The acceleration of large-scale sequencing and the progress in high-throughput computational analyses, defined as omics, was a hallmark for the comprehension of the biological processes in human health and diseases. In cancerology, the omics approach, initiated by genomics and transcriptomics studies, has revealed an incredible complexity with unsuspected molecular diversity within a same tumor type as well as spatial and temporal heterogeneity of tumors. The integration of multiple biological layers of omics studies brought oncology to a new paradigm, from tumor site classification to pan-cancer molecular classification, offering new therapeutic opportunities for precision medicine. In this review, we will provide a comprehensive overview of the latest innovations for multi-omics integration in oncology and summarize the largest multi-omics dataset available for adult and pediatric cancers. We will present multi-omics techniques for characterizing cancer biology and show how multi-omics data can be combined with clinical data for the identification of prognostic and treatment-specific biomarkers, opening the way to personalized therapy. To conclude, we will detail the newest strategies for dissecting the tumor immune environment and host–tumor interaction. We will explore the advances in immunomics and microbiomics for biomarker identification to guide therapeutic decision in immuno-oncology. Cancer is an important cause of death worldwide, even if its mortality has declined during the last decade . The IntUntil recently, cancer was widely considered as organ dependent and characterized by the site of apparition of the tumor. This hypothesis was slowly abandoned due to the heterogeneity present between two patients with a similar tumor  that wasThe use of single omics, such as genomics and transcriptomics, uncovered many driver genes to better comprehend the genomic landscape of cancer. Interestingly, some of these studies revealed the wide complementarity of genomics and transcriptomics, many of the driver genes being identified by either one or the other modality . This foTo this day, even with the new information that multi-omics approaches can bring, understanding how cancer develops and maintains is puzzling. Indeed, cancer cells interact with many different components including the host immune system . These iIn the first part, due to the growing interest in multi-omics, this review will address the challenges raised by omics and how to overcome them. In the second part, we will provide an overview of different databases that are useful in cancer research. In the third part, this review will attempt to illustrate how to integrate mutli-omics to clarify cancer complexity, and how the use of machine learning can help to predict survival and treatment response. Finally, we will give an overview of new methods to decipher the interaction between the host and the cancer cells and how it can bring opportunity for personalized therapy .To integrate the massive data flow generated through the different biological elements explored by NGS, innovative computational tools are needed for diminishing the data dimension, then making them manipulable by human hands. These tools allow researchers to fulfill the gap of missing knowledges and contribute to the discovery of novel biomarkers, deciphering the complexity of cancers . DespiteOne of these challenges is the heterogeneity that exists across the biological layers. It may differ in type, with numerical or categorical features, discrete or continuous variables with different ranges. The number of features can also vary between the different data sources, creating novel struggles to consider. Another burden, operating during sample collection, are missing values, setting up uninterpretable elements difficult to handle by some algorithms. Also, processing outliers and highly correlated variables might be burdensome to integrate as they might be irrelevant, create noise, or overfeed a system .To face these challenges, different tools have been created that we will summarize . Two maiStarting with exploratory matrices, herein is a brief overview of two algorithms using this approach: matrix factorization and logic data mining. These methods would be preferred for well-defined exhaustive datasets containing extreme values that need to be considered.Matrix factorization: joint and individual variation explained (JIVE), non-negative factorization (NMF), and structural learning and integrative decomposition (SLIDE) are some of the tools based on this approach. This algorithm uses a system of multiplication between columns and rows on a dataset decomposed in multiple matrices of smaller dimension. Each matrices have n columns, referring to n common objects, that will be multiplied by m rows, referring to other common objects. The matrix can contain different biological elements, that is, gene expression and miRNA measurement. The strength of matrix factorization is to cluster different matrices together but requires a dataset with scarce missing values .Logic data mining is a multistep procedure: 1) feature binarization, that assigns a threshold to convert each feature in binary values (0 or 1); 2) feature selection, that uses a machine learning approach to select a subset of data with relevant features introduced in a model; 3) extraction of logic formulas to build the final classification model. As an example, microarray logic analyzer (MALA) is based on this approach and has been used to identify overexpressed interrelated genes and proteins involved in a common pathway .We will now detail four types of algorithms to better conceptualize the probabilistic matrix approach. These models follow the principles of Gaussian probability, linear regression, or Bayesian statistics and are very useful to troubleshoot datasets with missing values.Gaussian latent variable model uses a probabilistic matrix starting with the dimension reduction of the dataset. The matrix composed of N columns and D rows will be decreased to a matrix with a lower dimensionality on D. D will be reduced in Q most relevant data. Relevant data will then be extracted to identify a pattern explaining the results. Because the algorithm is based on Gaussian probability, dataset with a Gaussian distribution perform better with this instrument than expression profiles near 0. Lower outliers close to the null value may cause misinterpretation during the analysis. However, missing values are well managed by the algorithm. A tool using this algorithm, iCluster, will be further explained in the next chapter .Generalized linear regression is one of the subcategories found in the generalized linear model. This algorithm is composed of three components: 1) random component; 2) systematic component; and 3) link function. The first step is the matrix generation, then a linear regression will be applied to the matrix finding the best possible linear relation to fit the expected values. The systematic components are distributed on the line while random components are outside of the line. The link function will identify a relationship between the linear predictor and the distribution of the random components. It aims to explain why some values are following a linear repartition while others are not. This model performs well when using data that are already explained on unexplained data. The accuracy might, however, be hindered by outliers that may introduce misinterpretation. This model is used in the icluster + tools .Z with n x k dimensions, following the rule of multivariate normal distribution. A joint integrative clustering will capture relevant features to individualize distinct clusters across omics datasets. The use of Baye’s theorem enables the analysis of factors with various distributions and correlation among datasets. The probabilistic model is also permissive of missing values  family tools used for multi-omics data integration. This unsupervised method infers principal component-based factors to decompose each matrix of M different omics components. The matrices will be transformed into a Z matrix of factors for each sample and M weight (W) matrices with features in rows and factors in columns, for each omics element. Downstream analysis will identify inference across Z and W matrices. Also, because the algorithm is based on a Bayesian framework, the distributions of the data are placed on the unobserved variables of the models and the algorithm will keep running until all the data have been characterized .To finish, despite the numerous tools that are now available, the perfect tool does not exist. The continual development of computational methods necessitates systematic evaluation (benchmarking) of the omics data analyses tools and methods . The majThe expansion and acceleration of NGS have generated a tremendous amount of data. In a collaborative effort, extensive omics databases have been created to stock and make those data publicly accessible to researchers, allowing for large meta-analysis for a tumor category or across cancer types. These datasets can host the sequencing output of different biological elements from bulk tumor or single-cell sequencing as well as clinical and treatment information. We will provide a summary of the main databases that are available for cancer research and detail the characteristics of each library with tumor types included and the biological and clinical contents provided .The Cancer Genome Atlas (TCGA) is the largest pan-cancer multi-omics database with clinical annotation allowing for large meta-analysis. TCGA is widely used by the research community, promoting new discoveries on tumor biology, evolution, and treatment specific biomarkers validated on meta-analysis across cancer types .The Cancer Immunome Database (TCIA) is a new database of immunogenic analysis of NGS data from 20 different types of solid tumors derived from TCGA database. This database has served for the elaboration of a pan-cancer immunogenomic classification for checkpoint blockade sensitivity .The International Cancer Genomics Consortium (ICGC) is the most ambitious biomedical efforts research since the human genome project. This database has permitted novel observation of cancer biology through the whole genome annotated alterations from 2,800 samples .The Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) contains genomic data from breast cancers. This database helped improving the classification and subtyping of breast tumors and already led to the discovery of 10 subgroups .Therapeutically Applicable Research to Generate Effective Treatments (TARGET) is a clinically annotate database hosting genomics and transcriptomics data from pediatric tumors. This dataset empowered the description of the driving process of childhood cancers, and was used, for example, for characterizing the immune environment of pediatric cancers and its prognostic impact .Cancer Research Institute (CRI) iAtlas is a database platform allowing the study of interaction between tumors and immune microenvironment, granting the possibility to explore immune response across genomics and clinical phenotypes. iAtlas (first version) allows researchers to explore these readouts and the relation between tumor types and immune response . CRI iAtThe dramatic acceleration in data production and the policy for data sharing have increased the comprehension of cancer complexity and fostered the integration of interrelated biological elements of omics analysis in more and more complex computational models that we will show in detail in the following.Tumor heterogeneity remains a hurdle to understand tumor biology. For example, for a same tumor type, there is a wide variation in clinical evolution across population of patients, highlighting differences in tumor cells to progress or mutate . These dOne of these tools that allows the sample classification is a microarray logic analyzer (MALA). MALA is based on logic data mining algorithm. Its follows a three-step process:  discreteTo better understand cancer, researchers need to characterize the different cancer subtypes. Subtyping cancer is more complicated and remains one of the major challenges in cancer research. The identification of subtypes can provide an understanding of the underlying molecular mechanisms and thereby help design precise treatment strategies for efficient cancer management. Contrary to classification that is more histologic, the subtypes are influenced by oncogenic