Sequence Selectivity Research Articles

Shapelet selection for time series classification

In recent times, increasing attention has been given to shapelet-based methods for time series classification. However, in the majority of current methods, similar subsequences were often selected as shapelets, thereby reducing the final interpretability of these methods. Aiming to circumvent the selection of similar subsequences as the final shapelets, a novel shapelet selection method (SSM) was proposed in this paper. Firstly, shapelet candidates were generated by SSM through time series segmentation to avoid excessive generation of similar candidates from a single time series. Secondly, all shapelet candidates were evaluated simultaneously to improve evaluation efficiency. Finally, SSM introduced a position-based filter to prevent the selection of similar sequences repeatedly. The results obtained on the UCR TSC archive demonstrated the effectiveness of the proposed method.

“A show of confidence and authority”: Philopseudes and More’s Lucian

Of the three Lucianic dialogues translated into Latin by Thomas More and published in 1506, Philopseudes is the last, longest, and most narratively complex. The majority of it is taken up by a recital of incredible lies, yet it presents a nuanced view of lying that accounts for its appeal to More, who calls it both “amusing” and “instructive” in his dedicatory letter to Thomas Ruthall. Lucian’s Philopseudes instructs by demonstrating various strategies of plausible liars and suggesting that “truth and sound reason” are an antidote ( alexipharmakon) to the drug ( pharmakon) or infectious disease of lying. But this solution is undercut, with typical Lucianic irony, by the failings of the characters who suggest it. More’s translation of Philopseudes is alert to Lucian’s ironies, but more confidently suggests a union of divinely revealed truth and human reason as a cure to the plague of lies. After analyzing both versions of Philopseudes, I defend three claims about the selection and arrangement of the whole sequence of dialogues in More’s Lucian— Cynicus, Menippus, and Philopseudes. 1) They form a unified progression of explicit and implicit critiques from which none of their characters escape, and which threaten to involve the author and his readers. 2) Later dialogues in More’s selection encourage ironic readings of earlier ones, guiding readers from a simple understanding of explicit critiques to a deeper understanding of implicit critiques, and finally to self-examination. 3) Beyond selecting and arranging the dialogues in this enlightening way, More scatters humorous interpretive mistakes and dubious allusions to authority throughout his own prefatory letter to Ruthall, even as he warns his readers not to fall for “a show of confidence and authority.” More’s ironic preface to Lucian thus requires of us, and trains us in, the prudent reading that Lucian himself demands.

Igome Graphs Suggest the Changes in the IgM and IgG Repertoires in Antiphospholipid Syndrome Are in Part Idiotypically Defined

Antiphospholipid Syndrome (APS) is characterized by the persistence of high-affinity antiphospholipid antibodies, manifesting clinically as pregnancy complications, thrombosis, or neurological symptoms. This study explores the changes in the IgM and IgG antibody repertoires in APS patients using igome graphs, which represent antibody repertoires through peptide libraries selected from phage display on the complete immunoglobulin fraction of the serum. Serum samples from healthy women and APS patients were processed to isolate IgM and IgG fractions, which were then used for phage selection and next-generation sequencing. Peptide sequences were analyzed using graph representation, clustering, and spectral embedding to identify significant differences between the repertoires. The study revealed that while IgM repertoires in APS patients exhibit a loss of public reactivities, the IgG repertoires do not follow this trend. Three orthogonal sequence motifs were identified in the IgM and IgG repertoires, suggesting potential idiotypic interactions. These findings highlight the intricate mechanisms of antibody selection and cross-reactivity in APS, offering insights into potential therapeutic approaches. The study underscores the importance of system-level analysis in understanding immune mechanisms and suggests that idiotypic interactions, though complex, could play a crucial role in APS pathogenesis.

Detection of drug resistance in Escherichia coli from calves with diarrhea in the Tongliao region: an analysis of multidrug-resistant strains.

