Questions related to Cancer Genomics
As in cancer genomic studies, we have databases such as TCGA and CBio Portal, do we have a similar database for hematological disease? These are databases where we can explore the hemoglobin gene but I am specifically looking for the whole-genome database. A dataset of WGS of a couple of patients could also be very helpful.
-can eliminate DNA doublestrand breaks(DSB)
-enables hope curing chromosomal abnormalities and induced maladies
However, chromothripsis should be taken into serious account to remain objective in judging it
#crispr #crisprcas9 #Chromothripsis #mutation #genomics #micronucleimodel #science #research
I am working on cancer genomics. I downloaded TCGA BRCA FPKM data for my analysis. after some preliminary analysis, I categorised 250 samples in total 2 distinct categories. Now I want to analyse and compare their epigenetic and mutation patterns.
After downloading all mutation and epigenetic data from TCGA BRCA, none of the samples from my previous analysis is matching.
Suppose, TCGA-AN-A0AK-01A-21R-A00Z-07 sample is present in my previously downloaded FPKM data.
But TCGA-AN-A0AK-01A-21W-A019-09 is available in the mutation data. Are these ids the same? For FPKM data and mutation data, do the same sample (individual) be represented by different Ids?
Please help me, Thank you in advance.
This might be a very vague question, but I'm trying to understand what the common stumbling blocks are when trying to make full use of cBioPortal?
For example, researchers are limited to querying only 100 genes from the cBioPortal.
What sort of work can you do through R/python environment that you can't do directly through cBIoPortal?
The project's budget is 12,000$ does not include buying any equipments except (for example) a genotyping analysis kit, I did a project for analyzing genetic diversity and selection signatures in four endangered cattle breeds using Illumina BovineHD kit but it was not satisfying, any suggestions? it is very important and crucial for my career.
Thanks in advance,
Cbioportal provides an online tool for generating oncoprints but I want to generate one for customized data. It would be great to know if a tool is already existing for this.
Has anyone have used TCGA analysis to get useful publications. If yes, would it be possible to get a copy of R code to use it as a road map for the procedure.
Gene copy number variation is one type of genomic diversity among different subjects.
Now, over time, people accumulate somatic mutations in their genome. Some of these may include gene duplication or deletion.
Has anyone addressed the question whether, in a given individual, the number of copies of a given gene can change over time in a particular cell lineage? Or in large cell populations (PBMNCs)?
I am a bioinformatics scientist focusing on cancer genomics. I recently came across you paper 'Genomic characterization of human brain metastases identifies drivers of metastatic lung adenocarcinoma ' and found very impressive.
We are doing some research similar to yours on identifying significantly altered CNV events between two groups. I am wondering if you mind sharing your source code for identifing brain metastatic drivers corresponding the part of Copy-number driver analysis in the methods? This reference would be really helpful to our study.
Can you combine and analyse RNAseq data from different TCGA datasets? I am asking specifically about STAD and ESCA, as STAD contains several gastro-esophageal adenocarcinomas which I would like to analyse together with the adenocarcinomas from ESCA to construct a larger 'esophageal adenocarcinoma' cohort.
I am wondering if each dataset has gone through a different pipeline meaning that it is not possible to do this (due to different batch effects etc.)?
I am exploring the sORF-encoded peptides which are less than 100 AAs long. As i read these kind of peptides are the direct product of gene; they did not came from processing of large precursor proteins. So, i need to clarify my doubt whether the signal peptides are present in this type of peptides or not. Any one working or having the knowledge of these peptides please clear this doubt.
I would like to conduct a project on analyzing commonalities of certain mutations in repetitive DNA of patients with certain types of cancers. To do this, I will need to find the whole genome sequence of different patients with the same type of cancer. I would very much appreciate it if you could direct me to a database where I could obtain this information, or if you have any tips on where I could find this information!
Thanks from a grateful undergraduate,
By certain type of cancer, mutations are well known. Genome/epigenome editing could possibly "repair" such genes or silence them by methylation of their promoter.
I’m performing a cancer genomic analysis and I need a negative control of non-cancer genes (200-400 genes). So, I appreciate any help.
I am working with a PDCL cancer cells and I want to transfect GFP in the cells. I am using lipofectamine 2000 and I incubated it with the cells for 6 hour and seems to be good but after 6 hours they start to show some death due to Lipofectamine toxicity.
So I only incubated cells with the Lipofactamine and the vectors for 6 hours and then changed the medium to the normal medium that I am using for the cells.
I used in this experiment 10.000 cells and they showed some positive cells after 72 hour however they all died when I tranfered them to a larger T25 Flask,
Any suggestions ?
I have been looking into cancer The Cancer Genome Atlas with no luck. Where could I find studies comparing samples prior and post any kind of triple negative cancer treatment? Comparison of microscopy imaging, expression, etc.
