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492 results for “sequence modeling”
Data from: The importance of being genomic: non-coding and coding sequences suggest different models of toxin multi-gene family evolution
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Dynamics of a host-parasitoid interaction clarified by modelling and DNA sequencing
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Data from: Genome-wide single nucleotide polymorphism (SNP) identification and characterization in a non-model organism, the African buffalo (Syncerus caffer), using next generation sequencing
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Data from: Genotyping-by-sequencing for estimating relatedness in non-model organisms: avoiding the trap of precise bias
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Figure 3. D in Neotypification of Drawida hattamimizu Hatai, 1930 (Annelida, Oligochaeta, Megadrili, Moniligastridae) as a model linking mtDNA (COI) sequences to an earthworm type, with a response to the 'Can of Worms' theory of cryptic species
Figure 3. D. hattamimizu detailed internal anatomy showing the disputed paired nephridial funnels ("n.m.") sketched for only three of the nephridia (after Hatai, 1930: fig. 4).
Model trees and associated simulated nucleotide sequences for testing phylogenetic inference methods
<p>This repository contains 142 tar.gz archive files, each containing nucleotide sequence data that have been simulated using <a href="http://abacus.gene.ucl.ac.uk/software/indelible/"><em>INDELible</em></a> for testing alignment-free phylogenetic inference methods. These datasets were generated by using the results (trees and model parameters) of 142 phylogenomic analyses of real-case data as model (available <a href="https://zenodo.org/record/4034261">here</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>Each archive contains the following files/directories:</p> <ul> <li><code>GTR.params.trees.tsv </code> a tab-delimited file summarizing the real-case GTR+Γ model parameters and the phylogenetic tree used to simulate the sequence dataset (gathered from <a href="https://zenodo.org/record/4034261">https://zenodo.org/record/4034261</a>)</li> <li><code>tax.tsv </code> a tab-delimited file containing the initial (col 1) and simplified (col 2) taxon names</li> <li><code>model.nwk </code> a <a href="https://evolution.genetics.washington.edu/phylip/newicktree.html">Newick</a>-formatted file containing the initial model tree (gathered from <code>GTR.params.trees.tsv</code>) with simplified leaf names (following <code>tax.tsv</code>)</li> <li><code>control.txt </code> the <em>INDELible</em> input file used to simulate the evolution of a sequence along the tree in <code>model.nwk</code></li> <li><code>seq/ </code> a directory containing the simulated sequences (one FASTA file per leaf in the tree in <code>model.nwk</code>)</li> </ul> <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>
Data from: Testing models of speciation from genome sequences: divergence and asymmetric admixture in Island Southeast Asian Sus species during the Plio-Pleistocene climatic fluctuations
In many temperate regions, ice ages promoted range contractions into refugia resulting in divergence (and potentially speciation), while warmer periods led to range expansions and hybridization. However, the impact these climatic oscillations had in many parts of the tropics remains elusive. Here, we investigate this issue using genome sequences of three pig (Sus) species, two of which are found on islands of the Sunda-shelf shallow seas in Island Southeast Asia (ISEA). A previous study revealed signatures of inter-specific admixture between these Sus species (Frantz et al. (2013) Genome sequencing reveals fine scale diversification and reticulation history during speciation in Sus. Genome biology, 14, R107). However, the timing, directionality and extent of this admixture remain unknown. Here we use a likelihood based model comparison to more finely resolve this admixture history and test whether it was mediated by humans or occurred naturally. Our analyses suggest that inter-specific admixture between Sunda-shelf species was most likely asymmetric and occurred long before the arrival of humans in the region. More precisely, we show that these species diverged during the late Pliocene but around 23% of their genomes have been affected by admixture during the later Pleistocene climatic transition. In addition, we show that our method provides a significant improvement over D-statistics which are uninformative about the direction of admixture.
