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Functional Genomics - Science topic

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Questions related to Functional Genomics
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I want to annotate each gene in the Homo sapiens taxon with its respective GO terms and its hierarchical parent terms in the GO database. How can I systematically do that? While I am aware that the obo file contains information such as "is a," "part of," and "regulates," it lacks a comprehensive hierarchy from child GO terms to all their parent terms. Is there an existing method available to achieve this systematic annotation, or do I need to develop a custom script to extract this information from the obo file?
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Mohammad Shahbaz Khan Certainly! Although the data is currently presented in Gene Ontology (GO) format, I want to create a comprehensive graph that visualizes the entire information. Further, I intend to annotate each gene with its corresponding GO term, including all parent terms associated with each gene.
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It says they are updating the developer software, but has been like this for a week. Any updates on this? I would like to gather data from this platform.
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Contact them via provided email.
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I'm interested in studying specific missense mutations in a human gene. My goal is to determine whether the mutated region of the protein is conserved across various species. Could you please guide me on how I can use in silico tools to find homologous protein sequences and identify their conserved regions?
Thank you very much
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That's a good approach Susanta Roy I would add that once you are working with your multiple sequence alignment (MSA) in Jalview (https://www.jalview.org), you load an experimental 3D protein structure, or an AlphaFold model (all possible from Jalview, just right-click on a sequence label), and visualise the mutations and conservation scores on the structure too. Jalview makes this easy by colouring the structure by the sequence, so you can choose to colour by conservation and add features to represent your mutations and they will instantly be viewable on the structure.
The other thing I would add is that in addition to BLASTing the full-length protein, have a look at it on InterPro and see what domains it has. Then you can work with curated MSAs from the individual domains too.
Great question Muhammad Abrar Yousaf !
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Hi all,
I want to learn how to work with these databases? I do not know how to learn them step by step, and there is no instructional video.
-Genevestigator
- ProteoCloud
-PeptideAtlas
- Chorus
Thanks for all your help.
#molecular_biology
#proteomics
#functional_genomics
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First, find the problem, the learning will be added to your mindset by default. Anyhow, youtube, Coursera or even some text (pdf) based tutorials are already available on the internet.
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There is very little publication where functional characterization(cloning, overexpression, silencing, etc.) of genes identified through GWAS has been performed. However, most of the publications on functional characterization are on genes identified through transcriptome. Why is this? I doubt whether there is any usefulness of GWAS on crop improvement or not? if yes then give me some successful publication examples?
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The reason is that there are very few publications where functional characterization (cloning, overexpression, silencing, etc.) of the genes identified through GWAS has been performed. However, most of the publications on functional characterization revolve around genes identified through transcription because these are quantitative traits and are controlled by many genes and the influence of the environment is very high and the effect of each gene is weak (Minor genes) and they have small-effect genes rather than Major genes that have large -effects
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I need to compare the human gut microbes with human to identify functional genome similarity. Is there any online tool for comparing the genome between different species?
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Do you mixing two similar but different things, i.e., functional genomics and comparative genomics.
If you want to compare genomes, it will be comparative genomics which would require genome (nucleotide) data.
For functional analysis and comparison, several other datasets would be required depending on the experimental design.
For comparative genomics too, you cannot use whole microbiome to compare against human genome. Such comparison itself is illogical. Why would one need to compare genome of a microbe to that of human.
  • There are several tools which can be used for the comparative analysis, but that can be discussed once the basics has been understood.
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How to find the candidate genes to validate their role through functional genomics experiments such as cloning, transgenics, over-expression, localization, and its interaction with other proteins and DNA, etc.
1)Do we need to study a lot of literature and see which genes role is not deciphered in particular traits e.g. drought stress?
2.) Do we need to perform our own transcriptome or comparative genomic studies or analyze already published studies from literature?
3. ) Do we need to perform our own marker traits association(QTLs) study or already published studies?
4.) Some people functionally characterize already known genes(say arabidopsis) to plant of their interest (legumes). But is it a significant or novel research problem to work upon?
5. all of the above.
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Hey Shubham,
it depends on what questions you would like to answer with your experiments. I'm also not sure if i fully understand your question but maybe this helps:
1) -> it is always useful to read and know the important literature.
2) -> transcriptomic data and comparative genomic studies are useful to identify relevant genes for a certain issue. When it comes to transcriptomic data a useful approach is to expose your organism/cells to a certain stress to detect up-/downregulated genes compared to non exposed cells. Therefore it is important to design your own experiments to answer your individual questions.
Finding only homologous genes/proteins, you can use several bioinformatic databases (BLAST, UniProt...) in this case you should know your target genes.
4) -> characterizing already known genes of the same organism/cells is not useful. Why would you do that when it's already reported? But you can investigate homologous genes of another organism (not reported!) to check if comparable gene sets/proteins are involved in e.g. draught/starvation etc... Finding completely unknown genes and classify them to a cellular event is not that easy as you might think :)
good luck!
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I look forward to implying a good CRISPR/Cas9 system with a selective maker for functional genomics of important genes from environmental fungi and yeast.
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As i know András Kis works with CRISPR/Cas9 systems, and has really good results. Maybe You could ask him directly.
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I have several ORF's that I am trying to find the function of, I am wondering what are the newest tools abvailable are or if there are any obscure tools I can use. I have already tried the following Tools: BlastP, Interproscan, PredictProtein, ITasser, DeepGoPlus.
Any help or advice would be much appreciated
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Try expasy tools portal.
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I have blasted an amino acid sequence in Blast P nd recieved multiple partiol matches (multiple helix turn helix domain, mulitple DNA binding region, multiple replication protien matches) all with low expect values. However when I search the same amino acid sequence on interproscan I recieve no results. Why would this be the case?
