Flexible Pipeline Research Articles

Assessing the influence of cyclic bending on pearlitic wire's microstructural evolution: a simulation of spiraling and armoring processing effects

Flexible pipelines are widely used offshore for oil and gas transportation and are subjected to several complex stress modes during service. Stress tensors cause different alterations in the macro and microstructure of the material. Therefore, studies involving the relationship between mechanical behavior and the crystallographic orientation of tensile armor wires need deep investigation. This work investigated the role of complex plastic deformation during the spiraling tensile armor on the microstructure and crystallographic texture of pearlitic steel with 0.74 wt%C. A heat-treated sample was prepared as a starting point. Further specimens were subjected to cold bending to investigate the strain accumulation during spiraling and armoring processing and then compared to an industrial twisted tensile armor sample. The microstructural and texture evolution was characterized by scanning electron microscopy, X-ray diffraction, and backscattered electron diffraction. Specimens were submitted to the Vickers microhardness and tensile tests to evaluate the mechanical properties. Results revealed that the bending deformation causes a reduction of (001)//ND fiber and increases the mechanical resistance of the material, which may prevent the onset and crack propagation. (111)//ND fiber increased while (110)//ND decreased, leading to stability in the slip systems. The formation of shear band structural changes resulted from the increase of (110)//ND with armoring and a decrease of (111)//ND fibers. Bending strains enhance the desirable texture components while diminishing the undesirable ones. Deformations to which the materials were subjected provide less mobility of dislocations, which causes an increase in mechanical strength. Little change in texture and microstructure was also observed.

Effect of solubilization heat treatment on microstructure and corrosion resistance of joints welded with the autogenous TIG process duplex stainless steel

Duplex stainless steels (DSSs), are used commonly under severe working conditions, subject to high stresses associated with corrosion. A typical DSS is composed of the combination involving two types of microstructures, ferritic and austenitic, which characterizes it as a material with corrosion resistance in aggressive media, due to its ability to passivate. The use of DSS has been increasingly exploited in the oil industry, and its use has occurred in numerous areas, standing out in the production of flexible pipelines, which in most applications, is associated with a welding process. They require special care when welded by the tungsten inert gas (TIG) welding process. Temperature variations in this process, along with heat treatment, promote the formation of precipitates in the matrix and change the phase balance. This significantly influences the mechanical and corrosion properties of DSSs. The objective of this work is to study the influence of two different parameters of solubilization heat treatment after welding on the microstructure and corrosion resistance of a DSSs welded joints. After the autogenous TIG welding process, the welds were subjected to thermal solubilization treatments for 10 and 25 s at 1050 °C. The conditions subjected to the solubilization heat treatment were compared with the as TIG welded control condition. The microstructure was analyzed by confocal microscopy and the corrosion resistance was evaluated by the "A" method of the ASTM G48 standard.

Automated identification of unstandardized medication data: a scalable and flexible data standardization pipeline using RxNorm on GEMINI multicenter hospital data.

Patient data repositories often assemble medication data from multiple sources, necessitating standardization prior to analysis. We implemented and evaluated a medication standardization procedure for use with a wide range of pharmacy data inputs across all drug categories, which supports research queries at multiple levels of granularity. The GEMINI-RxNorm system automates the use of multiple RxNorm tools in tandem with other datasets to identify drug concepts from pharmacy orders. GEMINI-RxNorm was used to process 2090155 pharmacy orders from 245258 hospitalizations between 2010 and 2017 at 7 hospitals in Ontario, Canada. The GEMINI-RxNorm system matches drug-identifying information from pharmacy data (including free-text fields) to RxNorm concept identifiers. A user interface allows researchers to search for drug terms and returns the relevant original pharmacy data through the matched RxNorm concepts. Users can then manually validate the predicted matches and discard false positives. We designed the system to maximize recall (sensitivity) and enable excellent precision (positive predictive value) with efficient manual validation. We compared the performance of this system to manual coding (by a physician and pharmacist) of 13 medication classes. Manual coding was performed for 1948817 pharmacy orders and GEMINI-RxNorm successfully returned 1941389 (99.6%) orders. Recall was greater than 0.985 in all 13 drug classes, and the F1-score and precision remained above 0.90 in all drug classes, facilitating efficient manual review to achieve 100% precision. GEMINI-RxNorm saved time substantially compared with manual standardization, reducing the time taken to review a pharmacy order row from an estimated 30 to 5 s and reducing the number of rows needed to be reviewed by up to 99.99%. GEMINI-RxNorm presents a novel combination of RxNorm tools and other datasets to enable accurate, efficient, flexible, and scalable standardization of pharmacy data. By facilitating efficient manual validation, the GEMINI-RxNorm system can allow researchers to achieve near-perfect accuracy in medication data standardization.

