Computational Approaches and Challenges in Spatial Transcriptomics

The development of spatial transcriptomics(ST)technologies has transformed genetic research from a single-cell data level to a two-dimensional spatial coordinate system and facilitated the study of the composition and function of various cell subsets in different environments and organs.The large-scale data generated by these ST technologies,which contain spatial gene expression information,have elicited the need for spatially resolved approaches to meet the requirements of computational and biological data interpretation.These requirements include dealing with the explosive growth of data to determine the cell-level and gene-level expression,correcting the inner batch effect and loss of expression to improve the data quality,conducting efficient interpretation and in-depth knowledge mining both at the single-cell and tissue-wide levels,and conducting multi-omics integration analysis to provide an extensible framework toward the in-depth understanding of biological processes.However,algorithms designed specifically for ST technologies to meet these requirements are still in their infancy.Here,we review computational approaches to these problems in light of corresponding issues and challenges,and present forward-looking insights into algorithm development.

CRISPR-detector:fast and accurate detection,visualization,and annotation of genome-wide mutations induced by genome editing events

The leading-edge CRISPR/CRISPR-associated technology is revolutionizing biotechnologies through genome editing.To track on/off-target events with emerging new editing techniques,improved bioinformatic tools are indispensable.Existing tools suffer from limitations in speed and scalability,especially with whole-genome sequencing(WGS)data analysis.To address these limitations,we have developed a comprehensive tool called CRISPR-detector,a web-based and locally deployable pipeline for genome editing sequence analysis.The core analysis module of CRISPR-detector is based on the Sentieon TNscope pipeline,with additional novel annotation and visualization modules designed to fit CRISPR applications.Co-analysis of the treated and control samples is performed to remove existing background variants prior to genome editing.CRISPR-detector offers optimized scalability,enabling WGS data analysis beyond Browser Extensible Data file-defined regions,with improved accuracy due to haplotype-based variant calling to handle sequencing errors.In addition,the tool also provides integrated structural variation calling and includes functional and clinical annotations of editing-induced mutations appreciated by users.These advantages facilitate rapid and efficient detection of mutations induced by genome editing events,especially for datasets generated from WGS.The web-based version of CRISPR-detector is available at https://db.cngb.org/crispr-detector,and the locally deployable version is available at https://github.com/hlcas/CRISPR-detector.

Corrigendum to“CRISPR-detector:fast and accurate detection,visualization,and annotation of genome-wide mutations induced by genome editing events”[Journal of Genetics and Genomics(2023)563-572]

This corrigendum clarifies information in the article“CRISPR-detector:fast and accurate detection,visualization,and annotation of genomewide mutations induced by genome editing events”by Huang et al.(2023).In the figure legend of Fig.S1,the sentence“CRISPR-detector failed to report insertions larger than 72 bp while CRISPResso2 failed to report insertions larger than 53 bp.”should be corrected into“CRISPR-detector failed to report deletions larger than 71 bp while CRISPResso2 failed to report deletions larger than 53 bp.”