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Interpro default percentage identity threshold is 50%. And I guess your blast result percentage identity is less than 50%. So, nothing passed the interproscan threshold and thus no result.
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Hi all, I want to download a gene sequnce from Genome Browser, but I am not so sure about the direction of the sequence I get. Does it always show the coding strand sequence of one gene in the 5' to 3' from left to right?
In NCBI, we can identify that one specific gene on chromosome is on plus or minus strand and thus can select "showing reverse complement" or not depending on the direction. I wonder if there is similar function on Genome Browser?
Thank you for answering!
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wherever you want to download gene sequence it will always be from 5' to 3', even if the gene in reverse stand.
from UCSC, downloading data will always give you the sequence and some text with informations as positions and sense.
fred
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It's known that the yeast Saccharomyces cerevisiae does not have P450 enzymes required for biotransformation of exogenous compounds.
However, many pharmacological and toxicological studies utilize yeast as a model organism.
Does anyone have an idea on how yeast cells metabolize these chemical agents before they exert their biological activity?
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Hello! Actually their metabolism is heterotrophic,and they degrade organic substrates as an exogenous source of carbon.
I think perhaps a recombinant Saccharomyces is used. As the recombinant ones have a positive stable P450 expression.
Hope this helps :)
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I would like to perform gene diversity analysis to study their ecological role and function from the bacterial whole genome data sets. This will be comparative study of different bacterial genomes will be published as a research review article.So, can I download and use genome data sets submitted by some one else in the GenBank? Can it be legal to use those genome data sets to use for my analysis?
Valuable suggestions and comments are welcome.
Thanks in advance.
Siva
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the reference used in the database for the dataset must be noted in your text (GSExxxxx for exemple if it's taken from GEO/NCBI).
fred
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I want to be able, in some quantitative way, to compare several plant species with respect to how much is known about the functions of all known loci in their genomes.
It seems like looking at the tags for each locus in different functional annotation or protein databases (i.e. Pfam, PANTHER, KOG, GO BP) might just be reflective of the species' evolutionary relationship to Arabidopsis, for which most of what we know about plant protein function has been determined. Are there any publicly available datasets that I could use to approximate the sum of all functional work done in each plant that would be comparable across species? How could I analyze them to roughly answer the question: "what proportion of the genome has been functionally characterized?"
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Use arabidopsis gene number (27,655) as a lower boundary of plant gene number estimation related to the species you're interested, because of low repetitive DNA content in arabidopsis genome.
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what is the function Genomic Lysis / Binding Buffer?
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Hi, may be you mean
PureLink™ Genomic Lysis/Binding Buffer?
As I know, the functions of the buffer are:
1. Cell membrane disruption
2. Creating conditions to DNA binding on the surface of silica-spin column membrane
Usually it is 2-6 M of guanidin chloride in the case of DNA or guanidine thyocianate in the case of RNA + solt buffers (for example sodium cytrate) + surface-active substance (SDS, triton, tween, sodium lauroilsarcosine etc).
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How can the same modification of the standard genetic code happen in two organelle genomes (chloroplasts and mitochondria) and in the same organism?
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It may be possible because mitochondria and chloroplast have similar system (Evolutionary) . However, differences are rare in terms of codons, so genetic code can be as universal.
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Hi, I am characterizing the transcriptional regulator of Mycobacteirum tuberculosis .
Because we don't have facility for M.tuberculosis culture, I am trying to over express (using PVV16 which has Hsp60 promoter) it in M.smegmatis. but I never got colonies though the clone and vector back bone are fine. So I thought over expression might be toxic to M.smegmatis. so to check its toxicity I did frame shift mutations and found its over expression is not lethal.
However for the same protein Chip-seq data available in MTB Network Portal. By going through their publication in NAR, I realised they over expressed corresponding transcription factors in M.tuberculosis using Tet-inducible system and did Chip-seq analysis.
My question is, is over expression of my protein not toxic to M.tuberculosis but toxic to M.smegmatis or am I simply doing something wrong?
How can I get a over expression strain of M.smegmatis?
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Functional genomics is a field of molecular biology.
What are the common and uncommon tools between them?
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Molecular Biology exclusively refers to wet lab work, functional genomics uses almost all of the molecular biology tools but adds bioinformatics to it since it usually deals with large datasets.
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I am studying fungal whole genome sequence specifically (basidiomycetes) secondary metabolite biosynthesis gene clusters. Antismash and SMURF have identified several terpenes, PKS, NRPS clusters but did not provide any putative end products from such clusters. But NapDos specified some end products and one of them (epothilone) was confirmed through analytical studies. When the biosynthesis cluster of epothilone was compared using local blast, it showed almost a 2kb similarity out of the 80 kb cluster for epothilone. However, the 2kb is not a continuous sequence (an average of 50 bp), sparsed among all the PKS clusters identified by ANtismash and SMURF. It may be noted that the biosynthesis gene cluster of epothilone till date is only confined within the bacterial kingdom.
Is sparsed sequence similarity relevant? Am I on the right track in searching sequence similarity among different kingdoms? It may be mentioned that the problem is similar for several other clusters. A valuable suggestion and comments will be highly appreciated.
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You shouldn't expect DNA sequences to line up across sp. or Kingdoms. Predicted amino-acid sequences will be a better indicator for sequence similarity, but keep in mind that even protein sequences with 10% identity can have the same fold. ANTISmash, SMURF, and NapDos use Stachelhaus code to predict the substrate specificity of Adenylation domains in the NRPS. The code is derived from many different experimental analysis of adenylation activation domains, but the fungal NRPSs are underrepresented. For instance, if Adenylation domain 1 from a predicted NRPS operon in ANTISmash suggests a Leucine then it might be any volumetrically and non-polar similar residue. Since fungal NRPS often incorporate Isovaline and 2-amino-isobutyric acid (think about the peptaibols) you should consider that these residues might be present in the final product of the predicted operon. The rules are more clearly defined for the NRPSs due to the biosynthetic modularity and wealth of literature.