alterations and/or modifications in the gene/protein expression . Moleculi.e., gene expression and miRNA), while the latter is intrinsic of one component and unrelated to others  algorithm. This tool aims to individualize two types of structures: joint and individual structures. The former is the biological patterns of the samples that are shared between the different component types  consists in multiplication between the columns and the rows of a matrix. The input data sets are formed by a matrix called mponents .WHI and intNMF considers heterogenous effects . nNMF involves a two-step process with network generation and integration of the network. To generate the network, a consensus matrix is built with a binary value attributed to each sample to reflect the connectivity between samples. Successive cycles are performed attributing a new binary value for every novel entry until the matrix is stabilized. The mean of the consensus matrix is made for each iterative cycle until the last cycle. This generates a consensus matrix for each data type integrated. The larger elements of the consensus matrix reflect the higher similarity between samples. After the networks are generated, samples can be considered as vertices and the consensus values as the edges. The network integration is based on the message passing theory (updating and combining network) that is processed on two ways: strong signals present in any data are conserved and consistent signals in multiple data are added up during an iterative process. Weak signals disappear while filtering out the noise. An advantage of nNMF is its ability to detect true cluster of small size with high reliability. Clinical application of nNMF demonstrated the capacity to establish novel clusters on head and neck squamous cell carcinoma, glioblastoma, and low-grade glioma data sets, suggesting a new comprehensive subtyping that eliminates previously unclassified samples .NMF and its extensions are based on matrix factorization algorithm that are efficient tools to treat a massive amount of data but are underperforming with missing values. To complement this part, iCluster family tools are good alternatives. iCluster is based on probabilistic matrices algorithm and iCluster is based on a Gaussian latent variable model. The basic concept of iCluster is to jointly estimate the link between the data with a dimension reduction principle: data and features are clustered together to maximize the correlation between data types . iClusteCNTN4 and RFTN1) for lung adenocarcinoma (The first extension of iCluster was iCluster+ (or iClusterplus), a model based on a generalized linear regression combined to the basic algorithm of iCluster. Compared to different models, iCluster + produces the best classification when integrating unknown datasets. It has been useful to construct two molecular subtypes and identified two core genes  in ovarian cancers. But it also demonstrated the role of BIN2 in ovarian cancer, and how it can be a favorable survival outcome (GPX2 for smoking induced cancer (lung squamous cell carcinoma), but also identified OAS2 and TRIM22 as modulator of the immune response  of glioblastoma oncogene rognosis . iProFun outcome . Finallyresponse .via supervised or unsupervised learning. Supervised learning relies on a labelled input dataset used to train and develop a function to predict the outcome on an experimental dataset. The algorithm attempts to find patterns relating the features to the given label. The second step is to integrate data test and to classify the validation data in the right label by following the identified patterns. The correlation between the given and predicted labels can be compared to assess the accuracy of the model. Unsupervised learning clusters the samples by identifying and regrouping different features following a similar pattern . It can be used for machine learning and for deep learning. ANN is a simple approach to create an artificial model, composed of neurons organized in layers. This is composed of an input layer, hidden layer, and an output layer. The input layer corresponds to the dataset given to the algorithm and the output layer corresponds to the possible outputs of the algorithm, in this case, a classification of the data. The hidden layer is what defines the complexity of the algorithm, it represents possible pathways linking the input layer to the output layer. For the integration of multi-omics, different blocks, specific to each type of omics data, are created. Depending on the biological question, different machine learning algorithms can be preferred.A vast diversity of algorithms are available for machine learning; random forest classifier and k-nearest neighbor are commonly supervised classifiers that are easily suitable for biomarker discovery. A random forest algorithm builds multiple decision trees that are randomly generated. Each decision tree provides a classification. The classification that is chosen in a majority of cases is used to classify each datapoint of the dataset . K-neareThese different approaches have been successfully applied to biological data to improve prognoses prediction. For example, ANN has already been used to analyze the survival in two breast cancer datasets. The model was able to predict the prognoses (favorable or unfavorable) and relapse probability . In the Deep learning approaches are a subset of machine learning that contains multiple hidden layers organized in a network allowing for progressive learning . Deep leSurvival analysis learning with multi-omics neural network (SALMON) attempts to aggregate and simplify the gene expression data to enable prognosis prediction. Deep learning approaches typically introduce an extensive number of parameters from a limited sample set that can introduce data overfitting rendering the models ineffective. In comparison, SALMON favorizes the use of eigengene matrices of gene co-expression modules to feed the model. This model was compared to other survival prognostic models on breast cancer datasets, including DeepSurv and random survival forest. SALMON predictor reached a higher survival concordance index than other methods and showed that the integration of multi-omics data combined with clinical and demographical features can increase the prediction performance. This algorithm was used to develop an age-specific prognostic score for breast cancer .CNN is very useful for deconvolutional observations of histological images used to classify and predict cancer prognoses. CNN was successful to detect and to predict patient outcomes on haematoxylin-eosin-stained tumor tissue microarray with a higher accuracy than the visual prognostic prediction. For that, the image was cut into spots that were characterized with a pre-trained convolutional neural network . MesoNetDrugCell is a visible neural network for drug response prediction. The model is built with two branches. The first one modulates the hierarchical organization of molecular subsystems in human cells. Each subsystem, involving small protein complexes, is connected to larger pathways and to cellular functions assigned from a bank of artificial neurons. The input layer is the mutation status from genomics data and the output corresponds to the state of the whole cell based on the genotype. The second branch of the system is a CNN assessing the fingerprint of a drug. The output from the two branches of the model is combined in a single layer of neurons integrating the response of a genotype to a certain treatment. To test on the first side of the model, they used two large drug screening resources: the Cancer Therapeutics Response Portal (CTRP) v2 and the Genomics of Drug Sensitivity in Cancer (GDSC) database. Using these two resources, it covers 684 drugs and 1,235 cell lines. Each cell-line genotype was represented with a binary vector (mutated or non-mutated). The chemical structure of the drug was represented by an average of 81 activated bits in the Morgan fingerprint vector. Drug cell was trained to associate each genotype-drug paired with its corresponding drug–response curve. As a last point, drug cell was clinically tested stratify cancer patients depending on their response to treatment. They tested drug cell on clinical trial data from 221 estrogen receptor positive metastatic breast cancer patients and showed that the model was able to predict the response to mTOR and CDK4/6 inhibitors. Drug cell also includes a feature for predicting the sensitivity to drug combination .In a number of studies, DNN was used to predict prognoses. As an example, DeepSurv is a tool defined as a prognostic model. With the integration of time-dependent and treatment-sensitive survival data to the model, it was trained to provide treatment recommendations. DeepSurv was tested on four real-life datasets: Worcester Heart Attack Study (WHAS), the Study to Understand Prognoses Preferences Outcomes and Risks of Treatment (SUPPORT), the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC), and Rotterdam & the German Breast Cancer Study Group (GBSG). DeepSurv takes input as the patient’s baseline data. The output is composed of one node estimating the log-risk function in the Cox model (estimation of the effect parameters without any consideration of the hazard function). As the second part, it uses a treatment recommender system. This system takes into account a group of patients assigned to a specific treatment group to predict the log-risk of a given treatment. Using the same base line hazard functions, it is possible to calculate the personal risk-ratio of prescribing one treatment and then, determine a treatment recommendation algorithm. To assess the possibility of a treatment recommendation system, DeepSurv was trained on the Rotterdam tumor bank to build the recommendation system that was then tested on GBSG. The treatment recommendation system demonstrated that, on a real clinical dataset, the patient’s survival would be increased by following the treatment recommendation of the algorithm. In comparison, a random survival forest-based recommendation system was not able to improve the patient’s outcome . To concOther examples of machine and deep learning models applicable for biomarker discovery are detailed in a recent review articles devoted to this subject  Zhu et . So far,Novel fields of interest have emerged to unravel host–tumor interactions  through The host-tumor interaction comprises a succession of interrelated steps facilitating or refraining an efficient immune recognition of tumor cells. These steps are dependent of the tumor biology, the host ability to recognize the tumor, and the mechanisms that will promote immune inflammation or immune evasion. Briefly, the somatic mutations of tumors result in the expression of neoantigens that can be exposed to and recognized by the host antigen presenting cells (APCs). This will activate an immune cascade responsible for the activation and recruitment of effector T cells in the tumor . These pIFNγ) or INFγ gene expression profile signature (The identification of biomarkers was first made possible by immunohistochemistry (IHC) assay that has the advantage of spatial annotation but is limited by the sparse number of features that can be assessed. A classification in three IHC immune states for tumor infiltrated lymphocytes (TILs) density is usually admitted with immune inflamed (or “hot”), immune excluded, and immune deserted (or “cold”) tumors . Few biomarkers are so far broadly recognized. Some from the IHC like tumor infiltrated lymphocytes (TILs) density, mostly for CD8, and the protein expression of PD-L1 in the tumor. Other biomarkers derive from NGS test: tumor mutation burden (TMB) and the level of expression of interferon-gamma  method  in threeTumor types were unevenly distributed between the immune groups, and thus highlighting the association of the immune phenotype and the diagnosis. Furthermore, the immune subtyping was correlated with the prognosis, inflammatory subtype having the best clinical outcome while wound healing dominant subtype had the worse survival. The clinical impact was observed across cancers and within a same cancer type. This work demonstrates that access to NGS, well clinically annotated large tumor database, and immunogenomics integration help understand the interplay of host immune system and tumor cells. The immune landscape characterization can also improve tumor classification and prognostication as shown in this