Data from: A branch-heterogeneous model of protein evolution for efficient inference of ancestral sequences
Most models of nucleotide or amino acid substitution used in phylogenetic studies assume that the evolutionary process has been homogeneous across lineages and that composition of nucleotides or amino acids has remained the same throughout the tree. These oversimplified assumptions are refuted by the observation that compositional variability characterizes extant biological sequences. Branch-heterogeneous models of protein evolution that account for compositional variability have been developed, but are not yet in common use because of the large number of parameters required, leading to high computational costs and potential overparameterization. Here, we present a new branch-nonhomogeneous and nonstationary model of protein evolution that captures more accurately the high complexity of sequence evolution. This model, henceforth called Correspondence and likelihood analysis (COaLA), makes use of a correspondence analysis to reduce the number of parameters to be optimized through maximum likelihood, focusing on most of the compositional variation observed in the data. The model was thoroughly tested on both simulated and biological data sets to show its high performance in terms of data fitting and CPU time. COaLA efficiently estimates ancestral amino acid frequencies and sequences, making it relevant for studies aiming at reconstructing and resurrecting ancestral amino acid sequences. Finally, we applied COaLA on a concatenate of universal amino acid sequences to confirm previous results obtained with a nonhomogeneous Bayesian model regarding the early pattern of adaptation to optimal growth temperature, supporting the mesophilic nature of the Last Universal Common Ancestor.
Data from: Kakusan4 and Aminosan: two programs for comparing nonpartitioned, proportional, and separate models for combined molecular phylogenetic analyses of multilocus sequence data
Proportional and separate models able to apply different combination of substitution rate matrix and among-site rate variation model to each locus are frequently used in phylogenetic studies of multilocus data. However, the selection from among nonpartitioned (i.e., a common combination of models is applied to all-loci concatenated sequences), proportional, and separate models is usually based on the researcher's preference rather than on any information criteria. The present study describes two programs, "Kakusan4" (for DNA sequences) and "Aminosan" (for amino-acid sequences), that allow the selection of evolutionary models based on several types of information criteria. The programs can handle both multilocus and single-locus data, in addition to providing an easy-to-use wizard interface and a non-interactive command line interface. In the case of multilocus data, substitution rate matrices and among-site rate variation models are compared at each locus and at all-loci concatenated sequences, after which nonpartitioned, proportional, and separate models are compared based on information criteria. The programs also provide model configuration files for MrBayes, PAUP*, PHYML, RAxML, and Treefinder to support further phylogenetic analysis using a selected model. The best-fit models were found to differ depending on the data set. Furthermore, differences in the information criteria among nonpartitioned, proportional, and separate models were much larger than those among the nonpartitioned models. These findings suggest that selecting from nonpartitioned, proportional, and separate models results in a better phylogenetic tree. Kakusan4 and Aminosan are available at http://www.fifthdimension.jp/. They are licensed under GNU GPL Ver.2, and are able to run on Windows, MacOS X, and Linux.
Data from: Complex models of sequence evolution require accurate estimators as exemplified with the invariable site plus Gamma model
The invariable site plus Γ model is widely used to model rate heterogeneity among alignment sites in maximum likelihood and Bayesian phylogenetic analyses. The proof that the invariable site plus continuous Γ model is identifiable (model parameters can be inferred correctly given enough data) has increased the creditability of its application to phylogeny reconstruction. However, most phylogenetic software implement the invariable site plus discrete Γ model, whose identifiability is likely but unproven. How well the parameters of the invariable site plus discrete Γ model are estimated is still disputed. Especially the correlation of the fraction of invariable sites with the fractions of sites with a slow evolutionary rate is discussed as being problematic. We show that optimization heuristics as implemented in frequently used phylogenetic software cannot always reliably estimate the shape parameter, the proportion of invariable sites and the tree length. Here, we propose an improved optimization heuristic that accurately estimates the three parameters. While research efforts mainly focus on tree search methods, our results signify the equal importance of verifying and developing effective estimation methods for complex models of sequence evolution.