“Oncomine Research Premium Edition is designed for laboratories engaged in advanced cancer genomics research and can be purchased in 2-user or 10-user packages for $5,000 and $10,000 respectively. ”(http://www.bio-itworld.com/newsitems/2007/oct/18-briefs/)
So I almost have no chance to use this edition for lacking money?And the researchers won't borrow the account to strangers?
What is the difference between RNA sequencing and Exome Capture RNA Sequencing and how it differentiated with exome and RNA Seq?
I have been searching for SNPs in miRNA genes (formulated panel of 65) in a designated breast cancer population by iPLEX/MALDI-TOF-MS. But after that just got stuck into and couldn't go further.
Expression analysis was suggested by someone, but all I need is some strong, supportive strategy to back my genotype data.
All I need is a follow-up strategy.
thanks in advance.
NB: I am just at the very beginning of my PhD career on Cancer Genomics. So, need some expert opinion to walk through the path.
It's beyond doubt tailor made treatment options for a specific diseases and patients will help the health care system better. Amongst other things individual therapy by gene IS a viable alternative. But for the economic aspects.
In cancer cells, can autophagy inhibition leads genomic instability in order to promote cancer progression?? thank you for your help. please provide references if you will find
I would like to look for functional polymorphisms upstream of the transcription start site of my gene of interest. Has anyone got any experience in doing this in populations of cancer patients?
What we know: It's been established that keap1 is involved in the regulation of transcription factor Nrf2, facilitating its ubiquitination and degradation in basal conditions (either by sequestering in the cytoplasm or actively shuttling it out the nuclei). When oxidative stress triggers a change in this mechanism Nrf2 migrates to the nuclei, where is binds the antioxidant response element (ARE), an upstreaming promoter region of many antioxidative, drug-metabolising and cytoprotective genes. This seems to be fairly well established.
But it gets more complicated than that: It appears that to do this Nrf2 requires dimerisation with other proteins containing bzip complexes. I had read that small Maf proteins were normally involved in this activation of ARE. However, I have recently come across a different model that suggests that Maf proteins actually inhibit expression of this genes and it is binding of Nrf2 to other bzip-containing factors that leads to upregulation (I've attached the model, from the NCI's cancer genome anatomy project, below).
Confusing models: Most of the literature I've found supports the opposing idea of the Nrf2-Maf dimers having an upregulating effect. Are these just different models? Has one premise been proven valid over the other? Or have I simply misunderstood this? If anyone has a better grasp of this than myself any comments would be great.
Microsatellite instability caused by defective mismatch repair machinery is observed in up to one fifth of colorectal adenocarcinomas. When the aberrant number of short tandem repeats in the tumor cells when compared to the germline genome are detected in 30% or higher in the examined microsatellite loci, it is referred to as microsatellite instability-high (MSI-H). I find no good explanation so far to explain why MSI-H tumor is more likely originated from the right-sided colon whereas, left-sided colon got more microsatellite stable (MSS) tumors? Strange.
I am interested in calculating differential expression of genes for tumor vs. normal samples from RNASeq V2 level 3 datasets for TCGA (downloaded from UCSC Cancer Browser). This data is from *.rsem.genes.normalized_results, log2(x+1) transformed and upper quantile normalized
After browsing a lot of literature, blogs and forums, I am able to note following methods of calculate differential expression from TCGA datasets.
1. Fold change Since the values from UCSC are already log 2 transformed, for a gene x, of tumor y, Log 2 fold change can be calculated as (log 2 transformed gene expression value of tumor sample) - (log 2 transformed gene expression value of matched normal sample).
2. Packages like limma to fit a linear model to the log2-transformed data using an empirical Bayes method to moderate standard errors.
3. Package EBSeq, as RSEM’s original paper since that algorithm takes into account the stochastic nature of the RSEM output.
4. Or, I should get expected_counts or scaled-estimates from TCGA data-portal and then use voom() to normalize, as well as packages like DESeq, EBSeq, limma for differential expression
5. Z-score (if comparing tumor vs. normal) = [(value gene X in tumor Y) - (mean gene X in normal)] / (standard deviation of gene X in normal)
I also see variation of Z-score application as
(A) in the TCGA cases where tumor samples are more than normal like say 500 tumor samples out of which only 65 have matched normal sample
(B) In TCGA cases where you have only tumor samples or you want to consider only tumor samples and you can compare it with data of normal tissues as from GTEx
(C) In cases where you have only tumor samples and twisting the calculations as
Z-score= [(gene x log value – mean of log values gene x for all tumor tissues)/ standard deviation of gene x from mean of all tumor tissues]
I would appreciate you, comment or suggest or share your experience on dealing with such kind of datasets, on the following lines:
Query 1: Have I understood these methods correct? How else we can calculate differential from TCGA datasets?