The PKS and terpenes are more difficult to examine in silico. Their rules are less clearly defined due to the inherent complexity of their biosynthetic processes. However, if you have fractionated the extracts of the fungi in question then you can look through the bioinformatic analysis for a biosynthetic scheme that makes since in the context of an isolated product that you purified from the fungus in question. Anytime I examine biosynthetic operons from sequence I compare with experimentally identified and confirmed natural product databases (SciFinder, Dictionary of Natural Products, MarineLit) using the source organism genus ors species as a search vector.     
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Is there a reference file (bed) for enhancer regions in the mouse genome (mm9) ?
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Thank you!
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I want to clone an enhancer that is translocated upstream of a regulatory gene by a translocation. How can I do that? Which protocol to follow?
I could see the upregulation of the gene by RNA sequencing, now I need to figure out which enhancer is the responsible one for this upregulation.
Thank you!
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The question posed by u probably has solution in this link
https://enhancer.lbl.gov/, check ur sequence in this database.
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I'm confused whether UCSC includes the promoter region or starts at the TSS
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UCSC gives only the proper gene coordinates, starting at TSS and stopping at TTS. If you want to include some upstream (like promoter) or downstream sequence, then you have to add or subtract (depending on the gene orientation / DNA strand position) the number of bases you want. Translated parts of the gene (that'll actually give the protein sequence) are indicated by wider bars than untranslated ones (5' and 3' UTRs). Promoters can also extend a bit more than 1kb from the TSS and on both sides, but there is no strict consensus about where to start and end a promoter, I think you'll have to decide a definition and stick to it.
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Does anyone know were I can download CGAP: A new comprehensive platform for the comparative analysis of chloroplast genomes? The link in their paper is broken.  
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Sorry, I thought that there is a problem in accessing paper.
I think you should drop mail to the corresponding author regarding this issue at given email id: zhliu@implad.ac.cn
As the problem is on their in-house server, nobody else can do anything, apart from their lab members.
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 i am working on s.aureus genomics. I have sequenced genome on illumina next seq 500 platform.
i have with me raw sequence files generated from illumina next seq 500.
i am looking for work flow for the analysis of genome and comparative analysis.
other wise suggest tools and server .
thank you
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thank you sir
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I have a list of genes ranked according to their expression. I want to know which gene encodes transcription factor. I cannot search one by one, because there are too many genes. Any suggestions to find genes encoding transcription factors? Any tools? Thanks.
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I am not sure for what specific organism you are looking them, but here is a good comprehensive list for TFs of rats/mice and humans 
Also I can redirect you to this thread where someone else asked the same question. You could start looking for something here as well? https://www.biostars.org/p/8042/
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I have RNA seq data for testis and ovary samples of 2 Drosophila species. I don't have biological or experimental replicates. How do I quantify differential expression for this. I have used reads from species X and species y testis data to build one assembly using TRINITY and aligned the raw reads of both species for abundance estimation using RSEM. Later used EdgeR to perform differential expression. EdgeR has done pairwise comparison for the samples. Is this a right approch to go about. From literature it appears this is the only possible way to compare and quantify genes if replicates are not available. Any suggestions . 
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edgeR is OK for DEGs analysis in single replicate experiment. You will be required to set a parameter of Biological coefficient of variation (BCV), which is default as 0.2.
You should overlook the statistical significance (such as p-value, FDR), because this is meaningless in your single replicate design. You can just look at the fold changes of genes calculated by edgeR.
Hope these can be helpful
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m
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The question is not clear
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The 'Viral Informatics Resources for Metagenome Exploration (VIROME)' uses CD - HIT 454 algorithm during its screening, does it mean that this portal is more useful when we have our data set generated by a 454 platform? 
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Thank you, Dr. Youssar for your response.
Solly
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I' try to detect the differentially expressed gene using human RNASEQ data. And I don't care about the  alternative splice and so on right now.
Could I just use tophat to map my reads and cuffdiff to deal with the bam file and get the DEG?
Or I must follow the tophat-cufflink-coffmerge-cuffdiff pipeline strictly?
I have got the gtf file from UCSC and iGenome.
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Dear Zhang,
I suggest you have to execute the steps strictly tophat followed by cufflink, cuffmerge and cuffdiff to get appropriate DEGs.
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hi
i am currently deciding on whether to use DNA microarray or RNA-seq for my project. I am interested in studying the mechanosensitive genes: genes which expression level changes upon mechanical stimuli. I was told that RNA-seq can identify unknown transcripts while DNA microarray is limited to probes for only known sequences.
My question is, arent all the DNA sequences for human genes are known? what is this unknown transcripts referring to?
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There is also the additional complication that there is a lot of features you will not capture with microarrays but you will with RNA-seq - the most prominent is you can find isoform switches with RNA-seq data.
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Why segmented genomes like bipartite, tripartitie etc are not seen in double stranded DNA plant viruses? any unique reason for this?
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Thanks sir
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I
Thanks for guidance! I will follow the ınstructions!
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This article represents the beautiful research you all do. Thanks Pat. You have amassed the strongest and creative minds around and this article does a great duty to them and itself by occupying the space and depth of the research. From cover to end we follow the drama of life. The figures are a great way to give the reader a progression from top to bottom.
