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opencc-zeroDec 2015View details →

Data from: Identification and qualification of 500 nuclear, single-copy, orthologous genes for the Eupulmonata (Gastropoda) using transcriptome sequencing and exon capture

The qualification of orthology is a significant challenge when developing large, multiloci phylogenetic data sets from assembled transcripts. Transcriptome assemblies have various attributes, such as fragmentation, frameshifts and mis-indexing, which pose problems to automated methods of orthology assessment. Here, we identify a set of orthologous single-copy genes from transcriptome assemblies for the land snails and slugs (Eupulmonata) using a thorough approach to orthology determination involving manual alignment curation, gene tree assessment and sequencing from genomic DNA. We qualified the orthology of 500 nuclear, protein-coding genes from the transcriptome assemblies of 21 eupulmonate species to produce the most complete phylogenetic data matrix for a major molluscan lineage to date, both in terms of taxon and character completeness. Exon capture targeting 490 of the 500 genes (those with at least one exon >120 bp) from 22 species of Australian Camaenidae successfully captured sequences of 2825 exons (representing all targeted genes), with only a 3.7% reduction in the data matrix due to the presence of putative paralogs or pseudogenes. The automated pipeline Agalma retrieved the majority of the manually qualified 500 single-copy gene set and identified a further 375 putative single-copy genes, although it failed to account for fragmented transcripts resulting in lower data matrix completeness when considering the original 500 genes. This could potentially explain the minor inconsistencies we observed in the supported topologies for the 21 eupulmonate species between the manually curated and 'Agalma-equivalent' data set (sharing 458 genes). Overall, our study confirms the utility of the 500 gene set to resolve phylogenetic relationships at a range of evolutionary depths and highlights the importance of addressing fragmentation at the homolog alignment stage for probe design.

opencc-zeroDec 2012View details →

Data from: An improved hypergeometric probability method for identification of functionally linked proteins using phylogenetic profiles

Predicting functions of proteins and alternatively spliced isoforms encoded in a genome is one of the important applications of bioinformatics in the post-genome era. Due to the practical limitation of experimental characterization of all proteins encoded in a genome using biochemical studies, bioinformatics methods provide powerful tools for function annotation and prediction. These methods also help minimize the growing sequence-to-function gap. Phylogenetic profiling is a bioinformatics approach to identify the influence of a trait across species and can be employed to infer the evolutionary history of proteins encoded in genomes. Here we propose an improved phylogenetic profile-based method which considers the co-evolution of the reference genome to derive the basic similarity measure, the background phylogeny of target genomes for profile generation and assigning weights to target genomes. The ordering of genomes and the runs of consecutive matches between the proteins were used to define phylogenetic relationships in the approach. We used Escherichia coli K12 genome as the reference genome and its 4195 proteins were used in the current analysis. We compared our approach with two existing methods and our initial results show that the predictions have outperformed two of the existing approaches. In addition, we have validated our method using a targeted protein-protein interaction network derived from protein-protein interaction database STRING. Our preliminary results indicates that improvement in function prediction can be attained by using coevolution-based similarity measures and the runs on to the same scale instead of computing them in different scales. Our method can be applied at the whole-genome level for annotating hypothetical proteins from prokaryotic genomes.

opencc-zeroDec 2014View details →

Data from: A multiscale vibrational spectroscopic approach for identification and biochemical characterization of pollen

