pwc-archive/papers-with-abstracts huggingface dataset:
Reconciling Multiple Connectivity Scores for Drug Repurposing
Authors: Kewalin Samart, Phoebe Tuyishime, Arjun Krishnan, Janani Ravi
Date: 2020-09-19 00:00:00

The basis of several recent methods for drug repurposing is the key principle that an efficacious drug will reverse the disease molecular 'signature' with minimal side-effects. This principle was defined and popularized by the influential 'connectivity map' study in 2006 regarding reversal relationships between disease- and drug-induced gene expression profiles, quantified by a disease-drug 'connectivity score.' Over the past 15 years, several studies have proposed variations in calculating connectivity scores towards improving accuracy and robustness in light of massive growth in reference drug profiles. However, these variations have been formulated inconsistently using various notations and terminologies even though they are based on a common set of conceptual and statistical ideas. Therefore, we present a systematic reconciliation of multiple disease-drug similarity metrics (ES, css, Sum, Cosine, XSum, XCor, XSpe, XCos, EWCos) and connectivity scores (CS, RGES, NCS, WCS, Tau, CSS, EMUDRA) by defining them using consistent notation and terminology. In addition to providing clarity and deeper insights, this coherent definition of connectivity scores and their relationships provides a unified scheme that newer methods can adopt, enabling the computational drug-development community to compare and investigate different approaches easily. To facilitate the continuous and transparent integration of newer methods, this article will be available as a live document (https://jravilab.github.io/connectivity_scores) coupled with a GitHub repository (https://github.com/jravilab/connectivity_scores) that any researcher can build on and push changes to.
The unmasking of Mitochondrial Adam and Structural Variants larger than point mutations as stronger candidates for traits, disease phenotype and sex determination
Authors: Abhishek Narain Singh
Date: 2021-02-24 00:00:00

Background: Structural Variations, SVs, in a genome can be linked to a disease or characteristic phenotype. The variations come in many types and it is a challenge, not only determining the variations accurately, but also conducting the downstream statistical and analytical procedure. Method: Structural variations, SVs, with size 1 base-pair to 1000s of base-pairs with their precise breakpoints and single-nucleotide polymorphisms, SNPs, were determined for members of a family. The genome was assembled using optimal metrics of ABySS and SOAPdenovo assembly tools using paired-end DNA sequence. Results: An interesting discovery was the mitochondrial DNA could have paternal leakage of inheritance or that the mutations could be high from maternal inheritance. It is also discovered that the mitochondrial DNA is less prone to SVs re-arrangements than SNPs, which propose better standards for determining ancestry and divergence between races and species over a long-time frame. Sex determination of an individual is found to be strongly confirmed using calls of nucleotide bases of SVs to the Y chromosome, more strongly determined than SNPs. We note that in general there is a larger variance -and thus the standard deviation, in the sum of SVs nucleotide compared to sum of SNPs of an individual when compared to reference sequence, and thus SVs serve as a stronger means to characterize an individual for a given trait or phenotype or to determine sex. The SVs and SNPs in HLA loci would also serve as a medical transformation method for determining the success of an organ transplant for a patient, and predisposition to diseases apriori.
Feature set optimization by clustering, univariate association, Deep & Machine learning omics Wide Association Study (DMWAS) for Biomarkers discovery as tested on GTEx pilot dataset for death due to heart attack
Authors: Abhishek Narain Singh
Date: 2021-02-24 00:00:00
Tasks: Clustering

