bluuebunny/crossref_metadata_2025_split huggingface dataset:
psupertime: supervised pseudotime analysis for time-series single-cell RNA-seq data
Authors: Macnair Will , Gupta Revant , Claassen Manfred 
Published: April 2022

AbstractMotivationImprovements in single-cell RNA-seq technologies mean that studies measuring multiple experimental conditions, such as time series, have become more common. At present, few computational methods exist to infer time series-specific transcriptome changes, and such studies have therefore typically used unsupervised pseudotime methods. While these methods identify cell subpopulations and the transitions between them, they are not appropriate for identifying the genes that vary coherently along the time series. In addition, the orderings they estimate are based only on the major sources of variation in the data, which may not correspond to the processes related to the time labels.ResultsWe introduce psupertime, a supervised pseudotime approach based on a regression model, which explicitly uses time-series labels as input. It identifies genes that vary coherently along a time series, in addition to pseudotime values for individual cells, and a classifier that can be used to estimate labels for new data with unknown or differing labels. We show that psupertime outperforms benchmark classifiers in terms of identifying time-varying genes and provides better individual cell orderings than popular unsupervised pseudotime techniques. psupertime is applicable to any single-cell RNA-seq dataset with sequential labels (e.g. principally time series but also drug dosage and disease progression), derived from either experimental design and provides a fast, interpretable tool for targeted identification of genes varying along with specific biological processes.Availability and implementationR package available at github.com/wmacnair/psupertime and code for results reproduction at github.com/wmacnair/psupplementary.Supplementary informationSupplementary data are available at Bioinformatics online.
Characterizing domain-specific open educational resources by linking ISCB Communities of Special Interest to Wikipedia
Authors: Kilpatrick Alastair M , Rahman Farzana , Anjum Audra , Shome Sayane , Andalib K M Salim , Banik Shrabonti , Chowdhury Sanjana F , Coombe Peter , Astroz Yesid Cuesta , Douglas J Maxwell , Eranti Pradeep , Kiran Aleyna D , Kumar Sachendra , Lim Hyeri , Lorenzi Valentina , Lubiana Tiago , Mahmud Sakib , Puche Rafael , Rybarczyk Agnieszka , Al Sium Syed Muktadir , Twesigomwe David , Zok Tomasz , Orengo Christine A , Friedberg Iddo , Kelso Janet F , Welch Lonnie 
Published: April 2022

AbstractMotivationWikipedia is one of the most important channels for the public communication of science and is frequently accessed as an educational resource in computational biology. Joint efforts between the International Society for Computational Biology (ISCB) and the Computational Biology taskforce of WikiProject Molecular Biology (a group of expert Wikipedia editors) have considerably improved computational biology representation on Wikipedia in recent years. However, there is still an urgent need for further improvement in quality, especially when compared to related scientific fields such as genetics and medicine. Facilitating involvement of members from ISCB Communities of Special Interest (COSIs) would improve a vital open education resource in computational biology, additionally allowing COSIs to provide a quality educational resource highly specific to their subfield.ResultsWe generate a list of around 1500 English Wikipedia articles relating to computational biology and describe the development of a binary COSI-Article matrix, linking COSIs to relevant articles and thereby defining domain-specific open educational resources. Our analysis of the COSI-Article matrix data provides a quantitative assessment of computational biology representation on Wikipedia against other fields and at a COSI-specific level. Furthermore, we conducted similarity analysis and subsequent clustering of COSI-Article data to provide insight into potential relationships between COSIs. Finally, based on our analysis, we suggest courses of action to improve the quality of computational biology representation on Wikipedia.
CMash: fast, multi-resolution estimation of k-mer-based Jaccard and containment indices
Authors: Liu Shaopeng , Koslicki David 
Published: April 2022

