ATM controls meiotic DNA double-strand break formation and recombination and affects synaptonemal complex organization in plants.

Meiosis is a specialized cell division that gives rise to genetically distinct gametic cells. Meiosis relies on the tightly controlled formation of DNA double-strand breaks (DSBs) and their repair via homologous recombination for correct chromosome segregation. Like all forms of DNA damage, meiotic DSBs are potentially harmful and their formation activates an elaborate response to inhibit excessive DNA break formation and ensure successful repair. Previous studies established the protein kinase ATM as a DSB sensor and meiotic regulator in several organisms. Here we show that Arabidopsis ATM acts at multiple steps during DSB formation and processing, as well as crossover (CO) formation and synaptonemal complex (SC) organization, all vital for the successful completion of meiosis. We developed a single-molecule approach to quantify meiotic breaks and determined that ATM is essential to limit the number of meiotic DSBs. Local and genome-wide recombination screens showed that ATM restricts the number of interference-insensitive COs, while super-resolution STED nanoscopy of meiotic chromosomes revealed that the kinase affects chromatin loop size and SC length and width. Our study extends our understanding of how ATM functions during plant meiosis and establishes it as an integral factor of the meiotic program.

GHOST: Recovering Historical Signal from Heterotachously Evolved Sequence Alignments.

Molecular sequence data that have evolved under the influence of heterotachous evolutionary processes are known to mislead phylogenetic inference. We introduce the General Heterogeneous evolution On a Single Topology (GHOST) model of sequence evolution, implemented under a maximum-likelihood framework in the phylogenetic program IQ-TREE (http://www.iqtree.org). Simulations show that using the GHOST model, IQ-TREE can accurately recover the tree topology, branch lengths, and substitution model parameters from heterotachously evolved sequences. We investigate the performance of the GHOST model on empirical data by sampling phylogenomic alignments of varying lengths from a plastome alignment. We then carry out inference under the GHOST model on a phylogenomic data set composed of 248 genes from 16 taxa, where we find the GHOST model concurs with the currently accepted view, placing turtles as a sister lineage of archosaurs, in contrast to results obtained using traditional variable rates-across-sites models. Finally, we apply the model to a data set composed of a sodium channel gene of 11 fish taxa, finding that the GHOST model is able to elucidate a subtle component of the historical signal, linked to the previously established convergent evolution of the electric organ in two geographically distinct lineages of electric fish. We compare inference under the GHOST model to partitioning by codon position and show that, owing to the minimization of model constraints, the GHOST model offers unique biological insights when applied to empirical data.

An Enumerative Combinatorics Model for Fragmentation Patterns in RNA Sequencing Provides Insights into Nonuniformity of the Expected Fragment Starting-Point and Coverage Profile.

RNA sequencing (RNA-seq) has emerged as the method of choice for measuring the expression of RNAs in a given cell population. In most RNA-seq technologies, sequencing the full length of RNA molecules requires fragmentation into smaller pieces. Unfortunately, the issue of nonuniform sequencing coverage across a genomic feature has been a concern in RNA-seq and is attributed to biases for certain fragments in RNA-seq library preparation and sequencing. To investigate the expected coverage obtained from fragmentation, we develop a simple fragmentation model that is independent of bias from the experimental method and is not specific to the transcript sequence. Essentially, we enumerate all configurations for maximal placement of a given fragment length, F, on transcript length, T, to represent every possible fragmentation pattern, from which we compute the expected coverage profile across a transcript. We extend this model to incorporate general empirical attributes such as read length, fragment length distribution, and number of molecules of the transcript. We further introduce the fragment starting-point, fragment coverage, and read coverage profiles. We find that the expected profiles are not uniform and that factors such as fragment length to transcript length ratio, read length to fragment length ratio, fragment length distribution, and number of molecules influence the variability of coverage across a transcript. Finally, we explore a potential application of the model where, with simulations, we show that it is possible to correctly estimate the transcript copy number for any transcript in the RNA-seq experiment.