perfectphyloR: An R package for reconstructing perfect phylogenies

Abstract Background A perfect phylogeny is a rooted binary tree that recursively partitions sequences. The nested partitions of a perfect phylogeny provide insight into the pattern of ancestry of genetic sequence data. For example, sequences may cluster together in a partition indicating that they arise from a common ancestral haplotype. Results We present an R package to reconstruct the local perfect phylogenies underlying a sample of binary sequences. The package enables users to associate the reconstructed partitions with a user-defined partition. We describe and demonstrate the major functionality of the package. Conclusion The package should be of use to researchers seeking insight into the ancestral structure of their sequence data. The reconstructed partitions have many applications, including the mapping of trait-influencing variants.

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perfectphyloR: An R package for reconstructing perfect phylogenies

10.1101/674523 ◽

2019 ◽

Author(s):

Charith Bhagya Karunarathna ◽

Jinko Graham

Keyword(s):

Binary Tree ◽

Sequence Data ◽

R Package ◽

Binary Sequences ◽

Ancestral Haplotype ◽

Perfect Phylogeny ◽

Nested Partitions ◽

Genetic Sequence ◽

Insight Into ◽

Rooted Binary Tree

AbstractBackgroundA perfect phylogeny is a rooted binary tree that recursively partitions sequences. The nested partitions of a perfect phylogeny provide insight into the pattern of ancestry of genetic sequence data. For example, sequences may cluster together in a partition indicating that they arise from a common ancestral haplotype.ResultsWe present an R packageperfectphyloRto reconstruct the local perfect phylogenies underlying a sample of binary sequences. The package enables users to associate the reconstructed partitions with a user-defined partition. We describe and demonstrate the major functionality of the package.ConclusionTheperfectphyloRpackage should be of use to researchers seeking insight into the ancestral structure of their sequence data. The reconstructed partitions have many applications, including the mapping of trait-influencing variants.

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SimRVSequences: an R package to simulate genetic sequence data for pedigrees

Bioinformatics ◽

10.1093/bioinformatics/btz881 ◽

2019 ◽

Vol 36 (7) ◽

pp. 2295-2297

Author(s):

Christina Nieuwoudt ◽

Angela Brooks-Wilson ◽

Jinko Graham

Keyword(s):

Sequence Data ◽

R Package ◽

Supplementary Information ◽

Supplementary Data ◽

Single Nucleotide Variants ◽

Single Nucleotide ◽

Genetic Sequence ◽

Large Numbers

Abstract Summary We present the R package SimRVSequences to simulate sequence data for pedigrees. SimRVSequences allows for simulations of large numbers of single-nucleotide variants (SNVs) and scales well with increasing numbers of pedigrees. Users provide a sample of pedigrees and SNV data from a sample of unrelated individuals. Availability and implementation SimRVSequences is publicly-available on CRAN https://cran.r-project.org/web/packages/SimRVSequences/. Supplementary information Supplementary data are available at Bioinformatics online.

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Tuberculosis (TB) and Noodletree: Verifying Genetic Sequence Data and Functionality of a Generally Transduced M. Tuberculosis Toxin through a Viral Carrier

Biophysical Journal ◽

10.1016/j.bpj.2017.11.3592 ◽

2018 ◽

Vol 114 (3) ◽

pp. 666a

Author(s):

Britt Int-Hout ◽

Lydia Flores

Keyword(s):

Sequence Data ◽

Genetic Sequence

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How Genetic Sequence Data Can Guide Vaccine Design

IEEE Potentials ◽

10.1109/mpot.2020.2967896 ◽

2020 ◽

Vol 39 (3) ◽

pp. 31-37 ◽

Cited By ~ 1

Author(s):

Muhammad Saqib Sohail ◽

Ahmed A. Quadeer ◽

Matthew R. McKay

Keyword(s):

Sequence Data ◽

Vaccine Design ◽

Genetic Sequence

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Codon usage tabulated from the GenBank genetic sequence data

Nucleic Acids Research ◽

10.1093/nar/18.suppl.2367 ◽

1990 ◽

Vol 18 (suppl) ◽

pp. 2367-2411 ◽

Cited By ~ 103

Author(s):

K.-n. Wada ◽

S.-i. Aota ◽

R. Tsuchiya ◽

F. Ishibashi ◽

T. Gojobori ◽

...

