Human Protein-coding Genes Research Articles

From proteome to pathogenesis: investigating polycystic ovary syndrome with Mendelian randomization analysis.

Polycystic ovary syndrome (PCOS) is defined by oligo/anovulation, hyperandrogenism, and polycystic ovaries with uncertain pathogenesis. The proteome represents a substantial source of therapeutic targets, and their coding genes may elucidate the mechanisms underlying PCOS. However, reports on the profiles of the human plasma protein-coding genes and PCOS are limited. Here, we aimed to investigate novel biomarkers or drug targets for PCOS by integrating genetics and the human plasma proteome. Our study acquired the protein quantitative trait loci from DECODE Genetics, offering 4,907 proteins in 35,559 individuals while obtaining PCOS summary statistics by accessing the FinnGen biobank (1,639 cases and 218,970 controls) and the genome-wide association study catalog (797 cases and 140,558 controls). Herein, we sequentially used two-sample Mendelian randomization (MR) analyses and colocalization to verify the causal link between candidate proteins, their coding genes, and PCOS. Further PCOS data download was conducted by accessing the Gene Expression Omnibus and Zenodo platforms. Gene expression level analysis, pathway enrichment analysis, immune cell infiltration, and transcription factor prediction were performed, aiming at detecting specific cell types with enriched expression and exploring potential optimized treatments for PCOS. MR analysis revealed 243 protein-coding genes with a causal relationship to PCOS risk, of which 12 were prioritized with the most significant evidence. Through colocalization analysis, three key genes, CUB domain-containing protein 1 (CDCP1), glutaredoxin 2 (GLRX2), and kirre-like nephrin family adhesion molecule 2 (KIRREL2), were identified. Subsequently, the three genes were strongly related to immune function and metabolism in terms of biological significance. In single-cell analysis, the expression levels of genes in ovarian theca cells were explored. Overall, three protein-coding genes (CDCP1, GLRX2, and KIRREL2) may be related to a higher PCOS risk, suggesting that they may be entry points for exploration of PCOS pathogenesis and treatment, warranting further clinical investigations.

The GC-content at the 5′ ends of human protein-coding genes is undergoing mutational decay

BackgroundIn vertebrates, most protein-coding genes have a peak of GC-content near their 5′ transcriptional start site (TSS). This feature promotes both the efficient nuclear export and translation of mRNAs. Despite the importance of GC-content for RNA metabolism, its general features, origin, and maintenance remain mysterious. We investigate the evolutionary forces shaping GC-content at the transcriptional start site (TSS) of genes through both comparative genomic analysis of nucleotide substitution rates between different species and by examining human de novo mutations.ResultsOur data suggests that GC-peaks at TSSs were present in the last common ancestor of amniotes, and likely that of vertebrates. We observe that in apes and rodents, where recombination is directed away from TSSs by PRDM9, GC-content at the 5′ end of protein-coding gene is currently undergoing mutational decay. In canids, which lack PRDM9 and perform recombination at TSSs, GC-content at the 5′ end of protein-coding is increasing. We show that these patterns extend into the 5′ end of the open reading frame, thus impacting synonymous codon position choices.ConclusionsOur results indicate that the dynamics of this GC-peak in amniotes is largely shaped by historic patterns of recombination. Since decay of GC-content towards the mutation rate equilibrium is the default state for non-functional DNA, the observed decrease in GC-content at TSSs in apes and rodents indicates that the GC-peak is not being maintained by selection on most protein-coding genes in those species.

A large-scale in vivo screen to investigate the roles of human genes in Drosophila melanogaster.

Understanding the signaling pathways in which genes participate is essential for discovering the etiology of diseases in humans. The model organism, Drosophila melanogaster, has been crucial in understanding the signaling pathways in humans, given the evolutionary conservation of a significant number of genes between the two species. Genetic screens using Drosophila are a useful way of testing large number of genes to study their function and roles within signaling pathways. We conducted a large-scale genetic screen to identify which human genes cause an alteration in the morphology of the Drosophila eye. The GMR-Gal4 was employed to activate a single UAS-human gene in the eye tissue. In total, we screened 802 UAS-human gene stocks, corresponding to 787 human protein-coding genes, for the ability to influence eye development. We found that overexpression of 64 human genes were capable of disrupting eye development, as determined by phenotypic changes in eye texture, size, shape, bristle morphology, and ommatidia organization. Subsequent analysis revealed that the fly genome encodes proteins that are homologous to a majority of the 64 human genes, raising the possibility that overexpression of these transgenes altered eye development by altering the activity of evolutionarily conserved developmental signaling pathways. Consistent with this hypothesis, a secondary screen demonstrated that overexpression of fly homologs produced phenotypes that mimicked those produced by overexpression of the human gene. Our screening has identified 64 human genes capable of inducing phenotypes in the fly, offering a foundation for ongoing research aimed at understanding functionally conserved pathways across species.

