Deep learning neural network tools for proteomics

Meyer, Jesse G.

doi:10.1016/j.crmeth.2021.100003

Cited by 68 publications

(50 citation statements)

References 82 publications

(124 reference statements)

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“…We imagine that students and researchers with novel algorithmic ideas can use this paradigm to add their functionality in a transparent and efficient manner, without having to re-create the entire pipeline. This could especially enable increasingly powerful machine learning and deep learning technologies to be integrated into computational proteomics (Torun et al 2021;Wen et al 2020;Meyer 2021).…”

Section: Discussionmentioning

confidence: 99%

AlphaPept, a modern and open framework for MS-based proteomics

Strauss

Bludau

Zeng

et al. 2021

Preprint

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In common with other omics technologies, mass spectrometry (MS)-based proteomics produces ever-increasing amounts of raw data, making their efficient analysis a principal challenge. There is a plethora of different computational tools that process the raw MS data and derive peptide and protein identification and quantification. During the last decade, there has been dramatic progress in computer science and software engineering, including collaboration tools that have transformed research and industry. To leverage these advances, we developed AlphaPept, a Python-based open-source framework for efficient processing of large high-resolution MS data sets. Using Numba for just-in-time machine code compilation on CPU and GPU, we achieve hundred-fold speed improvements while maintaining clear syntax and rapid development speed. AlphaPept uses the Python scientific stack of highly optimized packages, reducing the code base to domain-specific tasks while providing access to the latest advances in machine learning. We provide an easy on-ramp for community validation and contributions through the concept of literate programming, implemented in Jupyter Notebooks of the different modules. A framework for continuous integration, testing, and benchmarking enforces solid software engineering principles. Large datasets can rapidly be processed as shown by the analysis of hundreds of cellular proteomes in minutes per file, many-fold faster than the data acquisiton. The AlphaPept framework can be used to build automated processing pipelines using efficient HDF5 based file formats, web-serving functionality and compatibility with downstream analysis tools. Easy access for end-users is provided by one-click installation of the graphical user interface, for advanced users via a modular Python library, and for developers via a fully open GitHub repository.

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Section: Discussionmentioning

confidence: 99%

AlphaPept, a modern and open framework for MS-based proteomics

Strauss

Bludau

Zeng

et al. 2021

Preprint

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“…The output value is compared to the known real value of y; the weights are slightly modified to get the output value closer to the known actual value of y. A straightforward illustration of this is presented in (B), where weight1 equals 2, function equals 2Xx, and weight2 equals 2 [ 74 ].…”

Section: Overview Of Deep Learning (Dl) Modelsmentioning

confidence: 99%

Kidney Tumor Semantic Segmentation Using Deep Learning: A Survey of State-of-the-Art

Abdelrahman

Viriri

2022

J. Imaging

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Cure rates for kidney cancer vary according to stage and grade; hence, accurate diagnostic procedures for early detection and diagnosis are crucial. Some difficulties with manual segmentation have necessitated the use of deep learning models to assist clinicians in effectively recognizing and segmenting tumors. Deep learning (DL), particularly convolutional neural networks, has produced outstanding success in classifying and segmenting images. Simultaneously, researchers in the field of medical image segmentation employ DL approaches to solve problems such as tumor segmentation, cell segmentation, and organ segmentation. Segmentation of tumors semantically is critical in radiation and therapeutic practice. This article discusses current advances in kidney tumor segmentation systems based on DL. We discuss the various types of medical images and segmentation techniques and the assessment criteria for segmentation outcomes in kidney tumor segmentation, highlighting their building blocks and various strategies.

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“…Several methodologies exist for identifying peptides from DIA MS data, including EncyclopeDIA, 8 PECAN, 9 Spectronaut, 10 DIA-Umpire, 11 DIA-NN, 12 Thesaurus, 13 Open-SWATH, 14 Skyline, 15 mProphet, 16 LFQbench, 17 and PIQED. 18 Recent advances in machine learning 19 have opened up the possibility of de novo sequencing 20 or matching to predicted MS/MS spectra, such as Prosit, 21 DeepMass, 22 and DeepDIA; 23 however, many DIA data analysis methods require scoring the presence of peptides by comparing to spectra previously identified by DDA. Because nearly all proteomics DIA relies on LC, this is often achieved by assigning possible peptides a score based on the co-elution of peptide fragment ion signals over time.…”

Section: ■ Introductionmentioning

confidence: 99%

CsoDIAq Software for Direct Infusion Shotgun Proteome Analysis

Cranney

Meyer

2021

Anal. Chem.

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Direct infusion shotgun proteome analysis (DISPA) is a new paradigm for expedited mass spectrometry-based proteomics, but the original data analysis workflow was onerous. Here, we introduce CsoDIAq, a userfriendly software package for the identification and quantification of peptides and proteins from DISPA data. In addition to establishing a complete and automated analysis workflow with a graphical user interface, CsoDIAq introduces algorithmic concepts to spectrum-spectrum matching to improve peptide identification speed and sensitivity. These include spectra pooling to reduce search time complexity and a new spectrum−spectrum match score called match count and cosine, which improves target discrimination in a target-decoy analysis. Fragment mass tolerance correction also increased the number of peptide identifications. Finally, we adapt CsoDIAq to standard LC−MS DIA and show that it outperforms other spectrum−spectrum matching software.

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Deep learning neural network tools for proteomics

Cited by 68 publications

References 82 publications

AlphaPept, a modern and open framework for MS-based proteomics

AlphaPept, a modern and open framework for MS-based proteomics

Kidney Tumor Semantic Segmentation Using Deep Learning: A Survey of State-of-the-Art

CsoDIAq Software for Direct Infusion Shotgun Proteome Analysis

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