All codes are provided under GNU General Public License (GPL) or as a web-service, which guarantees your freedom to use the software for academic purposes. For more information, help or comments please contact Dr. Saeed at fsaeed@fiu.edu

HiCOPS
Database peptide search algorithms deduce peptides from mass spectrometry data. There has been substantial effort in improving their computational efficiency to achieve larger and more complex systems biology studies. However, modern serial and high-performance computing (HPC) algorithms exhibit suboptimal performance mainly due to their ineffective parallel designs (low resource utilization) and high overhead costs. We present an HPC framework, called HiCOPS, for efficient acceleration of the database peptide search algorithms on distributed-memory supercomputers. HiCOPS provides, on average, more than tenfold improvement in speed and superior parallel performance over several existing HPC database search software. We also formulate a mathematical model for performance analysis and optimization, and report near-optimal results for several key metrics including strong-scale efficiency, hardware utilization, load-balance, inter-process communication and I/O overheads. The core parallel design, techniques and optimizations presented in HiCOPS are search-algorithm-independent and can be extended to efficiently accelerate the existing and future algorithms and software. Our HPC framework can be downloaded from the following link: https://hicops.github.io/

If you use this HPC framework please cite: Haseeb, Muhammad, and Fahad Saeed. “High performance computing framework for tera-scale database search of mass spectrometry data.” Nature Computational Science 1, no. 8 (2021): 550-561. (https://www.nature.com/articles/s43588-021-00113-z)

SpeCollate
We designed and developed 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. Our machine-learnign model can be downloaded from the following link: https://deepspecs.github.io/

If you use this machine-learning model please cite: Tariq, Muhammad Usman, and Fahad Saeed. “SpeCollate: Deep cross-modal similarity network for mass spectrometry data based peptide deductions.” PloS one 16, no. 10 (2021): e0259349. (https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0259349)

ASD-DiagNet
We propose a framework called ASD-DiagNet for classifying subjects with Austism Spectrum Disorder (ASD) from healthy subjects by using only fMRI data. We designed and implemented a joint learning procedure using an autoencoder and a single layer perceptron which results in improved quality of extracted features and optimized parameters for the model. Further, we designed and implemented a data augmentation strategy, based on linear interpolation on available feature vectors, that allows us to produce synthetic datasets needed for training of machine learning models. The proposed approach is evaluated on a public dataset provided by Autism Brain Imaging Data Exchange including 1035 subjects coming from 17 different brain imaging centers. Our machine learning model outperforms other state of the art methods from 13 imaging centers with increase in classification accuracy up to 20% with maximum accuracy of 80%. The machine learning technique presented in this paper, in addition to yielding better quality, gives enormous advantages in terms of execution time (40 minutes vs. 6 hours on other methods). Our machine-learnign model can be downloaded from the following link: https://github.com/pcdslab/ASD-DiagNet

If you use this machine-learning model please cite: Taban Eslami, Vahid Mirjalili, Alvis Fong, Angela Laird, and Fahad Saeed, “ASD-DiagNet: A hybrid learning approach for detection of Autism Spectrum Disorder using fMRI data.” Frontiers in Neuroinformatics 13 (2019): 70. (https://www.frontiersin.org/articles/10.3389/fninf.2019.00070/full)

MaSS-Simulator
MaSS-Simulator is capable of simulating MS/MS spectra for LC-MS/MS based proteomics experiments. Our recently introduced MaSS-Simulator is capable of simulating highly accurate MS/MS spectra for LC-MS/MS based proteomics experiments. It provides great degree of control over the simulation by providing multiple configurable parameters. MaSS-Simulator offers a platform to assess and mark the limitations of MS-proteomics algorithms by testing them against a curated set of data and whole range of parameters. A complete evaluation report of an algorithm using all possible parametric verifications will provide a much deeper insight to the performance of of a given algorithm. Such evaluation will solve the reproducibility issues that are frequently faced in proteomics algorithm development. Testing of tools using such curated data set for which parameters (e.g. peptide coverage, S/N ratio etc.) can be carefully explored will rigorously evaluate the proteomics algorithms. In general, one can vary (to a practical degree) the size of the peptide, the coverage, the S/N ratios of the spectra and addition of PTM’s in the simulated spectra created for benchmarking. Such benchmarks when available for algorithms will inform proteomics practitioners which tool will work specifically for their data. Further, such reproducible evaluation of proteomics algorithms will enable method developers to endorse algorithms with confidence and reliability. Such an approach will also be helpful for the users, who can evaluate their dataset and cross-reference its properties with the algorithm’s evaluation report to conclude if the given algorithm will serve their purpose or if it will be able to achieve the required level of performance.

