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  1. AU="Paten, Benedict"
  2. AU="Molokhia, Ashraf"
  3. AU="Zirone, Eleonora"
  4. AU="Tong, Wenxia"
  5. AU="Chalira, Davie"
  6. AU="Hodane Yonis"
  7. AU="Boggiano, César"
  8. AU="Gainor, Justin F"
  9. AU="Scurr, David J"
  10. AU="Mahuran, Don J"
  11. AU="Dominguez, Dana A"
  12. AU=Mota Gustavo R
  13. AU="Pandey, Gayathri"
  14. AU=Kiesslich Ralf
  15. AU="Wiskar, Katie"
  16. AU=Bennett Kaila M.
  17. AU=Bacci Jennifer L
  18. AU="Wildman, Ricky D."
  19. AU="Alshibani, Nouf"
  20. AU="Loens, Christopher"
  21. AU="Friedman, Lawrence M."
  22. AU="Johnstone, Damian"
  23. AU="Maleki, Shayan"
  24. AU="G. E-S. Batiha"
  25. AU=Johnson Guyla G
  26. AU="Patel, Parth H"
  27. AU="Manassero, Carlo"
  28. AU="Kirk, Tom"
  29. AU="Bezabih, Yihienew Mequanint"
  30. AU="Hirsinger, Estelle"
  31. AU="Robles-Musso Castillo, Emilio"
  32. AU="Vahdatihassani, Faezeh"
  33. AU="Maria Pala"
  34. AU=Singh Indra
  35. AU="Gallacher, Nicola"
  36. AU="Chen, Pei-Min"
  37. AU=Andre L
  38. AU="Aleksandra I. Pivovarova"
  39. AU="Cruz, Thainá Gabriele Camargo da"
  40. AU="Atkins, Peter"

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  1. Artikel ; Online: GBZ file format for pangenome graphs.

    Sirén, Jouni / Paten, Benedict

    Bioinformatics (Oxford, England)

    2022  Band 38, Heft 22, Seite(n) 5012–5018

    Abstract: Motivation: Pangenome graphs representing aligned genome assemblies are being shared in the text-based Graphical Fragment Assembly format. As the number of assemblies grows, there is a need for a file format that can store the highly repetitive data ... ...

    Abstract Motivation: Pangenome graphs representing aligned genome assemblies are being shared in the text-based Graphical Fragment Assembly format. As the number of assemblies grows, there is a need for a file format that can store the highly repetitive data space efficiently.
    Results: We propose the GBZ file format based on data structures used in the Giraffe short-read aligner. The format provides good compression, and the files can be efficiently loaded into in-memory data structures. We provide compression and decompression tools and libraries for using GBZ graphs, and we show that they can be efficiently used on a variety of systems.
    Availability and implementation: C++ and Rust implementations are available at https://github.com/jltsiren/gbwtgraph and https://github.com/jltsiren/gbwt-rs, respectively.
    Supplementary information: Supplementary data are available at Bioinformatics online.
    Mesh-Begriff(e) High-Throughput Nucleotide Sequencing ; Software ; Data Compression ; Libraries
    Sprache Englisch
    Erscheinungsdatum 2022-09-30
    Erscheinungsland England
    Dokumenttyp Journal Article ; Research Support, N.I.H., Extramural
    ZDB-ID 1422668-6
    ISSN 1367-4811 ; 1367-4803
    ISSN (online) 1367-4811
    ISSN 1367-4803
    DOI 10.1093/bioinformatics/btac656
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  2. Artikel ; Online: A unified pipeline for FISH spatial transcriptomics.

    Cisar, Cecilia / Keener, Nicholas / Ruffalo, Mathew / Paten, Benedict

    Cell genomics

    2023  Band 3, Heft 9, Seite(n) 100384

    Abstract: High-throughput spatial transcriptomics has emerged as a powerful tool for investigating the spatial distribution of mRNA expression and its effects on cellular function. There is a lack of standardized tools for analyzing spatial transcriptomics data, ... ...

