pwc-archive/papers-with-abstracts huggingface dataset:
Partition Quantitative Assessment (PQA): A quantitative methodology to assess the embedded noise in clustered omics and systems biology data
Authors: Diego A. Camacho-Hernández, Victor E. Nieto-Caballero, José E. León-Burguete, Julio A. Freyre-González
Date: 2021-01-08 00:00:00
Tasks: Clustering

Identifying groups that share common features among datasets through clustering analysis is a typical problem in many fields of science, particularly in post-omics and systems biology research. In respect of this, quantifying how a measure can cluster or organize intrinsic groups is important since currently there is no statistical evaluation of how ordered is, or how much noise is embedded in the resulting clustered vector. Many of the literature focuses on how well the clustering algorithm orders the data, with several measures regarding external and internal statistical measures; but none measure has been developed to statistically quantify the noise in an arranged vector posterior a clustering algorithm, i.e., how much of the clustering is due to randomness. Here, we present a quantitative methodology, based on autocorrelation, to assess this problem.
EPIHC: Improving Enhancer-Promoter Interaction Prediction by using Hybrid features and Communicative learning
Authors: Shuai Liu, Xinran Xu, Zhihao Yang, Xiaohan Zhao, Wen Zhang
Date: 2020-12-31 00:00:00

Enhancer-promoter interactions (EPIs) regulate the expression of specific genes in cells, and EPIs are important for understanding gene regulation, cell differentiation and disease mechanisms. EPI identification through the wet experiments is costly and time-consuming, and computational methods are in demand. In this paper, we propose a deep neural network-based method EPIHC based on sequence-derived features and genomic features for the EPI prediction. EPIHC extracts features from enhancer and promoter sequences respectively using convolutional neural networks (CNN), and then design a communicative learning module to captures the communicative information between enhancer and promoter sequences. EPIHC also take the genomic features of enhancers and promoters into account. At last, EPIHC combines sequence-derived features and genomic features to predict EPIs. The computational experiments show that EPIHC outperforms the existing state-of-the-art EPI prediction methods on the benchmark datasets and chromosome-split datasets, and the study reveal that the communicative learning module can bring explicit information about EPIs, which is ignore by CNN. Moreover, we consider two strategies to improve performances of EPIHC in the cross-cell line prediction, and experimental results show that EPIHC constructed on training cell lines exhibit improved performances for the other cell lines.
Co-evolution between Codon Usage and Protein-Protein Interaction in Bacteria
Authors: Maddalena Dilucca, Giulio Cimini, Sergio Forcelloni, Andrea Giansanti
Date: 2020-12-19 00:00:00

We study the correlation between the codon usage bias of genetic sequences and the network features of protein-protein interaction (PPI) in bacterial species. We use PCA techniques in the space of codon bias indices to show that genes with similar patterns of codon usage have a significantly higher probability that their encoded proteins are functionally connected and interacting. Importantly, this signal emerges when multiple aspects of codon bias are taken into account at the same time. The present study extends our previous observations on E.Coli over a wide set of 34 bacteria. These findings could allow for future investigations on the possible effects of codon bias on the topology of the PPI network, with the aim of improving existing bioinformatics methods for predicting protein interactions.
Towards a robust out-of-the-box neural network model for genomic data
Authors: Zhaoyi Zhang, Songyang Cheng, Claudia Solis-Lemus
Date: 2020-12-10 00:00:00
Tasks: Marketing, Prediction

The accurate prediction of biological features from genomic data is paramount for precision medicine and sustainable agriculture. For decades, neural network models have been widely popular in fields like computer vision, astrophysics and targeted marketing given their prediction accuracy and their robust performance under big data settings. Yet neural network models have not made a successful transition into the medical and biological world due to the ubiquitous characteristics of biological data such as modest sample sizes, sparsity, and extreme heterogeneity. Here, we investigate the robustness, generalization potential and prediction accuracy of widely used convolutional neural network and natural language processing models with a variety of heterogeneous genomic datasets. Mainly, recurrent neural network models outperform convolutional neural network models in terms of prediction accuracy, overfitting and transferability across the datasets under study. While the perspective of a robust out-of-the-box neural network model is out of reach, we identify certain model characteristics that translate well across datasets and could serve as a baseline model for translational researchers.
Bacterial protein interaction networks: connectivity is ruled by gene conservation, essentiality and function
Authors: Maddalena Dilucca, Giulio Cimini, Andrea Giansanti
Date: 2017-08-07 00:00:00