Data from: Breakdown of phylogenetic signal: a survey of microsatellite densities in 454 shotgun sequences from 154 non model eukaryote species
Microsatellites are ubiquitous in Eukaryotic genomes. A more complete understanding of their origin and spread can be gained from a comparison of their distribution within a phylogenetic context. Although information for model species is accumulating rapidly, it is insufficient due to a lack of species depth, thus intragroup variation is necessarily ignored. As such, apparent differences between groups may be overinflated and generalizations cannot be inferred until an analysis of the variation that exists within groups has been conducted. In this study, we examined microsatellite coverage and motif patterns from 454 shotgun sequences of 154 Eukaryote species from eight distantly related phyla (Cnidaria, Arthropoda, Onychophora, Bryozoa, Mollusca, Echinodermata, Chordata and Streptophyta) to test if a consistent phylogenetic pattern emerges from the microsatellite composition of these species. It is clear from our results that data from model species provide incomplete information regarding the existing microsatellite variability within the Eukaryotes. A very strong heterogeneity of microsatellite composition was found within most phyla, classes and even orders. Autocorrelation analyses indicated that while microsatellite contents of species within clades more recent than 200 Mya tend to be similar, the autocorrelation breaks down and becomes negative or non-significant with increasing divergence time. Therefore, the age of the taxon seems to be a primary factor in degrading the phylogenetic pattern present among related groups. The most recent classes or orders of Chordates still retain the pattern of their common ancestor. However, within older groups, such as classes of Arthropods, the phylogenetic pattern has been scrambled by the long independent evolution of the lineages.
Data from: Sequence Capture using PCR-generated Probes (SCPP): a cost-effective method of targeted high-throughput sequencing for non-model organisms
Recent advances in high-throughput sequencing library preparation and subgenomic enrichment methods have opened new avenues for population genetics and phylogenetics of non-model organisms. To multiplex large numbers of indexed samples while sequencing predominantly orthologous, targeted regions of the genome, we propose modifications to an existing, in-solution capture that utilizes PCR products as target probes to enrich library pools for the genomic subset of interest. The sequence capture using PCR-generated probes (SCPP) protocol requires no specialized equipment, is highly flexible, and significantly reduces experimental costs for projects where a modest scale of genetic data is optimal (25-100 genomic loci). Our alterations enable application of this method across a wider phylogenetic range of taxa and result in higher capture efficiencies and coverage at each locus. Efficient and consistent capture over multiple SCPP experiments and at various phylogenetic distances is demonstrated, extending the utility of this method to both phylogeographic and phylogenomic studies.
Data from: Secondary structure models of 18S and 28S rRNAs of the true bugs based on complete rDNA sequences of Eurydema maracandica Oshanin, 1871 (Heteroptera: Pentatomidae)
The sequences of 18S and 28S rDNAs have been used as molecular markers to resolve phylogenetic relationships of Heteroptera for two decades. The complete sequences of 18S rDNAs have been used in many studies, while in most studies only partial sequences of 28S rDNAs have been used due to technical difficulties of amplifying the complete lengths. In this study, we amplified the complete 18S and 28S rDNA sequences of Eurydema maracandica Oshanin, 1871, and reconstructed the secondary structure models of the corresponding rRNAs. In addition, and more importantly, all of the length variable regions of 18S rRNA were compared among 37 families of Heteroptera based on 140 sequences, and the D3 region of 28S rRNA was compared among 51 families based on 84 sequences. It was found that 8 length variable regions could potentially serve as molecular synapomorphies for some monophyletic groups. Therefore discoveries of more molecular synapomorphies for specific clades can be anticipated from amplification of complete 18S and 28S rDNAs of more representatives of Heteroptera.
