Human Molecular Genetics - Science topic

I saw this book, it is perfect.

Copy Number Variants

Methods and Protocols

Editors: Bickhart, Derek M. (Ed.)

It is a big myth that curcumin and ginger make us vulnerable to coronavirus (COVID-19).

It is reported that there are many "RR type" players in measurement items that require instantaneous power such as back muscle strength and long jump.

Please take a look at the following RG links.

Thank you very much.

Hi, bro, as per my expereince, i will suggest you to contact with your supervisor first, for sure he/she will give you some outlines & then according to that university requirments you can write an research proposal. First, find Professor related to your research theme , mail him & tell him that you want to do PhD under his supervision, if he agreed then for sure he will sent you a letter in which he will mention the topic of your research, then you can write an your research proposal.

I hope this will help you. If any other other help is needed, feel free to ask.

Thanks

Hi there,

I understand that your question is about evaluating the association between a set of SNPs, in relationship to a disease. I thus imagine that you have at your disposal SNPs data for cases (individual with the disease) and controls (comparable individuals without the disease).

There are different scales to evaluate this association. A key information that could hold for all of them: SNPs are associated with a disease if they bring you information on the risk that an individual has the disease. Therefore, any classifier that tries, from SNP(s), to predict if an individual has the disease is susceptible to be used to assess association.

1. At the individual SNP level

a) Indepentently

Here you consider each SNP without considering the other. The most used association measure for that are Chi-Square and Fisher's Exact test. Statistics may have to be controlled for eventual population substructure (see ).

Some more complex method also exist (see Paragraph "Deriving a gene-wide test statistic for each gene" in ).

b) In relationship with the other SNPs

Any classifier that is able to give you information of the feature's importance can be used to assess the level of predictivity given by a SNP in relationship to the other. For instance, the weight of a logistic model trained to predict cases can be used. For more complex models, the loss of accuracy occurring when you permutate the values of a SNP can give you a sense of its importance in the prediction.

2. At the set of SNP level

To compare multiple set of SNPs that may be good predictors of a disease, you can use the accuracy (or any metric that evaluate predictive quality) of a classifier (e.g. Random Forest, Naive Bayes Classifier, etc.).

For instance, if you ranked all SNPs based on their individual association, you can evaluate the top-N SNPs by training a model and computing its accuracy, to identify at which point adding a SNP does not improve the model anymore.

A few additional words:

Consider Cross-Validation in order to avoid overfitting when finding best parameters of a model.

Do not forget to evaluate your association measures (e.g. accuracy of a set of SNP or their individual association) on an independant validation set, in order to have a real sense of the replicability of your results. See for instance [1] to understand a bit more about overfitting.

[1] https://en.wikipedia.org/wiki/Overfitting

Irfan, Please let me know if you find the attached papers informative to the underlying basis of your question. This is an area of investigation that we are actively pursuing. I think our most recent papers on the subject open up the discussion a bit further than is afforded by the functional/non-functional paradigm.

Dear Sir, this is very useful source about DNA evidence I would like to share with you. https://www.nij.gov/topics/forensics/evidence/dna/basics/pages/identifying-to-transporting.aspx

There are 20-25000 protein coding genes in human and we are able to manage with fewer genes due to multitasking by these genes. Multitasking is facilitated by formation multiple transcripts from the same gene. Multiple transcripts are formed by alternate splicing, alternate promoters and alternate poly A sites.

In addition to protein coding genes there are hundreds of small RNA coding genes which are regulatory RNAs (example miRNAs)

I am not sure in animals. In plants, we use colchicine for chromosome doubling. For example, a developed haploid line (ex. developed from anther culture) can be induced into double-hapolid (DH) line through chromosome-doubling agents. DH lines are important sources for plant breeding.

CRISPR /Cas9 has been implemented for editing human embryos

Ma H, Marti-Gutierrez N, Park SW, Wu J, Lee Y, Suzuki K, et al. Correction of a pathogenic gene mutation in human embryos. Nature. 2017;548:413–9. 7

See also,

Fogarty NM, McCarthy A, Snijders KE, Powell BE, Kubikova N, Blakeley P, et al. Genome editing reveals a role for OCT4 in human embryogenesis. Nature. 2017;550:67–73.

I also found this online:

How do you know if you have identical twins?

