bluuebunny/crossref_metadata_2025_split huggingface dataset:
RoDiCE: robust differential protein co-expression analysis for cancer complexome
Authors: Matsui Yusuke , Abe Yuichi , Uno Kohei , Miyano Satoru 
Published: September 2021

Abstract

Motivation
The full spectrum of abnormalities in cancer-associated protein complexes remains largely unknown. Comparing the co-expression structure of each protein complex between tumor and healthy cells may provide insights regarding cancer-specific protein dysfunction. However, the technical limitations of mass spectrometry-based proteomics, including contamination with biological protein variants, causes noise that leads to non-negligible over- (or under-) estimating co-expression.

Results
We propose a robust algorithm for identifying protein complex aberrations in cancer based on differential protein co-expression testing. Our method based on a copula is sufficient for improving identification accuracy with noisy data compared to conventional linear correlation-based approaches. As an application, we use large-scale proteomic data from renal cancer to show that important protein complexes, regulatory signaling pathways and drug targets can be identified. The proposed approach surpasses traditional linear correlations to provide insights into higher-order differential co-expression structures.

Availability and implementation
https://github.com/ymatts/RoDiCE.

Supplementary information
Supplementary data are available at Bioinformatics online.
PsiNorm: a scalable normalization for single-cell RNA-seq data
Authors: Borella Matteo , Martello Graziano , Risso Davide , Romualdi Chiara 
Published: September 2021

Abstract

Motivation
Single-cell RNA sequencing (scRNA-seq) enables transcriptome-wide gene expression measurements at single-cell resolution providing a comprehensive view of the compositions and dynamics of tissue and organism development. The evolution of scRNA-seq protocols has led to a dramatic increase of cells throughput, exacerbating many of the computational and statistical issues that previously arose for bulk sequencing. In particular, with scRNA-seq data all the analyses steps, including normalization, have become computationally intensive, both in terms of memory usage and computational time. In this perspective, new accurate methods able to scale efficiently are desirable.

Results
Here, we propose PsiNorm, a between-sample normalization method based on the power-law Pareto distribution parameter estimate. Here, we show that the Pareto distribution well resembles scRNA-seq data, especially those coming from platforms that use unique molecular identifiers. Motivated by this result, we implement PsiNorm, a simple and highly scalable normalization method. We benchmark PsiNorm against seven other methods in terms of cluster identification, concordance and computational resources required. We demonstrate that PsiNorm is among the top performing methods showing a good trade-off between accuracy and scalability. Moreover, PsiNorm does not need a reference, a characteristic that makes it useful in supervised classification settings, in which new out-of-sample data need to be normalized.

Availability and implementation
PsiNorm is implemented in the scone Bioconductor package and available at https://bioconductor.org/packages/scone/.

Supplementary information
Supplementary data are available at Bioinformatics online.
Structure-aware protein–protein interaction site prediction using deep graph convolutional network
Authors: Yuan Qianmu , Chen Jianwen , Zhao Huiying , Zhou Yaoqi , Yang Yuedong 
Published: September 2021

Abstract

Motivation
Protein–protein interactions (PPI) play crucial roles in many biological processes, and identifying PPI sites is an important step for mechanistic understanding of diseases and design of novel drugs. Since experimental approaches for PPI site identification are expensive and time-consuming, many computational methods have been developed as screening tools. However, these methods are mostly based on neighbored features in sequence, and thus limited to capture spatial information.

Results
We propose a deep graph-based framework deep Graph convolutional network for Protein–Protein-Interacting Site prediction (GraphPPIS) for PPI site prediction, where the PPI site prediction problem was converted into a graph node classification task and solved by deep learning using the initial residual and identity mapping techniques. We showed that a deeper architecture (up to eight layers) allows significant performance improvement over other sequence-based and structure-based methods by more than 12.5% and 10.5% on AUPRC and MCC, respectively. Further analyses indicated that the predicted interacting sites by GraphPPIS are more spatially clustered and closer to the native ones even when false-positive predictions are made. The results highlight the importance of capturing spatially neighboring residues for interacting site prediction.

