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Electron tomography is an imaging technique that allows for the elucidation of three-dimensional structural information of biological specimens in a very general context, including cellular in situ observations. The approach starts by collecting a set of images at different projection directions by tilting the specimen stage inside the microscope. Therefore, a crucial preliminary step is to precisely define the acquisition geometry by aligning all the tilt images to a common reference. Errors introduced in this step will lead to the appearance of artifacts in the tomographic reconstruction, rendering them unsuitable for the sample study. Focusing on fiducial-based acquisition strategies, this work proposes a deep-learning algorithm to detect misalignment artifacts in tomographic reconstructions by analyzing the characteristics of these fiducial markers in the tomogram. In addition, we propose an algorithm designed to detect fiducial markers in the tomogram with which to feed the classification algorithm in case the alignment algorithm does not provide the location of the markers. This open-source software is available as part of the Xmipp software package inside of the Scipion framework, and also through the command-line in the standalone version of Xmipp.

© 2006 Carmona-Saez et al; licensee BioMed Central Ltd. This work has been supported by the Spanish grants GR/SAL/0653/2004, CICYT BFU2004-00217/BMC, GEN2003-20235-c05-05, TIN2005-5619, PR27/05-13964-BSCH and a collaborative grant between the Spanish Research Council and the National Research Council of Canada (CSIC050402040003). The authors also thank the KEY Foundation for Brain-Mind Research in Zurich for partial economical support of this work. P.C.S. is the recipient of a fellowship from Comunidad de Madrid (CAM). A.P.M. acknowledges the support of the Spanish Ramón y Cajal program., Background: The extended use of microarray technologies has enabled the generation and accumulation of gene expression datasets that contain expression levels of thousands of genes across tens or hundreds of different experimental conditions. One of the major challenges in the analysis of such datasets is to discover local structures composed by sets of genes that show coherent expression patterns across subsets of experimental conditions. These patterns may provide clues about the main biological processes associated to different physiological states. Results: In this work we present a methodology able to cluster genes and conditions highly related in sub-portions of the data. Our approach is based on a new data mining technique, Non-smooth Non-Negative Matrix Factorization (nsNMF), able to identify localized patterns in large datasets. We assessed the potential of this methodology analyzing several synthetic datasets as well as two large and heterogeneous sets of gene expression profiles. In all cases the method was able to identify localized features related to sets of genes that show consistent expression patterns across subsets of experimental conditions. The uncovered structures showed a clear biological meaning in terms of relationships among functional annotations of genes and the phenotypes or physiological states of the associated conditions. Conclusion: The proposed approach can be a useful tool to analyze large and heterogeneous gene expression datasets. The method is able to identify complex relationships among genes and conditions that are difficult to identify by standard clustering algorithms., Spanish Research Council, National Research Council of Canada, KEY Foundation for Brain- Mind Research (Zurich), Comunidad de Madrid (CAM)., Spanish Ramón y Cajal program, Sección Deptal. de Arquitectura de Computadores y Automática (Físicas), Fac. de Ciencias Físicas, TRUE, pub

