Biology subjects

Abadi, S.

Publications and source records attributed to Abadi, S..

3 recordsLinked to original sources

bioRxiv2020

ModelTeller: model selection for optimal phylogenetic reconstruction using machine learning

Statistical criteria have long been the standard for selecting the best model for phylogenetic reconstruction and downstream statistical inference. While model selection is regarded as a fundamental step in phylogenetics, existing methods for this task consume computational resources for long processing time, they are not always feasible, and sometimes depend on preliminary assumptions which do not hold for sequence data. Moreover, while these methods are dedicated to revealing the processes that underlie the sequence data, in most cases they do not produce the most accurate trees. Notably, phylogeny reconstruction consists of two related tasks, topology reconstruction and branch-length estimation. It was previously shown that in many cases the most complex model, GTR+I+G, leads to topologies that are as accurate as using existing model selection criteria, but overestimates branch lengths. Here, we present ModelTeller, a computational methodology for phylogenetic model selection, devised within the machine-learning framework, optimized to predict the most accurate model for branch-length estimation accuracy. ModelTeller relies on a readily implemented machine-learning model and thus the prediction according to features extracted from the sequence data results in a substantial decrease in running time compared to existing strategies. We show that on datasets simulated under simple homogenous substitution models ModelTeller leads to branch-length estimation that is as accurate as the statistical model selection criteria. We then demonstrate that ModelTeller outperforms these criteria when more intricate patterns - that aim at mimicking realistic processes - are considered.

evolutionary biology↗

bioRxiv2020

Prediction of synonymous corrections by the BE-FF computational tool expands the targeting scope of base editing

Base editing is a genome-editing approach that employs the CRISPR/Cas system to precisely install point mutations within the genome. A cytidine or adenosine deaminase enzyme is fused to a deactivated Cas and converts C to T or A to G, respectively. The diversified repertoire of base editors, varied in their Cas and deaminase proteins, provides a wide range of functionality. However, existing base-editors can only induce transition substitutions in a specified region determined by the base editor, thus, they are incompatible for many point mutations. Here, we present BE-FF (Base Editors Functional Finder), a novel computational tool that identifies suitable base editors to correct the translated sequence erred by a given single nucleotide variation. Even if a perfect correction of the single nucleotide variation is not possible, BE-FF detects synonymous corrections to produce the reference protein. To assess the potential of BE-FF, we analysed a database of human pathogenic point mutations and found suitable base editors for 60.9% of the transition mutations. Importantly, 19.4% of them were made possible only by synonymous corrections. Moreover, we detected 298 cases in which pathogenic mutations caused by transversions were potentially repairable by base editing via synonymous corrections, although it had been thought impractical. The BE-FF tool and the database are available at https://www.danioffenlab.com/be-ff. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=76 SRC="FIGDIR/small/890244v1_ufig1.gif" ALT="Figure 1"> View larger version (17K): org.highwire.dtl.DTLVardef@36330borg.highwire.dtl.DTLVardef@514ca0org.highwire.dtl.DTLVardef@17dbc56org.highwire.dtl.DTLVardef@1d7d8d2_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2017

CRISPys: Optimal sgRNA design for editing multiple members of a gene family using the CRISPR system

The discovery and development of the CRISPR-Cas9 system in the past few years has made eukaryotic genome editing, and specifically gene knockout for reverse genetics, a simpler, efficient, and effective task. The system is directed to the genomic target site by a programmed single-guide RNA (sgRNA) that base-pairs with the DNA target, subsequently leading to site-specific double-strand breaks. However, many gene families in eukaryotic genomes exhibit partially overlapping functions and, thus, the knockout of one gene might be concealed by the function of the other. In such cases, the reduced specificity of the CRISPR-Cas9 system, which may lead to the cleavage of genomic sites that are not identical to the sgRNA, can be harnessed for the simultaneous knockout of multiple homologous genes. Here, we introduce CRISPys, an algorithm for the optimal design of sgRNAs that would potentially target multiple members of a given gene family. CRISPys first clusters all the potential targets in the input sequences into a hierarchical tree structure that specifies the similarity among them. Then, sgRNAs are proposed in the internal nodes of the tree by embedding mismatches where needed, such that the cleavage efficiencies of the induced targets are maximized. We suggest several approaches for designing the optimal individual sgRNA, and an approach that provides a set of sgRNAs that also accounts for the homologous relationships among gene-family members. We further show by in-silico examination over all gene families in the Solanum lycopersicum genome that our suggested approach outperforms simpler alignment-based techniques.\n\nGraphical abstract O_FIG_DISPLAY_L [Figure 1] M_FIG_DISPLAY C_FIG_DISPLAY\n\nHighlightsO_LIMany genes in eukaryotic genomes exhibit partially overlapping functions. This imposes difficulties on reverse-genetics, as the knockout of one gene might be concealed by the function of the other.\nC_LIO_LIWe present CRISPys, a graph-based algorithm for the optimal design of CRISPR systems given a set of redundant genes.\nC_LIO_LICRISPys harnesses the lack of specificity of the CRISPR-Cas9 genome editing technique, providing researchers the ability to simultaneously mutate multiple genes.\nC_LIO_LIWe show that CRISPys outperforms existing approaches that are based on simple alignment of the input gene family.\nC_LI

bioinformatics↗