Escherichia coli is a major pathogen responsible for calf diarrhea, which has been exacerbated by the irrational and unscientific use of antimicrobial drugs, leading to significant drug resistance. This study focused on the isolation and identification of E. coli from calf diarrhea samples in the Tongliao area of China. Isolation was conducted using selective media, Gram staining, and 16S rRNA sequencing. The minimum inhibitory concentration (MIC) of E. coli was determined through the microbroth dilution method. Additionally, the presence of antibiotic-resistant genes was detected, and multidrug-resistant strains were selected for whole-genome sequencing (WGS). The results revealed that all 40 isolated strains of E. coli exhibited resistance to sulfadiazine sodium, enrofloxacin, and ciprofloxacin, with 90% of the strains being susceptible to polymyxin B. Notably, strains 11, 23, and 24 demonstrated severe resistance. The detection rates of the antibiotic resistance genes TEM-1, TEM-206, strA, strB, qacH, and blaCTX were 100%, indicating a high prevalence of these genes. Moreover, the majority of strains carried antibiotic resistance genes consistent with their resistance phenotypes. WGS of strains 11, 23, and 24 revealed genome sizes of 4,897,185 bp, 4,920,234 bp, and 4,912,320 bp, respectively. These strains carried two, one, and two plasmids, respectively. The prediction of antibiotic resistance genes showed a substantial number of these genes within the genomes, with strain 24 harboring the highest number, totaling 77 subspecies containing 88 antibiotic resistance genes. In conclusion, all 40 isolated strains of E. coli from calf diarrhea in this study were multidrug-resistant, exhibiting a broad distribution of antibiotic resistance genes and mobile components. This poses a significant risk of horizontal gene transfer, highlighting the critical situation of antibiotic resistance in this region.

NIMG-41. MULTI-SITE, PROSPECTIVE TRIAL EVALUATING FET-PET IN GLIOBLASTOMA (FIG) STUDY (TROG 18.06): PRELIMINARY RESULTS OF CENTRAL NUCLEAR MEDICINE REVIEW OF FET-PET BIOLOGIC TARGET VOLUME DELINEATION FOR RADIATION PLANNING

Abstract BACKGROUND To provide an update from the multi-site trial evaluating O-(2-[18F]-fluoroethyl)-L-tyrosine Positron Emission Tomography (FET-PET) in Glioblastoma (FIG) study and 2) assess the impact of central Nuclear Medicine physician (NMP) review of prospective FET-PET1 delineation of the biological target volume (BTV) for radiotherapy (RT) planning. Material and methods Up to 210 adult GBM participants across 11 Australian sites will undergo FET-PET post-surgery/pre-chemo-RT [CRT] (FET-PET1), one month post CRT (FET-PET2) and at suspected progression (FET-PET3). Group 1 participants enter at timepoint 1 (FET-PET1 with MRI1), with Group 2 entering at timepoint 2. Adjuvant RT target volumes are derived per standard contrast MRI with hybrid post-hoc RT volumes then incorporating the FET-PET1 NM-derived BTV utilising MiM version 7.0. All trial sites and NMP have passed credentialling which included three benchmarking cases involving BTV delineation. RESULTS Trial recruitment commenced in January 2021, with 189 (n=126 Group 1 and n=63 Group 2) participants enrolled to date, with a target of 140 Group 1 participants. During trial credentialling, results demonstrated variations in FET-PET1-derived BTV in 25/72 (34.7%) - 13 minor and 12 major.Currently, 42 of 112 participant FET-PET1 with BTV delineation cases across 10 sites have undergone central review, with 7/42 (16.7%) requiring resubmission. Reasons for resubmission/protocol deviation included incorrect imaging sequence selection within the MiM workflow (n=3), Static GTV overcontouring (n=3) and dynamic volume of interest change in size/position during MiM workflow use (n=1). CONCLUSION The FIG trial will complete recruitment in 2024 with analyses planned at one year post CRT completion. Despite improvements in resubmission rates compared to the credentialling phase, central review of prospective FET-PET1-derived BTV delineation remains important in ensuring protocol adherence. The FIG study is the largest prospective multi-site study of its kind addressing FET-PET’s impact on adjuvant radiation planning and its role in management of pseudoprogression and prognostication.

Challenges and Prospects of DNA-Encoded Library Data Interpretation.

DNA-encoded library (DEL) technology is a powerful platform for the efficient identification of novel chemical matter in the early drug discovery process enabled by parallel screening of vast libraries of encoded small molecules through affinity selection and deep sequencing. While DEL selections provide rich data sets for computational drug discovery, the underlying technical factors influencing DEL data remain incompletely understood. This review systematically examines the key parameters affecting the chemical information in DEL data and their impact on hit triaging and machine learning integration. The need for rigorous data handling and interpretation is emphasized, with standardized methods being critical for the success of DEL-based approaches. Major challenges include the relationship between sequence counts and binding affinities, frequent hitters, and the influence of factors such as inhomogeneous library composition, DNA damage, and linkers on binding modes. Experimental artifacts, such as those caused by protein immobilization and screening matrix effects, further complicate data interpretation. Recent advancements in using machine learning to denoise DEL data and predict drug candidates are highlighted. This review offers practical guidance on adopting best practices for integrating robust methodologies, comprehensive data analysis, and computational tools to improve the accuracy and efficacy of DEL-driven hit discovery.

