PTMProphet: Fast and Accurate Mass Modification Localization for the Trans-Proteomic Pipeline
Ontology highlight
ABSTRACT:
INSTRUMENT(S): TripleTOF 5600
ORGANISM(S): Homo Sapiens (ncbitaxon:9606)
SUBMITTER: 
Robert Moritz
PROVIDER: MSV000096828 | MassIVE | Fri Jan 10 15:58:00 GMT 2025
SECONDARY ACCESSION(S): PXD013210
REPOSITORIES: MassIVE
Publications
PTMProphet: Fast and Accurate Mass Modification Localization for the Trans-Proteomic Pipeline.
Shteynberg David D DD Deutsch Eric W EW Campbell David S DS Hoopmann Michael R MR Kusebauch Ulrike U Lee Dave D Mendoza Luis L Midha Mukul K MK Sun Zhi Z Whetton Anthony D AD Moritz Robert L RL
Journal of proteome research 20190722 12
Spectral matching sequence database search engines commonly used on mass spectrometry-based proteomics experiments excel at identifying peptide sequence ions, and in addition, possible sequence ions carrying post-translational modifications (PTMs), but most do not provide confidence metrics for the exact localization of those PTMs when several possible sites are available. Localization is absolutely required for downstream molecular cell biology analysis of PTM function in vitro and in vivo. The ...[more]
Similar Datasets
Project description:Analysis of synthetic peptide reference datasets to demonstrate the performance of PTMProphet, a free and open-source software tool integrated into the Trans-Proteomic Pipeline, which reanalyzes identified spectra from any search engine for which pepXML output is available to provide localization confidence to enable appropriate further characterization of biologic events.
2019-07-18 | PXD013210 | Pride
Project description:Spectral matching sequence database search engines commonly used on mass spectrometry-based proteomics experiments excel at identifying peptide sequence ions, and in addition, possible sequence ions carrying post-translational modifications (PTMs), but most do not provide confidence metrics for the exact localization of those PTMs when several possible sites are available. Localization is absolutely required for downstream molecular cell biology analysis of PTM function in vitro and in vivo. Therefore, we developed PTMProphet, a free and open-source software tool integrated into the Trans-Proteomic Pipeline, which reanalyzes identified spectra from any search engine for which pepXML output is available to provide localization confidence to enable appropriate further characterization of biologic events. Localization of any type of mass modification (e.g., phosphorylation) is supported. PTMProphet applies Bayesian mixture models to compute probabilities for each site/peptide spectrum match where a PTM has been identified. These probabilities can be combined to compute a global false localization rate at any threshold to guide downstream analysis. We describe the PTMProphet tool, its underlying algorithms, and demonstrate its performance on ground-truth synthetic peptide reference data sets, one previously published small data set, one new larger data set, and also on a previously published phosphoenriched data set where the correct sites of modification are unknown. Data have been deposited to ProteomeXchange with identifier PXD013210.
| S-EPMC6898736 | biostudies-literature
Project description:Nanopore signal analysis enables detection of nucleotide modifications from native DNA and RNA sequencing, providing both accurate genetic/transcriptomic and epigenetic information without additional library preparation. Presently, only a limited set of modifications can be directly basecalled (e.g. 5-methylcytosine), while most others require exploratory methods that often begin with alignment of nanopore signal to a nucleotide reference. We present Uncalled4, a toolkit for nanopore signal alignment, analysis, and visualization. Uncalled4 features an efficient banded signal alignment algorithm, BAM signal alignment file format, statistics for comparing signal alignment methods, and a reproducible de novo training method for k-mer-based pore models, revealing potential errors in ONT's state-of-the-art DNA model. We apply Uncalled4 to RNA 6-methyladenine (m6A) detection in seven human cell lines, identifying 26% more modifications than Nanopolish using m6Anet, including in several genes where m6A has known implications in cancer. Uncalled4 is available open-source at github.com/skovaka/uncalled4.