AbstractMotivationK-mer-based methods are used ubiquitously in the field of computational biology. However, determining the optimal value of k for a specific application often remains heuristic. Simply reconstructing a new k-mer set with another k-mer size is computationally expensive, especially in metagenomic analysis where datasets are large. Here, we introduce a hashing-based technique that leverages a kind of bottom-m sketch as well as a k-mer ternary search tree (KTST) to obtain k-mer-based similarity estimates for a range of k values. By truncating k-mers stored in a pre-built KTST with a large k=kmax value, we can simultaneously obtain k-mer-based estimates for all k values up to kmax. This truncation approach circumvents the reconstruction of new k-mer sets when changing k values, making analysis more time and space-efficient.ResultsWe derived the theoretical expression of the bias factor due to truncation. And we showed that the biases are negligible in practice: when using a KTST to estimate the containment index between a RefSeq-based microbial reference database and simulated metagenome data for 10 values of k, the running time was close to 10× faster compared to a classic MinHash approach while using less than one-fifth the space to store the data structure.Availability and implementationA python implementation of this method, CMash, is available at https://github.com/dkoslicki/CMash. The reproduction of all experiments presented herein can be accessed via https://github.com/KoslickiLab/CMASH-reproducibles.Supplementary informationSupplementary data are available at Bioinformatics online.
Fast and interpretable genomic data analysis using multiple approximate kernel learning
Authors: Bektaş Ayyüce Begüm , Ak Çiğdem , Gönen Mehmet 
Published: April 2022

AbstractMotivationDataset sizes in computational biology have been increased drastically with the help of improved data collection tools and increasing size of patient cohorts. Previous kernel-based machine learning algorithms proposed for increased interpretability started to fail with large sample sizes, owing to their lack of scalability. To overcome this problem, we proposed a fast and efficient multiple kernel learning (MKL) algorithm to be particularly used with large-scale data that integrates kernel approximation and group Lasso formulations into a conjoint model. Our method extracts significant and meaningful information from the genomic data while conjointly learning a model for out-of-sample prediction. It is scalable with increasing sample size by approximating instead of calculating distinct kernel matrices.ResultsTo test our computational framework, namely, Multiple Approximate Kernel Learning (MAKL), we demonstrated our experiments on three cancer datasets and showed that MAKL is capable to outperform the baseline algorithm while using only a small fraction of the input features. We also reported selection frequencies of approximated kernel matrices associated with feature subsets (i.e. gene sets/pathways), which helps to see their relevance for the given classification task. Our fast and interpretable MKL algorithm producing sparse solutions is promising for computational biology applications considering its scalability and highly correlated structure of genomic datasets, and it can be used to discover new biomarkers and new therapeutic guidelines.Availability and implementationMAKL is available at https://github.com/begumbektas/makl together with the scripts that replicate the reported experiments. MAKL is also available as an R package at https://cran.r-project.org/web/packages/MAKL.Supplementary informationSupplementary data are available at Bioinformatics online.
The minimizer Jaccard estimator is biased and inconsistent
Authors: Belbasi Mahdi , Blanca Antonio , Harris Robert S , Koslicki David , Medvedev Paul 
Published: April 2022

AbstractMotivationSketching is now widely used in bioinformatics to reduce data size and increase data processing speed. Sketching approaches entice with improved scalability but also carry the danger of decreased accuracy and added bias. In this article, we investigate the minimizer sketch and its use to estimate the Jaccard similarity between two sequences.ResultsWe show that the minimizer Jaccard estimator is biased and inconsistent, which means that the expected difference (i.e. the bias) between the estimator and the true value is not zero, even in the limit as the lengths of the sequences grow. We derive an analytical formula for the bias as a function of how the shared k-mers are laid out along the sequences. We show both theoretically and empirically that there are families of sequences where the bias can be substantial (e.g. the true Jaccard can be more than double the estimate). Finally, we demonstrate that this bias affects the accuracy of the widely used mashmap read mapping tool.Availability and implementationScripts to reproduce our experiments are available at https://github.com/medvedevgroup/minimizer-jaccard-estimator/tree/main/reproduce.Supplementary informationSupplementary data are available at Bioinformatics online.
Sparse and skew hashing of K-mers
Authors: Pibiri Giulio Ermanno 
Published: April 2022

