Biology subjects

Martin Hemberg

Publications and source records attributed to Martin Hemberg.

5 recordsLinked to original sources

bioRxiv2016

Modelling dropouts allows for unbiased identification of marker genes in scRNASeq experiments

Features selection is a key step in many single-cell RNASeq (scRNASeq) analyses. Feature selection is intended to preserve biologically relevant information while removing genes only subject to technical noise. As it is frequently performed prior to dimensionality reduction, clustering and pseudotime analyses, feature selection can have a major impact on the results. Several different approaches have been proposed for unsupervised feature selection from unprocessed single-cell expression matrices, most based upon identifying highly variable genes in the dataset. We present two methods which take advantage of the prevalence of zeros (dropouts) in scRNASeq data to identify features. We show that dropout-based feature selection outperforms variance-based feature selection for multiple applications of single-cell RNASeq.

Bioinformatics↗

bioRxiv2016

Genomic positional conservation identifies topological anchor point (tap)RNAs linked to developmental loci

The mammalian genome is transcribed into large numbers of long noncoding RNAs (lncRNAs), but the definition of functional lncRNA groups has proven difficult, partly due to their low sequence conservation and lack of identified shared properties. Here we consider positional conservation across mammalian genomes as an indicator of functional commonality. We identify 665 conserved lncRNA promoters in mouse and human genomes that are preserved in genomic position relative to orthologous coding genes. The identified positionally conserved lncRNA genes are primarily associated with developmental transcription factor loci with which they are co-expressed in a tissue-specific manner. Strikingly, over half of all positionally conserved RNAs in this set are linked to distinct chromatin organization structures, overlapping the binding sites for the CTCF chromatin organizer and located at chromatin loop anchor points and borders of topologically associating domains (TADs). These topological anchor point (tap)RNAs possess conserved sequence domains that are enriched in potential recognition motifs for Zinc Finger proteins. Characterization of these noncoding RNAs and their associated coding genes shows that they are functionally connected: they regulate each others expression and influence the metastatic phenotype of cancer cells in vitro in a similar fashion. Thus, interrogation of positionally conserved lncRNAs identifies a new subset of tapRNAs with shared functional properties. These results provide a large dataset of lncRNAs that conform to the \"extended gene\" model, in which conserved developmental genes are genomically and functionally linked to regulatory lncRNA loci across mammalian evolution.

Molecular Biology↗

bioRxiv2016

SC3 - consensus clustering of single-cell RNA-Seq data

Using single-cell RNA-seq (scRNA-seq), the full transcriptome of individual cells can be acquired, enabling a quantitative cell-type characterisation based on expression profiles. However, due to the large variability in gene expression, identifying cell types based on the transcriptome remains challenging. We present Single-Cell Consensus Clustering (SC3), a tool for unsupervised clustering of scRNA-seq data. SC3 achieves high accuracy and robustness by consistently integrating different clustering solutions through a consensus approach. Tests on twelve published datasets show that SC3 outperforms five existing methods while remaining scalable, as shown by the analysis of a large dataset containing 44,808 cells. Moreover, an interactive graphical implementation makes SC3 accessible to a wide audience of users, and SC3 aids biological interpretation by identifying marker genes, differentially expressed genes and outlier cells. We illustrate the capabilities of SC3 by characterising newly obtained transcriptomes from subclones of neoplastic cells collected from patients.

Bioinformatics↗

bioRxiv2015

MPRAnator: a web-based tool for the design of Massively Parallel Reporter Assay experiments.

DNA regulatory elements contain short motifs where transcription factors (TFs) can bind to modulate gene expression. Although the broad principles of TF regulation are well understood, the rules that dictate how combinatorial TF binding translates into transcriptional activity remain largely unknown. With the rapid advances in DNA synthesis and sequencing technologies and the continuing decline in the associated costs, high-throughput experiments can be performed to investigate the regulatory role of thousands of oligonucleotide sequences simultaneously. Nevertheless, designing high-throughput reporter assay experiments such as Massively Parallel Reporter Assays (MPRAs) and similar methods remains challenging. We introduce MPRAnator, a set of tools that facilitate rapid design of MPRA experiments. With MPRA Motif design, a set of variables provides fine control of how motifs are placed into sequences therefore allowing the user to investigate the rules that govern TF occupancy. MPRA SNP design can be used to investigate the functional effects of single or combinations of SNPs at regulatory sequences. Finally, the Transmutation tool allows for the design of negative controls by permitting scrambling, reversing, complementing or introducing multiple random mutations in the input sequences or motifs.

Bioinformatics↗

bioRxiv2015

Discrete Distributional Differential Expression (D3E) - A Tool for Gene Expression Analysis of Single-cell RNA-seq Data

The advent of high throughput RNA-seq at the single-cell level has opened up new opportunities to elucidate the heterogeneity of gene expression. One of the most widespread applications of RNA-seq is to identify genes which are differentially expressed between two experimental conditions. Here, we present a discrete, distributional method for differential gene expression (D3E), a novel algorithm specifically designed for single-cell RNA-seq data. We use synthetic data to evaluate D3E, demonstrating that it can detect changes in expression, even when the mean level remains unchanged. Since D3E is based on an analytically tractable stochastic model, it provides additional biological insights by quantifying biologically meaningful properties, such as the average burst size and frequency. We use D3E to investigate experimental data, and with the help of the underlying model, we directly test hypotheses about the driving mechanism behind changes in gene expression.

Bioinformatics↗