Biology subjects

Bi, C.

Publications and source records attributed to Bi, C..

21 records · Page 2Linked to original sources

bioRxiv2020

Long-read Individual-molecule Sequencing Reveals CRISPR-induced Genetic Heterogeneity in Human ESCs

Accurately quantifying the genetic heterogeneity of a cell population is essential to understanding of biological systems. We develop a universal method to label individual DNA molecules for analyzing diverse types of rare genetic variants, with frequency as low as 4x10-5, using short- or long-read sequencing. It enables base-resolution haplotype-resolved quantitative characterization of rare variants. It provides the first quantitative evidence of persistent nonrandom large deletions and insertions following DNA repair of double-strand breaks induced by CRISPR-Cas9 in human pluripotent stem cells.

molecular biology↗

bioRxiv2019

CRISPR-Cas9-assisted native end-joining editing offers a simple strategy for efficient genetic engineering in Escherichia coli

Unlike eukaryotes, bacteria are less proficient in homologous recombination (HR) and non-homologous end joining (NHEJ). All existing genomic editing methods for Escherichia coli rely on exogenous HR or NHEJ systems to repair DNA double-strand breaks (DSBs). Although an E. coli native end-joining (ENEJ) system has been reported, its potential in chromosomal engineering has not yet been explored. Here, we present a CRISPR-Cas9-assisted native end-joining editing and show that ENEJ-dependent DNA repair can be used to conduct rapid and efficient knocking-out of E. coli genomic sequence of up to 83 kb. Moreover, the positive rate and editing efficiency is independent of high-efficiency competent cells. The method requires neither exogenous DNA repair systems nor introduced editing template. The Cas9 complex is the only foreign element in this method. This study is the first successful engineering effort to utilize ENEJ mechanism in genomic editing and provides an effective strategy for genetic engineering in bacteria that are inefficient in HR and NHEJ.\n\nSignificanceThe application in prokaryotes is difficult because of the weak homologous recombination and non-homologous end joining systems. E. coli, as the most-used prokaryote in metabolic engineering, has no NHEJ system. All existing genomic editing methods for E. coli rely on exogenous HR or NHEJ systems to repair double-strand breaks introduced by CRISPR/Cas9. In this report, we firstly demonstrate that the weak and previously ignored end-joining mechanism in E. coli can be used for efficient large-scale genetic engineering assisted by CRISPR/Cas9. Our efforts greatly simplify the genomic editing procedure of E. coli and provide an effective strategy for genetic engineering in bacteria that are inefficient in HR and NHEJ.

bioengineering↗

bioRxiv2017

DeepSimulator: a deep simulator for Nanopore sequencing

MotivationOxford Nanopore sequencing is a rapidly developed sequencing technology in recent years. To keep pace with the explosion of the downstream data analytical tools, a versatile Nanopore sequencing simulator is needed to complement the experimental data as well as to benchmark those newly developed tools. However, all the currently available simulators are based on simple statistics of the produced reads, which have difficulty in capturing the complex nature of the Nanopore sequencing procedure, the main task of which is the generation of raw electrical current signals.\n\nResultsHere we propose a deep learning based simulator, DeepSimulator, to mimic the entire pipeline of Nanopore sequencing. Starting from a given reference genome or assembled contigs, we simulate the electrical current signals by a context-dependent deep learning model, followed by a base-calling procedure to yield simulated reads. This workflow mimics the sequencing procedure more naturally. The thorough experiments performed across four species show that the signals generated by our context-dependent model are more similar to the experimentally obtained signals than the ones generated by the official context-independent pore model. In terms of the simulated reads, we provide a parameter interface to users so that they can obtain the reads with different accuracies ranging from 83% to 97%. The reads generated by the default parameter have almost the same properties as the real data. Two case studies demonstrate the application of DeepSimulator to benefit the development of tools in de novo assembly and in low coverage SNP detection.\n\nAvailabilityThe software can be accessed freely at: https://github.com/lykaust15/deep_simulator.

bioinformatics↗