Supplementary Material for Paper "Distilling Event Sequence Knowledge From Large Language Models"
<p>Supplementary Material for Paper:<br>Distilling Event Sequence Knowledge From Large Language Models<br>Somin Wadhwa, Oktie Hassanzadeh, Debarun Bhattacharjya, Ken Barker, and Jian Ni</p> <p>Appendix:<br>- <code>Appendix.pdf</code>: contains our prompts and a description of our human evaluation details.</p> <p>Data:<br>- <code>base_kg_v7.jsonl</code>: Our Wikidata-based Event Causal Knowledge Graph.</p> <p>Outputs:<br>- <code>sample_new_patterns_discovered.txt</code>: examples of observed new patters through application of sequential pattern mining algorithms, described in section 3.<br>- <code>precision_eval_sample.txt:</code> examples of output evaluated with a precision-evaluator model. <br>- <code>bsumm_output.txt</code>: sample outputs of identified influencing events through the application of binary summary markov model, described in section 5.2.</p> <p>Code:<br>- <code>src/generator.py</code>: ingests ICL prompts and generates requisite event sequences.<br>- <code>src/benchmarking.py</code>: ingests a _trained_ Flan-style seq2seq model to evaluate precision, and recall from the base KG.<br>- <code>src/utils.py</code>: utilities for generator and benchmarking.</p> <p>To cite:</p> <pre><code>@inproceedings{WadhwaHBBN24, author = {Somin Wadhwa and Oktie Hassanzadeh and Debarun Bhattacharjya and Ken Barker and Jian Ni}, title = {Distilling Event Sequence Knowledge From Large Language Models}, booktitle = {The Semantic Web - 23rd International Conference, {ISWC} 2024}, series = {Lecture Notes in Computer Science}, publisher = {Springer}, year = {2024}, }</code></pre>
Data from: An integrated model of phenotypic trait changes and site-specific sequence evolution
Recent years have seen a constant rise in the availability of trait data, including morphological features, ecological preferences, and life history characteristics. These phenotypic data provide means to associate genomic regions with phenotypic attributes, thus allowing the identification of phenotypic traits associated with the rate of genome and sequence evolution. However, inference methodologies that analyze sequence and phenotypic data in a unified statistical framework are still scarce. Here, we present TraitRateProp, a probabilistic method that allows testing whether the rate of sequence evolution is associated with a binary phenotypic character trait. The method further allows the detection of specific sequence sites whose evolutionary rate is most noticeably affected following the character transition, suggesting a shift in functional/structural constraints. TraitRateProp is first evaluated in simulations and then applied to study the evolutionary process of plastid plant genomes upon a transition to a heterotrophic lifestyle. To this end, we analyze 25 plastid genes across 85 orchid species, spanning different lifestyles and representing different genera in this large family of flowering plants. Our results indicate higher evolutionary rates following repeated transitions to a heterotrophic lifestyle in all but four of the loci analyzed.
Figure 3 from: Xie Q, Yu S, Wang Y, Rédei D, Bu W (2013) Secondary structure models of 18S and 28S rRNAs of the true bugs based on complete rDNA sequences of Eurydema maracandica Oshanin, 1871 (Heteroptera, Pentatomidae). ZooKeys 319: 363-377. https://doi.org/10.3897/zookeys.319.4178
Figure 3 - The 3'-half part of secondary structure model of 28S rRNA of Eurydema maracandica. The numbers D8 to D11 represent four LVRs.
Figure 4 from: Xie Q, Yu S, Wang Y, Rédei D, Bu W (2013) Secondary structure models of 18S and 28S rRNAs of the true bugs based on complete rDNA sequences of Eurydema maracandica Oshanin, 1871 (Heteroptera, Pentatomidae). ZooKeys 319: 363-377. https://doi.org/10.3897/zookeys.319.4178
Figure 4 - Secondary structure models of LVR W of Naboidea and Cimicoidea. These sequences are from 15 genera, 19 species of Naboidea and Cimicoidea. The species names and GenBank Accession numbers are as follow: Nabidae (a) Nabis ferus EF487300 (b) Nabis flavomarginatus GQ258424 (c) Himacerus apterus GQ258425; Lyctocoridae (d) Lyctocoris beneficus EF487298; Anthocoridae (e) Anthocoris sp. AY252319 (f) Anthocoris confusus EF487297 (g) Anthocoris montanus EF487307 (h) Tetraphleps aterrimus EF487295 (i) Amphiareus obscuriceps EF487301 (j) Orius agilis EF487296 (k) Physopleurella armata EF487308 (l) Montandoniola moraguesi EF487310 (m) Xylocoris cerealis GQ258395 (n) Bilia sp. GQ258406 (o) Buchananiella crassicornis GQ258407 (p) Lasiochilus japonicus GQ258410 (q) Lasiochilus luceonotatus GQ258411; Cimicidae (r) Cimex lectularius GQ258396; Curaliidae (s) Curalium cronini EU683128.
