Browse
Submit Data
Databases
API
Help

Dataset Information

18 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Personalized Proteome: Comparing Proteogenomics and Open Variant Search Approaches for Single Amino Acid Variant Detection.

ABSTRACT: Discovery of variant peptides such as a single amino acid variant (SAAV) in shotgun proteomics data is essential for personalized proteomics. Both the resolution of shotgun proteomics methods and the search engines have improved dramatically, allowing for confident identification of SAAV peptides. However, it is not yet known if these methods are truly successful in accurately identifying SAAV peptides without prior genomic information in the search database. We studied this in unprecedented detail by exploiting publicly available long-read RNA sequences and shotgun proteomics data from the gold standard reference cell line NA12878. Searching spectra from this cell line with the state-of-the-art open modification search engine ionbot against carefully curated search databases resulted in 96.7% false-positive SAAVs and an 85% lower true positive rate than searching with peptide search databases that incorporate prior genetic information. While adding genetic variants to the search database remains indispensable for correct peptide identification, inclusion of long-read RNA sequences in the search database contributes only 0.3% new peptide identifications. These findings reveal the differences in SAAV detection that result from various approaches, providing guidance to researchers studying SAAV peptides and developers of peptide spectrum identification tools.

SUBMITTER: Salz R

PROVIDER: S-EPMC8280751 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Similar Datasets

Hypertension pharmacogenomics: in search of personalized treatment approaches.

Project description:Cardiovascular and renal diseases are associated with many risk factors, of which hypertension is one of the most prevalent. Worldwide, blood pressure control is only achieved in ?50% of those treated for hypertension, despite the availability of a considerable number of antihypertensive drugs from different pharmacological classes. Although many reasons exist for poor blood pressure control, a likely contributor is the inability to predict to which antihypertensive drug an individual is most likely to respond. Hypertension pharmacogenomics and other 'omics' technologies have the potential to identify genetic signals that are predictive of response or adverse outcome to particular drugs, and guide selection of hypertension treatment for a given individual. Continued research in this field will enhance our understanding of how to maximally deploy the various antihypertensive drug classes to optimize blood pressure response at the individual level. This Review summarizes the available literature on the most convincing genetic signals associated with antihypertensive drug responses and adverse cardiovascular outcomes. Future research in this area will be facilitated by enhancing collaboration between research groups through consortia such as the International Consortium for Antihypertensives Pharmacogenomics Studies, with the goal of translating replicated findings into clinical implementation.

| S-EPMC4778736 | biostudies-literature

OryzaPG-DB: rice proteome database based on shotgun proteogenomics.

Project description:BackgroundProteogenomics aims to utilize experimental proteome information for refinement of genome annotation. Since mass spectrometry-based shotgun proteomics approaches provide large-scale peptide sequencing data with high throughput, a data repository for shotgun proteogenomics would represent a valuable source of gene expression evidence at the translational level for genome re-annotation.DescriptionHere, we present OryzaPG-DB, a rice proteome database based on shotgun proteogenomics, which incorporates the genomic features of experimental shotgun proteomics data. This version of the database was created from the results of 27 nanoLC-MS/MS runs on a hybrid ion trap-orbitrap mass spectrometer, which offers high accuracy for analyzing tryptic digests from undifferentiated cultured rice cells. Peptides were identified by searching the product ion spectra against the protein, cDNA, transcript and genome databases from Michigan State University, and were mapped to the rice genome. Approximately 3200 genes were covered by these peptides and 40 of them contained novel genomic features. Users can search, download or navigate the database per chromosome, gene, protein, cDNA or transcript and download the updated annotations in standard GFF3 format, with visualization in PNG format. In addition, the database scheme of OryzaPG was designed to be generic and can be reused to host similar proteogenomic information for other species. OryzaPG is the first proteogenomics-based database of the rice proteome, providing peptide-based expression profiles, together with the corresponding genomic origin, including the annotation of novelty for each peptide.ConclusionsThe OryzaPG database was constructed and is freely available at http://oryzapg.iab.keio.ac.jp/.

| S-EPMC3094275 | biostudies-literature

PerSVade: personalized structural variant detection in any species of interest.

