In proteomics, a crucial aspect is to identify peptide sequences. De novo sequencing methods have been widely employed to identify peptide sequences, and numerous tools have been proposed over the past two decades. Recently, deep learning approaches have been introduced for de novo sequencing. Previous methods focused on encoding tandem mass spectra and predicting peptide sequences from the first amino acid onwards. However, when predicting peptides using tandem mass spectra, the peptide sequence can be predicted not only from the first amino acid but also from the last amino acid due to the coexistence of b-ion (or a- or c-ion) and y-ion (or x- or z-ion) fragments in the tandem mass spectra. Therefore, it is essential to predict peptide sequences bidirectionally. Our approach, called NovoB, utilizes a Transformer model to predict peptide sequences bidirectionally, starting with both the first and last amino acids. In comparison to Casanovo, our method achieved an improvement of the average peptide-level accuracy rate of approximately 9.8% across all species.

BACKGROUND: High-throughput Proteomics has been accelerated by (tandem) mass spectrometry. However, the slow speed of mass spectra analysis prevents the analysis results from being up-to-date. Tandem mass spectrometry database search requires O(|S||D|) time where S is the set of spectra and D is the set of peptides in a database. With usual values of |S| and |D|, database search is quite time consuming. Meanwhile, the database for search is usually updated every month, with 0.5-2% changes. Although the change in the database is usually very small, it may cause extensive changes in the overall analysis results because individual PSM scores such as deltaCn and E-value depend on the entire search results. Therefore, to keep the search results up-to-date, one needs to perform database search from scratch every time the database is updated, which is very inefficient. RESULTS: Thus, we present a very efficient method to keep the search results up-to-date where the results are the same as those achieved by the normal search from scratch. This method, called progressive search, runs in O(|S||ΔD|) time on average where ΔD is the difference between the old and the new databases. The experimental results show that the progressive search is up to 53.9 times faster for PSM update only and up to 16.5 times faster for both PSM and E-value update. CONCLUSIONS: Progressive search is a novel approach to efficiently obtain analysis results for updated database in tandem mass spectrometry. Compared to performing a normal search from scratch, progressive search achieves the same results much faster. Progressive search is freely available at: https://isa.hanyang.ac.kr/ProgSearch.html .

BACKGROUND: False discovery rate (FDR) estimation is very important in proteomics. The target-decoy strategy (TDS), which is often used for FDR estimation, estimates the FDR under the assumption that when spectra are identified incorrectly, the probabilities of the spectra matching the target or decoy peptides are identical. However, no spectra matching target or decoy peptide probabilities are identical. We propose cTDS (target-decoy strategy with candidate peptides) for accurate estimation of the FDR using the probability that the spectrum is identified incorrectly as a target or decoy peptide. RESULTS: Most spectrum cases result in a probability of having the spectrum identified incorrectly as a target or decoy peptide of close to 0.5, but only about 1.14-4.85% of the total spectra have an exact probability of 0.5. We used an entrapment sequence method to demonstrate the accuracy of cTDS. For fixed FDR thresholds (1-10%), the false match rate (FMR) in cTDS is closer than the FMR in TDS. We compared the number of peptide-spectrum matches (PSMs) obtained with TDS and cTDS at a 1% FDR threshold with the HEK293 dataset. In the first and third replications, the number of PSMs obtained with cTDS for the reverse, pseudo-reverse, shuffle, and de Bruijn databases exceeded those obtained with TDS (about 0.001-0.132%), with the pseudo-shuffle database containing less compared to TDS (about 0.05-0.126%). In the second replication, the number of PSMs obtained with cTDS for all databases exceeds that obtained with TDS (about 0.013-0.274%). CONCLUSIONS: When spectra are actually identified incorrectly, most probabilities of the spectra matching a target or decoy peptide are not identical. Therefore, we propose cTDS, which estimates the FDR more accurately using the probability of the spectrum being identified incorrectly as a target or decoy peptide.

Matrix computation, in particular, matrix multiplication is time-consuming, but essentially and widely used in a large number of applications in science and industry. The existing distributed matrix multiplication methods only focus on either low communication cost (i.e., high performance) with the risk of out of memory or large-scale processing with high communication overhead. We propose a distributed elastic matrix multiplication method called CuboidMM that achieves both high performance and large-scale processing. We also propose a GPU acceleration method that can be combined with CuboidMM. CuboidMM partitions matrices into cuboids for optimizing the network communication cost with considering memory usage per task, and the GPU acceleration method partitions a cuboid into subcuboids for optimizing the PCI-E communication cost with considering GPU memory usage. We implement a fast and elastic matrix computation engine called DistME by integrating CuboidMM with GPU acceleration on top of Apache Spark. Through extensive experiments, we have demonstrated that CuboidMM and DistME significantly outperform the state-of-the-art methods and systems, respectively, in terms of both performance and data size.