pediatric database.A systematic meta-analysis of tumor and microenvironmental biomarkers for ICB sensitivity has investigated the relative impact of independent and combined biomarkers. An immunomics analysis from bulked-tumor transcriptomics and genomics was performed on a dataset from seven cancer types from 1,008 adult patients (CPI1000+) treated with immune checkpoint blockade (ICB) . The goaCXCL9 expression, a CD8 attractive chemokine, were the strongest predictors for ICB response, followed by CD8A expression, T-cell inflamed INFG gene expression, and CD274 expression. Due to the high prevalence of TMB in the literature, researchers decided to subdivide the somatic mutation by mutations fitting in an immunogenic signature. Some signatures like dinucleotide variants, that are source of amino-acid changes and generate immunogenic epitopes, ultraviolet (UV), or tobacco mutation signatures also came out as significant determinant of ICB response. In somatic copy number analysis, two copy number anomalies were identified as positive or negative predictor of response. Surprisingly, host factors like the loss of HLA heterogeneity or HLA subtypes did not show any impact on ICB sensitivity. They also performed single-cell RNA sequencing of a reactive CD8 TILs from patients to identify T-cell intrinsic markers of ICB sensitivity and highlighted CXCL13 and CCR5 gene involvement. The immune cell enumeration or BCR/TCR clonality assay were not assessed in this study.In univariate analysis, clonal and total TMB and CXCL9 expression had a better prediction accuracy than clonal TMB alone, yet inferior to the multivariate model. The integration of multiparameter biomarkers could explain approximatively 60% of the response to ICB in the different cancer types.A machine learning model for multivariate analysis including the significant determinant of response was a better predictor of ICB response than TMB alone in the initial cohort and three external independent validation datasets. Also, a two-parameter biomarker model combining clonal TMB and This study showed that multilayers integration of immunogenomics data and multiparameter models can improve the prediction of biomarker to anticipate the response to immunotherapy. It is open the opportunity to mechanistically solve host-tumor interaction and to understand how ICB remodel the tumor environment.IFNG, MZB1, JCHAIN, and IGLL5) in ICB responders. Using TRUST-algorithm for BCR clonotype enumeration, they observed an increase of clonal counts for heavy and light chains and in BCR diversity in responders. MCP-counter deconvolution algorithm also confirmed the enrichment of B cells in responders compared to non-responders. To confirm the role of B cell in ICB activity, they used IHC to assess B-cell density and to interrogate the spatial repartition of B cell and they performed functional study. The IHC confirmed an increase in B-cell density in responders and revealed a spatial organization in tertiary lymphoid structure (TLS) where CD20+ B cells colocalize with CD4+, CD8+, and FOXP3+ T cells. A previous study similarly showed a correlation between B-cell signature and increased the expression of CD8A and CD8+ T-cell infiltration  that is abundant and specific to each species. The 16 S rRNA genes are isolated and amplified from microbiome containing samples and then sequenced for species characterization. The downside of this technique is the need of high accuracy in the primer for amplification . A seconClostridium species) . In murine model of melanoma, it was shown that the use of different antibiotics decreases the response of PD-1 treatment compromising its tumor effect . The samspecies) . A recenspecies) . They dereatment .The role of microbiomics for modeling the host-tumor interaction and the immune response to cancer is now established, however, microbiomics has not yet been routinely implemented in multi-omics analysis but is probably a key element of cancer biology. Considering the high plasticity of the microbiome constitution, longitudinal analysis of the flora should be preferred . Most imAs we have shown, combining multiple biological elements of the tumor and of the host environment is an extremely powerful way to anatomize the TiME and to discover new biomarkers. However, there is no standardized method for immunomics and most of the computational integration tools are home made. To improve the generalization of the observation and to accelerate the possibility of patient selection for personalized therapy, standardization of the method is urging.In that sense, an available R package for computational tool for immuno-oncology biological research (IOBR) has been developed to comprehensively combine and interpret multi-omics data in the context of immuno-oncology . IOBR haFuture directions to fully characterize the immune environment and its dynamic would be to implement microbiomics data to such model and to construct longitudinal models with the introduction of time dependent models . The incCancer is a complex and highly heterogenous disease that is yet partially solved. Despite the molecular characterization made possible by the advances of NGS technologies, many of the underlying oncogenic mechanisms remain puzzling. The integration of the multilayers of the omics elements is an avenue to further elucidate cancer biology. In this review, we highlighted the challenges of computational modelization of large multi-omics datasets and we provided some clues for how to overcome these barriers. We presented some innovative bioinformatic tools developed to enlighten the implication of all the interconnected biological elements of cancer and showed how determinant they are to refine the disease subtyping and classification, and to discover biomarkers. Multi-omics integration has also heightened the field of immunomics and microbiomics, and thus has dramatically accelerated the identification of robust biomarkers for ICB efficacy toward the development of tailored immunotherapy.The constant modernization of the models endows analyses of increasingly larger datasets with a growing number of components. Collaborative effort for high-quality and clinically well-annotated databases, combining all the elements of high-throughput sequencing, is crucial to feed the models. Finally, the standardization of the methods would aid in replicating and confirming the results to increase the global knowledge and, ultimately, improve cancer treatments.


The prognosis of patients with different types of cancer is usually predicted with the help of the TNM staging guidelines; however, in recent years, there has been increased interest in using the primary tumor’s immune environment and characteristics to predict patient prognosis. Interventional radiologists play an important role in the evaluation of this immune microenvironment by performing image-guided biopsies that are paramount for obtaining appropriate tissue samples. The goal of this review is to describe the different techniques that are used for immune microenvironment evaluation, analysis steps for the data collected, and its relevance to patient care, including implications for patients undergoing interventional oncology procedures.In recent years there has been increased interest in using the immune contexture of the primary tumors to predict the patient’s prognosis. The tumor microenvironment of patients with cancers consists of different types of lymphocytes, tumor-infiltrating leukocytes, dendritic cells, and others. Different technologies can be used for the evaluation of the tumor microenvironment, all of which require a tissue or cell sample. Image-guided tissue sampling is a cornerstone in the diagnosis, stratification, and longitudinal evaluation of therapeutic efficacy for cancer patients receiving immunotherapies. Therefore, interventional radiologists (IRs) play an essential role in the evaluation of patients treated with systemically administered immunotherapies. This review provides a detailed description of different technologies used for immune assessment and analysis of the data collected from the use of these technologies. The detailed approach provided herein is intended to provide the reader with the knowledge necessary to not only interpret studies containing such data but also design and apply these tools for clinical practice and future research studies. The patient prognosis in various types of cancers has traditionally been predicted with the use of the TNM staging guidelines provided by the American Joint Committee on Cancer and Union for International Cancer Control . HoweverOne example of how data collected from immune contexture has been used to derive a simple, yet powerful classification system is called the ‘Immunoscore’. The Immunoscore is calculated after the assessment of CD3+ lymphocytes and cytotoxic CD8+ cells is performed using the Immunoscore assay in the tumor center (CT) and tumor margins (IM). The Immunoscore is calculated by looking at the number of cells in CT and TM, and based on this, the patients are divided into five groups from 0 to 4. Immunoscore has been validated internationally by the Society of Immunotherapy for Cancer, which created a consortium to globally evaluate the prognostic value of the Immunoscore in patients with stage I-III colon cancer . In thisInterventional radiologists (IRs) play an important role in the biomarker-based evaluation of patients on systemically administered immunotherapies. Image-guided tissue sampling is a cornerstone in the diagnosis, stratification, and longitudinal evaluation of therapeutic efficacy for cancer patients receiving immunotherapies. Furthermore, locoregional interventions performed by IRs inarguably have a profound influence on the local tumor immune microenvironment, and strategies to manipulate local tumor immunity towards an immunostimulatory state through such locoregional interventions has the potential to overcome barriers to systemically administered immunotherapies ,27,28,29Technologies based on tissue sections are amongst the most common tools used for both morphologic and molecular characterization of cancers. These tools provide several unique advantages, most notably the preservation of spatial information. Furthermore, many of these assays can be performed on archival tissue, allowing for the post hoc analysis of preserved paraffin embedded samples in tissue banks. The archetype assay for tissue section-based analysis is hematoxylin and eosin (HE) staining. This stain can be used to evaluate the TILs in sections of the malignant tissue. The use of HE staining is relatively easy in the clinical practice due to its convenience and low cost. The evaluation of HE-sections is done according to the International Immuno-Oncology Biomarkers Working Group recommendations . A standImmunohistochemistry (IHC)  uses mon® RNeasy® FFPE Kit. Nanostring nCounter kits designed to work with low quality FFPE samples with as little as 1 ng RNA can allow for analysis of a large number of gene transcripts from severely degraded samples. In total, 100 nanograms of RNA from the sample is hybridized to a custom human gene CodeSet and then processed on the nCounter platform after immobilization with Capture probe. Once the hybridization of the CodeSet with the mRNA has been completed, the next step is transferring the sample to the nCounter Prep Station. At the nCounter Prep Station, the target probes are first aligned and then immobilized in the nCounter Cartridge along with reduction in the excess probes that are inadvertently transferred to the station. The data collection then takes placed in the nCounter Digital Analyzer once the cartridge is placed in it. The target molecule of interest is identified by looking for the reporter probe which has six ordered fluorescent probes. This technology has various disadvantages such as the presence of only 800-predetermined genes which are mainly identified based on publication needs, and it does not provide any spatial information about the expression of genes.Nanostring nCounter is a technology that allows direct quantification of the RNA present in the sample without the need for more complicated traditional microarray techniques such as quantitative PCR (qPCR) and RNA-seq. The sensitivity of RNA levels detected with the help of Nanostring nCounter has been found to be similar to that of traditional techniques without the need for amplification or separate enzymatic processing ,37,38. AMultiplex