Project description:Structural variants (SVs) underlie genomic variation but are often overlooked due to difficult detection from short reads. Most algorithms have been tested on humans, and it remains unclear how applicable they are in other organisms. To solve this, we develop perSVade (personalized structural variation detection), a sample-tailored pipeline that provides optimally called SVs and their inferred accuracy, as well as small and copy number variants. PerSVade increases SV calling accuracy on a benchmark of six eukaryotes. We find no universal set of optimal parameters, underscoring the need for sample-specific parameter optimization. PerSVade will facilitate SV detection and study across diverse organisms.

| S-EPMC9380391 | biostudies-literature

Proteogenomics of Pristionchus pacificus reveals distinct proteome structure of nematode models.

Project description:Pristionchus pacificus is a nematode model organism whose genome has recently been sequenced. To refine the genome annotation we performed transcriptome and proteome analysis and gathered comprehensive experimental information on gene expression. Transcriptome analysis on a 454 Life Sciences (Roche) FLX platform generated >700,000 expressed sequence tags (ESTs) from two normalized EST libraries, whereas proteome analysis on an LTQ-Orbitrap mass spectrometer detected >27,000 nonredundant peptide sequences from more than 4000 proteins at sub-parts-per-million (ppm) mass accuracy and a false discovery rate of <1%. Retraining of the SNAP gene prediction algorithm using the gene expression data led to a decrease in the number of previously predicted protein-coding genes from 29,000 to 24,000 and refinement of numerous gene models. The P. pacificus proteome contains a high proportion of small proteins with no known homologs in other species ("pioneer" proteins). Some of these proteins appear to be products of highly homologous genes, pointing to their common origin. We show that >50% of all pioneer genes are transcribed under standard culture conditions and that pioneer proteins significantly contribute to a unimodal distribution of predicted protein sizes in P. pacificus, which has an unusually low median size of 240 amino acids (26.8 kDa). In contrast, the predicted proteome of Caenorhabditis elegans follows a distinct bimodal protein size distribution, with significant functional differences between small and large protein populations. Combined, these results provide the first catalog of the expressed genome of P. pacificus, refinement of its genome annotation, and the first comparison of related nematode models at the proteome level.

| S-EPMC2877580 | biostudies-literature

Artificial intelligence and machine learning approaches using gene expression and variant data for personalized medicine.

Project description:Precision medicine uses genetic, environmental and lifestyle factors to more accurately diagnose and treat disease in specific groups of patients, and it is considered one of the most promising medical efforts of our time. The use of genetics is arguably the most data-rich and complex components of precision medicine. The grand challenge today is the successful assimilation of genetics into precision medicine that translates across different ancestries, diverse diseases and other distinct populations, which will require clever use of artificial intelligence (AI) and machine learning (ML) methods. Our goal here was to review and compare scientific objectives, methodologies, datasets, data sources, ethics and gaps of AI/ML approaches used in genomics and precision medicine. We selected high-quality literature published within the last 5 years that were indexed and available through PubMed Central. Our scope was narrowed to articles that reported application of AI/ML algorithms for statistical and predictive analyses using whole genome and/or whole exome sequencing for gene variants, and RNA-seq and microarrays for gene expression. We did not limit our search to specific diseases or data sources. Based on the scope of our review and comparative analysis criteria, we identified 32 different AI/ML approaches applied in variable genomics studies and report widely adapted AI/ML algorithms for predictive diagnostics across several diseases.

| S-EPMC10233311 | biostudies-literature

VARSCOT: variant-aware detection and scoring enables sensitive and personalized off-target detection for CRISPR-Cas9.

