CoPixie, a novel algorithm for single-particle track colocalization, enables efficient quantification of telomerase dynamics at telomeres.

Single-particle imaging and tracking can be combined with colocalization analysis to study the dynamic interactions between macromolecules in living cells. Indeed, single-particle tracking has been extensively used to study protein-DNA interactions and dynamics. Still, unbiased identification and quantification of binding events at specific genomic loci remains challenging. Herein, we describe CoPixie, a new software that identifies colocalization events between a theoretically unlimited number of imaging channels, including single-particle movies. CoPixie is an object-based colocalization algorithm that relies on both pixel and trajectory overlap to determine colocalization between molecules. We employed CoPixie with live-cell single-molecule imaging of telomerase and telomeres, to test the model that cancer-associated POT1 mutations facilitate telomere accessibility. We show that POT1 mutants Y223C, D224N or K90E increase telomere accessibility for telomerase interaction. However, unlike the POT1-D224N mutant, the POT1-Y223C and POT1-K90E mutations also increase the duration of long-lasting telomerase interactions at telomeres. Our data reveal that telomere elongation in cells expressing cancer-associated POT1 mutants arises from the dual impact of these mutations on telomere accessibility and telomerase retention at telomeres. CoPixie can be used to explore a variety of questions involving macromolecular interactions in living cells, including between proteins and nucleic acids, from multicolor single-particle tracks.

Mitochondrial genome annotation with MFannot: a critical analysis of gene identification and gene model prediction.

Compared to nuclear genomes, mitochondrial genomes (mitogenomes) are small and usually code for only a few dozen genes. Still, identifying genes and their structure can be challenging and time-consuming. Even automated tools for mitochondrial genome annotation often require manual analysis and curation by skilled experts. The most difficult steps are (i) the structural modelling of intron-containing genes; (ii) the identification and delineation of Group I and II introns; and (iii) the identification of moderately conserved, non-coding RNA (ncRNA) genes specifying 5S rRNAs, tmRNAs and RNase P RNAs. Additional challenges arise through genetic code evolution which can redefine the translational identity of both start and stop codons, thus obscuring protein-coding genes. Further, RNA editing can render gene identification difficult, if not impossible, without additional RNA sequence data. Current automated mito- and plastid-genome annotators are limited as they are typically tailored to specific eukaryotic groups. The MFannot annotator we developed is unique in its applicability to a broad taxonomic scope, its accuracy in gene model inference, and its capabilities in intron identification and classification. The pipeline leverages curated profile Hidden Markov Models (HMMs), covariance (CMs) and ERPIN models to better capture evolutionarily conserved signatures in the primary sequence (HMMs and CMs) as well as secondary structure (CMs and ERPIN). Here we formally describe MFannot, which has been available as a web-accessible service (https://megasun.bch.umontreal.ca/apps/mfannot/) to the research community for nearly 16 years. Further, we report its performance on particularly intron-rich mitogenomes and describe ongoing and future developments.

Refining Mitochondrial Intron Classification With ERPIN: Identification Based on Conservation of Sequence Plus Secondary Structure Motifs.

Mitochondrial genomes-in particular those of fungi-often encode genes with a large number of Group I and Group II introns that are conserved at both the sequence and the RNA structure level. They provide a rich resource for the investigation of intron and gene structure, self- and protein-guided splicing mechanisms, and intron evolution. Yet, the degree of sequence conservation of introns is limited, and the primary sequence differs considerably among the distinct intron sub-groups. It makes intron identification, classification, structural modeling, and the inference of gene models a most challenging and error-prone task-frequently passed on to an "expert" for manual intervention. To reduce the need for manual curation of intron structures and mitochondrial gene models, computational methods using ERPIN sequence profiles were initially developed in 2007. Here we present a refinement of search models and alignments using the now abundant publicly available fungal mtDNA sequences. In addition, we have tested in how far members of the originally proposed sub-groups are clearly distinguished and validated by our computational approach. We confirm clearly distinct mitochondrial Group I sub-groups IA1, IA3, IB3, IC1, IC2, and ID. Yet, IB1, IB2, and IB4 ERPIN models are overlapping substantially in predictions, and are therefore combined and reported as IB. We have further explored the conversion of our ERPIN profiles into covariance models (CM). Current limitations and prospects of the CM approach will be discussed.