Sudip Sharma

• Doctor of Philosophy
• Graduate Reserach Assistant at Temple University

Cases abound in which nearly identical traits have appeared in distant species facing similar environments. These unmistakable examples of adaptive evolution offer opportunities to gain insight into their genetic origins and mechanisms through comparative analyses. Here, we present an approach to build genetic models that underlie the independent o...

The relative rate framework (RRF) can estimate divergence times from branch lengths in a phylogeny, which is the theoretical basis of the RelTime method frequently applied, a relaxed clock approach for molecular dating that scales well for large phylogenies. The use of RRF has also enabled the development of computationally efficient and accurate m...

The relative rate framework (RRF) can estimate divergence times from branch lengths in a phylogeny, which is the theoretical basis of the RelTime method frequently-applied, relaxed clock approach for molecular dating that scales well for large phylogenies. The use of RRF has also enabled the development of computationally efficient and accurate met...

Evolutionary sparse learning (ESL) uses a supervised machine learning approach, Least Absolute Shrinkage and Selection Operator (LASSO), to build models explaining the relationship between a hypothesis and the variation across genomic features (e.g., sites) in sequences alignments. ESL employs sparsity between and within the groups of genomic featu...

Evolutionary sparse learning (ESL) uses a supervised machine learning approach, Least Absolute Shrinkage and Selection Operator (LASSO), to build models explaining the relationship between a hypothesis and the variation across genomic features (e.g., sites) in sequence alignments. ESL employs sparsity between and within the groups of genomic featur...

Cases abound in which nearly identical traits have appeared in distant species facing similar environments. These unmistakable examples of adaptive evolution offer opportunities to gain insight into their genetic origins and mechanisms through comparative analyses. Here, we present a novel comparative genomics approach to build genetic models that...

We introduce the 12th version of the Molecular Evolutionary Genetics Analysis (MEGA12) software. This latest version brings many significant improvements by reducing the computational time needed for selecting optimal substitution models and conducting bootstrap tests on phylogenies using maximum likelihood (ML) methods. These improvements are achi...

We introduce the 12th version of the Molecular Evolutionary Genetics Analysis ( MEGA ) software. This latest version brings many significant improvements by reducing the computational time needed for selecting optimal substitution models and conducting bootstrap tests on phylogenies using maximum likelihood (ML) methods. These improvements are achi...

Phylogenomic analyses of long sequences, consisting of many genes and genomic segments, infer organismal relationships with high statistical confidence. But, these relationships can be sensitive to excluding just a few sequences. Currently, there is no direct way to identify fragile relationships and the associated individual gene sequences in spec...

Phylogenomic analyses of long sequences, consisting of many genes and genomic segments, infer organismal relationships with high statistical confidence. But, these relationships can be sensitive to excluding just a few sequences. Currently, there is no direct way to identify fragile relationships and the associated individual gene sequences in spec...

An individual’s chronological age does not always correspond to the health of different tissues in their body, especially in cases of disease. Therefore, estimating and contrasting the physiological age of tissues with an individual’s chronological age may be a useful tool to diagnose disease and its progression. Here, we present novel metrics to q...

A common practice in molecular systematics is to infer phylogeny and then scale it to time by using a relaxed clock method and calibrations. This sequential analysis practice ignores the effect of phylogenetic uncertainty on divergence time estimates and their confidence/credibility intervals. An alternative is to infer phylogeny and times jointly...

The selection of the optimal substitution model of molecular evolution imposes a high computational burden for long sequence alignments in phylogenomics. We discovered that the analysis of multiple tiny subsamples of site patterns from a full sequence alignment recovers the correct optimal substitution model when sites in the subsample are upsample...

Felsenstein’s bootstrap approach is widely used to assess confidence in species relationships inferred from multiple sequence alignments. It resamples sites randomly with replacement to build alignment replicates of the same size as the original alignment and infers a phylogeny from each replicate dataset. The proportion of phylogenies recovering t...

We introduce a supervised machine learning approach with sparsity constraints for phylogenomics, referred to as evolutionary sparse learning (ESL). ESL builds models with genomic loci—such as genes, proteins, genomic segments, and positions—as parameters. Using the Least Absolute Shrinkage and Selection Operator (LASSO), ESL selects only the most i...

Felsenstein's bootstrap resampling approach, applied in thousands of research articles, imposes a high computational burden for very long sequence alignments. We show that the bootstrapping of a collection of little subsamples, coupled with median bagging of subsample confidence limits, produces accurate bootstrap confidence for phylogenetic relati...

A bstract We introduce a supervised machine learning approach with sparsity constraints for phylogenomics, referred to as evolutionary sparse learning (ESL). ESL builds models with genomic loci—such as genes, proteins, genomic segments, and positions—as parameters. Using the Least Absolute Shrinkage and Selection Operator (LASSO), ESL selects only...

Global sequencing of hundreds of thousands of genomes of Severe acute respiratory syndrome coronavirus 2, SARS-CoV-2, has continued to reveal new genetic variants that are the key to unraveling its early evolutionary history and tracking its global spread over time. Here, we present the heretofore cryptic mutational history and spatiotemporal dynam...

Severe acute respiratory syndrome coronavirus 2, SARS-CoV-2, was quickly identified as the cause of COVID-19 disease soon after its earliest reports. The knowledge of the contemporary evolution of SARS-CoV-2 is urgently needed not only for a retrospective on how, when, and why COVID-19 has emerged and spread, but also for creating remedies through...