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302 results for “protein sequence”
Accurate annotation of protein coding sequences with IDTAXA - Training Data
<p>Training data used to test IDTAXA, HMMER, and BLAST performance of classification of amino acid and nucleotide sequences.</p>
Accurate annotation of protein coding sequences with IDTAXA - classification results
<p>Raw classification results generated by IDTAXA, BLAST, and HMMER on a training set scraped from KEGG of > 1.5M sequences. HMMER and BLAST results are also converted into IDTAXA like objects for ease of comparison.</p>
Data from: Latent generative landscapes as maps of functional diversity in protein sequence space
<p>Variational autoencoders are unsupervised learning models with generative capabilities, when applied to protein data, they classify sequences by phylogeny and generate de novo sequences which preserve statistical properties of protein composition. While previous studies focus on clustering and generative features, here, we evaluate the underlying latent manifold in which sequence information is embedded. To investigate properties of the latent manifold, we utilize direct coupling analysis and a Potts Hamiltonian model to construct a latent generative landscape. We showcase how this landscape captures phylogenetic groupings, functional and fitness properties of several systems including Globins, β-lactamases, ion channels, and transcription factors. We provide support on how the landscape helps us understand the effects of sequence variability observed in experimental data and provides insights on directed and natural protein evolution. We propose that combining generative properties and functional predictive power of variational autoencoders and coevolutionary analysis could be beneficial in applications for protein engineering and design.</p>
Supplementary data for: DNA sequences are as useful as protein sequences for inferring deep phylogenies
<p>Inference of deep phylogenies has almost exclusively used protein rather than DNA sequences, based on the perception that protein sequences are less prone to homoplasy and saturation or to issues of compositional heterogeneity than DNA sequences. Here we analyze a model of codon evolution under an idealized genetic code and demonstrate that those perceptions may be misconceptions. We conduct a simulation study to assess the utility of protein versus DNA sequences for inferring deep phylogenies, with protein-coding data generated under models of heterogeneous substitution processes across sites in the sequence and among lineages on the tree, and then analyzed using nucleotide, amino acid, and codon models. Analysis of DNA sequences under nucleotide-substitution models (possibly with the third codon positions excluded) recovered the correct tree at least as often as analysis of the corresponding protein sequences under modern amino acid models. We also applied the different data-analysis strategies to an empirical dataset to infer the metazoan phylogeny. Our results from both simulated and real data suggest that DNA sequences may be as useful as proteins for inferring deep phylogenies and should not be excluded from such analyses. Analysis of DNA data under nucleotide models has a major computational advantage over protein-data analysis, potentially making it feasible to use advanced models that account for among-site and among-lineage heterogeneity in the nucleotide-substitution process in inference of deep phylogenies.</p>
Supplementary dataset for Enzyme promiscuous profiles for protein sequence and reaction annotation
<p>The data and scripts used to produce, analyze, and visualize the results of the manuscript Enzyme promiscuous profiles for protein sequence and reaction annotation by Homa MohammadiPeyhani, Anastasia Sveshnikova, Ljubisa Miskovic, and Vassily Hatzimanikatis. The detailed description of the datafiles and scripts is provided in the accompanying README.rtf file.</p>
RefSeq bacterial protein (amino acid) sequences
<p><strong>Bacteria_Protein.fas.gz</strong></p><p>151,835,459 protein (amino acid) sequences extracted from 44,831 randomly selected bacterial genomes from NCBI's RefSeq (release 220). Sequences are named by their accession number, followed by "|" and their PGAP predicted function ("protein" tag). For example, the first sequence is named:</p><blockquote><p>WP_125174066.1|iron ABC transporter permease</p></blockquote><p>The process of creating the file involved the following steps.<br><strong>Step 1.</strong> Download 318,613 faa and fna files associated with a bacterial assembly in RefSeq. The following query was used:<br><i>esearch -db assembly -query '"Bacteria"[Organism] AND "latest refseq"[properties] AND "refseq has annotation"[properties]' | esummary | xtract -pattern DocumentSummary -element FtpPath_RefSeq</i><br><strong>Step 2.</strong> Verify all protein coding sequences match the expected protein sequence lengths within three codons, otherwise skip the assembly.