Background: Analysis of pollen grains reveals valuable information on biology, ecology, forensics, climate change, insect migration, food sources and aeroallergens. Vibrational (infrared and Raman) spectroscopies offer chemical characterization of pollen via identifiable spectral features without any sample pretreatment. We have compared the level of chemical information that can be obtained by different multiscale vibrational spectroscopic techniques. Methodology: Pollen from 15 different species of Pinales (conifers) were measured by seven infrared and Raman methodologies. In order to obtain infrared spectra, both reflectance and transmission measurements were performed on ground and intact pollen grains (bulk measurements), in addition, infrared spectra were obtained by microspectroscopy of multigrain and single pollen grain measurements. For Raman microspectroscopy measurements, spectra were obtained from the same pollen grains by focusing two different substructures of pollen grain. The spectral data from the seven methodologies were integrated into one data model by the Consensus Principal Component Analysis, in order to obtain the relations between the molecular signatures traced by different techniques. Results: The vibrational spectroscopy enabled biochemical characterization of pollen and detection of phylogenetic variation. The spectral differences were clearly connected to specific chemical constituents, such as lipids, carbohydrates, carotenoids and sporopollenins. The extensive differences between pollen of Cedrus and the rest of Pinaceae family were unambiguously connected with molecular composition of sporopollenins in pollen grain wall, while pollen of Picea has apparently higher concentration of carotenoids than the rest of the family. It is shown that vibrational methodologies have great potential for systematic collection of data on ecosystems and that the obtained phylogenetic variation can be well explained by the biochemical composition of pollen. Out of the seven tested methodologies, the best taxonomical differentiation of pollen was obtained by infrared measurements on bulk samples, as well as by Raman microspectroscopy measurements of the corpus region of the pollen grain. Raman microspectroscopy measurements indicate that measurement area, as well as the depth of focus, can have crucial influence on the obtained data.

opencc-zeroDec 2012View details →

Data from: Identification and mapping of linear antibody epitopes in human serum albumin using high-density peptide arrays

We have recently developed a high-density photolithographic, peptide array technology with a theoretical upper limit of 2 million different peptides per array of 2 cm2. Here, we have used this to perform complete and exhaustive analyses of linear B cell epitopes of a medium sized protein target using human serum albumin (HSA) as an example. All possible overlapping 15-mers from HSA were synthesized and probed with a commercially available polyclonal rabbit anti-HSA antibody preparation. To allow for identification of even the weakest epitopes and at the same time perform a detailed characterization of key residues involved in antibody binding, the array also included complete single substitution scans (i.e. including each of the 20 common amino acids) at each position of each 15-mer peptide. As specificity controls, all possible 15-mer peptides from bovine serum albumin (BSA) and from rabbit serum albumin (RSA) were included as well. The resulting layout contained more than 200.000 peptide fields and could be synthesized in a single array on a microscope slide. More than 20 linear epitope candidates were identified and characterized at high resolution i.e. identifying which amino acids in which positions were needed, or not needed, for antibody interaction. As expected, moderate cross-reaction with some peptides in BSA was identified whereas no cross-reaction was observed with peptides from RSA. We conclude that high-density peptide microarrays are a very powerful methodology to identify and characterize linear antibody epitopes, and should advance detailed description of individual specificities at the single antibody level as well as serologic analysis at the proteome-wide level.

opencc-zeroDec 2017View details →

Data from: Identification of reactive intermediate formation and bioactivation pathways in Abemaciclib metabolism by LC–MS/MS: in vitro metabolic investigation

Abemaciclib (Verzenio®) is approved as tyrosine kinase inhibitor (TKI) for breast cancer treatment. In this study, in vitro phase I metabolic profiling of Abemaciclib (ABC) was done using rat liver microsomes (RLMs). We checked the formation of reactive intermediates in ABC metabolism using (RLMs) in the presence of potassium cyanide (KCN) that was used as capturing agent for iminium reactive intermediates forming a stable complex that can be characterized by LC-MS/MS. Nine in vitro phase I metabolites and three cyano adducts were identified. The metabolic reactions involved in formation of these metabolites and adducts are reduction, oxidation, hydroxylation and cyanide addition. The bioactivation pathway was also proposed. Knowing the electrodeficient bioactive centre in ABC structure helped in making targeted modifications to improve its safty and retain its efficacy. Blocking or isosteric replacement of α carbon to the tertiary nitrogen atoms of piperazine ring can aid in reducing toxic side effects of ABC. No previous articles were found about in vitro metabolic profiling for ABC or structural identification of the formed reactive metabolites for ABC.

openother-pdNov 2023View details →

Data from: Identification of prokaryotic and eukaryotic virus-derived sequences in virome using deep learning

