Stand-alone Tool Research Articles

OrthoRefine: automated enhancement of prior ortholog identification via synteny

BackgroundIdentifying orthologs continues to be an early and imperative step in genome analysis but remains a challenging problem. While synteny (conservation of gene order) has previously been used independently and in combination with other methods to identify orthologs, applying synteny in ortholog identification has yet to be automated in a user-friendly manner. This desire for automation and ease-of-use led us to develop OrthoRefine, a standalone program that uses synteny to refine ortholog identification.ResultsWe developed OrthoRefine to improve the detection of orthologous genes by implementing a look-around window approach to detect synteny. We tested OrthoRefine in tandem with OrthoFinder, one of the most used software for identification of orthologs in recent years. We evaluated improvements provided by OrthoRefine in several bacterial and a eukaryotic dataset. OrthoRefine efficiently eliminates paralogs from orthologous groups detected by OrthoFinder. Using synteny increased specificity and functional ortholog identification; additionally, analysis of BLAST e-value, phylogenetics, and operon occurrence further supported using synteny for ortholog identification. A comparison of several window sizes suggested that smaller window sizes (eight genes) were generally the most suitable for identifying orthologs via synteny. However, larger windows (30 genes) performed better in datasets containing less closely related genomes. A typical run of OrthoRefine with ~ 10 bacterial genomes can be completed in a few minutes on a regular desktop PC.ConclusionOrthoRefine is a simple-to-use, standalone tool that automates the application of synteny to improve ortholog detection. OrthoRefine is particularly efficient in eliminating paralogs from orthologous groups delineated by standard methods.

3Dmapper: a command line tool for BioBank-scale mapping of variants to protein structures.

The interpretation of genomic data is crucial to understand the molecular mechanisms of biological processes. Protein structures play a vital role in facilitating this interpretation by providing functional context to genetic coding variants. However, mapping genes to proteins is a tedious and error-prone task due to inconsistencies in data formats. Over the past two decades, numerous tools and databases have been developed to automatically map annotated positions and variants to protein structures. However, most of these tools are web-based and not well-suited for large-scale genomic data analysis. To address this issue, we introduce 3Dmapper, a stand-alone command-line tool developed in Python and R. It systematically maps annotated protein positions and variants to protein structures, providing a solution that is both efficient and reliable. https://github.com/vicruiser/3Dmapper.

BSXplorer: analytical framework for exploratory analysis of BS-seq data

BackgroundBisulfite sequencing detects and quantifies DNA methylation patterns, contributing to our understanding of gene expression regulation, genome stability maintenance, conservation of epigenetic mechanisms across divergent taxa, epigenetic inheritance and, eventually, phenotypic variation. Graphical representation of methylation data is crucial in exploring epigenetic regulation on a genome-wide scale in both plants and animals. This is especially relevant for non-model organisms with poorly annotated genomes and/or organisms where genome sequences are not yet assembled on chromosome level. Despite being a technology of choice to profile DNA methylation for many years now there are surprisingly few lightweight and robust standalone tools available for efficient graphical analysis of data in non-model systems. This significantly limits evolutionary studies and agrigenomics research. BSXplorer is a tool specifically developed to fill this gap and assist researchers in explorative data analysis and in visualising and interpreting bisulfite sequencing data more easily.ResultsBSXplorer provides in-depth graphical analysis of sequencing data encompassing (a) profiling of methylation levels in metagenes or in user-defined regions using line plots and heatmaps, generation of summary statistics charts, (b) enabling comparative analyses of methylation patterns across experimental samples, methylation contexts and species, and (c) identification of modules sharing similar methylation signatures at functional genomic elements. The tool processes methylation data quickly and offers API and CLI capabilities, along with the ability to create high-quality figures suitable for publication.ConclusionsBSXplorer facilitates efficient methylation data mining, contrasting and visualization, making it an easy-to-use package that is highly useful for epigenetic research.

UniSpec: Deep Learning for Predicting the Full Range of Peptide Fragment Ion Series to Enhance the Proteomics Data Analysis Workflow.