    Abstract High-throughput spatial transcriptomics has emerged as a powerful tool for investigating the spatial distribution of mRNA expression and its effects on cellular function. There is a lack of standardized tools for analyzing spatial transcriptomics data, leading many groups to write their own in-house tools that are often poorly documented and not generalizable. To address this, we have expanded and improved the starfish library and used those tools to create PIPEFISH, a semi-automated and generalizable pipeline that performs transcript annotation for fluorescence
    Sprache Englisch
    Erscheinungsdatum 2023-08-21
    Erscheinungsland United States
    Dokumenttyp Journal Article
    ISSN 2666-979X
    ISSN (online) 2666-979X
    DOI 10.1016/j.xgen.2023.100384
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  3. Artikel ; Online: Optimal gap-affine alignment in O(s) space.

    Marco-Sola, Santiago / Eizenga, Jordan M / Guarracino, Andrea / Paten, Benedict / Garrison, Erik / Moreto, Miquel

    Bioinformatics (Oxford, England)

    2023  Band 39, Heft 2

    Abstract: Motivation: Pairwise sequence alignment remains a fundamental problem in computational biology and bioinformatics. Recent advances in genomics and sequencing technologies demand faster and scalable algorithms that can cope with the ever-increasing ... ...

    Abstract Motivation: Pairwise sequence alignment remains a fundamental problem in computational biology and bioinformatics. Recent advances in genomics and sequencing technologies demand faster and scalable algorithms that can cope with the ever-increasing sequence lengths. Classical pairwise alignment algorithms based on dynamic programming are strongly limited by quadratic requirements in time and memory. The recently proposed wavefront alignment algorithm (WFA) introduced an efficient algorithm to perform exact gap-affine alignment in O(ns) time, where s is the optimal score and n is the sequence length. Notwithstanding these bounds, WFA's O(s2) memory requirements become computationally impractical for genome-scale alignments, leading to a need for further improvement.
    Results: In this article, we present the bidirectional WFA algorithm, the first gap-affine algorithm capable of computing optimal alignments in O(s) memory while retaining WFA's time complexity of O(ns). As a result, this work improves the lowest known memory bound O(n) to compute gap-affine alignments. In practice, our implementation never requires more than a few hundred MBs aligning noisy Oxford Nanopore Technologies reads up to 1 Mbp long while maintaining competitive execution times.
    Availability and implementation: All code is publicly available at https://github.com/smarco/BiWFA-paper.
    Supplementary information: Supplementary data are available at Bioinformatics online.
    Mesh-Begriff(e) Algorithms ; Genomics ; Computational Biology ; Genome ; Sequence Analysis, DNA ; Software
    Sprache Englisch
    Erscheinungsdatum 2023-02-07
    Erscheinungsland England
    Dokumenttyp Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
    ZDB-ID 1422668-6
    ISSN 1367-4811 ; 1367-4803
    ISSN (online) 1367-4811
    ISSN 1367-4803
    DOI 10.1093/bioinformatics/btad074
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  4. Artikel: An average-case sublinear forward algorithm for the haploid Li and Stephens model.

    Rosen, Yohei M / Paten, Benedict J

    Algorithms for molecular biology : AMB

    2019  Band 14, Seite(n) 11

    Abstract: Background: Hidden Markov models of haplotype inheritance such as the Li and Stephens model allow for computationally tractable probability calculations using the forward algorithm as long as the representative reference panel used in the model is ... ...

    Abstract Background: Hidden Markov models of haplotype inheritance such as the Li and Stephens model allow for computationally tractable probability calculations using the forward algorithm as long as the representative reference panel used in the model is sufficiently small. Specifically, the monoploid Li and Stephens model and its variants are linear in reference panel size unless heuristic approximations are used. However, sequencing projects numbering in the thousands to hundreds of thousands of individuals are underway, and others numbering in the millions are anticipated.
    Results: To make the forward algorithm for the haploid Li and Stephens model computationally tractable for these datasets, we have created a numerically exact version of the algorithm with observed average case sublinear runtime with respect to reference panel size
    Conclusions: We show a forward algorithm which avoids any tradeoff between runtime and model complexity. Our algorithm makes use of two general strategies which might be applicable to improving the time complexity of other future sequence analysis algorithms: sparse dynamic programming matrices and lazy evaluation.
    Sprache Englisch
    Erscheinungsdatum 2019-04-02
    Erscheinungsland England
    Dokumenttyp Journal Article
    ZDB-ID 2224970-9
    ISSN 1748-7188
    ISSN 1748-7188
    DOI 10.1186/s13015-019-0144-9
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  5. Artikel: Unraveling Neuronal Identities Using SIMS: A Deep Learning Label Transfer Tool for Single-Cell RNA Sequencing Analysis.