Query 2. Since, Z-score assumes normal distribution and for RNASeq expression values one would not expect normal distribution, one should go for quantile normalization, but as TCGA datasets in UCSC is already quantile normalized, should these values be used directly and simply calculate fold change with that.
Query 3. Are all the variations of Z-score mentioned in point 5 valid?
Query 4: Which of the above or any other method is the most recommended method to calculate differential expression for tumor vs. normal samples from TCGA datasets and why?
I am searching for Structural Variation caller compatible with Mate-pair sequencing data. Any suggestion will be appreciated.
I have tried to search the patient`s description of the colon tumors analyzed in TCGA (the cancer genome atlas), but I have not found it; could somebody give me some information about how I might obtain the tumor characteristics associated with the analyzed samples (i.e. Ras mutation status, primary tumor, metastatic tumor... )?
I have methylation data from Infinium 450K and I found out differentially methylated regions. Is there a way to find the equally methylated regions as well, particularly in R.
Recommendation for any Package with commands would be highly appreciated.
I'm trying to determine if I can do an unpaired copy number analysis in the Partek Genomics Suite (PGS) v6.6 using .CEL files obtained from CytoScanHD chips (Affymetrix). I am able to download all necessary annotation files, however I cannot find a .cnmodels HapMap file to download, which I need to generate copy number from unpaired samples. PGS v6.6 seems to require a .cnmodels file to do this and does not accept other HapMap reference files like .REF_MODEL. I have previously used .cnmodels for SNP6 and 500K arrays in PGS however I cannot find one for CytoScanHD.
I have 2 questions which I am hoping someone can help me with:
1. Does anyone have a link to a .cnmodels HapMap file for CycoScanHD which I could download?
2. Is it possible to use a different HapMap reference file other than .cnmodels to do unpaired copy number analysis in PGS and if so which reference file and how?
I want to compare levels of an isoform between prostate cancer database and a normal database. As I am not a bioinformatician, I am looking for any help on how to do so... Are there any tools or tutorials for this that can help on this issue? I am looking at genomic databases and the protein isoform is a splice variant.
As far as I am concerned, the standard coverage 50x for the analysis of highly heterogeneous tumor samples such as melanoma is insufficient and scientific literature recommends base coverage around 500x or even 1000x. However, with that coverage the cost of analysis increase enormously.
If not, should there be? When we look upstream from genes, we are confronted with our personally unique 'elementome'. Cancer as a mitochondrial metabolic disease (2015 Seyfried ) considers environment but focuses on one organelle. While it is hard to argue the great science behind SMT, TOFL (Sonneschein& Soto), Bissel's stromal related studies, and Seyfried above...but they all seem downstream in that they fail to mention, downplay the environment, or key on specific micro env. The way I see it, it seems highly unlikely a single organelle/compartment in a cell is responsible for the root cause. Thoughts?
In TCGA cancer data, some miRNA expression(RPM) values are '0'. So do I need to consider those miRNA expression or not? Can any one give details regarding miRNA expression(RPM) data.
Thanks in advance.
we consider non-genetic cell phenotype plasticity as a central process in therapy resistance. We take into account theability of cells to produce discretely distinct phenotypes, switch between them without genomic alterations and inherit the new
phenotype non-genetically across cell generations (Brock et al,2009).
We have knockout, wide type and heterozygotic mice, only finding heterozygotic mice got tumor(4/10). I am really confused about this outcome. I wish someone give me some ideas about this heterozygote specific phenomenon.
Science has provided us with countless discoveries thought to potentially improve cancer outcomes. However, only a handful of them have been translated into clinical care, and at a quite prohibitive price tag (eg new generation TKIs, monoclonal antibodies, genomic testing etc). Some other, more cost effective, are yet to be fully adopted by health care providers. Among the later I would count maximizing use of metformin in patients diagnosed with type 2 diabetes and cancer. While use of metformin as an anti-tumor agent is currently tested in a concerning high (cost-wise) number of clinical trials, maximizing its benefit among patients with type 2 diabetes is yet to be a focus despite the drug being the first line therapy.
What are your thoughts?
I am looking for a database which has some annoatations for intergenic gene polymorphims that can help me out for cancer genomics report analysis. Can any help me out
I am looking for a database which has some annoatations for intergenic gene polymorphims that can help me out for cancer genomics report analysis. Can any help me out?
Can anybody suggest me a technique to measure genomic stability as histone modification read out. I am trying to measure the stability of cancer genome, what techniques can I use to measure the genomic instability in live cells, FFPE samples. Trying to establish an epigenetic signature for stability, what techniques could be of use here?