We present UniSpec, an attention-driven deep neural network designed to predict comprehensive collision-induced fragmentation spectra, thereby improving peptide identification in shotgun proteomics. Utilizing a training data set of 1.8 million unique high-quality tandem mass spectra (MS2) from 0.8 million unique peptide ions, UniSpec learned with a peptide fragmentation dictionary encompassing 7919 fragment peaks. Among these, 5712 are neutral loss peaks, with 2310 corresponding to modification-specific neutral losses. Remarkably, UniSpec can predict 73%-77% of fragment intensities based on our NIST reference library spectra, a significant leap from the 35%-45% coverage of only b and y ions. Comparative studies with Prosit elucidate that while both models are strong at predicting their respective fragment ion series, UniSpec particularly shines in generating more complex MS2 spectra with diverse ion annotations. The integration of UniSpec's predictions into shotgun proteomics data analysis boosts the identification rate of tryptic peptides by 48% at a 1% false discovery rate (FDR) and 60% at a more confident 0.1% FDR. Using UniSpec's predicted in-silico spectral library, the search results closely matched those from search engines and experimental spectral libraries used in peptide identification, highlighting its potential as a stand-alone identification tool. The source code and Python scripts are available on GitHub (https://github.com/usnistgov/UniSpec) and Zenodo (https://zenodo.org/records/10452792), and all data sets and analysis results generated in this work were deposited in Zenodo (https://zenodo.org/records/10052268).

Oral_voting_transfer: classification of oral microorganisms' function proteins with voting transfer model.

The oral microbial group typically represents the human body's highly complex microbial group ecosystem. Oral microorganisms take part in human diseases, including Oral cavity inflammation, mucosal disease, periodontal disease, tooth decay, and oral cancer. On the other hand, oral microbes can also cause endocrine disorders, digestive function, and nerve function disorders, such as diabetes, digestive system diseases, and Alzheimer's disease. It was noted that the proteins of oral microbes play significant roles in these serious diseases. Having a good knowledge of oral microbes can be helpful in analyzing the procession of related diseases. Moreover, the high-dimensional features and imbalanced data lead to the complexity of oral microbial issues, which can hardly be solved with traditional experimental methods. To deal with these challenges, we proposed a novel method, which is oral_voting_transfer, to deal with such classification issues in the field of oral microorganisms. Such a method employed three features to classify the five oral microorganisms, including Streptococcus mutans, Staphylococcus aureus, abiotrophy adjacent, bifidobacterial, and Capnocytophaga. Firstly, we utilized the highly effective model, which successfully classifies the organelle's proteins and transfers to deal with the oral microorganisms. And then, some classification methods can be treated as the local classifiers in this work. Finally, the results are voting from the transfer classifiers and the voting ones. The proposed method achieved the well performances in the five oral microorganisms. The oral_voting_transfer is a standalone tool, and all its source codes are publicly available at https://github.com/baowz12345/voting_transfer.

MRSLpred-a hybrid approach for predicting multi-label subcellular localization of mRNA at the genome scale.

In the past, several methods have been developed for predicting the single-label subcellular localization of messenger RNA (mRNA). However, only limited methods are designed to predict the multi-label subcellular localization of mRNA. Furthermore, the existing methods are slow and cannot be implemented at a transcriptome scale. In this study, a fast and reliable method has been developed for predicting the multi-label subcellular localization of mRNA that can be implemented at a genome scale. Machine learning-based methods have been developed using mRNA sequence composition, where the XGBoost-based classifier achieved an average area under the receiver operator characteristic (AUROC) of 0.709 (0.668-0.732). In addition to alignment-free methods, we developed alignment-based methods using motif search techniques. Finally, a hybrid technique that combines the XGBoost model and the motif-based approach has been developed, achieving an average AUROC of 0.742 (0.708-0.816). Our method-MRSLpred-outperforms the existing state-of-the-art classifier in terms of performance and computation efficiency. A publicly accessible webserver and a standalone tool have been developed to facilitate researchers (webserver: https://webs.iiitd.edu.in/raghava/mrslpred/).

Gesture-Based Interactions: Integrating Accelerometer and Gyroscope Sensors in the Use of Mobile Apps

This study investigates the feasibility and functionality of accelerometer and gyroscope sensors for gesture-based interactions in mobile app user experience. The core of this innovative approach lies in introducing a dynamic and intuitive user interaction model with the device sensors. The Android app developed for this purpose has been created for its use in controlled experiments. Methodologically, it was created as a stand-alone tool to both capture quantitative (time, automatically captured) and qualitative (behavior, collected with post-task questionnaires) variables. The app’s setting features a set of modules with two levels each (randomized presentation applied, minimizing potential learning effects), allowing users to interact with both sensor-based and traditional touch-based scenarios. Preliminary results with 22 participants reveal that tasks involving sensor-based interactions tend to take longer to complete when compared to the traditional ones. Remarkably, many participants rated sensor-based interactions as a better option than touch-based interactions, as seen in the post-task questionnaires. This apparent discrepancy between objective completion times and subjective user perceptions requires a future in-depth exploration of factors influencing user experiences, including potential learning curves, cognitive load, and task complexity. This study contributes to the evolving landscape of mobile app user experience, emphasizing the benefits of considering the integration of device sensors (and gesture-based interactions) in common mobile usage.