TCellR2Vec: efficient feature selection for TCR sequences for cancer classification

Cancer remains one of the leading causes of death globally. New immunotherapies that harness the patient’s immune system to fight cancer show promise, but their development requires analyzing the diversity of immune cells called T-cells. T-cells have receptors that recognize and bind to cancer cells. Sequencing these T-cell receptors allows to provide insights into their immune response, but extracting useful information is challenging. In this study, we propose a new computational method, TCellR2Vec, to select key features from T-cell receptor sequences for classifying different cancer types. We extracted features like amino acid composition, charge, and diversity measures and combined them with other sequence embedding techniques. For our experiments, we used a dataset of over 50,000 T-cell receptor sequences from five cancer types, which showed that TCellR2Vec improved classification accuracy and efficiency over baseline methods. These results demonstrate TCellR2Vec’s ability to capture informative aspects of complex T-cell receptor sequences. By improving computational analysis of the immune response, TCellR2Vec could aid the development of personalized immunotherapies tailored to each patient’s T-cells. This has important implications for creating more effective cancer treatments based on the individual’s immune system.

STALL-seq: mRNA-display selection of bacterial and eukaryotic translational arrest sequences from large random-sequence libraries

Translational arrest is a phenomenon wherein a temporary pause or slowing of the translation elongation reaction occurs due to the interaction between ribosome and nascent peptide. Recent studies have revealed that translational arrest peptides are involved in intracellular protein homeostasis regulatory functions, such as gene expression regulation at the translational level and regulation of cotranslational protein folding. Herein, we established a method for the large-scale in vitro selection of translational arrest peptides from DNA libraries by combining a modified mRNA display method and deep sequencing. We performed in vitro selection of translational arrest sequences from random-sequence libraries via mRNA display based on the E. coli PURE system or wheat germ extract. Following several rounds of affinity selection, we obtained various candidate sequences that were not similar to known arrest peptides and subsequently confirmed their ribosome stalling activity by peptidyl-tRNA detection and toeprinting assay. Following the site-directed mutagenesis of the selected sequences, these clones were found to contain novel arrest peptide motifs. This method, termed as STALL-seq (Selection of Translational Arrest sequences from Large Library sequencing), could be useful for the large-scale investigation of translational arrest sequences acting on both bacterial and eukaryotic ribosomes and could help discover novel intracellular regulatory mechanisms.

Synergistic solvent extraction system composed of bis(pyridin-2-ylmethyl) dodecan-1-amine and dinonylnaphthalene for enhanced selective extraction of nickel and cobalt

Simultaneous recovery of Ni and Co from Fe(III) and Al is a critical challenge in hydrometallurgical processes. Recognized solvent extraction systems often struggle with selectivity and effective performance in mixed metal ion environments. Herein, a new synergistic solvent extraction (SSX) system comprised of a novel pyridine analog, N,N-bis(pyridin-2-ylmethyl) dodecan-1-amine (BPMDA), and dinonylnaphthalene sulfonic acid (DNNSA) with tributyl phosphate as phase modifier is introduced. The SSX system demonstrates high extraction performance achieving >90% for Ni and >97% for Co in a single-stage extraction process, with high selectivity. Under optimal conditions, the selectivity sequence is observed as Co2+ (>97%) > Ni2+ (>90%) > Mn2+ (<20%) > Fe3+ (<10%) > Mg2+ (<5%) > Al3+ (<2%) > Ca2+ (<1%). Spectroscopic analysis evidences the preferential binding of BPMDA with Ni and Co in the presence of DNNSA, concurrently achieving a significant reduction in the co-extraction of Fe(III) and Al. The selective complexation of Ni and Co using the SSX system offers a highly efficient and selective approach for their extraction, with promising potential for applications in recovery-based processes.

HTD-TS3: Weakly Supervised Hyperspectral Target Detection Based on Transformer via Spectral-Spatial Similarity.

