bluuebunny/crossref_metadata_2025_split huggingface dataset:
Reprohackathons: promoting reproducibility in bioinformatics through training
Authors: Cokelaer Thomas , Cohen-Boulakia Sarah , Lemoine Frédéric 
Published: June 2023

Abstract

Motivation
The reproducibility crisis has highlighted the importance of improving the way bioinformatics data analyses are implemented, executed, and shared. To address this, various tools such as content versioning systems, workflow management systems, and software environment management systems have been developed. While these tools are becoming more widely used, there is still much work to be done to increase their adoption. The most effective way to ensure reproducibility becomes a standard part of most bioinformatics data analysis projects is to integrate it into the curriculum of bioinformatics Master’s programs.

Results
In this article, we present the Reprohackathon, a Master’s course that we have been running for the last 3 years at Université Paris-Saclay (France), and that has been attended by a total of 123 students. The course is divided into two parts. The first part includes lessons on the challenges related to reproducibility, content versioning systems, container management, and workflow systems. In the second part, students work on a data analysis project for 3–4 months, reanalyzing data from a previously published study. The Reprohackaton has taught us many valuable lessons, such as the fact that implementing reproducible analyses is a complex and challenging task that requires significant effort. However, providing in-depth teaching of the concepts and the tools during a Master’s degree program greatly improves students’ understanding and abilities in this area.
Bakdrive: identifying a minimum set of bacterial species driving interactions across multiple microbial communities
Authors: Wang Qi , Nute Michael , Treangen Todd J 
Published: June 2023

Abstract

Motivation
Interactions among microbes within microbial communities have been shown to play crucial roles in human health. In spite of recent progress, low-level knowledge of bacteria driving microbial interactions within microbiomes remains unknown, limiting our ability to fully decipher and control microbial communities.

Results
We present a novel approach for identifying species driving interactions within microbiomes. Bakdrive infers ecological networks of given metagenomic sequencing samples and identifies minimum sets of driver species (MDS) using control theory. Bakdrive has three key innovations in this space: (i) it leverages inherent information from metagenomic sequencing samples to identify driver species, (ii) it explicitly takes host-specific variation into consideration, and (iii) it does not require a known ecological network. In extensive simulated data, we demonstrate identifying driver species identified from healthy donor samples and introducing them to the disease samples, we can restore the gut microbiome in recurrent Clostridioides difficile (rCDI) infection patients to a healthy state. We also applied Bakdrive to two real datasets, rCDI and Crohn's disease patients, uncovering driver species consistent with previous work. Bakdrive represents a novel approach for capturing microbial interactions.

Availability and implementation
Bakdrive is open-source and available at: https://gitlab.com/treangenlab/bakdrive.
SynBa: improved estimation of drug combination synergies with uncertainty quantification
Authors: Zhang Haoting , Ek Carl Henrik , Rattray Magnus , Milo Marta 
Published: June 2023

Abstract

Motivation
There exists a range of different quantification frameworks to estimate the synergistic effect of drug combinations. The diversity and disagreement in estimates make it challenging to determine which combinations from a large drug screening should be proceeded with. Furthermore, the lack of accurate uncertainty quantification for those estimates precludes the choice of optimal drug combinations based on the most favourable synergistic effect.

Results
In this work, we propose SynBa, a flexible Bayesian approach to estimate the uncertainty of the synergistic efficacy and potency of drug combinations, so that actionable decisions can be derived from the model outputs. The actionability is enabled by incorporating the Hill equation into SynBa, so that the parameters representing the potency and the efficacy can be preserved. Existing knowledge may be conveniently inserted due to the flexibility of the prior, as shown by the empirical Beta prior defined for the normalized maximal inhibition. Through experiments on large combination screenings and comparison against benchmark methods, we show that SynBa provides improved accuracy of dose–response predictions and better-calibrated uncertainty estimation for the parameters and the predictions.

