PanDrugs2: prioritizing cancer therapies using integrated individual multi-omics data.

Genomics studies routinely confront researchers with long lists of tumor alterations detected in patients. Such lists are difficult to interpret since only a minority of the alterations are relevant biomarkers for diagnosis and for designing therapeutic strategies. PanDrugs is a methodology that facilitates the interpretation of tumor molecular alterations and guides the selection of personalized treatments. To do so, PanDrugs scores gene actionability and drug feasibility to provide a prioritized evidence-based list of drugs. Here, we introduce PanDrugs2, a major upgrade of PanDrugs that, in addition to somatic variant analysis, supports a new integrated multi-omics analysis which simultaneously combines somatic and germline variants, copy number variation and gene expression data. Moreover, PanDrugs2 now considers cancer genetic dependencies to extend tumor vulnerabilities providing therapeutic options for untargetable genes. Importantly, a novel intuitive report to support clinical decision-making is generated. PanDrugs database has been updated, integrating 23 primary sources that support >74K drug-gene associations obtained from 4642 genes and 14 659 unique compounds. The database has also been reimplemented to allow semi-automatic updates to facilitate maintenance and release of future versions. PanDrugs2 does not require login and is freely available at https://www.pandrugs.org/.

Dispersal history of SARS-CoV-2 in Galicia, Spain.

The dynamics of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission are influenced by a variety of factors, including social restrictions and the emergence of distinct variants. In this study, we delve into the origins and dissemination of the Alpha, Delta, and Omicron-BA.1 variants of concern in Galicia, northwest Spain. For this, we leveraged genomic data collected by the EPICOVIGAL Consortium and from the GISAID database, along with mobility information from other Spanish regions and foreign countries. Our analysis indicates that initial introductions during the Alpha phase were predominantly from other Spanish regions and France. However, as the pandemic progressed, introductions from Portugal and the United States became increasingly significant. The number of detected introductions varied from 96 and 101 for Alpha and Delta to 39 for Omicron-BA.1. Most of these introductions left a low number of descendants (<10), suggesting a limited impact on the evolution of the pandemic in Galicia. Notably, Galicia's major coastal cities emerged as critical hubs for viral transmission, highlighting their role in sustaining and spreading the virus. This research emphasizes the critical role of regional connectivity in the spread of SARS-CoV-2 and offers essential insights for enhancing public health strategies and surveillance measures.

DREIMT: a drug repositioning database and prioritization tool for immunomodulation.

MOTIVATION: Drug immunomodulation modifies the response of the immune system and can be therapeutically exploited in pathologies such as cancer and autoimmune diseases. RESULTS: DREIMT is a new hypothesis-generation web tool, which performs drug prioritization analysis for immunomodulation. DREIMT provides significant immunomodulatory drugs targeting up to 70 immune cells subtypes through a curated database that integrates 4960 drug profiles and ∼2600 immune gene expression signatures. The tool also suggests potential immunomodulatory drugs targeting user-supplied gene expression signatures. Final output includes drug-signature association scores, FDRs and downloadable plots and results tables. AVAILABILITYAND IMPLEMENTATION: http://www.dreimt.org. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Bicycle: a bioinformatics pipeline to analyze bisulfite sequencing data.

Summary: High-throughput sequencing of bisulfite-converted DNA is a technique used to measure DNA methylation levels. Although a considerable number of computational pipelines have been developed to analyze such data, none of them tackles all the peculiarities of the analysis together, revealing limitations that can force the user to manually perform additional steps needed for a complete processing of the data. This article presents bicycle, an integrated, flexible analysis pipeline for bisulfite sequencing data. Bicycle analyzes whole genome bisulfite sequencing data, targeted bisulfite sequencing data and hydroxymethylation data. To show how bicycle overtakes other available pipelines, we compared them on a defined number of features that are summarized in a table. We also tested bicycle with both simulated and real datasets, to show its level of performance, and compared it to different state-of-the-art methylation analysis pipelines. Availability and implementation: Bicycle is publicly available under GNU LGPL v3.0 license at http://www.sing-group.org/bicycle. Users can also download a customized Ubuntu LiveCD including bicycle and other bisulfite sequencing data pipelines compared here. In addition, a docker image with bicycle and its dependencies, which allows a straightforward use of bicycle in any platform (e.g. Linux, OS X or Windows), is also available. Contact: ograna@cnio.es or dgpena@uvigo.es. Supplementary information: Supplementary data are available at Bioinformatics online.