Univariate and multivariate methods for association of the genom-ic variations with the end-or-endo phenotype have been widely used for genome wide association studies. In addition to encoding the SNPs, we advocate usage of clustering as a novel method to encode the structural variations, SVs, in genomes, such as the deletions and insertions polymorphism (DIPs), Copy Number Variations (CNVs), translocation, inversion, etc., that can be used as an independent fea-ture variable value for downstream computation by artificial intelli-gence methods to predict the endo-or-end phenotype. We introduce a clustering based encoding scheme for structural variations and om-ics based analysis. We conducted a complete all genomic variants association with the phenotype using deep learning and other ma-chine learning techniques, though other methods such as genetic al-gorithm can also be applied. Applying this encoding of SVs and one-hot encoding of SNPs on GTEx V7 pilot DNA variation dataset, we were able to get high accuracy using various methods of DMWAS, and particularly found logistic regression to work the best for death due to heart-attack (MHHRTATT) phenotype. The genom-ic variants acting as feature sets were then arranged in descending order of power of impact on the disease or trait phenotype, which we call optimization and that also uses top univariate association into account. Variant Id P1_M_061510_3_402_P at chromosome 3 & position 192063195 was found to be most highly associated to MHHRTATT. We present here the top ten optimized genomic va-riant feature set for the MHHRTATT phenotypic cause of death.
Polygenic Risk Score in Africa Population: Progress and challenges
Authors: Yagoub Adam, Suraju Sadeeq, Judit Kumuthini, Olabode Ajayi, Gordon Wells, Rotimi Solomon, Olubanke Ogunlana, Emmmanuel Adetiba, Emeka Iweala, Benedikt Brors, Ezekiel Adebiyi
Date: 2021-02-16 00:00:00

Polygenic risk score (PRS) analysis is a powerful method been used to estimate an individual's genetic risk towards targeted traits. PRS analysis could be used to obtain evidence of a genetic effect beyond Genome-Wide Association Studies (GWAS) results i.e. when there are no significant markers. PRS analysis has been widely applied to investigate the genetic basis of several traits including rare diseases. However, the accuracy of PRS analysis depends on the genomic data of the underlying population. For instance, several studies showed that obtaining higher prediction power of PRS analysis is challenging for non-Europeans. In this manuscript, we reviewed the conventional PRS methods and their application to sub-saharan Africa communities. We concluded that the limiting factor of applying PRS analysis to sub-saharan populations is the lack of sufficient GWAS data. Also, we recommended developing African-specific PRS tools
Modern tools for annotation of small genomes of non-model eukaryotes
Authors: Marina Galchenkova, Aleksei Korzhenkov
Date: 2021-02-08 00:00:00

Nowadays, due to the increasing amount of experimental data obtained by sequencing, the most interest is focused on determining the functions and characteristics of its individual parts of the genome instead of determining the nucleotide sequence of the genome. The genome annotation includes the identification of coding and non-coding sequences, determining the structure of the gene and determining the functions of these sequences. Despite the significant achievements in computational technologies working with sequencing data, there is no general approach to the functional annotation of the genome in the reason of the large number of unresolved molecular determination of the function of some genomes parts. Nevertheless, the scientific community is trying to solve this problem. This review analyzed existing approaches to eukaryotic genome annotation. This work includes 3 main parts: introduction, main body and discussion. The introduction reflects the development of independent tools and automatic pipelines for annotation of eukaryotic genomes, which are associated with existing achievements in annotating prokaryotic ones. The main body consists of two distinguished parts, the first one is devoted to instructions for annotating genomes of non-model eukaryotes, and the second block is about recent versions of automatic pipelines that require minimal user's curation. The question of assessing the quality and completeness of the annotated genome is noted briefly, and the tools to conduct this analysis are discussed. Currently, there is no universal automatic software for eukaryotic genome annotation, covering the whole list of tasks, without manual curation or using additional external tools and resources. Thus it leads to the task of developing a wider functional and universal protocol for automatic annotation of small eukaryotic genomes.
Performance Evaluation of Transcriptomics Data Normalization for Survival Risk Prediction
Authors: Ai Ni, Li-Xuan Qin
Date: 2021-02-08 00:00:00
Tasks: Benchmarking, Prediction, Survival Prediction

