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14 results for “Alignment free”

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zenodo40/100

Supplementary material: Picosecond pulse-shaping for strong three-dimensional field-free alignment of generic asymmetric-top molecules

Supplementary material to the manuscript "Picosecond pulse-shaping for strong three-dimensional field-free alignment of generic asymmetric-top molecules" by Terry Mullins, Evangelos T. Karamatskos, Joss Wiese, Jolijn Onvlee, Arnaud Rouzée, Andrey Yachmenev, Sebastian Trippel, and Jochen Küpper, Nat Commun 13, 1431 (2022). <a href="https://doi.org/10.1038/s41467-022-28951-z">https://doi.org/10.1038/s41467-022-28951-z</a>, arXiv: <a href="https://arxiv.org/abs/2009.08157">2009.08157 </a> <ul> <li> simulations_part.z* is split zip archive containing simulations data for indole molecule, such as files with rotational probability density distributions computed at different times \(t=0..1500\) ps during the laser pulse and field-free evolution, and example python scripts for data retrieval.</li> <li>rawdata_part.z* is split zip archive containing raw experimental data.</li> <li>analysis_scripts.zip is zip archive containing experimental analysis codes.</li> </ul> The simulations_part.zip contains the following files and folders: <ul> <li>prob_density_euler_angles contains files rotdens_av_<time>.gz with simulated state-averaged rotational probability density distributions in terms of Euler angles for different times <time>, ranging from the beginning of the alignment laser pulse at \(t=0\) up to \(t=1500\) ps with a time step of 1 ps. Calculations of probability density distributions were done using <a href="https://github.com/CFEL-CMI/richmol">Richmol</a> program. The gzipped ASCII data files rotdens_av_<time>.gz contain in columns the values of the Euler angles \(\phi,\theta,\chi\) followed by the normalized probability density value.</li> <li>prob_density_atoms_xyz contains files monte_carlo_av_<time>.h5 with state-averaged rotational probability density distributions of all atoms in the indole molecule in terms of their Cartesian coordinates, for different times <time>, ranging from the beginning of the alignment pulse at \(t=0\) up to \(t=1500\) ps with a time step of 1 ps. Structure of monte_carlo_av_<time>.h5 HDF5 files: Key        Description -----        ---------------- 'C10'     - Cartesian coordinates of carbon atom no. 10 'C11'     - Cartesian coordinates of carbon atom no. 11 'C12'     - ... 'C14'     - ... 'C3'      - ... 'C6'      - ... 'C7'      - ... 'C9'      - ... 'N4'      - ... 'H1-C3'   - Cartesian coordinates of a vector pointing from carbon atom no. 3 to hydrogen atom no. 1 'H13-C11' - ... 'H15-C12' - ... 'H16-C14' - ... 'H2-N4'   - ... 'H5-C7'   - ... 'H8-C9'   - ... 'ref_vectors' - reference molecular-frame Cartesian coordinates of all atoms 'x'       - coordinates of the x-axis of Principal Axes of Inertia Frame 'y'       - coordinates of the y-axis of Principal Axes of Inertia Frame 'z'       - coordinates of the z-axis of Principal Axes of Inertia Frame 'pol_x'   - coordinates of the x-axis of Principal Axes of Polarizability Frame 'pol_y'   - coordinates of the y-axis of Principal Axes of Polarizability Frame 'pol_z'   - coordinates of the z-axis of Principal Axes of Polarizability Frame</li> <li>indole_deflected_states.txt ASCII file contains initial populations of rotational states of indole in the deflected beam. The following data is arranged in columns: m, J, id, energy, normalized population. The J and m are rotational quantum numbers of the total angular momentum and its Z-projection, the id number refers to the state's index in file indole_energies_j0_j20.txt listing rotational states of indole.</li> <li>indole_data.py Python module provides basic functions to extract information from HDF5 data files monte_carlo_av_<time>.h5. It can also be used to compute alignment and orientation.</li> <li>example_cos.py and example_dens.py Python scripts that demonstrate how to use indole_data.py module for computing and plotting alignment traces and a 2D projection of the probability density distribution, respectively.</li> <li>monte_carlo.py Python script that was used to compute through Monte-Carlo sampling probability density distributions for Cartesian positions of atoms in indole (monte_carlo_av_<time>.h5 files) using probability density distribution functions in Euler angles (outputs of Richmol program rotdens_av_<time>.gz).</li> </ul> The analysis_scripts.zip contains the following files and folders: <ul> <li>H_Plus folder contains codes relevant for the analysis of H+ ion data. <ul> <li>analyse_full_alignment_scans.m: subtracts background and combines delay scan data sets together, takes account of errors.</li> <li>calculate_resamped_df.m: called by analyse_full_alignment_scans.m to calculate the frequency sampling.</li> <li>unique_mean.m: called by analyse_full_alignment_scans.m when combining data sets. Combines non-unique data points into a single data point.</li> </ul> </li> <li>C_Plus2 folder contains codes for the analysis of C2+ ion data. The file descriptions are identical to those in the H_Plus directory.</li> <li>intensity/calculate_intensity.m: calculates peak intensity of the laser pulse from measured parameters as well as statistical error.</li> <li>intensity/compare_exp_sim.m: fits experimental and theoretical tomography and delay-dependent 2D projection values.</li> <li>intensity/nir2hdf5_kHz.py: converts raw data files (from rawdata_part.z* archive) into hdf5 files.</li> </ul>

opencc-by-4.0Jan 2022View details →

zenodo40/100

InpactorDB: A Plant classified lineage-level LTR retrotransposon reference library for free-alignment methods based on Machine Learning