Your ultrasound technician may be able to tell whether your twin babies are fraternal or identical by looking at the placenta. If you're having twins that are fraternal, they will have their own placentas.

So, even before they were born, one still have a chance to tell whether they are identical twins if ultrasound was taken.

there is no general problem using short sequencing primers ( pyrosequencing works well with them) so long as they only anneal at the right position and have no looping self complementarity and can anneal at the extension temperature of the sequencing kit but how short are you thinking. If the problem is getting as much sequence as possible from a short template then clone it and use vector primers to sequence and get the whole template sequence

The next questions might give a hint to the answer.

What are the variant/variants in question?(are these from genome build 37?). Please provide one or more examples.

How did you get the CADD annotations?

Is your gene annotated in Ensembl 75 at http://grch37.ensembl.org/index.html ?

Dear Dr Mahmood

Your question reminds me of what used to be done before sequencing became the standard practice. This method used to be known as clamped homogeneous electrical field electrophoresis.

Your choice of endonculease would be important here. If it were to cut at too many places the gel will be very difficult to understand. If it cuts at two few you will probably miss things.

It may be that you would be better off with an endonuclease that cuts at methylated bases. Others here may be able to better advise you on this point

Thank you for answer. I actually know the programs you have presented, however they do not meet my expectations completely. I have just wrote a script in Python which does what I want.

I intend to develop it and it is possible that I will pipe it with SAM or BAM tools if needed.

It works fast enough. It processes 1MB nucleotide sequence per 1 s on my old dual core 2.1 GHz laptop.

RRBS data shouldn't require validation by pyrosequencing. You could consider it if you have concerns, e.g. read depth in a specific region of interest, but I don't think it is usually necessary and reviewers are unlikely to demand it.

It would be far more useful and informative to consider 'validation' in other ways, such as analysing the region in an alternative cohort, or determining the impact upon gene expression and the subsequent functional implications.

Thank you everyone!

In very simple words, if you just want to see chromosomal aberrations then go for Array CGH, if you want to see point mutations in some selected group of genes then you may go for NGS gene panel sequencing, if you want to do exome for diagnostics then its OK. If you are still unable to identify your gene of choice then last option is whole genome sequencing.

The target gene sequencing ie. Sanger sequencing, arrayCGH and whole exome sequencing we have successfully used for our cases. 

Best of luck

Dear Aafaq

In general, the recurrent mutation remain in the population in low frequency, and that depends on the natural selection intensity against the mutant allele, and may be in another case the mutant allele may have a selection favorable and that factor will assist to increase the mutant allele frequency in the population.

Good Luck

Hi Sharif, if it's a relatively small region you could just use traditional bisulfite sequencing (Identify a SNP, bisulfite treat your DNA, PCR a product containing the SNP and region thought to be differentially methylated between alleles, clone PCR product into Ecoli vectors and send for Sanger sequencing). This would allow you to identify the different alleles and their methylation status. Alternatively if you have access to a pyrosequencer you can use that to identify allele specific methylation. Hope this helps. 

Dear Salam,

Loss of heterozygosity and copy number decay could indicate deletetion in one haplotype.

If you want to publish these results, you do need to confirm with another method.

Sorry, you might actually have to do an experiment to address the significance of SNPs. I don't think GWAS will ever have the resolution to answer this question directly.   

LOH, in its broadest sense, simply says that a locus for which a cell is heterozygous, meaning that the 2 parental alleles are distinguishable, becomes homozygous. This is often seen in cancer, where a locus that is heterozygous in a normal cell becomes homozygous in a cancer cell derived from this normal cell. The important issue is how does this happen and what does it mean. By far, the most frequent mechanism is a cell cycle error where a chromosome (let us say it is the paternal allele) gets duplicated as a result of this error, leading to transient triploidy, followed by loss of one of the 3 chromosomes. If the chromosome that gets lost is not the one that was duplicated (i.e.: the maternal allele), then you get loss of that allele resulting in loss of heterozygosity. An important point is that there is no net loss of DNA in that process. One parental allele is lost, but the other is duplicated so you end up with a pair of 2 chromosomes, which is what you started with. Other mechanisms include deletions or other chromosome rearrangements, but the one that I explained above is the most important, as evidenced by the fact that loss of heterozygosity is usually not associated with loss of any genetic material. This is an important mechanism of tumor suppressor gene inactivation, as it represents the second hit in Knudsen's two-hit hypothesis for tumor suppressor inactivation.

