ArticleGenome research2000
Expression-based genetic/physical maps of single-nucleotide polymorphisms identified by the cancer genome anatomy project.
Article in Genome research, 2000. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 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
7 citing papers in PubMed, 44 citations in OpenAlex.
- Online tools for bioinformatics analyses in nutrition sciences.Advances in nutrition (Bethesda, Md.) · 2012Review
- Functional nsSNPs from carcinogenesis-related genes expressed in breast tissue: potential breast cancer risk alleles and their distribution across human populations.Human genomics · 2006Article
- A transcript finishing initiative for closing gaps in the human transcriptome.Genome research · 2004Article
- The generation and utilization of a cancer-oriented representation of the human transcriptome by using expressed sequence tags.Proceedings of the National Academy of Sciences of the United States of America · 2003Article
- Mining for single nucleotide polymorphisms and insertions/deletions in maize expressed sequence tag data.Plant physiology · 2003Article
- Sequence variations in the public human genome data reflect a bottlenecked population history.Proceedings of the National Academy of Sciences of the United States of America · 2003Article
- The functional importance of disease-associated mutation.BMC bioinformatics · 2002Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
SNPs (Single-Nucleotide Polymorphisms), the most common DNA variant in humans, represent a valuable resource for the genetic analysis of cancer and other illnesses. These markers may be used in a variety of ways to investigate the genetic underpinnings of disease. In gene-based studies, the correlations between allelic variants of genes of interest and particular disease states are assessed. An extensive collection of SNP markers may enable entire molecular pathways regulating cell metabolism, growth, or differentiation to be analyzed by this approach. In addition, high-resolution genetic maps based on SNPs will greatly facilitate linkage analysis and positional cloning. The National Cancer Institute's CGAP-GAI (Cancer Genome Anatomy Project Genetic Annotation Initiative) group has identified 10,243 SNPs by examining publicly available EST (Expressed Sequence Tag) chromatograms. More than 6800 of these polymorphisms have been placed on expression-based integrated genetic/physical maps. In addition to a set of comprehensive SNP maps, we have produced maps containing single nucleotide polymorphisms in genes expressed in breast, colon, kidney, liver, lung, or prostate tissue. The integrated maps, a SNP search engine, and a Java-based tool for viewing candidate SNPs in the context of EST assemblies can be accessed via the CGAP-GAI web site (http://cgap.nci.nih.gov/GAI/). Our SNP detection tools are available to the public for noncommercial use.
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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.