IHC/immunofluorescence (IF) allows for visualization of multiple markers when evaluating a tissue section, whereas conventional IHC typically only evaluates one to two markers per tissue section. In multiplex IHC/IF, the target proteins are labeled using fluorochromes and then visualized using fluorescence microscopy tools to look at the distribution of these target proteins in a single tissue section . This fuLikewise, CO-Detection by inDEXing (CODEX) is a high-parameter multiplex tissue imaging platform that allows analysis of up to 60 markers in single cells on FFPE tissue sections ,47,48. IOver the past several years, new technologies have become available that marry the high-dimensional capabilities of bulk tissue genomic sequencing tools with the preserved spatial information of tissue section-based tools. These spatial transcriptomic tools are capable of providing unprecedented detail regarding cellular RNA and protein expression without sacrificing information from where the cells are located.The GeoMX Digital Spatial Profiler (DSP) is a platform that has been developed to use the barcoding technology of nCounter in order to both spatially resolve and digitally quantify the protein and mRNA expression. GeoMX DSP can be used to analyze any sample type such as core needle biopsy, FFPE tissue block, tissue microarray, slide mounted tissue, and fresh frozen tissue based on the availability of sample. This technology uses antibodies for protein detection and RNA probes for mRNA detection. These antibodies and RNA probes are linked to oligonucleotide tags . The DSPThe Visium Spatial Gene Expression platform helps with gene expression profiling at a high-resolution level ,60. VisiMultiplexed Error-Robust Fluorescence In Situ Hybridization (MERFISH) is a microscopy-based method that allows the study of hundreds to a few thousands of genes in addition to providing single-molecular analysis capability . The resMERFISH uses single-molecular fluorescence in situ hybridization (smFISH) in combination with labeling of RNA transcriptions using optical barcoding. MERFISH helps to assess the transcriptional activity in fixed samples by providing a highly multiplexed, single-molecule readout . TargeteImaging mass spectrometry (IMS) uses a matrix-assisted laser desorption ionization (MALDI) for molecular study of complex biological samples such as tissue sections . It alloStudies using MALDI IMS have been used in the past to find molecular signatures from different tumors of different types and grades of disease. It can also be used to find spatial distribution of a wide variety of biomarkers that have been shown to play an important role in cancers, including peptides, glycans, lipids, and metabolites ,74,75,76Even though MALDI has been used to investigate multiple disease states, full integration of this technology into cancer research is currently not possible as proteins which are not abundantly present in the sample cannot be detected preferentially due to the lack of techniques to amplify protein signal .Analysis of disaggregated tumor tissue allows for rapid and high dimensional analysis of molecular markers within the tissue. While spatial and individual cellular information is lost, these approaches can nonetheless provide a wealth of information regarding the tumor immune microenvironment.Polymerase chain reaction (PCR) is a relatively commonly used test which is used to amplify and detect the DNA and RNA sequences. In standard PCR, a set number of DNA sequences are chosen which can then be amplified to produce millions of copies for detection and analysis. Reverse transcriptase PCR (RT-PCR) is another technique that converts RNA templates in complementary DNA strands for molecular analysis. PCR is used to identify microsatellite instability in solid tumors, and metastasis workup in non-small cell lung cancers, colon cancers, and melanomas ,91,92.Western blots provide quantitative or semi-quantitative data about target proteins in a sample by identifying the normal proteins and modified proteins using a multi-step process . Over thEnzyme immunoassay (EIA) and enzyme-linked immunosorbent assay (ELISA) are used to identify the presence and concentration of different molecules in a sample. EIA and ELISA detect antigen and antibody reaction with the help of color changes by using an enzyme-linked conjugate and enzyme substrate . DependiDifferent cell mediators such as cytokines, chemokines, and growth factors play an important role in cell proliferation, cell migration, and immune response . In patiLEGENDplex is another bead-based assay that can be used for the measurement of multiple soluble factors such as cytokines and chemokines in serum, plasma, and cell culture supernatant . TherefoRNA sequencing (RNA-seq) allows for the study of the transcriptome of a tumor and its microenvironment and therefore, it has become an integral tool in immunogenomics. Investigators can use RNA-seq to identify tens of thousands of genes that are present in a single tumor-derived sample. RNA-seq allows the investigators to reconstruct the cellular heterogeneity of the tumor microenvironment from archival tissues . It can Whole exome sequencing (WES) is a sequencing technique in which the actionable areas of the genome are sequenced to identify variations in the exon regions along with causal variants of disease and/or disease-causing mutations ,135,136.Each T cell has unique antigen receptor (TCR) which are composed of alpha and beta chains whose specificity is based on the gene rearrangement process that occurs in the thymus. T cells have been shown to elicit response against its corresponding antigens (which can be cancer antigens); however, they have been shown to be driven by a wide number of T cell clones ,147,148.Characterization of single-cell suspensions of immune cells has been a mainstay of immune profiling for decades. The most common approach is to use flow cytometry, and with current technologies, it is now routinely possible to stain cells with numerous markers for more detailed characterization. Multi-color flow cytometry has been developed due to the advancement in the flow cytometry hardware, software, and reagents. It provides more measurements (up to 60 parameters) compared to the conventional flow cytometry techniques and due to the more detailed descriptions of cell phenotypes, it is possible to identify rare cell populations and new cell phenotypes. It is widely used in the phenotyping of tumor cells and assessment of tumor response to various treatment modalities such as immunotherapies ,164,165.Mass cytometry, which is also called CyTOF (cytometry by time-of-flight), blends flow cytometry with mass spectrometry using metal-conjugated antibodies to boost the number of detectable markers . CyTOF hSingle-cell RNA sequencing (scRNA-seq) allows the study of gene expression profile of individual cells when compared to bulk RNA sequencing (RNA-seq) which provides an average of the expression profile of an heterogenous population of cells . The useCellular Indexing of Transcriptomes and Epitopes by sequencing (CITE-Seq) is a technique that can be used with scRNA-seq for the detection of proteins. CITE-Seq allows uses antibodies that are conjugated to a DNA sequence that contains a PCR handle, an antibody barcode and a poly(dA) sequence ,203. ExtAssay for transposase-accessible chromatin using sequencing (ATAC-seq) is a method that can be used to explore chromatin accessibility by using a Tn5 transposase to integrates its adaptor load onto an accessible chromatin region . It requVarious single-cell epigenetics techniques are available that can used to understand gene regulation and cell identity by looking at cytosine modification (Bisulphite-seq), protein-DNA interaction , chromatin structure (DNase-seq), and three dimensional organization (HiC) ,215,216.As immune profiling tools increase in depth and complexity, the amount of data generated by each assay has increased many-fold. Fortunately, the tools and workflows for analyzing these data have kept abreast of the technologies that generate the data, and numerous software packages, both commercial and open source, are available to facilitate data analysis.Flow cytometry can help quantify a large number of parameters for millions of cells per sample; however, data analysis due to the high number of cells and parameters is often a bottleneck for the application of this technology. While commercial programs are available from multiple vendors, there are additionally easy-to-use packages for open-source platforms such as R (The R Foundation). The analysis of flow cytometry data can be categorized into six major stages: data pre-processing, cell population identification, cross-sample identification, cross-sample comparison, features extraction, and interpretation and visualization  218]..218].The flowCore package allows the users to organize and manipulate the data obtained by flow cytometry. It provides the users with an infrastructure for R-based flow cytometry analysis by organizing the data into flowFrames and flowSets. Each flowFrame reports that data along with the relative meta-data from a single experiment and multiple flowFrames make up a flowSet .The flowUtils package is used along with the flowCore package. It is designed to read the Gating Markup Language files (Gating-ML) which describe the gates transferable between different software packages. The Gating ML files provides data that are computationally reproductible .In the normalization stage of pre-processing step effects that arise from technical effects between samples are removed using the flowStats package. The gaussNorm and fdaNorm functions in the flowStats package normalize the data samples by identifying and aligning the high-density regions for each channel ,222.The quality assessment step is used to detect if there are any intersample measurement variation and it helps to identify the cause of these variations. Moreover, when the causes of variations are identified they can be removed from the analysis ,223. TheThe process of identifying homogenous cell populations in the data is known as gating. This is one of the most time-consuming processes in the data analysis if carried out manually. In this process, different cell populations within a sample are identified and then compared across the sample . Gating Sequential manual gating is the traditionally used process in which the analysis is performed manually and it is based on the visual comparison of one- or two-dimensional plots ,227. TheAutomated gating methods are based on mathematical modeling of the fluorescence intensity distribution of the cell populations . AutomatOnce the cell populations have been identified in the sample, the next step is to match the cell populations across samples to compare the characteristics of cell populations across the sample ,235. In In this step, different methods are used to determine associations between flow cytometry samples and class of interest such as healthy vs. unhealthy samples . SuperviIn order to analyze high-dimensional datasets, visualization of the data can be a helpful tool. With the help of visualization, different plots can be created to compare the distribution between different samples or cell populations. The flowViz package is commonly used for visualization which uses the data structures defined in the flowCore package .Genomic sequencing tools can generate hundreds of gigabytes of data per sample, and so the manipulation, let alone the investigation, of these data sets can appear daunting. However, as with flow cytometry, there are now freely available packages through platforms such as R that facilitate each step in the analysis pathway, namely, quality control, transcript identification, obtaining metrics for gene and transcript expression, and approaches for detecting differential gene expression.Quality control is needed at different steps in RNA-seq data acquisition, which includes obtaining raw reads, read alignment, and quantification in order to monitor the quality of the data. The quality check of raw reads helps to find presence of sequencing errors, PCR artifacts, or contamination. During the quality check, we look for the quality control of the raw reads, analysis of the quality of sequence, GC content, presence or absence of adaptors, and duplicated reads. FastQC and NGSQC are tools that can be used to perform the quality control on RNA-seq during raw reads . These tAs