I am actually interested in HLA-B, C and DBR1

Jack: Great question, though unfortunately there isn't yet a great answer.

First, what sort of DNA are you looking for: whole genome, whole exome, variant call sets relative to a reference sequence, etc.? Genome In A Bottle (http://jimb.stanford.edu/giab) is the go-to resource for benchmarking genome variant calls, but doesn't have much in the way of genotype-phenotype data based on samples with known diseases. Still, it's a great place to start.

dbGaP has lots of DNA+phenotype of data (https://www.ncbi.nlm.nih.gov/gap) but no standard sets used as reference, since access is controlled (and difficult). Coriell has DNA & cell lines from patients with known diseases but is not open source and you would need to have it sequenced (e.g. https://catalog.coriell.org/0/Sections/Search/Sample_Detail.aspx?Ref=NA11254&Product=DNA).

The Cancer Genomics Hub has tons of data for a variety of cancers, but would likely take a bit of wrangling to get it in the format you need https://wiki.nci.nih.gov/display/TCGA/Cancer+Genomics+Hub

The GA4GH (http://genomicsandhealth.org/) is working to create infrastructure to enable better clinical genome data sharing of the sort you are looking for, but there's still a ways to go.

Hi Nathaniel,

I have worked with miRNASNP tool (http://www.bioguo.org/miRNASNP/). It is a very good online database because it gives you delta G scores of interaction in wild-type and SNP conditions. However, it does not multiple entry option. 

To narrow down your SNP list, I suggest to investigate them in terms of global MAF, since SNPs with very low allele frequency in the population will not be clinically detectable and useful even if that SNP can change the interaction affinity so drastically.

thank you ...

of course you can use the browser from UCSC, but for a database prefer to go to the table browser from the same engine. the table will give you all what you can see in the viewer.

fred

Hi,

do you know if these SNPs could be associated with some traits? you can use tools as SIFT to predict exon variant consequences but still except for splice or stop codons, the effects should be tested in biological trials. in your case, at the exception of regulating sequences, I can't see how?

try the UCSC genome browser (try with cow at http://genome-euro.ucsc.edu/cgi-bin/hgTracks?db=bosTau8&lastVirtModeType=default&lastVirtModeExtraState=&virtModeType=default&virtMode=0&nonVirtPosition=&position=chr1%3A2772575%2D2921534&hgsid=219364117_IlvQGPx92M8GkcFH944RO3aT5k2x) to see if your SNPs are in interesting regions, browsing the few (for this specie) tracks.

fred

The question was about "regulatory genes". No about "regulatory sequences". I insist, we must be more precise. I consider "genes" sequences that encode a product, be that a protein or RNA. Of course, enhancers, recognition sequences for transcription factors and the like are extremely important but they are not "genes".

Dear Jennifer

I recommend you the following link:

-https://www3.appliedbiosystems.com/cms/groups/applied_markets_support/documents/generaldocuments/cms_049050.pdf

-www.prontodiagnostics.com/en/partners

-Forestier E, Frost BM, Harila-Saari A, et al: Genome-wide signatures of differential DNA methylation in pediatric acute lymphoblastic leukemia.Genome Biol 2013, 14:r105.

-Quantification of the human Y-Chromosome by real-time PCR in http://www.primerdesign.co.uk/get-file/id/1930

Hoping to be helpful

Marius

Hi Fahrettin,

Thanks for suggestion. I agree that PPARs are also expressed by macrophages and therefore it would be difficult to say from which cell type the expression is represented. 

I use Taqman assay and I certainly would like to assess  the genes which express early in the course of differentiation. As Maria mentioned about it would not be possible to measure the lipid droplet proteins in the preadipocytes ( SVF). Regarding MicroRNAs, I would look into that. 

You may also try Variowatch

Thank you Fredric, trying it out right now.

yes it is possible ,but very rare 

Sorry, My question only have title (the web problem in China or University- the content is Upload failed)。 Yes，i want to collect the T and B cell from blood and check their 

Dear Somnath Tagore,

I have used fastIBD already. It actually has its own method of phasing the genotype data. It's latest version only takes unphased data and phases it followed by IBD detection. What I wanted is a tool that takes the phased data(something that I have done using tools I prefer) as input. However I didn't know about SEQERR. I'll look this up. Thank you so much

Some condensed pyrazoles such as pyrazolo[3,4-b]pyridines have potency as CDK1 as shown below:

Pyrazolo[3,4-b]pyridines are attractive condensed heterocyclic compounds

and are reported to possess diverse range of biological and pharmaceutical activities such as antitumor [1], antibacterial [2], anti-inflammatory [3],  inhibitors of protein kinase [4], cyclin-dependent kinase 1 (CDK1) [5], glycogen synthase kinase-3 (GSK-3) [6], and HIV reverse transcriptase [7]. 