Microstructure, microtexture, and crack susceptibility in pearlitic steel during lab-simulated processes aiming tensile armor application in flexible pipelines

Microstructure, microtexture, and crack susceptibility in pearlitic steel during lab-simulated processes aiming tensile armor application in flexible pipelines

An Explanation for the Flutter Paradox in the Supercritical Region of a Simply-Supported Fluid-Conveying Pipe

Abstract Employing traditional Galerkin method, a coupled-mode flutter is predicted in the supercritical region of simply-supported pipes which constitutes a paradox since the internal flow effect is conservative and there is no energy to sustain the oscillation. Although there is a consensus that the flutter does not exist, the intrinsic mechanism remains to be clarified. This study has found that the internal flow induced Coriolis force term cannot be decoupled in traditional Galerkin method which leads to the dissatisfaction of the convergence conditions required in weighted residual approach (WRA). Moreover, the disparities in the predicted complex frequencies have been witnessed at different base function numbers when the internal flow velocity is sufficiently large. A modified Galerkin method adopting a new set of weighting functions is proposed based on WRA, and the Coriolis force term disappears by use of the orthogonality relations (it is stated that the Coriolis force is not directly omitted). Thus, a convergent solution for the set of residual functions which are identically equal to zeros can be guaranteed. Employing the modified method, the convergence in simulations is confirmed and the flutter phenomenon does not occur. This study can be a workbench for the study on the unsolved or partly solved issues in simulations of fluid-conveying pipes. Moreover, it has demonstrated that the predictions in traditional Galerkin method overestimate the natural frequencies, and it becomes more profound in higher-order natural modes at larger internal flow velocities which are of practice significance for dynamic analysis of flexible pipeline systems.

Assessing Combinations of Landsat, Sentinel-2 and Sentinel-1 Time series for Detecting Bark Beetle Infestations

ABSTRACT Bark beetle infestations are among the most substantial forest disturbance agents worldwide. Moreover, as a consequence of global climate change, they have increased in frequency and in the size and number of affected areas. Controlling bark beetle outbreaks requires consistent operational monitoring, as is possible using satellite data. However, while many satellite-based approaches have been developed, the full potential of dense, multi-sensor time series has yet to be fully explored. Here, for the first time, we used all available multispectral data from Landsat and Sentinel-2, Sentinel-1 SAR data, and combinations thereof to detect bark beetle infestations in the Bavarian Forest National Park. Based on a multi-year reference dataset of annual infested areas, we assessed the separability between healthy and infested forests for various vegetation indices calculated from the satellite data. We used two approaches to compute infestation probability time series from the different datasets: Bayesian conditional probabilities, based on the best-separating index from each satellite type, and random forest regression, based on all indices from each satellite type. Five different sensor configurations were tested for their detection capabilities: Landsat alone, Sentinel-1 alone, Sentinel-2 alone, Landsat and Sentinel-2 combined, and data from all satellite types combined. The best overall results in terms of spatial accuracy were achieved with Sentinel-2 (max. overall accuracy: 0.93). The detections of Sentinel-2 also were the closest to the onset of infestation estimated for each year. Sentinel-2 detected infested areas in larger contiguous patches with higher reliability compared to smaller patches. The results achieved with Landsat were somewhat inferior to those of Sentinel-2 (max. accuracy: 0.89). While yielding similar results, the combination of Landsat and Sentinel-2 did not provide any advantages over using Landsat or Sentinel-2 alone (max. accuracy: 0.87), while Sentinel-1 was unable to detect infested areas (max. accuracy: 0.62). The combined data of all three satellite types did not achieve satisfactory results either (max. accuracy: 0.67). Spatial accuracies were typically higher for Bayesian conditional probabilities than for random forest-derived probabilities, but the latter resulted in earlier detections. The approach presented herein provides a flexible disturbance detection pipeline well-suited for the monitoring of bark beetle outbreaks. Furthermore, it can also be applied to other disturbance types.

SENECA: building a fully digital neuromorphic processor, design trade-offs and challenges.

