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Fig. 3 in Relationships Of The Heteronchocleidids (Heteronchocleidus, Eutrianchoratus And Trianchoratus) As Inferred From Ribosomal Dna Nucleotide Sequence Data
Fig. 3. Bayesian consensus tree for the anabantoids, channids and catfishes (silurids, bagrids, clariids) obtained using partial Cytochrome b sequences with cyprinids as outgroup. The heteronchocleidids genera present on the anabantoids and channids are shown with their geographical areas. Values shown at each node refer to Bayesian posterior probabilities. (*refer to Table 3 for names used in GenBank).
Fig. 2. Bayesian consensus tree generated from partial 28S in Relationships Of The Heteronchocleidids (Heteronchocleidus, Eutrianchoratus And Trianchoratus) As Inferred From Ribosomal Dna Nucleotide Sequence Data
Fig. 2. Bayesian consensus tree generated from partial 28S rDNA sequences (D1 domain) with Diplectanum spp. and Gyrodactylus spp. as outgroups. Values shown at each node refer to Bayesian (BI) posterior probabilities/maximum likelihood (ML) percentages of the bootstrap values with 100 replicates. Bootstrap values lower than 50 are given as dashes (-).
Fig. 1 in Relationships Of The Heteronchocleidids (Heteronchocleidus, Eutrianchoratus And Trianchoratus) As Inferred From Ribosomal Dna Nucleotide Sequence Data
Fig. 1. Neighbour joining (NJ) tree constructed by PAUP* using partial 28S rDNA sequences (D1 domain) with Diplectanum spp. and Gyrodactylus spp. as outgroups. Percentages of the bootstrap values for neighbour joining (NJ)/maximum parsimony (MP) (NJ & MP=1,000 replicates) are shown along the branches. Bootstrap values lower than 50 are given as dashes (-).
Figure 1. Phylogenetic hypotheses for the Micropsectra atrofasciata species group, only including species with sequenced COII. A in A revision of West Palaearctic species of the Micropsectra atrofasciata species group (Diptera: Chironomidae)
Figure 1. Phylogenetic hypotheses for the Micropsectra atrofasciata species group, only including species with sequenced COII. A, result from Bayesian analysis of COII gene sequences. Posterior probabilities shown above branches, bootstrap support below branches from parsimony analysis of the same data set. B, result from parsimony analysis of the combined molecular and morphological data sets. Bremer support on branches. C, result from parsimony analysis of the combined molecular and morphological data sets, with third positions excluded. Bremer support on branches.
Solar flare forecasting based on magnetogram sequences learning with MViT and data augmentation
<p><strong>Source codes and dataset of the research "Solar flare forecasting based on magnetogram sequences learning with MViT and data augmentation".</strong></p><p>Our work employed PyTorch, a framework for training Deep Learning models with GPU support and automatic back-propagation, to load the MViTv2 s models with Kinetics-400 weights. To simplify the code implementation, eliminating the need for an explicit loop to train and the automation of some hyperparameters, we use the PyTorch Lightning module. The inputs were batches of 10 samples with 16 sequenced images in 3-channel resized to 224 × 224 pixels and normalized from 0 to 1.</p><p>Most of the papers in our literature survey split the original dataset chronologically. Some authors also apply k-fold cross-validation to emphasize the evaluation of the model stability. However, we adopt a hybrid split taking the first 50,000 to apply the 5-fold cross-validation between the training and validation sets (known data), with 40,000 samples for training and 10,000 for validation. Thus, we can evaluate performance and stability by analyzing the mean and standard deviation of all trained models in the test set, composed of the last 9,834 samples, preserving the chronological order (simulating unknown data).