Find research datasets worth reusing

opencc-by-4.0Oct 2022View details →

Shotgun metagenomic sequencing dataset of a synthetic mock community containing 20 genomes spiked-in at even and staggered concentrations.

Shotgun metagenomics (SM) sequencing is a popular method used in microbial ecology to obtain insights on microbial community structure and function potential in a given biological system without the need to cultivate microorganisms. The dataset described in this article describes technical triplicates of shotgun metagenomic sequence libraries generated from two purified and titrated mixes of 20 distinct reference bacterial genomes for which key characteristics such as genome size, sequence and spiked-in concentrations are known. In one of the genomic DNA mix, each genome is spiked-in at similar concentrations (representing an even microbial community) and in the other, genomes are spiked-in at different concentrations with some genomes highly abundant and other in low quantity, mimicking an uneven microbial community DNA extract. In order to be interpretable, SM sequencing data needs to be properly analyzed by complex analytical bioinformatic pipelines. Environments investigated with this method can range from simple to very complex. Typically, microbial communities contain microbes that are ubiquitous and some others much rarer. Analysis of rare microbes in a complex microbial community are challenging to perform as their sequencing signals get submerged by the microbial genomes that are more abundant. In this context, it is critical to have access to sequencing data of simple mock communities of mixes of well characterized genomes in order to develop and validate bioinformatic methods that aim to accurately analyze microbial communities.

opencc-by-4.0Nov 2022View details →

Emergenet models for HA/NA sequences of Influenza A strains

Models emergent dependencies between mutational variations of key viral proteins in Influenza A. Models can be read using the emergenet package available on pypi. (https://pypi.org/project/emergenet/)

Simultaneous profiling of histone modifications and DNA methylation via nanopore sequencing

Datasets that contain a minimum of nanopore reads sufficient for hidden Markov model training and for evaluating the performance of our computational tool - nanoHiMe at simultaneously calling CpG and/or adenine methylation on individual nanopore reads. Ecoli_PCR_amplicons_100k.tgz, Ecoli_PCR_MSssI_100k.tar.gz and Ecoli_PCR_pA-Hia5_100k.tar.gz are used for training new parameters of the emission distributions of individual k-mers from DNA template without modification, with fully methylated CpGs, and with partially methylated adenines, respectively. nanoHiMe_H3K27me3.fast5.tgz are the nanopore sequencing reads from H3K27me3 nanoHiMe-seq experiments in GM12878 cells and used for evaluating the performance of nanoHiMe at jointly calling CpG and adenine methylation.

opencc-by-4.0Sep 2022View details →

Additional files of scTensor paper "scTensor detects many-to-many cell-cell interactions from single cell RNA-sequencing data"

Complex biological systems are described as a multitude of cell-cell interactions (CCIs). Recent single-cell RNA-sequencing studies focus on CCIs based on ligand-receptor (L-R) gene co-expression. However, the analytical methods are still not mature; such methods cannot detect CCIs and the related L-R pairs simultaneously or also are not appropriate to detect many-to-many CCIs. In this work, we propose scTensor, a novel method for extracting representative triadic relationships (or hypergraphs), which include ligand-expression, receptor-expression, and related L-R pairs. Through extensive studies with simulated and empirical datasets, we have shown that scTensor could detect some hypergraphs, which cannot be detected by conventional methods, especially when those CCIs are many-to-many relationships.

CO1 Sequences and Material Table of Western Palearctic Weevil Taxa

The ZIP file contains the CO1 sequence dataset and its corresponding material table from the research article: Schütte A, Stüben PE, Astrin JJ (2023) Molecular Weevil Identification Project: A Thoroughly Curated Barcode Release of 1300 Western Palearctic Weevil Species (Coleoptera: Curculionoidea) - Biodiversity Data Journal 11.