A Bayesian approach for estimating the post-earthquake recovery trajectories of electric power systems in Japan

ABSTRACT Post-disaster recovery modelling of engineering systems has become an important facet of catastrophe risk modelling and management for natural hazards. The post-disaster recovery trajectory of a civil infrastructure system can be quantified using (a) the initial post-disaster functionality level, Q o ; (b) rapidity, h (i.e., the rate of functionality restoration); and (c) recovery time, R t . This study uses a Bayesian estimation approach to derive a set of probabilistic models to estimate Q o , R t , and h of electric power networks (EPNs) using post-earthquake recovery data from 16 large earthquakes in Japan between 2003 and 2022. The considered predictor (explanatory) variables include earthquake magnitude, year of occurrence, seismic intensity, and exposed population (PEX). Apart from being a simple and efficient stand-alone tool, the proposed data-driven models can be a useful benchmarking tool for simulation-based approaches for EPN recovery modelling.

Raman and photoluminescence signal separation in Raman hyperspectral imagery including noise reduction

AbstractRaman hyperspectral imaging (RHSI) is a valuable tool for gaining crucial information about the chemical composition of materials. However, obtaining clear Raman signals is not always a trivial task. Raw Raman signals can be susceptible to photoluminescence interference and noise. Hence, the preprocessing of RHSI is a required step for an effective and reliable chemical analysis. The main challenge is splitting the measured RHSI into separate Raman photoluminescence signals. Since no golden‐standard exists, it is non‐trivial to validate the correctness of the separated signals. While current state‐of‐the‐art preprocessing methods are effective, they require expert knowledge and involve unintuitive hyperparameters. Current approaches also lack generalizability, requiring extensive hyperparameter tuning on a case‐by‐case basis, while even then results are not always as expected. To this end, this work proposes a novel iterative RHSI preprocessing pipeline for splitting raw Raman signals and noise removal based on linear spline and radial basis function regression (IlsaRBF). The proposed method involves hyperparameters based on the physical properties of Raman spectroscopy, making them intuitive to use. This leads to more robust and stable hyperparameters, reducing the necessity for extensive hyperparameter tuning. A thorough evaluation shows that the proposed method outperforms the current state‐of‐the‐art. Additionally, a cosmic ray identification and removal algorithm (CRIR) and dynamic PCA for noise reduction are introduced. A standalone tool containing our proposed methods is provided, making RHSI preprocessing available to a broader audience, aiding further research and advancements in the field of Raman spectroscopy.

Asset Separation as a Bank Failure Management Tool

The article is devoted to the legal aspects of an asset separation tool as a bank failure management tool. Asset separation tool is provided by FSB Key Attributes of Effective Resolution Regimes for Financial Institutions and Directive 2014/59/EU (Bank Recovery and Resolution Directive, or BRRD). However, it has not been implemented in Ukrainian legislation to date. The article defines key elements required for effective transposition of the asset separation tool. It is fundamentally important to ensure clarity in the authority’s powers to conduct non-performing assets transfer to an asset management company and powers to transfer them back in cases provided in the law, as the framework should be predictable for all stakeholders. General rules for obtaining the consent of owners of the failed banks or any other parties should not be applicable. The assets should be transferred to the asset management company with the view to value maximization or orderly liquidation. An asset separation tool is not a standalone tool and shall be applied along with other tools to deal with bank failures. The article also defines key safeguards and requirements for an asset management company to operate efficiently and mitigate risks of political interference. Most asset management companies designed for bank failure management are owned by the state, and in line with this the BRRD envisages that an asset management shall wholly or partially belong to the state or a resolution authority. The BRRD provides that the resolution authority shall approve a statutory document of the asset management company, management and their remuneration, as well as the strategy and risk profile of the company. At the same time, there should be safeguards to ensure that the asset management company should be commercially oriented and operationally independent. The most efficient asset management companies have a narrow mandate. Special powers may be introduced for an asset management company when the general framework has impediments to the efficient operation of an asset management company. It should be noted that the asset management company has a temporary nature and should be wound up once its goal is reached. Necessary incentives should be introduced in this regard. This could be a sunset clause, defined interim key performance indicators, which should be flexible and regularly reviewed, and financial incentives for staff of the asset management company. It is suggested to introduce amendments to the Law of Ukraine ‘On household deposit guarantee system’ to implement the particularities described in the article.