</p><p>We develop three distinct models to evaluate the impact of oversampling magnetogram sequences through the dataset. The first model, Solar Flare MViT (SF MViT), has trained only with the original data from our base dataset without using oversampling. In the second model, Solar Flare MViT over Train (SF MViT oT), we only apply oversampling on training data, maintaining the original validation dataset. In the third model, Solar Flare MViT over Train and Validation (SF MViT oTV), we apply oversampling in both training and validation sets.</p><p>We also trained a model oversampling the entire dataset. We called it the "SF_MViT_oTV Test" to verify how resampling or adopting a test set with unreal data may bias the results positively.</p><p><strong>GitHub version</strong></p><p>The .zip hosted here contains all files from the project, including the checkpoint and the output files generated by the codes. We have a clean version hosted on GitHub (<a href="https://github.com/lfgrim/SFF_MagSeq_MViTs">https://github.com/lfgrim/SFF_MagSeq_MViTs</a>), without the magnetogram_jpg folder (which can be downloaded directly on <a href="https://tianchi-competition.oss-cn-hangzhou.aliyuncs.com/531804/dataset_ss2sff.zip">https://tianchi-competition.oss-cn-hangzhou.aliyuncs.com/531804/dataset_ss2sff.zip)</a> and the output and checkpoint files. Most code files hosted here also contain comments on the Portuguese language, which are being updated to English in the GitHub version.</p><p><strong>Folders Structure</strong></p><p>In the Root directory of the project, we have two folders: </p><ul><li>magnetogram_jpg: holds the source images provided by Space Environment Artificial Intelligence Early Warning Innovation Workshop through the link <a href="https://tianchi-competition.oss-cn-hangzhou.aliyuncs.com/531804/dataset_ss2sff.zip">https://tianchi-competition.oss-cn-hangzhou.aliyuncs.com/531804/dataset_ss2sff.zip. </a>It comprises 73,810 samples of high-quality magnetograms captured by HMI/SDO from 2010 May 4 to 2019 January 26. The HMI instrument provides these data (stored in hmi.sharp_720s dataset), making new samples available every 12 minutes. However, the images from this dataset were collected every 96 minutes. Each image has an associated magnetogram comprising a ready-made snippet of one or most solar ARs. It is essential to notice that the magnetograms cropped by SHARP can contain one or more solar ARs classified by the National Oceanic and Atmospheric Administration (NOAA).</li><li>Seq_Magnetogram: contains the references for source images with the corresponding labels in the next 24 h. and 48 h. in the respectively M24 and M48 sub-folders.<ul><li>M24/M48: both present the following sub-folders structure:<ul><li>Seqs16;</li><li>SF_MViT;</li><li>SF_MViT_oT;</li><li>SF_MViT_oTV;</li><li>SF_MViT_oTV_Test.</li></ul></li></ul></li></ul><p>There are also two files in root:</p><ul><li>inst_packages.sh: install the packages and dependencies to run the models.</li><li>download_MViTS.py: download the pre-trained MViTv2_S from PyTorch and store it in the cache.</li></ul><p>M24 and M48 folders hold reference text files (flare_Mclass...) linking the images in the magnetogram_jpg folders or the sequences (Seq16_flare_Mclass...) in the Seqs16 folders with their respective labels. They also hold "cria_seqs.py" which was responsible for creating the sequences and "test_pandas.py" to verify head info and check the number of samples categorized by the label of the text files. All the text files with the prefix "Seq16" and inside the Seqs16 folder were created by "criaseqs.py" code based on the correspondent "flare_Mclass" prefixed text files.</p><p>Seqs16 folder holds reference text files, in which each file contains a sequence of images that was pointed to the magnetogram_jpg folders.</p><p>All SF_MViT... folders hold the model training codes itself (SF_MViT...py) and the corresponding job submission (jobMViT...), temporary input (Seq16_flare...), output (saida_MVIT... and MViT_S...), error (err_MViT...) and checkpoint files (sample-FLARE...ckpt). Executed model training codes generate output, error, and checkpoint files. There is also a folder called "lightning_logs" that stores logs of trained models.</p><p><strong>Naming pattern for the files:</strong></p><ul><li>magnetogram_jpg: follows the format<i> </i>"hmi.sharp_720s.<SHARP-ID>.