    Gonzalez-Ferrer, Jesus / Lehrer, Julian / O'Farrell, Ash / Paten, Benedict / Teodorescu, Mircea / Haussler, David / Jonsson, Vanessa D / Mostajo-Radji, Mohammed A

    bioRxiv : the preprint server for biology

    2023  

    Abstract: Large single-cell RNA datasets have contributed to unprecedented biological insight. Often, these take the form of cell atlases and serve as a reference for automating cell labeling of newly sequenced samples. Yet, classification algorithms have lacked ... ...

    Abstract Large single-cell RNA datasets have contributed to unprecedented biological insight. Often, these take the form of cell atlases and serve as a reference for automating cell labeling of newly sequenced samples. Yet, classification algorithms have lacked the capacity to accurately annotate cells, particularly in complex datasets. Here we present SIMS (Scalable, Interpretable Machine Learning for Single-Cell), an end-to-end data-efficient machine learning pipeline for discrete classification of single-cell data that can be applied to new datasets with minimal coding. We benchmarked SIMS against common single-cell label transfer tools and demonstrated that it performs as well or better than state of the art algorithms. We then use SIMS to classify cells in one of the most complex tissues: the brain. We show that SIMS classifies cells of the adult cerebral cortex and hippocampus at a remarkably high accuracy. This accuracy is maintained in trans-sample label transfers of the adult human cerebral cortex. We then apply SIMS to classify cells in the developing brain and demonstrate a high level of accuracy at predicting neuronal subtypes, even in periods of fate refinement, shedding light on genetic changes affecting specific cell types across development. Finally, we apply SIMS to single cell datasets of cortical organoids to predict cell identities and unveil genetic variations between cell lines. SIMS identifies cell-line differences and misannotated cell lineages in human cortical organoids derived from different pluripotent stem cell lines. When cell types are obscured by stress signals, label transfer from primary tissue improves the accuracy of cortical organoid annotations, serving as a reliable ground truth. Altogether, we show that SIMS is a versatile and robust tool for cell-type classification from single-cell datasets.
    Sprache Englisch
    Erscheinungsdatum 2023-11-17
    Erscheinungsland United States
    Dokumenttyp Preprint
    DOI 10.1101/2023.02.28.529615
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  6. Artikel ; Online: Pangenome graph construction from genome alignments with Minigraph-Cactus.

    Hickey, Glenn / Monlong, Jean / Ebler, Jana / Novak, Adam M / Eizenga, Jordan M / Gao, Yan / Marschall, Tobias / Li, Heng / Paten, Benedict

    Nature biotechnology

    2023  Band 42, Heft 4, Seite(n) 663–673

    Abstract: Pangenome references address biases of reference genomes by storing a representative set of diverse haplotypes and their alignment, usually as a graph. Alternate alleles determined by variant callers can be used to construct pangenome graphs, but ... ...

    Abstract Pangenome references address biases of reference genomes by storing a representative set of diverse haplotypes and their alignment, usually as a graph. Alternate alleles determined by variant callers can be used to construct pangenome graphs, but advances in long-read sequencing are leading to widely available, high-quality phased assemblies. Constructing a pangenome graph directly from assemblies, as opposed to variant calls, leverages the graph's ability to represent variation at different scales. Here we present the Minigraph-Cactus pangenome pipeline, which creates pangenomes directly from whole-genome alignments, and demonstrate its ability to scale to 90 human haplotypes from the Human Pangenome Reference Consortium. The method builds graphs containing all forms of genetic variation while allowing use of current mapping and genotyping tools. We measure the effect of the quality and completeness of reference genomes used for analysis within the pangenomes and show that using the CHM13 reference from the Telomere-to-Telomere Consortium improves the accuracy of our methods. We also demonstrate construction of a Drosophila melanogaster pangenome.
    Mesh-Begriff(e) Humans ; Animals ; Drosophila melanogaster/genetics ; Haplotypes/genetics ; High-Throughput Nucleotide Sequencing/methods ; Alleles ; Sequence Analysis, DNA ; Genome, Human/genetics
    Sprache Englisch
    Erscheinungsdatum 2023-05-10
    Erscheinungsland United States
    Dokumenttyp Journal Article
    ZDB-ID 1311932-1
    ISSN 1546-1696 ; 1087-0156
    ISSN (online) 1546-1696
    ISSN 1087-0156
    DOI 10.1038/s41587-023-01793-w
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  7. Artikel ; Online: Haplotype-aware pantranscriptome analyses using spliced pangenome graphs.

