Potential Loss Of Information Research Articles

Recovery of Information Stored in Modified DNA with an Evolved Polymerase

DNA is increasingly being explored as an alternative medium for digital information storage, but the potential information loss from degradation and associated issues with error during reading challenge its wide-scale implementation. To address this, we propose an atomic-scale encoding standard for DNA, where information is encoded in degradation-resistant analogues of natural nucleic acids (xNAs). To better enable this approach, we used directed evolution to create a polymerase capable of transforming 2'-O-methyl templates into double-stranded DNA. Starting from a thermophilic, error-correcting reverse transcriptase, RTX, we evolved an enzyme (RTX-Ome v6) that relies on a fully functional proofreading domain to correct mismatches on DNA, RNA, and 2'-O-methyl templates. In addition, we implemented a downstream analysis strategy that accommodates deletions that arise during phosphoramidite synthesis, the most common type of synthesis error. By coupling and integrating new chemistries, enzymes, and algorithms, we further enable the large-scale use of nucleic acids for information storage.

Voxel-level analysis of normalized DSC-PWI time-intensity curves: a potential generalizable approach and its proof of concept in discriminating glioblastoma and metastasis

Standard DSC-PWI analyses are based on concrete parameters and values, but an approach that contemplates all points in the time-intensity curves and all voxels in the region-of-interest may provide improved information, and more generalizable models. Therefore, a method of DSC-PWI analysis by means of normalized time-intensity curves point-by-point and voxel-by-voxel is constructed, and its feasibility and performance are tested in presurgical discrimination of glioblastoma and metastasis. In this retrospective study, patients with histologically confirmed glioblastoma or solitary-brain-metastases and presurgical-MR with DSC-PWI (August 2007-March 2020) were retrieved. The enhancing tumor and immediate peritumoral region were segmented on CE-T1wi and coregistered to DSC-PWI. Time-intensity curves of the segmentations were normalized to normal-appearing white matter. For each participant, average and all-voxel-matrix of normalized-curves were obtained. The 10 best discriminatory time-points between each type of tumor were selected. Then, an intensity-histogram analysis on each of these 10 time-points allowed the selection of the best discriminatory voxel-percentile for each. Separate classifier models were trained for enhancing tumor and peritumoral region using binary logistic regressions. A total of 428 patients (321 glioblastomas, 107 metastases) fulfilled the inclusion criteria (256 men; mean age, 60years; range, 20-86years). Satisfactory results were obtained to segregate glioblastoma and metastases in training and test sets with AUCs 0.71-0.83, independent accuracies 65-79%, and combined accuracies up to 81-88%. This proof-of-concept study presents a different perspective on brain MR DSC-PWI evaluation by the inclusion of all time-points of the curves and all voxels of segmentations to generate robust diagnostic models of special interest in heterogeneous diseases and populations. The method allows satisfactory presurgical segregation of glioblastoma and metastases. • An original approach to brain MR DSC-PWI analysis, based on a point-by-point and voxel-by-voxel assessment of normalized time-intensity curves, is presented. • The method intends to extract optimized information from MR DSC-PWI sequences by impeding the potential loss of information that may represent the standard evaluation of single concrete perfusion parameters (cerebral blood volume, percentage of signal recovery, or peak height) and values (mean, maximum, or minimum). • The presented approach may be of special interest in technically heterogeneous samples, and intrinsically heterogeneous diseases. Its application enables satisfactory presurgical differentiation of GB and metastases, a usual but difficult diagnostic challenge for neuroradiologist with vital implications in patient management.

Probabilistic genotyping of single cell replicates from complex DNA mixtures recovers higher contributor LRs than standard analysis