Neuromorphic processors aim to emulate the biological principles of the brain to achieve high efficiency with low power consumption. However, the lack of flexibility in most neuromorphic architecture designs results in significant performance loss and inefficient memory usage when mapping various neural network algorithms. This paper proposes SENECA, a digital neuromorphic architecture that balances the trade-offs between flexibility and efficiency using a hierarchical-controlling system. A SENECA core contains two controllers, a flexible controller (RISC-V) and an optimized controller (Loop Buffer). This flexible computational pipeline allows for deploying efficient mapping for various neural networks, on-device learning, and pre-post processing algorithms. The hierarchical-controlling system introduced in SENECA makes it one of the most efficient neuromorphic processors, along with a higher level of programmability. This paper discusses the trade-offs in digital neuromorphic processor design, explains the SENECA architecture, and provides detailed experimental results when deploying various algorithms on the SENECA platform. The experimental results show that the proposed architecture improves energy and area efficiency and illustrates the effect of various trade-offs in algorithm design. A SENECA core consumes 0.47 mm2 when synthesized in the GF-22 nm technology node and consumes around 2.8 pJ per synaptic operation. SENECA architecture scales up by connecting many cores with a network-on-chip. The SENECA platform and the tools used in this project are freely available for academic research upon request.

ReporTree: a surveillance-oriented tool to strengthen the linkage between pathogen genetic clusters and epidemiological data

BackgroundGenomics-informed pathogen surveillance strengthens public health decision-making, playing an important role in infectious diseases’ prevention and control. A pivotal outcome of genomics surveillance is the identification of pathogen genetic clusters and their characterization in terms of geotemporal spread or linkage to clinical and demographic data. This task often consists of the visual exploration of (large) phylogenetic trees and associated metadata, being time-consuming and difficult to reproduce.ResultsWe developed ReporTree, a flexible bioinformatics pipeline that allows diving into the complexity of pathogen diversity to rapidly identify genetic clusters at any (or all) distance threshold(s) or cluster stability regions and to generate surveillance-oriented reports based on the available metadata, such as timespan, geography, or vaccination/clinical status. ReporTree is able to maintain cluster nomenclature in subsequent analyses and to generate a nomenclature code combining cluster information at different hierarchical levels, thus facilitating the active surveillance of clusters of interest. By handling several input formats and clustering methods, ReporTree is applicable to multiple pathogens, constituting a flexible resource that can be smoothly deployed in routine surveillance bioinformatics workflows with negligible computational and time costs. This is demonstrated through a comprehensive benchmarking of (i) the cg/wgMLST workflow with large datasets of four foodborne bacterial pathogens and (ii) the alignment-based SNP workflow with a large dataset of Mycobacterium tuberculosis. To further validate this tool, we reproduced a previous large-scale study on Neisseria gonorrhoeae, demonstrating how ReporTree is able to rapidly identify the main species genogroups and characterize them with key surveillance metadata, such as antibiotic resistance data. By providing examples for SARS-CoV-2 and the foodborne bacterial pathogen Listeria monocytogenes, we show how this tool is currently a useful asset in genomics-informed routine surveillance and outbreak detection of a wide variety of species.ConclusionsIn summary, ReporTree is a pan-pathogen tool for automated and reproducible identification and characterization of genetic clusters that contributes to a sustainable and efficient public health genomics-informed pathogen surveillance. ReporTree is implemented in python 3.8 and is freely available at https://github.com/insapathogenomics/ReporTree.

3D deconvolution of human skin immune architecture with Multiplex Annotated Tissue Imaging System.

Routine clinical assays, such as conventional immunohistochemistry, often fail to resolve the regional heterogeneity of complex inflammatory skin conditions. We introduce MANTIS (Multiplex Annotated Tissue Imaging System), a flexible analytic pipeline compatible with routine practice, specifically designed for spatially resolved immune phenotyping of the skin in experimental or clinical samples. On the basis of phenotype attribution matrices coupled to α-shape algorithms, MANTIS projects a representative digital immune landscape while enabling automated detection of major inflammatory clusters and concomitant single-cell data quantification of biomarkers. We observed that severe pathological lesions from systemic lupus erythematosus, Kawasaki syndrome, or COVID-19-associated skin manifestations share common quantitative immune features while displaying a nonrandom distribution of cells with the formation of disease-specific dermal immune structures. Given its accuracy and flexibility, MANTIS is designed to solve the spatial organization of complex immune environments to better apprehend the pathophysiology of skin manifestations.

