Naseri, A. | Search bioRxiv

bioRxiv2017

CRISPR-Based DNA Imaging in Living Cells Reveals Cell Cycle-Dependent Chromosome Dynamics

In contrast to the well-studied condensation and folding of chromosomes during mitosis, their dynamics in interphase are less understood. We developed a sensitive, multicolor system, CRISPR-Sirius, allowing the real-time tracking of the dynamics of chromosomal loci. We tracked loci kilobases to megabases apart and found significant variation in the inter-locus distances of each pair, indicating differing degrees of DNA contortion. We resolved two distinct modes of dynamics of loci: saltatory local movements as well as translational movements of the domain. The magnitude of both of these modes of movements increased from early to late G1, whereas the translational movements were reduced in early S. The local fluctuations decreased slightly in early S and more markedly in mid-late S. These newly observed movements and their cell cycle-dependence are indicative of a hitherto unrecognized compaction-relaxation dynamic of the chromosomal fiber operating concurrently with changes in the extent of observed genomic domain movements.\n\nIN BRIEFDistinct chromosome folding and dynamics during cell cycle progression were dissected by CRISPR-Sirius DNA imaging in living cells.\n\nHIGHLIGHTSO_LICRISPR-Sirius allows tracking of pairs of chromosomal loci having kilobase to megabase inter-locus distances\nC_LIO_LIPair-wise tracking of loci allows measurement of both local and domain dynamics\nC_LIO_LIChromosomal fiber relaxation is positively correlated with local dynamics\nC_LIO_LIGenomic region size contributes to local and domain movements\nC_LIO_LIDistinct chromosome dynamics were uncovered during cell cycle progression in interphase\nC_LI

cell biology↗

bioRxiv2017

Ultra-fast Identity by Descent Detection in Biobank-Scale Cohorts using Positional Burrows-Wheeler Transform

With the availability of genotyping data of very large samples, there is an increasing need for tools that can efficiently identify genetic relationships among all individuals in the sample. One fundamental measure of genetic relationship of a pair of individuals is identity by descent (IBD), chromosomal segments that are shared among two individuals due to common ancestry. However, the efficient identification of IBD segments among a large number of genotyped individuals is a challenging computational problem. Most existing methods are not feasible for even thousands of individuals because they are based on pairwise comparisons of all individuals and thus scale up quadratically with sample size. Some methods, such as GERMLINE, use fast dictionary lookup of short seed sequence matches to achieve a near-linear time efficiency. However, the number of short seed matches often scales up super-linearly in real population data.\n\nIn this paper we describe a new approach for IBD detection. We take advantage of an efficient population genotype index, Positional BWT (PBWT), by Richard Durbin. PBWT achieves linear time query of perfectly identical subsequences among all samples. However, the original PBWT is not tolerant to genotyping errors which often interrupt long IBD segments into short fragments. We introduce a randomized strategy by running PBWTs over random projections of the original sequences. To boost the detection power we run PBWT multiple times and merge the identified IBD segments through interval tree algorithms. Given a target IBD segment length, RaPID adjust parameters to optimize detection power and accuracy.\n\nSimulation results proved that our tool (RaPID) achieves almost linear scaling up to sample size and is orders of magnitude faster than GERMLINE. At the same time, RaPID maintains a detection power and accuracy comparable to existing mainstream algorithms, GERMLINE and IBDseq. Running multiple times with various target detection lengths over the 1000 Genomes Project data, RaPID can detect population events at different time scales. With our tool, it is feasible to identify IBDs among hundreds of thousands to millions of individuals, a sample size that will become reality in a few years.

genomics↗