<h4>This repository contains the data and Docker image to reproduce the results of our paper: identification of prokaryotic and eukaryotic virus-derived sequences in virome using deep learning</h4> Authors: Hengchuang Yin, Shufang Wu, Jie Tan, Qian Guo, Mo Li, Jinyuan Guo, Yaqi Wang, Xiaoqing Jiang, and Huaiqiu Zhu* This work has been accepted by GigaScience.  Hengchuang Yin, Shufang Wu, Jie Tan, Qian Guo, Mo Li, Jinyuan Guo, Yaqi Wang, Xiaoqing Jiang, and Huaiqiu Zhu. "IPEV: Identification of Prokaryotic and Eukaryotic Virus-Derived Sequences in Virome Using Deep Learning." GigaScience 13 (2024): giae018. <a href="https://doi.org/10.1093/gigascience/giae018" rel="nofollow">https://doi.org/10.1093/gigascience/giae018</a>. <div> </div>   Background: The virome obtained through virus-like particle enrichment contains a mixture of prokaryotic and eukaryotic virus-derived fragments. Accurate identification and classification of these elements are crucial to understanding their roles and functions in microbial communities. However, the rapid mutation rates of viral genomes pose challenges in developing high-performance tools for classification, potentially limiting downstream analyses. Findings: We present IPEV, a novel method to distinguish prokaryotic and eukaryotic viruses in viromes, with a 2D convolutional neural network combining trinucleotide pair relative distance and frequency. Cross-validation assessments of IPEV demonstrate its state-of-the-art precision, significantly improving the F1-score by approximately 22% on an independent test set compared to existing methods when query viruses share less than 30% sequence similarity with known viruses. Furthermore, IPEV outperforms other methods in accuracy on marine and gut virome samples based on annotations by sequence alignments. IPEV reduces runtime by at most 1,225 times compared to existing methods under the same computing configuration. We also utilized IPEV to analyze longitudinal samples and found that the gut virome exhibits a higher degree of temporal stability than previously observed in persistent personal viromes, providing novel insights into the resilience of the gut virome in individuals.  Conclusions: IPEV is a high-performance, user-friendly tool that assists biologists in identifying and classifying prokaryotic and eukaryotic viruses within viromes. The tool is available at https://github.com/basehc/IPEV.   5_fold_cross_validation.zip: Dataset of cross-validation of IPEV Eukaryotic_virus_CV_Dataset-1.csv: GI, and accession ID for the cross-validation Dataset-1 (eukaryotic virus) Prokaryotic_virus_CV_Dataset-1.csv: GI, and accession ID for the cross-validation Dataset-1 (prokaryotic virus) Test_Prokaryotic_virus_Dataset-1.fasta: An independent test set of IPEV (prokaryotic virus) Test_Eukaryotic_virus_Dataset-1.fasta: An independent test set of IPEV (eukaryotic virus)     Dataset_sequencing_error.zip: Simulated dataset with sequencing errors Cap_enzyme_sequence.fasta: Accession IDs of Receptor Binding Proteins (RBPs) in phages collected by our article Dataset_runtime_evaluation.zip: Dataset for evaluating the runtime of IPEV Receptor_binding_protein_accession_id: Accession IDs of Receptor Binding Proteins (RBPs) in phages collected by our article   archaea_ID.txt Accession ID information for the reference archaea dataset bacteria_ID.txt Accession ID information for the reference bacterial dataset marine_virome_id.csv: Ocean virome data information used in our paper gut_virome.csv:Gur virome data information used in our paper fungi.txt: Negative sequence information used to train, validate, and test the model in the decontamination function bacteria.txt: Negative sequence information used to train, validate, and test the model in the decontamination function     <h4>Reproduce the results of our paper from a Docker image.</h4>   We also provide a Docker image file that does not require any environment configuration. You can reproduce the results of our paper (e.g., train and test our IPEV model) in a Docker image. Pull the<a href="https://hub.docker.com/r/dryinhc/ipev_v1"> dryinhc/ipev_v1</a> image from Docker Hub. Open a terminal window and run the following command: docker pull dryinhc/ipev_v1 This will download the image to your local machine. Run the dryinhc/ipev_v1 image. In the same terminal window, run the following command: docker run -it --rm dryinhc/ipev_v1 This will start a container based on the image and run the IPEV tool. And you can run cd train or cd other file folders in the container. To exit the container, press Ctrl+D or type exit. It contains 4 directories, namely 5 fold cross validation, independent set, marine virome, and gut virome. The 5-fold cross-validation directory holds the scripts required for implementing the 5-fold cross-validation method. The independent set directory contains scripts necessary for working with an independent set. Lastly, the marine virome and gut virome directories store scripts for analyzing real datasets.         We hereby confirm that the dataset associated with the research described in this work is made available to the public under the Creative Commons Zero (CC0) license.     <h4>Contact </h4>   If you have any questions, please don't hesitate to ask me: yinhengchuang@pku.edu.cn or hqzhu@pku.edu.cn

opencc-by-4.0Nov 2024View details →

Processed mass spectrometry data - systematic identification of allosteric effectors in Escherichia coli metabolism