The Effect of Partial and Temporary Vaccination on African Swine Fever Eradication Rates

African swine fever (ASF) is one of the most severe diseases of pigs, with drastic impact on pig industry. Wild boar populations play the role of ASF virus epidemiological reservoir. No effective and safe ASF vaccine is available, yet, but a future vaccine will not be 100% effective and will provide protection for no more than a few months. We present an individual-based spatially explicit model of wild boar demography and ASF epidemiology, allowing to simulate a vaccination campaign. We tested how many animals should be vaccinated in relation to vaccine efficacy and to the duration of vaccine protection. We estimated how these parameters will affect ASF eradication probabilities. We also assessed how partial vaccination will interact with a series of ecological, epidemiological, and management-related factors linked to ASF persistence. In the case of a highly effective vaccine with short duration, eradication chances were generally low, and the virus disappeared only when simulating a high effort (2.5 vaccinated wild boars/km2). A vaccine with low efficacy and long duration was even less effective in eradicating the ASFV, as none of the simulated scenarios provided acceptable eradication rates. Our results indicate that, under realistic conditions, vaccination against the ASF genotype II virus cannot be seen as an effective stand-alone tool for eradication. Its use should be integrated into a more comprehensive strategy, making use of all the available management tools, such as density control through hunting and carcass removal. If the vaccine will exhibit 12 months or longer duration of its protection, splitting the vaccination effort into two or three bait distribution campaigns during the year will be a feasible option. If the vaccine will exhibit a duration of its immunization significantly shorter than 1 year, a single distribution at the end of winter will maximize the probability of eradication.

Comparison between transthoracic echocardiography and transoesophageal echocardiography in the diagnosis of acute aortic dissection from an emergency perspective. A systematic review and meta-analysis.

Acute aortic dissection (AAD) is a life-threatening medical condition with high early fatality. Therefore, a prompt and precise diagnosis, which can be achieved through invasive and non-invasive techniques is vital. Echocardiography, unlike MRI and CT, is accessible in emergency units and bedside-compatible. The recommended echocardiographic techniques for AAD are transthoracic and transoesophageal echocardiography (TTE and TOE). Therefore, our review compares their diagnostic roles in AAD. Studies relevant to our topic were attained through a database search and manual scrutiny of references lists of articles obtained from the electronic databases. The Quality Assessment of Diagnostic Accuracy Studies tool (QUADAS-2) has been used for quality assessment. All quantitative analyses were performed using either STATA 16 or Comprehensive Meta-Analyst software. The search strategy yielded 1,798 articles, of which only 11 were eligible for inclusion. Our subgroup analysis showed that conventional TTE had a sensitivity and specificity of 85.35% and 84.51% for the diagnosis of Stanford type A AAD and was 45.89% sensitive and 87.05% specific for the diagnosis of type B AAD. However, the subgroup analysis shows that contrast-enhancement of TTE results in a sensitivity and specificity of 93.30% and 97.60% for diagnosis of type A AAD, and 83.60% and 94.50% for diagnosis of type B AAD, respectively. On the other hand, conventional TOE was 93.64% sensitive and 95.50% specific for the diagnosis of type A AAD, 99.80% sensitive and 99.87% specific for the diagnosis of type B AAD. Moreover, our analyses show that TTE has pooled false negative and positive rates of 28.6% and 18.6%, while TOE has shown false negative and positive rates of 2.4% and 4.3%, respectively. TOE is the more favorable diagnostic tool for AAD diagnosis than TTE. However, it cannot be used as a stand-alone diagnostic tool since misdiagnosis cases are being reported. Contrast-enhanced TTE can also diagnose AAD since it provides similar results to conventional TOE.