Availability and implementation
The code for SynBa is available at https://github.com/HaotingZhang1/SynBa. The datasets are publicly available (DOI of DREAM: 10.7303/syn4231880; DOI of the NCI-ALMANAC subset: 10.5281/zenodo.4135059).
SpatialSort: a Bayesian model for clustering and cell population annotation of spatial proteomics data
Authors: Lee Eric , Chern Kevin , Nissen Michael , Wang Xuehai ,   IMAXT Consortium, Huang Chris , Gandhi Anita K , Bouchard-Côté Alexandre , Weng Andrew P , Roth Andrew 
Published: June 2023

Abstract

Motivation
Recent advances in spatial proteomics technologies have enabled the profiling of dozens of proteins in thousands of single cells in situ. This has created the opportunity to move beyond quantifying the composition of cell types in tissue, and instead probe the spatial relationships between cells. However, most current methods for clustering data from these assays only consider the expression values of cells and ignore the spatial context. Furthermore, existing approaches do not account for prior information about the expected cell populations in a sample.

Results
To address these shortcomings, we developed SpatialSort, a spatially aware Bayesian clustering approach that allows for the incorporation of prior biological knowledge. Our method is able to account for the affinities of cells of different types to neighbour in space, and by incorporating prior information about expected cell populations, it is able to simultaneously improve clustering accuracy and perform automated annotation of clusters. Using synthetic and real data, we show that by using spatial and prior information SpatialSort improves clustering accuracy. We also demonstrate how SpatialSort can perform label transfer between spatial and nonspatial modalities through the analysis of a real world diffuse large B-cell lymphoma dataset.

Availability and implementation
Source code is available on Github at: https://github.com/Roth-Lab/SpatialSort.
UNADON: transformer-based model to predict genome-wide chromosome spatial position
Authors: Yang Muyu , Ma Jian 
Published: June 2023

Abstract

Motivation
The spatial positioning of chromosomes relative to functional nuclear bodies is intertwined with genome functions such as transcription. However, the sequence patterns and epigenomic features that collectively influence chromatin spatial positioning in a genome-wide manner are not well understood.

Results
Here, we develop a new transformer-based deep learning model called UNADON, which predicts the genome-wide cytological distance to a specific type of nuclear body, as measured by TSA-seq, using both sequence features and epigenomic signals. Evaluations of UNADON in four cell lines (K562, H1, HFFc6, HCT116) show high accuracy in predicting chromatin spatial positioning to nuclear bodies when trained on a single cell line. UNADON also performed well in an unseen cell type. Importantly, we reveal potential sequence and epigenomic factors that affect large-scale chromatin compartmentalization in nuclear bodies. Together, UNADON provides new insights into the principles between sequence features and large-scale chromatin spatial localization, which has important implications for understanding nuclear structure and function.

Availability and implementation
The source code of UNADON can be found at https://github.com/ma-compbio/UNADON.
PPAD: a deep learning architecture to predict progression of Alzheimer’s disease
Authors: Al Olaimat Mohammad , Martinez Jared , Saeed Fahad , Bozdag Serdar ,   Alzheimer’s Disease Neuroimaging Initiative
Published: June 2023

Abstract

Motivation
Alzheimer’s disease (AD) is a neurodegenerative disease that affects millions of people worldwide. Mild cognitive impairment (MCI) is an intermediary stage between cognitively normal state and AD. Not all people who have MCI convert to AD. The diagnosis of AD is made after significant symptoms of dementia such as short-term memory loss are already present. Since AD is currently an irreversible disease, diagnosis at the onset of the disease brings a huge burden on patients, their caregivers, and the healthcare sector. Thus, there is a crucial need to develop methods for the early prediction AD for patients who have MCI. Recurrent neural networks (RNN) have been successfully used to handle electronic health records (EHR) for predicting conversion from MCI to AD. However, RNN ignores irregular time intervals between successive events which occurs common in electronic health record data. In this study, we propose two deep learning architectures based on RNN, namely Predicting Progression of Alzheimer’s Disease (PPAD) and PPAD-Autoencoder. PPAD and PPAD-Autoencoder are designed for early predicting conversion from MCI to AD at the next visit and multiple visits ahead for patients, respectively. To minimize the effect of the irregular time intervals between visits, we propose using age in each visit as an indicator of time change between successive visits.

