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2021 Journal article Open Access OPEN

Integration of multiple resolution data in 3D chromatin reconstruction using ChromStruct
Caudai C., Zoppè M., Tonazzini A., Merelli I., Salerno E.
The three-dimensional structure of chromatin in the cellular nucleus carries important information that is connected to physiological and pathological correlates and dysfunctional cell behaviour. As direct observation is not feasible at present, on one side, several experimental techniques have been developed to provide information on the spatial organization of the DNA in the cell; on the other side, several computational methods have been developed to elaborate experimental data and infer 3D chromatin conformations. The most relevant experimental methods are Chromosome Conformation Capture and its derivatives, chromatin immunoprecipitation and sequencing techniques (CHIP-seq), RNA-seq, fluorescence in situ hybridization (FISH) and other genetic and biochemical techniques. All of them provide important and complementary information that relate to the three-dimensional organization of chromatin. However, these techniques employ very different experimental protocols and provide information that is not easily integrated, due to different contexts and different resolutions. Here, we present an open-source tool, which is an expansion of the previously reported code ChromStruct, for inferring the 3D structure of chromatin that, by exploiting a multilevel approach, allows an easy integration of information derived from different experimental protocols and referred to different resolution levels of the structure, from a few kilobases up to Megabases. Our results show that the introduction of chromatin modelling features related to CTCF CHIA-PET data, histone modification CHIP-seq, and RNA-seq data produce appreciable improvements in ChromStruct's 3D reconstructions, compared to the use of HI-C data alone, at a local level and at a very high resolution.Source: Biology (Basel) 10 (2021): 338. doi:10.3390/biology10040338
DOI: 10.3390/biology10040338

See at: Europe PubMed Central Open Access | ISTI Repository Open Access | CNR ExploRA Open Access | www.mdpi.com Open Access | Biology Open Access


2021 Report Open Access OPEN

SI-Lab Annual Research Report 2020
Leone G. R., Righi M., Carboni A., Caudai C., Colantonio S., Kuruoglu E. E., Leporini B., Magrini M., Paradisi P., Pascali M. A., Pieri G., Reggiannini M., Salerno E., Scozzari A., Tonazzini A., Fusco G., Galesi G., Martinelli M., Pardini F., Tampucci M., Buongiorno R., Bruno A., Germanese D., Matarese F., Coscetti S., Coltelli P., Jalil B., Benassi A., Bertini G., Salvetti O., Moroni D.
The Signal & Images Laboratory (http://si.isti.cnr.it/) is an interdisciplinary research group in computer vision, signal analysis, smart vision systems and multimedia data understanding. It is part of the Institute for Information Science and Technologies of the National Research Council of Italy. This report accounts for the research activities of the Signal and Images Laboratory of the Institute of Information Science and Technologies during the year 2020.Source: ISTI Technical Report, ISTI-2021-TR/009, pp.1–38, 2021
DOI: 10.32079/isti-tr-2021/009

See at: ISTI Repository Open Access | CNR ExploRA Open Access


2021 Conference article Restricted

A deep Learning approach for hepatic steatosis estimation from ultrasound imaging
Colantonio S., Salvati A., Caudai C., Bonino F., De Rosa L., Pascali M. A., Germanese D., Brunetto M. R., Faita F.
This paper proposes a simple convolutional neural model as a novel method to predict the level of hepatic steatosis from ultrasound data. Hepatic steatosis is the major histologic feature of non-alcoholic fatty liver disease (NAFLD), which has become a major global health challenge. Recently a new definition for FLD, that take into account the risk factors and clinical characteristics of subjects, has been suggested; the proposed criteria for Metabolic Disfunction-Associated Fatty Liver Disease (MAFLD) are based on histological (biopsy), imaging or blood biomarker evidence of fat accumulation in the liver (hepatic steatosis), in subjects with overweight/obesity or presence of type 2 diabetes mellitus. In lean or normal weight, non-diabetic individuals with steatosis, MAFLD is diagnosed when at least two metabolic abnormalities are present. Ultrasound examinations are the most used technique to non-invasively identify liver steatosis in a screening settings. However, the diagnosis is operator dependent, as accurate image processing techniques have not entered yet in the diagnostic routine. In this paper, we discuss the adoption of simple convolutional neural models to estimate the degree of steatosis from echographic images in accordance with the state-of-the-art magnetic resonance spectroscopy measurements (expressed as percentage of the estimated liver fat). More than 22,000 ultrasound images were used to train three networks, and results show promising performances in our study (150 subjects).Source: ICCCI 2021 - 13th International Conference on Computational Collective Intelligence, pp. 703–714, Rhodes, Greece, 29/09/2021,1/10/ 2021
DOI: 10.1007/978-3-030-88113-9_57