Study on the Vibration Displacement of Hydraulic Pipeline System Excited by Fluid Impact

Hydraulic impact had an effect on pipeline vibration displacement, so it was important to study the external lateral impact periodic force on fluid-structure coupling vibration displacement of hydraulic pipeline. Based on Bernoulli beam model, the fluid-structure coupling vibration equation of flexible pipeline was established by analyzing the stress of flexible pipeline under the action of internal flow. The hydraulic system pipeline was simulated whether the pipeline was subject to periodic impact force. The displacement-time curves in x, y and z directions of vibration monitoring points of hydraulic flexible pipelines were obtained, and the maximum displacement-time curves of fluid-structure coupling vibration under different pressure hydraulic impact were obtained. The simulation results were shown that the vibration of monitoring points in x, y and z directions of hydraulic impact pressure increased and the total displacement of the pipeline middle section of the hydraulic system decrease continuously, and then they tend to stabilize after a period of attenuation. The maximum displacement value of the X-direction monitoring point in the middle part of the hydraulic system pipeline was 9.5e −5 m, and the displacement vibration was stable at 0.135s. The maximum y-displacement was 3.1e −4m, and the vibration was stable at 0.2 s. The maximum z displacement was 1.32e-4 m, and the vibration was stable at 0.36 s. The total displacement was 3.35e −4 m, and the vibration was stable at 0.43s. Increasing the impact pressure, the maximum displacement of the hydraulic pipeline vibrations were larger, and the attenuation of the maximum values were faster. The research results provide a reference for the hydraulic system to prevent pressure shock and reduce vibration.

Item Matching Model in E-Commerce: How Users Benefit

Abstract Research purpose. During the last decades, e-commerce sales have been rocketing, and this tendency is expected to increase over the following years. Due to the digital nature of e-commerce, one actual item can be sold on various e-commerce platforms, which leads to the exponential growth of the number of propositions. At the same time, the title and description of this item might differ. All these facts make more complicated for customers the process of searching on online platforms and change business approaches to the development of competitive strategy by e-commerce companies. The research question is how we can apply a machine learning algorithm to detect, based on the product information such as title and description, whether the items are actually relevant to the same product. Methodology. We suggest an approach that is based on a flexible textual data pipeline and the usage of a machine-learning model ensemble. Each step of the data processing is adjustable in dependence on domain issues and data features because we can achieve better results in solving the item-matching task. Findings. The item-matching model is developed. The proposed model is based on the semantic closeness of text descriptions of items and the usage of the core of keywords to present the reference item. Practical implications. We suggest an approach to improving the item searching process on different e-commerce platforms by dividing the process into two steps. The first step is searching for the related items among the set of reference items according to user preferences. The reference item description is created based on our item-matching model. The second step is surfing proposals of similar items on chosen e-commerce platforms. This approach can benefit buyers and sellers in various aspects, such as a low-price guarantee, a flexible strategy of similar products shown, and appropriate category-choosing recommendations.

High-Precision Measuring System for Internal Deformation Monitoring Pipeline of Rockfill Dams: Design and Validation

The monitoring of internal deformation in rockfill dams using pre-buried flexible pipelines is a promising technique; however, achieving precise measurements over a long-range pipeline is crucial and challenging. In this paper, we present a high-precision pipeline measuring system for monitoring internal deformation in rockfill dams, which can simultaneously measure the pipeline centerline and cross-section geometry. The system leverages the inertial measurement principle to perform positioning by fusing an inertial measurement unit (IMU) with odometers. It utilizes a Kalman filter and Rauch-Tung-Striebel (RTS) smoothing to estimate the optimal 3D curves of the pipeline. The pipeline’s inner diameter is measured using three wheel swing arms and a set of load-bearing wheels, and the pipeline cross-section geometry is fitted using a direct least-square ellipse fitting approach. Finally, we validated the measuring system through both model and on-site experiments and compared the measurements with the Leica AT-960LR absolute tracker and hydraulic overflow settlement gauge. The results indicate that the measuring system has excellent accuracy for measuring pipeline curves and deformation, and the settlement trend is consistent with the hydraulic overflow settlement gauge. We also assessed the cross-section deformation of the deformation monitoring pipeline in the Lianghekou dam for six months, and the results showed that if deployed in strict accordance with the design requirements, the monitoring pipeline buried inside the rockfill dam would not deform significantly. These findings provide a tremendous incentive for the continued development of rockfill dam internal deformation monitoring.