<br><strong>Step 3.</strong> Remove all redundant protein coding or protein sequences in a genome. Only exact duplicates were removed, but they were removed from both nucleotides and proteins. Hence, a duplicated amino acid sequence would be discarded along with its coding sequence even if the coding sequence was unique. This was done to keep the two sets of sequences consistent.<br><strong>Step 4.</strong> Name sequences by their accession and PGAP predicted function, separated by a "|" character. The PGAP predicted function is generally uniform, although there are subtle difference between some taxon specific predictions. The predicted function is reasonably dependable but certainly not perfect.<br><strong>Step 5.</strong> Discard any sequences without a predicted function ("hypothetical protein"). These were discarded under the assumption that the protein's function would be required for downstream uses of the sequences.<br><strong>Step 6.</strong> Append protein and protein coding (nucleotide) sequences from randomly ordered assemblies to separate gzipped FASTA formatted files until the Zenodo file size limit was met for either file. Hence, there are many exact duplicate sequences in the set, but none for the sequences from each genome.</p><p>The final sets of sequences are intended to provide large sets of matched protein coding (nucleotide) and protein (amino acid) sequences with consistent labels. The FASTA descriptions in both files are identical. Note, the protein coding sequences do not exactly translate into the protein sequences because of slight differences in length (typically inclusion/exclusion of the first or last codon), as well as use of different translation tables depending on the organism.</p><p>See <i>Related works</i> for the companion file of protein coding (nucleotide) sequences (DOI: 10.5281/zenodo.10031801).</p>
RefSeq bacterial protein coding (nucleotide) sequences
<p><strong>Bacteria_Nucleotide.fas.gz</strong></p><p>151,835,459 protein coding (nucleotide) sequences extracted from 44,831 randomly selected bacterial genomes from NCBI's RefSeq (release 220). Sequences are named by their accession number, followed by "|" and their PGAP predicted function ("protein" tag). For example, the first sequence is named:</p><blockquote><p>WP_125174066.1|iron ABC transporter permease</p></blockquote><p>The process of creating the file involved the following steps.<br><strong>Step 1.</strong> Download 318,613 faa and fna files associated with a bacterial assembly in RefSeq. The following query was used:<br><i>esearch -db assembly -query '"Bacteria"[Organism] AND "latest refseq"[properties] AND "refseq has annotation"[properties]' | esummary | xtract -pattern DocumentSummary -element FtpPath_RefSeq</i><br><strong>Step 2.</strong> Verify all protein coding sequences match the expected protein sequence lengths within three codons, otherwise skip the assembly.<br><strong>Step 3.</strong> Remove all redundant protein coding or protein sequences in a genome. Only exact duplicates were removed, but they were removed from both nucleotides and proteins. Hence, a duplicated amino acid sequence would be discarded along with its coding sequence even if the coding sequence was unique. This was done to keep the two sets of sequences consistent.<br><strong>Step 4.</strong> Name sequences by their accession and PGAP predicted function, separated by a "|" character. The PGAP predicted function is generally uniform, although there are subtle difference between some taxon specific predictions. The predicted function is reasonably dependable but certainly not perfect.<br><strong>Step 5.</strong> Discard any sequences without a predicted function ("hypothetical protein"). These were discarded under the assumption that the protein's function would be required for downstream uses of the sequences.<br><strong>Step 6.</strong> Append protein and protein coding (nucleotide) sequences from randomly ordered assemblies to separate gzipped FASTA formatted files until the Zenodo file size limit was met for either file. Hence, there are many exact duplicate sequences in the set, but none for the sequences from each genome.</p><p>The final sets of sequences are intended to provide large sets of matched protein coding (nucleotide) and protein (amino acid) sequences with consistent labels. The FASTA descriptions in both files are identical. Note, the protein coding sequences do not exactly translate into the protein sequences because of slight differences in length (typically inclusion/exclusion of the first or last codon), as well as use of different translation tables depending on the organism.</p><p>See <i>Related works</i> for the companion file of protein (amino acid) sequences (DOI: 10.5281/zenodo.10030000).</p>
Data from: Latent generative landscapes as maps of functional diversity in protein sequence space
Open the record for dataset details and reuse information.