See at: link.springer.com Restricted | CNR ExploRA Restricted


2020 Journal article Open Access OPEN

A multifunctional alternative lawn where warm-season grass and cold-season flowers coexist
Bretzel F., Gaetani M., Vannucchi F., Caudai C., Grossi N., Magni S., Caturegli L., Volterrani M.
Lawns provide green infrastructure and ecosystem services for anthropized areas. They have a strong impact on the environment in terms of inputs (water and fertilizers) and maintenance. The use of warm-season grasses, such as Cynodon dactylon (L.) Pers., provides a cost-effective and sustainable lawn in the dry summers of the Mediterranean. In winter, Bermudagrass is dormant and brown, which instead of being a problem could be an opportunity for biodiversity through the coexistence of flowering species. This study assesses the possibility of growing autumn-to-spring-flowering bulbs and forbs with Bermudagrass, to provide ecosystem services in urban areas. Eight geophytes and 18 forbs were incorporated into a mature turf of hybrid Bermudagrass, Cynodon dactylon × C. transvaalensis cv. "Tifway". At the same time, a commercial flowering mix was sown in the same conditions. Two different soil preparations, scalping and turf flaming, and two different nitrogen doses, 50 and 150 kg ha, were carried out before sowing and transplanting. The flowering plants were counted. All the bulbs and six of the 18 forbs were able to grow and flower in the first and second years. The commercial mix was in full bloom from April until the cutting time for the hybrid Bermudagrass, at the end of May. Adding the flowering species did not affect the healthy growth of the warm-season grass. The fertilization dose had no effect, while turf flaming led to a wider spread of Bellis perennis L. and Crocus spp. Several flower-visiting insects were observed in the spring.Source: Landscape and ecological engineering (Print) 16 (2020): 307–317. doi:10.1007/s11355-020-00423-w
DOI: 10.1007/s11355-020-00423-w

See at: ISTI Repository Open Access | Landscape and Ecological Engineering Restricted | Landscape and Ecological Engineering Restricted | Landscape and Ecological Engineering Restricted | Landscape and Ecological Engineering Restricted | link.springer.com | CNR ExploRA


2020 Journal article Open Access OPEN

Can Magnetic Resonance Radiomics Analysis Discriminate Parotid Gland Tumors? A Pilot Study
Gabelloni M., Faggioni L., Attanasio S., Vani V., Goddi A., Colantonio S., Germanese D., Caudai C., Bruschini L., Scarano M., Seccia V., Neri E.
Our purpose is to evaluate the performance of magnetic resonance (MR) radiomics analysis for differentiating between malignant and benign parotid neoplasms and, among the latter, between pleomorphic adenomas and Warthin tumors. We retrospectively evaluated 75 T2-weighted images of parotid gland lesions, of which 61 were benign tumors (32 pleomorphic adenomas, 23 Warthin tumors and 6 oncocytomas) and 14 were malignant tumors. A receiver operating characteristics (ROC) curve analysis was performed to find the threshold values for the most discriminative features and determine their sensitivity, specificity and area under the ROC curve (AUROC). The most discriminative features were used to train a support vector machine classifier. The best classification performance was obtained by comparing a pleomorphic adenoma with a Warthin tumor (yielding sensitivity, specificity and a diagnostic accuracy as high as 0.8695, 0.9062 and 0.8909, respectively) and a pleomorphic adenoma with malignant tumors (sensitivity, specificity and a diagnostic accuracy of 0.6666, 0.8709 and 0.8043, respectively). Radiomics analysis of parotid tumors on conventional T2-weighted MR images allows the discrimination of pleomorphic adenomas from Warthin tumors and malignant tumors with a high sensitivity, specificity and diagnostic accuracy.Source: Diagnostics (Basel) 10 (2020). doi:10.3390/diagnostics10110900
DOI: 10.3390/diagnostics10110900