DNA mixtures are a common source of crime scene evidence and are often one of the more difficult sources of biological evidence to interpret. With the implementation of probabilistic genotyping (PG), mixture analysis has been revolutionized allowing previously unresolvable mixed profiles to be analyzed and probative genotype information from contributors to be recovered. However, due to allele overlap, artifacts, or low-level minor contributors, genotype information loss inevitably occurs. In order to reduce the potential loss of significant DNA information from donors in complex mixtures, an alternative approach is to physically separate individual cells from mixtures prior to performing DNA typing thus obtaining single source profiles from contributors. In the present work, a simplified micro-manipulation technique combined with enhanced single-cell DNA typing was used to collect one or few cells, referred to as direct single-cell subsampling (DSCS). Using this approach, single and 2-cell subsamples were collected from 2 to 6 person mixtures. Single-cell subsamples resulted in single source DNA profiles while the 2-cell subsamples returned either single source DNA profiles or new mini-mixtures that are less complex than the original mixture due to the presence of fewer contributors. PG (STRmix™) was implemented, after appropriate validation, to analyze the original bulk mixtures, single source cell subsamples, and the 2-cell mini mixture subsamples from the original 2–6-person mixtures. PG further allowed replicate analysis to be employed which, in many instances, resulted in a significant gain of genotype information such that the returned donor likelihood ratios (LRs) were comparable to that seen in their single source reference profiles (i.e., the reciprocal of their random match probabilities). In every mixture, the DSCS approach gave improved results for each donor compared to standard bulk mixture analysis. With the 5- and 6- person complex mixtures, DSCS recovered highly probative LRs (≥1020) from donors that had returned non-probative LRs (<103) by standard methods.

Next generation models for portfolio risk management: An approach using financial big data

AbstractThis paper proposes a dynamic process of portfolio risk measurement to address potential information loss. The proposed model takes advantage of financial big data to incorporate out‐of‐target‐portfolio information that may be missed when one considers the value at risk (VaR) measures only from certain assets of the portfolio. We investigate how the curse of dimensionality can be overcome in the use of financial big data and discuss where and when benefits occur from a large number of assets. In this regard, the proposed approach is the first to suggest the use of financial big data to improve the accuracy of risk analysis. We compare the proposed model with benchmark approaches and empirically show that the use of financial big data improves small portfolio risk analysis. Our findings are useful for portfolio managers and financial regulators, who may seek for an innovation to improve the accuracy of portfolio risk estimation.

An Integrated Multi-criteria Structural Equation Model for Green Supplier Selection

The aims of the current study are to determine the criteria to be considered in a green supplier selection (GSS) process, develop an integrated decision-making approach using structural equation modelling (SEM) and fuzzy additive ratio assessment (ARAS) techniques to rank suitable suppliers, and investigate the effectiveness of this approach in collective decision-making. The study’s ultimate objective is to propose a method created from the perspective of fuzzy logic in which criterion weights are determined using SEM to resolve decision-making challenges such as GSS, in which a compromise solution is desired. In the first stage of the study, relevant literature as examined in detail. Eight main criteria and 27 sub-criteria were detected, taking into account classical, green, and social supplier selection (SS) criteria. A questionnaire was administered to 254 participants who were actively involved in SS processes for their companies, and the data were analyzed using SEM. In the second phase, six suppliers were evaluated considering the criteria found to have a positive impact on GSS, and fuzzy ARAS was applied. The results showed that the factors affecting GSS are quality (0.291), social responsibility (0.222), service (0.21), cost (0.149) and green product (0.128). The study presents an effective, objective, and fast method that can be used in decision-making problems such as GSS, that prevents potential loss of information and lack of transparency through an integrated approach, and that can be tested with a generalizable framework. The proposed hybrid method is an efficient and practical approach that can be tested on many decision-making problems, such as SS.

Recommendation algorithm based on attributed multiplex heterogeneous network

In the field of deep learning, the processing of large network models on billions or even tens of billions of nodes and numerous edge types is still flawed, and the accuracy of recommendations is greatly compromised when large network embeddings are applied to recommendation systems. To solve the problem of inaccurate recommendations caused by processing deficiencies in large networks, this paper combines the attributed multiplex heterogeneous network with the attention mechanism that introduces the softsign and sigmoid function characteristics and derives a new framework SSN_GATNE-T (S represents the softsign function, SN represents the attention mechanism introduced by the Softsign function, and GATNE-T represents the transductive embeddings learning for attribute multiple heterogeneous networks). The attributed multiplex heterogeneous network can help obtain more user-item information with more attributes. No matter how many nodes and types are included in the model, our model can handle it well, and the improved attention mechanism can help annotations to obtain more useful information via a combination of the two. This can help to mine more potential information to improve the recommendation effect; in addition, the application of the softsign function in the fully connected layer of the model can better reduce the loss of potential user information, which can be used for accurate recommendation by the model. Using the Adam optimizer to optimize the model can not only make our model converge faster, but it is also very helpful for model tuning. The proposed framework SSN_GATNE-T was tested for two different types of datasets, Amazon and YouTube, using three evaluation indices, ROC-AUC (receiver operating characteristic-area under curve), PR-AUC (precision recall-area under curve) and F1 (F1-score), and found that SSN_GATNE-T improved on all three evaluation indices compared to the mainstream recommendation models currently in existence. This not only demonstrates that the framework can deal well with the shortcomings of obtaining accurate interaction information due to the presence of a large number of nodes and edge types of the embedding of large network models, but also demonstrates the effectiveness of addressing the shortcomings of large networks to improve recommendation performance. In addition, the model is also a good solution to the cold start problem.