] R. Lin, P. J. Connolly, Y. Lu, G. Chiu, S. Li, Y. Yu, S. Huang, X. Li, S. L. Emanuel, S. A.                                        Middleton, R. H. Gruninger, M. Adams, A. R. Fuentes-Pesquera, L. M. Greenberger, Bioorg.       Med. Chem. Lett. 2007, 17, 4297; (b) Q. Ye, J. Cao, X. Zhou, D. Lv, Q. He, B. Yang, Y. Hu, Bioorg. Med. Chem. 2009, 17, 4763−4772.                                                                                     [2] (a) B. Leal, I. F. Afonso, C. R. Rodrigues, P. A. Abreu, R. Garrett, L. C. S. Pinheiro, A. R.     Azevedo, J. C. Borges, P. F. Vegi, C. C. C. Santos, F. C. A. da Silveira, L. M. Cabral, I. C. P. P. Frugulhetti, A. M. R. Bernardino, D. O. Santos, H. C. Castro, Bioorg. Med. Chem. 2008, 16, 8196; (b) F. E. Goda, A. A.-M. Abdel-Azizb, O. A. Attef, Bioorg. Med. Chem. 2004, 12, 1845; (c) H. Foks, D. Pancechowska-Ksepko, A. KVdzia, Z. Zwolska, M. Janowiec, E.     Augustynowicz-Kopec, II Farmaco 2005, 60, 513.

[3] S. B. Bharate, T. R. Mahajan, Y. R. Gole, M. Nambiar, T. T. Matan, A. Kulkarni-Almeida, S. Balachandran, H. Junjappa, A. Balakrishnan, R. A. Vishwakarma, Bioorg. Med. Chem. 2008,

16, 7167; (b) Y. Abe, K. Ohne, M. Inoue, M. Okumura, M. US2006/0004003 A1 2006, (Astellas Pharma Inc., Tokyo, Japan).

[4] M. Chioua, A. Samadi, E. Soriano, O. Lozach, L. Meijer, J. Marco-Contelles, Bioorg. Med. Chem. Lett. 2009, 19, 4566.

[5] S. Huang, R. Lin, Y. Yu, Y. Lu, P. Connolly, G. Chiu, S. Li, S. Emanuel, S. Middleton, Bioorg. Med. Chem. Lett. 2007, 17, 1243.

[6] J. Witherington, V. Bordas, A. Gaiba, N. S. Garton, A. Naylor, A. D. Rawlings, B. P. Slingsby, D. G. Smith, A. K. Takle, R. W. Ward, Bioorg. Med. Chem. Lett. 2003, 13, 3055.

[7] S. Saggar, J. Sisko, T. Tucker, R. Tynebor, D. Su, N. Anthony, U.S. Patent Appl. US 2,007,021,442, 2007, Chem. Abstr., 2007, 146, 163149

This may be too wide a question to get an answer to, so perhaps you can narrow it down. It really depends on what kinds of data you have and/or can generate. For example, depending on what data you have, you may be able to calculate how frequently the phenotype is inherited along with other traits that you do know the location of, and thus map the gene based on linkage, using classic genetics.

It depends on which gene and which species, etc.  A species like the cheetah which went through a severe bottleneck recently may be nearly homogeneous in most genes, while a species that is very diverse might have huge numbers of alleles for each gene.  Even within one species such as Homo sapiens, we have far more diversity in our MHC alleles than in most other genes, because of selection for diversity of immune system genes.

Dear Nathaniel, 

Thank you for your answer.

I am interested in working on a limited number of predefined SNPs.

Regards,

Olusegun

Hola Roberto: lamento no poder ayudarte; yo tengo formación matemática y aplico Matemática y Estadística al análisis de procesos biológicos. Tengo muy pobres conocimientos de Genética Molecular. Saludos.

