Biology subjects

Sarkar, I. N.

Publications and source records attributed to Sarkar, I. N..

4 recordsLinked to original sources

bioRxiv2024

Augmenting Bacterial Similarity Measures Using a Graph-Based Genome Representation

Relationships between bacterial taxa are traditionally defined using 16S rRNA nucleotide similarity or average nucleotide identity. Improvements in sequencing technology provides additional pairwise information on genome sequences, which may provide valuable information on genomic relationships. Mapping orthologous gene locations between genome pairs, known as synteny, is typically implemented in the discovery of new species and has not been systematically applied to bacterial genomes. Using a dataset of 378 bacterial genomes, we developed and tested a new measure of synteny similarity between a pair of genomes, which was scaled onto 16S rRNA distance using covariance matrices. Based on the input gene functions used (i.e., core, antibiotic resistance, and virulence), we observed varying topological arrangements of bacterial relationship networks by applying (1) complete linkage hierarchical clustering and (2) KNN graph structures to syntenic-scaled 16S data. Our metric improved clustering quality comparatively to state-of-the-art ANI metrics while preserving clustering assignments for the highest similarity relationships. Our findings indicate that syntenic relationships provide more granular and interpretable relationships for within-genera taxa compared to pairwise similarity measures, particularly in functional contexts. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=82 SRC="FIGDIR/small/588602v1_ufig1.gif" ALT="Figure 1"> View larger version (25K): org.highwire.dtl.DTLVardef@1bf81edorg.highwire.dtl.DTLVardef@1eaf18dorg.highwire.dtl.DTLVardef@198912dorg.highwire.dtl.DTLVardef@1633ee5_HPS_FORMAT_FIGEXP M_FIG GRAPHICAL ABSTRACT.This method uses a novel measure based on shared synteny between bacterial genomes to scale 16S rRNA distance values. Created with BioRender.com. C_FIG

microbiology↗

bioRxiv2021

Diet and Domestication Drive Evolution of the Gut Holobiome

The host microbiome encompasses all microorganisms of a host. Host and microbiome coevolution in the gut result in differing microbial compositions, functionality, and host diet [1]. Host diet modulates what macromolecules are used for gut microbial metabolism, which can determine digestion, health, and behavior [2, 3]. Microbial composition across animals provides data on how microbiomes segregate between species and diets [4]. Here we show that microbiome data from GenBank can model host evolution, providing a "holobiome" insight to the important roles of diet and domestication. The main findings of this study in respect to microbial composition among species were: (1) herbivores are more similar than hosts with other diets; (2) domesticated species are more similar than wild relatives; and (3) humans are distinct from primates. Microbial composition between diets indicates a difference in functionality, where protein and fiber degradation are seen more in carnivores and herbivores respectively. Additionally, herbivores show the most microbial diversity among the diets. Finally, this analysis informs us of gaps in current microbiome data collection, which is biased toward pathogens. Thus, the host-microbiome relationship depicts a complex web of microbial functionality, composition, and diet that impact coevolution.

bioinformatics↗

bioRxiv2021

LYRUS: A Machine Learning Model for Predicting the Pathogenicity of Missense Variants

Single amino acid variations (SAVs) are a primary contributor to variations in the human genome. Identifying pathogenic SAVs can aid in the diagnosis and understanding of the genetic architecture of complex diseases, such as cancer. Most approaches for predicting the functional effects or pathogenicity of SAVs rely on either sequence or structural information. Nevertheless, previous analyses have shown that methods that depend on only sequence or structural information may have limited accuracy. Recently, researchers have attempted to increase the accuracy of their predictions by incorporating protein dynamics into pathogenicity predictions. This study presents < Lai Yang Rubenstein Uzun Sarkar > (LYRUS), a machine learning method that uses an XGBoost classifier selected by TPOT to predict the pathogenicity of SAVs. LYRUS incorporates five sequence-based features, six structure-based features, and four dynamics-based features. Uniquely, LYRUS includes a newly-proposed sequence co-evolution feature called variation number. LYRUSs performance was evaluated using a dataset that contains 4,363 protein structures corresponding to 20,307 SAVs based on human genetic variant data from the ClinVar database. Based on our dataset, the LYRUS classifier has a higher accuracy, specificity, F-measure, and Matthews correlation coefficient (MCC) than alternative methods including PolyPhen2, PROVEAN, SIFT, Rhapsody, EVMutation, MutationAssessor, SuSPect, FATHMM, and MVP. Variation numbers used within LYRUS differ greatly between pathogenic and neutral SAVs, and have a high feature weight in the XGBoost classifier employed by this method. Applications of the method to PTEN and TP53 further corroborate LYRUSs strong performance. LYRUS is freely available and the source code can be found at https://github.com/jiaying2508/LYRUS.