Protein-protein interaction (PPI) networks are the backbone of all processes in living cells. In this work we relate conservation, essentiality and functional repertoire of a gene to the connectivity $k$ (i.e., the number of interaction links) of the corresponding protein in the PPI network. On a set of 42 bacterial genomes of different sizes, and with reasonably separated evolutionary trajectories, we investigate three issues: i) whether the distribution of connectivities changes between PPI subnetworks of essential and nonessential genes; ii) how gene conservation, measured both by the evolutionary retention index (ERI) and by evolutionary pressures, is related to the the connectivity of the corresponding protein; iii) how PPI connectivities are modulated by evolutionary and functional relationships, as represented by the Clusters of Orthologous Genes (COGs). We show that conservation, essentiality and functional specialisation of genes constrain the connectivity of the corresponding proteins in bacterial PPI networks. In particular, we isolate a core of highly connected proteins (with connectivities $k\ge40$), which is ubiquitous among the species considered here -- though mostly visible in the degree distributions of bacteria with small genomes (less than 1000 genes). The genes that belong to this highly connected core are conserved, essential and, in most cases, belong to the COG cluster J, related to ribosomal functions and to the processing of genetic information.
The bat coronavirus RmYN02 is characterized by a 6-nucleotide deletion at the S1/S2 junction, and its claimed PAA insertion is highly doubtful
Authors: Yuri Deigin, Rossana Segreto
Date: 2020-12-01 00:00:00

Zhou et al. reported the discovery of RmYN02, a strain closely related to SARS-CoV-2, which is claimed to contain a natural PAA amino acid insertion at the S1/S2 junction of the spike protein at the same position of the PRRA insertion that has created a polybasic furin cleavage site in SARS-CoV-2. The authors support with their findings the theory that the furin cleavage site insertion present in SARS-CoV-2 is natural. Because no nucleotide alignment with closely related strains of the region coding for the supposed insertion is provided by Zhou et al., we have applied several alignment algorithms to search for the most parsimonious alignments. We conclude that RmYN02 does not contain an insertion at the S1/S2 junction when compared to its closest relatives at the nucleotide level, but rather a 6-nucleotide deletion and that the claimed PAA insertion is more likely to be the result of mutations. A close examination of RmYN02 sequencing records and assembly methods is wishful. In conclusion, SARS-CoV-2, with its 12-nucleotide insertion at the S1/S2 junction remains unique among its sarbecovirus relatives.
Large-scale machine learning-based phenotyping significantly improves genomic discovery for optic nerve head morphology
Authors: Babak Alipanahi, Farhad Hormozdiari, Babak Behsaz, Justin Cosentino, Zachary R. McCaw, Emanuel Schorsch, D. Sculley, Elizabeth H. Dorfman, Sonia Phene, Naama Hammel, Andrew Carroll, Anthony P. Khawaja, Cory Y. McLean
Date: 2020-11-25 00:00:00
Tasks: BIG-bench Machine Learning, Diagnostic

Genome-wide association studies (GWAS) require accurate cohort phenotyping, but expert labeling can be costly, time-intensive, and variable. Here we develop a machine learning (ML) model to predict glaucomatous optic nerve head features from color fundus photographs. We used the model to predict vertical cup-to-disc ratio (VCDR), a diagnostic parameter and cardinal endophenotype for glaucoma, in 65,680 Europeans in the UK Biobank (UKB). A GWAS of ML-based VCDR identified 299 independent genome-wide significant (GWS; $P\leq5\times10^{-8}$) hits in 156 loci. The ML-based GWAS replicated 62 of 65 GWS loci from a recent VCDR GWAS in the UKB for which two ophthalmologists manually labeled images for 67,040 Europeans. The ML-based GWAS also identified 92 novel loci, significantly expanding our understanding of the genetic etiologies of glaucoma and VCDR. Pathway analyses support the biological significance of the novel hits to VCDR, with select loci near genes involved in neuronal and synaptic biology or known to cause severe Mendelian ophthalmic disease. Finally, the ML-based GWAS results significantly improve polygenic prediction of VCDR and primary open-angle glaucoma in the independent EPIC-Norfolk cohort.
Tracing the origins of SARS-CoV-2 in coronavirus phylogenies
Authors: Erwan Sallard, José Halloy, Didier Casane, Etienne Decroly, Jacques van Helden
Date: 2020-11-25 00:00:00