Web scraping technologies in an API world.

Web services are the de facto standard in biomedical data integration. However, there are data integration scenarios that cannot be fully covered by Web services. A number of Web databases and tools do not support Web services, and existing Web services do not cover for all possible user data demands. As a consequence, Web data scraping, one of the oldest techniques for extracting Web contents, is still in position to offer a valid and valuable service to a wide range of bioinformatics applications, ranging from simple extraction robots to online meta-servers. This article reviews existing scraping frameworks and tools, identifying their strengths and limitations in terms of extraction capabilities. The main focus is set on showing how straightforward it is today to set up a data scraping pipeline, with minimal programming effort, and answer a number of practical needs. For exemplification purposes, we introduce a biomedical data extraction scenario where the desired data sources, well-known in clinical microbiology and similar domains, do not offer programmatic interfaces yet. Moreover, we describe the operation of WhichGenes and PathJam, two bioinformatics meta-servers that use scraping as means to cope with gene set enrichment analysis.

Optical diagnosis in still images of colorectal polyps: comparison between expert endoscopists and PolyDeep, a Computer-Aided Diagnosis system.

Background: PolyDeep is a computer-aided detection and classification (CADe/x) system trained to detect and classify polyps. During colonoscopy, CADe/x systems help endoscopists to predict the histology of colonic lesions. Objective: To compare the diagnostic performance of PolyDeep and expert endoscopists for the optical diagnosis of colorectal polyps on still images. Methods: PolyDeep Image Classification (PIC) is an in vitro diagnostic test study. The PIC database contains NBI images of 491 colorectal polyps with histological diagnosis. We evaluated the diagnostic performance of PolyDeep and four expert endoscopists for neoplasia (adenoma, sessile serrated lesion, traditional serrated adenoma) and adenoma characterization and compared them with the McNemar test. Receiver operating characteristic curves were constructed to assess the overall discriminatory ability, comparing the area under the curve of endoscopists and PolyDeep with the chi- square homogeneity areas test. Results: The diagnostic performance of the endoscopists and PolyDeep in the characterization of neoplasia is similar in terms of sensitivity (PolyDeep: 89.05%; E1: 91.23%, p=0.5; E2: 96.11%, p<0.001; E3: 86.65%, p=0.3; E4: 91.26% p=0.3) and specificity (PolyDeep: 35.53%; E1: 33.80%, p=0.8; E2: 34.72%, p=1; E3: 39.24%, p=0.8; E4: 46.84%, p=0.2). The overall discriminative ability also showed no statistically significant differences (PolyDeep: 0.623; E1: 0.625, p=0.8; E2: 0.654, p=0.2; E3: 0.629, p=0.9; E4: 0.690, p=0.09). In the optical diagnosis of adenomatous polyps, we found that PolyDeep had a significantly higher sensitivity and a significantly lower specificity. The overall discriminative ability of adenomatous lesions by expert endoscopists is significantly higher than PolyDeep (PolyDeep: 0.582; E1: 0.685, p < 0.001; E2: 0.677, p < 0.0001; E3: 0.658, p < 0.01; E4: 0.694, p < 0.0001). Conclusion: PolyDeep and endoscopists have similar diagnostic performance in the optical diagnosis of neoplastic lesions. However, endoscopists have a better global discriminatory ability than PolyDeep in the optical diagnosis of adenomatous polyps.