Here the familia extensa circumdederunt plays out arm chair style. First the cover art sets the scene; Ancient Andes fauna where eons of the universe have played out, and we tell the story of one little flower and the marriage of life. From the start we were intrigued by the question of speciation, and asked. Can we capture the meaning of this by exploring biological and genetic mechanisms at play in the reproduction of Solanum pennellii? The physical space this organism occupies is the isolating and chaotic habitat of the Atacama desert. A dry niche. However rife with physical barriers to survival, this organism is a successful example of the Atacama experience. Bright ornaments are on display seeking and listening for a link... Action occurs but its not what was expected. Thru a market of energetic exchange a message was delivered.... But that's only the first space, the physical. As biologist read on. So many messages what do they say, what is the genetic space? Some messages of unknown content, others very familiar, and even still those we wrote. The flux of life is photographed and the courtship is a puzzled plot, answering some questions and raising more. Life is a struggle with ones own self (SI), and that of another (UI). None other than the true recipient and that of the original writer may know for certain, but we've opened the envelope and have peeked inside, inside the drama of life.
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Hi everyone,
I recently downloaded the histone modifications BAM and broadPeak files for GM12878 cells from UCSC (ENCODE histone modifications > Broad histone) from the link provided below:
However, the 'Additional Details' column for some of the files states the origAssembly=hg18 while it is hg19 for the others but the 'Alignment' sub-section in the 'Methods' section at the bottom of the page says GRCh37/hg19.
So my question is which human genome assembly (hg18 or hg19) was used for generating these files?
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Thanks Kévin and Abhishek for your replies.
I also emailed this question directly to the UCSC team and they confirmed that though the original assembly was different all files have since then been 'lifted over' to the hg19 coordinates.
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I have interval.list file with columns including chr, start, end, gene_name, TSS, Strand, as shown below:
chr1     958864565     58865165      A1BG          58864865      -
chr1     9 58863035    58863635      A1BG-AS1  58863335      +
chr10   52645135       52645735      A1CF          52645435       -
If any variant fall in to listed interval, How can I add gene name, TSS and Strand info relevant to that interval as well as variant to the VCF.
Thanks,
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I got the answer on BioStar, please follow the link if anybody wants answer.
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I want to study the expression of certain histone deacetylase (HDAC) genes and the expression of two pro-inflammatory genes – RIPK2 and COX2. Please let me know the best tools and methods  for the same. 
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Hi Andrew,
If you want to check the expression of these genes, I recommend you to perform RT-qPCR and Western blot analyses in your different conditions. 
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Hi, I haso worked on candidate genes exon regions in sugarcane and prepared a manuscript, would you be available for my article review ?
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 How urgent is it? I have not much time, but I can have a look. You may send it to eugenia.andrade@iniav.pt
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I need a BAC that spans the entire human FLI1 locus.  There is one annotated on the UCSC genome browser but, as bad luck would have it, this clone is no longer available.  How can I get a BAC That spans an interval of the FLI1 locus at ~200-kb in size?
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Thanks; i figured out that I was using release hg38 instead of hg19 where all the tracks for BACs are.  Thx!
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Dear RG users,
   I know that a series of genes are expressed in the same tissue of an eukaryotic organism; these genes map in close proximity in the genome, that is they lay hundreds of base pairs far away in the same chromosome; now my question is, is it possible to predict if all of the genes or at least some of them are under the control of the same promoter or if they are expressed in a polycistronic mRNA? any advice in this way will be highly appreciate
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Polycistronic mRNAs are very rare in eukaryotic organisms.  Usually, genes that are expressed in the same tissue type are regulated by a similar set of transcription factors. You could look at the regulatory elements for the genes to see if they have similar transcription factor binding sites.
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Hi All,
I am doing drug discovery work related to DNA methylation level in cancer cells. I wonder if there are new & good assay that allow me to efficiently track DNA methylation status on a specific promoter?
I know that I can do bisulfite sequencing, but its not fast and I cannot track many compounds. I wonder if there are newer assays out there that provide high throughput capacity?
Thanks,
Lyra
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Hi Lyra,
It also depends on the medicine you want to screen and what the impact on DNA methylation is at the specfic promoter. Large differences could need less accurate methods like MS-PCR.
I found that methylation specific high resolution melting worked quite OK on a specific promoter. With this method you also need to bisulfite convert your DNA, this is followed by QPCR and metlingcurve analysis. Of course, given that you have a QPCR machine capable of doing high res melting. But this method is quantitative and can be used for many samples at the same time. original paper doi:10.4161/epi.7.1.18815 and where I tried it on a specific promoter: dx.doi.org/10.1021/es405524b
Another method is BisPCR2, but does require an expensive illumina kit and a miSeq sequencer. It is a bisulfite sequencing method capable of measuring up to 48 samples at the same time, measuring multiple loci. 10.1186/s13072-015-0020-x
Good luck!
Jorke
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I find it confusing how the super enhancer can be defined practically. There has been 'standards' set to define mouse embryonic stem cell super enhancers using specific transcription factors. Is there any more general markers (like the enhancer markers H3K27ac or H3K4me1 and 2) that can be used to define super enhancers in the whole genome level in all cell types?
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Dear Rongrong,
what I usually do for defining the super-enhancers is first to delineate a set of active enhancers based on H3K4me1 and K3K27ac ChIP-seq. I then use this set of enriched regions to compute the H3K27ac signal using ROSE (https://bitbucket.org/young_computation/rose) as said above and it give reproducible results which are in accordance with the literature. Good luck!
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I want to develop a research proposal to investigate a possible hybridization of a bird species. Also need to understand the direction of the gene flow. If anybody knows the correct procedure to do this or any reference that I can use please let me know..
