and machine learning workflow for multiplexed proteomics.

Improvements in shotgun proteomics approaches are hampered by increases in multiplexed (chimeric) spectra, as improvements in peak capacity, sensitivity or dynamic range all increase the number of co-eluting peptides. This results in diminishing returns using traditional search algorithms, as co-fragmented spectra are known to decrease identification rates. Here we describe MSCypher, a freely available software suite that enables an extensible workflow including a hybrid supervised machine learned strategy that dynamically adjusts to individual datasets. This results in improved identification rates and quantification of low-abundant peptides and proteins. In addition, the integration of peptide de novo sequencing and database searching enables an unbiased view of variants and high-intensity unassigned peptide spectral matches.\n\nHighlightsO_LIOpen-source end-to-end label-free proteomics workflow\nC_LIO_LIIntegrated database searching and machine learning\nC_LIO_LICustomisable and extensible workflow including de novo sequencing\nC_LIO_LIOptimised for multiplexed spectra, challenging proteomics datasets and peptidomics applications\nC_LI

bioinformatics↗
bioRxiv2018

scMerge: Integration of multiple single-cell transcriptomics datasets leveraging stable expression and pseudo-replication

Concerted examination of multiple collections of single cell RNA-Seq (scRNA-Seq) data promises further biological insights that cannot be uncovered with individual datasets. However, such integrative analyses are challenging and require sophisticated methodologies. To enable effective interrogation of multiple scRNA-Seq datasets, we have developed a novel algorithm, named scMerge, that removes unwanted variation by combining stably expressed genes and utilizing pseudo-replicates across datasets. Analysis of large collections of publicly available datasets demonstrates that scMerge performs well in multiple scenarios and enhances biological discovery, including inferring cell developmental trajectories.

bioinformatics↗
bioRxiv2018

A new normalization for the Nanostring nCounter gene expression assay

The Nanostring nCounter gene expression assay uses molecular barcodes and single molecule imaging to detect and count hundreds of unique transcripts in a single reaction. These counts need to be normalized to adjust for the amount of sample, variations in assay efficiency, and other factors. Most users adopt the normalization approach described in the nSolver analysis software, which involves background correction based on the observed values of negative control probes, a within-sample normalization using the observed values of positive control probes and normalization across samples using reference (housekeeping) genes. Here we present a new normalization method, Removing Unwanted Variation-III (RUV-III), which makes vital use of technical replicates and suitable control genes. We also propose an approach using pseudo-replicates when technical replicates are not available. The effectiveness of RUV-III is illustrated on four different data sets. We also offer suggestions on the design and analysis of studies involving this technology.

bioinformatics↗
bioRxiv2018

Statistical Inference in Cell Lineage Trees

New approaches to lineage tracking allow the study of cell differentiation over many generations of cells during development in multicellular organisms. Understanding the variability observed in these lineage trees requires new statistical methods. Whereas invariant cell lineages, such as that for the nematode Caenorhabditis elegans, can be described using a lineage map, defined as the fixed pattern of phenotypes overlaid onto the binary tree structure, the variability of cell lineages from higher organisms makes it impossible to draw a single lineage map. Here, we introduce lineage variability maps which describe the pattern of second-order variation throughout the lineage tree. These maps can be undirected graphs of the partial correlations between every lineal position or directed graphs showing the dynamics of bifurcated patterns in each subtree. By using the symmetry invariance of a binary tree to develop a generalized spectral analysis for cell lineages, we show how to infer these graphical models for lineages of any depth from sample sizes of only a few pedigrees. When tested on pedigrees from C. elegans expressing a marker for pharyngeal differentiation potential, the maps recover essential features of the known lineage map. When applied to highly-variable pedigrees monitoring cell size in T lymphocytes, the maps show how most of the phenotype is set by the founder naive T cell. Lineage variability maps thus elevate the concept of the lineage map to the population level, addressing questions about the potency and dynamics of cell lineages and providing a way to quantify the progressive restriction of cell fate with increasing depth in the tree.\n\nAuthor summaryMulticellular organisms develop from a single fertilized egg by sequential cell divisions. The progeny from these divisions adopt different traits that are transmitted and modified through many generations. By tracking how cell traits change with each successive cell division throughout the family, or lineage, tree, it has been possible to understand where and how these modifications are controlled at the single-cell level, thereby addressing questions about, for example, the developmental origin of tissues, the sources of differentiation in immune cells, or the relationship between primary tumors and metastases. Such lineages often show large variability, with apparently identical founder cells giving rise to different patterns of descendants. Fundamental scientific questions, such as about the range of possible cell types a cell can give rise to, are often about this variability. To characterize this variation, and thus understand the lineage at the population level, we introduce lineage variability maps. Using data from worm and mammalian cell lineages we show how these maps provide quantifiable answers to questions about any developing lineage, such as the potency of founder cells and the progressive restriction of cell fate at each stage in the tree.

bioinformatics↗
bioRxiv2017

DECENT: Differential Expression with Capture Efficiency AdjustmeNT for Single-Cell RNA-seq Data

Dropout is a common phenomenon in single-cell RNA-seq (scRNA-seq) data, and when left unaddressed affects the validity of the statistical analyses. Despite this, few current methods for differential expression (DE) analysis of scRNA-seq data explicitly model the dropout process. We develop DECENT, a DE method for scRNA-seq data that explicitly models the dropout process and performs statistical analyses on the inferred pre-dropout counts. We demonstrate using simulated and real datasets the superior performance of DECENT compared to existing methods. DECENT does not require spike-in data, but spike-ins can be used to improve performance when available. The method is implemented in a publicly-available R package.

genomics↗
bioRxiv2017

GRIDSS: sensitive and specific genomic rearrangement detection using positional de Bruijn graph assembly

The identification of genomic rearrangements, particularly in cancers, with high sensitivity and specificity using massively parallel sequencing remains a major challenge. Here, we describe the Genome Rearrangement IDentification Software Suite (GRIDSS), a high-speed structural variant (SV) caller that performs efficient genome-wide break-end assembly prior to variant calling using a novel positional de Bruijn graph assembler. By combining assembly, split read and read pair evidence using a probabilistic scoring, GRIDSS achieves high sensitivity and specificity on simulated, cell line and patient tumour data, recently winning SV sub-challenge #5 of the ICGC-TCGA DREAM Somatic Mutation Calling Challenge. On human cell line data, GRIDSS halves the false discovery rate compared to other recent methods. GRIDSS identifies non-template sequence insertions, micro-homologies and large imperfect homologies, and supports multi-sample analysis. GRIDSS is freely available at https://github.com/PapenfussLab/gridss.

bioinformatics↗