<date>.magnetogram.fits.jpg" and</li><li>Seqs16: follows the format "hmi.sharp_720s.<i><</i>SHARP-ID<i>></i>.<init-date>.to.<end-date>", where:<ul><li>hmi: is the instrument that captured the image</li><li>sharp_720s: is the database source of SDO/HMI.</li><li><SHARP-ID>: is the identification of SHARP region, and can contain one or more solar ARs classified by the (NOAA).</li><li><date>: is the date-time the instrument captured the image in the format yyyymmdd_hhnnss_TAI (y:year, m:month, d:day, h:hours, n:minutes, s:seconds).</li><li><init-date>: is the date-time when the sequence starts, and follow the same format of <date>.</li><li><end-date>: is the date-time when the sequence ends, and follow the same format of <date>.</li></ul></li><li>Reference text files in M24 and M48 or inside SF_MViT... folders follows the format "<prefix>flare_Mclass_<forecasting-horizon>_<dataset>.txt<over>", where:<ul><li><prefix>: is Seq16 if refers to a sequence, or void if refers direct to images.</li><li><forecasting-horizon>: "24h" or "48h".</li><li><dataset>: is "TrainVal<n>" or "Test". The <n> refers to the split of Train/Val.</li><li><over>: void or "_over" after the extension (...txt_over): means temporary input reference that was over-sampled by a training model.</li></ul></li><li>All SF_MViT...folders:<ul><li>Model training codes: "SF_MViT_<oversampling-type>_M+_<forecasting-horizon>_<split-type><gpu-type>", where:<ul><li><oversampling -type>: void or "oT" (over Train) or "oTV" (over Train and Val) or "oTV_Test" (over Train, Val and Test);</li><li><forecasting-horizon>: "24h" or "48h";</li><li><split-type>: "oneSplit" for a specific split or "allSplits" if run all splits.</li><li><gpu-type>: void is default to run 1 GPU or "2gpu" to run into 2 gpus systems;</li></ul></li><li>Job submission files: "jobMViT_<queue>", where:<ul><li><queue>: point the queue in Lovelace environment hosted on CENAPAD-SP (<a href="https://www.cenapad.unicamp.br/parque/jobsLovelace">https://www.cenapad.unicamp.br/parque/jobsLovelace</a>)</li></ul></li><li>Temporary inputs: "Seq16_flare_Mclass_<forecasting-horizon>_<dataset>.txt<over>:<ul><li><dataset>: train or val;</li><li><over>: void or "_over" after the extension (...txt_over): means temporary input reference that was over-sampled by a training model.</li></ul></li><li>Outputs: "saida_MViT_Adam_10-7<split>", where:<ul><li><split>: k0 to k4, means the correlated split of the output, or void if the output is from all splits.</li></ul></li><li>Error files: "err_MViT_Adam_10-7<split>", where:<ul><li><split>: k0 to k4, means the correlated split of the error log file, or void if the error file is from all splits.</li></ul></li><li>Checkpoint files: "sample-FLARE_MViT_S_10-7-epoch=<n-epoch>-valid_loss=<loss-value>-Wloss_k=<n-split>.ckpt", where:<ul><li><n-opoch>: epoch number of the checkpoint;</li><li><loss-value>: corresponding valid loss;</li><li><n-split>: 0 to 4.</li></ul></li></ul></li></ul>
FIG. 1 in Analysis of Genomic Sequence Data Reveals the Origin and Evolutionary Separation of Hawaiian Hoary Bat Populations
FIG. 1.—Map of the Hawaiian Islands with collection sitesfor Hawaiian hoary bat tissues used inthis study. Sites with n> 1 are denoted with an asterisk.
FIG. 2 in Analysis of Genomic Sequence Data Reveals the Origin and Evolutionary Separation of Hawaiian Hoary Bat Populations
FIG. 2.—PCA result plot showing clustering of individual bats from four Hawaiian Islands using 21,808,031 SNPs. Sample information included in supplementary table S4, Supplementary Material online.
FIG. 4 in Analysis of Genomic Sequence Data Reveals the Origin and Evolutionary Separation of Hawaiian Hoary Bat Populations
FIG. 4.—SNAPP-based phylogenetic tree inference. (A) The maximum clade credibility or consensus tree, showing approximate divergence of hoary bats across the Hawaiian archipelago. The axis on the bottom of the figure corresponds to million years before present (Ma), using the emergence of Hawai'i (~0.43 Ma) as a calibration point (95% confidence intervals were given in square brackets). (B) The drawing of all sampled trees showing all ingroup nodes were supported by maximum posterior probabilities (1.00).