Data from: Cost-saving population genomic investigation of Daphnia longispina complex resting eggs using whole genome amplification and pre-sequencing screening

This dataset contains all paired MiSeq sequences that were generated for the study "Cost-saving population genomic investigation of Daphnia longispina complex resting eggs using whole genome amplification and pre-sequencing screening" by Nickel and Cordellier. The sample names used in the study and the associated file names are explained in the table "Study_sample_names.xlsx"

Single-cell naïve IgM VH:VL sequence data from 22 Kymice

Single-cell VH:VL sequencing data derived from naïve B-cells isolated from 22 Kymice. This dataset is published as part of the review process for the following preprint: https://www.biorxiv.org/content/10.1101/2022.06.27.497709v1.

AntiRef: reference clusters of human antibody sequences

Motivation: Biases in the human antibody repertoire result in publicly available antibody sequence datasets containing many duplicate or highly similar sequences. These redundant sequences are a barrier to rapid similarity searches and reduce the efficiency with which these datasets can be used to train statistical or machine learning models of human antibodies. Identity-based clustering provides a solution, however, the extremely large size of available antibody repertoire datasets make such clustering operations computationally intensive and potentially out of reach for many scientists and researchers who would benefit from such data. Results: AntiRef (Antibody Reference Clusters), which is modeled after UniRef, provides clustered datasets of filtered human antibody sequences. Starting from a dataset of ~335M unique, full-length, productive human antibody sequences from the Observed Antibody Space repository, several AntiRef cluster sets were generated. Due to the modular nature of recombined antibody genes, the clustering thresholds used by UniRef (100, 90 and 50 percent identity) to cluster general protein sequences are suboptimal for antibody clustering. AntiRef provides reference antibody sequence datasets clustered at a range of relevant identity thresholds: 100, 99, 98, 96, 94, 92 and 90 percent. AntiRef90, which uses the lowest clustering threshold of any AntiRef dataset, is roughly one-third the size of the filtered input dataset and less than half the size of the non-redundant AntiRef100. Datasets: AntiRef comprises a series of datasets, each representing one of several clustering thresholds. AntiRef datasets were generated by a nested clustering procedure similar to UniRef which, proceeding in order of decreasing stringency, clusters the representative sequences from the preceding round of clustering. AntiRef datasets can be found at the following links: <ul> <li><a href="https://doi.org/10.5281/zenodo.7474657">AntiRef100</a>: representative sequences resulting from clustering all filtered AntiRef input sequences at 100% identity.</li> <li><a href="https://doi.org/10.5281/zenodo.7475961">AntiRef99</a>: representative sequences resulting from clustering AntiRef100 at 99% identity.</li> <li><a href="https://doi.org/10.5281/zenodo.7476040">AntiRef98</a>: representative sequences resulting from clustering AntiRef99 at 98% identity.</li> <li><a href="https://doi.org/10.5281/zenodo.7487182">AntiRef96</a>: representative sequences resulting from clustering AntiRef98 at 96% identity.</li> <li><a href="https://doi.org/10.5281/zenodo.7487199">AntiRef94</a>: representative sequences resulting from clustering AntiRef96 at 94% identity.</li> <li><a href="https://doi.org/10.5281/zenodo.7487264">AntiRef92</a>: representative sequences resulting from clustering AntiRef94 at 92% identity.</li> <li><a href="https://doi.org/10.5281/zenodo.7487298">AntiRef90</a>: representative sequences resulting from clustering AntiRef92 at 90% identity.</li> </ul> Files: The following files are included in the primary AntiRef data repository: <ul> <li>antiref_cluster-manifest.csv.gz: A compressed CSV file containing the cluster assignments for every sequence in the AntiRef input dataset. For each AntiRef round, cluster names correspond to the sequence ID of the representative sequence (as determined by MMSeqs2). The nested clustering process conserves cluster names between iterations, meaning the clustering lineage of any sequence can easily be traced across all AntiRef datasets.</li> <li>download_heavy.txt: A plain text file (generated by the <a href="http://opig.stats.ox.ac.uk/webapps/oas/">Observed Antibody Space</a>) containing the commands necessary to download all antibody heavy chain sequences used to create AntiRef.</li> <li>download_light.txt: A plain text file (generated by the <a href="http://opig.stats.ox.ac.uk/webapps/oas/">Observed Antibody Space</a>) containing the commands necessary to download all antibody light chain sequences used to create AntiRef.</li> </ul> Code: All code used to generate AntiRef (data download, filtering, and clustering) is available under the MIT license on <a href="http://www.github.com/briney/antiref">GitHub</a>.