Eighteen genes primarily expressed in the testis are not required for male fertility in mice†.

There are approximately 20000 protein-coding genes in humans and mice. More than 1000 of these genes are predominantly expressed in the testis or are testis-specific and thought to play an important role in male reproduction. Through the production of gene knockout mouse models and phenotypic evaluations, many genes essential for spermatogenesis, sperm maturation, and fertilization have been discovered, greatly contributing to the elucidation of their molecular mechanisms. On the other hand, there are many cases in which single-gene knockout models do not affect fertility, indicating that tissue-specific genes are not always critical. Here, we selected 18 genes whose mRNA expression is restricted to the testis or higher than in other tissues, but whose function in male reproduction is unknown. We then created single-gene KO mouse models using the CRISPR/Cas9 system. The established KO males were subjected to mating tests and screened for effects on fecundity, revealing that these genes were not essential for spermatogenesis and male fertility. This knowledge will contribute to understanding the functions of genes characteristic of the testis and identify the cause of male infertility.

Human promoter directionality is determined by transcriptional initiation and the opposing activities of INTS11 and CDK9.

RNA polymerase II (RNAPII) transcription initiates bidirectionally at many human protein-coding genes. Sense transcription usually dominates and leads to messenger RNA production, whereas antisense transcription rapidly terminates. The basis for this directionality is not fully understood. Here, we show that sense transcriptional initiation is more efficient than in the antisense direction, which establishes initial promoter directionality. After transcription begins, the opposing functions of the endonucleolytic subunit of Integrator, INTS11, and cyclin-dependent kinase 9 (CDK9) maintain directionality. Specifically, INTS11 terminates antisense transcription, whereas sense transcription is protected from INTS11-dependent attenuation by CDK9 activity. Strikingly, INTS11 attenuates transcription in both directions upon CDK9 inhibition, and the engineered recruitment of CDK9 desensitises transcription to INTS11. Therefore, the preferential initiation of sense transcription and the opposing activities of CDK9 and INTS11 explain mammalian promoter directionality.

Human promoter directionality is determined by transcriptional initiation and the opposing activities of INTS11 and CDK9

RNA polymerase II (RNAPII) transcription initiates bidirectionally at many human protein-coding genes. Sense transcription usually dominates and leads to messenger RNA production, whereas antisense transcription rapidly terminates. The basis for this directionality is not fully understood. Here, we show that sense transcriptional initiation is more efficient than in the antisense direction, which establishes initial promoter directionality. After transcription begins, the opposing functions of the endonucleolytic subunit of Integrator, INTS11, and cyclin-dependent kinase 9 (CDK9) maintain directionality. Specifically, INTS11 terminates antisense transcription, whereas sense transcription is protected from INTS11-dependent attenuation by CDK9 activity. Strikingly, INTS11 attenuates transcription in both directions upon CDK9 inhibition, and the engineered recruitment of CDK9 desensitises transcription to INTS11. Therefore, the preferential initiation of sense transcription and the opposing activities of CDK9 and INTS11 explain mammalian promoter directionality.

Housekeeping protein-coding genes interrogated with tissue and individual variations

Housekeeping protein-coding genes are stably expressed genes in cells and tissues that are thought to be engaged in fundamental cellular biological functions. They are often utilized as normalization references in molecular biology research and are especially important in integrated bioinformatic investigations. Prior studies have examined human housekeeping protein-coding genes by analyzing various gene expression datasets. The inclusion of different tissue types significantly impacted the discovery of housekeeping genes. In this report, we investigated particularly individual human subject expression differences in protein-coding genes across different tissue types. We used GTEx V8 gene expression datasets obtained from more than 16,000 human normal tissue samples. Furthermore, the Gini index is utilized to investigate the expression variations of protein-coding genes between tissue and individual donor subjects. Housekeeping protein-coding genes found using Gini index profiles may vary depending on the tissue subtypes investigated, particularly given the diverse sample size collections across the GTEx tissue subtypes. We subsequently selected major tissues and identified subsets of housekeeping genes with stable expression levels among human donors within those tissues. In this work, we provide alternative sets of housekeeping protein-coding genes that show more consistent expression patterns in human subjects across major solid organs. Weblink: https://hpsv.ibms.sinica.edu.tw.