See at: Diagnostics Open Access | Europe PubMed Central Open Access | ISTI Repository Open Access | CNR ExploRA Open Access | Diagnostics Open Access | Diagnostics Open Access | Diagnostics Open Access


2019 Journal article Open Access OPEN

Estimation of the spatial chromatin structure based on a multiresolution bead-chain model
Caudai C., Salerno E., Zoppe M., Tonazzini A.
We present a method to infer 3D chromatin configurations from Chromosome Conformation Capture data. Quite a few methods have been proposed to estimate the structure of the nuclear DNA in homogeneous populations of cells from this kind of data. Many of them transform contact frequencies into Euclidean distances between pairs of chromatin fragments, and then reconstruct the structure by solving a distance-to-geometry problem. To avoid inconsistencies, our method is based on a score function that does not require any frequency-to-distance translation. We propose a multiscale chromatin model where the chromatin fibre is suitably partitioned at each scale. The partial structures are estimated independently, and connected to rebuild the whole fibre. Our score function consists in a data-fit part and a penalty part, balanced automatically at each scale and each subchain. The penalty part enforces "soft" geometric constraints. As many different structures can fit the data, our sampling strategy produces a set of solutions with similar scores. The procedure contains a few parameters, independent of both the scale and the genomic segment treated. The partition of the fibre, along with intrinsically parallel parts, make this method computationally efficient. Results from human genome data support the biological plausibility of our solutions.Source: IEEE/ACM transactions on computational biology and bioinformatics (Print) 16 (2019): 550–559. doi:10.1109/TCBB.2018.2791439
DOI: 10.1109/tcbb.2018.2791439

See at: ISTI Repository Open Access | IEEE/ACM Transactions on Computational Biology and Bioinformatics Restricted | IEEE/ACM Transactions on Computational Biology and Bioinformatics Restricted | IEEE/ACM Transactions on Computational Biology and Bioinformatics Restricted | CNR ExploRA Restricted | IEEE/ACM Transactions on Computational Biology and Bioinformatics Restricted | IEEE/ACM Transactions on Computational Biology and Bioinformatics Restricted


2019 Journal article Open Access OPEN

ChromStruct 4: a Python code to estimate the chromatin structure from Hi-C data
Caudai C., Salerno E., Zoppè M., Merelli I., Tonazzini A.
A method and a stand-alone Python(TM) code to estimate the 3D chromatin structure from chromosome conformation capture data are presented. The method is based on a multiresolution, modified-bead-chain chromatin model, evolved through quaternion operators in a Monte Carlo sampling. The solution space to be sampled is generated by a score function with a data-fit part and a constraint part where the available prior knowledge is implicitly coded. The final solution is a set of 3D configurations that are compatible with both the data and the prior knowledge. The iterative code, provided here as additional material, is equipped with a graphical user interface and stores its results in standard-format files for 3D visualization. We describe the mathematical-computational aspects of the method and explain the details of the code. Some experimental results are reported, with a demonstration of their fit to the data.Source: IEEE/ACM transactions on computational biology and bioinformatics (Online) 16 (2019): 1867–1878. doi:10.1109/TCBB.2018.2838669
DOI: 10.1109/tcbb.2018.2838669

See at: ISTI Repository Open Access | IEEE/ACM Transactions on Computational Biology and Bioinformatics Restricted | IEEE/ACM Transactions on Computational Biology and Bioinformatics Restricted | ieeexplore.ieee.org Restricted | CNR ExploRA Restricted | IEEE/ACM Transactions on Computational Biology and Bioinformatics Restricted | IEEE/ACM Transactions on Computational Biology and Bioinformatics Restricted