Datasets of the manuscript "Rational design of profile HMMs for sensitive and specific sequence detection with case studies applied to viruses, bacteriophages, and casposons"

DATASETS Rational design of profile HMMs for sensitive and specific sequence detection with case studies applied to viruses, bacteriophages, and casposons Liliane S. Oliveira, Alejandro Reyes, Bas E. Dutilh and Arthur Gruber* * Correspondence: <a href="mailto:argruber@usp.br">argruber@usp.br</a> (AG); Tel. +55 11 3091 7274   Here we provide different data of Microviridae, Flavivirus and casposons used throughout the work: <ul> <li>Microviridae folder <ul> <li>conserved_HMMs – profile HMMs constructed with TABAJARA in Conservation mode for Microviridae</li> <li>discriminative_HMMs – profile HMMs constructed with TABAJARA in Discrimination mode for Microviridae</li> <li>sequences – different sequence datasets and respective multiple sequence alignments <ul> <li>Microviridae_113-seq_training_set.fasta - 113 VP1 sequences covering diversity of the Microviridae family</li> <li>Microviridae_113-seq.aln – multiple sequence alignment of the 113-protein dataset</li> <li>Microviridae_1836-seq_testset.fasta - 1,836 sequence dataset covering 1,836 sequences of the major capsid protein (VP1) comprising 501 Alpavirinae sequences, 1,040 Gokushovirinae sequences and 295 Pichovirinae sequences</li> <li>Microviridae_1866-seq.aln - multiple sequence alignment of the 1,866-protein Microviridae dataset used in the experiment of Figure 4</li> </ul> </li> </ul> </li> <li>Flavivirus folder <ul> <li>conserved_HMMs – profile HMMs constructed with TABAJARA in Conservation mode for Flavivirus</li> <li>discriminative_HMMs – profile HMMs constructed with TABAJARA in Discrimination mode for Flavivirus <ul> <li>full-length – models constructed from full-length protein sequences</li> <li>short - models constructed from selected short alignment blocks of the protein sequences</li> </ul> </li> <li>sequences – different sequence datasets and respective multiple sequence alignments <ul> <li>Flavivirus_127-seq_training_set.fasta - 127 polyprotein sequences covering species diversity of the genus Flavivirus</li> <li>Flavivirus_127-seq.aln – multiple sequence alignment of the 127-protein dataset</li> <li>Flavivirus_6364-seq_testset.fasta - 6,364 sequence dataset covering species diversity of Flavivirus, including 3,919 of dengue virus (DENV), 327 of Zika virus (ZIKV), 63 of yellow fever virus (YFV), and the remaining 2,055 sequences covering other available flaviviruses</li> <li>Flavivirus_6364-seq.aln - multiple sequence alignment of the 6,364-protein Flavivirus dataset</li> </ul> </li> </ul> </li> <li>Casposons folder <ul> <li>casposon_generic_HMMs – profile HMMs constructed with TABAJARA in Discrimination mode for the generic detection of all casposons and discrimination from CRISPRs.</li> <li>casposon_family_discriminative_HMMs – profile HMMs constructed with TABAJARA in Discrimination mode for the specific discrimination among casposon families and from CRISPRs.</li> <li>sequences – different sequence datasets and respective multiple sequence alignments <ul> <li>casposons_crisprs.fasta – 106 Cas1 bona fide sequences derived from 52 CRISPRs and 54 casposons</li> <li>casposon_family_discrimination.aln - multiple sequence alignment of 52 bona fide CRISPR and 54 casposon sequences, with appropriate nomenclature to run TABAJARA for the discrimination of each casposon family.</li> <li>casposons_crisprs_discrimination.aln - multiple sequence alignment of 52 bona fide CRISPR and 54 casposon sequences, with appropriate nomenclature to run TABAJARA for discrimination of CRISPRs and casposons.</li> </ul> </li> </ul> </li> </ul>