Essentiality, protein–protein interactions and evolutionary properties are key predictors for identifying cancer-associated genes using machine learning

The distinctive nature of cancer as a disease prompts an exploration of the special characteristics the genes implicated in cancer exhibit. The identification of cancer-associated genes and their characteristics is crucial to further our understanding of this disease and enhanced likelihood of therapeutic drug targets success. However, the rate at which cancer genes are being identified experimentally is slow. Applying predictive analysis techniques, through the building of accurate machine learning models, is potentially a useful approach in enhancing the identification rate of these genes and their characteristics. Here, we investigated gene essentiality scores and found that they tend to be higher for cancer-associated genes compared to other protein-coding human genes. We built a dataset of extended gene properties linked to essentiality and used it to train a machine-learning model; this model reached 89% accuracy and > 0.85 for the Area Under Curve (AUC). The model showed that essentiality, evolutionary-related properties, and properties arising from protein–protein interaction networks are particularly effective in predicting cancer-associated genes. We were able to use the model to identify potential candidate genes that have not been previously linked to cancer. Prioritising genes that score highly by our methods could aid scientists in their cancer genes research.

Abstract 3513: A CNN-based approach with efficient transfer learning improves microRNA-mRNA prediction

Abstract MicroRNAs play a crucial role in post-transcriptional regulation, influencing over 60% of human protein-coding genes by targeting specific mRNA sites to suppress protein translation. Various predictive algorithms aim to discern potential microRNA-mRNA pairs. Current approaches primarily employ sequence alignment, machine learning, and deep learning, yet encounter challenges such as complex data pre-processing, time-consuming model generation, and limited binding site precision. Additionally, some methods rely on inefficient RNN-based models, resulting in sluggish predictions. To address these issues, we proposed a CNN-based algorithm combined with transfer learning for direct and precise prediction of microRNA-mRNA binding sites, eliminating the need for extensive preprocessing. We introduced two models in this study: the per-based model and the miRNA-target binding decision model. The former screens potential target sites on 3’-UTR sequences, while the latter guides the decision-making process for miRNA-target pairs. The per-based model utilized a public database, extracting 786,447 human microRNA-mRNA pairs verified by CLIP-seq. It employed sequence alignment approaches to determine putative binding sites and per-base binding states. MicroRNA sequences and seed regions served as the initial convolutional kernels in the deep learning model, combined with encoded full-length 3’-UTR sequences of mRNAs as inputs for the fine-tuned U-Net architecture. For the miRNA-target binding decision model, we excluded the per-based model's decoder and initialized a new classification layer for target-site prediction. Leveraging experimental validation datasets from public databases, we extracted 2,846 binding and 1,058 non-binding human microRNA-mRNA pairs. The pre-trained model was fine-tuned on these 3,904 pairs. Both models underwent training with cycle learning rate, focal loss, gradient clipping, and weighted decay to address dataset imbalances. The dataset was split into 80% training and 20% testing data, with balanced accuracy as the evaluation metric. The per-based model achieved a robust 82.14% balanced accuracy on the test data, excelling in handling imbalanced datasets in per-based tasks. It swiftly and accurately identified nucleotide binding states. The miRNA-target binding decision model outperformed existing methods with a balanced accuracy of 80.39% on the test data. In contrast to many deep learning methods requiring additional preprocessing, our algorithm directly predicts per-base binding states from full-length sequences. It seamlessly transfers knowledge from the per-based to the miRNA-target model. Importantly, our approach relies solely on seed regions, eliminating the need for prior knowledge and enhancing microRNA target prediction reliability. This advancement holds promise for biomedical and clinical researchers, offering valuable insights. Citation Format: Chen-Hao Peng, Hui-Yu Chen, Da-Chuan Cheng, Eric Y. Chuang, Chien-Yueh Lee. A CNN-based approach with efficient transfer learning improves microRNA-mRNA prediction [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3513.