A new unsupervised pseudo-siamese network with two filling strategies for image denoising and quality enhancement

Digital image noise may be introduced during acquisition, transmission, or processing and affects readability and image processing effectiveness. The accuracy of established image processing techniques, such as segmentation, recognition, and edge detection, is adversely impacted by noise. There exists an extensive body of work which focuses on circumventing such issues through digital image enhancement and noise reduction, but this work is limited by a number of constraints including the application of non-adaptive parameters, potential loss of edge detail information, and (with supervised approaches) a requirement for clean, labeled, training data. This paper, developed on the principle of Noise2Void, presents a new unsupervised learning approach incorporating a pseudo-siamese network. Our method enables image denoising without the need for clean images or paired noise images, instead requiring only noise images. Two independent branches of the network utilize different filling strategies, namely zero filling and adjacent pixel filling. Then, the network employs a loss function to improve the similarity of the results in the two branches. We also modify the Efficient Channel Attention module to extract more diverse features and improve performance on the basis of global average pooling. Experimental results show that compared with traditional methods, the pseudo-siamese network has a greater improvement on the ADNI dataset in terms of quantitative and qualitative evaluation. Our method therefore has practical utility in cases where clean images are difficult to obtain.

In Search of Species-Specific SNPs in a Non-Model Animal (European Bison (Bison bonasus))-Comparison of De Novo and Reference-Based Integrated Pipeline of STACKS Using Genotyping-by-Sequencing (GBS) Data.

Simple SummaryThe European bison is a dramatically low-diversified species, commonly analyzed using cattle-dedicated tools. Our aim was to compare two genotyping-by-sequencing (GBS) pipelines: de novo and reference pipeline, using the STACKS software and to reveal the maximum possible number of species-specific SNPs for our further project on European bison health. Therefore, we compared two genotyping-by-sequencing (GBS) pipelines: de novo (non-reference based) and a reference-based, using the STACKS software. We found a higher number of polymorphic loci from the reference pipeline in comparison to the de novo one. Next, we compared the results of the reference pipeline for the draft genome of European bison and completely annotated the Bos taurus genome. Higher numbers of polymorphic loci were revealed in European bison than in Bos taurus through the reference pipeline. We observed a possible effect of PCR duplicates on GBS data, as previously reported with the RADSeq approach. We recommend using a reference pipeline without PCR duplicates as a more efficient tool for species with low genetic diversity.The European bison is a non-model organism; thus, most of its genetic and genomic analyses have been performed using cattle-specific resources, such as BovineSNP50 BeadChip or Illumina Bovine 800 K HD Bead Chip. The problem with non-specific tools is the potential loss of evolutionary diversified information (ascertainment bias) and species-specific markers. Here, we have used a genotyping-by-sequencing (GBS) approach for genotyping 256 samples from the European bison population in Bialowieza Forest (Poland) and performed an analysis using two integrated pipelines of the STACKS software: one is de novo (without reference genome) and the other is a reference pipeline (with reference genome). Moreover, we used a reference pipeline with two different genomes, i.e., Bos taurus and European bison. Genotyping by sequencing (GBS) is a useful tool for SNP genotyping in non-model organisms due to its cost effectiveness. Our results support GBS with a reference pipeline without PCR duplicates as a powerful approach for studying the population structure and genotyping data of non-model organisms. We found more polymorphic markers in the reference pipeline in comparison to the de novo pipeline. The decreased number of SNPs from the de novo pipeline could be due to the extremely low level of heterozygosity in European bison. It has been confirmed that all the de novo/Bos taurus and Bos taurus reference pipeline obtained SNPs were unique and not included in 800 K BovineHD BeadChip.