Text-fig. 1. The Catefica exposure along the road between Catefica and Mugideira photographed in 1989 when the mesofossil flora was discovered. The exposed strata are mainly cross-bedded light colored sands, darker horizons of clay and dark lenses with mesofossils. The most productive sample, Catefica sample 49, was collected in the basal part of the exposed sequence (arrow head). One of the authors (PRC) exploring the middle part of the section. Photo K. R. Pedersen. in The Early Cretaceous Mesofossil Flora Of Catefica, Portugal: Angiosperms

Text-fig. 1. The Catefica exposure along the road between Catefica and Mugideira photographed in 1989 when the mesofossil flora was discovered. The exposed strata are mainly cross-bedded light colored sands, darker horizons of clay and dark lenses with mesofossils. The most productive sample, Catefica sample 49, was collected in the basal part of the exposed sequence (arrow head). One of the authors (PRC) exploring the middle part of the section. Photo K. R. Pedersen.

opencc-by-4.0Jan 2023View details →

Inventories of landslides triggered by the 2019 Cotabato - Davao del Sur (Philippines) seismic sequence

These files are related to the paper “Environmental effects following a seismic sequence: the 2019 Cotabato - Davao del Sur (Philippines) earthquakes” by Ferrario M. F., Perez J., Livio F., Rimando J., Michetti A. M., currently under review. Files include a multi-temporal inventory of landslides triggered during a seismic sequence. In October – December 2019, the Cotabato and Davao del Sur Provinces (Philippine) were hit by four Mw > 6.0 earthquakes. The sequence started with a Mw 6.4 on 16 October (EQ1), then three earthquakes with Mw of 6.6, 6.5 and 6.8 occurred on 29 October (EQ2), 31 October (EQ3) and 15 December 2019 (EQ4). Landslides were manually mapped on 3-m resolution PlanetScope images. Shapefiles are in WGS84 UTM Zone 51n and include: <ul> <li>Area study: shapefile of the study area (inventories 2 and 3)</li> <li>Area study_October 2019: shapefile of the study area for inventory 1</li> <li>Landslides_ott2019: polygonal inventory of landslides mapped on images acquired on 24 and 28/10/2019, that is after EQ1</li> <li>Landslides_nov2019: polygonal inventory of landslides mapped on images acquired on 15 and 16/11/2019, that is after EQ3</li> <li>Landslides_dic2019: polygonal inventory of landslides mapped on images acquired on 05/02/2020, that is after EQ4</li> </ul>

opencc-by-4.0Nov 2022View details →

Genome-wide SNP discovery in native American and Hungarian Robinia pseudoacacia genotypes using next-generation double-digest restriction-site-associated DNA sequencing (ddRAD-Seq)

Initial filtered ddRADseq dataset with highly variable SNP markers from native American and Hungarian Robinia pseudoacacia L. individuals

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Data for: High-throughput profiling of sequence recognition by tyrosine kinases and SH2 domains using bacterial peptide display

Tyrosine kinases and SH2 (phosphotyrosine recognition) domains have binding specificities that depend on the amino acid sequence surrounding the target (phospho)tyrosine residue. Although the preferred recognition motifs of many kinases and SH2 domains are known, we lack a quantitative description of sequence specificity that could guide predictions about signaling pathways or be used to design sequences for biomedical applications. Here, we present a platform that combines genetically-encoded peptide libraries and deep sequencing to profile sequence recognition by tyrosine kinases and SH2 domains. We screened several tyrosine kinases against a million-peptide random library and used the resulting profiles to design high-activity sequences. We also screened several kinases against a library containing thousands of human proteome-derived peptides and their naturally-occurring variants. These screens recapitulated independently measured phosphorylation rates and revealed hundreds of phosphosite-proximal mutations that impact phosphosite recognition by tyrosine kinases. We extended this platform to the analysis of SH2 domains and showed that screens could predict relative binding affinities. Finally, we expanded our method to assess the impact of non-canonical and post-translationally modified amino acids on sequence recognition. This specificity profiling platform will shed new light on phosphotyrosine signaling and could readily be adapted to other protein modification/recognition domains.

