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19,342 results for “sequence”
Geodetic model of the March 2021 Thessaly seismic sequence inferred from seismological and InSAR data
<p>A selection of Sentinel-1 (S1) wrapped and unwrapped measurements used in this study (from "a" to "u" files in tiff format as indicated in the word file attached). S1 data were processed by using our own internally developed InSAR processing chain.<br> <br> Earthquakes data locations.</p> <p><br> </p> <p> </p>
Data from: RAD sequencing, genotyping error estimation and de novo assembly optimization for population genetic inference
Restriction site-associated DNA sequencing (RADseq) provides researchers with the ability to record genetic polymorphism across thousands of loci for non-model organisms, potentially revolutionising the field of molecular ecology. However, as with other genotyping methods, RADseq is prone to a number of sources of error that may have consequential effects for population genetic inferences, and these have received only limited attention in terms of the estimation and reporting of genotyping error rates. Here we use individual sample replicates, under the expectation of identical genotypes, to quantify genotyping error in the absence of a reference genome. We then use sample replicates to (1) optimize de novo assembly parameters within the program Stacks, by minimizing error and maximizing the retrieval of informative loci, and; (2) quantify error rates for loci, alleles and SNPs. As an empirical example we use a double digest RAD dataset of a non-model plant species, Berberis alpina, collected from high altitude mountains in Mexico.
Data from: How "simple" methodological decisions affect interpretation of population structure based on reduced representation library DNA sequencing: a case study using the lake whitefish
Reduced representation (RRL) sequencing approaches (e.g., RADSeq, genotyping by sequencing) require decisions about how much to invest in genome coverage and sequencing depth (library quality), as well as choices of values for adjustable bioinformatics parameters. To empirically explore the importance of these "simple" decisions, we generated two independent sequencing libraries for the same 142 individual lake whitefish (Coregonus clupeaformis) using a nextRAD RRL approach: (1) A small number of loci and low sequencing depth (library A); and (2) more loci and higher sequencing depth (library B). The fish were selected from populations with different levels of expected genetic subdivision. Each library was analyzed using the STACKS pipeline followed by three types of population structure assessment (FST, DAPC and ADMIXTURE) with iterative increases in the stringency of sequencing depth and missing data requirements, as well as more specific a priori population maps. Library B was always able to resolve strong population differentiation in all three types of assessment regardless of the selected parameters. In contrast, library A produced more variable results; increasing the minimum sequencing depth threshold (-m) resulted in a reduced number of retained loci, and therefore lost resolution at high -m values for FST and ADMIXTURE, but not DAPC. FST and DAPC were robust to varying the population map and increasing the stringency of missing data requirements. In contrast, ADMIXTURE was unable to resolve strong population differentiation when increasing these same parameters in library A. Similarly, when examining fine scale population subdivision, library B was robust to changing parameters but library A lost resolution depending on the parameter set. We used library B to examine actual subdivision in our study populations. All three types of analysis found complete subdivision among populations in Lake Huron, ON and Dore Lake, SK, Canada using 10,640 SNP loci. Weak population subdivision was detected in Lake Huron with fish from sites in the north-west, Search Bay, North Point and Hammond Bay, showing slight differentiation. Overall, we show that apparently simple decisions about library quality and bioinformatics parameters can have potentially important impacts on the interpretation of population subdivision. Although costly, the early investment in a high-quality library and more conservative stringency settings on STACKS parameters lead to a final dataset that was more consistent and robust when examining both weak and strong population differentiation.
Training data for "Identification of allelic variants in SARS-CoV-2 from deep sequencing reads"
<p>Effectively monitoring global infectious disease crises, such as the COVID-19 pandemic, requires capacity to generate and analyze large volumes of sequencing data in near real time. These data have proven essential for monitoring the emergence and spread of new variants, and for understanding the evolutionary dynamics of the virus.</p> <p>Two sequencing platforms in combination with several established library preparation strategies are predominantly used to generate SARS-CoV-2 sequence data. However, data alone do not equal knowledge: they need to be analyzed. The Galaxy community developed analysis workflows to support the <strong>identification of allelic variants (AVs) in SARS-CoV-2 from deep sequencing reads</strong>.</p> <p>These workflows allow one to identify AVs and lineages in SARS-CoV-2 genomes with variant allele frequencies ranging from 5% to 100% (i.e., they detect variants with intermediate frequencies as well.</p> <p>In this tutorial we will see how to run these workflows for the different types of input data:</p> <ul> <li>Single end data derived from Illumina-based RNAseq experiments</li> <li>Paired end data derived from Illumina-based RNAseq experiments</li> <li>Paired-end data generated with Illumina-based Ampliconic (ARTIC) protocols</li> <li>ONT fastq files generated with Oxford nanopore (ONT)-based Ampliconic (ARTIC) protocols</li> </ul> <p>To illustrate the tutorial, we took some example datasets (paired-end data generated with Illumina-based Ampliconic (ARTIC) protocols) from COG-UK, the COVID-19 Genomics UK Consortium.</p>
Tutorial output for Tourmaline amplicon sequence processing workflow
<p>Tutorial output for the <a href="https://github.com/aomlomics/tourmaline">Tourmaline</a> amplicon sequence processing workflow.</p> <p>Tourmaline was run on the test data provided in the directory <a href="https://github.com/aomlomics/tourmaline/tree/master/00-data">00-data</a>, which were downloaded along with the rest of the repository using this command:</p> <pre><code>git clone https://github.com/aomlomics/tourmaline</code></pre> <p>Reference data were downloaded and symlinked using these commands:</p> <pre><code>cd tourmaline/01-imported wget https://data.qiime2.org/2021.2/common/silva-138-99-seqs-515-806.qza wget https://data.qiime2.org/2021.2/common/silva-138-99-tax-515-806.qza ln -s silva-138-99-seqs-515-806.qza refseqs.qza ln -s silva-138-99-tax-515-806.qza reftax.qza</code></pre> <p>Paths in 00-data/manifest_pe.csv and 00-data/manifest_se.csv were edited to match the local paths.</p> <p>Output for all modes of the workflow were then generated in series:</p> <pre><code>conda activate qiime2-2021.2 snakemake dada2_pe_report_unfiltered snakemake dada2_pe_report_filtered snakemake dada2_se_report_unfiltered snakemake dada2_se_report_filtered snakemake deblur_se_report_unfiltered snakemake deblur_se_report_filtered</code></pre> <p> </p>
EvolvingSTEM Bioinformatics mutant colony sequencing
<p>Mutant colony sequencing for EvolvingSTEM Bioinformatics</p>
Data from: A RAD-sequencing approach to genome-wide marker discovery, genotyping, and phylogenetic inference in a diverse radiation of primates
Until recently, most phylogenetic and population genetics studies of nonhuman primates have relied on mitochondrial DNA and/or a small number of nuclear DNA markers, which can limit our understanding of primate evolutionary and population history. Here, we describe a cost-effective reduced representation method (ddRAD-seq) for identifying and genotyping large numbers of SNP loci for taxa from across the New World monkeys, a diverse radiation of primates that shared a common ancestor ~20-26 mya. We also estimate, for the first time, the phylogenetic relationships among 15 of the 22 currently-recognized genera of New World monkeys using ddRAD-seq SNP data using both maximum likelihood and quartet-based coalescent methods. Our phylogenetic analyses robustly reconstructed three monophyletic clades corresponding to the three families of extant platyrrhines (Atelidae, Pitheciidae and Cebidae), with Pitheciidae as basal within the radiation. At the genus level, our results conformed well with previous phylogenetic studies and provide additional information relevant to the problematic position of the owl monkey (Aotus) within the family Cebidae, suggesting a need for further exploration of incomplete lineage sorting and other explanations for phylogenetic discordance, including introgression. Our study additionally provides one of the first applications of next-generation sequencing methods to the inference of phylogenetic history across an old, diverse radiation of mammals and highlights the broad promise and utility of ddRAD-seq data for molecular primatology.