Thanks
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Look up D-statistics- attached some papers. If you have whole genomes to work with, you could calculate this in sliding windows to examine enrichment of introgressed regions throughout the genome (see Dashamaptera et al. 2012). 
There are several variants (e.g. 5-taxon, "Dfoil"), but in the original 4-taxon test, for a fixed tree of ((A, B), C), D) you can test for introgression between taxon C and A or C and B by examining the relative frequency of discordant topologies inferred from your various loci throughout the genome. 
Of interest are two particular discordant patterns: a polymorphism that seems to unite A and C ("BABA", or a case in which B and D have the ancestral state, and A and C share the derived state) and those which unite B and C ("ABBA"). We could expect these patterns to show up simply due to stochasticity in lineage sorting, so in order to discriminate lineage sorting effects from "hybridization" we look for a bias towards one pattern or another. The assumption here is that random lineage sorting would tend to create a relatively equal number of "ABBA" to "BABA" sites... So we calculate the simple D statistic as sum(ABBA-BABA)/sum(ABBA+BABA). 
Hope that helps. There are other useful tests for introgression but the "ABBA-BABA" test (Durand et al. 2011) is very commonly used- but also see the extensions of Eaton and Ree (2012) and Pease and Hahn (2015). 
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What design of molecular techniques says about  the functional quality of a plant? Say for example if the plant is a timber yielding by its economics?
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Dear Binoy Kurian,
The characterization of wood is usually performed through the physical, mechanical, chemical and anatomical. Furthermore, there are destructive and non-destructive methods for the characterization of wood.
I send you some journals where you can easily find these characterizations.
I hope I understood your question and helped you.
Best Regards!
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I want to explore the transcription factor candidate for a human promoter gene. I want to mean that, let I do not know about any possible factor. So, now how can I find a transcriptional factor candidate for the promoter gene? What are the methods- using software only? Is there any manual method through which I can explore my query?
Please share as I am new in this cellular biology field. 
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Apart from predictions there are many sites that have been experimentally defined by ENCODE and other projects for a range of tissues. ENCODE data is easily accessible. It may also provide additional information that is valuable i.e. the epigenetic status largely defines the permission for TF binding. The absence of a permissive chromatin state in particular will prevent TF binding.
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Genome Assembly Statistics
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Thanks,  Professor Juan Luis Mateo.
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for example how can I find it on NCBI?
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You can try querying the SRA: https://www.ncbi.nlm.nih.gov/sra
Try several filters such as organism, place where the data was produced, name of the project, authors, etc.
Good luck!
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I've been wondering about gene model quality coming out of eukaryotic genome sequencing projects. Could anyone direct me to some good reviews on gene model prediction approaches and resulting prediction quality metrics? In short: what makes a eukaryotic gene model a good model?
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 Thank you for your answer, Jan. I'll take a look at your work. 
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Identifying bacteia present in healthy verses disease conditions in a given biological sample
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You can approach Eurofins Genomics India Pvt. Ltd. for faster turn around time.
Contact:
Dr. Abishek Malakar 8375898192
Dr. Rudra Prasanna Panda 8884112396
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Bioinformatic tools
Illumina Hi-Seq 2000 and Illumina Hi-Seq 2500
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There might be different errors cropping up with different machines, but there should hardly be any difference between HiSeqs 2000 and HiSeq2500. This Blog from Illumina also claims high concordance of the data: https://blog.basespace.illumina.com/2012/11/26/high-concordance-between-hiseq-2000-and-hiseq-2500-data/
I would be more concerned to compare HiSeq and NextSeq/NovaSeq data since the sequencing readout is different, or Illumina and Ion Torrent machines where the whole process is different
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Taxonomy, genetics, genome and something-else.
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Md. Abu:
Kindly see this link for insights:
Best
Syed
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Drosophila Testes transcriptome sample show a very high number of transcript counts, 26-29 thousand transcripts.
1.Can one expect this high transcript count from a testis sample?
2.After de novo assembly, we see that the number of annotated transcripts which has shown a homology with known gene of drosophila melanogaster to be around ~8000. Others are not showing any homology.  What about the remaining transcripts? this is a huge number to be neglected. 
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If you run a de novo assembly, keep in mind that all the SNPs (or even error during sequencing) might be contributed in the increment of transcript numbers. Also, how did you do the quality control for your raw reads ? Also, did you used pool samples - it might also increase the gene variance and caused the situation. Moreover, if you use Trinity for de novo assembly, it usually maximize the number of transcripts to increase the chance to detect "rare" genes. Also like Dr Prescott said previously, there could also be ncRNAs, miRNAs (given the fact that it has not been eliminated during the sequencing process - you didn't mentioned it here).
Usually I will do a clustering approach to cluster transcripts together to remove the redundant. But for Drosophilla, it might worth it to try a guided de novo assembly (based on a Drosophilla genome, of course). Trinity can handle the process quite well. 
Hope this help :)
Tuan
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Hi all,
how do I choose a genome to be used as reference if i want to order the contigs once they are assembled?I know that it is supposed to be the most closely related . But, for example, in my case I am seqeuncing a clinical isolate of Klebsiella pneumoniae but I have no information about this bacterium  and the most related species, Therefore, based on what  do I  choose a reference that is the most similar to my bacterium? 
Thanks
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Hi Silvia
You are right, the Medusa link seems expired.
Or try "http://contiguator.sourceforge.net/" and click on "Download latest version"
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I am looking for genes that have altered expression levels. When I use the GEO database I find multiple results for the same gene within one dataset (for example MCM4 in dataset "GSE9750"). In this example MCM4-results appear 4 times: 2x MCM4 is significantly elevated and 2x MCM4 shows no significant elevation. I'm wondering why one dataset contains multiple results for the same gene that also appear to contradict each other.