As an advanced technique in remote sensing, hyperspectral target detection (HTD) is widely concerned in civilian and military applications. However, the limitation of prior and heterogeneous backgrounds makes HTD models sensitive to data corruption under various interference from the environment. In this article, a novel united HTD framework based on the concept of transformer is proposed to extract [HTD based on transformer via spectral-spatial similarity (HTD-TS3)] under weak supervision, which opens up more flexible ways to study HTD. For the first time, the transformer mechanism is introduced into the HTD task to extract spectral and spatial features in a unified optimization procedure. By modeling long-range dependence among spectra, it realizes spectral-spatial joint inference based on long-range context, which addresses the issues of insufficient utilization of spatial information. To provide samples for weakly supervised learning (WSL), the coarse sample selection and spectral sequence construction in an efficient way are proposed, which makes full use of limited prior information. Finally, an exponential constrained nonlinear function is adopted to acquire pixel-level prediction via combining discriminative spectral-spatial features and coarse spatial information. Experiments on real hyperspectral images (HSIs) captured by different sensors at various scenes verify the effectiveness and efficiency of HTD-TS3.

A Test Report Optimization Method Fusing Reinforcement Learning and Genetic Algorithms

Filtering high-variability and high-severity defect reports from large test report databases is a challenging task in crowdtesting. Traditional optimization algorithms based on clustering and distance techniques have made progress but are limited by initial parameter settings and significantly decrease in efficiency with an increasing number of reports. To address this issue, this paper proposes a method that integrates reinforcement learning with genetic algorithms for crowdsourced testing report optimization, called Reinforcement Learning-based Genetic Algorithm for Crowdsourced Testing Report Optimization (RLGA). Its core goal is to identify distinct, high-severity defect reports from a large set. The method uses genetic algorithms to generate the optimal report selection sequence and adjusts the crossover probability (Pc) and mutation probability (Pm) dynamically with reinforcement learning based on the population’s average fitness, best fitness, and diversity. The reinforcement learning component uses a hybrid SARSA and Q-Learning strategy to update the Q-value table, allowing the algorithm to learn quickly in early iterations and expand the search space later to avoid local optima, thereby improving efficiency. To validate the RLGA method, this paper uses four public datasets and compares RLGA with six classic methods. The results indicate that RLGA outperforms BDDIV in terms of execution time and is less sensitive to the total number of test reports. In terms of optimization objectives, the test reports selected by RLGA have higher levels of defect severity and diversity than those selected by the random choice, BDDIV, and TSE methods. Regarding population diversity, RLGA effectively enhances the uniformity and diversity of individuals compared to random initialization. In terms of convergence speed, RLGA is superior to the GA, GA-SARSA, and GA-Q methods.

Crystal structure of the HMGA AT-hook 1 domain bound to the minor groove of AT-rich DNA and inhibition by antikinetoplastid drugs

High mobility group (HMG) proteins are intrinsically disordered nuclear non-histone chromosomal proteins that play an essential role in many biological processes by regulating the expression of numerous genes in eukaryote cells. HMGA proteins contain three DNA binding motifs, the “AT-hooks”, that bind preferentially to AT-rich sequences in the minor groove of B-form DNA. Understanding the interactions of AT-hook domains with DNA is very relevant from a medical point of view because HMGA proteins are involved in different conditions including cancer and parasitic diseases. We present here the first crystal structure (1.40 Å resolution) of the HMGA AT-hook 1 domain, bound to the minor groove of AT-rich DNA. In contrast to AT-hook 3 which bends DNA and shows a larger minor groove widening, AT-hook 1 binds neighbouring DNA molecules and displays moderate widening of DNA upon binding. The binding affinity and thermodynamics of binding were studied in solution with surface plasmon resonance (SPR)-biosensor and isothermal titration calorimetry (ITC) experiments. AT-hook 1 forms an entropy-driven 2:1 complex with (TTAA)2-containing DNA with relatively slow kinetics of association/dissociation. We show that N-phenylbenzamide-derived antikinetoplastid compounds (1–3) bind strongly and specifically to the minor groove of AT-DNA and compete with AT-hook 1 for binding. The central core of the molecule is the basis for the observed sequence selectivity of these compounds. These findings provide clues regarding a possible mode of action of DNA minor groove binding compounds that are relevant to major neglected tropical diseases such as leishmaniasis and trypanosomiasis.