the reference genome is available during the analysis of patient samples, RNA-seq analysis involves mapping of the reads onto the reference genome or transcriptome in order to identify which transcripts are expressed. After this, many of the reads may uniquely map or could be multi-mapped reads. Identification of novel transcripts using short reads is a challenging task in RNA-seq, but some methods such as Cufflinks, iReckon, SLIDE, StringTie or gene-finding tools such as Augustus can be used to increase chances of identification ,253,254.Transcript quantification is the most important step of analysis as it helps to estimate the gene and transcript expression . During Once the transcript has been quantified, the next step is differential gene expression analysis. In this step, the gene expression values are compared among samples. RPKM, FPKM, and TPM are used to normalize the sequencing depth of the samples. This process is performed by using normalizing methods such as TMM, DeSeq, PoissonSeq, and UpperQuartile, which ignore the highly variable and expressed features in order to prevent skewing of the count distribution ,264,265.Differential expression is first computed using discrete probabilistic distribution methods such as Poisson or negative binomial ,270. TheVisualization of the data collected from RNA-seq can be done at the level of reads or at the level of processed coverage. At the level of reads, a commonly use tool is ReadXplorer and at the level of processed coverage genome browsers such as UCSC browser, Integrative Genomics Viewer (IGV), Genoma Maps, or Savant can be used ,279,280.The Gene Ontology (GO) resource is a commonly used and comprehensive knowledgebase for sharing the functions of genes. The GO is structured in order to make it amenable to computational analysis, which makes it a powerful tool for comparisons and inferences about gene functions. It is structured by defining classes of gene functions that have specific relations to each other and they are often given logical definitions. Many different tools such as Categorizer, GAOTOOLS, and Map2Slim exist in order to use the GO annotations for analysis. These tools can be used to map specific GO terms to more specific GO terms ,286,287.p-values. After which, an adjustment to the p-values was made for multiple comparisons and a biological interpretation is made using these genes. However, this approach was associated with multiple disadvantages [Initially, the analysis of the gene expression data was performed using single-gene analysis by comparing each gene for a case and control group using parametric and non-parametric statistical tests to calculate vantages . Gene sevantages ,289,290.vantages . ORA metMolecular analysis of the tumor immune microenvironment starts with tissue acquisition, most typically via an image-guided biopsy performed by interventional radiology. Percutaneous needle biopsies (PNBs) are performed under imaging guidance to obtain tissue from different organs or lesions with a low risk of adverse events and producing excellent results . It is iTwo basic sampling techniques are used in PNBs: fine-needle aspiration (FNA) and core-needle biopsy. FNA is performed with the help of narrow-gauge needles from 20-gauge to 25-gauge, in order to obtain sufficient cells for both cytological and morphological analysis. The adequacy of samples obtained from FNA can be performed by an on-site pathologist which provides real-time feedback. Samples obtained from FNA can be used for pathological diagnosis, IHC, flow cytometry, and genetic testing. Core biopsies are performed with the help of larger needles from 16-gauge to 20-gauge, in order to obtain an appropriate amount of tissue for histological and architectural assessment of the specimen. The core biopsy samples are placed in a solution and then transferred to pathology where they are embedded in paraffin for further analysis. Selecting the appropriate type of biopsy is contingent upon a variety of factors including safety, technical feasibility, and desired molecular analysis. Excisional biopsies are the most invasive option but provide the greatest amount of tissue; furthermore, the spatial context is preserved, thus allowing for molecular analyses such as spatial transcriptomics. Accordingly, for some cancer types, excisional biopsies are the recommended approach. For example, in the National Comprehensive Cancer Network (NCCN) guidelines for B-cell lymphomas, excisional biopsies are explicitly recommended over FNA biopsies, with CNB a consideration if excisional biopsies are not feasible given the target lesion location . SpatialThe advent of immune checkpoint inhibitor (ICI)-based immunotherapies has revolutionized the landscape of systemically administered cancer therapies for solid organ malignancies. It has also stimulated tremendous excitement for the potential immunologic ramifications of locoregional therapies, including those performed by interventional radiology. In the last decade, immune profiling strategies have been used to understand the different factors associated with tumors, which can be responsible for the successful or unsuccessful treatment of cancer patients with ICI. Additionally, these technologies have been used to identify biomarkers, which can help predict the response of a particular patient to therapy .There are compelling preclinical and early clinical data to suggest that interventions such as thermal ablation and transarterial embolization can augment local and systemic tumor immunity ,303,304.Intratumoral immunotherapies can also be used to further unlock the potential of image-guided procedures by delivering potent immunologically active therapies directly into the tumor ,317,318.This review provides a detailed description of tissue-section based immune profiling techniques, bulk tissue analysis techniques, single-cell characterization techniques, workflow for analysis for the data collected from these techniques, and a focus review of sampling techniques and specifications along with immune changes after interventional oncology procedures. It is our hope that an empiric appreciation for the state-of-the-art immune profiling tools in this review will facilitate incorporation of these technologies into ongoing and future studies on locoregional therapies so that the key questions in the field can be answered.


Sus scrofa). This lack of knowledge hinders efforts to define and predict the effects of genetic variants in pig breeding programs. To address this knowledge gap, we need to identify regulatory sequences in the pig genome starting with regions of open chromatin. We used the “Improved Protocol for the Assay for Transposase-Accessible Chromatin (Omni-ATAC-Seq)” to identify putative regulatory regions in flash-frozen semitendinosus muscle from 24 male piglets. We collected samples from the smallest-, average-, and largest-sized male piglets from each litter through five developmental time points. Of the 4661 ATAC-Seq peaks identified that represent regions of open chromatin, >50% were within 1 kb of known transcription start sites. Differential read count analysis revealed 377 ATAC-Seq defined genomic regions where chromatin accessibility differed significantly across developmental time points. We found regions of open chromatin associated with downregulation of genes involved in muscle development that were present in small-sized fetal piglets but absent in large-sized fetal piglets at day 90 of gestation. The dataset that we have generated provides a resource for studies of genome regulation in pigs and contributes valuable functional annotation information to filter genetic variants for use in genomic selection in pig breeding programs.There is very little information about how the genome is regulated in domestic pigs ( Sus scrofa) is a hugely important farmed animal species globally, contributing a source of healthy animal protein to feed the growing human population. Meeting the increased demand for healthy sustainably produced food from pigs in coming decades will require novel breeding strategies and management practices that will rely on an improved ability to predict phenotype from genotype (MYH3) was shown to influence muscle fiber type composition in Korean native pigs  act as economic drivers in pig breeding programs. Prior to this study knowledge of open chromatin in pig muscle was limited to data from only two adult animals  to generate ATAC-Seq and RNA-Seq data from the same individuals to characterize the expressed and regulatory regions of the genome during development. The aims of the study were to: (1) map regions of open chromatin in semitendinosus muscle tissue from small-, average-, and large-sized male piglets at five developmental stages  and (2) analyze RNA-Seq data from the same tissues to generate gene expression profiles. The outcomes of the study will help to (1) understand the molecular drivers of muscle growth in pigs; (2) provide a foundation for functionally validating target genomic regions in vitro; and (3) identify high-quality causative variants for muscle growth with the goal of harnessing genetic variation and turning it into sustainable genetic gain in pig breeding programs. The dataset we generate will also provide valuable information for annotating the pig genome, Sscrofa 11.1 (n = 13) were fed the same diet and kept under one controlled husbandry condition at the research farm. For each of the developmental stages, the number of litters included in the study was as follows: prenatal day 45 = 1 litter, day 60 = 2 litters, day 90 = 5 litters, and postnatal 1 week = 2 litters and 6 weeks = 3 litters. Gilt identifier numbers for each litter are included in Tissue samples for this study were collected from Large White × Landrace pigs that were euthanized, not specifically for this study but for other on-going projects on the effects of fetal size on pig development at The Roslin Institute. All gilts  collected from the largest-, smallest-, and average-sized male piglets per litter at five different developmental stages . The larn = 3) and 60 (n = 3) time points. For the 6 weeks of age time point (n = 5), one large sample was unavailable for inclusion in the analysis. At day 90 of gestation (n = 11) three complete litters and one incomplete litter were included, while at 1 week of age samples were only available from the smallest and largest piglets from one litter (n = 2). We have included a flow chart describing which samples were analyzed at each stage of this study  for lysis and centrifuged for 10 min at 500g at 4°C. The pellet of nuclei was then washed in PBS and resuspended in 50 µl transposition mix  from the Nextera DNA Sample Prep Kit . The pellet was incubated with the transposition mix for 60 min at 37°C at 300 rpm on a thermomixer. The pellet of transposed nuclear DNA was purified with a MinElute PCR purification kit , eluted in 15 µl of Buffer EB and stored at −20°C. The full protocol is available via the FAANG Data Coordination Centre , filtered using a 70 µm corning cell strainer  then the filtrate (containing the nuclei) centrifuged at 500n Centre .g for 5 min. The pellet was resuspended in 400 μl 1×HB buffer and transferred to a 2 ml Eppendorf Protein Lo-Bind tube . Four-hundred microliter of 50% Iodixanol solution  was added to the 400 μl of cell solution . An Iodixanol gradient was then created and samples transferred to a swinging bucket centrifuge and spun for 25 min at maximum speed at 4°C with no brake. A thin “whitish” band appeared between layers two and three of the gradient. Evaluation and counting of nuclei was performed by staining with Trypan Blue . One milliliter of ATAC-RSB Buffer + 0.1% Tween 20 was then added to lyse the nuclei and the sample centrifuged for 10 min at 500g at 4°C. The pellet was then gently resuspended in 50 µl transposition mix for tagmentation as described for cryopreserved nuclei samples above.ATAC-Seq libraries were prepared using a version of the Omni-ATAC-Seq protocol  were sequenced to generate 50-nt paired-end reads on an Illumina NovaSeq 6000 platform using a single S2 flow cell. All of the libraries generated >90 M paired-end reads .ILLUMINACLIP:Trimmomatic-0.39/adapters/NexteraPE-PE.fa: 2:30:10:1:true SLIDINGWINDOW:5:20 MINLEN:30; —very-sensitive mode followed by excluding unmapped reads and marking PCR duplicates. PCR duplicates were marked using PicardTools v2.23.0 . Two rounds of peak calling were performed as follows: (1) peak calling