| S-EPMC10942365 | biostudies-literature
Project description:Infecting millions of people, the SARS-CoV-2 is evolving at an unprecedented rate, demanding advanced and specified analytic pipeline to capture the mutational spectra. In order to explore mutations and deletions in the spike (S) protein - the most-discussed protein of SARS-CoV-2 - we comprehensively analyzed 35,750 complete S protein-coding sequences through a custom Python-based pipeline. This GISAID-collected dataset of until 24 June 2020 covered six continents and five major climate zones. We identified 27,801 (77.77% sequences) mutated strains compared to reference Wuhan-Hu-1 wherein 84.40% of these strains mutated by only a single amino acid (aa). An outlier strain (EPI_ISL_463893) from Bosnia and Herzegovina possessed six aa substitutions. We also identified 11 residues with high aa mutation frequency, and each contains four types of aa variations. The infamous D614G variant has spread worldwide with ever-rising dominance and across regions with different climatic conditions alongside L5F and D936Y mutants, which have been documented throughout all regions and climate zones, respectively. We also found 988 unique aa substitutions spanned across 660 residues, which differed significantly among different continents (p = .003) and climatic zones (p = .021) as inferred with the Kruskal-Wallis test. Besides, 17 in-frame deletions at four sites adjacent to receptor-binding-domain were determined that may have a possible impact on attenuation. This study provides a fast and accurate pipeline for identifying mutations and deletions from the large dataset for coding and also non-coding sequences as evidenced by the representative analysis on existing S protein data. By using separate multi-sequence alignment, removing ambiguous sequences and in-frame stop codons, and utilizing pairwise alignment, this method can derive both synonymous and non-synonymous mutations (strain_ID reference aa:mutation position:strain aa). We suggest that the pipeline will aid in the evolutionary surveillance of any SARS-CoV-2 encoded proteins and will prove to be crucial in tracking the ever-increasing variation of many other divergent RNA viruses in the future. The code is available at https://github.com/SShaminur/Mutation-Analysis.
| S-EPMC7646266 | biostudies-literature
Project description:BackgroundBacteria carry a wide array of genes, some of which have multiple alleles. These different alleles are often responsible for distinct types of virulence and can determine the classification at the subspecies levels (e.g., housekeeping genes for Multi Locus Sequence Typing, MLST). Therefore, it is important to rapidly detect not only the gene of interest, but also the relevant allele. Current sequencing-based methods are limited to mapping reads to each of the known allele reference, which is a time-consuming procedure.ResultsTo address this limitation, we developed BacTag - a pipeline that rapidly and accurately detects which genes are present in a sequencing dataset and reports the allele of each of the identified genes. We exploit the fact that different alleles of the same gene have a high similarity. Instead of mapping the reads to each of the allele reference sequences, we preprocess the database prior to the analysis, which makes the subsequent gene and allele identification efficient. During the preprocessing, we determine a representative reference sequence for each gene and store the differences between all alleles and this chosen reference. Throughout the analysis we estimate whether the gene is present in the sequencing data by mapping the reads to this reference sequence; if the gene is found, we compare the variants to those in the preprocessed database. This allows to detect which specific allele is present in the sequencing data. Our pipeline was successfully tested on artificial WGS E. coli, S. pseudintermedius, P. gingivalis, M. bovis, Borrelia spp. and Streptomyces spp. data and real WGS E. coli and K. pneumoniae data in order to report alleles of MLST house-keeping genes.ConclusionsWe developed a new pipeline for fast and accurate gene and allele recognition based on database preprocessing and parallel computing and performed better or comparable to the current popular tools. We believe that our approach can be useful for a wide range of projects, including bacterial subspecies classification, clinical diagnostics of bacterial infections, and epidemiological studies.
| S-EPMC6501397 | biostudies-literature
Project description:BackgroundNext-generation sequencing (NGS) technologies have accelerated considerably the investigation into the composition of genomes and their functions. Genotyping-by-sequencing (GBS) is a genotyping approach that makes use of NGS to rapidly and economically scan a genome. It has been shown to allow the simultaneous discovery and genotyping of thousands to millions of SNPs across a wide range of species. For most users, the main challenge in GBS is the bioinformatics analysis of the large amount of sequence information derived from sequencing GBS libraries in view of calling alleles at SNP loci. Herein we describe a new GBS bioinformatics pipeline, Fast-GBS, designed to provide highly accurate genotyping, to require modest computing resources and to offer ease of use.ResultsFast-GBS is built upon standard bioinformatics language and file formats, is capable of handling data from different sequencing platforms, is capable of detecting different kinds of variants (SNPs, MNPs, and Indels). To illustrate its performance, we called variants in three collections of samples (soybean, barley, and potato) that cover a range of different genome sizes, levels of genome complexity, and ploidy. Within these small sets of samples, we called 35 k, 32 k and 38 k SNPs for soybean, barley and potato, respectively. To assess genotype accuracy, we compared these GBS-derived SNP genotypes with independent data sets obtained from whole-genome sequencing or SNP arrays. This analysis yielded estimated accuracies of 98.7, 95.2, and 94% for soybean, barley, and potato, respectively.ConclusionsWe conclude that Fast-GBS provides a highly efficient and reliable tool for calling SNPs from GBS data.