CMMamba: channel mixing Mamba for time series forecasting

Transformer-based methods have achieved excellent results in the field of time series forecasting due to their powerful ability to model sequences and capture their long-term dependencies. However, Transformer-based methods are still computationally inefficient and have high costs when modeling long sequences. In addition, previous work ignored the destruction of the original in-channel features by too many mixed features when performing channel mixing. To address the above issues, we introduce Mamba into the field of time series forecasting. The selective state-space model Mamba models long sequences more efficiently by expanding linearly according to the sequence length. Based on this, we propose Channel Mixing Mamba (CMMamba), which uses bidirectional Mamba to model sequences and uses a channel mixing mechanism to select appropriate mixed features to enhance the feature representation within the original channel. By introducing Mamba, we achieve efficient modeling of sequences. We also propose Deep Convolutional Structure (DCS) to learn cross-channel dependencies and temporal order information. Extensive experimental results on six real-world public datasets demonstrate the effectiveness of CMMamba.

Two remarks on properties of functions of bounded variation

In terms of variations, a sufficient condition for the uniform convergence of sequences of continuous functions is proved. Using this result, we obtain an addition to the classical Helly theorem on the selection of convergent sequences of functions with uniformly bounded variations in the case when the limit function is continuous. Also, by using an example we show that the condition of continuous differentiability of a function, ensuring the differentiability of its variation with the variable upper limit, is in a certain sense sharp.

An Expectation-Maximization framework for Personalized Itinerary Recommendation with POI Categories and Must-see POIs

In this article, we introduce a novel deterministic method based on Expectation Maximization (EM) to solve the rather complex problem of designing a tourist trip or Personalized Itinerary Recommendation (PIR). PIR objective is to recommend a personalized tour consisting of successive Points of Interest (POIs), which maximizes user satisfaction and respects user time-frame constraints. On top of that, the POIs are divided into categories, in order for travelers to be able to set limits on the maximum (and minimum) number of POIs that belong to one category and are included in the itinerary. In the proposed framework, emphasis is given on the POIs sequence selection, which exploits the customized POI recommendations offered by a recommender system. Additionally, the proposed methodology with POIs categories is able to solve the TourMustSee problem, so that the tour includes a set of POIs that must be visited. The proposed system has been successfully incorporated into a mobile app, offering a complete tourist trip design. The high performance, resilience, and computational efficiency of the proposed framework are demonstrated by experimental findings and comparisons to existing approaches on numerous synthetic and real datasets.

A Novel and Rapid Selective Viral Genome Amplification and Sequencing Method for African Swine Fever Virus.

African swine fever virus (ASFV) is the etiological agent of African swine fever, a highly contagious hemorrhagic disease affecting both wild boars and domestic pigs with lethality rates up to 100%. Until now, the most effective measure to prevent an outbreak of ASFV was early detection. In this situation, whole genome sequencing (WGS) allows the gathering of detailed information about the identity and epidemiology of the virus. However, due to the large genome size and complex genome ends, WGS is challenging. Current WGS workflows require either elaborate enrichment methods or are based on tiled PCR approaches, which are susceptible to genetic differences between ASFV strains. To overcome this, we developed a novel approach for WGS of ASFV, using the Phi29 DNA polymerase-based multiple displacement amplification in combination with only seven primers. Furthermore, we applied an alkaline-based DNA denaturation step to significantly increase the number of viral reads, which resolves the near-full genome of ASFV. This novel isothermal WGS approach can be used in authorized laboratories for the genomic epidemiological analysis of ASFV outbreaks caused by different genotypes.

Optimising ovarian tumor classification using a novel CT sequence selection algorithm

Gynaecological cancers, especially ovarian cancer, remain a critical public health issue, particularly in regions like India, where there are challenges related to cancer awareness, variable pathology, and limited access to screening facilities. These challenges often lead to the diagnosis of cancer at advanced stages, resulting in poorer outcomes for patients. The goal of this study is to enhance the accuracy of classifying ovarian tumours, with a focus on distinguishing between malignant and early-stage cases, by applying advanced deep learning methods. In our approach, we utilized three pre-trained deep learning models—Xception, ResNet50V2, and ResNet50V2FPN—to classify ovarian tumors using publicly available Computed Tomography (CT) scan data. To further improve the model’s performance, we developed a novel CT Sequence Selection Algorithm, which optimises the use of CT images for a more precise classification of ovarian tumours. The models were trained and evaluated on selected TIFF images, comparing the performance of the ResNet50V2FPN model with and without the CT Sequence Selection Algorithm. Our experimental results show the Comparative evaluation against the ResNet50V2 FPN model, both with and without the CT Sequence Selection Algorithm, demonstrates the superiority of the proposed algorithm over existing state-of-the-art methods. This research presents a promising approach for improving the early detection and management of gynecological cancers, with potential benefits for patient outcomes, especially in areas with limited healthcare resources.