Optimized SMRT-UMI protocol produces highly accurate sequence datasets from diverse populations – application to HIV-1 quasispecies
<p>Pathogen diversity resulting in quasispecies can enable persistence and adaptation to host defenses and therapies. However, accurate quasispecies characterization can be impeded by errors introduced during sample handling and sequencing which can require extensive optimizations to overcome. We present complete laboratory and bioinformatics workflows to overcome many of these hurdles. The Pacific Biosciences single molecule real-time platform was used to sequence PCR amplicons derived from cDNA templates tagged with universal molecular identifiers (SMRT-UMI). Optimized laboratory protocols were developed through extensive testing of different sample preparation conditions to minimize between-template recombination during PCR and the use of UMI allowed accurate template quantitation as well as removal of point mutations introduced during PCR and sequencing to produce a highly accurate consensus sequence from each template. Handling of the large datasets produced from SMRT-UMI sequencing was facilitated by a novel bioinformatic pipeline, Probabilistic Offspring Resolver for Primer IDs (PORPIDpipeline), that automatically filters and parses reads by sample, identifies and discards reads with UMIs likely created from PCR and sequencing errors, generates consensus sequences, checks for contamination within the dataset, and removes any sequence with evidence of PCR recombination or early cycle PCR errors, resulting in highly accurate sequence datasets. The optimized SMRT-UMI sequencing method presented here represents a highly adaptable and established starting point for accurate sequencing of diverse pathogens. These methods are illustrated through characterization of human immunodeficiency virus (HIV) quasispecies.</p>
Genbank annotation and sequence of the genome of a Rickettsiales symbiont of Reticulomyxa filosa
<p>The genome sequence of a Rickettsiales symbiont was selectively from the genome sequencing reads of its host, Reticulomyxa filosa. Those sequences were obtained from a previous third-party study (doi: 10.1016/j.cub.2013.11.027). The selective assembly procedure was based on GC content and coverage of contigs obtained from the total read sets with SPAdes. Full details are provided in the manuscript file.<br>The obtained symbiont sequence was then annotated with Prokka, and the gbk output file selected.</p><p>This genome was obtained and analysed in the context of a larger genome comparative studies on the Rickettsiales, aimed to investigate the evolutionary patterns within the whole lineage.</p>
Code and sequence data pertaining to: A phylogenomic perspective on interspecific competition
<p>Evolutionary processes may have substantial impacts on community assembly, but evidence for phylogenetic relatedness as a determinant of interspecific interaction strength remains mixed. In this perspective, we consider a possible role for discordance between gene trees and species trees in the interpretation of phylogenetic signal in studies of community ecology. Modern genomic data show that the evolutionary histories of many taxa are better described by a patchwork of histories that vary along the genome rather than a single species tree. If a subset of genomic loci harbor trait-related genetic variation, then the phylogeny at these loci may be more informative of interspecific trait differences than the genome background. We develop a simple method to detect loci harboring phylogenetic signal and demonstrate its application through a proof of principle analysis of Penicillium genomes and pairwise interaction strength. Our results show that phylogenetic signal that may be masked genome-wide could be detectable using phylogenomic techniques and may provide a window into the genetic basis for interspecific interactions.</p>
Supplementary material for "Exploring Conceptual Data Modeling Processes: Insights from Clustering and Visualizing Modeling Sequences"
<p>This material supplements the following conference publication:</p> <p>Winkler, Rosenthal, Strecker (2024). "Exploring Conceptual Data Modeling Processes: Insights from Clustering and Visualizing Modeling Sequences". Modellierung 2024.</p>
Asteroseismic Inversions for Internal Sound Speed Profiles of Main-sequence Stars with Radiative Cores