Dispersal history of SARS-CoV-2 in Galicia, Spain.

The dynamics of SARS-CoV-2 transmission are influenced by a variety of factors, including social restrictions and the emergence of distinct variants. In this study, we delve into the origins and dissemination of the Alpha, Delta, and Omicron variants of concern in Galicia, northwest Spain. For this, we leveraged genomic data collected by the EPICOVIGAL Consortium and from the GISAID database, along with mobility information from other Spanish regions and foreign countries. Our analysis indicates that initial introductions during the Alpha phase were predominantly from other Spanish regions and France. However, as the pandemic progressed, introductions from Portugal and the USA became increasingly significant. Notably, Galicia's major coastal cities emerged as critical hubs for viral transmission, highlighting their role in sustaining and spreading the virus. This research emphasizes the critical role of regional connectivity in the spread of SARS-CoV-2 and offers essential insights for enhancing public health strategies and surveillance measures.

PileLineGUI: a desktop environment for handling genome position files in next-generation sequencing studies.

Next-generation sequencing (NGS) technologies are making sequence data available on an unprecedented scale. In this context, new catalogs of Single Nucleotide Polymorphism and mutations generated by resequencing studies are usually stored in genome position files (e.g. Variant Call Format, SAMTools pileup, BED, GFF) comprising of large lists of genomic positions, which are difficult to handle by researchers. Here, we present PileLineGUI, a novel desktop application primarily designed for manipulating, browsing and analysing genome position files (GPF), with specific support to somatic mutation finding studies. The developed tool also integrates a new genome browser module specially designed for inspecting GPFs. PileLineGUI is free, multiplatform and designed to be intuitively used by biomedical researchers. PileLineGUI is available at: http://sing.ei.uvigo.es/pileline/pilelinegui.html.

Negative Samples for Improving Object Detection-A Case Study in AI-Assisted Colonoscopy for Polyp Detection.

Deep learning object-detection models are being successfully applied to develop computer-aided diagnosis systems for aiding polyp detection during colonoscopies. Here, we evidence the need to include negative samples for both (i) reducing false positives during the polyp-finding phase, by including images with artifacts that may confuse the detection models (e.g., medical instruments, water jets, feces, blood, excessive proximity of the camera to the colon wall, blurred images, etc.) that are usually not included in model development datasets, and (ii) correctly estimating a more realistic performance of the models. By retraining our previously developed YOLOv3-based detection model with a dataset that includes 15% of additional not-polyp images with a variety of artifacts, we were able to generally improve its F1 performance in our internal test datasets (from an average F1 of 0.869 to 0.893), which now include such type of images, as well as in four public datasets that include not-polyp images (from an average F1 of 0.695 to 0.722).

ALTER: program-oriented conversion of DNA and protein alignments.

ALTER is an open web-based tool to transform between different multiple sequence alignment formats. The originality of ALTER lies in the fact that it focuses on the specifications of mainstream alignment and analysis programs rather than on the conversion among more or less specific formats. In addition, ALTER is capable of identify and remove identical sequences during the transformation process. Besides its user-friendly environment, ALTER allows access to its functionalities in a programmatic way through a Representational State Transfer web service. ALTER's front-end and its API are freely available at http://sing.ei.uvigo.es/ALTER/ and http://sing.ei.uvigo.es/ALTER/api/, respectively.

Performance of Convolutional Neural Networks for Polyp Localization on Public Colonoscopy Image Datasets.