Training data for the Sei framework sequence model
<p>Training data for the Sei framework deep learning model. The data contains chromatin profiles from the Cistrome Project: <strong>please agree to the terms of usage at the Cistrome Project (http://cistrome.org/db/#/bdown) before downloading. </strong></p>
Supplementary Data for: Whole genome sequencing elucidates the species-wide diversity and evolution of fungicide resistance in the early blight pathogen Alternaria solani
<p>Supplementary Data for: Whole genome sequencing elucidates the species-wide diversity and evolution of fungicide resistance in the early blight pathogen Alternaria solani</p> <p>This repository contains:</p> <p>SNP call data / VCF file</p> <p>Scripts for all processing steps from mapping up to PCA and phylogenetic analyses (script.ts)<br> Scripts for population genomic analyses with LEA and PopGenome (scripts.SE)<br> All script names are self explanatory.</p>
Atmospheric turbulence distorted video sequence dataset
<p>This contains the full version of the dataset utilized in our paper "Neutralizing the impact of atmospheric turbulence on complex scene imaging via deep learning". Three main types of data are covered, which include algorithm simulated data, physical simulated data and real-world data. Specifically, the algorithm/physical simulated sequences are given with reference without turbulence distortion.</p>
Detection of Neoplasms by Metagenomic Sequencing of Cerebrospinal Fluid
<p>Detection of Neoplasms by Metagenomic Sequencing of Cerebrospinal Fluid</p> <p>Wei Gu, MD, PhD<sup>1,2</sup>*, Andreas M. Rauschecker, MD, PhD<sup>3</sup>, Elaine Hsu, BS<sup>1</sup>, Kelsey C. Zorn, BS<sup>4</sup>, Yasemin Sucu, BS<sup>1</sup>, Scot Federman, BS<sup>1</sup>, Allan Gopez, BS<sup>1</sup>, Shaun Arevalo, BS<sup>1</sup>, Hannah A. Sample, BS<sup>4</sup>, Eric Talevich, PhD<sup>5</sup>, Eric D. Nguyen, MD, PhD<sup> 1</sup>, Marc Gottschall, BS<sup>1</sup>, Bardia Nourbakhsh, MD, MAS<sup>6</sup>, Carl A. Gold, MD, MS<sup>7</sup>, Bruce A.C. Cree, MD, PhD, MAS<sup>8</sup>, Vanja Douglas, MD<sup>8</sup>, Megan B. Richie, MD<sup>8</sup>, Maulik P. Shah, MD, MHS<sup>8</sup>, S. Andrew Josephson, MD<sup>8</sup>, Jeffrey M. Gelfand, MD, MAS<sup>8</sup>, Steve Miller, MD, PhD<sup> 1</sup>, Linlin Wang, MD<sup>1</sup>, Tarik Tihan, MD, PhD<sup>9</sup>, Joseph L. DeRisi, PhD<sup>4,10</sup>, Charles Y. Chiu, MD, PhD<sup>11,12</sup>, Michael R. Wilson, MD, MAS<sup>8</sup>*</p> <p> </p> <p><sup>1</sup>Department of Laboratory Medicine, University of California San Francisco, CA 94107, USA</p> <p><sup>2</sup>Department of Pathology, Stanford University, CA 94305, USA</p> <p><sup>3</sup>Department of Radiology and Biomedical Imaging, University of California San Francisco, CA 94107, USA</p> <p><sup>4</sup>Department of Biochemistry and Biophysics, University of California San Francisco, CA 94107, USA</p> <p><sup>5</sup>DNANexus, Mountain View, CA 94040, USA</p> <p><sup>6</sup>Department of Neurology, Johns Hopkins University, MD 21287, USA</p> <p><sup>7</sup>Department of Neurology, Stanford University, CA 94305, USA</p> <p><sup>8</sup>Weill Institute for Neurosciences, Department of Neurology, University of California San Francisco, CA 94107, USA</p> <p><sup>9</sup>Department of Pathology, University of California San Francisco, CA 94107, USA</p> <p><sup>10</sup>Chan Zuckerberg Biohub, San Francisco, CA 94107, USA</p> <p><sup>11</sup>UCSF-Abbott Viral Diagnostics and Discovery Center, San Francisco, CA 91407, USA</p> <p><sup>12</sup>Department of Medicine, Division of Infectious Diseases, University of California San Francisco, CA 94107, USA</p> <p> </p> <p>* Corresponding authors:</p> <p>Michael Wilson, MD, MAS</p> <p>UCSF Department of Neurology</p> <p>675 Nelson Rising Lane, NS212</p> <p>San Francisco, CA 94158</p> <p><a href="mailto:Michael.Wilson@ucsf.edu">Michael.Wilson@ucsf.edu</a></p> <p> </p> <p>Wei Gu, MD, PhD</p> <p>Stanford University, Department of Pathology</p> <p>3373 Hillview Ave, 218</p> <p>Palo Alto, CA 94304</p> <p>650-723-1914</p> <p><a href="mailto:weigu@stanford.edu">weigu@stanford.edu</a></p>
Supplementary Information for Phylogenetic analyses of ray-finned fishes (Actinopterygii) using collagen type I protein sequences
<p>Ray-finned fishes (Actinopterygii) are the largest and most diverse group of vertebrates, comprising over half of all living vertebrate species. Phylogenetic relationships between ray-finned fishes have historically pivoted on the study of morphology, which has notoriously failed to resolve higher-order relationships, such as within the percomorphs. More recently, comprehensive genomic analyses have provided further resolution of actinopterygian phylogeny, including higher-order relationships. Such analyses are rightfully regarded as the 'gold standard' for phylogenetics. However, DNA retrieval requires modern or well-preserved tissue and is less likely to be preserved in archaeological or fossil specimens. In contrast some proteins, such as collagen, are phylogenetically informative and can survive into deep time. Here, we test the utility of collagen type I amino acid sequences for phylogenetic estimation of ray-finned fishes. We estimate topology