<p>This repository contains the accompanying inlists and run_star_extras files for Buchele et al. (2024): Asteroseismic Inversions for Internal Sound Speed Profiles of Main-sequence Stars with Radiative Cores.</p> <p> </p> <p>The MESA version used is r22.05.1. We have modified the /star/private/pulse_fgong.f90 file in the MESA source code to output the Gamma1 derivatives that are necessary to compute u-Y kernels by replacing the file with the version from <a href="https://github.com/MESAHub/mesa/pull/431">https://github.com/MESAHub/mesa/pull/431</a>. Additionally, we use the MESA SDK versions 22.6.1 and the GYRE version 6.0 that is included with the r22.05.1 release of MESA. </p> <p> </p> <p>The directory grid_base contains the inlist which set the parameters that do not change across the tracks of our grid</p> <p> </p> <ul> <li> <p>Inlist_all includes parameters that are constant across all tracks and evolutionary stages </p> </li> <li> <p>Inlist_prems includes parameters for the pre-main sequence evolution of all tracks </p> </li> <li> <p>Inlist_ms includes parameters for the main sequence evolution of all tracks </p> </li> <li> <p>run_star_extras.f90 includes the code which stops the evolution of a track when the central hydrogen abundance reaches the target value </p> </li> <li> <p>gyre.in includes parameters for calculating of frequencies using GYRE</p> </li> </ul> <p> </p> <p>The directory reference_models provides the inlists to generate our reference model for each star in our sample. This directory also includes the FGONG structure files of the computed reference models. Each file is labeled with the identifier used in the text, typically the KIC number. </p> <p> </p> <p>The physics_tweaks directory provides the inlists to generate the models with modified input physics. The suffixes refer to the physics that was changed in each model, as follows: </p> <ul> <li> <p>core_kap: Core opacity</p> </li> <li> <p>pp2: change to the He3+He4->Be7 reaction rate</p> </li> <li> <p>cno: change to the N14 + p -> O15. </p> </li> </ul>
Data from: A cost-effective blood DNA methylation-based age estimation method in domestic cats, Tsushima leopard cats (Prionailurus bengalensis euptilurus), and Panthera species, using targeted bisulfite sequencing and machine learning models
<p><span>Knowledge of individual age can help both in-situ and ex-situ conservation programs to design more efficient and suitable management plans for targeted wildlife species. DNA methylation is one of the epigenetic aging markers that has emerged as a promising tool that can estimate age with high accuracy using only a tiny amount of biological material, which can be collected in a minimally invasive way. Here, we sequenced five targeted genetic regions and used </span><span>8–23</span><span> selected CpG sites to build age estimation models with machine learning methods </span><span>with about only $3–7 per sample</span><span>, using blood samples of seven Felidae species—ranging from small to big, and domestic to endangered species: domestic cats (<em>Felis catus</em>, 139 samples), Tsushima leopard cats (<em>Prionailurus bengalensis euptilurus</em>, 84 samples), and five<em> Panthera </em>species (96 samples). </span><span>The models built achieved satisfactory accuracy—the mean absolute error of the best models was 1.966, 1.348, and 1.552 years in domestic cats, Tsushima leopard cats, and <em>Panthera</em> spp., respectively.</span><span> Our models in domestic cats and Tsushima leopard cats were applicable to individuals regardless of health conditions, indicating the high applicability of our models to samples collected from diverse situations, e.g., rescued individuals in the context of conservation. We also showed the possibility of developing universal age estimation models for the five<em> Panthera</em> spp. using two of the five genetic regions, suggesting an even lower cost to use our models for future applications.</span></p>
Full-length, single-cell RNA-sequencing of human bone marrow subpopulations reveals hidden complexity
<p><a href="http://www.biorxiv.org/content/10.1101/2021.07.28.454226v2">Full-length, single-cell RNA-sequencing of human bone marrow subpopulations reveals hidden complexity</a></p> <p>Bone marrow progenitor cell differentiation has frequently been used as a model for studying cellular plasticity and cell-fate decisions. Recent analysis at the level of single-cells has expanded knowledge of the transcriptional landscape of human hematopoietic cell lineages. Using single-molecule real-time (SMRT) full-length RNA sequencing, we have previously shown that human bone marrow lineage-negative (Lin-neg) cell populations contain a surprisingly diverse set of mRNA isoforms. Here, we report from single cell, full-length RNA sequencing that this