Supplementary data for: DNA sequences are as useful as protein sequences for inferring deep phylogenies
Open the record for dataset details and reuse information.
CusVarDB: A tool for building customized sample-specific variant protein database from Next-generation sequencing datasets
<p>CusVarDB is a windows based tool for creating a variant protein database from Next-generation sequencing datasets. The program supports variant calling for Genome, RNA-Seq and exome datasets.</p> <p>This repository will provide the resultant variant peptides identified in our study and its corresponding information. The detailed information of the table is given below.</p> <p>Supplementary Table 1. This table contains the resultant variant peptides along with its wild-type peptides from BT474, MDMAB157, MFM223, and HCC38 datasets. Along with mutant peptides, this section also provides additional information such as peptide-spectrum match (PSM), Protein accession, cross-correlation value from the search (Xcorr), and retention time (RT).</p> <p>Supplementary Table 2. This table provides the complete details of the resultant peptides. Here the mutant and corresponding wild-type peptides are mentioned in different sheets. For a given mutant peptide its wild-type peptide and corresponding information can be mapped using the VLOOKUP function in Excel by keeping column A (Sl.No) as lookup parameter.</p> <p>Supplementary Table 3. This table briefs about the variants which are already reported in other cancers.</p>
Arctic protistan N-related protein sequences
<p>Nitrogen metabolism related protein sequences in fasta format, originating from Arctic Ocean microbial eukaryotes. Sequences were produced from transcriptomes, sequenced by DoE-Joint Genome Institute (JGI) and the Marine Microbial Eukaryote Transcriptome Sequencing Project (MMETSP; Gordon and Betty Moore Foundation).</p>
Uncovering hundreds of RNA viral RdRps amongst uncharacterised sequences in public protein databases.
<p>These data are associated with the following manuscript:</p> <p>Brown, K., Firth, A. E. (2025)<br>Uncovering hundreds of RNA viral RdRps amongst uncharacterised sequences in public protein databases.<br><br></p>
NSRC-Search: Efficient searching for similar protein sequences of non-standard amino acid composition
<p>This research was funded by the National Science Centre in Poland (grant number 2021/41/N/ST6/01919)</p>
Derived Protein Sequence Data From Kaggle
<p>It is the dataset containing protein sequence and its associated go-terms, used for priliminary training process</p>
Dataset from: Sequence grammar underlying unfolding and phase separation of globular proteins
<p>Dataset related to the figures from the paper "Sequence grammar underlying unfolding and phase separation of globular proteins". The raw mass spectrometry proteomics data have been deposited via the PRIDE partner repository to the ProteomeXchange Consortium under the dataset identifier PXD033716. </p>
Long-read-based draft genome sequence of Indian black gram IPU-94-1 'Uttara': Insights into disease resistance and seed storage protein genes
<p>Black gram [Vigna mungo (L.) Hepper var. <em>mung<a>o</a></em>] [LAV1] is a warm-season legume highly prized for its protein content along with significant folate and iron proportions. To expedite the genetic enhancement of black gram, a high-quality draft genome from the center of origin of the crop is indispensable. Here, we established a draft genome sequence of an Indian black gram cultivar, 'Uttara' (IPU 94-1), known for its high resistance to mungbean yellow mosaic virus. Pacific Biosciences of California, Inc. (PacBio) single-molecule real-time (SMRT) and Illumina sequencing assembled a draft reference-guided assembly with a cumulative size of ~454.4 Mb, of which, 444.4 Mb was anchored on 11 pseudomolecules corresponding to 11 chromosomes. Uttara