Results
Our experimental results conducted on Alzheimer’s Disease Neuroimaging Initiative and National Alzheimer’s Coordinating Center datasets showed that our proposed models outperformed all baseline models for most prediction scenarios in terms of F2 and sensitivity. We also observed that the age feature was one of top features and was able to address irregular time interval problem.

Availability and implementation
https://github.com/bozdaglab/PPAD.
RNA design via structure-aware multifrontier ensemble optimization
Authors: Zhou Tianshuo , Dai Ning , Li Sizhen , Ward Max , Mathews David H , Huang Liang 
Published: June 2023

Abstract

Motivation
RNA design is the search for a sequence or set of sequences that will fold to desired structure, also known as the inverse problem of RNA folding. However, the sequences designed by existing algorithms often suffer from low ensemble stability, which worsens for long sequence design. Additionally, for many methods only a small number of sequences satisfying the MFE criterion can be found by each run of design. These drawbacks limit their use cases.

Results
We propose an innovative optimization paradigm, SAMFEO, which optimizes ensemble objectives (equilibrium probability or ensemble defect) by iterative search and yields a very large number of successfully designed RNA sequences as byproducts. We develop a search method which leverages structure level and ensemble level information at different stages of the optimization: initialization, sampling, mutation, and updating. Our work, while being less complicated than others, is the first algorithm that is able to design thousands of RNA sequences for the puzzles from the Eterna100 benchmark. In addition, our algorithm solves the most Eterna100 puzzles among all the general optimization based methods in our study. The only baseline solving more puzzles than our work is dependent on handcrafted heuristics designed for a specific folding model. Surprisingly, our approach shows superiority on designing long sequences for structures adapted from the database of 16S Ribosomal RNAs.

Availability and implementation
Our source code and data used in this article is available at https://github.com/shanry/SAMFEO.
Higher-order genetic interaction discovery with network-based biological priors
Authors: Pellizzoni Paolo , Muzio Giulia , Borgwardt Karsten 
Published: June 2023

Abstract

Motivation
Complex phenotypes, such as many common diseases and morphological traits, are controlled by multiple genetic factors, namely genetic mutations and genes, and are influenced by environmental conditions. Deciphering the genetics underlying such traits requires a systemic approach, where many different genetic factors and their interactions are considered simultaneously. Many association mapping techniques available nowadays follow this reasoning, but have some severe limitations. In particular, they require binary encodings for the genetic markers, forcing the user to decide beforehand whether to use, e.g. a recessive or a dominant encoding. Moreover, most methods cannot include any biological prior or are limited to testing only lower-order interactions among genes for association with the phenotype, potentially missing a large number of marker combinations.

Results
We propose HOGImine, a novel algorithm that expands the class of discoverable genetic meta-markers by considering higher-order interactions of genes and by allowing multiple encodings for the genetic variants. Our experimental evaluation shows that the algorithm has a substantially higher statistical power compared to previous methods, allowing it to discover genetic mutations statistically associated with the phenotype at hand that could not be found before. Our method can exploit prior biological knowledge on gene interactions, such as protein–protein interaction networks, genetic pathways, and protein complexes, to restrict its search space. Since computing higher-order gene interactions poses a high computational burden, we also develop a more efficient search strategy and support computation to make our approach applicable in practice, leading to substantial runtime improvements compared to state-of-the-art methods.

Availability and implementation
Code and data are available at https://github.com/BorgwardtLab/HOGImine
O044 The risk of surgical site infection and other wound complications following surgery to the lymph nodes for staging or treatment of malignancy: a systematic review and meta-analysis of 45,415 cases
Authors: Totty JP , Nzenwa IC , Hardie CM , Iqbal HA , Sidapra M , Matteucci PL , Chetter IC , Smith GE 
Published: June 2023

Abstract

Introduction
For completion of staging of malignant melanoma, sentinel lymph node biopsy (SLNB) may be offered to patients with stage 1B disease or greater, and in some positive cases, completion lymphadenectomy is offered. However, there is little evidence that either procedure are beneficial to patients, and both procedures carry a risk of wound complications. The aim of this study was to assess the current prevalence of wound complications in these procedures and quantify the change over time of this risk.