on individual samples (n = 24) and (2) aggregated multi-sample peak calling for each piglet size. All the scripts used for this analysis are found in ATAC-Seq peak calling was performed using Genrich v0.5  under thbedtools merge -i all_samples.bed -d10 -c 4,7,10,4 -o count_distinct,mean,mode,distinct). Peaks that were within ≤10 nucleotides of another peak were merged into one peak, and a support value  was calculated for each peak. Peaks with a support value of <3  were removed, resulting in a total of 12,090 ATAC-Seq peaks, which will now be referred to as the “consensus set.” A read fragment filtering and analysis workflow was devised similar to a recently published framework by samtools view -h -f2 -q10 -F1548 -bS). A read count for each sample  was then generated using ht-seq v0.13.5 (htseq-count--stranded=no –type=region). The library size for each BAM file was then used to normalize the read counts for multidimensional scaling analysis as described by A consensus set of ATAC-Seq peaks was created for the purpose of differential peak analysis. The consensus set, from individual sets that were called in all 24 samples, was created using bedtools v2.26.0  method and a 10% false discovery rate (FDR) was considered as the threshold of significance.DESeq2 v1.30.1  of the ATAC-Seq libraries was performed using the MASS::IsoMDS package  to ensurThe HMM-based Identification of Transcription factor footprints (HINT) pipeline from the Regulatory Genomics Toolbox  was estimated on an Agilent 2200 TapeStation System  to ensure that RNA quality was of RINe >7. RINe and other quality control metrics for the RNA samples are included in The RNA isolation protocol is described in full in Strand-specific paired-end reads with a fragment length of 100 bp for each sample were generated by Edinburgh Genomics, using the Illumina TruSeq mRNA library preparation protocol . mRNA-Seq libraries were sequenced on an Illumina NovaSeq 6000 platform to generate >66 M paired-end reads per sample .de novo) in kallisto with—bias option activated. The output tab-separated value files were then imported to R using txImport package  [DESeqDataSetFromTximport]. After multiple correction of P-values using the BH method (The transcript per million mapped (TPM) expression estimates for each sample were investigated using principal component analysis (PCA) in FactoMineR to identify any spurious samples that did not cluster as expected . Peak calling was performed using the same Genrich flags as previously described (aggregated multi-sample method), and we separated peaks shared between all size classes and size class-specific peaks with bedtools v2.26.0 . The sizi.e., positive values meant that the peak was either within the gene or within the 5′ 10-kb upstream region of the gene, and negative values corresponded to 10 kb from the 3′ end of the gene).An overlay of genes that were differentially expressed between the large- and small-sized fetal piglets at day 90 was performed using ATAC-Seq peaks within 10-kb vicinity of the differentially expressed genes (either upstream or downstream). This overlay would show us which of the differentially expressed genes had an ATAC-Seq peak in their vicinity and whether that peak was present in both large- and small-sized fetal piglets, or only in one of the two sizes. The distance from the start of the gene model to the start of the ATAC-Seq peak was used as a coordinate system  (CASQ1)  were multiplexed and sequenced to achieve 2.02e+08 average reads per sample . Reads were evenly distributed between barcodes across the first three batches. The fourth batch, which included only two samples that had a higher concentration of starting DNA, resulting in more reads per sequencing run compared to the other 22 samples (details in GAPDH) , and a g (CASQ1) . Read coWe also measured the fragment size distribution of the 24 ATAC-Seq samples, which showed that read density was highest at the putative nucleosome-free region (approximately 50 bp insert size) followed by the mononucleosome region (∼150–200 bp) and, then, the dinucleosome (∼300–400 bp) region as shown in i.e., samples from the same developmental stage should have a similar peak distribution and cluster together). NMDS was performed using the consensus set of peaks and the normalized ATAC-Seq read counts. The input matrix, which was converted to a Manhattan distance matrix prior to analysis, consisted of 12,090 consensus peaks and 24 samples. The sample separation on the first two components of the NMDS was driven by the developmental time points. The second and third components of the plot showed the greatest segregation between the developmental time points as shown in NMDS was used to ensure that the ATAC-Seq dataset was biologically meaningful, reproducible and there were no outlying samples (P > 0.05) except the TSS enrichment score . Detailed metrics are found in n = 3), day 90: average (n = 1), and day 90: small (n = 1)] most peaks were in distal intergenic regions  was used to validate the presence of the ATAC-Seq signal flanking transcription starts site (TSS) . ATAC-SeSscrofa11.1 corresponding to 724 unique transcripts (245 unique genes). One hundred and nine peaks were in unannotated intergenic regions. Nearly half of the peaks exhibiting differential read counts, between developmental time points, were in intronic regions and only 11.1% resided in promoters as shown in Differential peak analysis revealed 377 ATAC-Seq peaks from the consensus peak set in which the read count differed significantly between the developmental time points. These peaks were annotated using P-value >0.05).The read counts for the peaks that differed significantly between time points are shown in GMEB2, TFAP2C(var.2), HOXD12, FOXH1, and CEBPE) were detected using JASPAR2020 database annotation. The TF CEBPE CCAAT-Enhancer-Binding Protein-Beta showed the most extreme HINT z score  in comparison to the large piglets and showed visual evidence of a TF footprint  were calculated as TPM reads using Kallisto. The TPM expression estimates were then investigated using PCA  to identvs small-sized fetal piglets were compared, 58 upregulated and 31 downregulated genes were detected. When large- vs small-sized fetal piglets were compared, 54 upregulated and 35 downregulated genes were detected. Differentially expressed genes with an adjusted P-value (FDR <0.1) and log2FC ≥ 0.1 are annotated in n = 89) of differentially expressed genes. The list of differentially expressed genes and detailed analysis metrics are found in CASQ1), for example, which was 1.54-fold upregulated  in large-sized relative to small-sized fetal piglets is the skeletal muscle-specific member of the calsequestrin protein family and is highly expressed in skeletal muscle in adult pigs, see  in large-sized relative to small-sized fetal piglets  was downregulated , in small-sized relative to large-sized fetal piglets  at day 90 of gestation. Between the three sizes of fetal piglet 89 genes (FDR 10%) were found to be differentially expressed. When average-  piglets . It has  piglets .vs small-sized fetal piglets at day 90 (green) are shown in MYOG, ryanodine receptor 2 (RYR2), transmembrane 4L six family member 4 (TM4SF4), and interleukin 21 receptor  and differentially expressed genes between the large- r IL21R; , were onr IL21R; . There wr IL21R; . Of the  piglets , with a  piglets  but abse piglets .In this study, we used ATAC-Seq and RNA-Seq to improve our understanding of gene expression and regulation in developing pig muscle. We generated open chromatin profiles, in the form of ATAC-Seq peaks, for semitendinosus muscle from piglets from five developmental stages and compared these with gene expression profiles from the same tissue samples.longissimus dorsi muscle from pig embryos at days 45, 70, and 100 conducted by Of the 4661 ATAC-Seq peaks representing regions of open chromatin, that were identified in this study, >50% were within 1 kb of known transcription start sites. This result is consistent with studies across different species  in pig muscle has been previously reported by CASQ1 encodes the skeletal muscle-specific member of the calsequestrin protein family, is related to muscle metabolism, and has been shown to be highly expressed in fat pig breeds  cattle, MYBPC2 is highly expressed in fetal muscle, reflecting fast glycolytic fiber structural differentiation  at day 90 of gestation. Other studies have used a similar approach to investigate the effect of histone modification on the expression of genes involved in placental development in pigs  that was present in the small-sized fetal piglets but missing from the large-sized fetal piglets for day 90 of gestation. This result was surprising because MYOG was downregulated in the small-sized relative to the large-sized fetal piglets and as such we would have expected the chromatin to be less accessible and no peak to be present in the small-sized piglets. In future work, we plan to remove this peak using CRISPR genome editing and measure the effect on muscle progenitor cells in culture.elopment  and assoelopment  and backelopment , accordielopment . MYOG waMYH3 that influences muscle fiber type composition and intramuscular fat content in pigs . We have made the datasets available in the public archives and via the FAANG Data Portal for this purpose. The datasets also provide a foundation for incorporating functional information in statistical analyses, to increase the precision and power with which we can fine map high-quality causal variants in pigs. This would make it possible to increase the accuracy of genomic selection and the efficiency with which breeding turns genetic variation into genetic gain. Further investigation of genetic variants within the regulatory regions identified in this study would help to validate whether they might be driving muscle and growth phenotypes. Recently, a functional regulatory variant was identified in The dataset that we have generated provides a powerful foundation to investigate how the genome is regulated in production pigs and contributes valuable functional annotation information, for global FAANG efforts, and to define and predict the effects of genetic variants in pig breeding programs. The outcomes of the study will: (1) help us to understand the molecular drivers of muscle growth in pigs; (2) provide a foundation for functionally validating target genomic regions in in vitro systems; and (3) identify high-quality causative variants for muscle and growth traits with the goal of harnessing genetic variation and turning it into sustainable genetic gain in pig breeding programs.n = 24) are available via the European Nucleotide Archive (ENA) under accession number PRJEB41485. Details of all samples processed for the RNA-Seq dataset (n = 26) can be accessed via the ENA under accession number PRJEB41488. The sample metadata is available via the BioSamples database under sample accession numbers SAMEA7178119, SAMEA7178120, SAMEA7178122, SAMEA7178123, SAMEA7178124, SAMEA7178125, SAMEA7178126, SAMEA7178127, SAMEA7178134, SAMEA7178138, SAMEA7178142, SAMEA7178149, SAMEA7178150, SAMEA7178153, SAMEA7178159, SAMEA7178160, SAMEA7178164, SAMEA7178178, SAMEA7178179, SAMEA7178180, SAMEA7178182, SAMEA7178183, SAMEA7178184, SAMEA7178185, SAMEA7178187, and SAMEA7178188. These datasets are curated and submitted to FAANG data portal according to FAANG’s sample and experimental guidelines  and RNA-Seq  are available via a code repository in The raw sequence data for the ATAC-Seq samples (idelines , FAANG-Sidelines , FAANG-SG3 online.jkab424_Supplemental_Figure_S1Click here for additional data file.jkab424_Supplemental_Figure_S2Click here for additional data file.jkab424_Supplemental_Figure_S3Click here for additional data file.jkab424_Supplemental_Figure_S4Click here for additional data file.jkab424_Supplemental_File_S1Click here for additional data file.jkab424_Supplemental_File_S2Click here for additional data file.jkab424_Supplemental_File_S3Click here for additional data file.jkab424_Supplemental_Material_LegendsClick here for additional data file.jkab424_Supplemental_Table_1Click here for additional data file.jkab424_Supplemental_Table_2Click here for additional data file.jkab424_Supplemental_Table_3Click here for additional data file.jkab424_Supplemental_Table_4Click here for additional data file.jkab424_Supplemental_Table_5Click here for additional data file.jkab424_Supplemental_Table_6Click here for additional data file.