Project description:BACKGROUND:Natural variations in a genome can drastically alter the CRISPR-Cas9 off-target landscape by creating or removing sites. Despite the resulting potential side-effects from such unaccounted for sites, current off-target detection pipelines are not equipped to include variant information. To address this, we developed VARiant-aware detection and SCoring of Off-Targets (VARSCOT). RESULTS:VARSCOT identifies only 0.6% of off-targets to be common between 4 individual genomes and the reference, with an average of 82% of off-targets unique to an individual. VARSCOT is the most sensitive detection method for off-targets, finding 40 to 70% more experimentally verified off-targets compared to other popular software tools and its machine learning model allows for CRISPR-Cas9 concentration aware off-target activity scoring. CONCLUSIONS:VARSCOT allows researchers to take genomic variation into account when designing individual or population-wide targeting strategies. VARSCOT is available from https://github.com/BauerLab/VARSCOT .

| S-EPMC6598273 | biostudies-literature

Comparison of single-nuclei 5’ versus 3’-RNA-seq approaches: utility for somatic variant detection

Project description:Somatic mosaic variants are a major cause of human disease, including cancer and focal epilepsies, but can be challenging to study due to their mosaicism in bulk tissue biopsies. Coupling single-cell genotype and transcriptomic data has potential to provide insight into the role somatic variants play in disease etiology, such as by determining what cell types are affected or how the mutations affect gene expression. Here, we asked whether commonly used single-cell 3’- or 5’-RNA-sequencing assays can be used to derive single-cell genotype data for a priori known variants that are located near to either end of a transcript. To that end, we compared performance of commercially available single-cell 3’- and 5’- gene expression kits using resected brain samples from three pediatric patients with focal epilepsy. We quantified the ability to detect genetic variants in single-cell datasets depending on distance from the transcript end. Finally, we demonstrated the ability to identify affected cell types in a patient with a RHEB somatic variant causing an epilepsy-associated cortical malformation. Our results demonstrate that native single-cell 3’ or 5’-RNA-sequencing data can be used successfully to genotype single-cells for somatic variants that are expressed within proximity to a transcript end.

2023-01-09 | GSE210670 | GEO

Personalized Single-Cell Proteogenomics to Distinguish Acute Myeloid Leukemia from Non-Malignant Clonal Hematopoiesis.

Project description:Genetic mutations associated with acute myeloid leukemia (AML) also occur in age-related clonal hematopoiesis, often in the same individual. This makes confident assignment of detected variants to malignancy challenging. The issue is particularly crucial for AML post-treatment measurable residual disease monitoring, where results can be discordant between genetic sequencing and flow cytometry. We show here, that it is possible to distinguish AML from clonal hematopoiesis and to resolve the immunophenotypic identity of clonal architecture. To achieve this, we first design patient-specific DNA probes based on patient's whole-genome sequencing, and then use them for patient-personalized single-cell DNA sequencing with simultaneous single-cell antibody-oligonucleotide sequencing. Examples illustrate AML arising from DNMT3A and TET2 mutated clones as well as independently. The ability to personalize single-cell proteogenomic assessment for individual patients based on leukemia-specific genomic features has implications for ongoing AML precision medicine efforts.

| S-EPMC8265308 | biostudies-literature

Retracted: Generating Personalized Web Search Using Semantic Context.

Project description:[This retracts the article DOI: 10.1155/2015/462782.].

| S-EPMC5534311 | biostudies-other

Spectral Prediction Features as a Solution for the Search Space Size Problem in Proteogenomics.

Project description:Proteogenomics approaches often struggle with the distinction between true and false peptide-to-spectrum matches as the database size enlarges. However, features extracted from tandem mass spectrometry intensity predictors can enhance the peptide identification rate and can provide extra confidence for peptide-to-spectrum matching in a proteogenomics context. To that end, features from the spectral intensity pattern predictors MS2PIP and Prosit were combined with the canonical scores from MaxQuant in the Percolator postprocessing tool for protein sequence databases constructed out of ribosome profiling and nanopore RNA-Seq analyses. The presented results provide evidence that this approach enhances both the identification rate as well as the validation stringency in a proteogenomic setting.

| S-EPMC8214147 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data