Availability and implementation
The datasets, the pre-computed features, and the source codes along with the pre-trained models of GraphPPIS are available at https://github.com/biomed-AI/GraphPPIS. The GraphPPIS web server is freely available at https://biomed.nscc-gz.cn/apps/GraphPPIS.

Supplementary information
Supplementary data are available at Bioinformatics online.
Learning sparse log-ratios for high-throughput sequencing data
Authors: Gordon-Rodriguez Elliott , Quinn Thomas P , Cunningham John P 
Published: September 2021

AbstractMotivationThe automatic discovery of sparse biomarkers that are associated with an outcome of interest is a central goal of bioinformatics. In the context of high-throughput sequencing (HTS) data, and compositional data (CoDa) more generally, an important class of biomarkers are the log-ratios between the input variables. However, identifying predictive log-ratio biomarkers from HTS data is a combinatorial optimization problem, which is computationally challenging. Existing methods are slow to run and scale poorly with the dimension of the input, which has limited their application to low- and moderate-dimensional metagenomic datasets.ResultsBuilding on recent advances from the field of deep learning, we present CoDaCoRe, a novel learning algorithm that identifies sparse, interpretable and predictive log-ratio biomarkers. Our algorithm exploits a continuous relaxation to approximate the underlying combinatorial optimization problem. This relaxation can then be optimized efficiently using the modern ML toolbox, in particular, gradient descent. As a result, CoDaCoRe runs several orders of magnitude faster than competing methods, all while achieving state-of-the-art performance in terms of predictive accuracy and sparsity. We verify the outperformance of CoDaCoRe across a wide range of microbiome, metabolite and microRNA benchmark datasets, as well as a particularly high-dimensional dataset that is outright computationally intractable for existing sparse log-ratio selection methods.Availability and implementationThe CoDaCoRe package is available at https://github.com/egr95/R-codacore. Code and instructions for reproducing our results are available at https://github.com/cunningham-lab/codacore.Supplementary informationSupplementary data are available at Bioinformatics online.
MR-Corr2: a two-sample Mendelian randomization method that accounts for correlated horizontal pleiotropy using correlated instrumental variants
Authors: Cheng Qing , Qiu Tingting , Chai Xiaoran , Sun Baoluo , Xia Yingcun , Shi Xingjie , Liu Jin 
Published: September 2021

AbstractMotivationMendelian randomization (MR) is a valuable tool to examine the causal relationships between health risk factors and outcomes from observational studies. Along with the proliferation of genome-wide association studies, a variety of two-sample MR methods for summary data have been developed to account for horizontal pleiotropy (HP), primarily based on the assumption that the effects of variants on exposure (γ) and HP (α) are independent. In practice, this assumption is too strict and can be easily violated because of the correlated HP.ResultsTo account for this correlated HP, we propose a Bayesian approach, MR-Corr2, that uses the orthogonal projection to reparameterize the bivariate normal distribution for γ and α, and a spike-slab prior to mitigate the impact of correlated HP. We have also developed an efficient algorithm with paralleled Gibbs sampling. To demonstrate the advantages of MR-Corr2 over existing methods, we conducted comprehensive simulation studies to compare for both type-I error control and point estimates in various scenarios. By applying MR-Corr2 to study the relationships between exposure–outcome pairs in complex traits, we did not identify the contradictory causal relationship between HDL-c and CAD. Moreover, the results provide a new perspective of the causal network among complex traits.Availability and implementationThe developed R package and code to reproduce all the results are available at https://github.com/QingCheng0218/MR.Corr2.Supplementary informationSupplementary data are available at Bioinformatics online.
Integrating genome-scale metabolic modelling and transfer learning for human gene regulatory network reconstruction
Authors: Pio Gianvito , Mignone Paolo , Magazzù Giuseppe , Zampieri Guido , Ceci Michelangelo , Angione Claudio 
Published: September 2021