Colorectal cancer is one of the most frequent malignancies. Colonoscopy is the de facto standard for precancerous lesion detection in the colon, i.e., polyps, during screening studies or after facultative recommendation. In recent years, artificial intelligence, and especially deep learning techniques such as convolutional neural networks, have been applied to polyp detection and localization in order to develop real-time CADe systems. However, the performance of machine learning models is very sensitive to changes in the nature of the testing instances, especially when trying to reproduce results for totally different datasets to those used for model development, i.e., inter-dataset testing. Here, we report the results of testing of our previously published polyp detection model using ten public colonoscopy image datasets and analyze them in the context of the results of other 20 state-of-the-art publications using the same datasets. The F1-score of our recently published model was 0.88 when evaluated on a private test partition, i.e., intra-dataset testing, but it decayed, on average, by 13.65% when tested on ten public datasets. In the published research, the average intra-dataset F1-score is 0.91, and we observed that it also decays in the inter-dataset setting to an average F1-score of 0.83.

PileLine: a toolbox to handle genome position information in next-generation sequencing studies.

BACKGROUND: Genomic position (GP) files currently used in next-generation sequencing (NGS) studies are always difficult to manipulate due to their huge size and the lack of appropriate tools to properly manage them. The structure of these flat files is based on representing one line per position that has been covered by at least one aligned read, imposing significant restrictions from a computational performance perspective. RESULTS: PileLine implements a flexible command-line toolkit providing specific support to the management, filtering, comparison and annotation of GP files produced by NGS experiments. PileLine tools are coded in Java and run on both UNIX (Linux, Mac OS) and Windows platforms. The set of tools comprising PileLine are designed to be memory efficient by performing fast seek on-disk operations over sorted GP files. CONCLUSIONS: Our novel toolbox has been extensively tested taking into consideration performance issues. It is publicly available at http://sourceforge.net/projects/pilelinetools under the GNU LGPL license. Full documentation including common use cases and guided analysis workflows is available at http://sing.ei.uvigo.es/pileline.

WhichGenes: a web-based tool for gathering, building, storing and exporting gene sets with application in gene set enrichment analysis.

WhichGenes is a web-based interactive gene set building tool offering a very simple interface to extract always-updated gene lists from multiple databases and unstructured biological data sources. While the user can specify new gene sets of interest by following a simple four-step wizard, the tool is able to run several queries in parallel. Every time a new set is generated, it is automatically added to the private gene-set cart and the user is notified by an e-mail containing a direct link to the new set stored in the server. WhichGenes provides functionalities to edit, delete and rename existing sets as well as the capability of generating new ones by combining previous existing sets (intersection, union and difference operators). The user can export his sets configuring the output format and selecting among multiple gene identifiers. In addition to the user-friendly environment, WhichGenes allows programmers to access its functionalities in a programmatic way through a Representational State Transfer web service. WhichGenes front-end is freely available at http://www.whichgenes.org/, WhichGenes API is accessible at http://www.whichgenes.org/api/.

Compi: a framework for portable and reproducible pipelines.

Compi is an application framework to develop end-user, pipeline-based applications with a primary emphasis on: (i) user interface generation, by automatically generating a command-line interface based on the pipeline specific parameter definitions; (ii) application packaging, with compi-dk, which is a version-control-friendly tool to package the pipeline application and its dependencies into a Docker image; and (iii) application distribution provided through a public repository of Compi pipelines, named Compi Hub, which allows users to discover, browse and reuse them easily. By addressing these three aspects, Compi goes beyond traditional workflow engines, having been specially designed for researchers who want to take advantage of common workflow engine features (such as automatic job scheduling or logging, among others) while keeping the simplicity and readability of shell scripts without the need to learn a new programming language. Here we discuss the design of various pipelines developed with Compi to describe its main functionalities, as well as to highlight the similarities and differences with similar tools that are available. An open-source distribution under the Apache 2.0 License is available from GitHub (available at https://github.com/sing-group/compi). Documentation and installers are available from https://www.sing-group.org/compi. A specific repository for Compi pipelines is available from Compi Hub (available at https://www.sing-group.org/compihub.

A novel (18)O inverse labeling-based workflow for accurate bottom-up mass spectrometry quantification of proteins separated by gel electrophoresis.

