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  1. Article ; Online: Response to "The perpetual motion machine of AI-generated data and the distraction of ChatGPT as a 'scientist'".

    Noble, William Stafford

    Nature biotechnology

    2024  

    Language English
    Publishing date 2024-05-01
    Publishing country United States
    Document type Letter
    ZDB-ID 1311932-1
    ISSN 1546-1696 ; 1087-0156
    ISSN (online) 1546-1696
    ISSN 1087-0156
    DOI 10.1038/s41587-024-02230-2
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    Zs.A 2167: Show issues Location:
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  2. Article ; Online: Ten simple rules for defining a computational biology project.

    Noble, William Stafford

    PLoS computational biology

    2023  Volume 19, Issue 1, Page(s) e1010786

    MeSH term(s) Computational Biology
    Language English
    Publishing date 2023-01-05
    Publishing country United States
    Document type Journal Article
    ZDB-ID 2193340-6
    ISSN 1553-7358 ; 1553-734X
    ISSN (online) 1553-7358
    ISSN 1553-734X
    DOI 10.1371/journal.pcbi.1010786
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  3. Article ; Online: Reinvestigating the Correctness of Decoy-Based False Discovery Rate Control in Proteomics Tandem Mass Spectrometry.

    Freestone, Jack / Noble, William Stafford / Keich, Uri

    Journal of proteome research

    2024  

    Abstract: Traditional database search methods for the analysis of bottom-up proteomics tandem mass spectrometry (MS/MS) data are limited in their ability to detect peptides with post-translational modifications (PTMs). Recently, "open modification" database search ...

    Abstract Traditional database search methods for the analysis of bottom-up proteomics tandem mass spectrometry (MS/MS) data are limited in their ability to detect peptides with post-translational modifications (PTMs). Recently, "open modification" database search strategies, in which the requirement that the mass of the database peptide closely matches the observed precursor mass is relaxed, have become popular as ways to find a wider variety of types of PTMs. Indeed, in one study, Kong
    Language English
    Publishing date 2024-04-30
    Publishing country United States
    Document type Journal Article
    ZDB-ID 2078618-9
    ISSN 1535-3907 ; 1535-3893
    ISSN (online) 1535-3907
    ISSN 1535-3893
    DOI 10.1021/acs.jproteome.3c00902
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  4. Article: Predicting chromatin conformation contact maps.

    Min, Alan / Schreiber, Jacob / Kundaje, Anshul / Noble, William Stafford

    bioRxiv : the preprint server for biology

    2024  

    Abstract: Over the past 15 years, a variety of next-generation sequencing assays have been developed for measuring the 3D conformation of DNA in the nucleus. Each of these assays gives, for a particular cell or tissue type, a distinct picture of 3D chromatin ... ...

    Abstract Over the past 15 years, a variety of next-generation sequencing assays have been developed for measuring the 3D conformation of DNA in the nucleus. Each of these assays gives, for a particular cell or tissue type, a distinct picture of 3D chromatin architecture. Accordingly, making sense of the relationship between genome structure and function requires teasing apart two closely related questions: how does chromatin 3D structure change from one cell type to the next, and how do different measurements of that structure differ from one another, even when the two assays are carried out in the same cell type? In this work, we assemble a collection of chromatin 3D datasets-each represented as a 2D contact map- spanning multiple assay types and cell types. We then build a machine learning model that predicts missing contact maps in this collection. We use the model to systematically explore how genome 3D architecture changes, at the level of compartments, domains, and loops, between cell type and between assay types.
    Language English
    Publishing date 2024-04-14
    Publishing country United States
    Document type Preprint
    DOI 10.1101/2024.04.12.589240
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  5. Article: Systematic identification of inter-chromosomal interaction networks supports the existence of RNA factories.

    Hristov, Borislav Hrisimirov / Noble, William Stafford / Bertero, Alessandro

    bioRxiv : the preprint server for biology

    2023  

    Abstract: Most studies of genome organization have focused on intra-chromosomal ( ...

    Abstract Most studies of genome organization have focused on intra-chromosomal (
    Language English
    Publishing date 2023-09-22
    Publishing country United States
    Document type Preprint
    DOI 10.1101/2023.09.21.558852
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  6. Article ; Online: A wider field of view to predict expression.

    Lu, Yang Young / Noble, William Stafford

    Nature methods

    2021  Volume 18, Issue 10, Page(s) 1155–1156

    Language English
    Publishing date 2021-10-04
    Publishing country United States
    Document type Journal Article ; Research Support, N.I.H., Extramural ; Comment
    ZDB-ID 2169522-2
    ISSN 1548-7105 ; 1548-7091
    ISSN (online) 1548-7105
    ISSN 1548-7091
    DOI 10.1038/s41592-021-01259-4
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  7. Article ; Online: Target-decoy false discovery rate estimation using Crema.

    Lin, Andy / See, Donavan / Fondrie, William E / Keich, Uri / Noble, William Stafford

    Proteomics

    2024  Volume 24, Issue 8, Page(s) e2300084

    Abstract: Assigning statistical confidence estimates to discoveries produced by a tandem mass spectrometry proteomics experiment is critical to enabling principled interpretation of the results and assessing the cost/benefit ratio of experimental follow-up. The ... ...