    Sibbesen, Jonas A / Eizenga, Jordan M / Novak, Adam M / Sirén, Jouni / Chang, Xian / Garrison, Erik / Paten, Benedict

    Nature methods

    2023  Band 20, Heft 2, Seite(n) 239–247

    Abstract: Pangenomics is emerging as a powerful computational paradigm in bioinformatics. This field uses population-level genome reference structures, typically consisting of a sequence graph, to mitigate reference bias and facilitate analyses that were ... ...

    Abstract Pangenomics is emerging as a powerful computational paradigm in bioinformatics. This field uses population-level genome reference structures, typically consisting of a sequence graph, to mitigate reference bias and facilitate analyses that were challenging with previous reference-based methods. In this work, we extend these methods into transcriptomics to analyze sequencing data using the pantranscriptome: a population-level transcriptomic reference. Our toolchain, which consists of additions to the VG toolkit and a standalone tool, RPVG, can construct spliced pangenome graphs, map RNA sequencing data to these graphs, and perform haplotype-aware expression quantification of transcripts in a pantranscriptome. We show that this workflow improves accuracy over state-of-the-art RNA sequencing mapping methods, and that it can efficiently quantify haplotype-specific transcript expression without needing to characterize the haplotypes of a sample beforehand.
    Mesh-Begriff(e) Haplotypes ; Computational Biology ; Gene Expression Profiling ; Metagenomics ; Transcriptome
    Sprache Englisch
    Erscheinungsdatum 2023-01-16
    Erscheinungsland United States
    Dokumenttyp Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
    ZDB-ID 2169522-2
    ISSN 1548-7105 ; 1548-7091
    ISSN (online) 1548-7105
    ISSN 1548-7091
    DOI 10.1038/s41592-022-01731-9
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  8. Artikel: Assessing methylation detection for primary human tissue using Nanopore sequencing.

    Genner, Rylee / Akeson, Stuart / Meredith, Melissa / Jerez, Pilar Alvarez / Malik, Laksh / Baker, Breeana / Miano-Burkhardt, Abigail / Paten, Benedict / Billingsley, Kimberley J / Blauwendraat, Cornelis / Jain, Miten

    bioRxiv : the preprint server for biology

    2024  

    Abstract: DNA methylation most commonly occurs as 5-methylcytosine (5-mC) in the human genome and has been associated with human diseases. Recent developments in single-molecule sequencing technologies (Oxford Nanopore Technologies (ONT) and Pacific Biosciences) ... ...

    Abstract DNA methylation most commonly occurs as 5-methylcytosine (5-mC) in the human genome and has been associated with human diseases. Recent developments in single-molecule sequencing technologies (Oxford Nanopore Technologies (ONT) and Pacific Biosciences) have enabled readouts of long, native DNA molecules, including cytosine methylation. ONT recently upgraded their Nanopore sequencing chemistry and kits from R9 to the R10 version, which yielded increased accuracy and sequencing throughput. However the effects on methylation detection have not yet been documented. Here we performed a series of computational analyses to characterize differences in Nanopore-based 5mC detection between the ONT R9 and R10 chemistries. We compared 5mC calls in R9 and R10 for three human genome datasets: a cell line, a frontal cortex brain sample, and a blood sample. We performed an in-depth analysis on CpG islands and homopolymer regions, and documented high concordance for methylation detection among sequencing technologies. The strongest correlation was observed between Nanopore R10 and Illumina bisulfite technologies for cell line-derived datasets. Subtle differences in methylation datasets between technologies can impact analysis tools such as differential methylation calling software. Our findings show that comparisons can be drawn between methylation data from different Nanopore chemistries using guided hypotheses. This work will facilitate comparison among Nanopore data cohorts derived using different chemistries from large scale sequencing efforts, such as the NIH CARD Long Read Initiative.
    Sprache Englisch
    Erscheinungsdatum 2024-03-01
    Erscheinungsland United States
    Dokumenttyp Preprint
    DOI 10.1101/2024.02.29.581569
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  9. Artikel ; Online: Beyond the Human Genome Project: The Age of Complete Human Genome Sequences and Pangenome References.