using Bayesian approaches and compare the congruence of our estimated trees with published genomic phylogenies. Furthermore, we apply a Bayesian molecular clock approach and compare estimated divergence dates with previously published genomic clock analyses. Our collagen-derived trees exhibit 77% of node positions as congruent with recent genomic-derived trees, with the majority of discrepancies occurring in higher-order node positions, almost exclusively within the Percomorpha. Our molecular clock trees present divergence times that are fairly comparable with genomic-based phylogenetic analyses. We estimate the mean node age of Actinopteri at ~293 million years (Ma), the base of Teleostei at ~211 Ma and the radiation of percomorphs beginning at ~141 Ma (~350 Ma, ~250–283 Ma and ~120–133 Ma in genomic trees, respectively). Finally, we show that the average rate of collagen (I) sequence evolution is 0.9 amino acid substitutions for every million years of divergence, with the α3 (I) sequence evolving the fastest, followed by the α2 (I) chain. This is the quickest rate known for any vertebrate group. We demonstrate that phylogenetic analyses using collagen type I amino acid sequences generate tangible signals for actinopterygians that are highly congruent with recent genomic-level studies. However, there is limited congruence within percomorphs, perhaps due to clade-specific functional constraints acting upon collagen sequences. Our results provide important insights for future phylogenetic analyses incorporating extinct actinopterygian species via collagen (I) sequencing.</p>
Data from: Palynology of a short sequence of the Lower Devonian Beartooth Butte Formation at Cottonwood Canyon (Wyoming): Age, depositional environments and plant diversity
<p>The Beartooth Butte Formation hosts the most extensive Early Devonian macroflora of western North America. The age of the flora at Cottonwood Canyon (Wyoming) has been constrained to the Lochkovian-Pragian interval, based on fish biostratigraphy and unpublished palynological data. We present a detailed palynological analysis of the plant-bearing layers at Cottonwood Canyon. The palynomorphs comprise 32 spore, five cryptospore, two prasinophycean algae and an acritarch species. The stratigraphic ranges of these palynomorphs indicate a late Lochkovian - Pragian age, confirming previous age assignments. Analyses on samples from three different depositional environments of the plant-bearing sequence – layers with in situ lycophyte populations, flood layers that buried those populations and an organic matter accumulation zone within a flood layer – demonstrate distinct palynofacies. Comparisons between palynomorph and plant macrofossil diversity reveal some discrepancies. Whereas zosterophylls and lycophytes, most diverse and abundant among the macrofossils, have only one known corresponding spore type (assignable to zosterophylls) in the palynomorph assemblage, the trimerophytes, rare in the macrofossil assemblage, are represented by three spore types. Some of these discrepancies reflect taphonomic differences between macrofossils and palynomorphs, others could be due to the fact that the parent plants of most palynomorph types in the Cottonwood Canyon assemblage are unknown. These observations emphasize the need for concerted efforts to bring together the knowledge of macro- and microfloras within Early Devonian localities. Nevertheless, given the palaeophytogeographic significance of the Beartooth Butte Formation flora, its palyno- and macrofossil assemblages, taken together, provide new data relevant to future discussions of Early Devonian biogeography.</p>
Sequence-based genome-wide association study of individual milk mid-infrared wavenumbers in mixed-breed dairy cattle
<p>Fourier-transform mid-infrared (FT-MIR) spectroscopy provides a high-throughput and inexpensive method for predicting milk composition and other novel traits from milk samples. Whilst there have been many genome-wide association studies (GWAS) conducted on FT-MIR predicted traits, there have been few GWAS for individual FT-MIR wavenumbers. Here we examine associations between genomic regions and individual FT-MIR wavenumber phenotypes within a population of 38,085 mixed-breed New Zealand dairy cattle with imputed whole-genome sequence. GWAS were conducted for each of 895 individual FT-MIR wavenumber phenotypes and three FT-MIR predicted milk composition traits, and gene annotation and mammary tissue gene expression datasets were employed to identify candidate causative genes and variants. This resulted in the identification of 38 co-locating, co-segregating expression QTL (eQTL), and 31 protein-sequence mutations for FT-MIR wavenumber phenotypes, the latter including a null mutation in <i>ABO</i> that has a potential role in changing milk oligosaccharide profiles. For the candidate causative genes implicated in these analyses, the strength of association between relevant loci and each wavenumber across the mid-infrared spectrum revealed shared association patterns for groups of genomically-distant loci, highlighting clusters of loci linked through their biological roles in lactation and their presumed impacts on the chemical composition of milk.</p>
