Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

De novo identification of expressed cancer somatic mutations from single-cell RNA sequencing data.

ABSTRACT: Identifying expressed somatic mutations from single-cell RNA sequencing data de novo is challenging but highly valuable. We propose RESA - Recurrently Expressed SNV Analysis, a computational framework to identify expressed somatic mutations from scRNA-seq data. RESA achieves an average precision of 0.77 on three in silico spike-in datasets. In extensive benchmarking against existing methods using 19 datasets, RESA consistently outperforms them. Furthermore, we applied RESA to analyze intratumor mutational heterogeneity in a melanoma drug resistance dataset. By enabling high precision detection of expressed somatic mutations, RESA substantially enhances the reliability of mutational analysis in scRNA-seq. RESA is available at https://github.com/ShenLab-Genomics/RESA .

SUBMITTER: Zhang T

PROVIDER: S-EPMC10726641 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

De novo identification of expressed cancer somatic mutations from single-cell RNA sequencing data.

Zhang Tianyun T Jia Hanying H Song Tairan T Lv Lin L Gulhan Doga C DC Wang Haishuai H Guo Wei W Xi Ruibin R Guo Hongshan H Shen Ning N

Genome medicine 20231218 1

Identifying expressed somatic mutations from single-cell RNA sequencing data de novo is challenging but highly valuable. We propose RESA - Recurrently Expressed SNV Analysis, a computational framework to identify expressed somatic mutations from scRNA-seq data. RESA achieves an average precision of 0.77 on three in silico spike-in datasets. In extensive benchmarking against existing methods using 19 datasets, RESA consistently outperforms them. Furthermore, we applied RESA to analyze intratumor ...[more]

PMID: 38111063

Similar Datasets

DNApi: A De Novo Adapter Prediction Algorithm for Small RNA Sequencing Data.

Project description:With the rapid accumulation of publicly available small RNA sequencing datasets, third-party meta-analysis across many datasets is becoming increasingly powerful. Although removing the 3´ adapter is an essential step for small RNA sequencing analysis, the adapter sequence information is not always available in the metadata. The information can be also erroneous even when it is available. In this study, we developed DNApi, a lightweight Python software package that predicts the 3´ adapter sequence de novo and provides the user with cleansed small RNA sequences ready for down stream analysis. Tested on 539 publicly available small RNA libraries accompanied with 3´ adapter sequences in their metadata, DNApi shows near-perfect accuracy (98.5%) with fast runtime (~2.85 seconds per library) and efficient memory usage (~43 MB on average). In addition to 3´ adapter prediction, it is also important to classify whether the input small RNA libraries were already processed, i.e. the 3´ adapters were removed. DNApi perfectly judged that given another batch of datasets, 192 publicly available processed libraries were "ready-to-map" small RNA sequence. DNApi is compatible with Python 2 and 3, and is available at https://github.com/jnktsj/DNApi. The 731 small RNA libraries used for DNApi evaluation were from human tissues and were carefully and manually collected. This study also provides readers with the curated datasets that can be integrated into their studies.

| S-EPMC5063419 | biostudies-literature

SomaticSniper: identification of somatic point mutations in whole genome sequencing data.

Project description:MotivationThe sequencing of tumors and their matched normals is frequently used to study the genetic composition of cancer. Despite this fact, there remains a dearth of available software tools designed to compare sequences in pairs of samples and identify sites that are likely to be unique to one sample.ResultsIn this article, we describe the mathematical basis of our SomaticSniper software for comparing tumor and normal pairs. We estimate its sensitivity and precision, and present several common sources of error resulting in miscalls.Availability and implementationBinaries are freely available for download at http://gmt.genome.wustl.edu/somatic-sniper/current/, implemented in C and supported on Linux and Mac OS X.Contactdelarson@wustl.edu; lding@wustl.eduSupplementary informationSupplementary data are available at Bioinformatics online.

| S-EPMC3268238 | biostudies-literature

A framework for the detection of de novo mutations in family-based sequencing data.

