12,677 research outputs found
The inference of gene trees with species trees
Molecular phylogeny has focused mainly on improving models for the
reconstruction of gene trees based on sequence alignments. Yet, most
phylogeneticists seek to reveal the history of species. Although the histories
of genes and species are tightly linked, they are seldom identical, because
genes duplicate, are lost or horizontally transferred, and because alleles can
co-exist in populations for periods that may span several speciation events.
Building models describing the relationship between gene and species trees can
thus improve the reconstruction of gene trees when a species tree is known, and
vice-versa. Several approaches have been proposed to solve the problem in one
direction or the other, but in general neither gene trees nor species trees are
known. Only a few studies have attempted to jointly infer gene trees and
species trees. In this article we review the various models that have been used
to describe the relationship between gene trees and species trees. These models
account for gene duplication and loss, transfer or incomplete lineage sorting.
Some of them consider several types of events together, but none exists
currently that considers the full repertoire of processes that generate gene
trees along the species tree. Simulations as well as empirical studies on
genomic data show that combining gene tree-species tree models with models of
sequence evolution improves gene tree reconstruction. In turn, these better
gene trees provide a better basis for studying genome evolution or
reconstructing ancestral chromosomes and ancestral gene sequences. We predict
that gene tree-species tree methods that can deal with genomic data sets will
be instrumental to advancing our understanding of genomic evolution.Comment: Review article in relation to the "Mathematical and Computational
Evolutionary Biology" conference, Montpellier, 201
EXPLoRA-web: linkage analysis of quantitative trait loci using bulk segregant analysis
Identification of genomic regions associated with a phenotype of interest is a fundamental step toward solving questions in biology and improving industrial research. Bulk segregant analysis (BSA) combined with high-throughput sequencing is a technique to efficiently identify these genomic regions associated with a trait of interest. However, distinguishing true from spuriously linked genomic regions and accurately delineating the genomic positions of these truly linked regions requires the use of complex statistical models currently implemented in software tools that are generally difficult to operate for non-expert users. To facilitate the exploration and analysis of data generated by bulked segregant analysis, we present EXPLoRA-web, a web service wrapped around our previously published algorithm EXPLoRA, which exploits linkage disequilibrium to increase the power and accuracy of quantitative trait loci identification in BSA analysis. EXPLoRA-web provides a user friendly interface that enables easy data upload and parallel processing of different parameter configurations. Results are provided graphically and as BED file and/or text file and the input is expected in widely used formats, enabling straightforward BSA data analysis. The web server is available at http://bioinformatics.intec.ugent.be/explora-web/
BamView: visualizing and interpretation of next-generation sequencing read alignments.
So-called next-generation sequencing (NGS) has provided the ability to sequence on a massive scale at low cost, enabling biologists to perform powerful experiments and gain insight into biological processes. BamView has been developed to visualize and analyse sequence reads from NGS platforms, which have been aligned to a reference sequence. It is a desktop application for browsing the aligned or mapped reads [Ruffalo, M, LaFramboise, T, Koyutürk, M. Comparative analysis of algorithms for next-generation sequencing read alignment. Bioinformatics 2011;27:2790-6] at different levels of magnification, from nucleotide level, where the base qualities can be seen, to genome or chromosome level where overall coverage is shown. To enable in-depth investigation of NGS data, various views are provided that can be configured to highlight interesting aspects of the data. Multiple read alignment files can be overlaid to compare results from different experiments, and filters can be applied to facilitate the interpretation of the aligned reads. As well as being a standalone application it can be used as an integrated part of the Artemis genome browser, BamView allows the user to study NGS data in the context of the sequence and annotation of the reference genome. Single nucleotide polymorphism (SNP) density and candidate SNP sites can be highlighted and investigated, and read-pair information can be used to discover large structural insertions and deletions. The application will also calculate simple analyses of the read mapping, including reporting the read counts and reads per kilobase per million mapped reads (RPKM) for genes selected by the user
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