I appreciate your help!
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As expected, this is microarray data. Usually one probe is used to detect one gene (transcript) in an array but it's also common that some genes have multiple probes designed to hybridize to different regions of the transcript, perhaps for the purpose of variant/isoform studies. If you are interested in the overall expression of the gene, I would usually take an average of all probes for that gene. Otherwise you could look up the information of the probes (their locations, etc.) from the array company. In your case, http://www.affymetrix.com/estore/catalog/131537/AFFY/Human+Genome+U133A+2.0+Array#1_1.
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KBM-7 cells are a suspension myeloid cell line that are near-haploid. They are often used for gene-trap mutagenesis screens, or other functional genomic screens.
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Can anybody suggest me appropriate tool for aligning and assembly of 574 sequences of 68 Mb data together?
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I had a look at the PacBio webpage and they give some suggestions. Here's the link: http://www.pacb.com/products-and-services/analytical-software/ and that's where one can download the software: http://www.pacb.com/support/software-downloads/ and here are some tutorials: http://www.pacb.com/products-and-services/analytical-software/devnet/
I hope this helps.
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I am planning to overexpress a transcription factor in INS1 cells and at the same time I like to knockdown another receptor protein in the same cells that is over expressed of transcription factor (let say transcription factor X). For over expression lipofectamine LTX plus and for knockdown lipofectamine RNAiMAX will be used. my protocol will be : First transcription factor X overexpression will be performed and after 4 hours media will be replaced with complete media and knockdown of the 2nd gene will be done and incubate cells for 48h. Do you think this procedure will work? constructive criticism and suggestions are welcome
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Make a stable knocked out INS1 cell line of the first transcription factor using CRISPER or shRNA. After having  stable knocked out INS1 cells, overexpress the second transcription factor. Or you can do vice versa make stable overexpression of the first TF and then knockdown/ out the second TF.
If you do not want to make stable cell lines, then transient transfection is also possible but you have to check the timing of transfections. let me know.
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Can someone tell me if epperndorf biospectrophotometer with micro cuvette is really better than Nanodrop in nucleic acids quantification, even for small concentrations?
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I complitely agree with Abhijit. In my experince Qubit from Invitrogen (Broad range or High Sensitivity kit) allows to quantified the DNA better than Nanodrop, expecially when the concentration is low.
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I am new in learning chip data and methylation data. I want to know if I am given the methylation data and rna_seq data then how can I find that this TF has affinity for this methylated promoter or TF will not bind to this methylated site?
For knowing this first I need to have some info regarding TF binding affinity for different transcription factors or RNA-seq data and methylation data processing will itself give me this info? 
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thank alot to all
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I have an amino acid sequence of 110 which codes for a functional transporter. Utilizing which online tool and what are the possible way by which I can confirm the structure, functioning, or any other aspect related to the stretch of amino acid? Is 110 a.a. codes for a transporter? Please give your valuable suggestion.
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Hey,
I have used this one to predict the 3 D structure of the protein. In my case it was 77 aa protein.
Additionally, you can find out the potential domains in a protein from different online tools like
If it is a transporter protein it must carry a domain for ATP binding cassette or conserved domains responsible for transporter activity.
PS: I am not sure how efficient these algorithms are, its just I have used them only once.
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I have amplified a functional gene with my environmental samples and already did the cloning and sequencing. I'm trying to cluster similar genes and I came across two term commonly used but I do not know the difference between the two as essentially both cluster similar genes into one consensus gene right? Can anybody explain to me the difference between the two?
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A contig is a consensus from a "contiguous" alignment of reads, or the sequence resulting from the overlap of multiple smaller sequences...
So:
AAAGAGA
          AGATATGCTA
                            CTAACCA
Creates the contig:
AAAGAGATATGCTAACCA
OTU means "Operational Taxonomic Unit" and is a vague term. In the context of metagenomic and studies using 16S (etc) amplicon sequencing it refers to a cluster of sequences which have similarity above some threshold (say 3% or 4% etc) and can be assumed to come from the same "taxonomic unit" and is treated as such. A taxonomic unit (here's the vague part) could be species, genus, order... Depending on the sequence variation within that group for the sequences captured. BLASTing the consensus of that cluster would then give you some information as to the identity of the OTU.
Following the format of the above example:
GAGAGTACT (centroid)
GAGATTACT
GAGAGTATT
Would all cluster together with a similarity threshold of 10%, as the bottom two sequences are within 10% similarity of the top sequence. In the case of USearch, I believe the "centroid" sequences (or the sequence chosen to based similarity off of for determining cluster membership) is based on frequency of that sequence in the data.
Hopefully that helps.
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Classical definition of SPECIFICATION says:
"cell type is not yet determined and any bias the cell has toward a certain fate can be reversed or transformed to another fate"
Do you agree to this definition?
If yes. Can anyone tell me how do we quantitate the difference between SPECIFICATION and DETERMINATION with an experiment having a perfect positive and negative control and a perfect Null hypothesis to test this concept?
If anyone has come across any paper which specifically test this question, please do tell me about that.
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Dear Aritra Misra,
it is usually thought in the field that the fact that you can reproduce an animal out of a differentiated adult cell (as in the case of the sheep Dolly) implies that a determined state according to definition maybe never exists. Of course this has not been proven for all adult cell types. The problem to test this is that there are infinite testing conditions and therefore one can never prove that a differentiated state is truly ditermined, since an untried procedure might prove the opposite. Perhaps we need a weaker definition of what we mean with "determined". No doubt differentiating cells change progressively from less specified to more specified states, and what we usually call determined state just means virtually determined, as long as the cell is not put into quite extraordinary testing conditions occurring with low probability in its standard environment.