Keyword(s):

Codon Usage ◽

Sequence Data ◽

Genetic Sequence

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A Novel Insight into the Perfect Phylogeny Problem

Annals of Combinatorics ◽

10.1007/s00026-006-0276-8 ◽

2006 ◽

Vol 10 (1) ◽

pp. 97-109 ◽

Cited By ~ 1

Author(s):

S. Grünewald ◽

K. T. Huber

Keyword(s):

Perfect Phylogeny ◽

Insight Into

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Some Monotone Almost Balanced Knockout Tournament Plans

Probability in the Engineering and Informational Sciences ◽

10.1017/s0269964800002977 ◽

1993 ◽

Vol 7 (3) ◽

pp. 361-368

Author(s):

R. W. Chen ◽

F. K. Hwang ◽

Y. C. Yao ◽

A. Zame

Keyword(s):

Real World ◽

General Class ◽

Binary Tree ◽

The Real ◽

Random Labeling ◽

Knockout Tournament ◽

Rooted Binary Tree

Knockout tournaments are often used in sports (or experiments where preferences are registered by comparisons instead of measurements) to determine the champion of an event. A knockout tournament plan (KTP) for n players is a rooted binary tree with n leaves to be labeled by the n players. Each subtree of two leaves represents a match between the two players labeling the two leaves; the winner of the match then moves on to label the root of the subtree. While there are many KTPs to choose from for a given number of players, in the real world an almost balanced KTP is usually chosen. One reason could be the perception that a balanced KTP is “fair” to the players, in the sense that, given a random labeling of leaves by players, a stronger player has a better chance to win the tournament. Surprisingly, it has been shown that not all KTPs have this property, and it is difficult to prove this property for any general class of KTPs. So far the property has been shown to hold only for balanced KTPs. In this paper we extend it to some classes of almost balanced KTPs.

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coil: an R package for cytochrome c oxidase I (COI) DNA barcode data cleaning, translation, and error evaluation

Genome ◽

10.1139/gen-2019-0206 ◽

2020 ◽

Vol 63 (6) ◽

pp. 291-305 ◽

Cited By ~ 5

Author(s):

Cameron M. Nugent ◽

Tyler A. Elliott ◽

Sujeevan Ratnasingham ◽

Sarah J. Adamowicz

Keyword(s):

Cytochrome C ◽

Cytochrome C Oxidase ◽

Sequence Data ◽

Dna Barcode ◽

R Package ◽

Data Generation ◽

Error Identification ◽

Reading Frame ◽

Biological Contaminants ◽

And Function

Biological conclusions based on DNA barcoding and metabarcoding analyses can be strongly influenced by the methods utilized for data generation and curation, leading to varying levels of success in the separation of biological variation from experimental error. The 5′ region of cytochrome c oxidase subunit I (COI-5P) is the most common barcode gene for animals, with conserved structure and function that allows for biologically informed error identification. Here, we present coil ( https://CRAN.R-project.org/package=coil ), an R package for the pre-processing and frameshift error assessment of COI-5P animal barcode and metabarcode sequence data. The package contains functions for placement of barcodes into a common reading frame, accurate translation of sequences to amino acids, and highlighting insertion and deletion errors. The analysis of 10 000 barcode sequences of varying quality demonstrated how coil can place barcode sequences in reading frame and distinguish sequences containing indel errors from error-free sequences with greater than 97.5% accuracy. Package limitations were tested through the analysis of COI-5P sequences from the plant and fungal kingdoms as well as the analysis of potential contaminants: nuclear mitochondrial pseudogenes and Wolbachia COI-5P sequences. Results demonstrated that coil is a strong technical error identification method but is not reliable for detecting all biological contaminants.

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