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48 results for “Single molecule sequencing”
Widespread Polycistronic Transcripts in Fungi Revealed by Single-Molecule mRNA Sequencing
<p>Genes in prokaryotic genomes are often arranged into clusters and co-transcribed into poly- cistronic RNAs. Isolated examples of polycistronic RNAs were also reported in some higher eukaryotes but their presence was generally considered rare. Here we developed a long- read sequencing strategy to identify polycistronic transcripts in several mushroom forming fungal species including Plicaturopsis crispa, Phanerochaete chrysosporium, Trametes ver- sicolor, and Gloeophyllum trabeum. We found genome-wide prevalence of polycistronic transcription in these Agaricomycetes, involving up to 8% of the transcribed genes. Unlike polycistronic mRNAs in prokaryotes, these co-transcribed genes are also independently transcribed. We show that polycistronic transcription may interfere with expression of the downstream tandem gene. Further comparative genomic analysis indicates that polycis- tronic transcription is conserved among a wide range of mushroom forming fungi. In sum- mary, our study revealed, for the first time, the genome prevalence of polycistronic transcription in a phylogenetic range of higher fungi. Furthermore, we systematically show that our long-read sequencing approach and combined bioinformatics pipeline is a generic powerful tool for precise characterization of complex transcriptomes that enables identifica- tion of mRNA isoforms not recovered via short-read assembly.</p>
Single molecule, full-length transcript sequencing provides insight into the extreme metabolism of ruby-throated hummingbird Archilochus colubris
<p>Hummingbirds can support their high metabolic rates exclusively by oxidizing ingested sugars, which is unsurprising given their sugar-rich nectar diet and use of energetically expensive hovering flight. However, they cannot rely on dietary sugars as a fuel during fasting periods, such as during the night, at first light, or when undertaking long-distance migratory flights, and must instead rely exclusively on onboard lipids. This metabolic flexibility is remarkable both in that the birds can switch between exclusive use of each fuel type within minutes and in that de novo lipogenesis from dietary sugar precursors is the principle way in which fat stores are built, sometimes at exceptionally high rates, such as during the few days prior to a migratory flight. The hummingbird hepatopancreas is the principle location of de novo lipogenesis and likely plays a key role in fuel selection, fuel switching, and glucose homeostasis. Yet understanding how this tissue, and the whole organism, achieves and moderates high rates of energy turnover is hampered by a fundamental lack of information regarding how genes coding for relevant enzymes differ in their sequence, expression, and regulation in these unique animals. To address this knowledge gap, we generated a de novo transcriptome of the hummingbird liver using PacBio full-length cDNA sequencing (Iso-Seq), yielding a total of 8.6Gb of sequencing data, or 2.6M reads from 4 different size fractions. We analyzed data using the SMRTAnalysis v3.1 Iso-Seq pipeline, including classification of reads and clustering of isoforms (ICE) followed by error-correction (Arrow). We performed orthology analysis to identify closely related sequences between our transcriptome and other avian and human gene sets. We also aligned our transcriptome against the Calypte anna genome where possible. Finally, we closely examined homology of critical lipid metabolic genes between our transcriptome data and avian and human genomes. We confirmed high levels of sequence divergence within hummingbird lipogenic enzymes, suggesting a high probability of adaptive divergent function in the lipogenic liver pathways. Our results have leveraged cutting-edge technology and a novel bioinformatics pipeline to provide a compelling first direct look at the transcriptome of this incredible organism.</p>
Nanotiming: single-molecule based, telomere-to-telomere DNA replication timing profiling by nanopore sequencing
<p>Dataset for the manuscript "Nanotiming: telomere-to-telomere DNA replication timing profiling by nanopore sequencing" by Theulot et al ,2024 (<span>https://doi.org/10.1038/s41467-024-55520-3</span>) related to the github repository (https://github.com/LacroixLaurent/NanoTiming)</p> <ul> <li>WT_rep3.tar.gz contains fast5 file from an experiment where yeast BT1 strain was grown for one doubling time with 5µM BrdU then DNA was sequenced on R9.4.1 ONT flowcell</li> <li>mod_mapping.bam contains the bam file resulting from the BrdU base calling with megalodon (v2.2.9) using our BT1 reference genome and our BrdU aware model for base-calling</li> <li>WT_rep3_nanoT.bed.gz contains the reads coordinates from the mod_mappings file</li> <li>WT_rep3_nanoT_alldata.rds contains the BrdU profiles for each reads of the mod_mappings file, with the BrdU signal binned in 1kb non overlaping windows</li> <li>WT_rep3_nanoT.rds contains the genomic BrdU signal profiles by 1kb non overlaping windows</li> <li>TeloLengthDataNanoT.rds contains all the telomeric sequences extracted from the experiments reported in the Figure 4 and S19 to S23 of the manuscript with the associated filtering information and nanotiming signal.</li> </ul> <p> </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>
Single-molecule structural and kinetic studies across sequence space
<p>Data and analysis code related to the research article:</p> <p>Single-molecule structural and kinetic studies across sequence space</p>
Fibertools: fast and accurate m6A calling using single-molecule long-read sequencing (ML data)
<p>Fibertools is a convolutional neural network that permits the fast and accurate identification of endogenous and exogenous N6-methyladenine (m6A)-marked bases using single-molecule long-read sequencing.<strong> </strong>This dataset (ML data) provides training and validation data for training fibertools supervised and semi-supervised CNN models for three long-read chemistries.</p>
Pacbio single molecule sequencing
<p>As a leading genomics company, CD Genomics provides next-generation sequencing and bioinformatics services to pharmaceutical and biotech companies, as well as academic and government agencies around the world, relying on advanced sequencing instruments and rich project experience. We are offering long-read sequencing services from PacBio and Oxford Nanopore, giving researchers a wide range of cutting-edge sequencing services to suit the particular needs of any project. Here, we offer long-read sequencing services which are based on <a href="https://longseq.cd-genomics.com/pacbio-smrt-sequencing-technology.html">PacBio single molecule sequencing</a> technology, the Sequel II System. This technology can produce highly accurate (> 99.9% average concordance accuracy) long sequences up to 30 kb long, helping to identify biologically important structural variants, RNA splice site isoforms of cDNA, and provide insights into hard-to-sequence regions of the genome, among others.</p>
Single molecule image sequences of Thermothielavioides terrestris AA9E (TtAA9E) on cellulose in oxygen scavenging systems
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Raw data associated with the article: "Single-molecule DNA sequencing of widely varying GC-content using nucleotide release, capture and detection in microdroplets.", NAR, Puchtler et.al.