SARS-CoV-2 is a new human coronavirus (CoV), which emerged in China in late 2019 and is responsible for the global COVID-19 pandemic that caused more than 59 million infections and 1.4 million deaths in 11 months. Understanding the origin of this virus is an important issue and it is necessary to determine the mechanisms of its dissemination in order to contain future epidemics. Based on phylogenetic inferences, sequence analysis and structure-function relationships of coronavirus proteins, informed by the knowledge currently available on the virus, we discuss the different scenarios evoked to account for the origin - natural or synthetic - of the virus. The data currently available is not sufficient to firmly assert whether SARS-CoV2 results from a zoonotic emergence or from an accidental escape of a laboratory strain. This question needs to be solved because it has important consequences on the evaluation of risk/benefit balance of our interaction with ecosystems, the intensive breeding of wild and domestic animals, as well as some lab practices and on scientific policy and biosafety regulations. Regardless of its origin, studying the evolution of the molecular mechanisms involved in the emergence of pandemic viruses is essential to develop therapeutic and vaccine strategies and to prevent future zoonoses. This article is a translation and update of a French article published in M{\'e}decine/Sciences, Aug/Sept 2020 (http://doi.org/10.1051/medsci/2020123).
Structure learning for zero-inflated counts, with an application to single-cell RNA sequencing data
Authors: Thi Kim Hue Nguyen, Koen Van den Berge, Monica Chiogna, Davide Risso
Date: 2020-11-24 00:00:00

The problem of estimating the structure of a graph from observed data is of growing interest in the context of high-throughput genomic data, and single-cell RNA sequencing in particular. These, however, are challenging applications, since the data consist of high-dimensional counts with high variance and over-abundance of zeros. Here, we present a general framework for learning the structure of a graph from single-cell RNA-seq data, based on the zero-inflated negative binomial distribution. We demonstrate with simulations that our approach is able to retrieve the structure of a graph in a variety of settings and we show the utility of the approach on real data.
A robust and generalizable immune-relatedsignature for sepsis diagnostics
Authors: Yueran Yang, Yu Zhang, Shuai Li, Xubin Zheng, Man-Hon Wong, Kwong-Sak Leung, Lixin Cheng
Date: 2020-11-23 00:00:00
Tasks: Diagnostic

High-throughput sequencing can detect tens of thousands of genes in parallel, providing opportunities for improving the diagnostic accuracy of multiple diseases including sepsis, which is an aggressive inflammatory response to infection that can cause organ failure and death. Early screening of sepsis is essential in clinic, but no effective diagnostic biomarkers are available yet. Here, we present a novel method, Recurrent Logistic Regression, to identify diagnostic biomarkers for sepsis from the blood transcriptome data. A panel including five immune-related genes, LRRN3, IL2RB, FCER1A, TLR5, and S100A12, are determined as diagnostic biomarkers (LIFTS) for sepsis. LIFTS discriminates patients with sepsis from normal controls in high accuracy (AUROC = 0.9959 on average; IC = [0.9722-1.0]) on nine validation cohorts across three independent platforms, which outperforms existing markers. Our analysis determined an accurate prediction model and reproducible transcriptome biomarkers that can lay a foundation for clinical diagnostic tests and biological mechanistic studies.
TMEM240 mutations cause spinocerebellar ataxia 21 with mental retardation and severe cognitive impairment
Authors: Jérôme Delplanque, David Devos, Vincent Huin, Alexandre Genet, Olivier Sand, Caroline Moreau, Cyril Goizet, Perrine Charles, Mathieu Anheim, Marie Lorraine Monin, Luc Buée, Alain Destée, Guillaume Grolez, Christine Delmaire, Kathy Dujardin, Delphine Dellacherie, Alexis Brice, Giovanni Stevanin, Isabelle Strubi-Vuillaume, Alexandra Durr, Bernard Sablonnière
Date: 2020-11-21 00:00:00