AbstractMotivationA dictionary of k-mers is a data structure that stores a set of n distinct k-mers and supports membership queries. This data structure is at the hearth of many important tasks in computational biology. High-throughput sequencing of DNA can produce very large k-mer sets, in the size of billions of strings—in such cases, the memory consumption and query efficiency of the data structure is a concrete challenge.ResultsTo tackle this problem, we describe a compressed and associative dictionary for k-mers, that is: a data structure where strings are represented in compact form and each of them is associated to a unique integer identifier in the range [0,n). We show that some statistical properties of k-mer minimizers can be exploited by minimal perfect hashing to substantially improve the space/time trade-off of the dictionary compared to the best-known solutions.Availability and implementationhttps://github.com/jermp/sshash.Supplementary informationSupplementary data are available at Bioinformatics online.
Topsy-Turvy: integrating a global view into sequence-based PPI prediction
Authors: Singh Rohit , Devkota Kapil , Sledzieski Samuel , Berger Bonnie , Cowen Lenore 
Published: April 2022

AbstractSummaryComputational methods to predict protein–protein interaction (PPI) typically segregate into sequence-based ‘bottom-up’ methods that infer properties from the characteristics of the individual protein sequences, or global ‘top-down’ methods that infer properties from the pattern of already known PPIs in the species of interest. However, a way to incorporate top-down insights into sequence-based bottom-up PPI prediction methods has been elusive. We thus introduce Topsy-Turvy, a method that newly synthesizes both views in a sequence-based, multi-scale, deep-learning model for PPI prediction. While Topsy-Turvy makes predictions using only sequence data, during the training phase it takes a transfer-learning approach by incorporating patterns from both global and molecular-level views of protein interaction. In a cross-species context, we show it achieves state-of-the-art performance, offering the ability to perform genome-scale, interpretable PPI prediction for non-model organisms with no existing experimental PPI data. In species with available experimental PPI data, we further present a Topsy-Turvy hybrid (TT-Hybrid) model which integrates Topsy-Turvy with a purely network-based model for link prediction that provides information about species-specific network rewiring. TT-Hybrid makes accurate predictions for both well- and sparsely-characterized proteins, outperforming both its constituent components as well as other state-of-the-art PPI prediction methods. Furthermore, running Topsy-Turvy and TT-Hybrid screens is feasible for whole genomes, and thus these methods scale to settings where other methods (e.g. AlphaFold-Multimer) might be infeasible. The generalizability, accuracy and genome-level scalability of Topsy-Turvy and TT-Hybrid unlocks a more comprehensive map of protein interaction and organization in both model and non-model organisms.Availability and implementationhttps://topsyturvy.csail.mit.edu.Supplementary informationSupplementary data are available at Bioinformatics online.
Deep kernel learning improves molecular fingerprint prediction from tandem mass spectra
Authors: Dührkop Kai 
Published: April 2022

AbstractMotivationUntargeted metabolomics experiments rely on spectral libraries for structure annotation, but these libraries are vastly incomplete; in silico methods search in structure databases, allowing us to overcome this limitation. The best-performing in silico methods use machine learning to predict a molecular fingerprint from tandem mass spectra, then use the predicted fingerprint to search in a molecular structure database. Predicted molecular fingerprints are also of great interest for compound class annotation, de novo structure elucidation, and other tasks. So far, kernel support vector machines are the best tool for fingerprint prediction. However, they cannot be trained on all publicly available reference spectra because their training time scales cubically with the number of training data.ResultsWe use the Nyström approximation to transform the kernel into a linear feature map. We evaluate two methods that use this feature map as input: a linear support vector machine and a deep neural network (DNN). For evaluation, we use a cross-validated dataset of 156 017 compounds and three independent datasets with 1734 compounds. We show that the combination of kernel method and DNN outperforms the kernel support vector machine, which is the current gold standard, as well as a DNN on tandem mass spectra on all evaluation datasets.Availability and implementationThe deep kernel learning method for fingerprint prediction is part of the SIRIUS software, available at https://bio.informatik.uni-jena.de/software/sirius.
Self-supervised learning of cell type specificity from immunohistochemical images
Authors: Murphy Michael , Jegelka Stefanie , Fraenkel Ernest 
Published: April 2022

