Statistically Consistent <i>k</i>-mer Methods for Phylogenetic Tree Reconstruction

Allman, Elizabeth S.; Rhodes, John A.; Sullivant, Seth

doi:10.1089/cmb.2015.0216

Cited by 24 publications

(19 citation statements)

References 22 publications

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“…In the simplest case, each pairwise measure can be transformed into a distance, and a matrix of such distances used as input for computing a distance tree, e.g. by neighbour-joining 20 21 22 . Evidence is accumulating that in phylogenetic inference per se , these alignment-free methods can offer acceptable performance – in certain cases better than approaches based on multiple sequence alignment – at much greater computational speed and scalability 19 .…”

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confidence: 99%

Exploring lateral genetic transfer among microbial genomes using TF-IDF

Cong

Chan

Ragan

2016

Sci Rep

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Many microbes can acquire genetic material from their environment and incorporate it into their genome, a process known as lateral genetic transfer (LGT). Computational approaches have been developed to detect genomic regions of lateral origin, but typically lack sensitivity, ability to distinguish donor from recipient, and scalability to very large datasets. To address these issues we have introduced an alignment-free method based on ideas from document analysis, term frequency-inverse document frequency (TF-IDF). Here we examine the performance of TF-IDF on three empirical datasets: 27 genomes of Escherichia coli and Shigella, 110 genomes of enteric bacteria, and 143 genomes across 12 bacterial and three archaeal phyla. We investigate the effect of k-mer size, gap size and delineation of groups on the inference of genomic regions of lateral origin, finding an interplay among these parameters and sequence divergence. Because TF-IDF identifies donor groups and delineates regions of lateral origin within recipient genomes, aggregating these regions by gene enables us to explore, for the first time, the mosaic nature of lateral genes including the multiplicity of biological sources, ancestry of transfer and over-writing by subsequent transfers. We carry out Gene Ontology enrichment tests to investigate which biological processes are potentially affected by LGT.

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mentioning

confidence: 99%

Exploring lateral genetic transfer among microbial genomes using TF-IDF

Cong

Chan

Ragan

2016

Sci Rep

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“…An inner product between k -mer counts has long been used to detect and measure sequence similarity, and is referred to as the D 2 statistic. There have been many derivatives of the D 2 statistic that seek to enhance its accuracy in recreating evolutionary histories (e.g., , and [ 20 – 22 ]). does not attempt to re-create evolutionary histories, but rather estimates the similarity of genetic material as it exists today.…”

Section: Discussionmentioning

confidence: 99%

kWIP: The k-mer weighted inner product, a de novo estimator of genetic similarity

et al. 2017

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Modern genomics techniques generate overwhelming quantities of data. Extracting population genetic variation demands computationally efficient methods to determine genetic relatedness between individuals (or “samples”) in an unbiased manner, preferably de novo. Rapid estimation of genetic relatedness directly from sequencing data has the potential to overcome reference genome bias, and to verify that individuals belong to the correct genetic lineage before conclusions are drawn using mislabelled, or misidentified samples. We present the k-mer Weighted Inner Product (), an assembly-, and alignment-free estimator of genetic similarity. combines a probabilistic data structure with a novel metric, the weighted inner product (WIP), to efficiently calculate pairwise similarity between sequencing runs from their k-mer counts. It produces a distance matrix, which can then be further analysed and visualised. Our method does not require prior knowledge of the underlying genomes and applications include establishing sample identity and detecting mix-up, non-obvious genomic variation, and population structure. We show that can reconstruct the true relatedness between samples from simulated populations. By re-analysing several published datasets we show that our results are consistent with marker-based analyses. is written in C++, licensed under the GNU GPL, and is available from https://github.com/kdmurray91/kwip.

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“…The normalized frequency of each kind of k-mer consists of the k-mer encoding. It is widely used in genome assembly, clustering and capturing nucleotide or protein sequences' features [26][27][28]. 3) PseDNC/PseKNC:compared with k-mer, PseDNC/PseKNC considered more global information and introduced the physical and chemical properties of nucleotides.…”

Section: Sequence Encoding Strategymentioning

confidence: 99%

A Mini-review of the Computational Methods Used in Identifying RNA 5- Methylcytosine Sites

Huang

Zhou

2020

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RNA 5-methylcytosine (m5C) is one of the pillars of post-transcriptional modification (PTCM). A growing body of evidence suggests that m5C plays a vital role in RNA metabolism. Accurate localization of RNA m5C sites in tissue cells is the premise and basis for the in-depth understanding of the functions of m5C. However, the main experimental methods of detecting m5C sites are limited to varying degrees. Establishing a computational model to predict modification sites is an excellent complement to wet experiments for identifying m5C sites. In this review, we summarized some available m5C predictors and discussed the characteristics of these methods.

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Statistically Consistent k-mer Methods for Phylogenetic Tree Reconstruction

Cited by 24 publications

References 22 publications

Exploring lateral genetic transfer among microbial genomes using TF-IDF

Exploring lateral genetic transfer among microbial genomes using TF-IDF

kWIP: The k-mer weighted inner product, a de novo estimator of genetic similarity

A Mini-review of the Computational Methods Used in Identifying RNA 5- Methylcytosine Sites

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