AbstractMotivationGene regulation is responsible for controlling numerous physiological functions and dynamically responding to environmental fluctuations. Reconstructing the human network of gene regulatory interactions is thus paramount to understanding the cell functional organization across cell types, as well as to elucidating pathogenic processes and identifying molecular drug targets. Although significant effort has been devoted towards this direction, existing computational methods mainly rely on gene expression levels, possibly ignoring the information conveyed by mechanistic biochemical knowledge. Moreover, except for a few recent attempts, most of the existing approaches only consider the information of the organism under analysis, without exploiting the information of related model organisms.ResultsWe propose a novel method for the reconstruction of the human gene regulatory network, based on a transfer learning strategy that synergically exploits information from human and mouse, conveyed by gene-related metabolic features generated in silico from gene expression data. Specifically, we learn a predictive model from metabolic activity inferred via tissue-specific metabolic modelling of artificial gene knockouts. Our experiments show that the combination of our transfer learning approach with the constructed metabolic features provides a significant advantage in terms of reconstruction accuracy, as well as additional clues on the contribution of each constructed metabolic feature.Availability and implementationThe method, the datasets and all the results obtained in this study are available at: https://doi.org/10.6084/m9.figshare.c.5237687.Supplementary informationSupplementary data are available at Bioinformatics online.
SplicingFactory—splicing diversity analysis for transcriptome data
Authors: Dankó Benedek , Szikora Péter , Pór Tamás , Szeifert Alexa , Sebestyén Endre 
Published: September 2021

Abstract

Motivation
Alternative splicing contributes to the diversity of RNA found in biological samples. Current tools investigating patterns of alternative splicing check for coordinated changes in the expression or relative ratio of RNA isoforms where specific isoforms are up- or down-regulated in a condition. However, the molecular process of splicing is stochastic and changes in RNA isoform diversity for a gene might arise between samples or conditions. A specific condition can be dominated by a single isoform, while multiple isoforms with similar expression levels can be present in a different condition. These changes might be the result of mutations, drug treatments or differences in the cellular or tissue environment. Here, we present a tool for the characterization and analysis of RNA isoform diversity using isoform level expression measurements.

Results
We developed an R package called SplicingFactory, to calculate various RNA isoform diversity metrics, and compare them across conditions. Using the package, we tested the effect of RNA-seq quantification tools, quantification uncertainty, gene expression levels and isoform numbers on the isoform diversity calculation. We analyzed a set of CD34+ hematopoietic stem cells and myelodysplastic syndrome samples and found a set of genes whose isoform diversity change is associated with SF3B1 mutations.

Availability and implementation
The SplicingFactory package is freely available under the GPL-3.0 license from Bioconductor for the Windows, MacOS and Linux operating systems (https://www.bioconductor.org/packages/release/bioc/html/SplicingFactory.html).

Supplementary information
Supplementary data are available at Bioinformatics online.
VeloViz: RNA velocity-informed embeddings for visualizing cellular trajectories
Authors: Atta Lyla , Sahoo Arpan , Fan Jean 
Published: September 2021

Abstract

Motivation
Single-cell transcriptomics profiling technologies enable genome-wide gene expression measurements in individual cells but can currently only provide a static snapshot of cellular transcriptional states. RNA velocity analysis can help infer cell state changes using such single-cell transcriptomics data. To interpret these cell state changes inferred from RNA velocity analysis as part of underlying cellular trajectories, current approaches rely on visualization with principal components, t-distributed stochastic neighbor embedding and other 2D embeddings derived from the observed single-cell transcriptional states. However, these 2D embeddings can yield different representations of the underlying cellular trajectories, hindering the interpretation of cell state changes.

Results
We developed VeloViz to create RNA velocity-informed 2D and 3D embeddings from single-cell transcriptomics data. Using both real and simulated data, we demonstrate that VeloViz embeddings are able to capture underlying cellular trajectories across diverse trajectory topologies, even when intermediate cell states may be missing. By considering the predicted future transcriptional states from RNA velocity analysis, VeloViz can help visualize a more reliable representation of underlying cellular trajectories.

