ArticleScientific reports2016
Single-cell SNP analyses and interpretations based on RNA-Seq data for colon cancer research.
Article in Scientific reports, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
11 citing papers in PubMed.
- Article
- Application of Single-Cell Sequencing Technology in Research on Colorectal Cancer.Journal of personalized medicine · 2024Review
- Testing for Phylogenetic Signal in Single-Cell RNA-Seq Data.Journal of computational biology : a journal of computational molecular cell biology · 2023Article
- Single-Cell RNA Sequencing for Analyzing the Intestinal Tract in Healthy and Diseased Individuals.Frontiers in cell and developmental biology · 2022Review
- Clinically relevant fusion oncogenes: detection and practical implications.Therapeutic advances in medical oncology · 2022Review
- Cell-level somatic mutation detection from single-cell RNA sequencing.Bioinformatics (Oxford, England) · 2019Article
- Single cell immune profiling in transplantation research.American journal of transplantation : official journal of the American Society of Transplantation and the American Society of Transplant Surgeons · 2019Review
- Next-generation sequencing: recent applications to the analysis of colorectal cancer.Journal of translational medicine · 2017Review
- Single-cell sequencing and tumorigenesis: improved understanding of tumor evolution and metastasis.Clinical and translational medicine · 2017Review
- Fingerprints of Modified RNA Bases from Deep Sequencing Profiles.Journal of the American Chemical Society · 2017Article
- Heterogeneous circRNA expression profiles and regulatory functions among HEK293T single cells.Scientific reports · 2017Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Single-cell sequencing is useful for illustrating the cellular heterogeneities inherent in many intricate biological systems, particularly in human cancer. However, owing to the difficulties in acquiring, amplifying and analyzing single-cell genetic material, obstacles remain for single-cell diversity assessments such as single nucleotide polymorphism (SNP) analyses, rendering biological interpretations of single-cell omics data elusive. We used RNA-Seq data from single-cell and bulk colon cancer samples to analyze the SNP profiles for both structural and functional comparisons. Colon cancer-related pathways with single-cell level SNP enrichment, including the TGF-β and p53 signaling pathways, were also investigated based on both their SNP enrichment patterns and gene expression. We also detected a certain number of fusion transcripts, which may promote tumorigenesis, at the single-cell level. Based on these results, single-cell analyses not only recapitulated the SNP analysis results from the bulk samples but also detected cell-to-cell and cell-to-bulk variations, thereby aiding in early diagnosis and in identifying the precise mechanisms underlying cancers at the single-cell level.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.