<p>All data taken in the production of the corresponding paper: "Single-molecule DNA sequencing of widely varying GC-content using nucleotide release, capture and detection in microdroplets."</p> <p>The associated manuscript describes a method for DNA sequencing which involves the sequential release of nucleotides from a single, immobilised strand of DNA via pyrophosphorolysis (PPL). Released nucleotides, in the form of dNTPs, are captured in microdroplets which are manipulated using an optical-EWOD platform. A detection chemistry within each droplet releases a specific dye depending on which dNTPs are present, allowing the optical read-out of bases within each droplet. Hence, by capturing bases sequentially within droplets as they are cleaved from the strand of DNA, the sequence can be optically identified.</p>
Data from: Next-generation polyploid phylogenetics: rapid resolution of hybrid polyploid complexes using PacBio single-molecule sequencing
Difficulties in generating nuclear data for polyploids have impeded phylogenetic study of these groups. We describe a high-throughput protocol and an associated bioinformatics pipeline (PURC: "Pipeline for Untangling Reticulate Complexes") that is able to generate these data quickly and conveniently, and demonstrate its efficacy on accessions from the fern family Cystopteridaceae. We conclude with a demonstration of the downstream utility of these data by inferring a multilabeled species tree for a subset of our accessions. We amplified four ~1kb-long nuclear loci and sequenced them in a parallel-tagged amplicon sequencing approach using the PacBio platform. PURC infers the final sequences from the raw reads via an iterative approach that corrects PCR and sequencing errors and removes PCR-mediated recombinant sequences (chimeras). We generated data for all gene copies (homeologs, paralogs, and segregating alleles) present in each of three sets of 50 mostly-polyploid accessions, for four loci, in three PacBio runs (one run per set). From the raw sequencing reads PURC was able to accurately infer the underlying sequences. This approach makes it easy and economical to study the phylogenetics of polyploids, and in conjunction with recent analytical advances, facilitates investigation of broad patterns of polyploid evolution.
Data from: Advanced characterization of DNA molecules in rAAV vector preparations by single-stranded virus next-generation sequencing
Recent successful clinical trials with recombinant adeno-associated viral vectors (rAAVs) have led to a renewed interest in gene therapy. However, despite extensive developments to improve vector-manufacturing processes, undesirable DNA contaminants in rAAV preparations remain a major safety concern. Indeed, the presence of DNA fragments containing antibiotic resistance genes, wild-type AAV, and packaging cell genomes has been found in previous studies using quantitative polymerase chain reaction (qPCR) analyses. However, because qPCR only provides a partial view of the DNA molecules in rAAV preparations, we developed a method based on next-generation sequencing (NGS) to extensively characterize single-stranded DNA virus preparations (SSV-Seq). In order to validate SSV-Seq, we analyzed three rAAV vector preparations produced by transient transfection of mammalian cells. Our data were consistent with qPCR results and showed a quasi-random distribution of contaminants originating from the packaging cells genome. Finally, we found single-nucleotide variants (SNVs) along the vector genome but no evidence of large deletions. Altogether, SSV-Seq could provide a characterization of DNA contaminants and a map of the rAAV genome with unprecedented resolution and exhaustiveness. We expect SSV-Seq to pave the way for a new generation of quality controls, guiding process development toward rAAV preparations of higher potency and with improved safety profiles.
Single-Molecule Image Sequences of Trichoderma reesei Cel7A and Variants on Cellulose Fibrils
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Raw data associated with the article: "Single-molecule DNA sequencing of widely varying GC-content using nucleotide release, capture and detection in microdroplets.", NAR, Puchtler et.al.
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Data from: Single molecule sequencing resolves the detailed structure of complex satellite DNA loci in Drosophila melanogaster
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Data from: Next-generation polyploid phylogenetics: rapid resolution of hybrid polyploid complexes using PacBio single-molecule sequencing
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Data from: Advanced characterization of DNA molecules in rAAV vector preparations by single-stranded virus next-generation sequencing
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Data from: Direct sequencing of haplotypes from diploid individuals through a modified emulsion PCR-based single-molecule sequencing approach
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Non-destructive enzymatic deamination enables single molecule long read sequencing for the determination of 5-methylcytosine and 5- hydroxymethylcytosine at single base resolution
GEO Series GSE141908. Mus musculus. 16 samples. Type: Other; Methylation profiling by high throughput sequencing.
A comparison between single cell RNA sequencing and single molecule RNA FISH for rare cell analysis
GEO Series GSE99330. Homo sapiens. 801 samples. Type: Expression profiling by high throughput sequencing.
Unamplified Cap Analysis of Gene Expression on a single molecule sequencer (HeliScopeCAGE)
GEO Series GSE28148. Homo sapiens. 19 samples. Type: Expression profiling by array; Expression profiling by high throughput sequencing.
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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.