In the present work we report on a novel and fast protocol for accurate bottom-up protein quantification that overcomes the drawbacks of in-gel digestion and MALDI analysis, while maintaining their benefits. It relies on the following steps: (i) gel electrophoresis separation of proteins, (ii) fast in-gel protein digestion with trypsin, (iii) (18)O-labeling through the decoupled method, (iv) quantification through selected peptides previously chosen using the (18)O inverse labeling approach and that, finally, (v) it takes advantage of software specifically developed to select the peptides that will drive the quantification of the protein in an automated mode. We have accurately quantified the following six proteins: glycogen phosphorylase, BSA, ovalbumin, carbonic anhydrase, trypsin inhibitor, and α-lactalbumin. As a case study we have quantified the protein vitellogenin in plasma of Cyprinus carpio exposed to high levels of estrogens. The proposed new protocol was validated against the traditional ELISA method; both were found to provide comparable results (non-parametric Mann-Whitney U-test).

Metatax: Metataxonomics with a Compi-Based Pipeline for Precision Medicine.

The human body immune system, metabolism and homeostasis are affected by microbes. Dysbiosis occurs when the homeostatic equilibrium is disrupted due to an alteration in the normal microbiota of the intestine. Dysbiosis can cause cancer, and also affect a patient's ability to respond to treatment. Metataxonomics seeks to identify the bacteria present in a biological sample, based on the sequencing of the 16S rRNA genetic marker. Precision medicine attempts to find relationships between the microbiota and the risk of acquiring cancer, and design new therapies targeting bacteria. Flexible and portable bioinformatic pipelines are necessary to be able to bring metataxonomics to the clinical field, which allow groups of biological samples to be classified according to their diversity in the microbiota. With this aim we implemented Metatax, a new pipeline to analyze biological samples based on 16S rRNA gene sequencing. The results obtained with our pipeline should complement those obtained by sequencing a patient's DNA and RNA, in addition to clinical data, to improve knowledge of the possible reasons for a disease or a worse response to treatment.

An RNA-seq Based Machine Learning Approach Identifies Latent Tuberculosis Patients With an Active Tuberculosis Profile.

A better understanding of the response against Tuberculosis (TB) infection is required to accurately identify the individuals with an active or a latent TB infection (LTBI) and also those LTBI patients at higher risk of developing active TB. In this work, we have used the information obtained from studying the gene expression profile of active TB patients and their infected -LTBI- or uninfected -NoTBI- contacts, recruited in Spain and Mozambique, to build a class-prediction model that identifies individuals with a TB infection profile. Following this approach, we have identified several genes and metabolic pathways that provide important information of the immune mechanisms triggered against TB infection. As a novelty of our work, a combination of this class-prediction model and the direct measurement of different immunological parameters, was used to identify a subset of LTBI contacts (called TB-like) whose transcriptional and immunological profiles are suggestive of infection with a higher probability of developing active TB. Validation of this novel approach to identifying LTBI individuals with the highest risk of active TB disease merits further longitudinal studies on larger cohorts in TB endemic areas.

DFP: a Bioconductor package for fuzzy profile identification and gene reduction of microarray data.