Microstructural and Texture Evolution of Pearlite-Drawn Wires for Flexible Marine Pipelines: Investigating the Effect of Heat Treatments on Mechanical Properties

Flexible pipelines connect offshore platforms to subsea production systems due to their high flexibility, applicability, and recycling. Flexible armor layers in flexible pipelines are constructed using the parallel helical wrapping of several rectangular wires. The complex stress modes to which pipelines are subjected provide complex failure modes that are mostly unpredictable, requiring expensive pipeline integrity verification methods. This work investigates texture and microstructure evolution in pearlite-drawn wires due to different heat treatments. The material was subjected to annealing and isothermal heat treatments to obtain changes in its microstructure and texture. The changes were characterized using SEM, XRD, and EBSD techniques. Samples were subjected to tensile testing to evaluate their mechanical properties. This work revealed that annealing and isothermal treatments mainly modify the material microstructure, whereas annealing provides a material with grains with ease of deformation. In contrast, isothermal treatment provides grain growth with high internal energy and more deformation resistance. Annealing increases the intensity of all texture components, while isothermal treatment reduces intensity. These findings provide insights into the relationship between material properties and heat treatments, which can be used to optimize the design and performance of flexible pipelines, thereby reducing the need for expensive integrity verification methods.

SCREE: a comprehensive pipeline for single-cell multi-modal CRISPR screen data processing and analysis.

Single-cell CRISPR screens have been widely used to investigate gene regulatory circuits in diverse biological systems. The recent development of single-cell CRISPR screens has enabled multimodal profiling of perturbed cells with both gene expression, chromatin accessibility and protein levels. However, current methods cannot meet the analysis requirements of different types of data and have limited functions. Here, we introduce Single-cell CRISPR screens data analysEs and perturbation modEling (SCREE) as a comprehensive and flexible pipeline to facilitate the analyses of various types of single-cell CRISPR screens data. SCREE performs read alignment, sgRNA assignment, quality control, clustering and visualization, perturbation enrichment evaluation, perturbation efficiency modeling, gene regulatory score calculation and functional analyses of perturbations for single-cell CRISPR screens with both RNA, ATAC and multimodal readout. SCREE is available at https://github.com/wanglabtongji/SCREE.

Integrated data-driven modeling and experimental optimization of granular hydrogel matrices

Integrated data-driven modeling and experimental optimization of granular hydrogel matrices

Mapache: a flexible pipeline to map ancient DNA.

We introduce mapache, a flexible, robust and scalable pipeline to map, quantify and impute ancient and present-day DNA in a reproducible way. Mapache is implemented in the workflow manager Snakemake and is optimized for low-space consumption, allowing to efficiently (re)map large datasets-such as reference panels and multiple extracts and libraries per sample - to one or several genomes. Mapache can easily be customized or combined with other Snakemake tools. Mapache is freely available on GitHub (https://github.com/sneuensc/mapache). An extensive manual is provided at https://github.com/sneuensc/mapache/wiki. Supplementary data are available at Bioinformatics online.

An Open-Source Photogrammetry Workflow for Reconstructing 3D Models.

Acquiring accurate 3D biological models efficiently and economically is important for morphological data collection and analysis in organismal biology. In recent years, structure-from-motion (SFM) photogrammetry has become increasingly popular in biological research due to its flexibility and being relatively low cost. SFM photogrammetry registers 2D images for reconstructing camera positions as the basis for 3D modeling and texturing. However, most studies of organismal biology still relied on commercial software to reconstruct the 3D model from photographs, which impeded the adoption of this workflow in our field due the blocking issues such as cost and affordability. Also, prior investigations in photogrammetry did not sufficiently assess the geometric accuracy of the models reconstructed. Consequently, this study has two goals. First, we presented an affordable and highly flexible SFM photogrammetry pipeline based on the open-source package OpenDroneMap (ODM) and its user interface WebODM. Second, we assessed the geometric accuracy of the photogrammetric models acquired from the ODM pipeline by comparing them to the models acquired via microCT scanning, the de facto method to image skeleton. Our sample comprised 15 Aplodontia rufa (mountain beaver) skulls. Using models derived from microCT scans of the samples as reference, our results showed that the geometry of the models derived from ODM was sufficiently accurate for gross metric and morphometric analysis as the measurement errors are usually around or below 2%, and morphometric analysis captured consistent patterns of shape variations in both modalities. However, subtle but distinct differences between the photogrammetric and microCT-derived 3D models could affect the landmark placement, which in return affected the downstream shape analysis, especially when the variance within a sample is relatively small. At the minimum, we strongly advise not combining 3D models derived from these two modalities for geometric morphometric analysis. Our findings can be indictive of similar issues in other SFM photogrammetry tools since the underlying pipelines are similar. We recommend that users run a pilot test of geometric accuracy before using photogrammetric models for morphometric analysis. For the research community, we provide detailed guidance on using our pipeline for building 3D models from photographs.