assembly denotes features of a high-quality draft genome illustrated through high N50 value (42.88 Mb), gene completeness (benchmarking universal single-copy ortholog [BUSCO] score 94.17%), and low levels of ambiguous nucleotides (N) percent (0.0005%). Gene discovery using transcript evidence predicted 28,881 protein-coding genes, from which, ~95% were functionally annotated. A global survey of genes associated with disease resistance revealed 119 nucleotide binding site–leucine rich repeat (NBS-LRR) proteins, while 23 genes encoding seed storage proteins (SSPs) were discovered in black gram. A large set of microsatellite loci were discovered for marker development in the crop. Our draft genome of an Indian black gram provides the foundational genomic resources for the improvement of important agronomic traits and ultimately will help in accelerating black gram breeding programs.</p>
Amino acid sequences of RWP-RK domain containing proteins used for the construction of phylogenetic tree shown in Fig. 1
<p><span>The RWP-RK protein family is a group</span><span> of transcription factors containing </span><span>the RWP-RK DNA-binding domain. The RWP-RK DNA-binding domain is an ancient motif that emerged before the establishment of the Viridiplantae (green plants), which consist of green algae and land plants. This domain is mostly absent in other kingdoms but widely distributed in Viridiplantae. In green algae, a liverwort, and several angiosperms, RWP-RK proteins play essential roles in nitrogen responses and sexual reproduction-associated processes, which</span><span> </span><span>are seemingly unrelated phenomena but possible interdependent processes</span><span> </span><span>in autotrophs. Consistent with</span><span> related but diversified roles of the RWP-RK proteins in these organisms, the RWP-RK protein family appears to have expanded intensively, but independently, in the algal and land plant lineages. Therefore, bryophyte RWP-RK proteins occupy a unique position in the evolutionary process of establishing the RWP-RK protein family. In this review, we summarize current knowledge about the RWP-RK protein family in the Viridiplantae, and discuss the significance of bryophyte RWP-RK proteins in clarifying the relationship between diversification in the RWP-RK protein family and </span><span>procurement</span><span> of sophisticated mechanisms for adaptation to the terrestrial environment.</span></p>
Code and Data for "Real-time dynamic single-molecule protein sequencing on an integrated semiconductor device"
<p><strong>Code and Data for "Real-time dynamic single-molecule protein sequencing on an integrated semiconductor device".</strong></p> <pre>Code to analyze data produced by the Quantum-Si benchtop device and semiconductor chip is provided in a Python library <strong>qsi_algo</strong> under several submodules: - <strong>rs_caller.py</strong>: Algorithm for calling RS segments (also called ROI segments throughout code). - <strong>rs_caller_controller.py</strong>: Code framework for executing RS calling and property computation in a distributed manner - <strong>rs_properties</strong>: Code for computing properties of identified RS - <strong>rs_classifier</strong>: Algorithms for identifying peptide states (i.e. residue calls) associated with an RS - <strong>utils.py</strong>: shared helper code - <strong>pulse_reader</strong>: reader for binary pulse file - <strong>filters</strong>: ROI and pulse filtering utilities - <strong>plotting</strong>: functions for visualization of data relevant to the analyses presented Jupyter notebooks (<strong>.ipynb</strong>) files are named according to the manuscript figure they are associated with. Analysis code inside uses provided RS (recognition segment) data to demonstrate filtering and residue-calling techniques required to replicate analyses shown