LTR retrotransposons are mobile elements that make up the major part of most plant genomes. Their identification and annotation via bioinformatics approaches represent a major challenge in the era of massive plant genome sequencing. In addition to their involvement in the variation in genome size, these elements are also associated in the function and structure of different chromosomal regions and in the alteration of the function of coding regions, among others. Several plant retrotransposon sequence databases of LTR retrotransposons are available with public access such as PGSB, RepetDB or restricted access such as Repbase. Although they are useful for approaches to identify LTR-RTs in new genomes by similarity, the elements of these databases are not classified down to the lineage/family level. with great depth.  Here, we present InpactorDB a semi-curated dataset composed of 130,511 elements from 195 plant genomes (belonging to 108 plant species), classified down to the lineage level. This data set has been used to train two deep neural networks (one fully connected and one convolutional) for fast classification of elements. Used in lineage-level classification approaches, we obtain a score above 98% of F1-score, precision and recall.  In order to classify elements of the ‘LTR_STRUC’ and ‘EDTA’ datasets, we used the methodology proposed by Inpactor, which uses homology-based strategy with known coding domains belonging to LTR-RTs. We utilized the RexDB  domain library as reference. LTR-RTs were classified into superfamilies, Gypsy (RLG) or Copia (RLC) and sub-classified into lineages according to the similarities of five different amino acid reference domains (GAG, AP, RT, RNAseH, and INT domains). In addition, we applied filters to remove keep only intact elements: 1) to remove predicted elements with domains from two different superfamilies (i.e. Gypsy and Copia), 2) or elements with domains belonging to two or more different lineages, 3) to remove elements with lengths different than those reported by Gypsy Database (GyDB) with a tolerance of 20%, 4) to delete incomplete elements which has less than three identified domains, and 5) to remove elements with insertions of TE class II (reported in Repbase).  The final non-redundant version of InpactorDB consists of 67,305 LTR retrotransposons. Both redundant and non-redundant versions of InpactorDB are available in Fasta format in which sequences have identifiers with the following general Identification code: >Superfamily-Lineage-plant_family-specie-source-length-ID, Where Superfamily can is either RLC (for Copia) or RLG (for Gypsy), Lineage/family follows following the RexDB nomenclature, source (can be Repbase, RepetDB, PGSB, LTR_STRUC or EDTA datasets), length, and ID, is a unique number which identify each element inside the InpactorDB.

opencc-by-4.0Dec 2020View details →

zenodo40/100

Reference data for "Limitations of alignment-free tools in total RNA-seq quantification"

This repository contains reference data for "Limitations of alignment-free tools in total RNA-seq quantification"

opencc-by-4.0Apr 2018View details →

zenodo36/100

Alignment free identification of clones in Bcell receptor repertoires

The data sets are used for the analysis of the Alignment free [1] clonal identification approach. [1] "Alignment free identification of clones in B cell receptor repertoires", Ofir Lindenbaum,  Nima Nouri, Yuval Kluger, Steven H. Kleinstein . Preprint available at: https://www.biorxiv.org/content/10.1101/2020.03.30.017384v1

opencc-by-4.0Aug 2020View details →

zenodo32/100

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

opencc-zeroSep 2020View details →

zenodo32/100

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

opencc-zeroSep 2020View details →

zenodo32/100

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

opencc-zeroSep 2020View details →

zenodo32/100

The dataset for "Force-free current sheets in the Jovian magnetodisk: a key role of electron field-aligned anisotropy"

The dataset for "Force-free current sheets in the Jovian magnetodisk: a key role of electron field-aligned anisotropy" submitted to J. Geophys. Res. This dataset contains information about 18 current sheet in the Jovian magnetodisk. PDF file includes the table and eighteen figures with current sheets. The table lists corresponding dates/times of the current sheet crossings by Juno spacecraft. Archive includes ASCII flies with data plotted on these figures.

opencc-by-4.0Dec 2022View details →

zenodo28/100

Simulated pairs of nucleotide sequences for testing (alignment-free) genome distance estimate methods