Department of Pediatrics, Universoty of Szeged,Hungary,mol.gen.lab.

Tibor Kalmàr phd 

You probably want to look into clinvar.  It's free/gratis and consolidates many other sources.

It aggregates data from OMIM, GeneReviews, many locus-specific databases (LSDB), and various contributing testing laboratories.  It's also been published.

Publication

Nucl. Acids Res. (2013)

doi: 10.1093/nar/gkt1113

First published online: November 14, 2013

Abstract

ClinVar (http://www.ncbi.nlm.nih.gov/clinvar/) provides a freely available archive of reports of relationships among medically important variants and phenotypes. ClinVar accessions submissions reporting human variation, interpretations of the relationship of that variation to human health and the evidence supporting each interpretation. The database is tightly coupled with dbSNP and dbVar, which maintain information about the location of variation on human assemblies. ClinVar is also based on the phenotypic descriptions maintained in MedGen (http://www.ncbi.nlm.nih.gov/medgen). Each ClinVar record represents the submitter, the variation and the phenotype, i.e. the unit that is assigned an accession of the format SCV000000000.0. The submitter can update the submission at any time, in which case a new version is assigned. To facilitate evaluation of the medical importance of each variant, ClinVar aggregates submissions with the same variation/phenotype combination, adds value from other NCBI databases, assigns a distinct accession of the format RCV000000000.0 and reports if there are conflicting clinical interpretations. Data in ClinVar are available in multiple formats, including html, download as XML, VCF or tab-delimited subsets. Data from ClinVar are provided as annotation tracks on genomic RefSeqs and are used in tools such as Variation Reporter (http://www.ncbi.nlm.nih.gov/variation/tools/reporter), which reports what is known about variation based on user-supplied locations.

Published by Oxford University Press 2013. This work is written by US Government employees and is in the public domain in the US.

Apparently, the ABO blood groups are related to susceptibility to different pathogens.

See for example:

Dear Zaynab,

Concerning X-inactivation: Take in mind that X-inactivation in female individuals principally is random and stable. Meaning, that in about 50% of the cells you will find the maternal X beeing inactivated and in the other 50% it will be the paternal X chromosome. Therefore you have something like a "dose compensation" for the "bad" MAOA-L allel (which is extremly common and occurs in about 40% of the population) in females which you dont have in the male. About 20% of all the X-linked genes escape X-inactivation! So far as I know the MAOA gene does not belong to them! Therefore I dont agree with Nahi saying that both alleles have to "work" in female to give riese to the MAOA gene!

Concerning your question: Women are protected in two ways: (1) the MAOA gene is linked to the X chromosome so that women with the MAOA-L allel on one chromosome usually have a "normal" (different) allele on the other and due to X-inactivation you will find something like a mixture expression (50-50) of both alleles whereas you will find a 100% expression in men because there is only one allele (!); (2) there is circumstantial evidence that women are also protected by other genes from being disposed to violence.

Check the publication which I attached! Good luck

Christian

Hi Subrahamanyam, Thanks a lot for your prompt answer. We'll try the C. elegans miRNA.

Dear Brian

Essentially, before GWAS the candidate gene approach was the most popular methodology to use.  That said, it hasn't lost all favour - it is now used in conjunction with GWAS to provide more complete pictures for the molecular aetiology of complex traits considering a systems biology approach.

Finding an article comparing things before and after GWAS will be difficult but here are good examples discussing the impact of GWAS in research.

Regards,

The question is not quite clear.

But if you want to know about FUNCTIONAL genes with one copy per genome, look at the genes on the X in males !!  Or imprinted genes in autosomes, etc.

And multiple copies/genome, again there are many examples: Immunoglobulins, Hemoglobins, Histones, rRNA, microRNAs, etc.

Each person would have 24,000-40,000 sites where there is a base substitution on one copy of their gene (hence heterozygous) where the substitution results in a change in amino acid (non-synonymous substitution). 

Dear Agni

Our lab prepares competent cells in CCMB 80 buffer and stores at -80C in 50UL aliquots. We have been using these for more than 6 months and were able to get good results. The protocol is available online.

Good luck!

I think in 'About MutPred' section, it os explained about interpretation. It says it takes cues from SIFT. So there is conservation of aa residue taken into consideration plus how is each aa structurally conserved in a predicted topology is also considered. So does your residue fall apart while forming a sheet or loop is reflected by the scores. It says that it considers known variations reported in Human mutation database and predicts its impact on protein function.