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We have many samples and so want to set up 3 plates with an eBioscience kit and we have the Bio-rad machine and software. 1st plate has standards and samples and the other 2 are samples only (we assume that if we prepare all at once, there should be minimal variation between plates). Can the software handle this? or do we always need to run a standard curve on each plate?
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I would make sure that you run quality control samples on each plate (ie a pooled sample that you run in every assay and every plate) so you can check that plate does not introduce variation.
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The information encoded in DNA is more complex than previously thought:
• Alternative promoters.
• Products of RNA splicing.
• Non-coding repetitive DNA.
• Separate gene products of DNA sequences overlapping
• Gene sense and antisense transcripts.
• Multiple gene products combine to form a functional protein.
• Trans Action of gene enhancers located in different chromosomes.
The question is whether or not there is already a definition that encompasses these facts?
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I believe we are still a long way from fully incorporating all genomic features into the definition of the word "gene," but we're on our way there.
You may find the article below both interesting and informative.
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How does "junk DNA" help in regulation of gene expression ?
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Hi, you can also take a look at these papers
In fungal genomes, transposable elements (previously considered as junk DNA) shape the chromatin structure and therefore modify the transcriptional behavior of genes nearby.
Best,
Jonathan
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In my lab, a target gene was knockdowned (RNAi). Both RNAi and Control samples were separately prepared to construct libraries of small RNAs that were deep sequenced using Illumina platform (36 cycles). The sequencing results (performed in triplicate) showed a clear differential pattern in the length distribution of reads when Control and RNAi samples were compared. Most reads from RNAi samples ranges from 10 to 17 nt and many of them are rRNA. On the other hand, while reads generated from Control samples are longer than 20 nt. What these differential distribution patterns mean? How to explain the high abundance of ribosomal RNA in the RNAi samples and low abundance in Controls? Have you similar results and/or advices on how to interpret these data? Do you know any article discussing it? Please, share your expertise and beliefs.
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Thank you all for reply. The last answer was sent in April 2013. So, please, would you like to share updates on this topic? We could discuss not only RNAi vs. RNA-Seq but also CRISPR/Cas9 vs. RNA-Seq. Hope your contributions and advices.
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As epigenetic studies have significantly been increasing in Eukaryotes , I would ask if some bioinformaticians did create new databases or rather software to predict the epigenetic modifications and changes of a specific gene in comparison to other homologous or orthologous genes beside chromatin database (ChromDB: The Chromatin Database), EPIC https://www.plant-epigenome.org/links or even NCBI http://www.ncbi.nlm.nih.gov/epigenomics may have limited information and data on plant epigenetics? OR should we dig and retrieve these speculations by coming back to their experimental assays?
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Hi Abdullah,
there are several data published on comparative spatio-temporal gene structure and function at epigenetic level in plants. E.g. the ublication by Lafos_M et al., 2011 meets the criteria of your first question.
However, the other questions you raised at the end of your paragraph are more tricky to give ONE conclusove answer. Probably, you might gather a lot of different views from different researches.
In my oppinion:
Whatever you do, you should start from the raw data - -mostly sequence reads - and do it by hand, in a consistent manner forall  your datasets of interest. I don't have a solution for intercomparative analysis on metagenomics NGS data, and I think you can not do as many downstream comparative analyses from NGS data compared with e.g. microarrays - which is why I still like microarrays for expression analyses. In addition, for 'prediction of epigenetic modification' the data coverage is probably still to low and of varying quality - -besides problems of inter-experimental 'noise' and the impossibility to get rid of it.
As intercomparability of NGS data from different labs is possibly consistent with qualitytive information, different experiments will result in very different qualitative  data, which would be required for prediction of spatio-temporal effects at epigenetic level.
At best, make a large epigenmic experiment by yourself with all conditions and points-in-time that might be relevant for you - -this might be the only way to ensure full comparability of the data.
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Long read sequencing technologies are on the rise. Therefore it would be great to have a cost effective method for target selection using long DNA fragments.
Could molecular inversion probes be applied to capture long (>5kb) target fragments? Those would add the benefit that molecular indexing would be included.
What kind of (established?) capture technology would be best for such long fragments?
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Hi!
If you have patience check Molecular Cloning A Laboratory Manual by Sambrook et.al Volume 2. Chapter 11. DNA Sequencing...., where you get detail information about all size fragments.
I made DNA genomic library with bigger inserts up to 23 Kb.
Good Luck!
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Complete genome of my standard bacterial strain is not available and I saw some people using scaffold genome of this strain for RNA-seq. Is it really ok because some part of sequence might be roughly known. some fragments might be unmapped and lost during mapping. what are your opinion
thank you in advance for your answers
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Depends on the purpose of your study as well as how good the incomplete sequence is (how many scaffolds/contigs you have).
For a complete transcriptome, you would need to have the complete sequence. If you are interested in comparative transcriptomics and do not have to many gaps, scaffolded contigs will do just fine. You will loose data at the ends of the contigs, but that loss is presumably the same for all conditions under this setup.
Best,
Christian
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For example, if a tRNA gene's predicted "secondary structure score" is under 20 and a "cove score" is under 10 (applying tRNAscan-SE, Lowe, v. 1.21) could one assume this gene is fully functional? The gene is expressed as a typical clover leaf structure.
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If you're interested in few tRNAs (so you can manually curate the predictions), you can use information from biological knowledge to infer whether the prediction is right. E.g. if it's a tRNAAla, does it have the G3:U70 motif which is required for aminoacylation by AlaRS? (and so on for other synthetases).