Methods
This was a subgroup analysis of a larger systematic review (Prospero CRD42021239530). A systematic search was undertaken to identify all published material relating to surgery on the lymph nodes. The primary outcome was the proportion of surgical site infection and/or seroma. Studies relating to malignant melanoma were selected from the overall results, and random effects meta-analysis of proportions and meta-regression were undertaken.

Results
Seventy studies were suitable for inclusion in meta-analysis, constituting 7,585 patients. The proportion of patients developing infection following SLNB was 3.5% (95% CI 2.0–6.0%) and seroma 7.8% (4.0–14.3%). For completion lymphadenectomy, the risk of SSI was 17.4% (13.4– 22.2%) and seroma 24.7% (23.4–26.0%). Neither SSI nor seroma incidence have changed significantly in 30 years.

Conclusion
The risk of complications in these procedures remains high and has not changed in 30 years. Given a potential lack of benefit of these procedures, this data should be used as part of the decision-making process when counselling patients, and to guide future research into prevention of wound complications.

Take-home message
The risk of SSI following block dissection for melanoma is almost 20%, and the risk of seroma almost 25%. This unacceptably high risk has not improved in the last 30 years.
O055 Tenascin-C Expression and distribution following neoadjuvant chemotherapy for breast cancer
Authors: Tai JCJ , Ngai V , El Sheikh S , Ahmed M 
Published: June 2023

Abstract

Introduction
Tenascin-C (TNC), a matricellular glycoprotein linked to breast cancer invasion and metastasis, has never been studied exclusively in post-neoadjuvant chemotherapy (NACT) patients. This retrospective cohort analysis investigates whether TNC is expressed post-NACT and establishes potential links with patients’ clinicopathological characteristics.

Methods
Twenty-four invasive breast cancer patients who had received NACT and surgery were recruited from a single centre. The distribution and intensity of TNC expression in paraffin embedded breast specimens were analysed using a pre-determined scoring system. These scores were then correlated with the patients’ clinicopathological characteristics and ‘PREDICT’ scores.

Results
TNC was well expressed by the post-NACT cancer tissue, albeit more strongly in the tumour-associated stroma than in the tumour cells. More pronounced expression overall was seen in PR-positive patients, but greater TNC expression in the tumour invasive border was linked to ER-negativity, TNBC, and lack of lymph node involvement. A greater TNC expression in the central region of the tumour was linked to a better pathologic response to NACT. The degree of TNC expression was not statistically different between areas of response to NACT and areas with residual tumour. Patients with better post-surgical ‘PREDICT 10’ scores had greater TNC expression in the tumour.

Conclusion
This study shows that TNC remains well-expressed in the stroma of post-NACT breast cancer tissue, and may be linked to tumour phenotype, pathologic response to NACT, and prognosis. There are many potential clinical applications, including using TNC as a: (1) therapeutic target, (2) predictor of responsiveness to NACT and/or adjuvant therapies, (3) prognostic marker.

Take-home message
Tenascin-C is well-expressed in post-NACT breast cancer tissue obtained at the point of margin resection, and the degree and distribution of its expression is linked to tumour phenotype, pathologic response to NACT, and prognosis. This has potentially impactful implications for its use as a therapeutic target, predictor of responsiveness to chemotherapy, and prognostic marker.
Selective pressure on a saccharin intake phenotype and its correlates: a replication study
Authors: Dess Nancy K , Chapman Clinton D , Jacobi Paulina M 
Published: June 2023