5-hydroxymethylcytosine sequencing in plasma cell-free DNA identifies unique epigenomic features in prostate cancer patients resistant to androgen deprivation therapies.

Currently, no biomarkers are available to identify resistance to androgen-deprivation therapies (ADT) in men with hormone-naive prostate cancer. Since 5-hydroxymethylcytosines (5hmC) in gene body are associated with gene activation, in this study, we evaluated whether 5hmC signatures in cell-free DNA (cfDNA) predicts early resistance to ADT. We collected a total of 139 serial plasma samples from 55 prostate cancer patients receiving ADT at three time points including baseline (prior to initiating ADT, N=55), 3-month (after initiating ADT, N=55), and disease progression (N=15) within 24 months or 24-month if no progression was detected (N=14). To quantify 5hmC abundance across the genome, we used selective chemical labeling sequencing and mapped sequence reads to individual genes. Differential methylation analysis in baseline samples identified significant 5hmC difference in 1,642 of 23,433 genes between patients with and without progression (false discovery rate, FDR<0.1). Patients with disease progression showed significant 5hmC enrichments in multiple hallmark gene sets with androgen responses as top enriched gene set (FDR=1.19E-13). Interestingly, this enrichment was driven by a subgroup of patients featuring a significant 5hmC hypermethylation in the gene sets involving AR , FOXA1 and GRHL2 . To quantify overall activities of these gene sets, we developed a gene set activity scoring algorithm and observed significant association of high activity scores with poor progression-free survival (P<0.05). Longitudinal analysis showed that the high activity scores were significantly reduced after 3-months of initiating ADT (P<0.0001) but returned to higher levels when the disease was progressed (P<0.05). This study demonstrates that 5hmC-based activity scores from gene sets involved in AR , FOXA1 and GRHL2 may be used as biomarkers to determine early treatment resistance, monitor disease progression, and potentially identify patients who would benefit from upfront treatment intensification.

“Our American Optimism”: Race, Recognition, and Belonging in Claudia Rankine’s Don’t Let Me Be Lonely: An American Lyric

This paper explores Claudia Rankine’s representation of the feelings of racialisation in her 2004 poetry collection Don’t Let Me Be Lonely: An American Lyric. Contextualising this work within wider debates in Afropessimist philosophy, it considers how the poet’s portrayal of emotional pain, depression, and numbness exemplify a form of ‘social death’ instigated by the ubiquitous violence of antiblackness. Through close readings of select sequences, it argues that Rankine frames death as an ongoing structure of African American experience, rather than a singular event. In turn, this paper also considers how her innovative and hybrid “American Lyric” form functions to create a new grammar for Black self-expression in an ostensibly ‘post-race’ culture that obfuscates contemporary systems of racial inequality. It argues that Rankine’s collection cultivates an ethics of attention to the experiences and pain of others, offering poetry as a form of “exhausted hope” to challenge the dehumanising force of White supremacy.

De Novo Synthesis of Error-Free Long Oligos.

This protocol describes the synthesis of long oligonucleotides (up to 401-mer), their isolation from complex mixtures using the catching-by-polymerization (CBP) method, and the selection of error-free sequence via cloning followed by Sanger sequencing. Oligo synthesis is achieved under standard automated solid-phase synthesis conditions with only minor yet critical adjustments using readily available reagents. The CBP method involves tagging the full-length sequence with a polymerizable tagging phosphoramidite (PTP), co-polymerizing the sequence into a polymer, washing away failure sequences, and cleaving the full-length sequence from the polymer. Cloning and sequencing guided selection of error-free sequence overcome the problems of substitution, deletion, and addition errors that cannot be addressed using any other methods, including CBP. Long oligos are needed in many areas such as protein engineering and synthetic biology. The methods described here are particularly important for projects requiring long oligos containing long repeats or stable higher-order structures, which are difficult or impossible to produce using any other existing technologies. © 2024 Wiley Periodicals LLC. Basic Protocol 1: Long oligo synthesis Support Protocol 1: Synthesis of polymerizable tagging phosphoramidite (PTP) Support Protocol 2: Synthesis of 5'-O-Bz phosphoramidite Basic Protocol 2: Catching-by-polymerization (CBP) purification Basic Protocol 3: Error-free sequence selection via cloning and sequencing.