Project description:Germline mutation detection from human DNA sequence data is challenging due to the rarity of such events relative to the intrinsic error rates of sequencing technologies and the uneven coverage across the genome. We developed PhaseByTransmission (PBT) to identify de novo single nucleotide variants and short insertions and deletions (indels) from sequence data collected in parent-offspring trios. We compute the joint probability of the data given the genotype likelihoods in the individual family members, the known familial relationships and a prior probability for the mutation rate. Candidate de novo mutations (DNMs) are reported along with their posterior probability, providing a systematic way to prioritize them for validation. Our tool is integrated in the Genome Analysis Toolkit and can be used together with the ReadBackedPhasing module to infer the parental origin of DNMs based on phase-informative reads. Using simulated data, we show that PBT outperforms existing tools, especially in low coverage data and on the X chromosome. We further show that PBT displays high validation rates on empirical parent-offspring sequencing data for whole-exome data from 104 trios and X-chromosome data from 249 parent-offspring families. Finally, we demonstrate an association between father's age at conception and the number of DNMs in female offspring's X chromosome, consistent with previous literature reports.

| S-EPMC5255947 | biostudies-literature

SNV identification from single-cell RNA sequencing data.

Project description:Integrating single-cell RNA sequencing (scRNA-seq) data with genotypes obtained from DNA sequencing studies facilitates the detection of functional genetic variants underlying cell type-specific gene expression variation. Unfortunately, most existing scRNA-seq studies do not come with DNA sequencing data; thus, being able to call single nucleotide variants (SNVs) from scRNA-seq data alone can provide crucial and complementary information, detection of functional SNVs, maximizing the potential of existing scRNA-seq studies. Here, we perform extensive analyses to evaluate the utility of two SNV calling pipelines (GATK and Monovar), originally designed for SNV calling in either bulk or single-cell DNA sequencing data. In both pipelines, we examined various parameter settings to determine the accuracy of the final SNV call set and provide practical recommendations for applied analysts. We found that combining all reads from the single cells and following GATK Best Practices resulted in the highest number of SNVs identified with a high concordance. In individual single cells, Monovar resulted in better quality SNVs even though none of the pipelines analyzed is capable of calling a reasonable number of SNVs with high accuracy. In addition, we found that SNV calling quality varies across different functional genomic regions. Our results open doors for novel ways to leverage the use of scRNA-seq for the future investigation of SNV function.

| S-EPMC7279618 | biostudies-literature

De novo transcriptome sequencing of axolotl blastema for identification of differentially expressed genes during limb regeneration.

Project description:BACKGROUND: Salamanders are unique among vertebrates in their ability to completely regenerate amputated limbs through the mediation of blastema cells located at the stump ends. This regeneration is nerve-dependent because blastema formation and regeneration does not occur after limb denervation. To obtain the genomic information of blastema tissues, de novo transcriptomes from both blastema tissues and denervated stump ends of Ambystoma mexicanum (axolotls) 14 days post-amputation were sequenced and compared using Solexa DNA sequencing. RESULTS: The sequencing done for this study produced 40,688,892 reads that were assembled into 307,345 transcribed sequences. The N50 of transcribed sequence length was 562 bases. A similarity search with known proteins identified 39,200 different genes to be expressed during limb regeneration with a cut-off E-value exceeding 10-5. We annotated assembled sequences by using gene descriptions, gene ontology, and clusters of orthologous group terms. Targeted searches using these annotations showed that the majority of the genes were in the categories of essential metabolic pathways, transcription factors and conserved signaling pathways, and novel candidate genes for regenerative processes. We discovered and confirmed numerous sequences of the candidate genes by using quantitative polymerase chain reaction and in situ hybridization. CONCLUSION: The results of this study demonstrate that de novo transcriptome sequencing allows gene expression analysis in a species lacking genome information and provides the most comprehensive mRNA sequence resources for axolotls. The characterization of the axolotl transcriptome can help elucidate the molecular mechanisms underlying blastema formation during limb regeneration.

| S-EPMC3702472 | biostudies-literature

Somatic mutations and germline sequence variants in the expressed tyrosine kinase genes of patients with de novo acute myeloid leukemia.