BACKGROUND: Expression profiling assays done by using DNA microarray technology generate enormous data sets that are not amenable to simple analysis. The greatest challenge in maximizing the use of this huge amount of data is to develop algorithms to interpret and interconnect results from different genes under different conditions. In this context, fuzzy logic can provide a systematic and unbiased way to both (i) find biologically significant insights relating to meaningful genes, thereby removing the need for expert knowledge in preliminary steps of microarray data analyses and (ii) reduce the cost and complexity of later applied machine learning techniques being able to achieve interpretable models. RESULTS: DFP is a new Bioconductor R package that implements a method for discretizing and selecting differentially expressed genes based on the application of fuzzy logic. DFP takes advantage of fuzzy membership functions to assign linguistic labels to gene expression levels. The technique builds a reduced set of relevant genes (FP, Fuzzy Pattern) able to summarize and represent each underlying class (pathology). A last step constructs a biased set of genes (DFP, Discriminant Fuzzy Pattern) by intersecting existing fuzzy patterns in order to detect discriminative elements. In addition, the software provides new functions and visualisation tools that summarize achieved results and aid in the interpretation of differentially expressed genes from multiple microarray experiments. CONCLUSION: DFP integrates with other packages of the Bioconductor project, uses common data structures and is accompanied by ample documentation. It has the advantage that its parameters are highly configurable, facilitating the discovery of biologically relevant connections between sets of genes belonging to different pathologies. This information makes it possible to automatically filter irrelevant genes thereby reducing the large volume of data supplied by microarray experiments. Based on these contributions GENECBR, a successful tool for cancer diagnosis using microarray datasets, has recently been released.

geneCBR: a translational tool for multiple-microarray analysis and integrative information retrieval for aiding diagnosis in cancer research.

BACKGROUND: Bioinformatics and medical informatics are two research fields that serve the needs of different but related communities. Both domains share the common goal of providing new algorithms, methods and technological solutions to biomedical research, and contributing to the treatment and cure of diseases. Although different microarray techniques have been successfully used to investigate useful information for cancer diagnosis at the gene expression level, the true integration of existing methods into day-to-day clinical practice is still a long way off. Within this context, case-based reasoning emerges as a suitable paradigm specially intended for the development of biomedical informatics applications and decision support systems, given the support and collaboration involved in such a translational development. With the goals of removing barriers against multi-disciplinary collaboration and facilitating the dissemination and transfer of knowledge to real practice, case-based reasoning systems have the potential to be applied to translational research mainly because their computational reasoning paradigm is similar to the way clinicians gather, analyze and process information in their own practice of clinical medicine. RESULTS: In addressing the issue of bridging the existing gap between biomedical researchers and clinicians who work in the domain of cancer diagnosis, prognosis and treatment, we have developed and made accessible a common interactive framework. Our geneCBR system implements a freely available software tool that allows the use of combined techniques that can be applied to gene selection, clustering, knowledge extraction and prediction for aiding diagnosis in cancer research. For biomedical researches, geneCBR expert mode offers a core workbench for designing and testing new techniques and experiments. For pathologists or oncologists, geneCBR diagnostic mode implements an effective and reliable system that can diagnose cancer subtypes based on the analysis of microarray data using a CBR architecture. For programmers, geneCBR programming mode includes an advanced edition module for run-time modification of previous coded techniques. CONCLUSION: geneCBR is a new translational tool that can effectively support the integrative work of programmers, biomedical researches and clinicians working together in a common framework. The code is freely available under the GPL license and can be obtained at http://www.genecbr.org.

@Note: a workbench for biomedical text mining.

Biomedical Text Mining (BioTM) is providing valuable approaches to the automated curation of scientific literature. However, most efforts have addressed the benchmarking of new algorithms rather than user operational needs. Bridging the gap between BioTM researchers and biologists' needs is crucial to solve real-world problems and promote further research. We present @Note, a platform for BioTM that aims at the effective translation of the advances between three distinct classes of users: biologists, text miners and software developers. Its main functional contributions are the ability to process abstracts and full-texts; an information retrieval module enabling PubMed search and journal crawling; a pre-processing module with PDF-to-text conversion, tokenisation and stopword removal; a semantic annotation schema; a lexicon-based annotator; a user-friendly annotation view that allows to correct annotations and a Text Mining Module supporting dataset preparation and algorithm evaluation. @Note improves the interoperability, modularity and flexibility when integrating in-home and open-source third-party components. Its component-based architecture allows the rapid development of new applications, emphasizing the principles of transparency and simplicity of use. Although it is still on-going, it has already allowed the development of applications that are currently being used.