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 d 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/">INDELible</a> was used to simulate the evolution of 200 nucleotide sequence pairs with d substitution events per character under the models GTR and GTR+Γ. Each model was adjusted with three different equilibrium frequencies: <ul> <li>f1: equal frequencies, i.e. freq(A) = freq(C) = freq(G) = freq(T) = 0.25,</li> <li>f2: GC-rich, i.e. freq(A) = 0.1, freq(C) = 0.3, freq(G) = 0.4, freq(T) = 0.2,</li> <li>f3: AT-rich, i.e. freq(A) = freq(T) = 0.4, freq(C) = freq(G) = 0.1.</li> </ul> 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 INDELible <a href="http://abacus.gene.ucl.ac.uk/software/indelible/manual/model.shtml">manual</a>).   For each of the 20 evolutionary distances d = 0.05, 0.10, ..., 1.00, six XZ-compressed files containing 200 simulation data are available: <ul> <li><code>data-d-f1-nogam.tsv.xz</code>   data simulated under the model GTR with equilibrium frequencies f1</li> <li><code>data-d-f1-gamma.tsv.xz</code>   data simulated under the model GTR+Γ with equilibrium frequencies f1</li> <li><code>data-d-f2-nogam.tsv.xz</code>   data simulated under the model GTR with equilibrium frequencies f2</li> <li><code>data-d-f2-gamma.tsv.xz</code>   data simulated under the model GTR+Γ with equilibrium frequencies f2</li> <li><code>data-d-f3-nogam.tsv.xz</code>   data simulated under the model GTR with equilibrium frequencies f3</li> <li><code>data-d-f3-gamma.tsv.xz</code>   data simulated under the model GTR+Γ with equilibrium frequencies f3</li> </ul>   Each file is tab-delimited and contains the 18 following fields: <ul> <li><code>[1]   </code>   integer seed value specified to INDELible,</li> <li><code>[2-5] </code>   frequencies of T, C, A, G, respectively, specified to INDELible,</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 INDELible,</li> <li><code>[11]   </code>   Γ shape parameter alpha (= 0 in the <code>nogam</code> files, i.e. GTR substitution model without Γ) specified to INDELible,</li> <li><code>[12]   </code>   length lgt1 of the first sequence seq1 (i.e. no. A, C, G, T in seq1),</li> <li><code>[13]   </code>   length lgt2 of the second sequence seq2 (i.e. no. A, C, G, T in seq2),</li> <li><code>[14]   </code>   no. sites in aligned sequences seq1 and seq2 (i.e. no. A, C, G, T and gap character states in seq1 or seq2),</li> <li><code>[15]   </code>   no. non-gapped sites (core sites) in aligned sequences seq1 and seq2,</li> <li><code>[16]   </code>   observed p-distance between aligned sequences seq1 and seq2 (i.e. no. nucleotide mismatches divided by no. core sites),</li> <li><code>[17]   </code>   aligned seq1 (containing indel gaps),</li> <li><code>[18]   </code>   aligned seq2 (containing indel gaps).</li> </ul> Of note, seq1 and seq2 (fields <code>[17-18]</code>) being aligned, these two entries are two strings with identical no. sites (field <code>[14]</code>). Gap character states (<code>-</code>) should be removed from seq1 and seq2 to obtain the unaligned sequences. _____ Criscuolo A (2020) On the transformation of MinHash-based uncorrected distances into proper evolutionary distances for phylogenetic inference. F1000Research, 9:1309. <a href="https://doi.org/10.12688/f1000research.26930.1">doi:10.12688/f1000research.26930.1</a>

opencc-by-4.0Sep 2020View details →

zenodo28/100

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

opencc-zeroSep 2020View details →

zenodo24/100

Simulated nucleotide sequences for testing alignment-free genome distance estimates

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 d 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 d substitution events per character (GTR+Γ evolutionary model). For each of the 12 evolutionary distances d = 0.05, 0.10, ..., 0.60, an XZ-compressed file containing 500 lines is available. Each line contains 18 fields separated by blank spaces:   [1]     seed value used during simulation,   [2]     true evolutionary distance d between the two simulated sequences,   [3]     total number of simulated characters,   [4]     number of non-indel characters with nucleotide mismatch,   [5]     number of non-indel characters,   [6-9]   A, C, G, T frequencies used during simulation,   [10-15]   GTR parameters used during simulation,   [16]     Γ distribution parameter used during simulation,   [17-18]   two simulated sequences with indel events as gaps. 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: <pre>(t1:d,t2:0.000);</pre> where d is given in field [2]. ___ Criscuolo A (2019) A fast alignment-free bioinformatics procedure to infer accurate distance-based phylogenetic trees from genome assemblies. Research Ideas and Outcomes, 5:e36178. doi:<a href="https://doi.org/10.3897/rio.5.e36178">10.3897/rio.5.e36178</a>

opencc-by-4.0Sep 2020View details →

dryad24/100

Alignment-free methods for polyploid genomes: quick and reliable genetic distance estimation

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.

opencc-zeroJul 2021View details →

dryad24/100

Alignment-free methods for polyploid genomes: quick and reliable genetic distance estimation

Open the record for dataset details and reuse information.

publicJul 2021View details →

zenodo4/100

Seeker: Alignment-free identification of bacteriophage genomes by deep learning

Training and testing data.

restrictedSep 2020View details →

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