diversity is also reflected at the single-cell level. From fresh human bone marrow unselected and lineage-negative progenitor cells were isolated by droplet-based single-cell selection (10xGenomics). The single cell-derived mRNAs were analyzed by full-length SMRT and short-read sequencing. In both samples we detected an average of 8000 different genes using short-read sequencing. Differential expression analysis arranged the single-cells of the total bone marrow into only four clusters whereas the Lin-neg population was much more diverse with nine clusters. mRNA isoform analysis of the single-cell populations using full-length sequencing revealed that Lin-neg cells contain on average 24% more novel splice variants than the total bone marrow cells. Interestingly, among the most frequent genes expressing novel isoforms were members of the spliceosome, e.g. HNRNPs, DEAD box helicases and SRSFs. Mapping the isoforms from all genes to the cell type clusters revealed that total bone marrow cells express novel isoforms only in a small subset of clusters. On the other hand, lineage-negative progenitor cells expressing novel isoforms were present in nearly all subpopulations. In conclusion, on a single-cell level lineage-negative cells express a higher diversity of genes and more alternatively spliced novel isoforms suggesting that cells in this subpopulation are poised for different fates. </p> <p> </p>
Station Data and Earthquake Catalogs - Distinct yet adjacent earthquake sequences near the Mendocino Triple Junction: 20 December 2021 Mw 6.1 and 6.0 Petrolia, and 20 December 2022 Mw 6.4 Ferndale
<p>Supplemental Material for publication from The Seismic Record (TSR):</p> <div> <div> <div> <p>Yoon, C. E. and D. R. Shelly (2024). Distinct Yet Adjacent Earthquake Sequences near the Mendocino Triple Junction: 20 December 2021 Mw 6.1 and 6.0 Petrolia, and 20 December 2022 Mw 6.4 Ferndale, The Seismic Record. 4(1), 81–92, doi: 10.1785/0320230053.</p> <p>Data Sets S0-S4 with station data and earthquake catalogs in text format</p> <p>See README_Data_Supplement.pdf for more details about contents of each data file. Please refer to the accompanying publication and its supplement for figures, tables, and equations.</p> </div> </div> </div> <p> </p>
PubMLST allele profiles and sequence alignments of 382 carbapenem-resistant Pseudomonas aeruginosa isolates collected from Japanese hospitals in 2019-2020
<p>This dataset provides PubMLST allele profiles, sequence alignments, and Mash distance data used in the study of "Nationwide genome surveillance of carbapenem-resistant Pseudomonas aeruginosa in Japan". </p>
Deep sequencing datasets from: RNA-catalyzed evolution of catalytic RNA
<p>This dataset includes raw and processed sequencing data from evolving RNA populations described in Nikos Papastavrou, David P Horning, Gerald F Joyce. "RNA-Catalyzed Evolution of Catalytic RNA" (submitted). Briefly, directed evolution of a hammerhead ribozyme sequence was carried out over eight rounds of three steps each: 1) templated synthesis of the reverse-complement hammerhead RNA by a polymerase ribozyme; 2) templated synthesis of a new copy of the hammerhead RNA from the reverse-complement by a polymerase ribozyme; 3) selective recovery of hammerhead RNA that cleaved an attached RNA substrate. Cleaved RNA was reverse transcribed, PCR amplified, and archived for sequencing, while a portion was in vitro transcribed with T7 RNA polymerase to initiate the next round of evolution. Two distinct branches of evolution were carried out for 8 rounds, using the '52-2' or '71-89' polymerase ribozymes to replicate RNA, respectively. Sequenced RNA populations were analyzed to determine polymerase ribozyme fidelity and study the evolution of hammerhead sequences replicated by polymerases with low or high RNA copying fidelities. The dataset includes raw sequence files, processed tables of mutations by position along the sequence for each polymerase, processed tables of the sequence frequency distribution from each round in the evolving populations, and spreadsheets containing final processed data used directly in manuscript figures and tables.</p>
Complex models of sequence evolution improve fit, but not gene tree discordance, for tetrapod mitogenomes
