# Tag Info

7

If you are looking for cancer mutations, the primary resource is COSMIC and they provide GRCh38 VCFs. The download page is here: http://cancer.sanger.ac.uk/cosmic/download It'll be up to you how you define "common", but the VCF includes a lot of information you can use.

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I don't know if there's a database that does exactly what you want, but there are some places that might help you figure this out, especially if you already have a list of CNAs/genes/regions in mind. ICGC is the first that comes to mind, as it has samples from many types of cancer and copy number data for most of them. Depending on what you want though, you ...

7

Your question doesn't give enough information for a specific answer but this should do for a start. Get the VCF file describing all variants in Clinvar from NCBI: wget ftp://ftp.ncbi.nlm.nih.gov/pub/clinvar/vcf_GRCh38/clinvar.vcf.gz Extract any variants whose VCF line contains the word "cancer": zgrep -iE '^#|CLNDBN=[^;]*cancer' clinvar.vcf.gz > ...

5

I’m no longer working in tumour sequencing so I’m by no means an expert. But in a nutshell, the reason is that, as indicated, homozygous SNPs aren’t informative: if your allelic fraction is 100%, meaning you only observe a single nucleotide at a given position, we don’t know whether we’re dealing with a tumour related mutation. In fact, we probably don’t ...

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Try the Gene Expression Omnibus - it looks like they have some datasets.

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There was a Structural Variant breakout session at the London Calling conference this year. Unfortunately I didn't attend that session, but MinION community members have access to Constance Donnell's summary of that: https://community.nanoporetech.com/posts/breakout-structural-varia Here are my attempts at grabbing non-creative chunks from those notes: ...

5

While your question is specific to cancerous germline mutations, I'd suggest you look at the COSMIC database of somatic mutations to include in your analysis. There are other factors to include in this kind of analysis you're suggesting, such as predictive deleterious effects (PolyPhen for example can perform such predictions). If you have 10M variants/...

5

I've been using the LoFreq* caller for exactly this. It is designed to find variants with very low frequency, so is well suited for this type of analysis. LoFreq* (i.e. LoFreq version 2) is a fast and sensitive variant-caller for inferring SNVs and indels from next-generation sequencing data. It makes full use of base-call qualities and other sources of ...

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There is a recent paper that attempts to do this: ISOWN: accurate somatic mutation identification in the absence of normal tissue controls. In this work, we describe the development, implementation, and validation of ISOWN, an accurate algorithm for predicting somatic mutations in cancer tissues in the absence of matching normal tissues. ...

4

There are many databases that have used publication scraping for oncogenic gene fusions. There are publications for the individual methods they used for their scraping and aggregation. COSMIC - http://cancer.sanger.ac.uk/cosmic TICdb - http://www.unav.es/genetica/TICdb/ ChimerDB - http://ercsb.ewha.ac.kr/fusiongene/ - https://www.ncbi.nlm.nih.gov/pubmed/...

4

There is no reason your t-test should reproduce edgeR. In fact, edgeR exists because t-test is inappropriate. edgeR does the tests by pooling information from all genes, because with the low number of replicates your t-test doesn't give sufficient statistical power. What you need to do is: check visually your gene and make a decision yourself based on ...

4

Each color is a clone (set of mutations) or an individual mutation (depends on how the input data was generated). This is a cancer with the bottleneck likely being induced by a treatment (e.g. UV, chemo, drugs, etc...) Based on the vignette this is likely an AML (leukemia). It looks like grey are healthy cells. At day 0 a specific clone (red) existed at a ...

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If you have access to the UK Biobank dataset, then they have urine data for healthy individuals. https://www.nature.com/articles/s41588-020-00757-z

3

The $log(CPM)$ of any low-moderately expressed gene will be negative. There is nothing unexpected there. Your statistics are inappropriate for a variety of reasons. Firstly, a CPM is not a robust value that's comparable between samples (this is why CPMs aren't used for statistics). edgeR performs more appropriate normalization and incorporates that into its ...

3

Please take a look at An open access pilot freely sharing cancer genomic data from participants in Texas Although I have not worked on this paper but author claimed that both tumor and normal data is publicly available. In case if you still need something more, than you can work on virtual normal data. And there are several papers which provide machnine ...

