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10 results for “alignment-free”
Reference data for "Limitations of alignment-free tools in total RNA-seq quantification"
<p>This repository contains reference data for "Limitations of alignment-free tools in total RNA-seq quantification"</p>
Supplementary material 2 from: Nugent CM, Adamowicz SJ (2020) Alignment-free classification of COI DNA barcode data with the Python package Alfie. Metabarcoding and Metagenomics 4: e55815. https://doi.org/10.3897/mbmg.4.55815
File S2 – Python script for custom grid search of hyperparameters for optimization of the neural network
Supplementary material 3 from: Nugent CM, Adamowicz SJ (2020) Alignment-free classification of COI DNA barcode data with the Python package Alfie. Metabarcoding and Metagenomics 4: e55815. https://doi.org/10.3897/mbmg.4.55815
File S3 – The parameters utilized in the grid search for each of the five machine learning algorithms tested in the design of the Alfie package
Supplementary material 4 from: Nugent CM, Adamowicz SJ (2020) Alignment-free classification of COI DNA barcode data with the Python package Alfie. Metabarcoding and Metagenomics 4: e55815. https://doi.org/10.3897/mbmg.4.55815
File S4 – Jupyter notebook with tutorial demonstrating how to apply the Alfie classifier in the Python programming language, and how to train custom alignment-free classifiers using the Alfie training module
Simulated pairs of nucleotide sequences for testing (alignment-free) genome distance estimate methods
<p>This repository contains 24,000 pairs of nucleotide sequences (and associated parameters) that have been simulated for testing alignment-free genome distance estimates. Given an evolutionary distance <em>d</em> varying from 0.05 to 1.00 nucleotide substitutions per character (step = 0.05), the program <a href="http://abacus.gene.ucl.ac.uk/software/indelible/"><em>INDELible</em></a> was used to simulate the evolution of 200 nucleotide sequence pairs with <em>d</em> substitution events per character under the models GTR and GTR+Γ. Each model was adjusted with three different equilibrium frequencies:</p> <ul> <li><em>f</em><sub>1</sub>: equal frequencies, i.e. freq(A) = freq(C) = freq(G) = freq(T) = 0.25,</li> <li><em>f</em><sub>2</sub>: GC-rich, i.e. freq(A) = 0.1, freq(C) = 0.3, freq(G) = 0.4, freq(T) = 0.2,</li> <li><em>f</em><sub>3</sub>: AT-rich, i.e. freq(A) = freq(T) = 0.4, freq(C) = freq(G) = 0.1.</li> </ul> <p>For each simulated sequence pair, model parameters (i.e. GTR: six relative rates of nucleotide substitution; GTR+Γ: six rates and one Γ shape parameter) were randomly drawn from 142 sets of parameters derived from real-case data (see file <a href="https://zenodo.org/record/4034261/files/GTR.params.trees.tsv?download=1">GTR.params.trees.tsv</a> at <a href="https://zenodo.org/record/4034261">https://zenodo.org/record/4034261</a>). Initial sequence length was 5 Mbs, and an indel rate of 0.01 was set with indel length drawn from [1, 50000] according to a Zipf distribution with parameter 1.5 (see <em>INDELible</em> <a href="http://abacus.gene.ucl.ac.uk/software/indelible/manual/model.shtml">manual</a>).</p> <p> </p> <p>For each of the 20 evolutionary distances <em>d</em> = 0.05, 0.10, ..., 1.00, six XZ-compressed files containing 200 simulation data are available:</p> <ul> <li><code>data-d-f1-nogam.tsv.xz</code> data simulated under the model GTR with equilibrium frequencies <em>f</em><sub>1</sub></li> <li><code>data-d-f1-gamma.tsv.xz</code> data simulated under the model GTR+Γ with equilibrium frequencies <em>f</em><sub>1</sub></li> <li><code>data-d-f2-nogam.tsv.xz</code> data simulated under the model GTR with equilibrium frequencies <em>f</em><sub>2</sub></li> <li><code>data-d-f2-gamma.tsv.xz</code> data simulated under the model GTR+Γ with equilibrium frequencies <em>f</em><sub>2</sub></li> <li><code>data-d-f3-nogam.tsv.xz</code> data simulated under the model GTR with equilibrium frequencies <em>f</em><sub>3</sub></li> <li><code>data-d-f3-gamma.tsv.xz</code> data simulated under the model GTR+Γ with equilibrium frequencies <em>f</em><sub>3</sub></li> </ul> <p> </p> <p>Each file is tab-delimited and contains the 18 following fields:</p> <ul> <li><code>[1] </code> integer <em>seed</em> value specified to <em>INDELible</em>,</li> <li><code>[2-5] </code> frequencies of T, C, A, G, respectively, specified to <em>INDELible</em>,</li> <li><code>[6-10] </code> C-T, A-T, G-T, A-C, C-G rate parameters, respectivly (normalized such that A-G rate = 1), specified to <em>INDELible</em>,</li> <li><code>[11] </code> Γ shape parameter <em>alpha</em> (= 0 in the <code>nogam</code> files, i.e. GTR substitution model without Γ) specified to <em>INDELible</em>,</li> <li><code>[12] </code> length <em>lgt1</em> of the first sequence <em>seq1</em> (i.e. no. A, C, G, T in <em>seq1</em>),</li> <li><code>[13] </code> length <em>lgt2</em> of the second sequence <em>seq2</em> (i.e. no. A, C, G, T in <em>seq2</em>),</li> <li><code>[14] </code> no. <em>sites</em> in aligned sequences <em>seq1</em> and <em>seq2</em> (i.e. no. A, C, G, T and gap character states in <em>seq1</em> or <em>seq2</em>),</li> <li><code>[15] </code> no. non-gapped sites (<em>core</em> sites) in aligned sequences <em>seq1</em> and <em>seq2</em>,</li> <li><code>[16] </code> observed <em>p-distance</em> between aligned sequences <em>seq1</em> and <em>seq2</em> (i.e. no. nucleotide mismatches divided by no. <em>core</em> sites),</li> <li><code>[17] </code> aligned <em>seq1</em> (containing indel gaps),</li> <li><code>[18] </code> aligned <em>seq2</em> (containing indel gaps).