Mottle: Accurate pairwise substitution distance at high divergence through the exploitation of short-read mappers and gradient descent.

Current tools for estimating the substitution distance between two related sequences struggle to remain accurate at a high divergence. Difficulties at distant homologies, such as false seeding and over-alignment, create a high barrier for the development of a stable estimator. This is especially true for viral genomes, which carry a high rate of mutation, small size, and sparse taxonomy. Developing an accurate substitution distance measure would help to elucidate the relationship between highly divergent sequences, interrogate their evolutionary history, and better facilitate the discovery of new viral genomes. To tackle these problems, we propose an approach that uses short-read mappers to create whole-genome maps, and gradient descent to isolate the homologous fraction and calculate the final distance value. We implement this approach as Mottle. With the use of simulated and biological sequences, Mottle was able to remain stable to 0.66-0.96 substitutions per base pair and identify viral outgroup genomes with 95% accuracy at the family-order level. Our results indicate that Mottle performs as well as existing programs in identifying taxonomic relationships, with more accurate numerical estimation of genomic distance over greater divergences. By contrast, one limitation is a reduced numerical accuracy at low divergences, and on genomes where insertions and deletions are uncommon, when compared to alternative approaches. We propose that Mottle may therefore be of particular interest in the study of viruses, viral relationships, and notably for viral discovery platforms, helping in benchmarking of homology search tools and defining the limits of taxonomic classification methods. The code for Mottle is available at https://github.com/tphoward/Mottle_Repo.

Machine learning based lineage tree reconstruction improved with knowledge of higher level relationships between cells and genomic barcodes.

Tracking cells as they divide and progress through differentiation is a fundamental step in understanding many biological processes, such as the development of organisms and progression of diseases. In this study, we investigate a machine learning approach to reconstruct lineage trees in experimental systems based on mutating synthetic genomic barcodes. We refine previously proposed methodology by embedding information of higher level relationships between cells and single-cell barcode values into a feature space. We test performance of the algorithm on shallow trees (up to 100 cells) and deep trees (up to 10 000 cells). Our proposed algorithm can improve tree reconstruction accuracy in comparison to reconstructions based on a maximum parsimony method, but this comes at a higher computational time requirement.

Benchmarked approaches for reconstruction of in vitro cell lineages and in silico models of C. elegans and M. musculus developmental trees.

The recent advent of CRISPR and other molecular tools enabled the reconstruction of cell lineages based on induced DNA mutations and promises to solve the ones of more complex organisms. To date, no lineage reconstruction algorithms have been rigorously examined for their performance and robustness across dataset types and number of cells. To benchmark such methods, we decided to organize a DREAM challenge using in vitro experimental intMEMOIR recordings and in silico data for a C. elegans lineage tree of about 1,000 cells and a Mus musculus tree of 10,000 cells. Some of the 22 approaches submitted had excellent performance, but structural features of the trees prevented optimal reconstructions. Using smaller sub-trees as training sets proved to be a good approach for tuning algorithms to reconstruct larger trees. The simulation and reconstruction methods here generated delineate a potential way forward for solving larger cell lineage trees such as in mouse.