4,207 research outputs found
On the weight of indels in genomic distances
Dias Vieira Braga M, Machado R, Ribeiro LC, Stoye J. On the weight of indels in genomic distances. BMC Bioinformatics. 2011;12(Suppl 9: RECOMB-CG 2011): S13.Background:
Classical approaches to compute the genomic distance are usually limited to genomes with the same content, without duplicated markers. However, differences in the gene content are frequently observed and can reflect important evolutionary aspects. A few polynomial time algorithms that include genome rearrangements, insertions and deletions (or substitutions) were already proposed. These methods often allow a block of contiguous markers to be inserted, deleted or substituted at once but result in distance functions that do not respect the triangular inequality and hence do not constitute metrics.
Results:
In the present study we discuss the disruption of the triangular inequality in some of the available methods and give a framework to establish an efficient correction for two models recently proposed, one that includes insertions, deletions and double cut and join (DCJ) operations, and one that includes substitutions and DCJ operations.
Conclusions:
We show that the proposed framework establishes the triangular inequality in both distances, by summing a surcharge on indel operations and on substitutions that depends only on the number of markers affected by these operations. This correction can be applied a posteriori, without interfering with the already available formulas to compute these distances. We claim that this correction leads to distances that are biologically more plausible
Progressive Mauve: Multiple alignment of genomes with gene flux and rearrangement
Multiple genome alignment remains a challenging problem. Effects of
recombination including rearrangement, segmental duplication, gain, and loss
can create a mosaic pattern of homology even among closely related organisms.
We describe a method to align two or more genomes that have undergone
large-scale recombination, particularly genomes that have undergone substantial
amounts of gene gain and loss (gene flux). The method utilizes a novel
alignment objective score, referred to as a sum-of-pairs breakpoint score. We
also apply a probabilistic alignment filtering method to remove erroneous
alignments of unrelated sequences, which are commonly observed in other genome
alignment methods. We describe new metrics for quantifying genome alignment
accuracy which measure the quality of rearrangement breakpoint predictions and
indel predictions. The progressive genome alignment algorithm demonstrates
markedly improved accuracy over previous approaches in situations where genomes
have undergone realistic amounts of genome rearrangement, gene gain, loss, and
duplication. We apply the progressive genome alignment algorithm to a set of 23
completely sequenced genomes from the genera Escherichia, Shigella, and
Salmonella. The 23 enterobacteria have an estimated 2.46Mbp of genomic content
conserved among all taxa and total unique content of 15.2Mbp. We document
substantial population-level variability among these organisms driven by
homologous recombination, gene gain, and gene loss. Free, open-source software
implementing the described genome alignment approach is available from
http://gel.ahabs.wisc.edu/mauve .Comment: Revision dated June 19, 200
Generalizations of the genomic rank distance to indels
MOTIVATION: The rank distance model represents genome rearrangements in multi-chromosomal genomes as matrix operations, which allows the reconstruction of parsimonious histories of evolution by rearrangements. We seek to generalize this model by allowing for genomes with different gene content, to accommodate a broader range of biological contexts. We approach this generalization by using a matrix representation of genomes. This leads to simple distance formulas and sorting algorithms for genomes with different gene contents, but without duplications. RESULTS: We generalize the rank distance to genomes with different gene content in two different ways. The first approach adds insertions, deletions and the substitution of a single extremity to the basic operations. We show how to efficiently compute this distance. To avoid genomes with incomplete markers, our alternative distance, the rank-indel distance, only uses insertions and deletions of entire chromosomes. We construct phylogenetic trees with our distances and the DCJ-Indel distance for simulated data and real prokaryotic genomes, and compare them against reference trees. For simulated data, our distances outperform the DCJ-Indel distance using the Quartet metric as baseline. This suggests that rank distances are more robust for comparing distantly related species. For real prokaryotic genomes, all rearrangement-based distances yield phylogenetic trees that are topologically distant from the reference (65% similarity with Quartet metric), but are able to cluster related species within their respective clades and distinguish the Shigella strains as the farthest relative of the Escherichia coli strains, a feature not seen in the reference tree. AVAILABILITY AND IMPLEMENTATION: Code and instructions are available at https://github.com/meidanis-lab/rank-indel. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online
CLEVER: Clique-Enumerating Variant Finder
Next-generation sequencing techniques have facilitated a large scale analysis
of human genetic variation. Despite the advances in sequencing speeds, the
computational discovery of structural variants is not yet standard. It is
likely that many variants have remained undiscovered in most sequenced
individuals. Here we present a novel internal segment size based approach,
which organizes all, including also concordant reads into a read alignment
graph where max-cliques represent maximal contradiction-free groups of
alignments. A specifically engineered algorithm then enumerates all max-cliques
and statistically evaluates them for their potential to reflect insertions or
deletions (indels). For the first time in the literature, we compare a large
range of state-of-the-art approaches using simulated Illumina reads from a
fully annotated genome and present various relevant performance statistics. We
achieve superior performance rates in particular on indels of sizes 20--100,
which have been exposed as a current major challenge in the SV discovery
literature and where prior insert size based approaches have limitations. In
that size range, we outperform even split read aligners. We achieve good
results also on real data where we make a substantial amount of correct
predictions as the only tool, which complement the predictions of split-read
aligners. CLEVER is open source (GPL) and available from
http://clever-sv.googlecode.com.Comment: 30 pages, 8 figure
Hidden breakpoints in genome alignments
During the course of evolution, an organism's genome can undergo changes that
affect the large-scale structure of the genome. These changes include gene
gain, loss, duplication, chromosome fusion, fission, and rearrangement. When
gene gain and loss occurs in addition to other types of rearrangement,
breakpoints of rearrangement can exist that are only detectable by comparison
of three or more genomes. An arbitrarily large number of these "hidden"
breakpoints can exist among genomes that exhibit no rearrangements in pairwise
comparisons.