One pivotal feature of transcriptomics data is the unwanted variations caused by disparate experimental handling, known as handling effects. Various data normalization methods were developed to alleviate the adverse impact of handling effects in the setting of differential expression analysis. However, little research has been done to evaluate their performance in the setting of survival outcome prediction, an important analysis goal for transcriptomics data in biomedical research. Leveraging a unique pair of datasets for the same set of tumor samples-one with handling effects and the other without, we developed a benchmarking tool for conducting such an evaluation in microRNA microarrays. We applied this tool to evaluate the performance of three popular normalization methods-quantile normalization, median normalization, and variance stabilizing normalization-in survival prediction using various approaches for model building and designs for sample assignment. We showed that handling effects can have a strong impact on survival prediction, and that quantile normalization, a most popular method in current practice, tends to underperform median normalization and variance stabilizing normalization. We demonstrated with a small example the reason for quantile normalization's poor performance in this setting. Our finding highlights the importance of putting normalization evaluation in the context of the downstream analysis setting and the potential of improving the development of survival predictors by applying median normalization. We make available our benchmarking tool for performing such evaluation on additional normalization methods in connection with prediction modeling approaches.
Efficient construction of the extended BWT from grammar-compressed DNA sequencing reads
Authors: Diego Diaz-Dominguez annd Gonzalo Navarro
Date: 2021-02-08 00:00:00

We present an algorithm for building the extended BWT (eBWT) of a string collection from its grammar-compressed representation. Our technique exploits the string repetitions captured by the grammar to boost the computation of the eBWT. Thus, the more repetitive the collection is, the lower are the resources we use per input symbol. We rely on a new grammar recently proposed at DCC'21 whose nonterminals serve as building blocks for inducing the eBWT. A relevant application for this idea is the construction of self-indexes for analyzing sequencing reads -- massive and repetitive string collections of raw genomic data. Self-indexes have become increasingly popular in Bioinformatics as they can encode more information in less space. Our efficient eBWT construction opens the door to perform accurate bioinformatic analyses on more massive sequence datasets, which are not tractable with current eBWT construction techniques.
NoisET: Noise learning and Expansion detection of T-cell receptors
Authors: Meriem Bensouda Koraichi, Maximilian Puelma Touzel, Andrea Mazzolini, Thierry Mora, Aleksandra M. Walczak
Date: 2021-02-06 00:00:00

High-throughput sequencing of T- and B-cell receptors makes it possible to track immune repertoires across time, in different tissues, in acute and chronic diseases and in healthy individuals. However quantitative comparison between repertoires is confounded by variability in the read count of each receptor clonotype due to sampling, library preparation, and expression noise. We review methods for accounting for both biological and experimental noise and present an easy-to-use python package NoisET that implements and generalizes a previously developed Bayesian method. It can be used to learn experimental noise models for repertoire sequencing from replicates, and to detect responding clones following a stimulus. We test the package on different repertoire sequencing technologies and datasets. We review how such approaches have been used to identify responding clonotypes in vaccination and disease data. Availability: NoisET is freely available to use with source code at github.com/statbiophys/NoisET.
Polysomally Protected Viruses
Authors: Michael Wilkinson, David Yllanes, Greg Huber
Date: 2021-01-30 00:00:00
Tasks: Vocal Bursts Type Prediction

It is conceivable that an RNA virus could use a polysome, that is, a string of ribosomes covering the RNA strand, to protect the genetic material from degradation inside a host cell. This paper discusses how such a virus might operate, and how its presence might be detected by ribosome profiling. There are two possible forms for such a polysomally protected virus, depending upon whether just the forward strand or both the forward and complementary strands can be encased by ribosomes (these will be termed type 1 and type 2, respectively). It is argued that in the type 2 case the viral RNA would evolve an ambigrammatic property, whereby the viral genes are free of stop codons in a reverse reading frame (with forward and reverse codons aligned). Recent observations of ribosome profiles of ambigrammatic narnavirus sequences are consistent with our predictions for the type 2 case.
Protocol for Executing and Benchmarking Eight Computational Doublet-Detection Methods in Single-Cell RNA Sequencing Data Analysis
Authors: Nan Miles Xi, Jingyi Jessica Li
Date: 2021-01-21 00:00:00
Tasks: Benchmarking

