GraphChainer: Co-linear Chaining for Accurate Alignment of Long Reads to Variation Graphs

Aligning reads to a variation graph is a standard task in pangenomics, with downstream applications in e.g., improving variant calling. While the vg toolkit (Garrison et al., Nature Biotechnology, 2018) is a popular aligner of short reads, GraphAligner (Rautiainen and Marschall, Genome Biology, 2020) is the state-of-the-art aligner of long reads. GraphAligner works by finding candidate read occurrences based on individually extending the best seeds of the read in the variation graph. However, a more principled approach recognized in the community is to co-linearly chain multiple seeds. We present a new algorithm to co-linearly chain a set of seeds in an acyclic variation graph, together with the first efficient implementation of such a co-linear chaining algorithm into a new aligner of long reads to variation graphs, GraphChainer. Compared to GraphAligner, at a normalized edit distance threshold of 40%, it aligns 9% to 12% more reads, and 15% to 19% more total read length, on real PacBio reads from human chromosomes 1 and 22. On both simulated and real data, GraphChainer aligns between 97% and 99% of all reads, and of total read length. At the more stringent normalized edit distance threshold of 30%, GraphChainer aligns up to 29% more total real read length than GraphAligner. GraphChainer is freely available at https://github.com/algbio/GraphChainer

UMAP does not preserve global structure any better than t-SNE when using the same initialization

One of the most ubiquitous analysis tools employed in single-cell transcriptomics and cytometry is t-distributed stochastic neighbor embedding (t-SNE) [1], used to visualize individual cells as points on a 2D scatter plot such that similar cells are positioned close together. Recently, a related algorithm, called uniform manifold approximation and projection (UMAP) [2] has attracted substantial attention in the single-cell community. In Nature Biotechnology, Becht et al. [3] argued that UMAP is preferable to t-SNE because it better preserves the global structure of the data and is more consistent across runs. Here we show that this alleged superiority of UMAP can be entirely attributed to different choices of initialization in the implementations used by Becht et al.: t-SNE implementations by default used random initialization, while the UMAP implementation used a technique called Laplacian eigenmaps [4] to initialize the embedding. We show that UMAP with random initialization preserves global structure as poorly as t-SNE with random initialization, while t-SNE with informative initialization performs as well as UMAP with informative initialization. Hence, contrary to the claims of Becht et al., their experiments do not demonstrate any advantage of the UMAP algorithm per se, but rather warn against using random initialization.

Aplicación de la nanobiotecnología con el sistema CRISPR-cas

Introducción: La nanobiotecnología y la biología sintética son ciencias que impactan en la actualidad con el lanzamiento de aplicaciones innovadoras y beneficiosas para el ser humano, estas ciencias se han fusionado para fabricar nuevos componentes para la construcción de células totalmente artificiales y la creación de biomoléculas sintéticas. Objetivo: Conocer las aplicaciones de la nanobiotecnología relacionadas con el uso del sistema CRISPR/Cas en el almacenamiento de información en el ADN bacteriano y alternativas terapéuticas. Materiales y métodos: Se realizó una revisión bibliográfica sobre las principales aplicaciones de la nanobiotecnología, en las bases de datos ScienceDirect, SciELO, PubMed y en revistas como: Nature biotechnology, Biochemistry, Science y Journal Microbiology. Resultados: La revisión de literatura describe y analiza las nuevas aplicaciones nanobiotecnológicas utilizadas para escribir información en el código genético de las células bacterianas, en el que se emplean el sistema basado en repeticiones palindrómicas cortas agrupadas y regularmente interespaciadas (CRISPR/Cas) y la producción de ADN sintético, así como las alternativas terapéuticas relacionadas con la terapia génica. Conclusión: Entre las aplicaciones nanobiotecnológicas se han demostrado dos métodos para grabar información en el ADN de células bacterianas, de Escherichia coli y Sulfolobus tokodai vinculados con el empleo del sistema CRISPR/Cas y la producción de ADN sintético, así como el uso del CRISPR/Cas en la terapia génica y celular.