Comprehensive pan-carcinoma analysis of ITGB1 distortion and its potential clinical significance for cancer immunity

The human protein-coding gene ITGB1 (Integrin 1), also known as CD29, has a length of 58048 base pairs. The Integrin family's most prevalent subunit, it participates in the transmission of numerous intracellular signaling pathways. A thorough examination of ITGB1's functions in human malignancies, however, is inadequate and many of their relationships to the onset and development of human cancers remain unknown. In this work, we examined ITGB1's role in 33 human cancers. Finally, a multi-platform analysis revealed that three of the 33 malignancies had significantly altered ITGB1 expression in tumor tissues in comparison to normal tissues. In addition, it was discovered through survival analysis that ITGB1 was a stand-alone prognostic factor in a number of cancers. ITGB1 expression was linked to immune cell infiltration in colon cancer, according to an investigation of immune infiltration in pan-cancer. In the gene co-expression research, ITGB1 showed a positive connection with the majority of the cell proliferation and EMT indicators, indicating that ITGB1 may have an essential function in controlling cancer metastasis and proliferation. Our pan-cancer analysis of ITGB1 gives evidence in favor of a further investigation into its oncogenic function in various cancer types.

Physical Peculiarity of Two Sites in Human Promoters: Universality and Diverse Usage in Gene Function.

Since the discovery of physical peculiarities around transcription start sites (TSSs) and a site corresponding to the TATA box, research has revealed only the average features of these sites. Unsettled enigmas include the individual genes with these features and whether they relate to gene function. Herein, using 10 physical properties of DNA, including duplex DNA free energy, base stacking energy, protein-induced deformability, and stabilizing energy of Z-DNA, we clarified for the first time that approximately 97% of the promoters of 21,056 human protein-coding genes have distinctive physical properties around the TSS and/or position -27; of these, nearly 65% exhibited such properties at both sites. Furthermore, about 55% of the 21,056 genes had a minimum value of regional duplex DNA free energy within TSS-centered ±300 bp regions. Notably, distinctive physical properties within the promoters and free energies of the surrounding regions separated human protein-coding genes into five groups; each contained specific gene ontology (GO) terms. The group represented by immune response genes differed distinctly from the other four regarding the parameter of the free energies of the surrounding regions. A vital suggestion from this study is that physical-feature-based analyses of genomes may reveal new aspects of the organization and regulation of genes.

The Landscape of Point Mutations in Human Protein Coding Genes Leading to Pregnancy Loss.

Pregnancy loss is the most frequent complication of a pregnancy which is devastating for affected families and poses a significant challenge for the health care system. Genetic factors are known to play an important role in the etiology of pregnancy loss; however, despite advances in diagnostics, the causes remain unexplained in more than 30% of cases. In this review, we aggregated the results of the decade-long studies into the genetic risk factors of pregnancy loss (including miscarriage, termination for fetal abnormality, and recurrent pregnancy loss) in euploid pregnancies, focusing on the spectrum of point mutations associated with these conditions. We reviewed the evolution of molecular genetics methods used for the genetic research into causes of pregnancy loss, and collected information about 270 individual genetic variants in 196 unique genes reported as genetic cause of pregnancy loss. Among these, variants in 18 genes have been reported by multiple studies, and two or more variants were reported as causing pregnancy loss for 57 genes. Further analysis of the properties of all known pregnancy loss genes showed that they correspond to broadly expressed, highly evolutionary conserved genes involved in crucial cell differentiation and developmental processes and related signaling pathways. Given the features of known genes, we made an effort to construct a list of candidate genes, variants in which may be expected to contribute to pregnancy loss. We believe that our results may be useful for prediction of pregnancy loss risk in couples, as well as for further investigation and revealing genetic etiology of pregnancy loss.

Alternative Transcripts Diversify Genome Function for Phenome Relevance to Health and Diseases.