</li> </ul> <p>Of note, <em>seq1</em> and <em>seq2</em> (fields <code>[17-18]</code>) being aligned, these two entries are two strings with identical no. <em>sites</em> (field <code>[14]</code>). Gap character states (<code>-</code>) should be removed from <em>seq1</em> and <em>seq2</em> to obtain the unaligned sequences.</p> <p>_____</p> <p>Criscuolo A (2020) <em>On the transformation of MinHash-based uncorrected distances into proper evolutionary distances for phylogenetic inference</em>. F1000Research, 9:1309. <a href="https://doi.org/10.12688/f1000research.26930.1">doi:10.12688/f1000research.26930.1</a></p>
Supplementary material 1 from: Nugent CM, Adamowicz SJ (2020) Alignment-free classification of COI DNA barcode data with the Python package Alfie. Metabarcoding and Metagenomics 4: e55815. https://doi.org/10.3897/mbmg.4.55815
File S1 – Training, test, and validation data sets used in model training and analysis
Simulated nucleotide sequences for testing alignment-free genome distance estimates
<p>This repository contains (12×500=)6,000 pairs of nucleotide sequences that have been simulated for testing alignment-free genome distance estimates, as described in <a href="https://riojournal.com/article/36178/">Criscuolo (2019)</a>. Given an evolutionary distance <em>d</em> varying from 0.05 to 0.60 (step = 0.05), the program <a href="http://tree.bio.ed.ac.uk/software/seqgen/">SeqGen</a> was used to simulate the evolution of 500 nucleotide sequence pairs with <em>d</em> substitution events per character (GTR+Γ evolutionary model).</p> <p>For each of the 12 evolutionary distances <em>d</em> = 0.05, 0.10, ..., 0.60, an XZ-compressed file containing 500 lines is available. Each line contains 18 fields separated by blank spaces:<br> [1] seed value used during simulation,<br> [2] true evolutionary distance <em>d</em> between the two simulated sequences,<br> [3] total number of simulated characters,<br> [4] number of non-indel characters with nucleotide mismatch,<br> [5] number of non-indel characters,<br> [6-9] A, C, G, T frequencies used during simulation,<br> [10-15] GTR parameters used during simulation,<br> [16] Γ distribution parameter used during simulation,<br> [17-18] two simulated sequences with indel events as gaps.</p> <p>Of note, each pair of aligned sequences without gaps can be regenerated using <a href="http://tree.bio.ed.ac.uk/software/seqgen/">SeqGen</a> v1.3.4 with parameters from fields [1,3,6-16] and the following two-leaf model tree:</p> <pre>(t1:d,t2:0.000);</pre> <p>where <em>d</em> is given in field [2].</p> <p>___</p> <p>Criscuolo A (2019) <em>A fast alignment-free bioinformatics procedure to infer accurate distance-based phylogenetic trees from genome assemblies</em>. Research Ideas and Outcomes, 5:e36178. doi:<a href="https://doi.org/10.3897/rio.5.e36178">10.3897/rio.5.e36178</a></p>
Alignment-free methods for polyploid genomes: quick and reliable genetic distance estimation
<p>Polyploid genomes pose several inherent challenges to population genetic analyses. While alignment-based methods are fundamentally limited in their applicability to polyploids, alignment-free methods bypass most of these limits. We investigated the use of Mash, a k-mer analysis tool that uses the MinHash method to reduce complexity in large genomic datasets, for basic population genetic analyses of polyploid sequences. We measured the degree to which Mash correctly estimated pairwise genetic distance in simulated haploid and polyploid short-read sequences with various levels of missing data. Mash-based estimates of genetic distance were comparable to alignment-based estimates, and were less impacted by missing data. We also used Mash to analyze publicly available short-read data for three polyploid and one diploid species, then compared Mash results to published results. For both simulated and real data, Mash accurately estimated pairwise genetic differences for polyploids as well as diploids as much as 476 times faster than alignment-based methods, though we found that Mash genetic distance estimates could be biased by per-sample read depth. Mash may be a particularly useful addition to the toolkit of polyploid geneticists for rapid confirmation of alignment-based results and for basic population genetics in reference-free systems or those with only poor quality sequence data available.</p>
Alignment-free methods for polyploid genomes: quick and reliable genetic distance estimation
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Seeker: Alignment-free identification of bacteriophage genomes by deep learning
<p>Training and testing data.</p>
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