3

It seems like I was wrong and COSMIC does store information about cell lines. You need to download the files from Genotypes: Genotypes Files listing the SNP calls for each cell line identified by PICNIC analysis of Affymetrix SNP6.0 array data. Both a simple genotype (AA, BB - homozygous or AB - heterozygous) and a complex interpretation of ...

3

This existed as a closed silo, at least in 2015. Qiagen has a team of hired students and Post-Docs for collating research papers into their Knowledge Base, an extensive database that is integrated into a few of their commercial products. Qiagen's claim is that by providing a consistently-structured and well-formatted database, the process of research ...

3

I don't think it will be possible to do what you ask, right now with current knowledge. Selecting variants relevant to cancer risk is still an open problem and usually requires quite a lot of human intervention. You can use different measures of population frequency to filter common variants with the assumption that frequent variants won't be pathogenic. ...

3

I have previously estimated tumour purity with the EXPANDS an inferred tumour heterogeneity program which is designed to calculate the number of clonal subpopulations in matched tumour/normal samples. The purity is essentially the size of the largest subpopulation identified in that sample - this is discussed in the programs FAQ. In addition to a matched ...

3

Example 2 and 3 seem symmetric because they are forced to by the model. The Cox proportional hazard relies on the proportional hazards assumption. This basically means that the ratio of the hazards are constant over time. If you were to plot the hazard ratio, it would look like a flat line over all Event_at. Another way of saying this is that the hazard ...

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There is a CSV table in this paper with 33 sets of genes.

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It's usually CNV callers that make use of Tumour/Normal WGS pairs to estimate purity. It can also be done with WES (exome) Tumour/Normal pairs. There are several tools out there, I have some experience with the one written by Illumina (public on Github): https://github.com/Illumina/canvas It requires realigning things with bowtie2, so I don't think it can ...

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I already see for links when I try to search. Take a look at them below. Also if you can be precise about kind of data are you trying to find, it would be better https://pdmr.cancer.gov/ http://cdt.northwestern.edu/news/patient-derived-xenograft-repository-now-available-researchers http://www.epo-berlin.com/epo-tumor-models-xenografts.html http://www....

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The trouble with looking for cancer-associated variants is that it can be difficult to tease out spurious effects (e.g. ethnicity) from causative variants. If you're interested in what genes are implicated in most types of cancer, the annotations for the NanoString panels are quite good: cancer pathways cancer progression The "Support documents" section of ...

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EMT describe cells' change in their state from being epithelial to the mesenchymal class. So, if a cancer cell line is gaining properties that allow it to move, it might reach the metastasis phase, were a cell can move to any point of the body and start a new tumor there. As in the wikipedia page you linked: EMT has also been shown to occur in wound ...

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All the popular variant calling tools such as Strelka, VarScan etc require a normal sample. Strelka and VarScan require a normal sample in somatic mode, but they both have a germline mode for unpaired analysis. One of the most popular somatic variant callers is MuTect and that does not require a normal.

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In the TCGA datasets there is a variable (type) containing the information if the sample was from the tumor or from an adjacent region, which is usually considered as healthy. As this samples come from the same patient and the same tissues they are used in the analysis for the comparisons and classifications.

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Without control data from your subjects, I don't think there's really no way to distinguish somatic mutations from germ-line mutations. The best you can do is to screen out common variants, which are germ-line mutations that are shared by large numbers of individuals using the population frequencies from something like the Exome Aggregation Consortium: ...

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I'm not sure what I'm talking about, but I guess one could re-phrase Konrad's answer in terms of information theory. The information content of a message (a packet of bits) is inversely proportional to the frequency of those bits. For example, if you randomly extract a word (the message) from an English book and the word is "the", you don't get much ...

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You can find CNV (and other structural variant) information in: DECIPHER DECIPHER is used by the clinical community to share and compare phenotypic and genotypic data. The DECIPHER database contains data from 29603 patients who have given consent for broad data-sharing; DECIPHER also supports more limited sharing via consortia. Have a look at the numbers....

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