Quad division prototype selection-based k-nearest neighbor classifier for click fraud detection from highly skewed user click dataset

In online advertising, the user-clicks dataset based fraudulent publishers’ classification models exhibit poor performance due to high skewness in class distribution of the publishers. The nearest-neighbor based classification techniques are popularly used to reduce the impact of class skewness on performance. The Nearest-Neighbor techniques use Prototype Selection (PS) methods to select promising samples before classifying them for reducing the size of training data. Although Nearest-Neighbor techniques are simple to use and reduce the negative impact of the loss of potential information, they suffer from higher storage requirements and slower classification speed when applied on datasets with skewed class distributions. In this paper, we propose a Quad Division Prototype Selection-based k-Nearest Neighbor classifier (QDPSKNN) by introducing quad division method for handling uneven class distribution. The quad-division divides the data into four quartiles (groups) and performs controlled under-sampling for balancing class distribution. It reduces the size of the training dataset by selecting only the relevant prototypes in the form of nearest-neighbors. The performance of QDPSKNN is evaluated on Fraud Detection in Mobile Advertising (FDMA) user-click dataset and fifteen other benchmark imbalanced datasets to test its generalizing behaviour. The performance is also compared with one baseline model (k-NN) and four other prototype selection methods such as NearMiss-1, NearMiss-2, NearMiss-3, and Condensed Nearest-Neighbor. The results show improved classification performance with QDPSKNN in terms of precision, recall, f-measure, g-mean, reduction rate and execution time, compared to existing prototype selection methods in the classification of fraudulent publishers as well as on other benchmark imbalanced datasets. Wilcoxon signed ranked test is conducted to demonstrate significant differences amid QDPSKNN and state-of-the-art methods.

Identifying Temporal Aggregation Effect on Crash-Frequency Modeling

Aggregation of spatiotemporal data can encounter potential information loss or distort attributes via individual observation, which would influence modeling results and lead to an erroneous inference, named the ecological fallacy. Therefore, deciding spatial and temporal resolution is a fundamental consideration in a spatiotemporal analysis. The modifiable temporal unit problem (MTUP) occurs when using data that is temporally aggregated. While consideration of the spatial dimension has been increasingly studied, the counterpart, a temporal unit, is rarely considered, particularly in the traffic safety modeling field. The purpose of this research is to identify the MTUP effect in crash-frequency modeling using data with various temporal scales. A sensitivity analysis framework is adopted with four negative binomial regression models and four random effect negative binomial models having yearly, quarterly, monthly, and weekly temporal units. As the different temporal unit was applied, the result of the model estimation also changed in terms of the mean and significance of the parameter estimates. Increasing temporal correlation due to using the small temporal unit can be handled with the random effect models.

MultiGBS: A multi-layer graph approach to biomedical summarization

Automatic text summarization methods generate a shorter version of the input text to assist the reader in gaining a quick yet informative gist. Existing text summarization methods generally focus on a single aspect of text when selecting sentences, causing the potential loss of essential information. In this study, we propose a domain-specific method that models a document as a multi-layer graph to enable multiple features of the text to be processed at the same time. The features we used in this paper are word similarity, semantic similarity, and co-reference similarity, which are modelled as three different layers. The unsupervised method selects sentences from the multi-layer graph based on the MultiRank algorithm and the number of concepts. The proposed MultiGBS algorithm employs UMLS and extracts the concepts and relationships using different tools such as SemRep, MetaMap, and OGER. Extensive evaluation by ROUGE and BERTScore shows increased F-measure values.

Deep Statistical Comparison for Multi-Objective Stochastic Optimization Algorithms

Abstract Making a statistical comparison of meta-heuristic multi-objective optimization algorithms is crucial for identifying the strengths and weaknesses of a newly proposed algorithm. Currently, state-of-the-art comparison approaches involve user-preference-based selection of a single quality indicator or an ensemble of quality indicators as a comparison metric. Using these quality indicators, high-dimensional data is transformed into one-dimensional data. By doing this, information contained in the high-dimensional space can be lost, which will affect the results of the comparison. To avoid losing this information, we propose a novel ranking scheme that compares the distributions of high-dimensional data. Experimental results show that the proposed approach reduces potential information loss when statistical significance is not observed in high-dimensional data. Consequently, the selection of a quality indicator is required only in cases when statistical significance is observed in high-dimensional data. With this the cases that are affected by the user preference selection are reduced.