Pre-processed IgH repertoire sequencing data from BioProject PRJNA748239
<p><strong>Data Processing</strong></p> <p>Samples were demultiplexed via their Illumina indices, and processed using the Immcantation toolkit(1,2). Raw fastq files were filtered based on a quality score threshold of 20. Paired reads were joined if they had a minimum length of 10 nt, maximum error rate of 0.3 and a significance threshold of 0.0001. Reads with identical UMI were collapsed to a consensus sequence. Reads with identical full-length sequence and identical constant primer but differing UMI were further collapsed. Sequences were then submitted to IgBlast (3) for VDJ assignment and sequence annotation. Constant region sequences were mapped to germline using Stampy(4). The number and type of V gene mutations was calculated using the shazam R package.(2)</p> <p> </p> <p>software_versions pRESTO:0.5.3,Change-O:0.3.4,IgBlast 1.6.1, stampy1.0.21. shazam0.1.8</p> <p>quality_thresholds FilterSeq.py pRESTO Q>20</p> <p>paired_reads_assembly AssemblePairs.py pRESTO minlen 10 maxerror 0.3 alpha 0.0001</p> <p>primer_match_cutoffs MaskPrimers.py pRESTO C primer & V primer maxerror 0.2</p> <p>consensus_building BuildConsensus.py pRESTO maxerror 0.1 maxgap 0.5</p> <p>collapsing_method CollapseSeq.py pRESTO</p> <p>germline_database IMGT</p> <p> </p> <p>Format</p> <p> </p> <p>Processed sequences are provided in a tab delimited file format, including the following annotations:</p> <p> </p> <p><strong>C_CALL</strong> Isotype subclass</p> <p><strong>SEQUENCE_ID</strong> Sequence identifier</p> <p><strong>V_CALL</strong> V segment gene and allele</p> <p><strong>D_CALL</strong> D segment gene and allele</p> <p><strong>J_CALL</strong> J segment gene and allele</p> <p><strong>JUNCTION_LENGTH</strong> Junction length</p> <p><strong>CONSCOUNT</strong> Raw read count from which UMI consensus sequences were generated, summed over all UMIs for the given unique sequence.</p> <p><strong>DUPCOUNT</strong> UMI count for the given unique sequence</p> <p><strong>ISOTYPE</strong> Constant region primer (isotype)</p> <p><strong>MU_COUNT_CDR_R</strong> Number of replacement mutations in CDR region</p> <p><strong>MU_COUNT_CDR_S</strong> Number of silent mutations in CDR region</p> <p><strong>MU_COUNT_FWR_R</strong> Number of replacement mutations in FWR region</p> <p><strong>MU_COUNT_FWR_S</strong> Number of silent mutations in FWR region</p> <p><strong>MUT_TOTAL</strong> Total number of mutations in V gene </p> <p><strong>NP_LENGTH</strong> Total number of N and P additions<strong> </strong></p> <p><strong>SEQUENCE_INPUT</strong> Full length sequence</p> <p><strong>SEQUENCE_IMGT</strong> Gapped IMGT sequence</p> <p><strong>V_GERM_START_VDJ</strong> position of the first nucleotide in ungapped V germline sequence alignment</p> <p><strong>JUNCTION</strong> Junction nucleotide sequence</p> <p><strong>GERMLINE_IMGT_D_MASK</strong> IMGT-gapped germline nucleotide sequence with ns masking the NP1-D-NP2 regions</p> <p><strong>CDR3_AA_GRAVY</strong> CDR3 hydrophobicity</p> <p><strong>CDR3_AA_BULK</strong> CDR3 bulkiness</p> <p><strong>CDR3_AA_ALIPHATIC</strong> Normalized aliphatic index</p> <p><strong>CDR3_AA_POLARITY</strong> CDR3 polarity</p> <p><strong>CDR3_AA_CHARGE</strong> normalised net charge</p> <p><strong>CDR3_AA_BASIC</strong> Basic side chain residue content</p> <p><strong>CDR3_AA_ACIDIC</strong> Acidic side chain residue content</p> <p><strong>CDR3_AA_AROMATIC</strong> aromatic side chain conten</p> <p><strong>Subset</strong> Defined B cell subset </p> <p><strong>Repertoire</strong> Defined B cell repertoire (Naive, Memory IgM/IgD, IgA, IgG)</p> <p><strong>R_SCDR</strong> R/S ratio in CDR region</p> <p><strong>R_SFWR</strong> R/S ratio in FWR region</p> <p><strong>V_GENE</strong> V segment gene</p> <p><strong>D_GENE</strong> D segment gene</p> <p><strong>J_GENE</strong> J segment gene</p> <p><strong>V_FAM</strong> V family gene</p> <p><strong>Run</strong> ID of sequencing run</p> <p><strong>Sex</strong> Sex of the Subject</p> <p><strong>Age</strong> Age of the subject</p> <p><strong>UNIQUE_ID</strong> Subject identifier </p> <p><strong>SAMPLE</strong> Sample identifier, linking back to raw data</p> <p><strong>Bcellno</strong> Number of input B cells</p> <p><strong>Cells</strong> Cell type </p> <p> </p> <p><strong>References</strong></p> <p>1. Vander Heiden, J. A., G. Yaari, M. Uduman, J. N. H. Stern, K. C. O’Connor, D. A. Hafler, F. Vigneault, and S. H. Kleinstein. 2014. PRESTO: A toolkit for processing high-throughput sequencing raw reads of lymphocyte receptor repertoires. <em>Bioinformatics</em>30: 1930–1932.</p> <p>2. Gupta, N. T., J. A. Vander Heiden, M. Uduman, D. Gadala-Maria, G. Yaari, and S. H. Kleinstein. 2015. Change-O: A toolkit for analyzing large-scale B cell immunoglobulin repertoire sequencing data. <em>Bioinformatics</em>31: 3356–3358.</p> <p>3. Ye, J., N. Ma, T. L. Madden, and J. M. Ostell. 2013. IgBLAST: an immunoglobulin variable domain sequence analysis tool. <em>Nucleic Acids Res.</em>41.</p> <p>4. Lunter, G., and M. Goodson. 2011. Stampy: A statistical algorithm for sensitive and fast mapping of Illumina sequence reads. <em>Genome Res.</em>21: 936–939.</p>