MATLAB files of processed mass spectrometry data, i.e. full data table after peak picking, annotation and quantification. Additionally, for each of the tested enzymes, the relevant ion traces of substrates and products are extracted, sorted by timepoint and replicate and saved in separate tables.

opencc-zeroNov 2021View details →

Identification of the Oligocene to early Miocene loricariid catfish †Taubateia paraiba as a member of the Rhinelepinae: List of taxa for which comparative CT-data were examined

One of the earliest and most complete loricariid fossils is †Taubateia paraiba, which was described by Malabarba and Lundberg (2007) from southeastern Brazil based on a ventral impression of a neurocranium and some vertebrae. The fossil is from lacustrine habitat of the Tremembé Formation of the Taubaté Group, which is dated to the Oligocene to Early Miocene (~30–20 Mya). The locality (22°50' S, 45°52' W) is currently part of the Paraná River basin. Based on an extensive database of measurements of loricariids by the authors, the 54.5 mm long skull, equates to approximately 70 mm head length (snout to tip of supraoccipital), indicating a specimen that is ~175–210 mm long, which is fairly large for loricariids. In the original description, Malabarba and Lundberg described the anatomy of the fossil in detail, but they were unable to resolve the identity of the fish beyond its placement within Loricariidae.

opencc-zeroJan 2022View details →

Identification of species by combining molecular and morphological data using convolutional neural networks

Integrative taxonomy is central to modern taxonomy and systematic biology, including behavior, niche preference, distribution, morphological analysis, and DNA barcoding. However, decades of use demonstrate that these methods can face challenges when used in isolation, for instance, potential misidentifications due to phenotypic plasticity for morphological methods, and incorrect identifications because of introgression, incomplete lineage sorting, and horizontal gene transfer for DNA barcoding. Although researchers have advocated the use of integrative taxonomy, few detailed algorithms have been proposed. Here, we develop a convolutional neural network method (morphology-molecule network [MMNet]) that integrates morphological and molecular data for species identification. The newly proposed method (MMNet) worked better than four currently available alternative methods when tested with 10 independent data sets representing varying genetic diversity from different taxa. High accuracies were achieved for all groups, including beetles (98.1% of 123 species), butterflies (98.8% of 24 species), fishes (96.3% of 214 species), and moths (96.4% of 150 total species). Further, MMNet demonstrated a high degree of accuracy (>98%) in four data sets including closely related species from the same genus. The average accuracy of two modest subgenomic (single nucleotide polymorphism) data sets, comprising eight putative subspecies respectively, is 90%. Additional tests show that the success rate of species identification under this method most strongly depends on the amount of training data, and is robust to sequence length and image size. Analyses on the contribution of different data types (image vs. gene) indicate that both morphological and genetic data are important to the model, and that genetic data contribute slightly more. The approaches developed here serve as a foundation for the future integration of multimodal information for integrative taxonomy, such as image, audio, video, 3D scanning, and biosensor data, to characterize organisms more comprehensively as a basis for improved investigation, monitoring, and conservation of biodiversity.