I work with a protein that has 2 different common SNP variants in the population, and we discovered that only one form can bind a protein required for degradation. Is there a resource that links cancer panel genome wide SNP (DNA sequence) with genome wide protein expression level (eg mass spec, protein or IHC microarray) from the same samples? Or do I have to find a patient/control set and do it myself?!
i am checking for TSG (p16,p21,PTEN,p53) Methylation, mutation status of ovarian cancer cell line like oaw42,oaw28 ,ov167,ov177
I have some old CGH data from Roche/NimbleGen that I would like to organize into some publication quality figures. I find the software supplied by them to be inadequate for anything but scanning the results.
The problem in downloading from FTP is that it provides Genbank file of whole genome. How to extract coding region from these Genbank file?
Does anyone know a way to identify the main GO Terms associated with cancer?
I have a list of genes annotated with GO biological processes and wanted to filter only those annotated in biological processes related to cancer.
This project is in a rare type of lymphoid cancer. Due to this rarity, there are only 3 cell lines available for the disease, which I have in the lab. They are drug-sensitive cell lines (drug disclosed only if you are interested in collaboration) and I also have their 3 drug-resistant clones, which were developed in the lab (so total sample n=6). Unfortunately, I do not have any patient samples. I wish to conduct RNA-seq to examine changes at the mRNA level as well as SNV's in these cells. The goal is to identify changes that characterize drug-resistant cells vs. their drug-sensitive WT counterparts. Please tell me if this type of an analysis is possible with such low sample numbers? If so, please let me know if you are interested in collaborating to analyze the resultant RNA-Seq data?
I would like to perform a in-silico validation for my research study where I need to combine some published datasets (from GEO portal) to increase the number of samples (n) and then analyse them for differential expression for specific genes. I would to do all these analysis in R. Any suggestion for R software package, combining package or batch effect removal + their r scripts?
Thanks a lot for anticipation
There are controversial questions about the role of viruses in some types of cancer. There are some viruses "associated with" or "suspected to play a role in" some types of cancer. The major problem is that there is a lack of evidence for the presence of the virus in the majority of the observed cases leading to the conclusion that the virus cannot be considered as the cause of cancer.
So my question is simple, can we certify that there is an absence of a virus in a cell with today's techniques or are we limited with a detection threshold if a small number of copies may be sufficient to trigger cancerogenicity?
When does the term transcriptome come in case of the cancer cells cultured in vitro? If we are thinking for similarity determination of the cancer cells in comparison to original tumor tissue from which it has been derived the early passage, cells are considered for studying NGS data because there is some common hypothesis and prove for some cancer cell lines that after some passages (5-15) genomic and transcriptomic alteration occurs. I am not sure in between the early passages, which passage will be utmost suitable because NGS run is always associated with high costs.
Genome sequencing of tumors could help in dissecting clones within a tumor, is that possible to predict drug targets for each clone, so that a cocktail could be applied to the patient?
My project involves screening for novel SNPs in tumor samples. I've identified a novel SNP locus in a particular gene. How can I proceed to design primers for PCR testing on a bigger tumor sample? Is there any easy to use software to do that?
The most used definition for "Neoplasia" was put forth by Willis RA in 1952 based upon the understanding of the disease process of that era. Since then our understanding of neoplastic process has increased so much.
1. Do you think even after 60 years, the definition is still valid or needs modification? For eg., the hall mark features of cancer proposed by Douglas Hanahan and Robert Weinberg in 2000 conveys much better fundamentals than old definition.
2. If so, has any agency such as IARC has taken steps to define an universally acceptable definition that encompass all current knowledge of the process ?
3. Do you think this exercise is a waste of time and we need to focus efforts for more in depth understanding of the disease than on defining the disease?
When everyone is thinking of bad about cancer, I wish to know much about my friend "Cancer" who is the nexus of the Healthcare business and emerging burden of the society. I would like to know some goodness of the cancer cells. I hope this will add a caliber for some of the research hypothesis with the help of the scientific society.
" When one door closes another opens"
Yours suggestions and comments are highly appreciated
In NGS, scientist go through different processing steps using standard tools and public databases to align sequences, map then on reference databases and identify different mutations known to be relevant in particular diseases. I would like to survey about the most widely used tools and reference databases.
Does anybody have experience with (or know of anybody) performing whole genome sequencing (WGS) of genetically matched (i.e., from the same individual) peripheral blood leukocyte vs lymphoblastoid cell line samples as constitutive tissue controls for tumors WGS? Unfortunately, we are in a position where we do not have matching PBL for all tumor samples, but (fortunate to?) have immortalized LBCLs. How good of a surrogate are the LBCLs for such studies if no PBL Is available? In addition to SNVs I am particularly concerned about possible differences in CNVs.