Building a grey-based multi-criteria decision-making model for offshore wind farm site selection

The selection of suitable sites is a critical step for the successful development of offshore wind power. This study aims to establish an assessment framework to select the optimal alternative under multiple conflicting criteria. A hybrid model that combines the grey decision-making trial-and-evaluation laboratory-based analytic network process (grey DANP) and probability-based grey relational analysis (P-GRA) methods is proposed. Grey DANP can not only map the influential network relationship of the criteria, but also generate a set of criteria influence weights. In addition, P-GRA, which uses the concept of interval analysis, can be used to determine the gap between the alternatives and the ideal referential solution (IRS). P-GRA retains the interval value type in the calculation process to avoid the loss of potential information. The proposed model optimizes the traditional DANP and GRA methods, which improving their effectiveness and practicability for practical applications. This paper contributes to solving the problem of farm site selection based on the potential for sustainable development and the novelty of the analysis model. A case study of a Taiwan example is used to examine the feasibility of the proposed model. Finally, a sensitivity analysis was conducted to demonstrate the robustness and innovativeness of the proposed model.

Facilitating Cross-Border Criminal Justice Cooperation Between the UK and Ireland After Brexit: ‘Keeping the Lights On’ to Ensure the Safety of the Common Travel Area

Much of the cooperation on criminal justice matters between the United Kingdom and the Republic of Ireland is based on EU level instruments. While there has been consideration of the broader impact of Brexit on the Good Friday Agreement and consensus on the need to avoid a return to a hard border between Ireland and Northern Ireland, more detailed consideration has not been given to the effect that Brexit may have on continued criminal justice cooperation across the border. This article highlights the combined risks that Brexit presents for Northern Ireland in the form of increased criminality at a time when the loss of EU police cooperation mechanisms may result in a reduction of operational capacity and the removal of the legal architecture underpinning informal cooperation. Part 1 seeks to highlight the historical context of UK-Irish cooperation in policing matters. Part 2 explores the risk that post Brexit the Irish border may become a focus for criminal activity. The risks relating to increased immigration crime, smuggling of commodities and potential rise in terrorist activities are explored. Part 3 considers how the risks of increased criminal threats are exacerbated by the loss of EU criminal justice cooperation mechanisms and how this will affect UK-Irish cooperation specifically. Consideration is particularly given to the loss of information sharing systems. Part 4 considers how loss of EU level cooperation mechanisms could be mitigated. The viability of bilateral agreements between the UK and Ireland is considered alongside ways which police cooperation can be formalised to compensate for the potential loss of EU criminal justice information sharing systems. Nordic police cooperation is considered as a potential blueprint for the UK and Ireland.

Data-Driven Supervised Learning for Life Science Data

Life science data are often encoded in a non-standard way by means of alpha-numeric sequences, graph representations, numerical vectors of variable length, or other formats. Domain-specific or data-driven similarity measures like alignment functions have been employed with great success. The vast majority of more complex data analysis algorithms require fixed-length vectorial input data, asking for substantial preprocessing of life science data. Data-driven measures are widely ignored in favor of simple encodings. These preprocessing steps are not always easy to perform nor particularly effective, with a potential loss of information and interpretability. We present some strategies and concepts of how to employ data-driven similarity measures in the life science context and other complex biological systems. In particular, we show how to use data-driven similarity measures effectively in standard learning algorithms.

HealthyBlock: Blockchain-Based IT Architecture for Electronic Medical Records Resilient to Connectivity Failures.

The current information systems for the registration and control of electronic medical records (EMR) present a series of problems in terms of the fragmentation, security, and privacy of medical information, since each health institution, laboratory, doctor, etc. has its own database and manages its own information, without the intervention of patients. This situation does not favor effective treatment and prevention of diseases for the population, due to potential information loss, misinformation, or data leaks related to a patient, which in turn may imply a direct risk for the individual and high public health costs for governments. One of the proposed solutions to this problem has been the creation of electronic medical record (EMR) systems using blockchain networks; however, most of them do not take into account the occurrence of connectivity failures, such as those found in various developing countries, which can lead to failures in the integrity of the system data. To address these problems, HealthyBlock is presented in this paper as an architecture based on blockchain networks, which proposes a unified electronic medical record system that considers different clinical providers, with resilience in data integrity during connectivity failure and with usability, security, and privacy characteristics. On the basis of the HealthyBlock architecture, a prototype was implemented for the care of patients in a network of hospitals. The results of the evaluation showed high efficiency in keeping the EMRs of patients unified, updated, and secure, regardless of the network clinical provider they consult.

Virtual care: a 'Zoombie' apocalypse?