2019 Conference article Open Access OPEN

La radiomica come elemento fondante della medicina di precisione in ambito oncologico
Colantonio S., Carlini E., Caudai C., Germanese D., Manghi P., Pascali M. A., Barucci A., Farnesi D., Zoppetti N., Colcelli V., Pini R., Carpi R., Esposito M., Neri E., Romei C., Occhipinti M.
Questo documento introduce e inquadra le attività che un gruppo interdisciplinare di ricercatori e clinici sta portando avanti grazie a tecniche di analisi di immagini, machine learning e intelligenza artificiale, a supporto della medicina di precisione in ambito oncologico. Partendo dalla comprensione del fenomeno fisico e dalla caratterizzazione dei processi biologici che sottendono alla formazione delle immagini biomedicali, attraverso tecniche di analisi radiomica dei dati radiologici e di mining di dati complessi, terogenei e multisorgente, le soluzioni studiate mirano a supportare i clinici nel continuum dei processi diagnostici, prognostici e terapeutici in ambito oncologico.Source: Ital-IA: primo Convegno Nazionale CINI sull'Intelligenza Artificiale, Roma, Italy, 18-19 marzo 2019

See at: ISTI Repository Open Access | CNR ExploRA Open Access | www.ital-ia.it Open Access


2019 Conference article Restricted

Radiomics to predict prostate cancer aggressiveness: a preliminary study
Germanese D., Mercatelli L., Colantonio S., Miele V., Pascali M. A., Caudai C., Zoppetti N., Carpi R., Barucci A., Bertelli E., Agostini S.
Radiomics is encouraging a paradigm shift in oncological diagnostics towards the symbiosis of radiology and Artificial Intelligence (AI) techniques. The aim is to exploit very accurate, robust image processing algorithms and provide quantitative information about the phenotypic differences of cancer traits. By exploring the association between this quantitative information and patients' prognosis, AI algorithms are boosting the power of radiomics in the perspective of precision oncology. However, the choice of the most suitable AI method can determine the success of a radiomic application. The current state-of-the art methods in radiomics aim at extracting statistical features from biomedical images and, then, process them with Machine Learning (ML) techniques. Many works have been reported in the literature presenting various combinations of radiomic features and ML methods. In this preliminary study, we aim to analyse the performance of a radiomic approach to predict prostate cancer (PCa) aggressiveness from multiarametric Magnetic Resonance Imaging (mp-MRI). Clinical mp-MRI data were collected from patients with histology-confirmed PCa and labelled by a team of expert radiologists. Such data were used to extract and select two sets of radiomic features; hence, the classification performances of five classifiers were assessed. This analysis is meant as a preliminary step towards the overall goal of investigating the potential of radiomic-based analyses.Source: BIBE 2019: 19th annual IEEE International Conference on Bioinformatics and Bioengineering, pp. 972–976, Athens, Greece, 28-30 October 2019
DOI: 10.1109/bibe.2019.00181

See at: academic.microsoft.com Restricted | dblp.uni-trier.de Restricted | ieeexplore.ieee.org Restricted | CNR ExploRA Restricted | xplorestaging.ieee.org Restricted


2019 Conference article Open Access OPEN

May radiomic data predict prostate cancer aggressiveness?
Germanese D., Colantonio S., Caudai C., Pascali M. A., Barucci A., Zoppetti N., Agostini S., Bertelli E., Mercatelli L., Miele V., Carpi R.
Radiomics can quantify tumor phenotypic characteristics non-invasively by defining a signature correlated with biological information. Thanks to algorithms derived from computer vision to extract features from images, and machine learning methods to mine data, Radiomics is the perfect case study of application of Artificial Intelligence in the context of precision medicine. In this study we investigated the association between radiomic features extracted from multi-parametric magnetic resonance imaging (mp-MRI)of prostate cancer (PCa) and the tumor histologic subtypes (using Gleason Score) using machine learning algorithms, in order to identify which of the mp-MRI derived radiomic features can distinguish high and low risk PCa.Source: CAIP 2019 - International Conference on Computer Analysis of Images and Patterns, pp. 65–75, Salerno, Italy, 6 September, 2019
DOI: 10.1007/978-3-030-29930-9_7

