Biology subjects

Stern, A.

Publications and source records attributed to Stern, A..

24 records · Page 2Linked to original sources

bioRxiv2020

Unsupervised cellular phenotypic hierarchy enables spatial intratumor heterogeneity characterization, recurrence-associated microdomains discovery, and harnesses network biology from hyperplexed in-situ fluorescence images of colorectal carcinoma

LEAPH is an unsupervised machine learning algorithm for characterizing in situ phenotypic heterogeneity in tissue samples. LEAPH builds a phenotypic hierarchy of cell types, cell states and their spatial configurations. The recursive modeling steps involve determining cell types with low-ranked mixtures of factor analyzers and optimizing cell states with spatial regularization. We applied LEAPH to hyperplexed (51 biomarkers) immunofluorescence images of colorectal carcinoma primary tumors (N=213). LEAPH, combined with pointwise mutual information (PMI), enables the discovery of phenotypically distinct microdomains, composed of spatially configured computational phenotypes. LEAPH identified a subset of microdomains visualized as the spatial configuration of recurrence-specific signaling networks whose intracellular and intercellular interactions support cancer stem cell maintenance and immunosuppression in the evolving tumor microenvironment. The LEAPH framework, when combined with microdomain discovery and microdomain-specific network biology, has the potential to provide insights into pathophysiological mechanisms, identify novel drug targets and inform therapeutic strategies for individual patients.

cancer biology↗

bioRxiv2019

Sequencing Complete Genomes of RNA Viruses With MinION Nanopore: Finding Associations Between Mutations

One of the key challenges in the field of genetics is the inference of haplotypes from next generation sequencing data. The MinION Oxford Nanopore sequencer allows sequencing long reads, with the potential of sequencing complete genes, and even complete genomes of viruses, in individual reads. However, MinION suffers from high error rates, rendering the detection of true variants difficult. Here we propose a new statistical approach named AssociVar, which differentiates between true mutations and sequencing errors from direct RNA/DNA sequencing using MinION. Our strategy relies on the assumption that sequencing errors will be dispersed randomly along sequencing reads, and hence will not be associated with each other, whereas real mutations will display a non-random pattern of association with other mutations. We demonstrate our approach using direct RNA sequencing data from evolved populations of the MS2 bacteriophage, whose small genome makes it ideal for MinION sequencing. AssociVar inferred several mutations in the phage genome, which were corroborated using parallel Illumina sequencing. This allowed us to reconstruct full genome viral haplotypes constituting different strains that were present in the sample. Our approach is applicable to long read sequencing data from any organism for accurate detection of bona fide mutations and inter-strain polymorphisms.

genetics↗

bioRxiv2019

A Bayesian framework for inferring the influence of sequence context on single base modifications

The probability of single base modifications (mutations and DNA/RNA modifications) is expected to be highly influenced by the flanking nucleotides that surround them, known as the sequence context. This phenomenon may be mainly attributed to the enzyme that modifies or mutates the genetic material, since most enzymes tend to have specific sequence contexts that dictate their activity. Thus, identification of context effects may lead to the discovery of additional editing sites or unknown enzymatic factors. Here, we develop a statistical model that allows for the detection and evaluation of the effects of different sequence contexts on mutation rates from deep population sequencing data. This task is computationally challenging, as the complexity of the model increases exponentially as the context size increases. We established our novel Bayesian method based on sparse model selection methods, with the leading assumption that the number of actual sequence contexts that directly influence mutation rates is minuscule compared to the number of possible sequence contexts. We show that our method is highly accurate on simulated data using pentanucleotide contexts, even when accounting for noisy data. We next analyze empirical population sequencing data from polioviruses and detect a significant enrichment in sequence contexts associated with deamination by the cellular deaminases ADAR 1/2. In the current era, where next generation sequencing data is highly abundant, our approach can be used on any population sequencing data to reveal context-dependent base alterations, and may assist in the discovery of novel mutable sites or editing sites.

bioinformatics↗

bioRxiv2018

Inferring Population Genetics Parameters of Evolving Viruses Using Time-series Data

1With the advent of deep sequencing techniques, it is now possible to track the evolution of viruses with ever-increasing detail. Here we present FITS (Flexible Inference from Time-Series) - a computational framework that allows inference of either the fitness of a mutation, the mutation rate or the population size from genomic time-series sequencing data. FITS was designed first and foremost for analysis of either short-term Evolve & Resequence (E&R) experiments, or for rapidly recombining populations of viruses. We thoroughly explore the performance of FITS on noisy simulated data, and highlight its ability to infer meaningful information even in those circumstances. In particular FITS is able to categorize a mutation as Advantageous, Neutral or Deleterious. We next apply FITS to empirical data from an E&R experiment on poliovirus where parameters were determined experimentally and demonstrate extremely high accuracy in inference. We highlight the ease of use of FITS for step-wise or iterative inference of mutation rates, population size, and fitness values for each mutation sequenced, when deep sequencing data is available at multiple time-points.\n\nAvailabilityFITS is written in C++ and is available both with a highly user friendly graphical user interface but also as a command line program that allows parallel high throughput analyses. Source code, binaries (Windows and Mac) and complementary scripts, are available from GitHub at https://github.com/SternLabTAU/FITS.\n\nContactsternadi@tau.ac.il

evolutionary biology↗

bioRxiv2018

AccuNGS: a sequencing protocol for detection of ultra-rare variants reveals extensive variation in HIV during first days of infection

Mutations fuel evolution and facilitate adaptation to novel environments. However, characterizing the spectrum of mutations in a population is obscured by high error rates of next generation sequencing. Here, we present AccuNGS, a novel in vivo sequencing approach that detects variants as rare as 1:10,000. Applying it to 46 clinical samples taken from early infections of the human-infecting viruses HIV, RSV and CMV, revealed large differences in within-host genetic diversity among virus populations. Haplotype reconstruction revealed that increased diversity was mostly driven by multiple transmitted/founder viruses in HIV and CMV samples. Conversely, we detected an abundance of defective virus genomes (DVGs) in RSV samples, including hyper-edited genomes, nonsense mutations and single point deletions. Higher proportions of DVGs correlated with increased viral loads, suggesting increased cellular co-infection rates, which enable DVG persistence. AccuNGS establishes a general platform that allows detecting DVGs, and in general, rare variants that drive evolution.

bioinformatics↗

bioRxiv2017

Evolutionary rate shifts suggest species-specific adaptation events in HIV-1 and SIV

The process of molecular adaptation following a cross-species virus transmission event is currently poorly understood. Here, we identified 137 protein sites that experienced deceleration in their rate of evolution along the HIV-1/SIV phylogeny, likely indicating gain-of-function and consequent adaptation. The majority of such events occurred in parallel to cross-species transmission events and varied between HIV-1 groups, indicating independent adaptation strategies. The evolutionary rate decelerations we found were particularly prominent in accessory proteins that counteract host antiviral restriction factors, suggesting that these factors are a major barrier to viral adaptation to a new host. Surprisingly, we observed that the non-pandemic HIV-1 group O, derived from gorillas, exhibited more rate deceleration events than the pandemic group M, derived from chimpanzees. We suggest that the species barrier is higher when the genetic distance of the hosts increases. Our approach paves the way for subsequent studies on cross-species transfers in other major pathogens.

evolutionary biology↗