opencc-zeroJan 2023View details →

opencc-by-4.0Jan 2023View details →

Supplementary data of the paper 'Adaptive trends of sequence compositional complexity over pandemic time in the SARS CoV 2 coronavirus'

Supplement of the paper "Adaptive trends of sequence compositional complexity over pandemic time in the SARS-CoV-2 coronavirus” During the spread of the COVID-19 pandemic, the SARS-CoV-2 coronavirus underwent mutation and recombination events that altered its genome compositional structure, thus providing an unprecedented opportunity to check an evolutionary process in real time. The mutation rate is known to be lower than expected for neutral evolution, suggesting natural selection and convergent evolution. We begin by summarizing the compositional heterogeneity of each viral genome by computing its Sequence Compositional Complexity (SCC). To analyze the full range of SCC diversity, we select random samples of high quality coronavirus genomes covering the full span of the pandemic. We then search for evolutionary trends that could inform us on the adaptive process of the virus to its human host by computing the phylogenetic ridge regression of SCC against time (i.e., the collection date of each viral isolate). In early samples, we find no statistical support for any trend in SCC values, although the viral genome appears to evolve faster than Brownian Motion (BM) expectation. However, in samples taken after the emergence of high fitness variants, and despite the brief time span elapsed, a driven decreasing trend for SCC and an increasing one for its absolute evolutionary rate are detected, pointing to a role for selection in the evolution of SCC in the coronavirus. We conclude that the higher fitness of variant genomes may have leads to adaptive trends of SCC over pandemic time in the coronavirus. Supplementary files <table> <tbody> <tr> <td> File </td> <td> Description </td> </tr> <tr> <td> SupplementaryTables S1-S19.zip </td> <td> Excel supplementary tables: The strain name, the collection date, and the SCC values for each analyzed genome. </td> </tr> <tr> <td>nextstrain_ncov_open_global_timetree.nwk</td> <td>ML phylodynamic tree for the Nextstrain sample</td> </tr> <tr> <td> SupplementaryTable S20.pdf </td> <td> A complete list acknowledging the authors, originating and submitting laboratories of the genetic sequences we used for the analysis of the Nextstrain sample. </td> </tr> <tr> <td>Nextstrain_sample_fasta_3059.zip</td> <td>Nextstrain sample (sequences in Fasta format)</td> </tr> <tr> <td> PhylogeneticTimetrees_NewickFormat.zip </td> <td> Phylogenetic timetrees (Newick format). </td> </tr> </tbody> </table>

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Data corresponding to: Evaluation of sequencing and PCR-based methods for the quantification of the viral genome formula

Viruses show great diversity in their genome organisation. Multipartite viruses package their genome segments into separate particles, most or all of which are required to initiate infection in the host cell. The benefits of such seemingly inefficient genome organization are not well understood. One hypothesised benefit of multipartition is that it allows for flexible changes in gene expression by altering the frequency of each genome segment in different environments, such as encountering different host species. The ratio of the frequency of segments is termed the genome formula (GF). Thus far, formal studies quantifying the GF have been performed for well-characterised virus-host systems in experimental settings using RT-qPCR. However, to understand GF variation in natural populations or novel virus-host systems, a comparison of several methods for GF estimation including high-throughput sequencing (HTS) based methods is needed. Currently, it is unclear how HTS-methods compare a golden standard, such as RT-qPCR. Here we show a comparison of multiple GF quantification methods (RT-qPCR, RT-digital PCR, Illumina RNAseq and Nanopore direct RNA sequencing) using three host plants (Nicotiana tabacum, Nicotiana benthamiana, and Chenopodium quinoa) infected with cucumber mosaic virus (CMV), a tripartite RNA virus. Our results show that all methods give roughly similar results, though there is a significant method effect on genome formula estimates. While the RT-qPCR and RT-dPCR GF estimates are congruent, the GF estimates from HTS methods deviate from those found with PCR. Our findings emphasise the need to tailor the GF quantification method to the experimental aim, and highlight that it may not be possible to compare HTS and PCR-based methods directly. The difference in results between PCR-based methods and HTS highlights that the choice of quantification technique is not trivial.