We present an extension of the multichromosomal breakpoint median problem to
genomes that have undergone gene gain and loss. We then demonstrate that the
median distance among three genomes can be used to calculate a lower bound on
the number of hidden breakpoints present. We provide an implementation of this
calculation including the median distance, along with some practical
improvements on the time complexity of the underlying algorithm.
We apply our approach to measure the abundance of hidden breakpoints in
simulated data sets under a wide range of evolutionary scenarios. We
demonstrate that in simulations the hidden breakpoint counts depend strongly on
relative rates of inversion and gene gain/loss. Finally we apply current
multiple genome aligners to the simulated genomes, and show that all aligners
introduce a high degree of error in hidden breakpoint counts, and that this
error grows with evolutionary distance in the simulation. Our results suggest
that hidden breakpoint error may be pervasive in genome alignments.Comment: 13 pages, 4 figure
Sequence alignment, mutual information, and dissimilarity measures for constructing phylogenies
Existing sequence alignment algorithms use heuristic scoring schemes which
cannot be used as objective distance metrics. Therefore one relies on measures
like the p- or log-det distances, or makes explicit, and often simplistic,
assumptions about sequence evolution. Information theory provides an
alternative, in the form of mutual information (MI) which is, in principle, an
objective and model independent similarity measure. MI can be estimated by
concatenating and zipping sequences, yielding thereby the "normalized
compression distance". So far this has produced promising results, but with
uncontrolled errors. We describe a simple approach to get robust estimates of
MI from global pairwise alignments. Using standard alignment algorithms, this
gives for animal mitochondrial DNA estimates that are strikingly close to
estimates obtained from the alignment free methods mentioned above. Our main
result uses algorithmic (Kolmogorov) information theory, but we show that
similar results can also be obtained from Shannon theory. Due to the fact that
it is not additive, normalized compression distance is not an optimal metric
for phylogenetics, but we propose a simple modification that overcomes the
issue of additivity. We test several versions of our MI based distance measures
on a large number of randomly chosen quartets and demonstrate that they all
perform better than traditional measures like the Kimura or log-det (resp.
paralinear) distances. Even a simplified version based on single letter Shannon
entropies, which can be easily incorporated in existing software packages, gave
superior results throughout the entire animal kingdom. But we see the main
virtue of our approach in a more general way. For example, it can also help to
judge the relative merits of different alignment algorithms, by estimating the
significance of specific alignments.Comment: 19 pages + 16 pages of supplementary materia
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The genomic diversification of grapevine clones.
BACKGROUND:Vegetatively propagated clones accumulate somatic mutations. The purpose of this study was to better appreciate clone diversity and involved defining the nature of somatic mutations throughout the genome. Fifteen Zinfandel winegrape clone genomes were sequenced and compared to one another using a highly contiguous genome reference produced from one of the clones, Zinfandel 03. RESULTS:Though most heterozygous variants were shared, somatic mutations accumulated in individual and subsets of clones. Overall, heterozygous mutations were most frequent in intergenic space and more frequent in introns than exons. A significantly larger percentage of CpG, CHG, and CHH sites in repetitive intergenic space experienced transition mutations than in genic and non-repetitive intergenic spaces, likely because of higher levels of methylation in the region and because methylated cytosines often spontaneously deaminate. Of the minority of mutations that occurred in exons, larger proportions of these were putatively deleterious when they occurred in relatively few clones. CONCLUSIONS:These data support three major conclusions. First, repetitive intergenic space is a major driver of clone genome diversification. Second, clones accumulate putatively deleterious mutations. Third, the data suggest selection against deleterious variants in coding regions or some mechanism by which mutations are less frequent in coding than noncoding regions of the genome
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Inference of single-cell phylogenies from lineage tracing data using Cassiopeia.
The pairing of CRISPR/Cas9-based gene editing with massively parallel single-cell readouts now enables large-scale lineage tracing. However, the rapid growth in complexity of data from these assays has outpaced our ability to accurately infer phylogenetic relationships. First, we introduce Cassiopeia-a suite of scalable maximum parsimony approaches for tree reconstruction. Second, we provide a simulation framework for evaluating algorithms and exploring lineage tracer design principles. Finally, we generate the most complex experimental lineage tracing dataset to date, 34,557 human cells continuously traced over 15 generations, and use it for benchmarking phylogenetic inference approaches. We show that Cassiopeia outperforms traditional methods by several metrics and under a wide variety of parameter regimes, and provide insight into the principles for the design of improved Cas9-enabled recorders. Together, these should broadly enable large-scale mammalian lineage tracing efforts. Cassiopeia and its benchmarking resources are publicly available at www.github.com/YosefLab/Cassiopeia
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