Impact of point-of-care clinical decision support on referrer behavior, imaging volume, patient radiation dose exposure, and sustainability

ObjectivesWhen referring patients to radiology, it is important that the most appropriate test is chosen to avoid inappropriate imaging that may lead to delayed diagnosis, unnecessary radiation dose, worse patient outcome, and poor patient experience. The current radiology appropriateness guidance standard at our institution is via access to a standalone web-based clinical decision support tool (CDST). A point-of-care (POC) CDST that incorporates guidance directly into the physician workflow was implemented within a subset of head and neck cancer specialist referrers. The purpose of this audit was to evaluate the imaging pathway, pre- and post-implementation to assess changes in referral behavior.MethodsCT and MRI neck data were collected retrospectively to examine the relationship between imaging referrals pre- and post-POC CDST implementation. Effective radiation dose and estimated carbon emissions were also compared.ResultsThere was an overall reduction in absolute advanced imaging volume by 8.2%, and a reduction in duplicate CT and MRI imaging by 61%, p < 0.0001. There was also a shift in ordering behavior in favor of MRI (OR [95% CI] = 1.50 [1.02–2.22], p = 0.049). These changes resulted in an effective radiation dose reduction of 0.27 mSv per patient, or 13 equivalent chest x-rays saved per patient, p < 0.0001. Additionally, the reduction in unnecessary duplicate imaging led to a 13.5% reduction in carbon emissions, p = 0.0002.ConclusionsImplementation of the POC CDST resulted in a significant impact on advanced imaging volume, saved effective dose, and reduction in carbon emissions.Critical relevance statementThe implementation of a point-of-care clinical decision support tool may reduce multimodality ordering and advanced imaging volume, manifesting in reduced effective dose per patient and reduced estimated carbon emissions. Widespread utilization of the point-of-care clinical decision support tool has the potential to reduce imaging wait times.Key points• Implementation of the point-of-care clinical decision support tool reduced the number of patients who simultaneously had a CT and MRI ordered for the same clinical indication compared to a standalone web-based clinical decision support tool.• The point-of-care clinical decision support tool reduced the absolute number of CT/MRI scans requested compared to the standalone web-based clinical decision support tool.• Utilization of the point-of-care clinical decision support tool led to a significant reduction in the effective dose per patient compared to the standalone web-based clinical decision support tool.Graphical

FORGEdb: a tool for identifying candidate functional variants and uncovering target genes and mechanisms for complex diseases.

The majority of disease-associated variants identified through genome-wide association studies are located outside of protein-coding regions. Prioritizing candidate regulatory variants and gene targets to identify potential biological mechanisms for further functional experiments can be challenging. To address this challenge, we developed FORGEdb ( https://forgedb.cancer.gov/ ; https://forge2.altiusinstitute.org/files/forgedb.html ; and https://doi.org/10.5281/zenodo.10067458 ), a standalone and web-based tool that integrates multiple datasets, delivering information on associated regulatory elements, transcription factor binding sites, and target genes for over 37 million variants. FORGEdb scores provide researchers with a quantitative assessment of the relative importance of each variant for targeted functional experiments.

CheRRI-Accurate classification of the biological relevance of putative RNA-RNA interaction sites.

RNA-RNA interactions are key to a wide range of cellular functions. The detection of potential interactions helps to understand the underlying processes. However, potential interactions identified via in silico or experimental high-throughput methods can lack precision because of a high false-positive rate. We present CheRRI, the first tool to evaluate the biological relevance of putative RNA-RNA interaction sites. CheRRI filters candidates via a machine learning-based model trained on experimental RNA-RNA interactome data. Its unique setup combines interactome data and an established thermodynamic prediction tool to integrate experimental data with state-of-the-art computational models. Applying these data to an automated machine learning approach provides the opportunity to not only filter data for potential false positives but also tailor the underlying interaction site model to specific needs. CheRRI is a stand-alone postprocessing tool to filter either predicted or experimentally identified potential RNA-RNA interactions on a genomic level to enhance the quality of interaction candidates. It is easy to install (via conda, pip packages), use (via Galaxy), and integrate into existing RNA-RNA interaction pipelines.

Endoscopic Microvascular Decompression.

Microvascular decompression is a widely accepted surgical treatment for compressive cranial nerve pathologies such as trigeminal neuralgia, hemifacial spasm, glossopharyngeal neuralgia, and other craniofacial pain syndromes. Endoscopy has risen as a safe and effective minimally invasive tool to optimize microvascular decompression. Endoscopy offers improved visualization, minimizes retraction, and allows for smaller surgical openings compared to traditional microscopic approaches. There are several reports of improved neuralgia outcomes and reduced post-operative complications after endoscopic microvascular decompression. In skilled surgical hands, endoscopy is an excellent option for microvascular decompression as stand-alone tool or adjunct to the microscope. An overview of the history, operative considerations, and techniques is provided in this chapter.