    Abstract Assigning statistical confidence estimates to discoveries produced by a tandem mass spectrometry proteomics experiment is critical to enabling principled interpretation of the results and assessing the cost/benefit ratio of experimental follow-up. The most common technique for computing such estimates is to use target-decoy competition (TDC), in which observed spectra are searched against a database of real (target) peptides and a database of shuffled or reversed (decoy) peptides. TDC procedures for estimating the false discovery rate (FDR) at a given score threshold have been developed for application at the level of spectra, peptides, or proteins. Although these techniques are relatively straightforward to implement, it is common in the literature to skip over the implementation details or even to make mistakes in how the TDC procedures are applied in practice. Here we present Crema, an open-source Python tool that implements several TDC methods of spectrum-, peptide- and protein-level FDR estimation. Crema is compatible with a variety of existing database search tools and provides a straightforward way to obtain robust FDR estimates.
    MeSH term(s) Algorithms ; Databases, Protein ; Peptides/chemistry ; Proteins/analysis ; Proteomics/methods
    Chemical Substances Peptides ; Proteins
    Language English
    Publishing date 2024-02-21
    Publishing country Germany
    Document type Journal Article
    ZDB-ID 2032093-0
    ISSN 1615-9861 ; 1615-9853
    ISSN (online) 1615-9861
    ISSN 1615-9853
    DOI 10.1002/pmic.202300084
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    Zs.A 5386: Show issues Location:
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    Zs.A 1868: Show issues Location:
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  8. Article ; Online: Matrix prior for data transfer between single cell data types in latent Dirichlet allocation.

    Min, Alan / Durham, Timothy / Gevirtzman, Louis / Noble, William Stafford

    PLoS computational biology

    2023  Volume 19, Issue 5, Page(s) e1011049

    Abstract: Single cell ATAC-seq (scATAC-seq) enables the mapping of regulatory elements in fine-grained cell types. Despite this advance, analysis of the resulting data is challenging, and large scale scATAC-seq data are difficult to obtain and expensive to ... ...

    Abstract Single cell ATAC-seq (scATAC-seq) enables the mapping of regulatory elements in fine-grained cell types. Despite this advance, analysis of the resulting data is challenging, and large scale scATAC-seq data are difficult to obtain and expensive to generate. This motivates a method to leverage information from previously generated large scale scATAC-seq or scRNA-seq data to guide our analysis of new scATAC-seq datasets. We analyze scATAC-seq data using latent Dirichlet allocation (LDA), a Bayesian algorithm that was developed to model text corpora, summarizing documents as mixtures of topics defined based on the words that distinguish the documents. When applied to scATAC-seq, LDA treats cells as documents and their accessible sites as words, identifying "topics" based on the cell type-specific accessible sites in those cells. Previous work used uniform symmetric priors in LDA, but we hypothesized that nonuniform matrix priors generated from LDA models trained on existing data sets may enable improved detection of cell types in new data sets, especially if they have relatively few cells. In this work, we test this hypothesis in scATAC-seq data from whole C. elegans nematodes and SHARE-seq data from mouse skin cells. We show that nonsymmetric matrix priors for LDA improve our ability to capture cell type information from small scATAC-seq datasets.
    MeSH term(s) Animals ; Mice ; Caenorhabditis elegans/genetics ; Bayes Theorem ; Algorithms ; Chromatin ; Regulatory Sequences, Nucleic Acid ; Single-Cell Analysis/methods
    Chemical Substances Chromatin
    Language English
    Publishing date 2023-05-05
    Publishing country United States
    Document type Journal Article ; Research Support, U.S. Gov't, Non-P.H.S.
    ZDB-ID 2193340-6
    ISSN 1553-7358 ; 1553-734X
    ISSN (online) 1553-7358
    ISSN 1553-734X
    DOI 10.1371/journal.pcbi.1011049
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  9. Article ; Online: Efficient Indexing of Peptides for Database Search Using Tide.

    Nii Adoquaye Acquaye, Frank Lawrence / Kertesz-Farkas, Attila / Noble, William Stafford

    Journal of proteome research

    2023  Volume 22, Issue 2, Page(s) 577–584

    Abstract: The first step in the analysis of protein tandem mass spectrometry data typically involves searching the observed spectra against a protein database. During database search, the search engine must digest the proteins in the database into peptides, ... ...

    Abstract The first step in the analysis of protein tandem mass spectrometry data typically involves searching the observed spectra against a protein database. During database search, the search engine must digest the proteins in the database into peptides, subject to digestion rules that are under user control. The choice of these digestion parameters, as well as selection of post-translational modifications (PTMs), can dramatically affect the size of the search space and hence the statistical power of the search. The Tide search engine separates the creation of the peptide index from the database search step, thereby saving time by allowing a peptide index to be reused in multiple searches. Here we describe an improved implementation of the indexing component of Tide that consumes around four times less resources (CPU and RAM) than the previous version and can generate arbitrarily large peptide databases, limited by only the amount of available disk space. We use this improved implementation to explore the relationship between database size and the parameters controlling digestion and PTMs, as well as database size and statistical power. Our results can help guide practitioners in proper selection of these important parameters.
    MeSH term(s) Algorithms ; Peptides/chemistry ; Proteins/metabolism ; Search Engine ; Databases, Protein ; Software
    Chemical Substances Peptides ; Proteins
    Language English
    Publishing date 2023-01-12
    Publishing country United States
    Document type Journal Article
    ZDB-ID 2078618-9
    ISSN 1535-3907 ; 1535-3893
    ISSN (online) 1535-3907
    ISSN 1535-3893
    DOI 10.1021/acs.jproteome.2c00617
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  10. Article ; Online: Ten simple rules for writing a response to reviewers.

    Noble, William Stafford

    PLoS computational biology

    2017  Volume 13, Issue 10, Page(s) e1005730

    Language English
    Publishing date 2017-10
    Publishing country United States
    Document type Editorial
    ZDB-ID 2193340-6
    ISSN 1553-7358 ; 1553-734X
    ISSN (online) 1553-7358
    ISSN 1553-734X
    DOI 10.1371/journal.pcbi.1005730
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