Abstract
The Occidental High- and Low-Saccharin rats (respectively, HiS and LoS lines) were selectively bred for decades to examine mechanisms and correlates of a saccharin intake phenotype. Observed line differences ranged from taste and eating to drug self-administration and defensive behavior, paralleling human research on relationships between gustation, personality, and psychopathology. The original lines were terminated in 2019, and replicate lines (HiS-R and LoS-R) were selectively bred for 5 generations to test for reproducible, rapid selection for the phenotype and its correlates. The line differences chosen for replication included intake of tastants (saccharin, sugars, quinine-adulterated sucrose, sodium chloride, and ethanol) and foods (cheese, peas, Spam, and chocolate) and several noningestive behaviors (deprivation-induced hyperactivity, acoustic startle, and open field behavior). The HiS-R and LoS-R lines diverged on intake of saccharin, disaccharides, quinine-adulterated sucrose, sodium chloride, and complex foods, and open field behavior. Differences from the original lines also were observed. Reasons for and implications of the pattern of replication and lack thereof in 5 generations are discussed.
Low birthweight is associated with epigenetic age acceleration in the first 3 years of life
Authors: Quinn Edward B , Hsiao Chu J , Maisha Felicien M , Mulligan Connie J 
Published: June 2023

AbstractBackground and objectivesThe Developmental Origins of Health and Disease hypothesis posits that early life adversity is associated with poor adult health outcomes. Epidemiological evidence has supported this framework by linking low birthweight with adult health and mortality, but the mechanisms remain unclear. Accelerated epigenetic aging may be a pathway to connect early life experiences with adult health outcomes, based on associations of accelerated epigenetic aging with increased morbidity and mortality.MethodologySixty-seven mother-infant dyads were recruited in the eastern Democratic Republic of Congo. Birthweight data were collected at birth, and blood samples were collected at birth and follow-up visits up to age 3. DNA methylation data were generated with the Illumina MethylationEPIC array and used to estimate epigenetic age. A multilevel model was used to test for associations between birthweight and epigenetic age acceleration.ResultsChronological age was highly correlated with epigenetic age from birth to age 3 (r = 0.95, p &lt; 2.2 × 10−16). Variation in epigenetic age acceleration increased over time. Birthweight, dichotomized around 2500 g, predicted epigenetic age acceleration over the first 3 years of life (b = −0.39, p = 0.005).Conclusions and implicationsOur longitudinal analysis provides the first evidence for accelerated epigenetic aging that emerges between birth and age 3 and associates with low birthweight. These results suggest that early life experiences, such as low birthweight, may shape the trajectory of epigenetic aging in early childhood. Furthermore, accelerated epigenetic aging may be a pathway that links low birthweight and poor adult health outcomes.
Genome Comparison Reveals Inversions and Alternative Evolutionary History of Nutritional Endosymbionts in Planthoppers (Hemiptera: Fulgoromorpha)
Authors: Deng Junchen , Bennett Gordon M , Franco Diego C , Prus-Frankowska Monika , Stroiński Adam , Michalik Anna , Łukasik Piotr 
Published: June 2023

Abstract
The evolutionary success of sap-feeding hemipteran insects in the suborder Auchenorrhyncha was enabled by nutritional contributions from their heritable endosymbiotic bacteria. However, the symbiont diversity, functions, and evolutionary origins in this large insect group have not been broadly characterized using genomic tools. In particular, the origins and relationships among ancient betaproteobacterial symbionts Vidania (in Fulgoromorpha) and Nasuia/Zinderia (in Cicadomorpha) are uncertain. Here, we characterized the genomes of Vidania and Sulcia from three Pyrops planthoppers (family Fulgoridae) to understand their metabolic functions and evolutionary histories. We find that, like in previously characterized planthoppers, these symbionts share nutritional responsibilities, with Vidania providing seven out of ten essential amino acids. Sulcia lineages across the Auchenorrhyncha have a highly conserved genome but with multiple independent rearrangements occurring in an early ancestor of Cicadomorpha or Fulgoromorpha and in a few succeeding lineages. Genomic synteny was also observed within each of the betaproteobacterial symbiont genera Nasuia, Zinderia, and Vidania, but not across them, which challenges the expectation of a shared ancestry for these symbionts. The further comparison of other biological traits strongly suggests an independent origin of Vidania early in the planthopper evolution and possibly of Nasuia and Zinderia in their respective host lineages. This hypothesis further links the potential acquisition of novel nutritional endosymbiont lineages with the emergence of auchenorrhynchan superfamilies.