CRISPR-Analytics (CRISPR-A): A platform for precise analytics and simulations for gene editing

Sanvicente-García, Marta; García-Valiente, Albert; Jouide, Socayna; Jaraba-Wallace, Jessica; Bautista, Eric; Escobosa, Marc; Sánchez-Mejías, Avencia; Güell, Marc

doi:10.1371/journal.pcbi.1011137

Cited by 3 publications

(2 citation statements)

References 51 publications

(71 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Furthermore, in our analysis of the gene editing data of the AAVS1 locus, KMAP identified four distinct editing patterns. Remarkably, these patterns correspond to the top 4 patterns reported in the original publication (Sanvicente-García et al, 2023), reinforcing KMAP’s capability to discern and visualize complex genetic patterns.…”

Section: Introductionsupporting

confidence: 72%

“…We use KMAP to detect editing patterns from gene editing data of the Adeno-Associated Virus Site 1 (AAVS1) locus. Sanvicente-García et al (2023) used a previously described (Doench et al, 2016; Mali et al, 2013) guide RNA with a 20nt protospacer that targeted the genomic location (chr19:55,115,752-55,115,771; GRCh38/hg38) on the human genome. The Streptococcus pyogenes Cas9 (SpCas9) protein will make a double stranded break (DSB) at three nucleotides upstream of the PAM, after that the broken DNAs are ligated by non-homologous end joining (NHEJ), which leads to different DNA editing results (mainly random small indels).…”

Section: Ht-selex Data Analysismentioning

confidence: 99%

See 1 more Smart Citation

KMAP: Kmer Manifold Approximation and Projection for visualizing DNA sequences

Fu,

Niskanen,

Wei

et al. 2024

Preprint

Self Cite

View full text Add to dashboard Cite

Identifying and illustrating patterns in DNA sequences is a crucial task in various biological data analyses. In this task, patterns are often represented by sets of kmers, the fundamental building blocks of DNA sequences. To visually unveil these patterns, we could project each kmer onto a point in two-dimensional (2D) space. However, this projection poses challenges due to the high-dimensional nature of kmers and their unique mathematical properties. Here, we established a mathematical system to address the peculiarities of the kmer manifold. Leveraging this kmer manifold theory, we developed a statistical method named KMAP for detecting kmer patterns and visualizing them in 2D space. We applied KMAP to three distinct datasets to showcase its utility. KMAP achieved a comparable performance to the classical method MEME, with approximately 90% similarity in motif discovery from HT-SELEX data. In the analysis of H3K27ac ChIP-seq data from Ewing Sarcoma (EWS), we found that BACH1, OTX2 and ERG1 might affect EWS prognosis by binding to promoter and enhancer regions across the genome. We also found that FLI1 bound to the enhancer regions after ETV6 degradation, which showed the competitive binding between ETV6 and FLI1. Moreover, KMAP identified four prevalent patterns in gene editing data of the AAVS1 locus, aligning with findings reported in the literature. These applications underscore that KMAP could be a valuable tool across various biological contexts. KMAP is freely available at: https://github.com/chengl7-lab/kmap.

show abstract

Section: Introductionsupporting

confidence: 72%

Section: Ht-selex Data Analysismentioning

confidence: 99%

KMAP: Kmer Manifold Approximation and Projection for visualizing DNA sequences

Fu,

Niskanen,

Wei

et al. 2024

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

A qualitative assessment of using ChatGPT as large language model for scientific workflow development

Sänger,

De Mecquenem,

Lewińska

et al. 2024

GigaScience

View full text Add to dashboard Cite

Background Scientific workflow systems are increasingly popular for expressing and executing complex data analysis pipelines over large datasets, as they offer reproducibility, dependability, and scalability of analyses by automatic parallelization on large compute clusters. However, implementing workflows is difficult due to the involvement of many black-box tools and the deep infrastructure stack necessary for their execution. Simultaneously, user-supporting tools are rare, and the number of available examples is much lower than in classical programming languages. Results To address these challenges, we investigate the efficiency of large language models (LLMs), specifically ChatGPT, to support users when dealing with scientific workflows. We performed 3 user studies in 2 scientific domains to evaluate ChatGPT for comprehending, adapting, and extending workflows. Our results indicate that LLMs efficiently interpret workflows but achieve lower performance for exchanging components or purposeful workflow extensions. We characterize their limitations in these challenging scenarios and suggest future research directions. Conclusions Our results show a high accuracy for comprehending and explaining scientific workflows while achieving a reduced performance for modifying and extending workflow descriptions. These findings clearly illustrate the need for further research in this area.

show abstract

Quantifying allele-specific CRISPR editing activity with CRISPECTOR2.0

Assa,

Kalter,

Rosenberg

et al. 2024

Nucleic Acids Research

View full text Add to dashboard Cite

Off-target effects present a significant impediment to the safe and efficient use of CRISPR-Cas genome editing. Since off-target activity is influenced by the genomic sequence, the presence of sequence variants leads to varying on- and off-target profiles among different alleles or individuals. However, a reliable tool that quantifies genome editing activity in an allelic context is not available. Here, we introduce CRISPECTOR2.0, an extended version of our previously published software tool CRISPECTOR, with an allele-specific editing activity quantification option. CRISPECTOR2.0 enables reference-free, allele-aware, precise quantification of on- and off-target activity, by using de novo sample-specific single nucleotide variant (SNV) detection and statistical-based allele-calling algorithms. We demonstrate CRISPECTOR2.0 efficacy in analyzing samples containing multiple alleles and quantifying allele-specific editing activity, using data from diverse cell types, including primary human cells, plants, and an original extensive human cell line database. We identified instances where an SNV induced changes in the protospacer adjacent motif sequence, resulting in allele-specific editing. Intriguingly, differential allelic editing was also observed in regions carrying distal SNVs, hinting at the involvement of additional epigenetic factors. Our findings highlight the importance of allele-specific editing measurement as a milestone in the adaptation of efficient, accurate, and safe personalized genome editing.

show abstract

CRISPR-Analytics (CRISPR-A): A platform for precise analytics and simulations for gene editing

Cited by 3 publications

References 51 publications

KMAP: Kmer Manifold Approximation and Projection for visualizing DNA sequences

KMAP: Kmer Manifold Approximation and Projection for visualizing DNA sequences

A qualitative assessment of using ChatGPT as large language model for scientific workflow development

Quantifying allele-specific CRISPR editing activity with CRISPECTOR2.0

Contact Info

Product

Resources

About