AbstractMotivationAdvances in bioimaging now permit in situ proteomic characterization of cell–cell interactions in complex tissues, with important applications across a spectrum of biological problems from development to disease. These methods depend on selection of antibodies targeting proteins that are expressed specifically in particular cell types. Candidate marker proteins are often identified from single-cell transcriptomic data, with variable rates of success, in part due to divergence between expression levels of proteins and the genes that encode them. In principle, marker identification could be improved by using existing databases of immunohistochemistry for thousands of antibodies in human tissue, such as the Human Protein Atlas. However, these data lack detailed annotations of the types of cells in each image.ResultsWe develop a method to predict cell type specificity of protein markers from unlabeled images. We train a convolutional neural network with a self-supervised objective to generate embeddings of the images. Using non-linear dimensionality reduction, we observe that the model clusters images according to cell types and anatomical regions for which the stained proteins are specific. We then use estimates of cell type specificity derived from an independent single-cell transcriptomics dataset to train an image classifier, without requiring any human labelling of images. Our scheme demonstrates superior classification of known proteomic markers in kidney compared to selection via single-cell transcriptomics.Availability and implementationCode and trained model are available at www.github.com/murphy17/HPA-SimCLR.Supplementary informationSupplementary data are available at Bioinformatics online.
spicyR: spatial analysis ofin situcytometry data in R
Authors: Canete Nicolas P , Iyengar Sourish S , Ormerod John T , Baharlou Heeva , Harman Andrew N , Patrick Ellis 
Published: April 2022

AbstractMotivationHigh parameter histological techniques have allowed for the identification of a variety of distinct cell types within an image, providing a comprehensive overview of the tissue environment. This allows the complex cellular architecture and environment of diseased tissue to be explored. While spatial analysis techniques have revealed how cell–cell interactions are important within the disease pathology, there remains a gap in exploring changes in these interactions within the disease process. Specifically, there are currently few established methods for performing inference on cell-type co-localization changes across images, hindering an understanding of how cellular environments change with a disease pathology.ResultsWe have developed the spicyR R package to perform inference on changes in the spatial co-localization of types across groups of images. Application to simulated data demonstrates a high sensitivity and specificity. We the utility of spicyR by applying it to a type 1 diabetes imaging mass cytometry dataset, revealing changes in cellular associations that were relevant to the disease progression. Ultimately, spicyR allows changes in cellular environments to be explored under different pathologies or disease states.Availability and implementationR package is freely available at http://bioconductor.org/packages/release/bioc/html/spicyR.html and shiny app implementation at http://shiny.maths.usyd.edu.au/spicyR/.Supplementary informationSupplementary data are available at Bioinformatics online.
PltDB: a blood platelets-based gene expression database for disease investigation
Authors: Zou Danyi , Yuan Ye , Xu Luming , Lei Shijun , Li Xingbo , Lu Xiaohuan , Wang Xingyue , Li XiaoQiong , Wang Lin , Wang Zheng 
Published: April 2022