Availability and implementation
Source code is available on GitHub (https://github.com/JEFworks-Lab/veloviz) and Bioconductor (https://bioconductor.org/packages/veloviz) with additional tutorials at https://JEF.works/veloviz/. Datasets used can be found on Zenodo (https://doi.org/10.5281/zenodo.4632471).

Supplementary information
Supplementary data are available at Bioinformatics online.
Evolutionary drivers of morphological differentiation among three bottlenose dolphin lineages,Tursiopsspp. (Delphinidae), in the northwest Indian Ocean utilizing linear and geometric morphometric techniques
Authors: Gray Howard , van Waerebeek Koen , Owen Joseph , Collins Tim , Minton Gianna , Ponnampalam Louisa , Willson Andrew , Baldwin Robert , Hoelzel A Rus 
Published: September 2021

AbstractLocal adaptation and adaptive radiations are typically associated with phenotypic variation suited to alternative environments. In the marine environment, the nature of relevant ecological or environmental transitions is poorly understood, especially for highly mobile species. Here we compare three genetic lineages in the genus Tursiops (bottlenose dolphins), using linear measurements and geometric morphometric techniques, in the context of environmental variation in the northwest Indian Ocean. Cranial morphology was clearly differentiated comparing Tursiops truncatus and Tursiops aduncus, while a recently discovered genetic lineage, found in the Arabian Sea, was morphologically most similar to T. aduncus from the same region, but distinct for various measures, particularly metrics associated with the lateral dimension of the skull. The extent of divergence between T. truncatus and T. aduncus compared to differences between the T. aduncus lineages is consistent with the recent phylogeny for these species. Therefore, with the corroboration of genetic and morphological inference, we propose two conservation units of T. aduncus be recognized in the region at a sub-specific level so that their conservation can be managed effectively. We consider possible evolutionary mechanisms associated with regional habitat characteristics and the exploitation of distinct prey resources.
Treatment and long-term outcome of breast cancer in very young women: nationwide population-based study
Authors: Chen L -J , Chang Y -J , Chang Y -J 
Published: September 2021

AbstractBackgroundThe study aimed to assess the correlation between long-term survival and treatment in very young women with breast cancer.MethodsData on women with breast cancer were retrieved from the Taiwan Cancer Registry between 2004 and 2014. Patients who did not undergo surgery or who had stage 0 or IV disease were excluded. Survival analysis was conducted. The participants were divided into very young (20–29.9 years), young (30–39.9 years), and adult (40–50.0 years) groups.ResultsAmong 104 115 women, 24 474 (572 very young, 5565 young, and 18 337 adult) were eligible for the study. Median follow-up was 79.5 (range 24–158) months. The mortality rates in the very young, young, and adult groups were 12.9, 10.0, and 8.2 per cent respectively (P &lt; 0.001). Very young patients had higher histological grade, unfavourable subtype, higher TNM stage, and received more breast-conserving surgery (BCS). Kaplan–Meier survival analysis showed that very young patients had the poorest long-term survival. Very young patients with stage II disease had the worst prognosis. In the multivariable regression model, radiotherapy was associated with decreased local recurrence but not with improved overall, cancer-specific, or disease-free survival for stage II disease in the very young group. Surgery type and chemotherapy were not associated with significant improvement in overall survival.ConclusionVery young patients with stage II disease had poor long-term outcomes. BCS had no detrimental effects on long-term outcomes.
Decision making in emergency laparotomy: the role of predicted life expectancy
Authors: Choong J X , McIlveen E , Quasim T , Moug S J 
Published: September 2021

Abstract

Introduction
Increasing numbers of older patients are undergoing emergency laparotomy (EL). They are at increased risk of adverse outcomes, making the shared decision on whether to operate challenging. This retrospective cohort study aimed to assess the role of age and life-expectancy predictions on short- and long-term survival in patients undergoing EL.