In the wake of COVID-19, clinicians took to telehealth to continue providing services to their patients, mostly via telephone or videoconferencing technology. Telehealth has many promised and proven benefits including convenience to the patient, potentially less distraction from the electronic health record (EHR), saves in travel time and expenses, and lowering patients’ wait time in the clinic. However, there could be some unintended negative consequences including increased clinician burnout due to screen fatigue, potential loss of information due to the limitations of the medium, difficulty discussing sensitive issues and impacts on patient-clinician relationship, empathy, and compassion. In this perspective, we discuss some of the positives and potential negatives of telehealth and highlight some considerations that could guide the choice of media. We submit that for telehealth to become a sustainable solution that is widely applied, it is important to take these issues into consideration in both research and implementation of telehealth solutions.

The wolf from the Middle Pleistocene site of Ostiense (Rome): the last occurrence of Canis mosbachensis (Canidae, Mammalia) in Italy

ABSTRACT The phyletic relationship between Canis lupus and the Early-Middle Pleistocene Canis mosbachensis is widely accepted among scholars, although the taxonomy of several European fossil specimens is still debated. In the last decades, many studies focused on the evolution of Pleistocene wolves have been proposed considering new materials as well as specimens belonging to historical collections. The canid remains recovered during the last century and housed in museums, undoubtedly represent a valuable source of biometric and morphological data which require, in some cases, an updated review of the fossil material. Indeed, cases of stratigraphic and taxonomic misinterpretations represent a potential loss of information for understanding the palaeobiology of these iconic predators. Here we provide the first updated description of the specimen belonging to the collection of the Italian geologist and palaeontologist Alessandro Portis dating back to the early last century. Previously reported as Canis lupus, the cranium from the historical excavation of Ostiense palaeontological site (Rome, Italy) is ascribed to the Early-Middle Pleistocene wolf Canis mosbachensis. Moreover, the reassessment of the stratigraphic and geological data of the Ostiense site led to consider the studied specimen as the last occurrence of the Mosbach wolf in the Italian fossil record.

A 3D Fully Convolutional Neural Network With Top-Down Attention-Guided Refinement for Accurate and Robust Automatic Segmentation of Amygdala and Its Subnuclei.

Recent advances in deep learning have improved the segmentation accuracy of subcortical brain structures, which would be useful in neuroimaging studies of many neurological disorders. However, most existing deep learning based approaches in neuroimaging do not investigate the specific difficulties that exist in segmenting extremely small but important brain regions such as the subnuclei of the amygdala. To tackle this challenging task, we developed a dual-branch dilated residual 3D fully convolutional network with parallel convolutions to extract more global context and alleviate the class imbalance issue by maintaining a small receptive field that is just the size of the regions of interest (ROIs). We also conduct multi-scale feature fusion in both parallel and series to compensate the potential information loss during convolutions, which has been shown to be important for small objects. The serial feature fusion enabled by residual connections is further enhanced by a proposed top-down attention-guided refinement unit, where the high-resolution low-level spatial details are selectively integrated to complement the high-level but coarse semantic information, enriching the final feature representations. As a result, the segmentations resulting from our method are more accurate both volumetrically and morphologically, compared with other deep learning based approaches. To the best of our knowledge, this work is the first deep learning-based approach that targets the subregions of the amygdala. We also demonstrated the feasibility of using a cycle-consistent generative adversarial network (CycleGAN) to harmonize multi-site MRI data, and show that our method generalizes well to challenging traumatic brain injury (TBI) datasets collected from multiple centers. This appears to be a promising strategy for image segmentation for multiple site studies and increased morphological variability from significant brain pathology.

Transcriptome-Wide Profiling of Protein-RNA Interactions by Cross-Linking and Immunoprecipitation Mediated by FLAG-Biotin Tandem Purification.

RNA and RNA-binding proteins (RBPs) control multiple biological processes. The spatial and temporal arrangement of RNAs and RBPs underlies the delicate regulation of these processes. A strategy called CLIP-seq (cross-linking and immunoprecipitation) has been developed to capture endogenous protein-RNA interactions with UV cross-linking followed by immunoprecipitation. Despite the wide use of conventional CLIP-seq method in RBP study, the CLIP method is limited by the availability of high-quality antibodies, potential contaminants from the copurified RBPs, requirement of isotope manipulation, and potential loss of information during a tedious experimental procedure. Here we describe a modified CLIP-seq method called FbioCLIP-seq using the FLAG-biotin tag tandem purification. Through tandem purification and stringent wash conditions, almost all the interacting RNA-binding proteins are removed. Thus, the RNAs interacting indirectly mediated by these copurified RBPs are also reduced. Our FbioCLIP-seq method allows efficient detection of direct protein-bound RNAs without SDS-PAGE and membrane transfer procedures in an isotope-free and protein-specific antibody-free manner.