Identifying cell state-associated alternative splicing events and their coregulation.

Alternative splicing shapes the transcriptome and contributes to each cell's unique identity, but single-cell RNA sequencing (scRNA-seq) has struggled to capture the impact of alternative splicing. We previously showed that low recovery of mRNAs from single cells led to erroneous conclusions about the cell-to-cell variability of alternative splicing. Here, we present a method, Psix, to confidently identify splicing that changes across a landscape of single cells, using a probabilistic model that is robust against the data limitations of scRNA-seq. Its autocorrelation-inspired approach finds patterns of alternative splicing that correspond to patterns of cell identity, such as cell type or developmental stage, without the need for explicit cell clustering, labeling, or trajectory inference. Applying Psix to data that follow the trajectory of mouse brain development, we identify exons whose alternative splicing patterns cluster into modules of coregulation. We show that the exons in these modules are enriched for binding by distinct neuronal splicing factors and that their changes in splicing correspond to changes in expression of these splicing factors. Thus, Psix reveals cell type-dependent splicing patterns and the wiring of the splicing regulatory networks that control them. Our new method will enable scRNA-seq analysis to go beyond transcription to understand the roles of post-transcriptional regulation in determining cell identity.

A method for positive forensic identification of samples from extremely low-coverage sequence data.

BACKGROUND: Determining whether two DNA samples originate from the same individual is difficult when the amount of retrievable DNA is limited. This is often the case for ancient, historic, and forensic samples. The most widely used approaches rely on amplification of a defined panel of multi-allelic markers and comparison to similar data from other samples. When the amount retrievable DNA is low these approaches fail. RESULTS: We describe a new method for assessing whether shotgun DNA sequence data from two samples are consistent with originating from the same or different individuals. Our approach makes use of the large catalogs of single nucleotide polymorphism (SNP) markers to maximize the chances of observing potentially discriminating alleles. We further reduce the amount of data required by taking advantage of patterns of linkage disequilibrium modeled by a reference panel of haplotypes to indirectly compare observations at pairs of linked SNPs. Using both coalescent simulations and real sequencing data from modern and ancient sources, we show that this approach is robust with respect to the reference panel and has power to detect positive identity from DNA libraries with less than 1 % random and non-overlapping genome coverage in each sample. CONCLUSION: We present a powerful new approach that can determine whether DNA from two samples originated from the same individual even when only minute quantities of DNA are recoverable from each.

The dynamics of functional classes of plant genes in rediploidized ancient polyploids.

RESULTS: We measure the simultaneous dynamics of duplicate orthologous gene loss in rosids, in asterids, and in monocots, as influenced by biological functional class. This pan-angiosperm view confirms common tendencies and consistency through time for both ancient and more recent whole genome polyploidization events. CONCLUSIONS: The gene loss analysis represents an assessment of post-polyploidization evolution, at the level of individual gene families within and across sister genomes. Functional analysis confirms universal trends previously reported for more recent plant polyploidy events: genes involved with regulation and responses were retained in multiple copies, while genes involved with metabolic and catalytic processes tended to lose copies, across all three groups of plants.To understand the particular evolutionary patterns of plant genomes, there is a need to systematically survey the fate of the subgenomes of polyploids fixed as whole genome duplicates, including patterns of retention of duplicate, triplicate, etc. genes.

Coverage-dependent bias creates the appearance of binary splicing in single cells.

Single-cell RNA sequencing provides powerful insight into the factors that determine each cell's unique identity. Previous studies led to the surprising observation that alternative splicing among single cells is highly variable and follows a bimodal pattern: a given cell consistently produces either one or the other isoform for a particular splicing choice, with few cells producing both isoforms. Here, we show that this pattern arises almost entirely from technical limitations. We analyze alternative splicing in human and mouse single-cell RNA-seq datasets, and model them with a probabilistic simulator. Our simulations show that low gene expression and low capture efficiency distort the observed distribution of isoforms. This gives the appearance of binary splicing outcomes, even when the underlying reality is consistent with more than one isoform per cell. We show that accounting for the true amount of information recovered can produce biologically meaningful measurements of splicing in single cells.