I just did for my protein.

MoRF binding, well this pdf might help you.

It is a bit surprising to have PCR inhibitors if you work with cDNA (even if it is possible). Because usually, inhibitors come from the tissue sample or from the extraction/purification procedure, but if you managed to perform an efficient reverse transcription, I don't think that you can have inhibitors that act on polymerase and not on reverse transcriptase. I mean that if you had inhibitors from the samples or from the extraction buffer (or other), one can think that reverse transcription would have failed.

Did you look to the purity of the RNA (260/280 and 260/230 absorbance ratios)? and integrity ?

Anyway, I think that diluton of the cDNA can be a good test... let me know if it works, or not

I don't know for miRNA. I just know that all purification procedures are risky for small nucleic acid molecules.

Dear Shilu Mathew

Thanks for your paper offers. I found some of these paper before. However, maybe my question was misleading, I only want to know what is the proportion of common SNP variants and rare SNP variants in average of sequencing human data. For instance, if we want to simulate a part of human sequence data for a length of 500 kb, what is the proportion of rare and common SNP variant in the noted genome region?

Regards

Sajjad 

As someone who also looks into expression of gene homologues,

1. A 100 gene assay is doable, only lengthy.

2. A better approach would be to reconstruct a phylogenetic tree, identify the major groups these gene fall into and then study representatives from each group.

3. Even better, study.orthologues from the same group in a pair of interesting species.

Hi

you have to remember what I tell you before

Different types of library can behave differently in the emulsion PCR. So you could try to ad more library. Do you see primer dimers or adaptor dimers in the Bioanalyser traces?

"Besides, the position is required to in this format: 1:100-200(chr:stat position-end position), which is required by the analysis tool rvtest I am using."

so for "position" you mean "coordinates of the genes that you identified through GSEA"..which btw has both pathways and signatures!

alright...you can use table browser in UCSC, similar to what Jun Hoe Lee wrote:

1) go to http://genome.ucsc.edu/

2) tools -> table browser

3) choose your assembly

4) group -> genes and gene predictions

5) track -> RefSeq

6) table ->  refGenes

7) region -> genome

8) identifiers -> paste list (o upload it, depending from what you have)

9) output format -> all fields

10) write file name (i.e. myOutput.txt) and click "get output"

I would read some of this papers in order to get into the topic...

1: Wiener D, Gajardo-Meneses P, Ortega-Hernández V, Herrera-Cares C, Díaz S,

Fernández W, Cornejo V, Gamboa J, Tapia T, Alvarez C, Carvallo P. BRCA1 and BARD1

colocalize mainly in the cytoplasm of breast cancer tumors, and their isoforms

show differential expression. Breast Cancer Res Treat. 2015 Oct;153(3):669-78.

doi: 10.1007/s10549-015-3575-0. Epub 2015 Sep 22. PubMed PMID: 26395808.

2: Gambino G, Tancredi M, Falaschi E, Aretini P, Caligo MA. Characterization of

three alternative transcripts of the BRCA1 gene in patients with breast cancer

and a family history of breast and/or ovarian cancer who tested negative for

pathogenic mutations. Int J Mol Med. 2015 Apr;35(4):950-6. doi:

10.3892/ijmm.2015.2103. Epub 2015 Feb 16. PubMed PMID: 25683334; PubMed Central

PMCID: PMC4356434.

3: Raponi M, Smith LD, Silipo M, Stuani C, Buratti E, Baralle D. BRCA1 exon 11 a

model of long exon splicing regulation. RNA Biol. 2014;11(4):351-9. doi:

10.4161/rna.28458. Epub 2014 Mar 18. PubMed PMID: 24658338; PubMed Central PMCID:

PMC4075520.

4: Colombo M, Blok MJ, Whiley P, Santamariña M, Gutiérrez-Enríquez S, Romero A,

Garre P, Becker A, Smith LD, De Vecchi G, Brandão RD, Tserpelis D, Brown M,

Blanco A, Bonache S, Menéndez M, Houdayer C, Foglia C, Fackenthal JD, Baralle D,

Wappenschmidt B; kConFaB Investigators, Díaz-Rubio E, Caldés T, Walker L, Díez O,

Vega A, Spurdle AB, Radice P, De La Hoya M. Comprehensive annotation of splice

junctions supports pervasive alternative splicing at the BRCA1 locus: a report

from the ENIGMA consortium. Hum Mol Genet. 2014 Jul 15;23(14):3666-80. doi:

10.1093/hmg/ddu075. Epub 2014 Feb 25. PubMed PMID: 24569164.