BACKGROUND: One of the most important steps in peptide identification is to estimate the false discovery rate (FDR). The most commonly used method for estimating FDR is the target-decoy search strategy (TDS). While this method is simple and effective, it is time/space-inefficient because it searches a database that is twice as large as the original protein database. This inefficiency problem becomes more evident as protein databases get bigger and bigger. We propose a target-small decoy search strategy and present a rigorous verification that it reduces the database size and search time while retaining the accuracy of target-decoy search strategy (TDS). RESULTS: We show that peptide spectrum matches (PSMs) obtained at 1% FDR in TDS overlap ~ 99% with those in our method. (Considering that 1% FDR is used, 99% overlap means our method is very accurate.) Moreover, our method is more time/space-efficient than TDS. The search time of our method is reduced to only 1/4 of that of TDS when UniProt and its 1/8 decoy database are used. CONCLUSIONS: We demonstrate that our method is almost as accurate as TDS and more time/space-efficient than TDS. Since the efficiency of our method is more evident as the database size increases, our method is expected to be useful for identifying peptides in proteogenomics databases constructed from inflated databases using genomic data.

BACKGROUND: A cross-correlation (XCorr) score function is one of the most popular score functions utilized to search peptide identifications in databases, and many computer programs, such as SEQUEST, Comet, and Tide, currently use this score function. Recently, the HiXCorr algorithm was developed to speed up this score function for high-resolution spectra by improving the preprocessing step of the tandem mass spectra. However, despite the development of the HiXCorr algorithm, the score function is still slow because candidate peptides increase when post-translational modifications (PTMs) are considered in the search. RESULTS: We used a graphics processing unit (GPU) to develop the accelerating score function derived by combining Tide's XCorr score function and the HiXCorr algorithm. Our method is 2.7 and 5.8 times faster than the original Tide and Tide-Hi, respectively, for 50 Da precursor tolerance. Our GPU-based method produced identical scores as did the CPU-based Tide and Tide-Hi. CONCLUSION: We propose the accelerating score function to search modifications using a single GPU. The software is available at https://github.com/Tide-for-PTM-search/Tide-for-PTM-search .

Satellite image data generated from remote sensors around the world have different resolutions and are processed at varying levels from Level 0 to Level 3, with each level containing vast amounts of information. Due to the problem of data size, many ocean science researchers use L3 images, which have a spatial resolution of 4 km or 9 km. However, in order to overcome problems such as red tides or to analyze the marine ecosystem based on ocean color satellite research, researchers must generate data by changing the parameters of images at various levels. There is also a need for immediate access to satellite image information using analytical and visualization tools. Considering those requirements, TuPiX-OC (Turning PiXels into knowledge and science-OceanColor) provides an environment to design and execute algorithms for data processing and analysis of satellite image data by data type. TuPix-OC, which has a distributed architecture, is an analytical platform that supports data import, level conversion, DB integration, analysis and processing, and visualization. TuPiX-OC stores satellite data in a massive storage device, and provides an online platform for satellite data conversion/analysis/ visualization. For satellite data processing, TuPiX-OC converts NASA-provided binary files into files that can be analyzed by users. Moreover, TuPiX-OC includes various algorithms for satellite data selection and utilization of satellite images. Preliminary Experiments of TuPiX-OC's satellite image data processing capability showed that it was able to process 35 times as many images as the open source software SeaDAS.

Summary: In many proteogenomic applications, mapping peptide sequences onto genome sequences can be very useful, because it allows us to understand origins of the gene products. Existing software tools either take the genomic position of a peptide start site as an input or assume that the peptide sequence exactly matches the coding sequence of a given gene model. In case of novel peptides resulting from genomic variations, especially structural variations such as alternative splicing, these existing tools cannot be directly applied unless users supply information about the variant, either its genomic position or its transcription model. Mapping potentially novel peptides to genome sequences, while allowing certain genomic variations, requires introducing novel gene models when aligning peptide sequences to gene structures. We have developed a new tool called ACTG (Amino aCids To Genome), which maps peptides to genome, assuming all possible single exon skipping, junction variation allowing three edit distances from the original splice sites, exon extension and frame shift. In addition, it can also consider SNVs (single nucleotide variations) during mapping phase if a user provides the VCF (variant call format) file as an input. Availability and Implementation: Available at http://prix.hanyang.ac.kr/ACTG/search.jsp . Contact: [email protected]. Supplementary information: Supplementary data are available at Bioinformatics online.

UNLABELLED: Peptide identification is an important problem in proteomics. One of the most popular scoring schemes for peptide identification is XCorr (cross-correlation). Since calculating XCorr is computationally intensive, a lot of efforts have been made to develop fast XCorr engines. However, the existing XCorr engines are not suitable for high-resolution MS/MS spectrometry because they are either slow or require a specific type of CPU. We present a portable high-speed XCorr engine for high-resolution tandem mass spectrometry by developing a novel algorithm for calculating XCorr. The algorithm enables XCorr calculation 1.25-49 times faster than previous algorithms for 0.01 Da fragment tolerance. Furthermore, our engine is easily portable to any machine with different types of CPU because it is developed in C language. Hence, our XCorr engine will expedite peptide identification by high-resolution tandem mass spectrometry. AVAILABILITY AND IMPLEMENTATION: Available at http://isa.hanyang.ac.kr/HiXCorr/HiXCorr.html.