Figure 1 from: Xie Q, Yu S, Wang Y, Rédei D, Bu W (2013) Secondary structure models of 18S and 28S rRNAs of the true bugs based on complete rDNA sequences of Eurydema maracandica Oshanin, 1871 (Heteroptera, Pentatomidae). ZooKeys 319: 363-377. https://doi.org/10.3897/zookeys.319.4178
Figure 1 - Secondary structure model of 18S rRNA of Eurydema maracandica. The bases marked in black represent length-conservative regions, and the bases labeled as capital letters B to W in red represent 13 LVRs. CP and NC represent monophyletic groups Cimicomorpha+Pentatomomorpha and Naboidea+Cimicoidea, respectively. Base pairing is indicated as follows: standard canonical pairs by lines (G–C, A–U), wobble G:U pairs by dots (G·U), A:G or A:C pairs by open circles (A○G, A○C), and other non-canonical pairs by filled circles (e.g., A●A).
Figure 2 from: Xie Q, Yu S, Wang Y, Rédei D, Bu W (2013) Secondary structure models of 18S and 28S rRNAs of the true bugs based on complete rDNA sequences of Eurydema maracandica Oshanin, 1871 (Heteroptera, Pentatomidae). ZooKeys 319: 363-377. https://doi.org/10.3897/zookeys.319.4178
Figure 2 - The 5'-half part of secondary structure model of 28S rRNA of Eurydema maracandica. The numbers D2 to D7 represent six LVRs. PM, GO and AL represent monophyletic groups Paraphrynoveliidae+Macroveliidae, Gelastocoridae+Ochteridae and Acanthosomatidae+Lestoniidae, respectively.
Data and code for, "Large language models design sequence-defined macromolecules via evolutionary optimization"
<div> <pre># Codes and data for "Large language models design sequence-defined macromolecules via evolutionary optimization"<br><br>Note this repository contains codes and data files for the manuscript. This is a snapshot of the repository, frozen at the time of submission.<br><br># Codes<br><br>## LLM codes<br>- `run_claude.py` - the routine for performing LLM-based rollouts; intended for command line execution using argparse<br>- `message_utils.py` - utilities for constructing and parsing messages for LLM I/O<br>- `model_utils.py` - lightweight utilities for retrieving formatted predictions from the RNN ensemble<br>- `target_defs.py` - defines the sequence, locations, and natural language descriptions of the target structures<br>- `ask_about_oracle.ipynb` - asks the LLM to speculate about the nature of the optimization task<br><br>## other algorithms<br>- `active_learning.ipynb` - use EI acquisition with RF surrogate to label new sequences; includes an unused tokenization scheme<br>- `evolutionary_algorithm.ipynb` - use DEAP library to perform evolutionary optimization<br>- `random_sampling.ipynb` - sample sequences randomly from all possible sequences<br><br>## postprocessing<br>- `process_aggregated_logs.py` - reads data from the raw log files and prepares them for visualization<br>- `process_sample_rollouts.py` - reads data from the raw log files and prepares individual rollouts<br><br>## visualization<br>- `figure1b.ipynb` - renders panel b of Fig. 1<br>- `figure1efg.ipynb` - renders the last row of Fig. 1 (panels e-g)<br>- `figure2.ipynb` - renders all of Fig. 2<br>- `figure_si.ipynb` - renders Figs. S1 and S2<br>- `figure_md_validation.ipynb` - renders Fig. S3<br><br># Data files<br><br>- `prompts/`<br> - `prompt-scientific-v4.4.yml` - the full text of the scientific prompt, to be read by `run_claude.py`<br> - `prompt-oracle-v4.4.yml` - the full text of the oracle prompt, to be read by `run_claude.py`<br>- `models/` - the TorchScript RNN models used to make predictions<br>- `data/`<br> - `embeddings` - calculated embeddings for a collection of sequences from our prior work<br> - `llm-logs` - the raw logs obtained from the Claude 3.5 Sonnet LLM (other algorithms made to look like the LLM logs after the fact)<br> - `llm-logs-opus` - the raw logs obtained from the Claude 3.0 Opus LLM (used in the first draft of the article, replaced by Claude 3.5 Sonnet) <br> - `all-rollouts-kltd.csv` - postprocessed logs for all the rollouts using the "top $k < d^*$" metric<br> - `all-rollouts-topkd.csv` - postprocessed logs for all the rollouts using the "mean $d$ for top $k$" metric<br> - `sample-rollout-membranes-x-3.csv` - postprocessed logs for a single rollout replica, `x` = each algorithm type<br> - `snapshots` - png snapshots of MD simulation results at different locations in the manifold</pre> </div>
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