Biology subjects

Mirarab, S.

Publications and source records attributed to Mirarab, S..

5 recordsLinked to original sources

bioRxiv2018

Multi-allele species reconstruction using ASTRAL

Genome-wide phylogeny reconstruction is becoming increasingly common, and one driving factor behind these phylogenomic studies is the promise that the potential discordance between gene trees and the species tree can be modeled. Incomplete lineage sorting is one cause of discordance that bridges population genetic and phylogenetic processes. ASTRAL is a species tree reconstruction method that seeks to find the tree with minimum quartet distance to an input set of inferred gene trees. However, the published ASTRAL algorithm only works with one sample per species. To account for polymorphisms in present-day species, one can sample multiple individuals per species to create multi-allele datasets. Here, we introduce how ASTRAL can handle multi-allele datasets. We show that the quartet-based optimization problem extends naturally, and we introduce heuristic methods for building the search space specifically for the case of multi-individual datasets. We study the accuracy and scalability of the multi-individual version of ASTRAL-III using extensive simulation studies and compare it to NJst, the only other scalable method that can handle these datasets. We do not find strong evidence that using multiple individuals dramatically improves accuracy. When we study the trade-off between sampling more genes versus more individuals, we find that sampling more genes is more effective than sampling more individuals, even under conditions that we study where trees are shallow (median length: {approx} 1Ne) and ILS is extremely high.

bioinformatics↗

bioRxiv2018

INSTRAL: Discordance-aware Phylogenetic Placement using Quartet Scores

Phylogenomic analyses have increasingly adopted species tree reconstruction using methods that account for gene tree discordance using pipelines that require both human effort and computational resources. As the number of available genomes continues to increase, a new problem is facing researchers. Once more species become available, they have to repeat the whole process from the beginning because updating species trees is currently not possible. However, the de novo inference can be prohibitively costly in human effort or machine time. In this paper, we introduce INSTRAL, a method that extends ASTRAL to enable phylogenetic placement. INSTRAL is designed to place a new species on an existing species tree after sequences from the new species have already been added to gene trees; thus, INSTRAL is complementary to existing placement methods that update gene trees.

bioinformatics↗

bioRxiv2018

FAVITES: simultaneous simulation of transmission networks, phylogenetic trees, and sequences

MotivationThe ability to simulate epidemics as a function of model parameters allows insights that are unobtainable from real datasets. Further, reconstructing transmission networks for fast-evolving viruses like HIV may have the potential to greatly enhance epidemic intervention, but transmission network reconstruction methods have been inadequately studied, largely because it is difficult to obtain \"truth\" sets on which to test them and properly measure their performance.\n\nResultsWe introduce FAVITES, a robust framework for simulating realistic datasets for epidemics that are caused by fast-evolving pathogens like HIV. FAVITES creates a generative model to produce contact networks, transmission networks, phylogenetic trees, and sequence datasets, and to add error to the data. FAVITES is designed to be extensible by dividing the generative model into modules, each of which is expressed as a fixed API that can be implemented using various models. We use FAVITES to simulate HIV datasets and study the realism of the simulated datasets. We then use the simulated data to study the impact of the increased treatment efforts on epidemiological outcomes. We also study two transmission network reconstruction methods and their effectiveness in detecting fast-growing clusters.\n\nAvailability and implementationFAVITES is available at https://github.com/niemasd/FAVITES, and a Docker image can be found on DockerHub (https://hub.docker.com/r/niemasd/favites).

bioinformatics↗

bioRxiv2017

Assembly-free and alignment-free sample identification using genome skims

The ability to quickly and inexpensively describe taxonomic diversity is critical in this era of rapid climate and biodiversity changes. The currently preferred molecular technique, barcoding, has been very successful, but is based on short organelle markers. Recently, an alternative genome-skimming approach has been proposed: low-pass sequencing (100Mb - several Gb per sample) is applied to voucher and/or query samples, and marker genes and/or organelle genomes are recovered computationally. The current practice of genome-skimming discards the vast majority of the data because the low coverage of genome-skims prevents assembling the nuclear genomes. In contrast, we suggest using all unassembled reads directly, but existing methods poorly support this goal. We introduce a new alignment-free tool, Skmer, to estimate genomic distances between the query and each reference genome-skim using the k-mer decomposition of reads. We test Skmer on a large set of insect and bird genomes, sub-sampled to create genome-skims. Skmer shows great accuracy in estimating genomic distances, identifying the closest match in a reference dataset, and inferring the phylogeny. The software is publicly available on https://github.com/shahab-sarmashghi/Skmer.git

ecology↗

bioRxiv2017

Fine-mapping the Favored Mutation in a Positive Selective Sweep

Methods to identify signatures of selective sweeps in population genomics data have been actively developed, but mostly do not identify the specific mutation favored by the selective sweep. We present a method, iSAFE, that uses a statistic derived solely from population genetics signals to pinpoint the favored mutation even when the signature of selection extends to 5Mbp. iSAFE was tested extensively on simulated data and in human populations from the 1000 Genomes Project, at 22 loci with previously characterized selective sweeps. For 14 of the 22 loci, iSAFE ranked the previously characterized candidate mutation among the 13 highest scoring (out of [~] 21, 000 variants). Three loci did not show a strong signal. For the remaining loci, iSAFE identified previously unreported mutations as being favored. In these regions, all of which involve pigmentation related genes, iSAFE identified identical selected mutations in multiple non-African populations suggesting an out-of-Africa onset of selection. The iSAFE software can be downloaded from https://github.com/alek0991/iSAFE.

evolutionary biology↗