The complete chloroplast genome of Glycyrrhiza uralensis Fisch. isolated in Korea (Fabaceae)

The chloroplast genome of Glycyrrhiza uralensis Fisch was sequenced to investigate intraspecific variations on the chloroplast genome. Its length is 127,689 bp long (34.3% GC ratio) with atypical structure of chloroplast genome, which is congruent to those of Glycyrrhiza genus. It includes 110 genes (76 protein-coding genes, four rRNAs, and 30 tRNAs). Intronic region of ndhA presented the highest nucleotide diversity based on the six G. uralenesis chloroplast genomes. A total of 150 single nucleotide polymorphisms and 10 insertion and deletion (INDEL) regions were identified from the six G. uralensis chloroplast genomes. Phylogenetic trees show that the six chloroplast genomes of G. uralensis formed the two clades, requiring additional studies to understand it.

R-CRISPR: A Deep Learning Network to Predict Off-Target Activities with Mismatch, Insertion and Deletion in CRISPR-Cas9 System

The Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)—associated protein 9 (Cas9) system is a groundbreaking gene-editing tool, which has been widely adopted in biomedical research. However, the guide RNAs in CRISPR-Cas9 system may induce unwanted off-target activities and further affect the practical application of the technique. Most existing in silico prediction methods that focused on off-target activities possess limited predictive precision and remain to be improved. Hence, it is necessary to propose a new in silico prediction method to address this problem. In this work, a deep learning framework named R-CRISPR is presented, which devises an encoding scheme to encode gRNA-target sequences into binary matrices, a convolutional neural network as feature extractor, and a recurrent neural network to predict off-target activities with mismatch, insertion, or deletion. It is demonstrated that R-CRISPR surpasses six mainstream prediction methods with a significant improvement on mismatch-only datasets verified by GUIDE-seq. Compared with the state-of-art prediction methods, R-CRISPR also achieves competitive performance on datasets with mismatch, insertion, and deletion. Furthermore, experiments show that data concatenate could influence the quality of training data, and investigate the optimal combination of datasets.

A Complete Pedigree-Based Graph Workflow for Rare Candidate Variant Analysis

Methods that use a linear genome reference for genome sequencing data analysis are reference biased. In the field of clinical genetics for rare diseases, a resulting reduction in genotyping accuracy in some regions has likely prevented the resolution of some cases. Pangenome graphs embed population variation into a reference structure. While pangenome graphs have helped to reduce reference mapping bias, further performance improvements are possible. We introduce VG-Pedigree, a pedigree-aware workflow based on the pangenome-mapping tool of Giraffe (Sirén et al. 2021) and the variant-calling tool DeepTrio (Kolesnikov et al. 2021) using a specially-trained model for Giraffe-based alignments. We demonstrate mapping and variant calling improvements in both single-nucleotide variants (SNVs) and insertion and deletion (INDEL) variants over those produced by alignments created using BWA MEM to a linear-reference and Giraffe mapping to a pangenome graph containing data from the 1000 Genomes Project. We have also adapted and upgraded the deleterious-variant (DV) detecting methods and programs of Gu et al. into a streamlined workflow (Gu et al. 2019). We used these workflows in combination to detect small lists of candidate DVs among 15 family quartets and quintets of the Undiagnosed Diseases Program (UDP). All candidate DVs that were previously diagnosed using the mendelian models covered by the previously published Gu et al. methods were recapitulated by these workflows. The results of these experiments indicate a slightly greater absolute count of DVs are detected in the proband population than in their matched unaffected siblings.

Modification of improved-genome editing via oviductal nucleic acids delivery (i-GONAD)-mediated knock-in in rats

Background Improved genome-editing via oviductal nucleic acids delivery (i-GONAD) is a new technology that facilitates in situ genome-editing of mammalian zygotes exiting the oviductal lumen. The i-GONAD technology has been developed for use in mice, rats, and hamsters; however, oligonucleotide (ODN)-based knock-in (KI) is more inefficient in rats than mice. To improve the efficiency of i-GONAD in rats we examined KI efficiency using three guide RNAs (gRNA), crRNA1, crRNA2 and crRNA3. These gRNAs recognize different portions of the target locus, but also overlap each other in the target locus. We also examined the effects of commercially available KI -enhancing drugs (including SCR7, L755,507, RS-1, and HDR enhancer) on i-GONAD-mediated KI efficiency. Results The KI efficiency in rat fetuses generated after i-GONAD with crRNA2 and single-stranded ODN was significantly higher (24%) than crRNA1 (5%; p < 0.05) or crRNA3 (0%; p < 0.01). The KI efficiency of i-GONAD with triple gRNAs was 11%. These findings suggest that KI efficiency largely depends on the type of gRNA used. Furthermore, the KI efficiency drugs, SCR7, L755,507 and HDR enhancer, all of which are known to enhance KI efficiency, increased KI efficiency using the i-GONAD with crRNA1 protocol. In contrast, only L755,507 (15 μM) increased KI efficiency using the i-GONAD with crRNA2 protocol. None of them were significantly different. Conclusions We attempted to improve the KI efficiency of i-GONAD in rats. We demonstrated that the choice of gRNA is important for determining KI efficiency and insertion and deletion rates. Some drugs (e.g. SCR7, L755,507 and HDR enhancer) that are known to increase KI efficiency in culture cells were found to be effective in i-GONAD in rats, but their effects were limited.

Zip Trees

We introduce the zip tree , 1 a form of randomized binary search tree that integrates previous ideas into one practical, performant, and pleasant-to-implement package. A zip tree is a binary search tree in which each node has a numeric rank and the tree is (max)-heap-ordered with respect to ranks, with rank ties broken in favor of smaller keys. Zip trees are essentially treaps [8], except that ranks are drawn from a geometric distribution instead of a uniform distribution, and we allow rank ties. These changes enable us to use fewer random bits per node. We perform insertions and deletions by unmerging and merging paths ( unzipping and zipping ) rather than by doing rotations, which avoids some pointer changes and improves efficiency. The methods of zipping and unzipping take inspiration from previous top-down approaches to insertion and deletion by Stephenson [10], Martínez and Roura [5], and Sprugnoli [9]. From a theoretical standpoint, this work provides two main results. First, zip trees require only O (log log n ) bits (with high probability) to represent the largest rank in an n -node binary search tree; previous data structures require O (log n ) bits for the largest rank. Second, zip trees are naturally isomorphic to skip lists [7], and simplify Dean and Jones’ mapping between skip lists

HyINDEL – A Hybrid approach for Detection of Insertions and Deletions

Insertion and deletion (INDELs) mutations, the most common type of structural variation in the human genome, have been implicated in numerous human traits and diseases including rare genetic disorders and cancer. Next generation sequencing (NGS) technologies have drastically reduced the cost of sequencing whole genomes, greatly contributing to genome-wide detection of structural variants. However, due to large variations in INDEL sizes and presence of low complexity and repeat regions, their detection remains a challenge. Here we present a hybrid approach, HyINDEL, which integrates clustering, split-mapping and assembly-based approaches, for the detection of INDELs of all sizes (from small to large) and also identifies the insertion sequences. The method starts with identifying clusters of discordant and soft-clip reads which are validated by depth-of-coverage and alignment of soft-clip reads to identify candidate INDELs, while the assembly -based approach is used in identifying the insertion sequence. Performance of HyINDEL is evaluated on both simulated and real datasets and compared with state-of-the-art tools. A significant improvement in recall and F-score metrics as well as in breakpoint support is observed on using soft-clip alignments. It is freely available at https://github.com/alok123t/HyINDEL.