© The Royal Society of Chemistry 2022. We are thankful to Philip Baldwin, Dmitry Lyumkis, and Gabriel Lander for making their validation methods available (4.h and A.c). Javier Vargas and Jordi Burguet would like to thank the Spanish Ministry of Science and Innovation for financial support through the call 2019 Proyectos de I+D+i - RTI Tipo A (PID2019-108850RA-I00) and Arrate Muñoz Barrutia, PID2019-109820RB-I00, MCIN/AEI/10.13039/501100011033/, cofinanced by European Regional Development Fund (ERDF), "A way of making Europe.". The authors acknowledge the economic support from MICIN of the Instruct Image Processing Center (I2PC) as part of the Spanish participation in Instruct-ERIC, the European Strategic Infrastructure Project (ESFRI) in the area of Structural Biology, Grant PID2019-104757RB-I00 funded by MCIN/AEI/10.13039/501100011033/ and "ERDF A way of making Europe", by the European Union and Grant PRE2020 - 093527 funded by MCIN/AEI/ 10.13039/501100011033 and by "ESF Investing in your future". We also acknowledge support from "Comunidad Autonoma de Madrid" through Grant: S2017/BMD-3817, Instituto de Salud Carlos III (project IMPaCT-Data, exp. IMP/00019), co-funded by the European Union, European Regional Development Fund (ERDF, "A way to make Europe"), and European Union (EU) and Horizon 2020 through grants: EOSC Life (INFRAEOSC-04-2018, Proposal: 824087), HighResCells (ERC - 2018 - SyG, Proposal: 810057), IMpaCT (WIDESPREAD-03-2018 - Proposal: 857203), EOSC - Synergy (EINFRA-EOSC-5, Proposal: 857647), iNEXT-Discovery (Proposal: 871037), EnLaCES (H2020-MSCA-IF-2020, Proposal: 101024130)., The number of maps deposited in public databases (Electron Microscopy Data Bank, EMDB) determined by cryo-electron microscopy has quickly grown in recent years. With this rapid growth, it is critical to guarantee their quality. So far, map validation has primarily focused on the agreement between maps and models. From the image processing perspective, the validation has been mostly restricted to using two half-maps and the measurement of their internal consistency. In this article, we suggest that map validation can be taken much further from the point of view of image processing if 2D classes, particles, angles, coordinates, defoci, and micrographs are also provided. We present a progressive validation scheme that qualifies a result validation status from 0 to 5 and offers three optional qualifiers (A, W, and O) that can be added. The simplest validation state is 0, while the most complete would be 5AWO. This scheme has been implemented in a website https://biocomp.cnb.csic.es/EMValidationService/ to which reconstructed maps and their ESI can be uploaded., Unión Europea. Horizonte 2020, Ministerio de Ciencia e Innovación (MICINN)/FEDER, Ministerio de Ciencia e Innovación (MICINN), Comunidad de Madrid, Depto. de Óptica, Fac. de Ciencias Físicas, TRUE, pub

Single-particle analysis by electron microscopy is a well established technique for analyzing the three-dimensional structures of biological macromolecules. Besides its ability to produce high-resolution structures, it also provides insights into the dynamic behavior of the structures by elucidating their conformational variability. Here, the different image-processing methods currently available to study continuous conformational changes are reviewed.

Three dimensional electron microscopy is becoming a very data-intensive field in which vast amounts of experimental images are acquired at high speed. To manage such large-scale projects, we had previously developed a modular workflow system called Scipion (de la Rosa-Trevfn et al., 2016). We present here a major extension of Scipion that allows processing of EM images while the data is being acquired. This approach helps to detect problems at early stages, saves computing time and provides users with a detailed evaluation of the data quality before the acquisition is finished. At present, Scipion has been deployed and is in production mode in seven Cryo-EM facilities throughout the world., QC 20190118

Nowadays it is possible to unravel complex information at all levels of cellular organization by obtaining multi-dimensional image information. at the macromolecular level, three-dimensional (3D) electron microscopy, together with other techniques, is able to reach resolutions at the nanometer or subnanometer level. The information is delivered in the form of 3D volumes containing samples of a given function, for example, the electron density distribution within a given macromolecule. The same situation happens at the cellular level with the new forms of light microscopy, particularly confocal microscopy, all of which produce biological 3D volume information. Furthermore, it is possible to record sequences of images over time (videos), as well as sequences of volumes, bringing key information on the dynamics of living biological systems. It is in this context that work on bioimage started two years ago, and that its first version is now presented here. In essence, Bioimage is a database specifically designed to contain multi-dimensional images, perform queries and interactively work with the resulting multi-dimensional information on the World Wide Web, as well as accomplish the required cross-database links. Two sister home pages of bioimage can be accessed at http://www.bioimage.org and http://www-embl.bioimage.org