Autosomal dominant cerebellar ataxia corresponds to a clinically and genetically heterogeneous group of neurodegenerative disorders that primarily affect the cerebellum. Here, we report the identification of the causative gene in spinocerebellar ataxia 21, an autosomal-dominant disorder previously mapped to chromosome 7p21.3-p15.1. This ataxia was firstly characterized in a large French family with slowly progressive cerebellar ataxia, accompanied by severe cognitive impairment and mental retardation in two young children. Following the recruitment of 12 additional young family members, linkage analysis enabled us to definitively map the disease locus to chromosome 1p36.33-p36.32. The causative mutation, (c.509C4T/p.P170L) in the transmembrane protein gene TMEM240, was identified by whole exome sequencing and then was confirmed by Sanger sequencing and co-segregation analyses. Index cases from 368 French families with autosomal-dominant cerebellar ataxia were also screened for mutations. In seven cases, we identified a range of missense mutations (c.509C4T/p.P170L, c.239C4T/p.T80M, c.346C4T/p.R116C, c.445G4A/p.E149K, c.511C4T/p.R171W), and a stop mutation (c.489C4G/p.Y163*) in the same gene. TMEM240 is a small, strongly conserved transmembrane protein of unknown function present in cerebellum and brain. Spinocerebellar ataxia 21 may be a particular early-onset disease associated with severe cognitive impairment.
Expanding the phenotype of SCA19/22: Parkinsonism, cognitive impairment and epilepsy
Authors: Vincent Huin, Isabelle Strubi-Vuillaume, Kathy Dujardin, Marine Brion, Marie Delliaux, Delphine Dellacherie, Jean-Christophe Cuvellier, Jean-Marie Cuisset, Audrey Riquet, Caroline Moreau, Luc Defebvre, Bernard Sablonniere, David Devos
Date: 2020-11-20 00:00:00

BACKGROUND: Spinocerebellar ataxia types 19 and 22 (SCA19/22) are rare conditions in which relatively isolated cerebellar involvement is frequently associated with cognitive impairment. Here, we report on new clinical features and provide details of the cognitive profile in two SCA19/22 families.METHODS: Two families displaying an autosomal-dominant form of cerebellar ataxia underwent clinical examinations and genetic testing.RESULTS: In addition to the classical clinical features of SCA, a wide spectrum of cognitive disorders (including visuospatial impairments) was observed. Eight patients had mild Parkinsonism, and five had epilepsy. Genetic testing showed that the KCND3 mutation (c.679_681delTTC, p.F227del) was present in both families.CONCLUSIONS: Our findings broaden the phenotypic spectrum of SCA19/22, and suggest that KCND3 should be included in the list of candidate genes for epilepsy, Parkinsonism and cognitive impairment.
The MAPT gene is differentially methylated in the progressive supranuclear palsy brain
Authors: Vincent Huin, Vincent Deramecourt, Dominique Caparros-Lefebvre, Claude-Alain Maurage, Charles Duyckaerts, Eniko Kovari, Florence Pasquier, Valérie Buée-Scherrer, Julien Labreuche, Hélène Behal, Luc Buée, Claire-Marie Dhaenens, Bernard Sablonnière
Date: 2020-11-20 00:00:00