AbstractMotivationMolecular profiling of blood-based liquid biopsies is a promising disease detection method, which overcomes the limitations of invasive diagnostic strategies. Recently, gene expression profiling of platelets reportedly provides valuable resource for developing new biomarkers for the detection of diseases, including cancer. However, there is no database containing RNAs in platelets.ResultsIn this study, we constructed PltDB (http://www.pltdb-hust.com), a blood platelets-based gene expression database featuring integration and visualization of RNA expression profiles based on RNA-seq and microarray data spanning both normal individuals and patients with different diseases. PltDB currently contains the expression landscape of mRNAs, lncRNAs, circRNAs and miRNAs in platelets from patients with different disease types and healthy controls. Moreover, PltDB provides users with the tools for visualizing results of comparison and correlation analysis and for downloading expression profiles and analysis results. A submission interface for the scientific community is also embraced for uploading novel RNA expression profiles derived from platelet samples. PltDB will offer a comprehensive review of the clinical use of platelets, overcome technical problems when analyzing data from diverse studies and serve as a powerful platform for developing new blood biomarkers.Availability and implementationPltDB is accessible at http://www.pltdb-hust.com.Supplementary informationSupplementary data are available at Bioinformatics online.
Freedom in Chains? Religious Freedom Rights in an International Frame: Addressing the Increasing Intolerance and Violence Directed Toward People of Faith
Authors: Hodge David R 
Published: April 2022

Abstract
Violations of religious freedom rights are accelerating across the globe, with levels of religious harassment reaching record levels. Yet, despite social work’s commitment to challenging religious discrimination and promoting human rights, little, if any, attention has focused on this issue in the profession’s literature. In keeping with calls from the United Nations, this article highlights a human rights concern of critical importance to many vulnerable people, namely those who experience acts of intolerance, hate and violence for peacefully practicing the tenets of their faith. Accordingly, the parameters of religious freedom are delineated along with the status of religious freedom rights in the world, in conjunction with the populations that experience particularly extensive discrimination. After discussing the systemic factors that legitimise discrimination against people of faith, strategies are offered that social workers might implement to promote more equitable and socially just societies.
Disclosure of Child Sexual Abuse: Experiences of Men Survivors in India
Authors: Sharma Alankaar 
Published: April 2022

AbstractLittle is known about men and boys’ experiences of child sexual abuse (CSA) in India, particularly about their experiences of disclosing abuse. Disclosure experiences are often important as they can potentially make the path to recovery and healing more tractable or challenging for survivors. Using the Interpretative Phenomenological Analysis approach, in-depth qualitative interviews were conducted with eleven adult men survivors of CSA in India varying in age, location and sexual orientation, to learn about their experiences of disclosing abuse. Barriers to disclosure included guilt, shame, protecting the perpetrator, protecting others from emotional stress and pain, stigma about same-sex sexual activity, and fear of minimisation of abuse experiences. Disclosure was further inhibited by pervasive silence in society about sexuality and sexual abuse. Responses to disclosure were varied and included supportive responses, silence and victim blaming. Findings demonstrate that disclosure experiences of men survivors were strongly influenced by patriarchal and heteronormative norms and practices. Social work has a role to play in building awareness of sexual abuse of boys, helping create a social environment where survivors can feel safe about disclosing abuse, and challenging the oppressive structures of patriarchy and heteronormativity.
(Re-)Imagining Social Work in the Anthropocene
Authors: Panagiotaros Chris Victor , Boddy Jennifer , Gray Tonia , Ife Jim 
Published: April 2022

AbstractThe ecological crisis, marked by the Anthropocene epoch, is having a major impact on the global ecosystem, and the consequences are predicted to become increasingly severe in coming decades. The turbulence and uncertainty of the crisis means social work must begin planning, reflecting and reorientating. The first half of the article contextualises the climate crisis within neoliberal capitalism, whereas the second half proposes alternatives for social work practice that attempt to exist outside these structures. We have argued that social work should have a greater focus on developing an eco-social transition which means engaging with alternative economic systems, intentional communities, community gardens and localism. These approaches can practically espouse the profession’s values whilst beginning to conceptualise a response to the climate crisis that operates outside neoliberal capitalism.