Project description:Activating mutations in tyrosine kinase (TK) genes (eg, FLT3 and KIT) are found in more than 30% of patients with de novo acute myeloid leukemia (AML); many groups have speculated that mutations in other TK genes may be present in the remaining 70%. We performed high-throughput resequencing of the kinase domains of 26 TK genes (11 receptor TK; 15 cytoplasmic TK) expressed in most AML patients using genomic DNA from the bone marrow (tumor) and matched skin biopsy samples ("germline") from 94 patients with de novo AML; sequence variants were validated in an additional 94 AML tumor samples (14.3 million base pairs of sequence were obtained and analyzed). We identified known somatic mutations in FLT3, KIT, and JAK2 TK genes at the expected frequencies and found 4 novel somatic mutations, JAK1(V623A), JAK1(T478S), DDR1(A803V), and NTRK1(S677N), once each in 4 respective patients of 188 tested. We also identified novel germline sequence changes encoding amino acid substitutions (ie, nonsynonymous changes) in 14 TK genes, including TYK2, which had the largest number of nonsynonymous sequence variants (11 total detected). Additional studies will be required to define the roles that these somatic and germline TK gene variants play in AML pathogenesis.

| S-EPMC2343607 | biostudies-literature

De novo assembly of a bell pepper endornavirus genome sequence using RNA sequencing data.

Project description:The genus Endornavirus is a double-stranded RNA virus that infects a wide range of hosts. In this study, we report on the de novo assembly of a bell pepper endornavirus genome sequence by RNA sequencing (RNA-Seq). Our result demonstrates the successful application of RNA-Seq to obtain a complete viral genome sequence from the transcriptome data.

| S-EPMC4395069 | biostudies-literature

RNAmut: robust identification of somatic mutations in acute myeloid leukemia using RNA-sequencing.

Project description: Not available

| S-EPMC7271607 | biostudies-literature

De novo analysis of bulk RNA-seq data at spatially resolved single-cell resolution.

Project description:Uncovering the tissue molecular architecture at single-cell resolution could help better understand organisms' biological and pathological processes. However, bulk RNA-seq can only measure gene expression in cell mixtures, without revealing the transcriptional heterogeneity and spatial patterns of single cells. Herein, we introduce Bulk2Space ( https://github.com/ZJUFanLab/bulk2space ), a deep learning framework-based spatial deconvolution algorithm that can simultaneously disclose the spatial and cellular heterogeneity of bulk RNA-seq data using existing single-cell and spatial transcriptomics references. The use of bulk transcriptomics to validate Bulk2Space unveils, in particular, the spatial variance of immune cells in different tumor regions, the molecular and spatial heterogeneity of tissues during inflammation-induced tumorigenesis, and spatial patterns of novel genes in different cell types. Moreover, Bulk2Space is utilized to perform spatial deconvolution analysis on bulk transcriptome data from two different mouse brain regions derived from our in-house developed sequencing approach termed Spatial-seq. We have not only reconstructed the hierarchical structure of the mouse isocortex but also further annotated cell types that were not identified by original methods in the mouse hypothalamus.

| S-EPMC9618574 | biostudies-literature

De novo gene signature identification from single-cell RNA-seq with hierarchical Poisson factorization.

Project description:Common approaches to gene signature discovery in single-cell RNA-sequencing (scRNA-seq) depend upon predefined structures like clusters or pseudo-temporal order, require prior normalization, or do not account for the sparsity of single-cell data. We present single-cell hierarchical Poisson factorization (scHPF), a Bayesian factorization method that adapts hierarchical Poisson factorization (Gopalan et al, 2015, Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence, 326) for de novo discovery of both continuous and discrete expression patterns from scRNA-seq. scHPF does not require prior normalization and captures statistical properties of single-cell data better than other methods in benchmark datasets. Applied to scRNA-seq of the core and margin of a high-grade glioma, scHPF uncovers marked differences in the abundance of glioma subpopulations across tumor regions and regionally associated expression biases within glioma subpopulations. scHFP revealed an expression signature that was spatially biased toward the glioma-infiltrated margins and associated with inferior survival in glioblastoma.

| S-EPMC6386217 | 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