The existence of doublets is a key confounder in single-cell RNA sequencing (scRNA-seq) data analysis. Computational methods have been developed for detecting doublets from scRNA-seq data. We developed an R package DoubletCollection to integrate the installation and execution of eight doublet-detection methods. DoubletCollection also provides a unified interface to perform and visualize downstream analysis after doublet detection. Here, we present a protocol of using DoubletCollection to benchmark doublet-detection methods. This protocol can automatically accommodate new doublet-detection methods in the fast-growing scRNA-seq field.
Reply: Early-onset phenotype of bi-allelic GRN mutations
Authors: Vincent Huin, Mathieu Barbier, Alexandra Durr, Isabelle Le Ber
Date: 2021-01-20 00:00:00

We would like to reply to Neuray et al. who report a series of five new patients from four unrelated families with bi-allelic mutations of GRN. Their work nicely completes the few existing reports of similar cases, and refers to our recent publication describing six homozygous GRN pathogenic variant carriers with divergent phenotypes and ages at onset (Huin et al., 2020). In summary, the Letter from Neuray et al., reports valuable findings that lead to better define CLN11 due to bi-allelic GRN pathogenic variants. Despite the small sample number that does not allow statistical analysis, the authors underlined the occurrence of cognitive deterioration and epilepsy. Further study of the CLN11 families with functional brain imaging and neuropsychological examinations may be highly informative for the understanding and the clinical characterization of this rare disease.
Update on the genetic and epigenetic etiology of gestational diabetes mellitus: a review
Authors: Tajudeen Yahaya, Titilola Salisu, Yusuf Abdulrahman, Abdulrazak Umar
Date: 2021-01-18 00:00:00
Tasks: Management

Background: Many studies have been conducted on the genetic and epigenetic etiology of gestational diabetes mellitus (GDM) in the last two decades because of the diseases increasing prevalence and role in the global diabetes mellitus (DM) explosion. An update on the genetic and epigenetic etiology of GDM then becomes imperative to better understand and stem the rising incidence of the disease. This review, therefore, articulated GDM candidate genes and their pathophysiology for the awareness of stakeholders. Main body (genetic and epigenetic etiology, GDM): The search discovered 83 GDM candidate genes, of which TCF7L2, MTNR1B, CDKAL1, IRS1, and KCNQ1 are the most prevalent. Certain polymorphisms of these genes can modulate beta-cell dysfunction, adiposity, obesity, and insulin resistance through several mechanisms. Environmental triggers such as diets, pollutants, and microbes may also cause epigenetic changes in these genes, resulting in a loss of insulin-boosting and glucose metabolism functions. Early detection and adequate management may resolve the condition after delivery; otherwise, it will progress to maternal type 2 diabetes mellitus (T2DM) and fetal configuration to future obesity and DM. This shows that GDM is a strong risk factor for T2DM and, in rare cases, type 1 diabetes mellitus (T1DM) and maturity-onset diabetes of the young (MODY). This further shows that GDM significantly contributes to the rising incidence and burden of DM worldwide and its prevention may reverse the trend. Conclusion: Mutations and epigenetic changes in certain genes are strong risk factors for GDM. For affected individuals with such etiologies, medical practitioners should formulate drugs and treatment procedures that target these genes and their pathophysiology.
Genetics and Pathophysiology of Maturity-onset Diabetes of the Young (MODY): A Review of Current Trends
Authors: Tajudeen O. Yahaya, Shemishere B. Ufuoma
Date: 2021-01-17 00:00:00

Single gene mutations have been implicated in the pathogenesis of a form of diabetes mellitus (DM) known as the maturity-onset diabetes of the young (MODY). However, there are diverse opinions on the suspect genes and pathophysiology, necessitating the need to review and communicate the genes to raise public awareness. We used the Google search engine to retrieve relevant information from reputable sources such as PubMed and Google Scholar. We identified 14 classified MODY genes as well as three new and unclassified genes linked with MODY. These genes are fundamentally embedded in the beta cells, the most common of which are HNF1A, HNF4A, HNF1B, and GCK genes. Mutations in these genes cause beta-cell dysfunction, resulting in decreased insulin production and hyperglycemia. MODY genes have distinct mechanisms of action and phenotypic presentations compared with type 1 and type 2 DM and other forms of DM. Healthcare professionals are therefore advised to formulate drugs and treatment based on the causal genes rather than the current generalized treatment for all types of DM. This will increase the effectiveness of diabetes drugs and treatment and reduce the burden of the disease.
Genes predisposing to type 1 diabetes mellitus and pathophysiology: a narrative review
Authors: Tajudeen Yahaya, Titilola Salisu
Date: 2021-01-17 00:00:00

