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1. SpeCollate: Deep cross-modal similarity network for mass spectrometry data based peptide deductions

   https://doi.org/10.1371/journal.pone.0259349  

   Tariq, Muhammad Usman ; Saeed, Fahad ( October 2021 , PLOS ONE)

   Lisacek, Frederique (Ed.)

   Historically, the database search algorithms have been the de facto standard for inferring peptides from mass spectrometry (MS) data. Database search algorithms deduce peptides by transforming theoretical peptides into theoretical spectra and matching them to the experimental spectra. Heuristic similarity-scoring functions are used to match an experimental spectrum to a theoretical spectrum. However, the heuristic nature of the scoring functions and the simple transformation of the peptides into theoretical spectra, along with noisy mass spectra for the less abundant peptides, can introduce a cascade of inaccuracies. In this paper, we design and implement a Deep Cross-Modal Similarity Network called SpeCollate , which overcomes these inaccuracies by learning the similarity function between experimental spectra and peptides directly from the labeled MS data. SpeCollate transforms spectra and peptides into a shared Euclidean subspace by learning fixed size embeddings for both. Our proposed deep-learning network trains on sextuplets of positive and negative examples coupled with our custom-designed SNAP-loss function. Online hardest negative mining is used to select the appropriate negative examples for optimal training performance. We use 4.8 million sextuplets obtained from the NIST and MassIVE peptide libraries to train the network and demonstrate that for closed search, SpeCollate is able to perform better than Crux and MSFragger in terms of the number of peptide-spectrum matches (PSMs) and unique peptides identified under 1% FDR for real-world data. SpeCollate also identifies a large number of peptides not reported by either Crux or MSFragger. To the best of our knowledge, our proposed SpeCollate is the first deep-learning network that can determine the cross-modal similarity between peptides and mass-spectra for MS-based proteomics. We believe SpeCollate is significant progress towards developing machine-learning solutions for MS-based omics data analysis. SpeCollate is available at https://deepspecs.github.io/ . 

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2. LBE: A Computational Load Balancing Algorithm for Speeding up Parallel Peptide Search in Mass-Spectrometry Based Proteomics

   https://doi.org/10.1109/IPDPSW.2019.00040  

   Haseeb, Muhammad ; Afzali, Fatima ; Saeed, Fahad ( May 2019 , Proceedings of 2019 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW))

   The most commonly employed method for peptide identification in mass-spectrometry based proteomics involves comparing experimentally obtained tandem MS/MS spectra against a set of theoretical MS/MS spectra. The theoretical MS/MS spectra data are predicted using protein sequence database. Most state-of-the-art peptide search algorithms index theoretical spectra data to quickly filter-in the relevant (similar) indexed spectra when searching an experimental MS/MS spectrum. Data filtration substantially reduces the required number of computationally expensive spectrum-to-spectrum comparison operations. However, the number of predicted (and indexed) theoretical spectra grows exponentially with increase in post-translational modifications creating a memory and I/O bottleneck. In this paper, we present a parallel algorithm, called LBE, for efficient partitioning of theoretical spectra data on a distributed-memory architecture. Our proposed algorithm first groups the similar theoretical spectra. The groups are then finely split across the system allowing machines to perform almost equal amount of work when querying a MS/MS spectrum. Our results show that the compute load imbalance using LBE based data distribution is ≤ 20% allowing speedups of order of magnitudes over existing methods. The proposed algorithm has been implemented on a compute cluster using MPI library. Experimental results for increasing index sizes are reported in terms of execution time, speedups and memory footprint. To the best of our knowledge, LBE is the first load-balancing technique for MS/MS proteomics data on memory-distributed clusters that incorporates proteomics domain knowledge for efficient load-balancing. Source code is made available at: https://github.com/pcdslab/lbdslim/tree/mpi. 

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3. Accelerating open modification spectral library searching on tensor core in high-dimensional space

   https://doi.org/10.1093/bioinformatics/btad404  

   Kang, Jaeyoung ; Xu, Weihong ; Bittremieux, Wout ; Moshiri, Niema ; Rosing, Tajana ; Kelso, ed., Janet ( June 2023 , Bioinformatics)

   Driven by technological advances, the throughput and cost of mass spectrometry (MS) proteomics experiments have improved by orders of magnitude in recent decades. Spectral library searching is a common approach to annotating experimental mass spectra by matching them against large libraries of reference spectra corresponding to known peptides. An important disadvantage, however, is that only peptides included in the spectral library can be found, whereas novel peptides, such as those with unexpected post-translational modifications (PTMs), will remain unknown. Open modification searching (OMS) is an increasingly popular approach to annotate modified peptides based on partial matches against their unmodified counterparts. Unfortunately, this leads to very large search spaces and excessive runtimes, which is especially problematic considering the continuously increasing sizes of MS proteomics datasets.