TargetFinder, Average-Rank, and the ABC model, using the three latest benchmarks from the field: a reference that combines 3D and eQTL data, called BENGI, and two genetic screening references, called CRiFF and CRiSPRi. Overall, none of the three methods performed best on the three references. CRiFF and CRISPRi reference sets are likely more reliable, but CRiFF is not genome-wide and CRiFF and CRISPRi are mostly available on the K562 cancer cell line. The BENGI reference set is genome-wide but likely contains many false positives. This study therefore calls for new reliable and genome-wide E/G reference data rather than new functional link E/G identification methods.Differences in cells’ functions arise from differential activity of regulatory elements, including enhancers. Enhancers are cis-regulatory elements that cooperate with promoters through transcription factors to activate the expression of one or several genes by getting physically close to them in the 3D space of the nucleus. There is increasing evidence that genetic variants associated with common diseases are enriched in enhancers active in cell types relevant to these diseases. Identifying the enhancers associated with genes and conversely, the sets of genes activated by each enhancer  across cell types, can help understanding the genetic mechanisms underlying human diseases. There are three broad approaches for the genome-wide identification of E/G relationships in a cell type: 1) genetic link methods or eQTL, 2) functional link methods based on 1D functional data such as open chromatin, histone mark or gene expression and 3) spatial link methods based on 3D data such as HiC. Since 1) and 3) are costly, the current strategy is to develop functional link methods and to use data from 1) and 3) as reference to evaluate them. However, there is still no consensus on the best functional link method to date, and method comparison remain seldom. Here, we compared the relative performances of three recent methods for the identification of enhancer-gene links,  Vertebrate organisms are made of billions of cells that all have the same genome, but able to deliver a wide range of biological functions. These functional differences are conveyed by the differential expression of genes across cell types, which is partly driven by the differential action of their regulatory elements . Among those regulatory elements, enhancers are particularly interesting, not only because they are predominant and cover more genomic space , but alsThe identification of enhancers and associated genes, i.e., which genes are the targets of which enhancers in a particular cell type, is an important objective in the field. There is increasing evidence that variants associated with common diseases are located in enhancers active in cell types relevant to these diseases . UnderstThere are three broad approaches that are currently used for the genome-wide identification of E/G relationships in a given cell type : 1) geneBecause genetic 1) and spatial link 3) methods are very costly and the generation of 3D data in spatial link methods requires a specific expertise, functional link methods 2) have become the most widely used approach to identify E/G relationships. This is confirmed by the plethora of functional link methods that have been developed since 2011 (see below). On the other hand, data underlying methods of types 1) and 3) are commonly considered as references to assess the reliability of methods of type 2) see .a posteriori using external reference data (most often 3D or genetic) .Functional link methods, also reviewed in genetic) . For illThe second category of methods uses machine learning approaches such as random forests or neural networks. They consist in training a model to discriminate true vs false E/G based on distinctive features from the 1D data they use, from a reference dataset of known E/G , and a dataset of unsupported E/G (ground negatives), as a negative control. When provided with new data, the model determines which E/G are more likely to be true .Two recent studies evaluated functional link methods . HoweverActivity-By-Contact (ABC) model that performs best in their evaluation. The second study  in a cell type. It then quantifies the enhancer activity (A) of these regions E by computing the geometric mean of the read counts of chromatin accessibility  and H3K27ac ChIP-seq at E. The contact (C) between E and G is then computed either as the Knight-Ruiz (KR) matrix-balancing normalized Hi-C contact frequency between E and the promoter of gene G, if cell type specific Hi-C data are available, or simply as the inverse of the distance  between E and G otherwise  and the logarithm of the expression of G across many cell types  of the ENCODE registry of candidate cis-regulatory elements (cCREs) (The  methods . The Shell types . The dis (cCREs) .TargetFinder defines true (ground positive) E/G based on 3D data (HiC) and learns features associated to those using gradient boosting. The learnt features are as diverse as open chromatin, methylation, histone marks or transcription factors, and can both be taken from enhancer and promoter regions and from the window between them  and flow cytometry”. In this approach, they “deliver KRAB-dCas9 to many candidate regulatory elements in a population of cells by using a library of guide RNAs”. The results of this technique are then subjected to a statistical framework to determine the sets of E/G that are active and inactive in the cell type. The technique was then applied to thirty genes in five genomic regions (spanning 1.1–4.0 Mb) for which they tested all DNase I hypersensitive (DHS) elements (representing open chromatin regions) in K562 cells within 450 kb of the gene of interest. Together with previous CRISPR experiments, this approach yielded 109 ground positives  and 3,754 ground negatives  E/G, which are considered as a reference set for the evaluation of numerous methods of the field including the ABC model and the distance method .e method . We willd  and is made of sets of E/G active and inactive in different cell lines according to different types of data . We focus our evaluation on the GM12878 cell line, which has the largest amount of annotation data, with 6 sets of active and inactive E/G available. The active E/G sets result from the processing of four types of 3D data, Hi-C . Note that while Fulco et al. provided a code associated to their proposed methodTargetFinder’s already thresholded predictions only allowed us to compute a single pair of  values for each reference set, and explains the absence of AUPR curves for this tool. In fact, two different pairs of  values, a pessimistic one and an optimistic one, were computed and plotted for TargetFinder, leading to two different dots for TargetFinder on the plots, TargetFinder_pes and TargetFinder_opt .For the  authors , with so authors 3. The Average-Rank and the distance methods of ABC model and the distance method of TargetFinder performs better than Average-Rank, except on eQTL reference sets. Altogether, these positive controls confirmed the validity of the pipeline we implemented.We replicated the results of the two evaluation papers . The curBENGI datasets, and that TargetFinder performs best overall, followed by Average-Rank, distance and ABC model. Note that TargetFinder is much more precise than sensitive, and performs much better on HiC and CTCF sets. This last result can be explained by the fact that TargetFinder learns true E/G based on HiC data. Nevertheless, CRiFF set, and that the ABC model performs best (AUPR = 0.63), before the distance method and finally the Average-Rank method. Contrary to its result on BENGI, TargetFinder does not perform well on CRiFF.CRISPRi with respect to CRiFF, if the methods that perform best (ABC model) and worst (TargetFinder) are the same, it has to be noted that Average-Rank performs better than distance and that the AUPRs are globally lower  were not specifically designed to identify E/G relationships. For instance, some E/G relationships may not need spatial proximity or the presence of CTCF to operate  on the CRiFF and CRISPRi data are more difficult to explain as these techniques should be quite exhaustive in identifying the enhancers of a given gene. However, the authors of CRiFF state in their paper that “CRISPRi might fail to discover certain regulatory elements, for example, due to differential sensitivity to KRAB-mediated inhibition”  of the  operate . Likewisibition” . The CRI CRISPRi , we know CRISPRi . AltogetCRiFF and the CRISPRi techniques seem to be better suited to generate true E/G reference/evaluation data. Therefore if we really had to select an E/G relationship identification method, then we would choose the one that performs best on the CRiFF and CRISPRi data, namely, the ABC model. Another, more practical, reason to select the ABC model over the Average-Rank and TargetFinder methods is that a dedicated and more importantly well documented software has been made available to the community by its authorsAverage-Rank and TargetFinder methods can be more challenging. Finally, contrary to the Average-Rank method that requires gene expression and chromatin accessibility data on many cell types (here 112), and to TargetFinder that requires tens of data types on the cell type of interest, the ABC model only requires two types of data (open chromatin and H3K27ac) on the cell type of interest. This substantially broadens the scope of application, as the actual amount of data available is likely going to be limited in many real data settings.Because they were specifically designed to identify E/G relationships for a selected set of genes or enhancers, the CRiFF technique is that it does not provide genome-wide results. In the present study, the CRiFF data we used cover 58 different genes located in 21 different genomic regions ranging from 1Mb to 4 Mb in size, which represents less than 1 percent of the genome. The CRISPRi technique is expected to be more representative of the genome, but it produced results that were similar to CRiFF. Another potential bias could come from the use of the K562 cancer cell line which is the only cell line for which there was sufficient CRiFF data. Even if the authors have performed more CRISPRi-FlowFISH experiments since our study , in order