Self-sufficiency of e-mail as a marketing tool

Through their development modern digital communication channels modify newly established patterns and very quickly influence changes in marketing practice. Starting from the questions of whether marketing messages sent to consumers via e-mail are useful or can they be used as a standalone tool, this paper presents a preliminary study conducted on a sample of 192 respondents. The study aims at collecting guidelines for a larger study on the impact of modern digital communication channels on consumer behavior, i.e. on the necessary adaptations in marketing practices that are already based on digital technologies. The paper specifically focuses on electronic marketing messages and newsletters as frequently used modern marketing tools. The research was conducted through a specially designed questionnaire and it has determined that e-mail as a standalone marketing tool is not effective and should be used in combination with some other tools. It has not been found that email positively influences the purchase decision nor is the newsletter as a marketing tool completely positively accepted by the respondents

Evaluating Workforce Mental Health and Well-Being: A Review of Assessments.

Given the importance of mental health and well-being assessments to employers' efforts to optimize employee health and well-being, this paper reviews mental health assessments that have utility in the workplace. A review of publicly available mental health and well-being assessments was conducted with a primary focus on burnout, general mental health and well-being, loneliness, psychological safety, resilience, and stress. Assessments had to be validated for adult populations; available in English as a stand-alone tool; have utility in an employer setting; and not have a primary purpose of diagnosing a mental health condition. All assessments were reviewed by a minimum of two expert reviewers to document number of questions, subscales, fee structure, international use, translations available, scoring/reporting, respondent (ie, employee or organization), and the target of the assessment (ie, mental health domain and organizational or individual level assessments. Sixty-six assessments across the six focus areas met inclusion criteria, enabling employers to select assessments that meet their self-identified measurement needs. This review provides employers with resources that can help them understand their workforce's mental health and well-being status across multiple domains, which can serve as a needs assessment, facilitate strategic planning of mental health and well-being initiatives, and optimize evaluation efforts.

Towards stochastic subgrid‐scale modeling of turbulent thermal convection in an under‐resolved off‐lattice Boltzmann method

AbstractA characteristic‐based Off‐Lattice Boltzmann Method (OLBM) and a stochastic One‐Dimensional Turbulence (ODT) model is utilized for numerical simulation of turbulent thermal convection. Standalone ODT results for low‐order statistics are compared with those from various eddy‐viscosity‐based subgrid‐scale models utilized in Large‐Eddy Simulations (LES) with OLBM. The predictive capabilities of both approaches are discussed by comparison with available reference Direct Numerical Simulation (DNS) results. All turbulence models are able to predicted the mean temperature, but fail to fully capture fluctuations. While the OLBM aims to represent large‐scale structures, it misses some constitutional small‐scale fluctuations. By contrast, the reduced‐order ODT model captures small‐scale fluctuations in the vicinity of the wall, but cannot resolve the organized bulk flow. Here, the modeling capabilities of both OLBM and ODT as standalone tools are discussed. On this basis, a strategy for the incorporation of ODT as wall model in OLBM is suggested.

Transcript assembly and annotations: Bias and adjustment.

Transcript annotations play a critical role in gene expression analysis as they serve as a reference for quantifying isoform-level expression. The two main sources of annotations are RefSeq and Ensembl/GENCODE, but discrepancies between their methodologies and information resources can lead to significant differences. It has been demonstrated that the choice of annotation can have a significant impact on gene expression analysis. Furthermore, transcript assembly is closely linked to annotations, as assembling large-scale available RNA-seq data is an effective data-driven way to construct annotations, and annotations are often served as benchmarks to evaluate the accuracy of assembly methods. However, the influence of different annotations on transcript assembly is not yet fully understood. We investigate the impact of annotations on transcript assembly. Surprisingly, we observe that opposite conclusions can arise when evaluating assemblers with different annotations. To understand this striking phenomenon, we compare the structural similarity of annotations at various levels and find that the primary structural difference across annotations occurs at the intron-chain level. Next, we examine the biotypes of annotated and assembled transcripts and uncover a significant bias towards annotating and assembling transcripts with intron retentions, which explains above the contradictory conclusions. We develop a standalone tool, available at https://github.com/Shao-Group/irtool, that can be combined with an assembler to generate an assembly without intron retentions. We evaluate the performance of such a pipeline and offer guidance to select appropriate assembling tools for different application scenarios.