   We propose an OMS algorithm, called HOMS-TC, that fully exploits parallelism in the entire pipeline of spectral library searching. We designed a new highly parallel encoding method based on the principle of hyperdimensional computing to encode mass spectral data to hypervectors while minimizing information loss. This process can be easily parallelized since each dimension is calculated independently. HOMS-TC processes two stages of existing cascade search in parallel and selects the most similar spectra while considering PTMs. We accelerate HOMS-TC on NVIDIA’s tensor core units, which is emerging and readily available in the recent graphics processing unit (GPU). Our evaluation shows that HOMS-TC is 31× faster on average than alternative search engines and provides comparable accuracy to competing search tools.

   HOMS-TC is freely available under the Apache 2.0 license as an open-source software project at https://github.com/tycheyoung/homs-tc.

    

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4. GPU-acceleration of the distributed-memory database peptide search of mass spectrometry data

   https://doi.org/10.1038/s41598-023-43033-w  

   Haseeb, Muhammad ; Saeed, Fahad ( October 2023 , Scientific Reports)

   Database peptide search is the primary computational technique for identifying peptides from the mass spectrometry (MS) data. Graphical Processing Units (GPU) computing is now ubiquitous in the current-generation of high-performance computing (HPC) systems, yet its application in the database peptide search domain remains limited. Part of the reason is the use of sub-optimal algorithms in the existing GPU-accelerated methods resulting in significantly inefficient hardware utilization. In this paper, we design and implement a new-age CPU-GPU HPC framework, calledGiCOPS, for efficient and complete GPU-acceleration of the modern database peptide search algorithms on supercomputers. Our experimentation shows that the GiCOPS exhibits between 1.2 to 5$$\times$$$\times$speed improvement over its CPU-only predecessor, HiCOPS, and over 10$$\times$$$\times$improvement over several existing GPU-based database search algorithms for sufficiently large experiment sizes. We further assess and optimize the performance of our framework using the Roofline Model and report near-optimal results for several metrics including computations per second, occupancy rate, memory workload, branch efficiency and shared memory performance. Finally, the CPU-GPU methods and optimizations proposed in our work for complex integer- and memory-bounded algorithmic pipelines can also be extended to accelerate the existing and future peptide identification algorithms. GiCOPS is now integrated with our umbrella HPC framework HiCOPS and is available at:https://github.com/pcdslab/gicops.

    

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5. Integrated Transcriptomic and Proteomic Analysis of Primary Human Umbilical Vein Endothelial Cells

   https://doi.org/10.1002/pmic.201800315  

   Madugundu, Anil K. ; Na, Chan Hyun ; Nirujogi, Raja Sekhar ; Renuse, Santosh ; Kim, Kwang Pyo ; Burns, Kathleen H. ; Wilks, Christopher ; Langmead, Ben ; Ellis, Shannon E. ; Collado‐Torres, Leonardo ; et al ( June 2019 , PROTEOMICS)

   Understanding the molecular profile of every human cell type is essential for understanding its role in normal physiology and disease. Technological advancements in DNA sequencing, mass spectrometry, and computational methods allow us to carry out multiomics analyses although such approaches are not routine yet. Human umbilical vein endothelial cells (HUVECs) are a widely used model system to study pathological and physiological processes associated with the cardiovascular system. In this study, next‐generation sequencing and high‐resolution mass spectrometry to profile the transcriptome and proteome of primary HUVECs is employed. Analysis of 145 million paired‐end reads from next‐generation sequencing confirmed expression of 12 186 protein‐coding genes (FPKM ≥0.1), 439 novel long non‐coding RNAs, and revealed 6089 novel isoforms that were not annotated in GENCODE. Proteomics analysis identifies 6477 proteins including confirmation ofN‐termini for 1091 proteins, isoforms for 149 proteins, and 1034 phosphosites. A database search to specifically identify other post‐translational modifications provide evidence for a number of modification sites on 117 proteins which include ubiquitylation, lysine acetylation, and mono‐, di‐ and tri‐methylation events. Evidence for 11 “missing proteins,” which are proteins for which there was insufficient or no protein level evidence, is provided. Peptides supporting missing protein and novel events are validated by comparison of MS/MS fragmentation patterns with synthetic peptides. Finally, 245 variant peptides derived from 207 expressed proteins in addition to alternate translational start sites for seven proteins and evidence for novel proteoforms for five proteins resulting from alternative splicing are identified. Overall, it is believed that the integrated approach employed in this study is widely applicable to study any primary cell type for deeper molecular characterization.

    

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