in manuscript figures. Please note: several methods rely on randomization for model initialization and/or data sampling which can cause small deviations from equivalent analyses in published figures. The raw data produced from the Quantum-Si benchtop device and semiconductor chip for the assays presented in the accompanying study is presented in a pulse-called binary file format. Pulses can be used as input for RS identification and peptide state identification. Pre-segmented (RS-identified) files are included for convenience. The data contained in the files include: <strong>{run_id}.bin</strong>: Binary format for storing pulse info. The reader provided in <strong>qsi_algo.pulse_reader</strong> produces the following columns: - <strong>aperture_index</strong>: unique aperture index on chip - <strong>start_f</strong>: index of first frame in pulse, counted from the beginning of the run - <strong>end_f</strong>: index of last frame in pulse, counted from the beginning of the run - <strong>dur_f</strong>: duration of pulse in frames - <strong>dur_s</strong>: duration of pulse in seconds - <strong>ipd_f</strong>: interpulse duration in frames (number of frames since end of preceding pulse) - <strong>ipd_s</strong>: interpulse duration in seconds (time in seconds elapsed since end of preceding pulse) - <strong>snr</strong>: signal-to-noise ratio (bin1_intensity / bin1_bg_std) - <strong>intensity</strong>: intensity of pulse (counts above baseline in bin1) - <strong>bin0_intensity</strong>: counts above baseline in bin0 - <strong>intensity_display</strong>: bin1_intensity + bin1_bg_mean - <strong>binratio</strong>: bin0_intensity / bin1_intensity - <strong>bg_mean</strong>: bin1 background mean in region of pulse - <strong>bg_std</strong>: bin1 background standard deviation in region pulse - <strong>bin0_bg_mean</strong>: bin0 background mean in region of pulse - <strong>bin0_bg_std</strong>: bin0 background standard deviation in region pulse <strong>{run_id}.csv.gz</strong>: Compressed comma-separated value file containing RS/ROI properties computed from raw pulses.bin file by included RS caller (example in <strong>rs_caller.py</strong>). - <strong>ap</strong>: unique aperture index on chip - <strong>ROI</strong>: ordinal ROI number in the aperture, 0-indexed - <strong>start_p</strong>: index (.loc) of first pulse in the ROI (inclusive) in pulse dataframe - <strong>end_p</strong>: index (.loc) of last pulse in the ROI (inclusive) in pulse dataframe - <strong>start_f</strong>: first frame of the first pulse in the ROI (inclusive) - <strong>end_f</strong>: Last frame of the last pulse in the ROI (exclusive) - <strong>start_s</strong>: Time (in seconds elapsed from beginning of run) of the start of the ROI - <strong>end_s</strong>: Time (in seconds elapsed from beginning of run) of the end of the ROI - <strong>dur_f</strong>: Duration in frames of the ROI - <strong>dur_s</strong>: Duration in seconds of the ROI - <strong>num_pulses</strong>: Number of pulses in the ROI (that also passed filtering during ROI-calling) - <strong>pw_mean</strong>: Mean pulse duration (in seconds) of pulses in the ROI - <strong>ipd_mean</strong>: Mean inter-pulse duration (in seconds) of pulses in the ROI - <strong>snr_mean</strong>: Mean signal-to-noise ratio of pulses in the ROI - <strong>intensity_mean</strong>: Mean intensity above baseline of pulses in the ROI - <strong>binratio_norm</strong>: Estimated pulse bin ratio of pulses in the ROI, according to the following equation: sum(bin0_intensity*dur_f) / np.sum(bin1_intensity*dur_f) - <strong>ROI_score</strong>: ROI quality score (0-1 from least to most likely to contain recognizer-peptide recognition pulsing) - <strong>binratio_skew</strong>: bin ratio correction factor accounting for binning signal timing differences across the chip. This factor has already been applied to the binratio_norm column</pre>