See at: ISTI Repository Open Access | academic.microsoft.com Restricted | dblp.uni-trier.de Restricted | link.springer.com Restricted | link.springer.com Restricted | CNR ExploRA Restricted


2018 Conference article Open Access OPEN

Parallelizable strategy for the estimation of the 3D structure of biological macromolecules
Caudai C., Zoppè M., Salerno E., Merelli I., Tonazzini A.
We present a parallelizzable, multilevel algorithm for the study of three-dimensional structure of biological macromolecules, applied to two fundamental topics: the 3D reconstruction of Chromatin and the elaboration of motion of proteins. For Chromatin, starting from contact data obtained through Chromosome Conformation Capture techniques, our method first subdivides the data matrix in biologically relevant blocks, and then treats them separately, at several levels, depending on the initial data resolution. The result is a family of configurations for the entire fiber, each one compatible with both experimental data and prior knowledge about specific genomes. For Proteins, the method is conceived as a solution for the problem of identifying motion and alternative conformations to the deposited structures. The algorithm, using quaternions, processes the main chain and the aminoacid side chains independently; it then exploits a Monte Carlo method for selection of biologically acceptable conformations, based on energy evaluation, and finally returns a family of conformations and of trajectories at single atom resolution.Source: PDP 2018 - 26th Euromicro International Conference on Parallel, Distributed and Network-based Processing (PDP), pp. 134–137, Cambridge, UK, 21-23 March 2018
DOI: 10.1109/pdp2018.2018.00026

See at: ISTI Repository Open Access | academic.microsoft.com Restricted | dblp.uni-trier.de Restricted | ieeexplore.ieee.org Restricted | CNR ExploRA Restricted | xplorestaging.ieee.org Restricted


2018 Software Unknown

ChromStruct v4.2 - Reconstruction of 3D chromatin structure from chromosome conformation capture data
Salerno E., Caudai C.
This Python (v.2.7.10) code provides an estimate of the 3D structure of the chromatin fibre in cell nuclei from the contact frequency data produced by a 'Chromosome conformation capture' experiment. The only input required is a text file containing a general real matrix of contact frequencies. The code features a GUI where all the tuneable parameters are made available to the user. The fibre is divided in independent segments whose structures are first estimated separately and then modelled as single elements of a lower-resolution fibre, which is treated iteratively in the same way until it cannot be divided anymore into independent segments. The full-resolution chain is then reconstructed by another iterative procedure. See the Readme file and the cited references for more detail.

See at: CNR ExploRA | www.researchgate.net


2018 Journal article Open Access OPEN

Culture and horticulture: protecting soil quality in urban gardening
Bretzel F., Caudai C., Tassi E., Rosellini I., Scatena M., Pini R.
Urban cultivation for food production is of growing importance. The quality of urban soil can be improved by tillage and the incorporation of organic matter, or can be degraded by chemical treatments. Urban gardeners have a role in this process, through the selection of various cultivation techniques. Our study focuses on an allotment area in the town of Pisa (Italy), which since 1995 has been run as a municipal vegetable garden by the residents. We analysed the soil and compared the data with those collected five years previously, to verify the possible changes in soil properties and fertility. We also interviewed the gardeners regarding their backgrounds, motivations and cultivation practices. We looked for possible changes in the soil quality attributable to the cultivation techniques. We found that the allotment holders influenced the soil quality through the cultivation techniques. Organic carbon, electrical conductivity and the content of copper increased unevenly in relation to the gardeners' cultivation practices. At the same time the study highlights that the urban gardeners were not completely aware of how to protect and enhance the fertility and the quality of urban soil. We believe that town councils should be responsible for providing correct information to the allotment holders and thus prevent the possible misuse of urban soil to grow food, as this can affect everyone's health.Source: Science of the total environment 644 (2018): 45–51. doi:10.1016/j.scitotenv.2018.06.289
DOI: 10.1016/j.scitotenv.2018.06.289

