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1

So the general sequencing analysis pipeline is to generate a bunch of read sequences, figure out how they match to a reference genome and align them, then call any differences between the reference genome and the sequencing reads. Interactive Genome Viewer from the Broad Institute may help you visualize what is happening here. As you can see in the image, ...


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There are many methods for the analysis of CNV. If you are an R user I would recommend you to take a look at the Bioconductor package list, in particular the section for copy number variation. Currently it contains 50 packages!


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Your suggested approach of comparing to the baseline distribution on a point-by-point basis isn't bad, although it's going to be susceptible to small false positives from noise. You'd probably want to only use events that span a certain minimum number of consecutive observations. You might also want to look into circular binary segmentation, as described ...


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That number refers to the number of reads that overlap that base pair that has the variant. Sequencing reads in this case almost always refer to next generation sequencing. These metrics and other VCF fields are used to give some indicator of confidence for variant.


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In a Chromatin Immunoprecipitation experiment we assume that most of the genome being assayed will not be captured by the antibody-coated beads. The 'control' reaction is typically beads lacking an antibody, or beads coated with a non-specific antibody--in some cases a small amount of genomic DNA suffices. The signal (either array-based or sequence-based) ...


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You will probably want to read up on Position Frequency Matrices (PFMs) and Position Weight Matrices (PWMs). These tend to be much more sensitive and useful for pattern recognition in nuclei acid sequences. Two examples of databases with these patterns are TRANSFAC and JASPAR. Regex patterns can be useful in protein sequences sometimes, although PWM have ...


1

Prosite (http://prosite.expasy.org/) uses regular expressions to search for protein domains, in contrast with Pfam. If you look at an entry, such as http://prosite.expasy.org/cgi-bin/prosite/prosite-search-ac?PDOC00022, you can see the consensus pattern under the PATTERN section towards the bottom. Prosite does not have the same coverage as Pfam, but the ...


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Regular expressions are not commonly used for DNA or Protein analysis, but many people use Hidden-Markov-Models (HMMs). If you are looking at protein domains, you can find many HMMs here: http://pfam.xfam.org/


1

Welcome to Biology and welcome to Biology.SE! It is hard to answer such a question as the domains you describe are very vast. If you want a book in an introductory book to biology, then you want to read this post and this post. If you are more interested in population genetics and molecular evolution then this post will interest you. Still talking about ...


1

MIT Open Course Ware has a course on genetics, which uses the following text: "An Introduction to Genetic Analysis", Griffiths, Anthony J. F., Jeffrey H. Miller, David T. Suzuki, Richard C. Lewontin, and William M. Gelbart, 7th ed. New York: W. H. Freeman, 2000.


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GENETICS: Analysis and Principles, 4th edition, Robert J. Booker This book is used in my school's ( NYU Poly) undergraduate genetics class.


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This post goes over what MuTec requires as input. mark duplicates and indel realignment will probably have to be done on bam file to use it as input. BQSR is optional and does not change the quality too much. HaplotypeCaller is used for germline not somatic variant calling. If you have followup bioinformatics questions, you might be able to find answers ...


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I don't think it's possible to do this via QuickGO. You can, however, do it from GO's website, using their GOOSE tool. This offers an interface to the GO SQL database and lets you run queries. It also offers example searches, one of which ("Find descendants of the node 'nucleus'") can easily be modified to return what you are asking for: SELECT COUNT(*) ...


5

There exists a bunch of population genetics forward and backward (coalescence) simulation platforms. Here is a non-exhaustive list. They all differ and you'll have to go through their manual to see what is more adapted to your needs. Here is an exhaustive (or almost exhaustive) list of such platforms. Some are more known than others. Personally, I already ...


0

Go this URL And click pBR322 origin in the map. Then, the corresponding sequence will be highlighted. 2248-2867: pBR322_origin 2201 cccttaacgt gagttttcgt tccactgagc gtcagacccc gtagaaaaga 2250 2251 tcaaaggatc ttcttgagat cctttttttc tgcgcgtaat ctgctgcttg 2300 2301 caaacaaaaa aaccaccgct accagcggtg gtttgtttgc cggatcaaga 2350 ...


3

Plasmapper Is quite good a recognizing common origins of replication. You will have to look up compatibility yourself. It is what I usually use when confronted with unnannotated plasmids.


0

Try this. I would suggest OriDB also, because it's precisely what you asked for. Getting the sequences out for easy blasting might be a chore, depending.


2

Christian, great idea to ask this question here before taking important decisions. Are those media articles a hype? Yes. Over the last 10 years I constantly see those hype stories in media about "revolutionary" large-scale-study/big data projects with mind-blowing numbers (gigabases, teraflops, terabytes, thousands of papers and hundreds of genes). ...


16

The paper by Lobo and Levin is an attempt to learn a model that represents the inner workings of a biological system by fitting parameters to data. This is a common topic in "systems biology", a model-based approach to studying biology that is popular in some fields. Even for small systems, this is a phenomenally hard problem. Unlike most machine learning ...


14

The fruit, sadly, does not hang so low. Short version Lobo et al (the work you refer to) is a nice and not especially novel application of basic Systems Biology modeling approaches to the wound healing system in flat worms. The main barrier to the wider application of such work is the lack of the necessary experimental data. Lobo et al themselves don't ...


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In babel: babel my.mol2 my.pdbqt -xh -x states options for writing .pdbqt files -h says keep hydrogens


1

As Alex M shows, many lengths of exons are 3n+1 or +2. It seems cells do not care about in-frame. In some cases, exon insertion by alternative splicing shift the frame and produce a short version of gene product or introduce premature stop codon (PTC). When PCT is recognized in cells, the spliced transcript is quickly degraded. This is called nonsense ...


2

As pointed out by others this is not true. I just verified this for humans (annotations from gencode21). Methodology: Obtained the start points of the CDS for all genes For each exon of each gene, calculated the distance between the CDS start and exon end Obtained the remainder after dividing this value by 3 (modulo) Commands: Command-1: awk ...


2

Here is exon size data of some random gene (human ROR-gamma). Link: http://www.ncbi.nlm.nih.gov/nuccore/NM_005060.3 It gets cut at all three possible positions of codon: exon 2 30 exon 3 86 exon 4 142 exon 5 513 exon 6 122 exon 7 133 exon 8 108 exon 9 111 exon 10 110 Alernatively spliced isoform B of this gene gets different first exon. ...


1

I don't have time to find examples right now but no, it's not true. You often get cases like this (lower case letters represent the intron): ACCTGTaccttgcaacttgcatAGCTGAC Which would be spliced to: ACCTCTGAC Note that the second codon consists of one nucleotide from exon1 and two from exon2. I'll try and update this with real world examples, but I can ...



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