to finally reach a consensus in this field, and be able to answer numerous questions related to cell function and disease.bam files) of DNAse-seq data (single end) from 10 cell types: stomach, HepG2, K562, thymus, adrenal gland, small intestine, GM12878, IMR-90, heart and H1-hESC, with accession numbers provided in In order to illustrate unsupervised/heuristic enhancer/gene identification methods, we chose the simplest one, the pairwise chromatin accessibility correlation across cell types, and represented it on bedtools merge, we then obtained 473,766 consensus peaks across all cell types. We then quantified the chromatin accessibility of the 473,766 consensus peaks in each cell type by simply counting the number of mapped reads of each cell type overlapping each consensus peak using bedtools intersect, and normalized the number of reads of each peak in each cell type by the total number of mapped reads in peaks for this cell type. Finally we computed the consensus peak pairwise Pearson correlation between the log10 of the normalized chromatin accessibility across the 10 cell types of these peaks for all pairs of peaks less distant than 500 kb using a script that we wrote: compute_correlations.pyWe obtained from about 60,000 (GM12878) to about 200,000 (IMR-90) peaks per cell type. By concatenating, sorting and merging on the genome the peaks called in each cell type using ABC model on BENGI and the Average-Rank and the TargetFinder methods on CRiFF, and since Moore et al. provided the code of the Average-Rank method, and Fulco et al. the code to run the ABC model, we decided to try and reproduce the evaluation of the Average-Rank method on BENGI and of the ABC model on CRiFF. We also used the code of the distance method provided by Moore et al. (with some modifications), to evaluate the baseline distance method on BENGI, CRiFF and CRISPRi . In addition, since the code to generate the Precision-Recall curves and the AUPRs was not provided in the papers, we generated our own R code to make these plots using existing R packages. The code used to perform all these analyses was stored in Jupyter notebooks that we provide below, together with additional details about these analyses.In total we evaluated four methods, the  CRISPRi , 4. It hBENGI github repositoryScripts directory included, on the one hand the scripts to make the BENGI sets, and on the other hand the scripts to run the evaluation of the methods on a given BENGI set (note that other cell types than GM12878 were provided). It is important to bear in mind that the script corresponding to a method was not a generic script allowing to retrieve all the E/G relationships called by this method in a particular cell type, but rather only produces evaluation data of this method on a given BENGI set, i.e., attaches to each true and false E/G of a BENGI set, the score of the method’s associated prediction . Since we could not run any of the scripts from Moore et al. without modifying them, sometimes quite deeply, we suspect these scripts were provided to give a general idea of the underlying analyses rather than to be used as such. No mention of program versions were provided neither, which again hampers reproducibility.The Moore et al.’s code, reference data and annotation were first downloaded from the distance method on the BENGI sets, we used a slightly modified version of the Run-Distance-Method.sh script provided by Moore et al. This script takes as input a string defining the BENGI set , the version of the BENGI set (here v3), the mode , the expression threshold  and the output path. In normal mode, this script calls the rank.distance.py script on the set of human TSSs, the set of all cCREs (candidate cis-regulatory elements) and the BENGI set. It then outputs a 2 column file including for each E/G of the BENGI set on a row, 1 or 0 according to whether this E/G is true or false according to the BENGI set and the score provided by the distance method which is defined as the inverse of the smallest distance between a TSS of G and the enhancer E. Our modification consisted in adding two additional columns to this tabulated file, one for the enhancer id and one for the gene id, this for an easier downstream fusion with the evaluation result of the Sheffield method. For this purpose we also had to modify the Run-Distance-Method.sh script so that it sorts the 4 column tabulated file provided by the python script according to the enhancer id and the gene id. After running the evaluation script we plotted the Precision-Recall curves using existing R packages. The following Jupyter notebook provides all the necessary information for evaluating the distance method on the BENGI setsTo evaluate the Average-Rank method on the BENGI sets, we first had to run the Sheffield method (correlation between open chromatin at E and expression level at G) on each BENGI set.To evaluate the dhs112_v3.bed file) and the genes with their expression levels in the same 112 cell types (exp112.bed file) from the webRun-Sheffield.sh script that evaluates the Sheffield method on a given BENGI set. This script takes as input a string defining the BENGI set, the version of the BENGI set and the output path. It then makes the set of enhancers of the BENGI set in bed format, the enhancer matrix with these enhancers in rows and their chromatin accessibility in the 112 cell types in columns, the genes of the BENGI set in bed format, the matrix of these genes in rows with their expression levels in the 112 cell types in columns, and then calls the sheffield.correlation.py script. This script takes as input a matrix of gene expression in the 112 cell types, the gene file in bed format, the enhancer matrix, a gene summary file, the BENGI set and the cell type. It then outputs a 6 column file including for each E/G of the BENGI set on a row, 1 or 0 according to whether this E/G is true or false in the BENGI set, the Pearson correlation between the chromatin accessibility at E and the expression level at G across the 112 cell types, the p-value, the Z-score, the enhancer id and the gene id.For this we first downloaded the DNAse Hypersensitivity (DHS) peaks with their chromatin accessibility in 112 cell types  on each set, i.e., TargetFinder’s predictions that corresponded to a ground positive E/G of the BENGI set. To do so, we first had to convert the enhancer and promoter coordinates of the TargetFinder’s prediction file into cCRE-ELS (candidate cis-regulatory elements with enhancer like signature) ids and gene ids respectively. For that we used bedtools intersect on the GM12878 cCRE-ELS file and the Gencode v19 TSS file from Moore et al. respectively. In total we found 342, 222, 143, 564, 9 and 2 TPs for CHIC, RNAPII ChIA-PET, CTCF ChIA-PET, HiC, Geuvadis and GTEx  and 2) by the subset of BENGI ground positive E/G that were also in the initial set of 44,313 E/G relationships given as input to TargetFinder .Recall was computed in two different ways: by dividing the TPs 1) by the total number of TargetFinder on BENGI are indicated on The performances of CRiFF reference set we first downloaded Table S6a from tsv file, and then obtained the 109 ground positive and the 3754 ground negative E/G relationships by performing the filters detailed inTo obtain the distance and the Average-Rank methods, we first intersected the enhancers of the CRiFF set with the ENCODE cCRE-ELS (candidate cis-regulatory element with enhancer like signature) provided and used by Moore et al. This process is described in the three notebooks below. We have to say that we only slightly modified the distance and Average-Rank methods scripts used above for BENGI and GM12878 in order to run then on CRiFF and K562 (see notebooks below).In order to be able to use almost the same scripts as above for the The complete process for this evaluation is provided in the following notebookThe complete process for this evaluation is provided in the following notebookThe complete process for this evaluation is provided in the following notebookTargetFinder on the CRiFF set, we first downloaded TargetFinder’s K562 predictions from the dedicated github repositoryTargetFinder predicted an E/G or not.In order to evaluate TargetFinder’s precision and recall on the CRiFF set, we first computed TargetFinder’s true positives (TPs), i.e., TargetFinder’s predictions that corresponded to a ground positive E/G of the CRiFF set. To do so, we first had to convert the enhancer and promoter coordinates of the TargetFinder’s prediction file into cCRE-ELS (candidate cis-regulatory elements with enhancer-like signature) ids and gene ids respectively. For that we used bedtools intersect on the K562 cCRE-ELS file and the Gencode v19 TSS file from Moore et al. respectively. In total we only found 4 TPs  and 2) by the subset of CRiFF ground positive E/G that were also in the initial set of 41,477 E/G relationships given as input to TargetFinder .Recall was computed in two different ways: by dividing the TPs 1) by the total number of TargetFinder on CRiFF are indicated on The performances of CRISPRi set was obtained from the BENGI’s github repositoryThe K562 distance method on the K562 CRISPRi set was done exactly the same way as on the GM12878 BENGI sets, but replacing the GM12878 cCREs by the K562 cCREs (see above).The evaluation of the Average-Rank method on the K562 CRISPRi set was done exactly the same was as on the GM12878 BENGI sets, but replacing the GM12878 cCREs by the K562 cCREs (see above).The evaluation of the ABC model on the K562 CRISPRi set, we had to rerun the ABC model on K562 but using a different white list as the one used for the evaluation on CRiFF. Indeed, the ABC model’s step 1.3 called make candidate region can take as input a white list of promoters and enhancers on which to enforce predictions, and it was important to use it to ensure that all CRISPRi ground positives and negatives could be predicted by the ABC model. Here the white list we used was made of the union of all K562 cCRE-ELS from Moore et al., and of the Gencode v19 TSS from Moore et al. that we extended by 250bp on each side.To evaluate the TargetFinder on the K562 CRISPRi set was done exactly the same way as on the GM12878 BENGI set (see above). The number of TPs was only 3. The performances of TargetFinder on CRISPRi are indicated on The evaluation of