Methods
All patients who underwent EL at one hospital in the West of Scotland between March 2014 to December 2016 were included. Clinical parameters were collected, and patients were followed up to allow reporting of 30-, 60- and 90-day and 1-year mortality rates. Period life expectancy was used to stratify patients into below life expectancy (bLEP) and at-or-above life expectancy (aLEP) groups at presentation. Remaining life expectancy was used to calculate the net years of life gained (NYLG).

Results
Some 462 patients underwent EL: 20 per cent in the aLEP group. These patients were older (P &lt; 0.001), had more co-morbidities (P &lt; 0.001) and were high risk on P-POSSUM scoring (P = 0.008). The 30-, 60- and 90-day and 1-year mortality rates were 11, 14, 16 and 23 per cent respectively. Advanced age (P = 0.011) and high ASA score (P = 0.004) and P-POSSUM score (P &lt; 0.001) were independent predictors of death at 1 year on multivariable analysis. The cohort NYLG were 19.2 years. Comparing patients aged less than 70 with those aged 70 years or older, the NYLG were 25.9 versus 5.5 years. Comparing bLEP and aLEP, the NYLG were 22.2 versus 4.4 years. In patients aged 70 years and older, NYLG decreased by more than half in patients with co-morbidities (ASA score 3,4,5) (9.3 versus 4.3 years).

Conclusion
Discussions around long-term outcomes after emergency surgery remain difficult. Although age is an influencing factor, predicted life expectancy alone does not provide additional value to shared decision making.
Organ preservation following short-course radiotherapy for rectal cancer
Authors: Nilsson Per J , Ahlberg Madelene , Kordnejad Shahrzad , Holm Torbjörn , Martling Anna 
Published: September 2021

Abstract

Background
Non-operative management of rectal cancer is increasingly being used in selected patients. Most reports include patients treated with chemoradiotherapy (CRT) before inclusion into a Watch & Wait (W&W) programme. The aim of this study was to report outcomes from a single-centre W&W programme involving a large cohort of patients.

Methods
Patients treated with chemoradiotherapy (CRT) or short-course radiotherapy (SCRT) with or without chemotherapy, between 2008 and 2020, who showed signs of a clinical complete response (cCR) were reviewed. Patients were assessed using digital rectal examination, flexible endoscopy, carcinoembryonic antigen measurement, MRI, and CT imaging, discussed at the multidisciplinary tumour board meeting, and followed up in a dedicated W&W programme as from 2015. Outcomes including regrowth and 3-year survival (time to regrowth or death) were prospectively evaluated.

Results
Of 142 patients who were assessed, 88 fulfilled the criteria for cCR. Treatment before cCR included CRT, SCRT with chemotherapy, and SCRT alone in 16 (18 per cent), 28 (32 per cent), and 44 (50 per cent) patients, respectively. Patients treated with CRT and SCRT with chemotherapy had more advanced clinical T- and N-stage, compared with patients treated with SCRT alone (clinical T-stage &gt; 2: 81 per cent and 89 per cent versus 47 per cent, respectively; clinical N-stage &gt; 0: 75 per cent and 93 per cent versus 68 per cent, respectively). Overall rate of regrowth was 19 per cent, with 31 per cent, 21 per cent, and 14 per cent following CRT, SCRT with chemotherapy, and SCRT alone, respectively. Uni- and multivariable analyses evaluating the clinical parameters revealed no statistically significant associations with risk of local regrowth. All but one patient with regrowth underwent salvage surgery. The 3-year survival rate (death with regrowth as competing risk) was 93 per cent, with no significant difference between treatment groups.

Conclusion
In this cohort of W&W patients, the vast majority received SCRT with or without chemotherapy and results consistent with previous W&W reports were obtained. No statistically significant differences in terms of regrowth rate were obtained when comparing CRT, SCRT with chemotherapy, and SCRT alone. SCRT can induce sustained cCR and may precede a W&W strategy.