Manipulation using alternative exon splicing (AES), alternative transcription start (ATS), and alternative polyadenylation (APA) sites are key to transcript diversity underlying health and disease. All three are pervasive in organisms, present in at least 50% of human protein-coding genes. In fact, ATS and APA site use has the highest impact on protein identity, with their ability to alter which first and last exons are utilized as well as impacting stability and translation efficiency. These RNA variants have been shown to be highly specific, both in tissue type and stage, with demonstrated importance to cell proliferation, differentiation and the transition from fetal to adult cells. While alternative exon splicing has a limited effect on protein identity, its ubiquity highlights the importance of these minor alterations, which can alter other features such as localization. The three processes are also highly interwoven, with overlapping, complementary, and competing factors, RNA polymerase II and its CTD (C-terminal domain) chief among them. Their role in development means dysregulation leads to a wide variety of disorders and cancers, with some forms of disease disproportionately affected by specific mechanisms (AES, ATS, or APA). Challenges associated with the genome-wide profiling of RNA variants and their potential solutions are also discussed in this review.

Systematic transcriptional analysis of human cell lines for gene expression landscape and tumor representation

Cell lines are valuable resources as model for human biology and translational medicine. It is thus important to explore the concordance between the expression in various cell lines vis-à-vis human native and disease tissues. In this study, we investigate the expression of all human protein-coding genes in more than 1,000 human cell lines representing 27 cancer types by a genome-wide transcriptomics analysis. The cell line gene expression is compared with the corresponding profiles in various tissues, organs, single-cell types and cancers. Here, we present the expression for each cell line and give guidance for the most appropriate cell line for a given experimental study. In addition, we explore the cancer-related pathway and cytokine activity of the cell lines to aid human biology studies and drug development projects. All data are presented in an open access cell line section of the Human Protein Atlas to facilitate the exploration of all human protein-coding genes across these cell lines.

Optimized minimal genome-wide human sgRNA library

Genome-wide clustered regularly interspaced short palindromic repeats (CRISPR)-based knockout screening is revolting the genetic analysis of a cellular or molecular phenotype in question but is challenged by the large size of single-guide RNA (sgRNA) library. Here we designed a minimal genome-wide human sgRNA library, H-mLib, which is composed of 21,159 sgRNA pairs assembled based on a dedicated selection strategy from all potential SpCas9/sgRNAs in the human genome. These sgRNA pairs were cloned into a dual-gRNA vector each targeting one gene, resulting in a compact library size nearly identical to the number of human protein-coding genes. The performance of the H-mLib was benchmarked to other CRISPR libraries in a proliferation screening conducted in K562 cells. We also identified groups of core essential genes and cell-type specific essential genes by comparing the screening results from the K562 and Jurkat cells. Together, the H-mLib exemplified high specificity and sensitivity in identifying essential genes while containing minimal library complexity, emphasizing its advantages and applications in CRISPR screening with limited cell numbers.

Building alternative splicing and evolution-aware sequence-structure maps for protein repeats

Alternative splicing of repeats in proteins provides a mechanism for rewiring and fine-tuning protein interaction networks. In this work, we developed a robust and versatile method, ASPRING, to identify alternatively spliced protein repeats from gene annotations. ASPRING leverages evolutionary meaningful alternative splicing-aware hierarchical graphs to provide maps between protein repeats sequences and 3D structures. We re-think the definition of repeats by explicitly accounting for transcript diversity across several genes/species. Using a stringent sequence-based similarity criterion, we detected over 5,000 evolutionary conserved repeats by screening virtually all human protein-coding genes and their orthologs across a dozen species. Through a joint analysis of their sequences and structures, we extracted specificity-determining sequence signatures and assessed their implication in experimentally resolved and modelled protein interactions. Our findings demonstrate the widespread alternative usage of protein repeats in modulating protein interactions and open avenues for targeting repeat-mediated interactions.

Construction of HBV-HCC prognostic model and immune characteristics based on potential genes mining through protein interaction networks.

To search for human protein-coding genes related to hepatocellular carcinoma (HCC) in the context of hepatitis B virus (HBV) infection, and perform prognosis risk assessment. Genes related to HBV-HCC were selected through literature screening and protein-protein interaction (PPI) network database analysis. Prognosis potential genes (PPGs) were identified using Cox regression analysis. Patients were divided into high-risk and low-risk groups based on PPGs, and risk scores were calculated. Kaplan-Meier plots were used to analyze overall survival rates, and the results were predicted based on clinicopathological variables. Association analysis was also conducted with immune infiltration, immune therapy, and drug sensitivity. Experimental verification of the expression of PPGs was done in patient liver cancer tissue and normal liver tissue adjacent to tumors. The use of a prognosis potential genes risk assessment model can reliably predict the prognosis risk of patients, demonstrating strong predictive ability. Kaplan-Meier analysis showed that the overall survival rate of the low-risk group was significantly higher than that of the high-risk group. There were significant differences between the two subgroups in terms of immune infiltration and IC50 association analysis. Experimental verification revealed that CYP2C19, FLNC, and HNRNPC were highly expressed in liver cancer tissue, while UBE3A was expressed at a lower level. PPGs can be used to predict the prognosis risk of HBV-HCC patients and play an important role in the diagnosis and treatment of liver cancer. They also reveal their potential role in the tumor immune microenvironment, clinical-pathological characteristics, and prognosis.