bioinformatics↗

bioRxiv2019

Curated Multiple Sequence Alignment for the Adenomatous Polyposis Coli (APC) Gene and Accuracy of In Silico Pathogenicity Predictions

Computational algorithms are often used to assess pathogenicity of Variants of Uncertain Significance (VUS) that are found in disease-associated genes. Most computational methods include analysis of protein multiple sequence alignments (PMSA), assessing interspecies variation. Careful validation of PMSA-based methods has been done for relatively few genes, partially because creation of curated PMSAs is labor-intensive. We assessed how PMSA-based computational tools predict the effects of the missense changes in the APC gene, in which pathogenic variants cause Familial Adenomatous Polyposis. Most Pathogenic or Likely Pathogenic APC variants are protein-truncating changes. However, public databases now contain thousands of variants reported as missense. We created a curated APC PMSA that contained >3 substitutions/site, which is large enough for statistically robust in silico analysis. The creation of the PMSA was not easily automated, requiring significant querying and computational analysis of protein and genome sequences. Of 1924 missense APC variants in the NCBI ClinVar database, 1800 (93.5%) are reported as VUS. All but two missense variants listed as P/LP occur at canonical splice or Exonic Splice Enhancer sites. Pathogenicity predictions by five computational tools (Align-GVGD, SIFT, PolyPhen2, MAPP, REVEL) differed widely in their predictions of Pathogenic/Likely Pathogenic (range 17.5-75.0%) and Benign/Likely Benign (range 25.0-82.5%) for APC missense variants in ClinVar. When applied to 21 missense variants reported in ClinVar as Benign, the five methods ranged in accuracy from 76.2-100%. Computational PMSA-based methods can be an excellent classifier for variants of some hereditary cancer genes. However, there may be characteristics of the APC gene and protein that confound the results of in silico algorithms. A systematic study of these features could greatly improve the automation of alignment-based techniques and the use of predictive algorithms in hereditary cancer genes.\n\nAuthor SummaryA critical problem in clinical genetics today is interpreting whether a genetic variant is benign or causes disease (pathogenic). Some of the hardest variants to interpret are those that change one amino acid for another in a protein sequence (a \"missense variant\"). Various computer programs are often used to predict whether mutations in disease-associated genes likely cause disease. Most computer programs involve studying how the gene has changed during evolution, comparing the protein sequences of different species by aligning them with each other. Variants in amino acids that have not tolerated mutation during evolution are usually predicted to be pathogenic, and variants in amino acids that have tolerated variation are usually predicted to be benign. High quality alignments are necessary to make accurate predictions. However, creating high quality alignments is difficult, not easily automated, and requires significant manual curation. Results from computer-generated predictions are used in current published guidelines as one tool for evaluating whether variants will disrupt the protein function and cause disease. These guidelines may be applied to genes in which single amino acid substitutions do not commonly cause disease. One such example is the APC gene, which is responsible for Familial Adenomatous Polyposis (FAP). Missense APC changes are not a common cause of FAP. Our analysis of APC demonstrated the difficulty of generating an accurate protein sequence alignment and the tendency of computer tools to overestimate the damaging effects of amino acid substitutions. Our results suggest that the rules for using computer-based tools to predict whether a variant causes disease should be modified when applied to genes in which missense variants rarely cause disease.

genetics↗