The possibility of targeting the causal genes along with the mechanisms of pathogenically complex diseases has led to numerous studies on the genetic etiology of some diseases. In particular, studies have added more genes to the list of type 1 diabetes mellitus (T1DM) suspect genes, necessitating an update for the interest of all stakeholders. Therefore this review articulates T1DM suspect genes and their pathophysiology. Notable electronic databases, including Medline, Scopus, PubMed, and Google-Scholar were searched for relevant information. The search identified over 73 genes suspected in the pathogenesis of T1DM, with human leukocyte antigen, insulin gene, and cytotoxic T lymphocyte-associated antigen 4 accounting for most of the cases. Mutations in these genes, along with environmental factors, may produce a defective immune response in the pancreas, resulting in \b{eta}-cell autoimmunity, insulin deficiency, and hyperglycemia. The mechanisms leading to these cellular reactions are gene-specific and, if targeted in diabetic individuals, may lead to improved treatment. Medical practitioners are advised to formulate treatment procedures that target these genes in patients with T1DM.
From Genotype to Phenotype: polygenic prediction of complex human traits
Authors: Timothy G. Raben, Louis Lello, Erik Widen, Stephen D. H. Hsu
Date: 2021-01-14 00:00:00

Decoding the genome confers the capability to predict characteristics of the organism(phenotype) from DNA (genotype). We describe the present status and future prospects of genomic prediction of complex traits in humans. Some highly heritable complex phenotypes such as height and other quantitative traits can already be predicted with reasonable accuracy from DNA alone. For many diseases, including important common conditions such as coronary artery disease, breast cancer, type I and II diabetes, individuals with outlier polygenic scores (e.g., top few percent) have been shown to have 5 or even 10 times higher risk than average. Several psychiatric conditions such as schizophrenia and autism also fall into this category. We discuss related topics such as the genetic architecture of complex traits, sibling validation of polygenic scores, and applications to adult health, in vitro fertilization (embryo selection), and genetic engineering.
Genes predisposing to syndromic and nonsyndromic infertility: a narrative review
Authors: Tajudeen O. Yahaya, Usman U. Liman, Haliru Abdullahi, Yahuza S. Koko, Samuel S. Ribah, Zulkarnain Adamu, Suleiman Abubakar
Date: 2021-01-13 00:00:00
Tasks: Management

Background: Advanced biological techniques have helped produce more insightful findings on the genetic etiology of infertility that may lead to better management of the condition. This review provides an update on genes predisposing to syndromic and nonsyndromic infertility. Main body: The review identified 65 genes linked with infertility and infertility-related disorders. These genes regulate fertility. However, mutational loss of the functions of the genes predisposes to infertility. Twenty-three (23) genes representing 35% were linked with syndromic infertility, while 42 genes (65%) cause nonsyndromic infertility. Of the 42 nonsyndromic genes, 26 predispose to spermatogenic failure and sperm morphological abnormalities, 11 cause ovarian failures, and 5 cause sex reversal and puberty delay. Overall, 31 genes (48%) predispose to male infertility, 15 genes (23%) cause female infertility, and 19 genes (29%) predispose to both. The common feature of male infertility was spermatogenic failure and sperm morphology abnormalities, while ovarian failure has been the most frequently reported among infertile females. The mechanisms leading to these pathologies are gene-specific, which, if targeted in the affected, may lead to improved treatment. Conclusions: Mutational loss of the functions of some genes involved in the development and maintenance of fertility may predispose to syndromic or nonsyndromic infertility via gene-specific mechanisms. A treatment procedure that targets the affected gene(s) in individuals expressing infertility may lead to improved treatment.