Supplementary material 6 from: Boroni NL, Lobo LS, Romano PSR, Lessa G (2017) Taxonomic identification using geometric morphometric approach and limited data: an example using the upper molars of two sympatric species of Calomys (Cricetidae: Rodentia). Zoologia 34: 1-11. https://doi.org/10.3897/zoologia.34.e19864

PCA with Calomys terrer and C. expulsus : Data type: statistical data

Supplementary material 5 from: Boroni NL, Lobo LS, Romano PSR, Lessa G (2017) Taxonomic identification using geometric morphometric approach and limited data: an example using the upper molars of two sympatric species of Calomys (Cricetidae: Rodentia). Zoologia 34: 1-11. https://doi.org/10.3897/zoologia.34.e19864

Tests for normality, hornoscedasticity and homogeneity : Data type: statistical data

Supplementary material 4 from: Boroni NL, Lobo LS, Romano PSR, Lessa G (2017) Taxonomic identification using geometric morphometric approach and limited data: an example using the upper molars of two sympatric species of Calomys (Cricetidae: Rodentia). Zoologia 34: 1-11. https://doi.org/10.3897/zoologia.34.e19864

Protocol for testing error in the digitalization : Data type: statistical data

Supplementary material 3 from: Boroni NL, Lobo LS, Romano PSR, Lessa G (2017) Taxonomic identification using geometric morphometric approach and limited data: an example using the upper molars of two sympatric species of Calomys (Cricetidae: Rodentia). Zoologia 34: 1-11. https://doi.org/10.3897/zoologia.34.e19864

Specimens of the wear category class 2 : Data type: species data

Supplementary material 2 from: Boroni NL, Lobo LS, Romano PSR, Lessa G (2017) Taxonomic identification using geometric morphometric approach and limited data: an example using the upper molars of two sympatric species of Calomys (Cricetidae: Rodentia). Zoologia 34: 1-11. https://doi.org/10.3897/zoologia.34.e19864

Wear categories : Data type: measurement

Supplementary material 1 from: Boroni NL, Lobo LS, Romano PSR, Lessa G (2017) Taxonomic identification using geometric morphometric approach and limited data: an example using the upper molars of two sympatric species of Calomys (Cricetidae: Rodentia). Zoologia 34: 1-11. https://doi.org/10.3897/zoologia.34.e19864

Alisphenoid strut : Data type: multimedia

Figure 3 from: Boroni NL, Lobo LS, Romano PSR, Lessa G (2017) Taxonomic identification using geometric morphometric approach and limited data: an example using the upper molars of two sympatric species of Calomys (Cricetidae: Rodentia). Zoologia 34: 1-11. https://doi.org/10.3897/zoologia.34.e19864

Figure 3 - PCA showing the individual projections of Calomys sp., C. expulsus and C. tener in the two major axis (PC1 28.7%, PC2 12.3% of the variance). The wireframes illustrate shape differences between most different specimens: CE 09, CT 05, CT 02, and SP 11. Black circles: C. expulsus; Gray triangle: C. tener; Black square: Calomys sp. identified as C. expulsus; Gray square: Calomys sp. identified as C. tener; Gray triangle with black edge: specimen CT 02; Stars: mean shape for each species.

Figure 7 from: Boroni NL, Lobo LS, Romano PSR, Lessa G (2017) Taxonomic identification using geometric morphometric approach and limited data: an example using the upper molars of two sympatric species of Calomys (Cricetidae: Rodentia). Zoologia 34: 1-11. https://doi.org/10.3897/zoologia.34.e19864

Figure 7 - Pooled regression within the two species, between shape (dependent variable) and centroid size (independent variable). Black circles: C. expulsus; Gray triangle: C. tener; Black square: Calomys sp. identified as C. expulsus; Gray square: Calomys sp. identified as C. tener.

opencc-by-4.0Oct 2019View details →

Figures 4-6 from: Boroni NL, Lobo LS, Romano PSR, Lessa G (2017) Taxonomic identification using geometric morphometric approach and limited data: an example using the upper molars of two sympatric species of Calomys (Cricetidae: Rodentia). Zoologia 34: 1-11. https://doi.org/10.3897/zoologia.34.e19864

Figures 4-6 Shape differences wireframes in PC1. Gray solid lines indicate C. tener (positive PC1 values) and black dotted lines indicate C. expulsus (negative PC1 values), see Fig. 3 for individual projections. (4) Differences between the mean shapes of C. tener and C. expulsus; (5) differences between the disparate shape of C. tener represented by CT 05 and C. expulsus represented by CE 09; (6) shape differences wireframes in PC2. Gray solid lines indicate the mean of shape of PC2 value and black dotted lines indicate specimen CT 02 (C. tener outlier).

opencc-by-4.0Apr 2024View details →

Raw data for pseudogene identification

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