Background: Progressive supranuclear palsy (PSP) is a rare neurodegenerative disease causing parkinsonian symptoms. Altered DNA methylation of the microtubule-associated protein tau gene correlates with the expression changes in Alzheimer's disease and Parkinson's disease brains. However, few studies examine the sequences beyond the constitutive promoter.Objectives: Because activating different microtubule associated protein tau gene control regions via methylation might regulate the differential tau expression constituting the specific signatures of individual tauopathies, we compared methylation of a candidate promoter, intron 0.Methods: We assessed DNA methylation in the brains of patients with different tauopathies (35 Alzheimer's disease, 10 corticobasal degeneration, and 18 PSP) and 19 controls by intron 0 pyrosequencing. We also evaluated methylation in an independent cohort of 11 PSP cases and 12 controls. Frontal (affected by tau pathology) and occipital (unaffected) cortices were analyzed.Results: In the initial samples, one CpG island site in intron 0 (CpG1) showed significant hypomethylation in PSP-affected frontal cortices when compared with controls (p = 0.022). Such hypomethylation was observed in replicate samples, but not in occipital cortices or other tauopathies. PSP and control samples (combining the initial and replicate samples) remained significantly different after adjustment for potential confounding factors (age, H1/H1 diplotype; p = 0.0005). PSP-affected tissues exhibited microtubule-associated protein tau RNA hyperexpression when compared with controls (p = 0.004), although no correlation with CpG1 methylation was observed.Conclusions: This exploratory study suggests that regions other than the constitutive promoter may be involved in microtubule-associated protein tau gene regulation in tauopathies and that intron 0 hypomethylation may be a specific epigenetic signature of PSP. These preliminary findings require confirmation.
Nonequilibrium model of short-range repression in gene transcription regulation
Authors: F. E. Garbuzov, V. V. Gursky
Date: 2020-11-09 00:00:00
Tasks: Blocking

Transcription factors are proteins that regulate gene activity by activating or repressing gene transcription. A special class of transcriptional repressors operates via a short-range mechanism, making local DNA regions inaccessible to binding by activators, and thus providing an indirect repressive action on the target gene. This mechanism is commonly modeled assuming that repressors interact with DNA under thermodynamic equilibrium and neglecting some configurations of the gene regulatory region. We elaborate on a more general nonequilibrium model of short-range repression using the graph formalism for transitions between gene states, and we apply analytical calculations to compare it with the equilibrium model in terms of the repression strength and expression noise. In contrast to the equilibrium approach, the new model allows us to separate two basic mechanisms of short-range repression. The first mechanism is associated with the recruiting of factors that mediate chromatin condensation, and the second one concerns the blocking of factors that mediate chromatin loosening. The nonequilibrium model demonstrates better performance on previously published gene expression data obtained for transcription factors controlling Drosophila development, and furthermore it predicts that the first repression mechanism is the most favorable in this system. The presented approach can be scaled to larger gene networks and can be used to infer specific modes and parameters of transcriptional regulation from gene expression data.
Immune Fingerprinting through Repertoire Similarity
Authors: Thomas Dupic, Meriem Bensouda Koraichi, Anastasia Minervina, Mikhail Pogorelyy, Thierry Mora, Aleksandra M. Walczak
Date: 2020-06-24 00:00:00

Immune repertoires provide a unique fingerprint reflecting the immune history of individuals, with potential applications in precision medicine. However, the question of how personal that information is and how it can be used to identify individuals has not been explored. Here, we show that individuals can be uniquely identified from repertoires of just a few thousands lymphocytes. We present "Immprint," a classifier using an information-theoretic measure of repertoire similarity to distinguish pairs of repertoire samples coming from the same versus different individuals. Using published T-cell receptor repertoires and statistical modeling, we tested its ability to identify individuals with great accuracy, including identical twins, by computing false positive and false negative rates $< 10^{-6}$ from samples composed of 10,000 T-cells. We verified through longitudinal datasets and simulations that the method is robust to acute infections and the passage of time. These results emphasize the private and personal nature of repertoire data.
Genome Compression Against a Reference
Authors: Anirduddha Laud, Gaurav Menghani, Madhava Keralapura
Date: 2020-10-05 00:00:00

Being able to store and transmit human genome sequences is an important part in genomic research and industrial applications. The complete human genome has 3.1 billion base pairs (haploid), and storing the entire genome naively takes about 3 GB, which is infeasible for large scale usage. However, human genomes are highly redundant. Any given individual's genome would differ from another individual's genome by less than 1%. There are tools like DNAZip, which express a given genome sequence by only noting down the differences between the given sequence and a reference genome sequence. This allows losslessly compressing the given genome to ~ 4 MB in size. In this work, we demonstrate additional improvements on top of the DNAZip library, where we show an additional ~ 11% compression on top of DNAZip's already impressive results. This would allow further savings in disk space and network costs for transmitting human genome sequences.