See at: ISTI Repository Open Access | The Science of The Total Environment Restricted | The Science of The Total Environment Restricted | The Science of The Total Environment Restricted | CNR ExploRA Restricted | The Science of The Total Environment Restricted | The Science of The Total Environment Restricted | The Science of The Total Environment Restricted | The Science of The Total Environment Restricted | www.sciencedirect.com Restricted


2017 Journal article Open Access OPEN

The SENSEable Pisa project: citizen-participation in monitoring acoustic climate of Mediterranean city centres
Vinci B., Tonacci A., Caudai C., De Rosa P., Nencini L., Pratali L.
The concept of urban sustainability and liveability closely depends on multi-level approaches to environmental issues. The ultimate goal in the field of noise management is to involve citizens and facilitate their participation in urban environmental decisions. The SENSEable Pisa project, based on the concept of Real-Time City and Smart City, presents an acoustic urban monitoring system based on a low-cost data acquisition method for a pervasive outdoor noise measurement. The system is based on the use of noise sensors located on private houses in the centre of Pisa, which provide a good model for the current acoustic climate of Mediterranean city centres. In this study, SENSEable acquisitions show a strong anthropogenic component not revealed by public strategic maps. The anthropogenic component, commonly known as movida, becomes increasingly critical in Mediterranean cities, therefore, it is necessary to explore methods highlighting this new source and to adopt strategies for the creation of reliable noise pollution maps.Source: Clean (Weinh., Internet) 45 (2017). doi:10.1002/clen.201600137
DOI: 10.1002/clen.201600137

See at: ISTI Repository Open Access | CLEAN - Soil Air Water Restricted | CLEAN - Soil Air Water Restricted | CLEAN - Soil Air Water Restricted | CLEAN - Soil Air Water Restricted | onlinelibrary.wiley.com Restricted | CLEAN - Soil Air Water Restricted | CLEAN - Soil Air Water Restricted | CNR ExploRA Restricted


2017 Contribution to conference Open Access OPEN

3D Chromatin structure estimation from chromosome conformation capture data
Caudai C., Salerno E., Zoppè M., Tonazzini A.
In this communication we describe ChromStruct4, a method to reconstruct a set of plausible chromatin configurations starting from contact data obtained through Chromosome Conformation Capture techniques. Chromating fibre is modeled as a kinematic chain made of consecutive and partially penetrable beads whose properties (bead size, elasticity, curvature, etc.) can be suitably constrained. The chain can be divided in segments corresonding to Topological Association Domains. We do not search for a unique consensus configuration, because the experimental data are not derived from a single cell, but from millions of cells. We use a coarse-grained recoursive approach, based on a Simulated Annealing algorithm in order to sample the solution space. As opposed to most popular methods, we do not translate contact frequencies deterministically into distances, since this often produces structures that are not consistent with the Euclidean geometry, but adopt the assumption that loci with very high contact frequencies are actually close, but loci with low contact frequencies are not necessarily far away. ChromStruct4 is tested against real Hi-C data and compared with other methods for the 3-dimesional reconstruction fo Chromatin structure starting from Chromosome Conformation Capture data.Source: BITS 2017 - 14th Annual Meeting of the Bioinformatics Italian Society, Cagliari, Italy, 5-7 July 2017

See at: ISTI Repository Open Access | CNR ExploRA Open Access


2017 Report Open Access OPEN

Comparison between ChromStruct4 and TADbit
Caudai C., Salerno E., Zoppé M., Tonazzini A.
In this report performances of ChromStruct4 and tadbit have been compared. These are two methods for the inference of chromatin three-dimensional conformations starting from Chromosome Conforma- tion Capture data. tadbit and ChromStruct4 have been tested against the same data sets from real hi-c experiments. With comparative experi- ments, also robustness of ChromStruct4 against biases have been tested.Source: ISTI Technical reports, 2017

See at: ISTI Repository Open Access | CNR ExploRA Restricted


2016 Journal article Open Access OPEN

Computational estimation of chromosome structure
Caudai C., Salerno E.
Research performed at ISTI-CNR in the framework of the national Flagship Project InterOmics includes the development of algorithms to reconstruct the chromosome structure from "chromosome conformation capture" data. One algorithm now being tested has already produced interesting results. Unlike most popular techniques, it does not derive a classical distance-to-geometry problem from the original contact data, and applies an efficient multiresolution approach to the genome under study.Source: ERCIM news 104 (2016): 21–22.