    Taylor, Dylan J / Eizenga, Jordan M / Li, Qiuhui / Das, Arun / Jenike, Katharine M / Kenny, Eimear E / Miga, Karen H / Monlong, Jean / McCoy, Rajiv C / Paten, Benedict / Schatz, Michael C

    Annual review of genomics and human genetics

    2024  

    Abstract: The Human Genome Project was an enormous accomplishment, providing a foundation for countless explorations into the genetics and genomics of the human species. Yet for many years, the human genome reference sequence remained incomplete and lacked ... ...

    Abstract The Human Genome Project was an enormous accomplishment, providing a foundation for countless explorations into the genetics and genomics of the human species. Yet for many years, the human genome reference sequence remained incomplete and lacked representation of human genetic diversity. Recently, two major advances have emerged to address these shortcomings: complete gap-free human genome sequences, such as the one developed by the Telomere-to-Telomere Consortium, and high-quality pangenomes, such as the one developed by the Human Pangenome Reference Consortium. Facilitated by advances in long-read DNA sequencing and genome assembly algorithms, complete human genome sequences resolve regions that have been historically difficult to sequence, including centromeres, telomeres, and segmental duplications. In parallel, pangenomes capture the extensive genetic diversity across populations worldwide. Together, these advances usher in a new era of genomics research, enhancing the accuracy of genomic analysis, paving the path for precision medicine, and contributing to deeper insights into human biology.
    Sprache Englisch
    Erscheinungsdatum 2024-04-25
    Erscheinungsland United States
    Dokumenttyp Journal Article ; Review
    ZDB-ID 2037670-4
    ISSN 1545-293X ; 1527-8204
    ISSN (online) 1545-293X
    ISSN 1527-8204
    DOI 10.1146/annurev-genom-021623-081639
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  10. Artikel ; Online: Towards inferring nanopore sequencing ionic currents from nucleotide chemical structures.

    Ding, Hongxu / Anastopoulos, Ioannis / Bailey, Andrew D / Stuart, Joshua / Paten, Benedict

    Nature communications

    2021  Band 12, Heft 1, Seite(n) 6545

    Abstract: The characteristic ionic currents of nucleotide kmers are commonly used in analyzing nanopore sequencing readouts. We present a graph convolutional network-based deep learning framework for predicting kmer characteristic ionic currents from corresponding ...

    Abstract The characteristic ionic currents of nucleotide kmers are commonly used in analyzing nanopore sequencing readouts. We present a graph convolutional network-based deep learning framework for predicting kmer characteristic ionic currents from corresponding chemical structures. We show such a framework can generalize the chemical information of the 5-methyl group from thymine to cytosine by correctly predicting 5-methylcytosine-containing DNA 6mers, thus shedding light on the de novo detection of nucleotide modifications.
    Mesh-Begriff(e) Cytosine/metabolism ; Nanopore Sequencing/methods ; Nucleotides/metabolism ; Sequence Analysis, DNA/methods
    Chemische Substanzen Nucleotides ; Cytosine (8J337D1HZY)
    Sprache Englisch
    Erscheinungsdatum 2021-11-11
    Erscheinungsland England
    Dokumenttyp Journal Article ; Research Support, N.I.H., Extramural
    ZDB-ID 2553671-0
    ISSN 2041-1723 ; 2041-1723
    ISSN (online) 2041-1723
    ISSN 2041-1723
    DOI 10.1038/s41467-021-26929-x
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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