High-resolution genome-wide mapping of chromosome-arm-scale truncations induced by CRISPR-Cas9 editing

Lazar, Nathan H.; Çelik, Safiye; Chen, Lu; Fay, Marta; Irish, Jonathan C.; Jensen, James; Tillinghast, Conor A.; Urbanik, John; Bone, William P.; Roberts, Genevieve H.L.; Gibson, Christopher C.; Haque, Imran S.

doi:10.1101/2023.04.15.537038

Cited by 5 publications

(2 citation statements)

References 64 publications

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“…It is particularly relevant when the measured intervals exceed the distances between genes, increasing the likelihood of multiple genes being contained within the intervals. In such situations, which are common in studying large genomic domains, but also relevant to CRISPR screens results [ 33 ], SAGO is essential because the assumption of conventional statistics (that there is independence between genes) does not hold. We have demonstrated this with replication timing, compartments, copy number alterations, LADs and large closed chromatin domains ( Figure 3 and Figure 4 ).…”

Section: Discussionmentioning

confidence: 99%

A Cyclic Permutation Approach to Removing Spatial Dependency between Clustered Gene Ontology Terms

Rapoport,

Greenberg,

Yakhini

et al. 2024

Biology

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Traditional gene set enrichment analysis falters when applied to large genomic domains, where neighboring genes often share functions. This spatial dependency creates misleading enrichments, mistaking mere physical proximity for genuine biological connections. Here we present Spatial Adjusted Gene Ontology (SAGO), a novel cyclic permutation-based approach, to tackle this challenge. SAGO separates enrichments due to spatial proximity from genuine biological links by incorporating the genes’ spatial arrangement into the analysis. We applied SAGO to various datasets in which the identified genomic intervals are large, including replication timing domains, large H3K9me3 and H3K27me3 domains, HiC compartments and lamina-associated domains (LADs). Intriguingly, applying SAGO to prostate cancer samples with large copy number alteration (CNA) domains eliminated most of the enriched GO terms, thus helping to accurately identify biologically relevant gene sets linked to oncogenic processes, free from spatial bias.

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Section: Discussionmentioning

confidence: 99%

A Cyclic Permutation Approach to Removing Spatial Dependency between Clustered Gene Ontology Terms

Rapoport,

Greenberg,

Yakhini

et al. 2024

Biology

View full text Add to dashboard Cite

show abstract

“…The visual interpretation of cellular phenotypes through microscopy is widely used to drive biomedical discovery across a variety of questions in biology, including subcellular protein localization 1,2 , mitochondrial phenotypes 3,4 , cell cycle stages 5,6 , as well as chemical 7 and genetic perturbations 8,9 . Machine learning has the potential to unlock rich feature extraction of cellular morphological phenotypes that has thus far remained largely inaccessible to investigators [10][11][12] , making single-cell morphological profiling among the current roster of single-cell omics in its own right [13][14][15][16] . Indeed, deep learning now powers robust cell segmentation methods [17][18][19][20] , as well as cell phenotyping using classification networks 21,22 .…”

Section: Introductionmentioning

confidence: 99%

Unbiased single-cell morphology with self-supervised vision transformers

Doron

Moutakanni²,

Chen

et al. 2023

Preprint

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Accurately quantifying cellular morphology at scale could substantially empower existing single-cell approaches. However, measuring cell morphology remains an active field of research, which has inspired multiple computer vision algorithms over the years. Here, we show that DINO, a vision-transformer based, self-supervised algorithm, has a remarkable ability for learning rich representations of cellular morphology without manual annotations or any other type of supervision. We evaluate DINO on a wide variety of tasks across three publicly available imaging datasets of diverse specifications and biological focus. We find that DINO encodes meaningful features of cellular morphology at multiple scales, from subcellular and single-cell resolution, to multi-cellular and aggregated experimental groups. Importantly, DINO successfully uncovers a hierarchy of biological and technical factors of variation in imaging datasets. The results show that DINO can support the study of unknown biological variation, including single-cell heterogeneity and relationships between samples, making it an excellent tool for image-based biological discovery.

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A benchmark of computational methods for correcting biases of established and unknown origin in CRISPR-Cas9 screening data

Vinceti,

Iannuzzi,

Boyle

et al. 2024

Preprint

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CRISPR-Cas9 screens stand as formidable tools for investigating biology with unprecedented precision and scale. One of their principal applications involves probing large panels of immortalised human cancer cell lines for viability reduction responses upon systematic genetic knock-out at a genome-wide level, to identify novel cancer dependencies and therapeutic targets. However, biases in CRISPR-Cas9 screens' data pose challenges, leading to potential confounding effects on their interpretation and compromising their overall quality. The mode of action of the Cas9 enzyme, exerted by the induction of DNA double-strand breaks at a locus targeted by a specifically designed single-guide RNA (sgRNA), is influenced by specific structural features of the target site, including copy number amplifications (CN bias). More worryingly, proximal targeted loci tend to generate similar gene-independent responses to CRISPR-Cas9 targeting (proximity bias), possibly due to Cas9-induced whole chromosome-arm truncations or other unknown genomic structural features and different chromatin accessibility levels. Different computational methods have been proposed to correct these biases in silico, each based on different modelling assumptions. We have benchmarked seven of the latest methods, rigorously evaluating their effectiveness for the first time in reducing both CN and proximity bias in the two largest publicly available cell-line-based CRISPR-Cas9 screens to date. We have also evaluated the ability of each method in preserving data quality and heterogeneity by assessing the extent to which the processed data allows accurate detection of true positive essential genes, established oncogenetic addictions, and known/novel biomarkers of cancer dependency. Our analysis sheds light on the ability of each method to correct biases arising from structural properties and other possible unknown factors associated with CRISPR-Cas9 screen data under different scenarios. In particular, it shows that among all tested methods CRISPRcleanR outperforms other methods in correcting both CN and proximity biases, while Chronos yields a final dataset better able to recapitulate known sets of essential and non-essential genes. Overall, our investigation provides guidance for the selection of the most appropriate bias-correction method, based on its strengths, weaknesses and experimental settings.

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High-resolution genome-wide mapping of chromosome-arm-scale truncations induced by CRISPR-Cas9 editing

Cited by 5 publications

References 64 publications

A Cyclic Permutation Approach to Removing Spatial Dependency between Clustered Gene Ontology Terms

A Cyclic Permutation Approach to Removing Spatial Dependency between Clustered Gene Ontology Terms

Unbiased single-cell morphology with self-supervised vision transformers

A benchmark of computational methods for correcting biases of established and unknown origin in CRISPR-Cas9 screening data

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