Protein sequences of LbNoxA, LbNoxB and LbNoxR in L. bicolor and other different species
<p>Ectomycorrhizal symbiosis, which involves mutually beneficial interactions between soil fungi and tree roots, is essential for promoting tree growth. To establish this symbiotic relationship, fungal symbionts must initiate and sustain mutualistic interactions with host plants while avoiding host defence responses. In recent years, the ectomycorrhizal association between the ectomycorrhizal basidiomycete<em> L. bicolor </em>and its host<em> Populus tremula × alba</em>,<em> </em>has been used to elucidate the molecular mechanisms that drive the development of ectomycorrhizal symbiosis. The gene repertoire of<em> L. bicolor</em> contained three <em>Nox</em> genes: <em>LbNoxA</em> (JGI ID Lacbi2|399780), <em>LbNoxB</em> (JGI ID Lacbi2|173015), and <em>LbNoxR</em> (JGI ID Lacbi2|383559). LbNoxA, protein sequence (JGI database + |gene numbers): Clapu1|8615, Sclsc1|12632, Morcra1|822478, Ophsi1|5059, PhlFC14_2|1813626, Pleery1|1506038, Armbor1|1922489, Trave1|68345, Dicsqu463_1|970488, Polbr1|1451927, Ganluc1|486672, Musmu1|NP_031833.3, Lacbi2|399780, LacbiH82_1|443856, Lacbi81306_1|757325, Lacbi55996_1|1021277, LacbiH53_1|522216, LacbiN203_1|820777, LacbiS238N_1|410357, LacbiS238O_1|87145, LacbiCham3_1|66106, LacbiH70_1|500846, Lacbi59489_1|1094392, LacbiH82×H70_1|455396, Lacbi9312_2|1966069, LacbiDR170_1|936588, LacbiD101_1|770957, Lacam2|675282. LbNoxB, protein sequence: Clapu1|2069, Sclsc1|4716, Morcra1|717130, Ophsi1|1953, PhlFC14_2|1930601, Pleery1|1513566, Armbor1|1788315, Trave1|154726, Dicsqu463_1|1002687, Polbr1|1407584, Ganluc1|380884, Musmu1|NP_031833.3, Lacbi2|173015, LacbiH82_1|402315, Lacbi81306_1|388494, Lacbi55996_1|1012552, LacbiN203_1|692258, LacbiS238N_1|466368, LacbiCham3_1|119176, LacbiS238O_1|142990, LacbiH70_1|481432, Lacbi59489_1|1017543, LacbiH82×H70_1|424508, Lacbi9312_2|2376025, LacbiDR170_1|961381, LacbiD101_1|744494, Lacam2|673882, LacbiH53_1|399281. LbNoxR, protein sequence: Clapu1|5169, Sclsc1|101, Morcra1|909770, Ophsi1|2065, PhlFC14_2|1902619, Pleery1|1379754, Armbor1|1887170, Trave1|31426, Dicsqu463_1|961540, Polbr1|1449735, Ganluc1|555878, Musmu1|NP_035007.1, Lacbi2|383559, LacbiH82_1|404736, Lacbi81306_1|796955, Lacbi55996_1|396143, LacbiH53_1|580999, LacbiN203_1|781452, LacbiS238N_1|524632, LacbiS238O_1|142040, LacbiCham3_1|116334, LacbiH70_1|479311, Lacbi59489_1|951497, LacbiDR170_1|1040569, Lacbi9312_2|2114762, Lacam2|671404, LacbiD101_1|660911, LacbiH82×H70_1|470284.</p>
Data for paper: Transfer learning for cross-context prediction of protein expression from 5'UTR sequence
<p>This depsit contains data for the paper entitled: "<strong>Transfer learning for cross-context prediction of protein expression from 5'UTR sequence</strong>".</p> <p>The <strong>rebeca.zip</strong> file contains a snapshot of the rebeca package which can be used to train, fine tune and test the CONV-LSTM model used in this study.</p> <p>The <strong>datasets.zip</strong> file contains the compiled sequence to expression datasets from across all Flow-seq expressions considered in this study. </p> <p>The <strong>analysis.zip</strong> file contains all data files and jupyter notebooks necessary to reproduce our analysis. Each Flow-seq study has a dedicated folder (e.g., `fepB') with two sub-folders: 1. The `data\_split' folder, which contains the steps necessary to split the Flow-seq data for our ML experiments (a `readme.txt' file describes the input and output files and a jupyter notebook is available to reproduce the data split); 2. The `data\_analysis' folder, which contains a jupyter notebook and the necessary input files to reproduce the analysis of our experiments.</p>
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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.