Stacking Sequence, Interlayer Bonding, Termination Group Stability and Li/Na/Mg diffusion in MXenes
<p>Input-files and structures.</p> <p>tree-structure of files: </p> <p>.<br> ├── h2_molecule_in_vacuum<br> │ └── output<br> │ ├── CONTCAR<br> │ ├── INCAR<br> │ ├── KPOINTS<br> │ ├── OUTCAR<br> │ └── POSCAR<br> ├── readme<br> ├── ti3c2t2<br> │ ├── octahedral<br> │ │ ├── 1x1_clcl<br> │ │ │ └── output<br> │ │ │ ├── CONTCAR<br> │ │ │ ├── INCAR<br> │ │ │ ├── KPOINTS<br> │ │ │ ├── OUTCAR<br> │ │ │ └── POSCAR<br> │ │ ├── 1x1_ff<br> │ │ │ └── output<br> │ │ │ ├── CONTCAR<br> │ │ │ ├── INCAR<br> │ │ │ ├── KPOINTS<br> │ │ │ ├── OUTCAR<br> │ │ │ └── POSCAR<br> │ │ ├── 1x1_fho<br> │ │ │ └── output<br> │ │ │ ├── CONTCAR<br> │ │ │ ├── INCAR<br> │ │ │ ├── KPOINTS<br> │ │ │ ├── OUTCAR<br> │ │ │ └── POSCAR<br> │ │ ├── 1x1_fo<br> │ │ │ └── output<br> │ │ │ ├── CONTCAR<br> │ │ │ ├── INCAR<br> │ │ │ ├── KPOINTS<br> │ │ │ ├── OUTCAR<br> │ │ │ └── POSCAR<br> │ │ ├── 1x1_ohho<br> │ │ │ └── output<br> │ │ │ ├── CONTCAR<br> │ │ │ ├── INCAR<br> │ │ │ ├── KPOINTS<br> │ │ │ ├── OUTCAR<br> │ │ │ └── POSCAR<br> │ │ ├── 1x1_oho<br> │ │ │ └── output<br> │ │ │ ├── CONTCAR<br> │ │ │ ├── INCAR<br> │ │ │ ├── KPOINTS<br> │ │ │ ├── OUTCAR<br> │ │ │ └── POSCAR<br> │ │ ├── 1x1_oo<br> │ │ │ └── output<br> │ │ │ ├── CONTCAR<br> │ │ │ ├── INCAR<br> │ │ │ ├── KPOINTS<br> │ │ │ ├── OUTCAR<br> │ │ │ └── POSCAR<br> │ │ ├── neb_li_2_image<br> │ │ │ └── output<br> │ │ │ ├── 00<br> │ │ │ │ ├── OUTCAR<br> │ │ │ │ ├── POSCAR<br> │ │ │ │ └── old_POSCAR<br> │ │ │ ├── 01<br> │ │ │ │ ├── CONTCAR<br> │ │ │ │ ├── OUTCAR<br> │ │ │ │ ├── POSCAR<br> │ │ │ │ └── old_POSCAR<br> │ │ │ ├── 02<br> │ │ │ │ ├── CONTCAR<br> │ │ │ │ ├── OUTCAR<br> │ │ │ │ ├── POSCAR<br> │ │ │ │ └── old_POSCAR<br> │ │ │ ├── 03<br> │ │ │ │ ├── OUTCAR<br> │ │ │ │ ├── POSCAR<br> │ │ │ │ └── old_POSCAR<br> │ │ │ ├── INCAR<br> │ │ │ ├── KPOINTS<br> │ │ │ └── neb.dat<br> │ │ ├── neb_li_5_image<br> │ │ │ ├── neb.dat<br> │ │ │ └── output<br> │ │ │ ├── 00<br> │ │ │ │ ├── OUTCAR<br> │ │ │ │ ├── POSCAR<br> │ │ │ │ └── old_POSCAR<br> │ │ │ ├── 01<br> │ │ │ │ ├── CONTCAR<br> │ │ │ │ ├── OUTCAR<br> │ │ │ │ ├── POSCAR<br> │ │ │ │ ├── fe.dat<br> │ │ │ │ └── old_POSCAR<br> │ │ │ ├── 02<br> │ │ │ │ ├── CONTCAR<br> │ │ │ │ ├── OUTCAR<br> │ │ │ │ ├── POSCAR<br> │ │ │ │ ├── fe.dat<br> │ │ │ │ └── old_POSCAR<br> │ │ │ ├── 03<br> │ │ │ │ ├── CONTCAR<br> │ │ │ │ ├── OUTCAR<br> │ │ │ │ ├── POSCAR<br> │ │ │ │ ├── fe.dat<br> │ │ │ │ └── old_POSCAR<br> │ │ │ ├── 04<br> │ │ │ │ ├── CONTCAR<br> │ │ │ │ ├── OUTCAR<br> │ │ │ │ ├── POSCAR<br> │ │ │ │ ├── fe.dat<br> │ │ │ │ └── old_POSCAR<br> │ │ │ ├── 05<br> │ │ │ │ ├── CONTCAR<br> │ │ │ │ ├── OUTCAR<br> │ │ │ │ ├── POSCAR<br> │ │ │ │ ├── fe.dat<br> │ │ │ │ └── old_POSCAR<br> │ │ │ ├── 06<br> │ │ │ │ ├── OUTCAR<br> │ │ │ │ ├── POSCAR<br> │ │ │ │ └── old_POSCAR<br> │ │ │ ├── INCAR<br> │ │ │ ├── KPOINTS<br> │ │ │ ├── POSCAR<br> │ │ │ └── neb.dat<br> │ │ ├── neb_li_endpoint_2image<br> │ │ │ └── output<br> │ │ │ ├── CONTCAR<br> │ │ │ ├── INCAR<br> │ │ │ ├── KPOINTS<br> │ │ │ ├── OUTCAR<br> │ │ │ └── POSCAR<br> │ │ ├── neb_li_endpoint_5image<br> │ │ │ └── output<br> │ │ │ ├── CONTCAR<br> │ │ │ ├── INCAR<br> │ │ │ ├── KPOINTS<br> │ │ │ ├── OUTCAR<br> │ │ │ └── POSCAR<br> │ │ ├── neb_li_startpoint<br> │ │ │ └── output<br> │ │ │ ├── CONTCAR<br> │ │ │ ├── INCAR<br> │ │ │ ├── KPOINTS<br> │ │ │ ├── OUTCAR<br> │ │ │ └── POSCAR<br> │ │ ├── neb_mg<br> │ │ │ └── output<br> │ │ │ ├── 00<br> │ │ │ │ ├── OUTCAR<br> │ │ │ │ └── POSCAR<br> │ │ │ ├── 01<br> │ │ │ │ ├── CONTCAR<br> │ │ │ │ ├── OUTCAR<br> │ │ │ │ ├── POSCAR<br> │ │ │ │ └── fe.dat<br> │ │ │ ├── 02<br> │ │ │ │ ├── CONTCAR<br> │ │ │ │ ├── OUTCAR<br> │ │ │ │ ├── POSCAR<br> │ │ │ │ └── fe.dat<br> │ │ │ ├── 03<br> │ │ │ │ ├── OUTCAR<br> │ │ │ │ └── POSCAR<br> │ │ │ ├── INCAR<br> │ │ │ ├── KPOINTS<br> │ │ │ └── neb.dat<br> │ │ ├── neb_mg_5_image<br> │ │ ├── neb_mg_endpoint<br> │ │ │ └── output<br> │ │ │ ├── CONTCAR<br> │ │ │ ├── INCAR<br> │ │ │ ├── KPOINTS<br> │ │ │ ├── OUTCAR<br> │ │ │ └── POSCAR<br> │ │ ├── neb_mg_startpoint<br> │ │ │ └── output<br> │ │ │ ├── CONTCAR<br> │ │ │ ├── INCAR<br> │ │ │ ├── KPOINTS<br> │ │ │ ├── OUTCAR<br> │ │ │ └── POSCAR<br> │ │ ├── neb_na<br> │ │ │ └── output<br> │ │ │ ├── 00<br> │ │ │ │ ├── OUTCAR<br> │ │ │ │ └── POSCAR<br> │ │ │ ├── 01<br> │ │ │ │ ├── CONTCAR<br> │ │ │ │ ├── OUTCAR<br> │ │ │ │ └── POSCAR<br> │ │ │ ├── 02<br> │ │ │ │ ├── CONTCAR<br> │ │ │ │ ├── OUTCAR<br> │ │ │ │ └── POSCAR<br> │ │ │ ├── 03<br> │ │ │ │ ├── OUTCAR<br> │ │ │ │ └── POSCAR<br> │ │ │ ├── INCAR<br> │ │ │ ├── KPOINTS<br> │ │ │ └── neb.dat<br> │ │ ├── neb_na_endpoint<br> │ │ │ └── output<br> │ │ │ ├── CONTCAR<br> │ │ │ ├── INCAR<br> │ │ │ ├── KPOINTS<br> │ │ │ ├── OUTCAR<br> │ │ │ └── POSCAR<br> │ │ ├── neb_na_startpoint<br> │ │ │ └── output<br> │ │ │ ├── CONTCAR<br> │ │ │ ├── INCAR<br> │ │ │ ├── KPOINTS<br> │ │ │ ├── OUTCAR<br> │ │ │ └── POSCAR<br> │ │ ├── neb_template<br> │ │ │ └── output<br> │ │ │ ├── CONTCAR<br> │ │ │ ├── INCAR<br> │ │ │ ├── KPOINTS<br> │ │ │ ├── OUTCAR<br> │ │ │ └── POSCAR<br> │ │ └── site_test<br> │ │ ├── cmcm<br> │ │ │ └── output<br> │ │ │ ├── CONTCAR<br> │ │ │ ├── INCAR<br> │ │ │ ├── KPOINTS<br> │ │ │ ├── OUTCAR<br> │ │ │ └── POSCAR<br> │ │ ├── mtmt<br> │ │ │ └── output<br> │ │ │ ├── CONTCAR<br> │ │ │ ├── INCAR<br> │ │ │ ├── KPOINTS<br> │ │ │ ├── OUTCAR<br> │ │ │ └── POSCAR<br> │ │ └── tctc<br> │ │ └── output<br> │ │ ├── CONTCAR<br> │ │ ├── INCAR<br> │ │ ├── KPOINTS<br> │ │ ├── OUTCAR<br> │ │ └── POSCAR<br> │ ├── singlelayer<br> │ │ ├── sl_clcl<br> │ │ │ └── output<br> │ │ │ ├── CONTCAR<br> │ │ │ ├── INCAR<br> │ │ │ ├── KPOINTS<br> │ │ │ ├── OUTCAR<br> │ │ │ └── POSCAR<br> │ │ ├── sl_ff<br> │ │ │ └── output<br> │ │ │ ├── CONTCAR<br> │ │ │ ├── INCAR<br> │ │ │ ├── KPOINTS<br> │ │ │ ├── OUTCAR<br> │ │ │ └── POSCAR<br> │ │ ├── sl_fho<br> │ │ │ └── output<br> │ │ │ ├── CONTCAR<br> │ │ │ ├── INCAR<br> │ │ │ ├── KPOINTS<br> │ │ │ ├── OUTCAR<br> │ │ │ └── POSCAR<br> │ │ ├── sl_fo<br> │ │ │ └── output<br> │ │ │ ├── CONTCAR<br> │ │ │ ├── INCAR<br> │ │ │ ├── KPOINTS<br> │ │ │ ├── OUTCAR<br> │ │ │ └── POSCAR<br> │ │ ├── sl_ohho<br> │ │ │ └── output<br> │ │ │ ├── CONTCAR<br> │ │ │ ├── INCAR<br> │ │ │ ├── KPOINTS<br> │ │ │ ├── OUTCAR<br> │ │ │ └── POSCAR<br> │ │ ├── sl_oho<br> │ │ │ └── output<br> │ │ │ ├── CONTCAR<br> │ │ │ ├── INCAR<br> │ │ │ ├── KPOINTS<br> │ │ │ ├── OUTCAR<br> │ │ │ └── POSCAR<br> │ │ └── sl_oo<br> │ │ └── output<br> │ │ ├── CONTCAR<br> │ │ ├── INCAR<br> │ │ ├── KPOINTS<br> │ │ ├── OUTCAR<br> │ │ └── POSCAR<br> │ └── trigonal<br> │ ├── 1x1_clcl<br> │ │ └── output<br> │ │ ├── CONTCAR<br> │ │ ├── INCAR<br> │ │ ├── KPOINTS<br> │ │ ├── OUTCAR<br> │ │ └── POSCAR<br> │ ├── 1x1_ff<br> │ │ └── output<br> │ │ ├── CONTCAR<br> │ │ ├── INCAR<br> │ │ ├── KPOINTS<br> │ │ ├── OUTCAR<br> │ │ └── POSCAR<br> │ ├── 1x1_fho<br> │ │ └── output<br> │ │ ├── CONTCAR<br> │ │ ├── INCAR<br> │ │ ├── KPOINTS<br> │ │ ├── OUTCAR<br> │ │ └── POSCAR<br> │ ├── 1x1_fo<br> │ │ └── output<br> │ │ ├── CONTCAR<br> │ │ ├── INCAR<br> │ │ ├── KPOINTS<br> │ │ ├── OUTCAR<br> │ │ └── POSCAR<br> │ ├── 1x1_ohho<br> │ │ └── output<br> │ │ ├── CONTCAR<br> │ │ ├── INCAR<br> │ │ ├── KPOINTS<br> │ │ ├── OUTCAR<br> │ │ └── POSCAR<br> │ ├── 1x1_oho<br> │ │ └── output<br> │ │ ├── CONTCAR<br> │ │ ├── INCAR<br> │ │ ├── KPOINTS<br> │ │ ├── OUTCAR<br> │ │ └── POSCAR<br> │ ├── 1x1_oo<br> │ │ └── output<br> │ │ ├── CONTCAR<br> │ │ ├── INCAR<br> │ │ ├── KPOINTS<br> │ │ ├── OUTCAR<br> │ │ └── POSCAR<br> │ ├── neb_li_2_image<br> │ │ └── output<br> │ │ ├── 00<br> │ │ │ ├── OUTCAR<br> │ │ │ └── POSCAR<br> │ │ ├── 01<br> │ │ │ ├── CONTCAR<br> │ │ │ ├── OUTCAR<br> │ │ │ └── POSCAR<br> │ │ ├── 02<br> │ │ │ ├── CONTCAR<br> │ │ │ ├── OUTCAR<br> │ │ │ └── POSCAR<br> │ │ ├── 03<br> │ │ │ ├── OUTCAR<br> │ │ │ └── POSCAR<br> │ │ ├── INCAR<br> │ │ ├── KPOINTS<br> │ │ └── neb.dat<br> │ ├── neb_li_5_image<br> │ │ └── output<br> │ │ ├── 00<br> │ │ │ ├── OUTCAR<br> │ │ │ └── POSCAR<br> │ │ ├── 01<br> │ │ │ ├── CONTCAR<br> │ │ │ ├── OUTCAR<br> │ │ │ └── POSCAR<br> │ │ ├── 02<br> │ │ │ ├── CONTCAR<br> │ │ │ ├── OUTCAR<br> │ │ │ └── POSCAR<br> │ │ ├── 03<br> │ │ │ ├── CONTCAR<br> │ │ │ ├── OUTCAR<br> │ │ │ └── POSCAR<br> │ │ ├── 04<br> │ │ │ ├── CONTCAR<br> │ │ │ ├── OUTCAR<br> │ │ │ └── POSCAR<br> │ │ ├── 05<br> │ │ │ ├── CONTCAR<br> │ │ │ ├── OUTCAR<br> │ │ │ └── POSCAR<br> │ │ ├── 06<br> │ │ │ ├── OUTCAR<br> │ │ │ └── POSCAR<br> │ │ ├── INCAR<br> │ │ ├── KPOINTS<br> │ │ └── neb.dat<br> │ ├── neb_li_endpoint_2image<br> │ │ └── output<br> │ │ ├── CONTCAR<br> │ │ ├── INCAR<br> │ │ ├── KPOINTS<br> │ │ ├── OUTCAR<br> │ │ └── POSCAR<br> │ ├── neb_li_endpoint_5image<br> │ │ └── output<br> │ │ ├── CONTCAR<br> │ │ ├── INCAR<br> │ │ ├── KPOINTS<br> │ │ ├── OUTCAR<br> │ │ └── POSCAR<br> │ ├── neb_li_startpoint<br> │ │ └── output<br> │ │ ├── CONTCAR<br> │ │ ├── INCAR<br> │ │ ├── KPOINTS<br> │ │ ├── OUTCAR<br> │ │ └── POSCAR<br> │ ├── neb_mg<br> │ │ └── output<br> │ │ ├── 00<br> │ │ │ ├── OUTCAR<br> │ │ │ └── POSCAR<br> │ │ ├── 01<br> │ │ │ ├── CONTCAR<br> │ │ │ ├── OUTCAR<br> │ │ │ └── POSCAR<br> │ │ ├── 02<br> │ │ │ ├── CONTCAR<br> │ │ │ ├── OUTCAR<br> │ │ │ └── POSCAR<br> │ │ ├── 03<br> │ │ │ ├── OUTCAR<br> │ │ │ └── POSCAR<br> │ │ ├── INCAR<br> │ │ ├── KPOINTS<br> │ │ └── neb.dat<br> │ ├── neb_mg_5_image<br> │ │ └── output<br> │ │ ├── 00<br> │ │ │ ├── OUTCAR<br> │ │ │ └── POSCAR<br> │ │ ├── 01<br> │ │ │ ├── CONTCAR<br> │ │ │ ├── OUTCAR<br> │ │ │ └── POSCAR<br> │ │ ├── 02<br> │ │ │ ├── CONTCAR<br> │ │ │ ├── OUTCAR<br> │ │ │ └── POSCAR<br> │ │ ├── 03<br> │ │ │ ├── CONTCAR<br> │ │ │ ├── OUTCAR<br> │ │ │ └── POSCAR<br> │ │ ├── 04<br> │ │ │ ├── CONTCAR<br> │ │ │ ├── OUTCAR<br> │ │ │ └── POSCAR<br> │ │ ├── 05<br> │ │ │ ├── CONTCAR<br> │ │ │ ├── OUTCAR<br> │ │ │ └── POSCAR<br> │ │ ├── 06<br> │ │ │ ├── OUTCAR<br> │ │ │ └── POSCAR<br> │ │ ├── INCAR<br> │ │ ├── KPOINTS<br> │ │ └── neb.dat<br> │ ├── neb_mg_endpoint<br> │ │ └── output<br> │ │ ├── CONTCAR<br> │ │ ├── INCAR<br> │ │ ├── KPOINTS<br> │ │ ├── OUTCAR<br> │ │ └── POSCAR<br> │ ├── neb_mg_startpoint<br> │ │ └── output<br> │ │ ├── CONTCAR<br> │ │ ├── INCAR<br> │ │ ├── KPOINTS<br> │ │ ├── OUTCAR<br> │ │ └── POSCAR<br> │ ├── neb_na<br> │ │ └── output<br> │ │ ├── 00<br> │ │ │ ├── OUTCAR<br> │ │ │ └── POSCAR<br> │ │ ├── 01<br> │ │ │ ├── CONTCAR<br> │ │ │ ├── OUTCAR<br> │ │ │ └── POSCAR<br> │ │ ├── 02<br> │ │ │ ├── CONTCAR<br> │ │ │ ├── OUTCAR<br> │ │ │ └── POSCAR<br> │ │ ├── 03<br> │ │ │ ├── OUTCAR<br> │ │ │ └── POSCAR<br> │ │ ├── INCAR<br> │ │ ├── KPOINTS<br> │ │ └── neb.dat<br> │ ├── neb_na_endpoint<br> │ │ └── output<br> │ │ ├── CONTCAR<br> │ │ ├── INCAR<br> │ │ ├── KPOINTS<br> │ │ ├── OUTCAR<br> │ │ └── POSCAR<br> │ ├── neb_na_startpoint<br> │ │ └── output<br> │ │ ├── CONTCAR<br> │ │ ├── INCAR<br> │ │ ├── KPOINTS<br> │ │ ├── OUTCAR<br> │ │ └── POSCAR<br> │ └── neb_template<br> │ └── output<br> │ ├── CONTCAR<br> │ ├── INCAR<br> │ ├── KPOINTS<br> │ ├── OUTCAR<br> │ └── POSCAR<br> ├── v2ct2<br> │ ├── octahedral<br> │ │ ├── 1x1_clcl<br> │ │ │ └── output<br> │ │ │ ├── CONTCAR<br> │ │ │ ├── INCAR<br> │ │ │ ├── KPOINTS<br> │ │ │ ├── OUTCAR<br> │ │ │ └── POSCAR<br> │ │ ├── 1x1_ff<br> │ │ │ └── output<br> │ │ │ ├── CONTCAR<br> │ │ │ ├── INCAR<br> │ │ │ ├── KPOINTS<br> │ │ │ ├── OUTCAR<br> │ │ │ └── POSCAR<br> │ │ ├── 1x1_fho<br> │ │ │ └── output<br> │ │ │ ├── CONTCAR<br> │ │ │ ├── INCAR<br> │ │ │ ├── KPOINTS<br> │ │ │ ├── OUTCAR<br> │ │ │ └── POSCAR<br> │ │ ├── 1x1_fo<br> │ │ │ └── output<br> │ │ │ ├── CONTCAR<br> │ │ │ ├── INCAR<br> │ │ │ ├── KPOINTS<br> │ │ │ ├── OUTCAR<br> │ │ │ └── POSCAR<br> │ │ ├── 1x1_ohho<br> │ │ │ └── output<br> │ │ │ ├── CONTCAR<br> │ │ │ ├── INCAR<br> │ │ │ ├── KPOINTS<br> │ │ │ ├── OUTCAR<br> │ │ │ └── POSCAR<br> │ │ ├── 1x1_oho<br> │ │ │ └── output<br> │ │ │ ├── CONTCAR<br> │ │ │ ├── INCAR<br> │ │ │ ├── KPOINTS<br> │ │ │ ├── OUTCAR<br> │ │ │ └── POSCAR<br> │ │ └── 1x1_oo<br> │ │ └── output<br> │ │ ├── CONTCAR<br> │ │ ├── INCAR<br> │ │ ├── KPOINTS<br> │ │ ├── OUTCAR<br> │ │ └── POSCAR<br> │ ├── singlelayer<br> │ │ ├── sl_clcl<br> │ │ │ └── output<br> │ │ │ ├── CONTCAR<br> │ │ │ ├── INCAR<br> │ │ │ ├── KPOINTS<br> │ │ │ ├── OUTCAR<br> │ │ │ └── POSCAR<br> │ │ ├── sl_ff<br> │ │ │ └── output<br> │ │ │ ├── CONTCAR<br> │ │ │ ├── INCAR<br> │ │ │ ├── KPOINTS<br> │ │ │ ├── OUTCAR<br> │ │ │ └── POSCAR<br> │ │ ├── sl_fho<br> │ │ │ └── output<br> │ │ │ ├── CONTCAR<br> │ │ │ ├── INCAR<br> │ │ │ ├── KPOINTS<br> │ │ │ ├── OUTCAR<br> │ │ │ └── POSCAR<br> │ │ ├── sl_fo<br> │ │ │ └── output<br> │ │ │ ├── CONTCAR<br> │ │ │ ├── INCAR<br> │ │ │ ├── KPOINTS<br> │ │ │ ├── OUTCAR<br> │ │ │ └── POSCAR<br> │ │ ├── sl_ohho<br> │ │ │ └── output<br> │ │ │ ├── CONTCAR<br> │ │ │ ├── INCAR<br> │ │ │ ├── KPOINTS<br> │ │ │ ├── OUTCAR<br> │ │ │ └── POSCAR<br> │ │ ├── sl_oho<br> │ │ │ └── output<br> │ │ │ ├── CONTCAR<br> │ │ │ ├── INCAR<br> │ │ │ ├── KPOINTS<br> │ │ │ ├── OUTCAR<br> │ │ │ └── POSCAR<br> │ │ └── sl_oo<br> │ │ └── output<br> │ │ ├── CONTCAR<br> │ │ ├── INCAR<br> │ │ ├── KPOINTS<br> │ │ ├── OUTCAR<br> │ │ └── POSCAR<br> │ └── trigonal<br> │ ├── 1x1_clcl<br> │ │ └── output<br> │ │ ├── CONTCAR<br> │ │ ├── INCAR<br> │ │ ├── KPOINTS<br> │ │ ├── OUTCAR<br> │ │ └── POSCAR<br> │ ├── 1x1_ff<br> │ │ └── output<br> │ │ ├── CONTCAR<br> │ │ ├── INCAR<br> │ │ ├── KPOINTS<br> │ │ ├── OUTCAR<br> │ │ └── POSCAR<br> │ ├── 1x1_fho<br> │ │ └── output<br> │ │ ├── CONTCAR<br> │ │ ├── INCAR<br> │ │ ├── KPOINTS<br> │ │ ├── OUTCAR<br> │ │ └── POSCAR<br> │ ├── 1x1_fo<br> │ │ └── output<br> │ │ ├── CONTCAR<br> │ │ ├── INCAR<br> │ │ ├── KPOINTS<br> │ │ ├── OUTCAR<br> │ │ └── POSCAR<br> │ ├── 1x1_ohho<br> │ │ └── output<br> │ │ ├── CONTCAR<br> │ │ ├── INCAR<br> │ │ ├── KPOINTS<br> │ │ ├── OUTCAR<br> │ │ └── POSCAR<br> │ ├── 1x1_oho<br> │ │ └── output<br> │ │ ├── CONTCAR<br> │ │ ├── INCAR<br> │ │ ├── KPOINTS<br> │ │ ├── OUTCAR<br> │ │ └── POSCAR<br> │ └── 1x1_oo<br> │ └── output<br> │ ├── CONTCAR<br> │ ├── INCAR<br> │ ├── KPOINTS<br> │ ├── OUTCAR<br> │ └── POSCAR<br> ├── xrd_ti3c2t2<br> │ ├── octahedral<br> │ │ ├── 1x1_ff<br> │ │ │ └── output<br> │ │ │ ├── CONTCAR<br> │ │ │ ├── INCAR<br> │ │ │ ├── KPOINTS<br> │ │ │ ├── OUTCAR<br> │ │ │ └── POSCAR<br> │ │ ├── 1x1_oo<br> │ │ │ └── output<br> │ │ │ ├── CONTCAR<br> │ │ │ ├── INCAR<br> │ │ │ ├── KPOINTS<br> │ │ │ ├── OUTCAR<br> │ │ │ └── POSCAR<br> │ │ ├── mixed1<br> │ │ │ └── output<br> │ │ │ ├── CONTCAR<br> │ │ │ ├── INCAR<br> │ │ │ ├── KPOINTS<br> │ │ │ ├── OUTCAR<br> │ │ │ └── POSCAR<br> │ │ ├── mixed2<br> │ │ │ └── output<br> │ │ │ ├── CONTCAR<br> │ │ │ ├── INCAR<br> │ │ │ ├── KPOINTS<br> │ │ │ ├── OUTCAR<br> │ │ │ └── POSCAR<br> │ │ └── mixed3<br> │ │ └── output<br> │ │ ├── CONTCAR<br> │ │ ├── INCAR<br> │ │ ├── KPOINTS<br> │ │ ├── OUTCAR<br> │ │ └── POSCAR<br> │ └── trigonal<br> │ ├── 1x1_ff<br> │ │ └── output<br> │ │ ├── CONTCAR<br> │ │ ├── INCAR<br> │ │ ├── KPOINTS<br> │ │ ├── OUTCAR<br> │ │ └── POSCAR<br> │ ├── 1x1_oo<br> │ │ └── output<br> │ │ ├── CONTCAR<br> │ │ ├── INCAR<br> │ │ ├── KPOINTS<br> │ │ ├── OUTCAR<br> │ │ └── POSCAR<br> │ ├── mixed1<br> │ │ └── output<br> │ │ ├── CONTCAR<br> │ │ ├── INCAR<br> │ │ ├── KPOINTS<br> │ │ ├── OUTCAR<br> │ │ └── POSCAR<br> │ ├── mixed2<br> │ │ └── output<br> │ │ ├── CONTCAR<br> │ │ ├── INCAR<br> │ │ ├── KPOINTS<br> │ │ ├── OUTCAR<br> │ │ └── POSCAR<br> │ └── mixed3<br> │ └── output<br> │ ├── CONTCAR<br> │ ├── INCAR<br> │ ├── KPOINTS<br> │ ├── OUTCAR<br> │ └── POSCAR<br> └── zr3c2t2<br> ├── octahedral<br> │ ├── 1x1_clcl<br> │ │ └── output<br> │ │ ├── CONTCAR<br> │ │ ├── INCAR<br> │ │ ├── KPOINTS<br> │ │ ├── OUTCAR<br> │ │ └── POSCAR<br> │ ├── 1x1_ff<br> │ │ └── output<br> │ │ ├── CONTCAR<br> │ │ ├── INCAR<br> │ │ ├── KPOINTS<br> │ │ ├── OUTCAR<br> │ │ └── POSCAR<br> │ ├── 