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When we run a known protein in blast, we can see many homologous proteins, some of them have name of proteins and even name of organisms but others have hypothetical proteins and name of organisms. My question here is during the collection of homologous proteins is that possible? Can we collect hypothetical proteins?
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The hypothetical protein annotation might come from the lack of homology to a well annotated protein. Have you check if such hypothetical proteins have any domain in PFAM? Perhaps using DELTA-BLAST you would be able to find any annotated domain that could tell you more about such proteins.
Nevertheless, such hypothetical proteins could be used for phylogenetics. Of course, keep in mind that they might be miss annotations or proteins with no expression data. Be sure that they have somewhat conserved structure when doing the alignment, before doing the phylogenetic analysis.
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Recently, I obtained three RNA-seq pair-ended libraries for transcriptome assembly using trinity. The read number in total is nearly 290M while my server has a limited memory for 64G, which could not meet the requirement. 
Could I do transcriptome assembly for each libraries and then merge them later somehow? Or other tools better than trinity in terms of memory usage efficiency?
Thank you!
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Thank you, Mr Zattara. I will try as you said.
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In huntingtin gene, 67 exons regions are available. In which exon does the disease causing variable number of CAG repeats occur?
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adding to what have been said by the last three experts, in general, CAG repeat variation happens in regions with a minimal number of CAG repeats. The molecular mechanism behind the variation is slippage during DNA replication, which happens in regions with simple repeats, resulting in reduction or expansion of these short tandem repeats. The same mechanism is responsible for causing other trinucleotide repeat variation disorders.  The reason that trinucleotide repeat disorders are seen more often than other types of simple nucleotide repeats (e.g. di-nucleotides or tetra-nuecleotides) is that trinucletoide repeat variation maintains the correct reading frame, thus it is less detrimental than others (which might be lethal).  
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I am scoring SSR markers for genetic diversity analysis in sugarcane genotypes and I found some of markers with more than 4 alleles, but when I analysed them I got negative values for PIC, Anybody have some suggestion for me?
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Dear Muhammad,
Why do you want to calculate PIC? That is the key question.There are two "PICs", and their classical formulas won't have a meaning for an octaploid. There are other scales of marker information, e.g. Shannon entropy. Please check my book chapter about SSR informativeness https://www.researchgate.net/publication/236097974_Informativeness_of_microsatellite_markers?ev=prf_pub
Best wishes
Humberto
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I have a very basic question. In genomic analysis we deal with coordinates. But I am little confused with the understanding about coordinates numbering system between positive and negative strand. I am aware of the 5'-3' orientations of positive and negative strands. But can someone explain me how coordinates are numbered for these strands. For example, If i have a position of nucleotide A at 432990 in Chromosome 11 positive strand, will the same position 432990 corresponds to the nucleotide T in the negative strand? Though I am not sure I guess there is no coordinates number for negative strand and the coordinate always refer to positive strand. However I expect someone to give me little more detail on this. Thanks.
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thank you  @Aleksandr
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I am interested in soil metagenomics and wish to do shotgun sequencing in order to see changes in populations' diversity and in functional genes. I want a library of 2.4 Gbp. I will start with ~200bp fragments.
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One argument for assembly has been to reduce the computational load of a BLASTX comparison of millions of DNA reads against a protein database such as NCBI-nr.
However, this problem has now been overcome: please try DIAMOND, a new tool that we have recently published in Nature Methods that is 20,000 times faster than BLASTX on Illumina reads. For example, BLASTX requires about 45 days to compare 5 million reads against NR, while DIAMOND does the same comparison in minutes (reporting over 80% of all matches that BLASTX reports)
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Dear all,
I am Juan Garcia, from the Marine Biology department at the University of Vienna. I have found a very interesting article, attached.
I am working with Nitrosopumilus genomes and I would like to create a figure similar to your figure S2, so I wonder if you would be so kind to help me with that.
I know it has been done with Circos but I am not sure which option has been used to create one of the inner circles, in concrete, the one showing the regions of genomic plasticity compared to N maritimus and C symbiosum.
Is it possible that you used a circular stacked bar plot? I appreciate your help.
Thanks a lot for your attention
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Hi Juan,
Did you try to contact the authors of this paper? Maybe they could share the specific code they used to make this figure.
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Are there kits available? If yes, which ones can you recommend?
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There are lots of available Crispr/Cas9 vectors suited for different oraganisms available in the Addgene site. Here is the link http://www.addgene.org/CRISPR/ , you can order it as low as 65$ which is very cheap. They provide all the necessary protocols for designing the guided RNA, plasmid infection and target efficiency.
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Tumor genotype affect therapeutic outcome, I wonder if there is a way to modulate tumor genotypes favorable to therapy.
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Hi Dr. Gupta,
I think that viral-based gene delivery is being tested in aimals for a plethora of malignancies, diseases and syndromes, and it seems a promising approach. I think it is a suitable approoach for monogenic diseases that require the therapy over a single gene or mutation. However, as cancer is a multifactorial disease, usually caused by multiple genetic and epigenetic disturbancies, I wonder if you could apply viral delivery of genes. Perhaps it is possible for genes such as p53, BRCA2, RAS or other genes with a well-known implication in tumour development and cancer prognosis.
But, sincerely, I have no expertise on that.
Regards,
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I have an average of 1.5 ul/ml total dna quantity on my vine dna samples. In this case, how should I calculate a touchdown PCR reaction for 20 - 25ul?
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3 ng into the reaction should be fine if your PCR is reliable.
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I want to investigate the functional genes of fungi responsible for N2O emission. Who can tell me how to do it? Many thanks in advance
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Geochip may be a more comprehensive choice. You can ask them about the probe design regarding of fungal genes responsible for N2O release. According to my experience, geochip can save lots of time and offer much more insights than qPCR.