See at: ercim-news.ercim.eu Open Access | CNR ExploRA Open Access


2016 Software Unknown

Reconstruction of 3D chromatin structure from chromosome conformation capture data (Release 2.0)
Salerno E., Caudai C.
This Python code provides an estimate of the 3D structure of the chromatin fibre in cell nuclei from the contact frequency data produced by a 'Chromosome conformation capture' experiment. The only input required is a text file containing a general real matrix of contact frequencies. The related genomic resolution, along with a few geometric parameters and the parameters for tuning the estimation algorithm must be set in advance in a special section of the source code. The whole fibre is divided in independent segments, whose structures are estimated and modelled as single elements of a lower-resolution fibre that is treated recursively in the same way, until it cannot be divided anymore into independent segments. The full-resolution chain is then reconstructed by another recursive procedure.

See at: CNR ExploRA


2016 Report Open Access OPEN

Consistency tests for a recursive multi-scale 3D chromatin structure reconstruction algorithm
Caudai C., Salerno E., Zoppè M., Tonazzini A.
In this report, we test the consistency and coherence of an algorithm obtained as an extension of a technique we proposed in the past. This implements a recursive multi-scale reconstruction of the 3d chromatin structure from Chromosome Conformation Capture data. These data derive from millions of cells, so we cannot expect that they lead to a unique solution; for this reason, we adopt a statistic approach to sample the space of the solutions generated by a suitable objective function, in order to achieve congurations compatible with the input data and the known constraints. The consistency of the algorithm has been tested by producing a large number of results and evaluating the dispersion of the nal values of the objective function. Using the same solutions, synthetic contact matrices have been produced and compared with the input matrix to test the coherence of our solutions with the initial data. Furthermore, we investigated the presence of typical structures in the solutions by hierarchical clustering.Source: Project report, InterOmic, 2016

See at: ISTI Repository Open Access | CNR ExploRA Open Access


2016 Other Open Access OPEN

3D chromatin structure estimation through a constraint-enhanced score function
Caudai C., Salerno E., Zoppè M., Tonazzini A.
Based on experimental techniques of the type "Chromosome Conformation Capture" (3C), several methods have been proposed in the literature to estimate the structure of the nuclear DNA in homogeneous populations of cells. Many of these methods transform contact frequencies into Euclidean distances between pairs of chromatin fragments, and then reconstruct the structure by solving a distance-to-geometry problem. To avoid the drawbacks of this strategy, we propose to abandon the frequency-distance translation and adopt a recursive multiscale procedure, where the chromatin fibre is modelled by a new kind of modified bead chain, the data are suitably partitioned at each scale, and the resulting partial structures are estimated independently of each other and then connected again to rebuild the whole chain. We propose a new score function to generate the solution space: it includes a data-fit part that does not require target distances, and a penalty part, which enforces "soft" geometric constraints on the solution, coherent with known physical and biological constraints. The relative weights of the two parts are balanced automatically at each scale and each subchain treated. Since it is reasonable to expect that many different structures fit any 3C-type data set, we sample the solution space by simulated annealing, with no search for an absolute optimum. A set of different solutions with similar scores is thus generated. The procedure can be managed through a minimum set of parameters, independent of both the scale and the particular genomic segment being treated. The user is thus allowed to control the solutions easily and effectively. The partition of the fibre, along with several intrinsically parallel parts, make this method computationally efficient. We report some results obtained with the new method and code, tested against real data, that support the reliability of our method and the biological plausibility of our solutions.

See at: ISTI Repository Open Access | CNR ExploRA Open Access | puma.isti.cnr.it Open Access