If you use the simulator please cite: Muaaz Gul Awan, and Fahad Saeed*, “MaSS‐Simulator: A Highly Configurable Simulator for Generating MS/MS Datasets for Benchmarking of Proteomics Algorithms.” Proteomics (2018): 1800206 (https://doi.org/10.1002/pmic.201800206)

The tool can be downloaded from the following link as open-source GPL code: https://github.com/pcdslab/MaSS-Simulator

Simulator is also available on CodeOcean as a executable code at:  https://codeocean.com/capsule/7820894

How to use Mass-Simulator: Tutorial (Pdf 1.4MB) 

J-Eros
J-Eros is a machine learning algorithm for computing similarity between two multivariate time series along with k-Nearest-Neighbor classifier, to classify healthy vs ADHD children using just fMRI data without using any other data or demographics. We applied this technique to the public data provided by ADHD-200 Consortium competition and our results show that J-Eros is capable of discriminating healthy from ADHD children such that we outperformed other state of the art techniques. This machine learning algorithm is a major step towards diagnosing ADHD using quantitative methods and will be an essential part for diagnosing mental illnesses.

The tool can be downloaded from the following link: https://github.com/pcdslab/J-Eros

If you use the tool please cite: Taban Eslami, and Fahad Saeed^*, “Similarity based classification of ADHD using Singular Value Decomposition“, Proceedings of ACM Conference on Computing Frontiers (ACM-CF), Ischia, Italy, May 2018 Tech Report | Presentation (YouTube)

J-Eros is also available on CodeOcean as a executable code at: https://codeocean.com/capsule/1256395

phyNGSC
phyNGSC is a hybrid strategy between MPI and OpenMP to accelerate the compression of big FASTQ datasets by combining the best features of distributed and shared memory architectures to balance the load of work among processes, alleviate memory latency by exploiting locality and accelerate I/O by reducing excessive read/write operations and inter-node message exchange. The algorithm introduces a novel timestamp-based approach which allows concurrent writing of compressed data in a non-deterministic order and thereby allows us to exploit a high amount of parallelism. As a proof-of-concept, we implemented some methods developed for DSRC v1 to underline the compression portion of our hybrid parallel strategy, since it exhibits superior performance for sequential solutions. The parallel algorithm is developed using C/C++, MPI and OPENMP constructs

The tool can be downloaded from the following link: https://github.com/pcdslab/PHYNGSC

If you use the tool please cite: Sandino Vargas-Pérez and Fahad Saeed*, “A Hybrid MPI-OpenMP Strategy to Speedup the Compression of Big Next-Generation Sequencing Datasets“, IEEE Transactions on Parallel and Distributed Systems, March 2017 Tech Report | IEEE Xplore

Sandino N. V. Perez and Fahad Saeed^*,  “A Parallel Algorithm for Compression of Big Next Generation Sequencing Datasets”, IEEE International Workshop on Parallelism in Bioinformatics (PBio), Proceedings of Parallel and Distributed Processing with Applications (IEEE ISPA-15), Helsinki Finland, August 2015 Tech Report

GPU-ArraySort

GPU-ArraySort is a highly scalable parallel algorithm for sorting large number of arrays using a GPU. Existing techniques focus on sorting a single large array and cannot be used for sorting large number of smaller arrays in an efficient manner. Such small number of large arrays are common in many big data applications in fields such as proteomics, genomics, connectomics, and astronomy. Our algorithm performs in-place operations and makes minimum use of any temporary run-time memory. Our results indicate that we can sort up to 2 million arrays having 1000 elements each, within few seconds. We compare our results with the unorthodox tagged array sorting technique based on NVIDIA’s Thrust library. GPU-ArraySort out-performs the tagged array sorting technique by sorting three times more data in a much smaller time. 

The tool can be downloaded from the following link: https://github.com/PCDS/GPU-ArraySort-2.0

If you use the tool please cite: Muaaz Gul Awan and Fahad Saeed^*, “GPU-ArraySort: A parallel, in-place algorithm for sorting large number of arrays“, Proceedings of Workshop on High Performance Computing for Big Data, International Conference on Parallel Processing (ICPP-2016), Philadelphia PA, August 2016 Tech Report | IEEE Xplore

MS-Reduce

MS-Reduce is a linear-time tool that allows massive reduction in amount of mass spectrometry data without significantly reducing the quality of the peptide deduction. Our novel data-reductive strategy for analysis of Big MS data is called MS-REDUCE and is capable of eliminating noisy peaks as well as peaks that do not contribute to peptide deduction before any peptide deduction is attempted. Our experiments have shown up to 100x speed up over existing state of the art noise elimination algorithms while maintaining comparable high quality matches. Using our approach we were able to process a million spectra in just under an hour on a moderate server which will be especially useful for processing in high-throughput environments. The algorithms has been implemented in Java and code/associated data sets.

The tool can be downloaded from the following link: https://github.com/pcdslab/MSREDUCE

If you use the tool please cite: Muaaz Awan and Fahad Saeed^*, “MS-REDUCE: An ultrafast technique for reduction of Big Mass Spectrometry Data for high-throughput processing“, Oxford Bioinformatics, Jan 2016 Tech Report | PubMed | Oxford

Muaaz Awan and Fahad Saeed^*, “On the sampling of Big Mass Spectrometry Data“, Proceedings of Bioinformatics and Computational Biology (BICoB) Conference, Honolulu Hawaii, March 2015 Tech Report