The Extracellular Vesicles: Their Role and Future in Reproduction and Embryonic Implantation

Intercellular communication is an integral part of the physiological process in a living organism. Evidence has shown that a significant amount of human protein-coding genes is endowed with the production of membrane and secreted protein types. Some of these proteins possessed post-translational influence that interferes with cellular functions. Recently the concept of cell communication gravitated to the use of membrane-bound particles called Extracellular Vesicles (EVs). EVs are released from the host cell and impact on the recipient cell through its contents (Cargo). The cargo contains various particles such as lipids, proteins, RNA, and glycolipids derived from the host cells. The current widespread research in EVs has brought its mediating role in physiological and pathological processes to limelight. Though the regulatory part of EVs has been widely embraced by researchers, the discrepancy associated with its collection and isolation as well as its analysis has remained a subject of debate in the literature. The facts emanating from the literature suggest that EVs, with its prospective application in Reproductive biology, has prompted extensive research in the last decade. Its knowledge has broadened the scope of understanding the physiology and pathology scenarios involved in reproductive processes. As a potential biomarker, it has become a valuable tool for diagnosis, prognostic, and therapeutic purposes, especially in the context of reproductive processes. However, the challenges of standardizing the isolation, purification, and analyzing EVs have remained a nightmare that need to be surmounted. Emerging evidence has demonstrated its impact in gamete development, fertilization, and embryo implantation. Thus, it could serve as a platform to understand the mechanism of conception and implantation. By extension, define a therapeutic approach for women with recurrent pregnancy loss.

An expectation-maximization framework for comprehensive prediction of isoform-specific functions.

Advances in RNA sequencing technologies have achieved an unprecedented accuracy in the quantification of mRNA isoforms, but our knowledge of isoform-specific functions has lagged behind. There is a need to understand the functional consequences of differential splicing, which could be supported by the generation of accurate and comprehensive isoform-specific gene ontology annotations. We present isoform interpretation, a method that uses expectation-maximization to infer isoform-specific functions based on the relationship between sequence and functional isoform similarity. We predicted isoform-specific functional annotations for 85617 isoforms of 17900 protein-coding human genes spanning a range of 17430 distinct gene ontology terms. Comparison with a gold-standard corpus of manually annotated human isoform functions showed that isoform interpretation significantly outperforms state-of-the-art competing methods. We provide experimental evidence that functionally related isoforms predicted by isoform interpretation show a higher degree of domain sharing and expression correlation than functionally related genes. We also show that isoform sequence similarity correlates better with inferred isoform function than with gene-level function. Source code, documentation, and resource files are freely available under a GNU3 license at https://github.com/TheJacksonLaboratory/isopretEM and https://zenodo.org/record/7594321.

A CRISPR-del-based pipeline for complete gene knockout in human diploid cells.

The advance of CRISPR/Cas9 technology has enabled us easily to generate gene knockout cell lines by introducing insertion-deletion mutations (indels) at the target site via the error-prone non-homologous end joining repair system. Frameshift-promoting indels can disrupt gene functions by generation of a premature stop codon. However, there is growing evidence that targeted genes are not always knocked out by the indel-based gene disruption. Here, we established a pipeline of CRISPR-del, which induces a large chromosomal deletion by cutting two different target sites, to perform 'complete' gene knockout efficiently in human diploid cells. Quantitative analyses show that the frequency of gene deletion with this approach is much higher than that of conventional CRISPR-del methods. The lengths of the deleted genomic regions demonstrated in this study are longer than those of 95% of the human protein-coding genes. Furthermore, the pipeline enabled the generation of a model cell line having a bi-allelic cancer-associated chromosomal deletion. Overall, these data lead us to propose that the CRISPR-del pipeline is an efficient and practical approach for producing 'complete' gene knockout cell lines in human diploid cells.