1x1_fho<br> │ │ └── output<br> │ │ ├── CONTCAR<br> │ │ ├── INCAR<br> │ │ ├── KPOINTS<br> │ │ ├── OUTCAR<br> │ │ └── POSCAR<br> │ ├── 1x1_fo<br> │ │ └── output<br> │ │ ├── CONTCAR<br> │ │ ├── INCAR<br> │ │ ├── KPOINTS<br> │ │ ├── OUTCAR<br> │ │ └── POSCAR<br> │ ├── 1x1_ohho<br> │ │ └── output<br> │ │ ├── CONTCAR<br> │ │ ├── INCAR<br> │ │ ├── KPOINTS<br> │ │ ├── OUTCAR<br> │ │ └── POSCAR<br> │ ├── 1x1_oho<br> │ │ └── output<br> │ │ ├── CONTCAR<br> │ │ ├── INCAR<br> │ │ ├── KPOINTS<br> │ │ ├── OUTCAR<br> │ │ └── POSCAR<br> │ └── 1x1_oo<br> │ └── output<br> │ ├── CONTCAR<br> │ ├── INCAR<br> │ ├── KPOINTS<br> │ ├── OUTCAR<br> │ └── POSCAR<br> ├── singlelayer<br> │ ├── sl_clcl<br> │ │ └── output<br> │ │ ├── CONTCAR<br> │ │ ├── INCAR<br> │ │ ├── KPOINTS<br> │ │ ├── OUTCAR<br> │ │ └── POSCAR<br> │ ├── sl_ff<br> │ │ └── output<br> │ │ ├── CONTCAR<br> │ │ ├── INCAR<br> │ │ ├── KPOINTS<br> │ │ ├── OUTCAR<br> │ │ └── POSCAR<br> │ ├── sl_fho<br> │ │ └── output<br> │ │ ├── CONTCAR<br> │ │ ├── INCAR<br> │ │ ├── KPOINTS<br> │ │ ├── OUTCAR<br> │ │ └── POSCAR<br> │ ├── sl_fo<br> │ │ └── output<br> │ │ ├── CONTCAR<br> │ │ ├── INCAR<br> │ │ ├── KPOINTS<br> │ │ ├── OUTCAR<br> │ │ └── POSCAR<br> │ ├── sl_ohho<br> │ │ └── output<br> │ │ ├── CONTCAR<br> │ │ ├── INCAR<br> │ │ ├── KPOINTS<br> │ │ ├── OUTCAR<br> │ │ └── POSCAR<br> │ ├── sl_oho<br> │ │ └── output<br> │ │ ├── CONTCAR<br> │ │ ├── INCAR<br> │ │ ├── KPOINTS<br> │ │ ├── OUTCAR<br> │ │ └── POSCAR<br> │ └── sl_oo<br> │ └── output<br> │ ├── CONTCAR<br> │ ├── INCAR<br> │ ├── KPOINTS<br> │ ├── OUTCAR<br> │ └── POSCAR<br> └── trigonal<br> ├── 1x1_clcl<br> │ └── output<br> │ ├── CONTCAR<br> │ ├── INCAR<br> │ ├── KPOINTS<br> │ ├── OUTCAR<br> │ └── POSCAR<br> ├── 1x1_ff<br> │ └── output<br> │ ├── CONTCAR<br> │ ├── INCAR<br> │ ├── KPOINTS<br> │ ├── OUTCAR<br> │ └── POSCAR<br> ├── 1x1_fho<br> │ └── output<br> │ ├── CONTCAR<br> │ ├── INCAR<br> │ ├── KPOINTS<br> │ ├── OUTCAR<br> │ └── POSCAR<br> ├── 1x1_fo<br> │ └── output<br> │ ├── CONTCAR<br> │ ├── INCAR<br> │ ├── KPOINTS<br> │ ├── OUTCAR<br> │ └── POSCAR<br> ├── 1x1_ohho<br> │ └── output<br> │ ├── CONTCAR<br> │ ├── INCAR<br> │ ├── KPOINTS<br> │ ├── OUTCAR<br> │ └── POSCAR<br> ├── 1x1_oho<br> │ └── output<br> │ ├── CONTCAR<br> │ ├── INCAR<br> │ ├── KPOINTS<br> │ ├── OUTCAR<br> │ └── POSCAR<br> └── 1x1_oo<br> └── output<br> ├── CONTCAR<br> ├── INCAR<br> ├── KPOINTS<br> ├── OUTCAR<br> └── POSCAR</p> <p> </p>
NPIP: A Comprehensive Analysis Pipeline for Rapid Pathogen Detection in Clinical Samples Based on Nanopore Sequencing
<p>Background: Rapid and accurate pathogen detection is important for effective control of infectious diseases. However, traditional pathogen culture methods have a very long detection time, as well as high rates of false-positive and false-negative results. Third generation sequencing (TGS) technology brings the new possibility of being used as a pathogen detection method. However, the practicability of a pathogen detection report based on TGS is still lacking. There is also a lack of professional and accurate report interpretation.</p> <p>Results: Here, we report on the development of a pathogen detection and analysis tool (NPIP) based on third generation nanopore sequencing technology. We also prove the practicability of nanopore sequencing and NPIP analysis tools in emergency and clinical pathogen detection by demonstrating its use in a practical case.</p> <p>Conclusions: This platform provides an effective, convenient, and fast analysis tool for clinicians and public health personnel to more successfully apply TGS in pathogen detection.</p>
The first 500 numbers in the Fibonacci sequence
<p>This dataset consists of a single text file containing the first 500 numbers in the Fibonacci sequence, one per line. This is a sample dataset used to showcase the features of the <a href="https://github.com/rodluger/showyourwork">showyourwork</a> package.</p>
Long reads and Hi-C sequencing illuminate the two-compartment genome of the model arbuscular mycorrhizal symbiont Rhizophagus irregularis
<p>This repository contains annotations for the strains of <em>R. irregularis</em> chromosome assemblies.</p>
Alfalfa genotyping-by-sequencing (GBS) data
<p>Alfalfa (<i>Medicago</i> <i>sativa</i> L.) quantitative trait loci (QTL) mapping population (184 F<sub>1</sub>) derived from cultivars 3010 (cold-tolerant) as female parent and CW 100 (cold-sensitive) as male parent were genotyped using genotyping-by-sequencing (GBS). Polymorphic SNPs unique to either 3010 (AB x AA) or CW 1010 (AA x AB) were identified as single dose allele (SDA) markers and used to generate the genetic linkage maps. Two sets of linkage maps, a set for each parent, were used to map the traits and the QTL were identified. With the genotyping and phenotyping informations we were able to map various alfalfa traits such as fall dormancy, winter-hardiness, freezing tolerance, flowering time, yield and leaf-rust resistance. The raw sequence data were deposited at NCBI SRA with the accession number SRP150116. This study identified several genomic regions and associated markers that can be further utilized in marker-assisted breeding to improve the alfalfa. </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.