<p>Variation in gene tree estimates is widely observed in empirical phylogenomic data and is often assumed to be the result of biological processes. However, a recent study using tetrapod mitochondrial genomes to control for biological sources of variation due to their haploid, uniparentally inherited, and non-recombining nature found that levels of discordance among mitochondrial gene trees were comparable to those found in studies that assume only biological sources of variation. Additionally, they found that several of the models of sequence evolution chosen to infer gene trees were doing an inadequate job of fitting the sequence data. These results indicated that significant amounts of gene tree discordance in empirical data may be due to poor fit of sequence evolution models and that more complex and biologically realistic models may be needed. To test how the fit of sequence evolution models relates to gene tree discordance, we analyzed the same mitochondrial datasets as the previous study using two additional, more complex models of sequence evolution that each model a different biologically realistic aspect of the evolutionary process: a covarion model to incorporate heterotachy, and a model partitioned model to incorporate variable evolutionary patterns by codon position. Our results show that both additional models fit the data better than the models used in the previous study, with the covarion being consistently and strongly preferred as tree size increases. However, even these more preferred models still inferred highly discordant mitochondrial gene trees, thus deepening the mystery around what we label the "Mito-Phylo Paradox" and leading us to ask whether the observed variation could be biological after all.</p>
16S rRNA Sequencing Data of Fecal Microbiota in an Italian Cohort of Patients with CDKL5 Deficiency Disorder
<h3>Summary of the study </h3> <p>CDKL5 deficiency disorder (CDD) is a neurodevelopmental condition characterized by global developmental delay, early-onset seizures, intellectual disability, visual and motor impairments, distinct from Rett Syndrome (RTT) due to the absence of a clear regression period. Gastrointestinal (GI) disturbances and signs of subclinical immune dysregulation are common in CDD patients, yet the underlying causes are unknown. Recent studies hint at a possible link between neurological disorders and gut microbiota, an unexplored area in CDD. In this groundbreaking study, we examined fecal microbiota in CDD patients and their healthy relatives, revealing differences in bacterial diversity and composition. We further investigated microbiota changes based on various factors, including the severity of GI issues, seizure frequency, sleep disorders, food intake type, neuro-behavioral features (assessed through the RTT Behaviour Questionnaire – RSBQ), and ambulation capacity. </p> <p>Our findings suggest a potential connection between CDD, microbiota, and symptom severity. This study represents the first exploration of the gut-microbiota-brain axis in CDD patients, contributing to the growing body of research on the role of gut microbiota in neurodevelopmental disorders. It opens doors to potential interventions targeting intestinal microbes to enhance the well-being of individuals with CDD.</p> <h3>Mehods</h3> <p>The Dataset represent the raw data (.fastq) obtained from the sequencing of the fecal samples from 17 Italian Patients with CDD, and 17 Healthy Relatives (i.e. siblings or mother), collected at a single time-point.</p> <p>Samples from Patients affected by CDD are called CDD, samples from Healthy Relatives are called HC-CDD (i.e. healthy controls of patients affected by CDD). For details about the sample names see the “Explanation Table”.</p> <p>Bacterial DNA was extracted from fecal samples using the QIAmp Powerfexal DNA Kit (Qiagen, Germany) following the manufacturer's protocol. The 16S rRNA sequencing and analysis was performed by a service offered by Zymo Research (Germany).</p> <p><em>Targeted Library Preparation</em>: The DNA samples were prepared for targeted sequencing with the Quick-16S™ NGS Library Prep Kit (Zymo Research). The primer sets used were Quick-16S™ Primer Set V3-V4 (Zymo Research). The sequencing library was prepared using an innovative library preparation process in which PCR reactions were performed in real-time PCR machines to control cycles and therefore limit PCR chimera formation. The final PCR products were quantified with qPCR fluorescence readings and pooled together based on equal molarity. The final pooled library was cleaned up with the Select-a-Size DNA Clean & Concentrator™, then quantified with TapeStation® (Agilent Technologies, Santa Clara, CA) and Qubit® (Thermo Fisher Scientific, Waltham, WA). </p> <p><em>Sequencing:</em> The final library was sequenced on Illumina® MiSeq™ with a v3 reagent kit (600 cycles). </p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.