5: Soyombo AA, Wu Y, Kolski L, Rios JJ, Rakheja D, Chen A, Kehler J, Hampel H,

Coughran A, Ross TS. Analysis of induced pluripotent stem cells from a BRCA1

mutant family. Stem Cell Reports. 2013 Oct 3;1(4):336-49. doi:

10.1016/j.stemcr.2013.08.004. eCollection 2013. PubMed PMID: 24319668; PubMed

Central PMCID: PMC3849250.

6: Tammaro C, Raponi M, Wilson DI, Baralle D. BRCA1 exon 11 alternative splicing,

multiple functions and the association with cancer. Biochem Soc Trans. 2012

Aug;40(4):768-72. doi: 10.1042/BST20120140. Review. PubMed PMID: 22817731.

7: Raponi M, Douglas AG, Tammaro C, Wilson DI, Baralle D. Evolutionary constraint

helps unmask a splicing regulatory region in BRCA1 exon 11. PLoS One.

2012;7(5):e37255. doi: 10.1371/journal.pone.0037255. Epub 2012 May 16. PubMed

PMID: 22615956; PubMed Central PMCID: PMC3353946.

8: Sevcik J, Falk M, Kleiblova P, Lhota F, Stefancikova L, Janatova M, Weiterova

L, Lukasova E, Kozubek S, Pohlreich P, Kleibl Z. The BRCA1 alternative splicing

variant Δ14-15 with an in-frame deletion of part of the regulatory

serine-containing domain (SCD) impairs the DNA repair capacity in MCF-7 cells.

Cell Signal. 2012 May;24(5):1023-30. doi: 10.1016/j.cellsig.2011.12.023. Epub

2012 Jan 3. PubMed PMID: 22245140.

9: Okumura N, Yoshida H, Kitagishi Y, Nishimura Y, Matsuda S. Alternative

splicings on p53, BRCA1 and PTEN genes involved in breast cancer. Biochem Biophys

Res Commun. 2011 Sep 30;413(3):395-9. doi: 10.1016/j.bbrc.2011.08.098. Epub 2011

Aug 27. Review. PubMed PMID: 21893034.

10: Thomassen M, Blanco A, Montagna M, Hansen TV, Pedersen IS, Gutiérrez-Enríquez

S, Menéndez M, Fachal L, Santamariña M, Steffensen AY, Jønson L, Agata S, Whiley

P, Tognazzo S, Tornero E, Jensen UB, Balmaña J, Kruse TA, Goldgar DE, Lázaro C,

Diez O, Spurdle AB, Vega A. Characterization of BRCA1 and BRCA2 splicing

variants: a collaborative report by ENIGMA consortium members. Breast Cancer Res

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Hi there,

I think the following should help:

Hello Dr. Parihar,

In our lab we have a mouse model that overexpresses a SIRT3 construct as well as adenoviruses for both a full-length SIRT3 as well as a truncated SIRT3 which lacks a mitochondrial localization signal. Would this be of any interest to your group?

Kyle

There are 5,392 SNPs reported by dbSNP for BRCA1. You can find a list of them here (http://www.genecards.org/cgi-bin/carddisp.pl?gene=BRCA1) along with their position on the chromosome. From there you can export the list, sort by chromosome location, determine the range of chr 17 positions for each exon, count the number of SNPs that fall within that range for each exon and then you will know which exon has the most SNPs. There are many resources that can help you find the location (chromosome position of each exon), such as UCSC but there are many others. I hope this is a sufficient amount of information for you to be able to generate an answer to your question. Good luck!

Regarding the problem of choose the right gene, I can suggest for normalization of gene expression experiment the approach based inter-run calibration. Basically it is a deltadelta-Ct method that take into account for better normalization multiple (potential) reference genes and their specific amplification efficiencies .

Here the link of the publication:

There is a great web-based tool called phenolyzer.  It accepts a list of HPO terms, or disease terms as input and provides a regression model to rank disease genes.  It is well documented so that you can adjust parameters to your specific focus.  Links are to the web sever and the paper published in nature methods.  Hope this helps!

