Dataset Information

Split-inducing indels in phylogenomic analysis.

ABSTRACT:

Background

Most phylogenetic studies using molecular data treat gaps in multiple sequence alignments as missing data or even completely exclude alignment columns that contain gaps.

Results

Here we show that gap patterns in large-scale, genome-wide alignments are themselves phylogenetically informative and can be used to infer reliable phylogenies provided the gap data are properly filtered to reduce noise introduced by the alignment method. We introduce here the notion of split-inducing indels (splids) that define an approximate bipartition of the taxon set. We show both in simulated data and in case studies on real-life data that splids can be efficiently extracted from phylogenomic data sets.

Conclusions

Suitably processed gap patterns extracted from genome-wide alignment provide a surprisingly clear phylogenetic signal and an allow the inference of accurate phylogenetic trees.

SUBMITTER: Donath A

PROVIDER: S-EPMC6047143 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Publications

Split-inducing indels in phylogenomic analysis.

Donath Alexander A Stadler Peter F PF

Algorithms for molecular biology : AMB 20180716

<h4>Background</h4>Most phylogenetic studies using molecular data treat gaps in multiple sequence alignments as missing data or even completely exclude alignment columns that contain gaps.<h4>Results</h4>Here we show that gap patterns in large-scale, genome-wide alignments are themselves phylogenetically informative and can be used to infer reliable phylogenies provided the gap data are properly filtered to reduce noise introduced by the alignment method. We introduce here the notion of split-in ...[more]

PMID: 30026791

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Project description:BackgroundRecent studies have demonstrated the genetic significance of insertions, deletions, and other more complex structural variants (SVs) in the human population. With the development of the next-generation sequencing technologies, high-throughput surveys of SVs on the whole-genome level have become possible. Here we present split-read identification, calibrated (SRiC), a sequence-based method for SV detection.ResultsWe start by mapping each read to the reference genome in standard fashion using gapped alignment. Then to identify SVs, we score each of the many initial mappings with an assessment strategy designed to take into account both sequencing and alignment errors (e.g. scoring more highly events gapped in the center of a read). All current SV calling methods have multilevel biases in their identifications due to both experimental and computational limitations (e.g. calling more deletions than insertions). A key aspect of our approach is that we calibrate all our calls against synthetic data sets generated from simulations of high-throughput sequencing (with realistic error models). This allows us to calculate sensitivity and the positive predictive value under different parameter-value scenarios and for different classes of events (e.g. long deletions vs. short insertions). We run our calculations on representative data from the 1000 Genomes Project. Coupling the observed numbers of events on chromosome 1 with the calibrations gleaned from the simulations (for different length events) allows us to construct a relatively unbiased estimate for the total number of SVs in the human genome across a wide range of length scales. We estimate in particular that an individual genome contains ~670,000 indels/SVs.ConclusionsCompared with the existing read-depth and read-pair approaches for SV identification, our method can pinpoint the exact breakpoints of SV events, reveal the actual sequence content of insertions, and cover the whole size spectrum for deletions. Moreover, with the advent of the third-generation sequencing technologies that produce longer reads, we expect our method to be even more useful.

Project description:BackgroundFaced with the ongoing global pandemic of coronavirus disease, the 'National Reference Centre for Whole Genome Sequencing of microbial pathogens: database and bioinformatic analysis' (GENPAT) formally established at the 'Istituto Zooprofilattico Sperimentale dell'Abruzzo e del Molise' (IZSAM) in Teramo (Italy) is in charge of the SARS-CoV-2 surveillance at the genomic scale. In a context of SARS-CoV-2 surveillance requiring correct and fast assessment of epidemiological clusters from substantial amount of samples, the present study proposes an analytical workflow for identifying accurately the PANGO lineages of SARS-CoV-2 samples and building of discriminant minimum spanning trees (MST) bypassing the usual time consuming phylogenomic inferences based on multiple sequence alignment (MSA) and substitution model.ResultsGENPAT constituted two collections of SARS-CoV-2 samples. The first collection consisted of SARS-CoV-2 positive swabs collected by IZSAM from the Abruzzo region (Italy), then sequenced by next generation sequencing (NGS) and analyzed in GENPAT (n = 1592), while the second collection included samples from several Italian provinces and retrieved from the reference Global Initiative on Sharing All Influenza Data (GISAID) (n = 17,201). The main results of the present work showed that (i) GENPAT and GISAID detected the same PANGO lineages, (ii) the PANGO lineages B.1.177 (i.e. historical in Italy) and B.1.1.7 (i.e. 'UK variant') are major concerns today in several Italian provinces, and the new MST-based method (iii) clusters most of the PANGO lineages together, (iv) with a higher dicriminatory power than PANGO lineages, (v) and faster that the usual phylogenomic methods based on MSA and substitution model.ConclusionsThe genome sequencing efforts of Italian provinces, combined with a structured national system of NGS data management, provided support for surveillance SARS-CoV-2 in Italy. We propose to build phylogenomic trees of SARS-CoV-2 variants through an accurate, discriminant and fast MST-based method avoiding the typical time consuming steps related to MSA and substitution model-based phylogenomic inference.

Dataset Information

Split-inducing indels in phylogenomic analysis.

Background

Results

Conclusions

Publications

Split-inducing indels in phylogenomic analysis.

Similar Datasets

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