The art of transfer learning: An adaptive and robust pipeline

Transfer learning is an essential tool for improving the performance of primary tasks by leveraging information from auxiliary data resources. In this work, we propose Adaptive Robust Transfer Learning (ART), a flexible pipeline of performing transfer learning with generic machine learning algorithms. We establish the nonasymptotic learning theory of ART, providing a provable theoretical guarantee for achieving adaptive transfer while preventing negative transfer. Additionally, we introduce an ART‐integrated‐aggregating machine that produces a single final model when multiple candidate algorithms are considered. We demonstrate the promising performance of ART through extensive empirical studies on regression, classification, and sparse learning. We further present a real‐data analysis for a mortality study.

Numerical Investigation on Buried Pipelines Subjected to Permanent Ground Deformations Due to Shallow Slope Failure

Permanent ground deformations (PGDs) induced by slope failures cause catastrophic damage to buried pipelines. This paper presents a 2D plane-strain numerical analysis of the behavior of a 800 mm water transport pipeline buried in the Aine-Tine slope (Mila, Algeria) subjected to shallow PGD, as it could be triggered by the recent earthquake of August 07th, 2020 (M= 4.9). The analysis is carried out through the application of an incremental displacement to simulate the soil-pipeline interaction while focusing on the effect of (1) the magnitude of the PGD and (2) the rigidity of the pipeline on the structural response of the pipeline. The elasticperfectly Mohr-Coulomb model was used to simulate the soil behavior and the elastic model was used to simulate that of the steel pipe. Pipeline deformations (i.e., translation and ovalization) and radial internal forces’ (i.e., axial forces F୅, shear forces Fୗ and bending moments M୆) results highlighted that shallow PGD can exert additional loads on pipelines that are proportional to the magnitude of the PGD. It has been found that the soil deformations as well as the internal forces induced on the pipeline ring are higher for rigid pipelines. Moreover, the results indicated that rigid pipelines are more effective than flexible ones as far as ovalization-serviceability limit state is concerned. In effect, for PGD magnitudes of 0.5, 1 and 2 m, the ovalization values of the flexible pipeline are, respectively, higher by 23%, 21% and 18% than those calculated for the rigid pipeline. Through a simplified linear numerical simulation such as that presented in this study, engineers and planners could be guided to foresee the possible causes of pipeline leaks and the mechanisms of ruptures that lead very often to severe disruption of pipelines’ normal operation. KEYWORDS: Soil-structure interaction, Slope failure, Permanent ground deformation, Pipelines, Radial internal forces, Ovalization

ARA: a flexible pipeline for automated exploration of NCBI SRA datasets.

One of the most effective and useful methods to explore the content of biological databases is searching with nucleotide or protein sequences as a query. However, especially in the case of nucleic acids, due to the large volume of data generated by the next-generation sequencing (NGS) technologies, this approach is often not available. The hierarchical organization of the NGS records is primarily designed for browsing or text-based searches of the information provided in metadata-related keywords, limiting the efficiency of database exploration. We developed an automated pipeline that incorporates the well-established NGS data-processing tools and procedures to allow easy and effective sampling of the NCBI SRA database records. Given a file with query nucleotide sequences, our tool estimates the matching content of SRA accessions by probing only a user-defined fraction of a record's sequences. Based on the selected parameters, it allows performing a full mapping experiment with records that meet the required criteria. The pipeline is designed to be easy to operate-it offers a fully automatic setup procedure and is fixed on tested supporting tools. The modular design and implemented usage modes allow a user to scale up the analyses into complex computational infrastructure. We present an easy-to-operate and automated tool that expands the way a user can access and explore the information contained within the records deposited in the NCBI SRA database.