Thank you everyone for all your valuable suggessions.. thanks Arpa, thanks Jamie

Thanks, I will check.

Dear Sanya Cirkovic,

Ya, you got it right. Just to be in line, all papers published on LGR in BRCA genes followed the new HGVS nomenclature.

I do think   Alamut software is an essential/ main/golden bioinformatics tool when we  perform genetic testing and genetics diagnosis.

Best regards

Dr Farid Cherbal

Hi Emma,

You can use Negative RT as a control.  So, basically, at the time of preparing the cDNA, you can make Negative RT COntrol = RNA (DNase treated) + dNTPS + Random Hexamers/Oligo dT + RT Buffer + Water WITHOUT RT ENzyme. Do the cDNA prep by whatever method you usually do.

Now, use both +RT and -RT samples and now do PCR with the primers for Dystrophin. If you don't have any gDNA contamination, your -RT will be clean and will not give any amplicon. +RT will obviously/should work.

If your -RT control shows any amplicon, this suggests that your DNase treatment of the RNA is not good enough and you need to treat it again.

Hope this helps. Good Luck!

Utkarsh

Hi,

The HGVS guidelines are a bit tricky to figure out sometimes. Do you mean large deletions for which you do not know the breakpoints, found by arrayCGH, MLPA etc?

For these, you should use c.(first deleted exon, first nucleotide)-?_(last deleted exon, last nucleotide)+?del

This is because the breakpoint could be anywhere in those two introns flanking the deleted exon(s)

Does that help?

All the best,

Gavin

If you run SIFT on the SIFT website (http://sift.jcvi.org/www/SIFT_chr_coords_submit.html) and also run Provean from the same website (http://provean.jcvi.org/genome_submit_2.php), you get different SIFT predictions returned. I've never found an explanation for this, but I'm guessing the results from the Provean search are from a more recent re-analysis of the data. ANNOVAR-SIFT agrees with the former set.

I recently benchmarked (unpublished) all 15 tools in ANNOVAR using a set of high confidence rare human pathogenic mutations vs randomly chosen private variants in ExAC. Even after tweaking the thresholds to maximise performance, SIFT predicted 49.2% of pathogenic mutations as damaging, but 29.3% of ExAC. I  ranked it 8th. In agreement with Oyediran, the best 3 methods by far were FATHMM (85.1% to 18.8%), MetaLR (80.8% to 20.7%) and MetaSVM (73.4% to 15.5%).

I would probably try formamide + heat if possible.

If the tissue have been frozen directly in Trizol, you need to disrupt the tissue using a mechanical device such as MagnaLyser (ROCHE) or an T8-Ika. After this disruption, you could extract your nucleic acid as describe in the manual of the trizol.

when you want to design the primer, It would be better to try the sequence with high C site to make more differences between treated  and untreated DNA.

Hi, Dr Dannemiller, did you find any ASE database? Thanks

Hi Alok,

depending on what your reactants are (purified proteins, cell extracts on the one hand, and oligos, plasmids, PCR fragments, genomic DNA of treated cells on the other hand), you can use several techniques like EMSA, pulldown assay, cell fractionation (i.e. chromatin fraction) + western blot or mass spec, dot blot, southwestern blot, etc.

Hope this helps.

Ph.

Hi Mesut

I'm not sure I understand you're question fully - but if you're looking at a 'memory' form of transcriptional regulation within tissues in humans you're most likely referring to methylation and epigenetic regulation.

The core genes involved in methylation are MTHFR, MTRR and MTR: however there are a number of other genes that could be involved in the methylation process.

I hope that helped somewhat.

The master mix is not a problem for a best amplification!!! all  master mix are good, yuor amplification is mosty dependent of your primer quality.

Hi Robert Trivers,

I own a copy of your book. I will do a deal with you - I will read the Chapters you suggest with an open mind, if you do likewise with the papers by Oliver & Greene & Oliver et al. and for good measure read Lynch et al. 2015 'Ancient Transposable Elements Transformed the Uterine Regulatory Landscape and Transcriptome during the Evolution of Mammalian  Pregnancy' and similar papers to get another angle on the subject of TEs and evolution, and also on TEs and cancer.  We can then resume this interesting discussion. Other interested people on Research Gate are invited to do the same,

Regards,

Keith