| S-EPMC5210301 | biostudies-literature
Project description:Accurate anatomical localization of intracranial electrodes is important for identifying the seizure foci in patients with epilepsy and for interpreting effects from cognitive studies employing intracranial electroencephalography. Localization is typically performed by coregistering postimplant computed tomography (CT) with preoperative magnetic resonance imaging (MRI). Electrodes are then detected in the CT, and the corresponding brain region is identified using the MRI. Many existing software packages for electrode localization chain together separate preexisting programs or rely on command line instructions to perform the various localization steps, making them difficult to install and operate for a typical user. Further, many packages provide solutions for some, but not all, of the steps needed for confident localization. We have developed software, Locate electrodes Graphical User Interface (LeGUI), that consists of a single interface to perform all steps needed to localize both surface and depth/penetrating intracranial electrodes, including coregistration of the CT to MRI, normalization of the MRI to the Montreal Neurological Institute template, automated electrode detection for multiple types of electrodes, electrode spacing correction and projection to the brain surface, electrode labeling, and anatomical targeting. The software is written in MATLAB, core image processing is performed using the Statistical Parametric Mapping toolbox, and standalone executable binaries are available for Windows, Mac, and Linux platforms. LeGUI was tested and validated on 51 datasets from two universities. The total user and computational time required to process a single dataset was approximately 1 h. Automatic electrode detection correctly identified 4362 of 4695 surface and depth electrodes with only 71 false positives. Anatomical targeting was verified by comparing electrode locations from LeGUI to locations that were assigned by an experienced neuroanatomist. LeGUI showed a 94% match with the 482 neuroanatomist-assigned locations. LeGUI combines all the features needed for fast and accurate anatomical localization of intracranial electrodes into a single interface, making it a valuable tool for intracranial electrophysiology research.
| S-EPMC8695687 | biostudies-literature
Project description:MotivationWith an increasing number of patient-derived xenograft (PDX) models being created and subsequently sequenced to study tumor heterogeneity and to guide therapy decisions, there is a similarly increasing need for methods to separate reads originating from the graft (human) tumor and reads originating from the host species' (mouse) surrounding tissue. Two kinds of methods are in use: On the one hand, alignment-based tools require that reads are mapped and aligned (by an external mapper/aligner) to the host and graft genomes separately first; the tool itself then processes the resulting alignments and quality metrics (typically BAM files) to assign each read or read pair. On the other hand, alignment-free tools work directly on the raw read data (typically FASTQ files). Recent studies compare different approaches and tools, with varying results.ResultsWe show that alignment-free methods for xenograft sorting are superior concerning CPU time usage and equivalent in accuracy. We improve upon the state of the art sorting by presenting a fast lightweight approach based on three-way bucketed quotiented Cuckoo hashing. Our hash table requires memory comparable to an FM index typically used for read alignment and less than other alignment-free approaches. It allows extremely fast lookups and uses less CPU time than other alignment-free methods and alignment-based methods at similar accuracy. Several engineering steps (e.g., shortcuts for unsuccessful lookups, software prefetching) improve the performance even further.AvailabilityOur software xengsort is available under the MIT license at http://gitlab.com/genomeinformatics/xengsort . It is written in numba-compiled Python and comes with sample Snakemake workflows for hash table construction and dataset processing.
| S-EPMC8017614 | biostudies-literature
Project description:To accelerate the isolation of plant protein complexes and study cellular localization and interaction of their components, an improved recombineering protocol is described for simple and fast site-directed modification of plant genes in bacterial artificial chromosomes (BACs). Coding sequences of fluorescent and affinity tags were inserted into genes and transferred together with flanking genomic sequences of desired size by recombination into Agrobacterium plant transformation vectors using three steps of E. coli transformation with PCR-amplified DNA fragments. Application of fast-track recombineering is illustrated by the simultaneous labelling of CYCLIN-DEPENDENT KINASE D (CDKD) and CYCLIN H (CYCH) subunits of kinase module of TFIIH general transcription factor and the CDKD-activating CDKF;1 kinase with green fluorescent protein (GFP) and mCherry (green and red fluorescent protein) tags, and a PIPL (His18 -StrepII-HA) epitope. Functionality of modified CDKF;1 gene constructs is verified by complementation of corresponding T-DNA insertion mutation. Interaction of CYCH with all three known CDKD homologues is confirmed by their co-localization and co-immunoprecipitation. Affinity purification and mass spectrometry analyses of CDKD;2, CYCH, and DNA-replication-coupled HISTONE H3.1 validate their association with conserved TFIIH subunits and components of CHROMATIN ASSEMBLY FACTOR 1, respectively. The results document that simple modification of plant gene products with suitable tags by fast-track recombineering is well suited to promote a wide range of protein interaction and proteomics studies.
| S-EPMC6852550 | biostudies-literature
Project description:The design of genetic markers is of particular relevance in crop breeding programs. Despite many economically important crops being polyploid organisms, the current primer design tools are tailored for diploid species. Bread wheat, for instance, is a hexaploid comprising of three related genomes and the performance of genetic markers is diminished if the primers are not genome specific. PolyMarker is a pipeline that generates SNP markers by selecting candidate primers for a specified genome using local alignments and standard primer design tools to test the viability of the primers. A command line tool and a web interface are available to the community.PolyMarker is available as a ruby BioGem: bio-polyploid-tools. Web interface: http://polymarker.tgac.ac.uk.
| S-EPMC4765872 | biostudies-literature