opencc-zeroJan 2023View details →

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Genome report: Genome sequence of 1S1, a transformable and highly regenerable diploid potato for use as a model for gene editing and genetic engineering

Generation of a genomic resource for a readily transformable diploid potato would provide a resource for high throughput functional analysis in potato. The heterozygous Solanum tuberosum Group Phureja clone 1S1 has a high regeneration rate, self-fertility, desirable tuber traits and is amenable to Agrobacterium-mediated transformation. To create a contiguous genome assembly, a homozygous doubled monoploid of 1S1 (DM1S1) was sequenced using 44 Gbp of long reads generated from Oxford Nanopore Technologies (ONT), yielding a 736 Mb assembly that encoded 31,145 protein-coding genes. The final assembly for DM1S1 represents a nearly complete genic space, shown by the presence of 99.6% (C:99.5%[S:97.8%, D:1.7%],F:0.1%,M:0.4%,n:1614) of the Benchmarking Universal Single Copy Orthologs. Variant analysis with Illumina reads from 1S1 was used to deduce its alternate haplotype using the variant calling tools Strelka2 (v2.9.10), GATK's Haplotypecaller (v4.1.4.1), and Freebayes (v1.3.2). These variants were used to create consensus fasta sequences with the DM1S1 assembly using bcftools (v1.9.64).

opencc-zeroFeb 2023View details →

FIG. 7 in A synopsis of Ptisana Murdock ferns (Marattiaceae) in New Caledonia based on sequence data and morphology with the recognition of a new vulnerable species, P. soluta (Compton) Murdock & Perrie, comb. nov., stat. nov.

FIG. 7. — Median-joining networks based on trnSGG and rps4-trnS sequences: A, the Ptisana attenuata clade; B, the P. salicina/P. soluta comb. nov., stat. nov./P. smithii clade. The size of each circle is proportional to the haplotype frequency. Undetected intermediate haplotypes on nodes are shown as black circles and hatch marks represent mutational steps separating haplotypes.

FIG. 6 in A synopsis of Ptisana Murdock ferns (Marattiaceae) in New Caledonia based on sequence data and morphology with the recognition of a new vulnerable species, P. soluta (Compton) Murdock & Perrie, comb. nov., stat. nov.

FIG. 6. — Phylogram from the Bayesian phylogenetic analysis of the chloroplast DNA sequence data for Ptisana Murdock. Support values for branches are given in the order of Bayesian inference posterior probability; maximum parsimony bootstrap support; and maximum likelihood bootstrap support. Only values>0.80 PP and 60% BS are shown.

FIG. 4 in A synopsis of Ptisana Murdock ferns (Marattiaceae) in New Caledonia based on sequence data and morphology with the recognition of a new vulnerable species, P. soluta (Compton) Murdock & Perrie, comb. nov., stat. nov.

FIG. 4. — Distribution map for the New Caledonian endemic species of Ptisana attenuata (Labill.) Murdock (), P. rolandi-principis (Rosenst.) Christenh. (Δ), and P. soluta (Compton) Murdock & Perrie, comb. nov., stat. nov. (, with unvouchered field observations indicated by a broken outline). Shaded areas are ultramafic substrates. The collecting sites of the sequenced P. attenuata samples are indicated.

FIG. 5 in A synopsis of Ptisana Murdock ferns (Marattiaceae) in New Caledonia based on sequence data and morphology with the recognition of a new vulnerable species, P. soluta (Compton) Murdock & Perrie, comb. nov., stat. nov.

FIG. 5. — The holotype of Ptisana soluta (Compton) Murdock & Perrie, comb. nov., stat. nov. (Compton 1674, Ignambi, 1914, BM[BM000787128]) showing how the lamina transitions from 3-pinnate proximally to 2-pinnate distally. CC BY The Trustees of the Natural History Museum, London.