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As the previous answer said, there are some public HP1 ChIP-seq data from D. melanogaster if not from the DGRP, from modENCODE and maybe others. In the case of modENCODE, they've published not only the reads, but also their peak calls (mapping with Eland + calling with MACS). BEDTools ( https://github.com/arq5x/bedtools2 )is a nice command line tool for ...


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for biomart goto below link http://central.biomart.org/converter/#!/ID_converter/gene_ensembl_config_2 Also there is one more converter which i found pretty useful http://biodbnet.abcc.ncifcrf.gov/db/db2db.php#biodb


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If you're working with BioCyc pathways you can use their REST-API for batch-downloading all genes for their pathways. You can run a wide range of queries including BioVelo-queries for all genes/compounds in a specified pathway, all pathways in organism etc. A query for all the pathways in B. subtilis would look like: ...


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Have a look at these ChIP-seq data for HP1 in Drosophila: 1, 2 and 3. From ChIP-seq data you can find the distance between the TFBS peaks and the TSS of the gene. You can also look for nucleosome positioning and DNAse hypersensitvity regions; for the former, I am sure that data is available for Drosophila.


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I've written a little script to remove identical sequences from fasta to get what I need. To print the list of removed sequences, uncomment line 22 #! /usr/bin/python3 # Removes identical sequences from fasta file import sys from Bio import SeqIO sequences={} #This is where sequences will be stored #likely calling str(seq.seq) on every test will be slower ...


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I'm not sure if there's a way on GenBank, but UniProt offers UniRef where you can cluster redundant sequences or specify a lower cutoff (like 90% identity).


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A couple of months ago I listened to a plant physiologist who strongly recommended recombination-based mapping over sequencing based mapping. The main reason he gave was the error-rate of 2nd generation sequencing. The error rate on Illumina platforms are about 1 % if I recall. In a small genome like that of Arabidopsis thaliana (157 Mbp) that accounts for ...


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There is no perfect cut-off. It always depends on what you're doing. The e-value is basically a measure of how many such alignments you would expect to find in a database this size by chance. Therefore, e-values greater than 1 mean that you'd expect at least one alignment similar to what you've found by chance alone. As others have stated, the e-value is ...


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The e-value is supposed to be a metric for the chance that an alignment could occur at random, but it is a crude estimate. As pointed out in other answers, this significantly does not include the length of the query sequence. It also does not include the conservation of the gene or the frequency of amino acids (in protein blasts). It does take into ...


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E-value refers to the expected number of random hits for a given alignment score. Smaller it is more reliable is your match. There is no hard and fast rule for e-value cutoff. You can keep whatever you want depending on the level of stringency that you require. But you should note that for smaller sequences (< 30nt) there is always a higher likelihood of ...


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http://ocw.mit.edu/courses/health-sciences-and-technology/hst-508-genomics-and-computational-biology-fall-2002/audio-lectures/ Download all the slides if you want good resources.


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Ants, slime molds, and brains. Ants and slime molds use simple rules to generate pretty good transportation networks in an emergent way, and brains wire and rewire themselves constantly(adding/removing edges, but not usually nodes). Evolutionary networks, metabolic networks, and ecological networks are much harder to get concrete data sets from, because ...


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I don't know whether it's exactly what you need, but there are formal algorithms for tree comparison. There are basically two approaches: one utilizing tree lengths (branch score distance) and the other one dealing with topologies only (symmetric-difference metric): details and references can be found in the manual to treedist from the phylip-package, which ...


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Download the BLOSUM data and source-code from here. Unzip the archive which has several files. The file called blosum'XX'.qij will have the co-occurence probabilities, and the subsitution probabilities can be calculated from them. Also have a look at this article.


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check out the sequence page at RCSB PDB, it can show SNPs mapped onto 3D for some of the proteins (you need to enable the SNP annotations in the drop-down) http://www.rcsb.org/pdb/explore/remediatedSequence.do?params.showJmol=true&structureId=4HHB


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I guess this arises because of the default cufflinks option --total-hits-norm in which it normalizes the FPKMs with total reads including the ones that are not mapped to a known gene or a predicted gene from the assembly. In the genes.fpkm_tracking file the FPKM values are reported for known/predicted genes. It is certainly possible that the number of genes ...


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Why a related genome helps: 1) Alignment of the reads first and assemble next. 2) The gene-space is already predefined ( the genes and their co-ordinates are already known), so if your assembly is fragmented or missing a portion of the gene information, that can be accomodated with reference genome. Limitations: Rather than assembling your own genome, you ...


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Try running software like Censor. If checks in the reference repeat databases and detects microsatellites. So, you can most probably be sure of the microsatellites generated. Moreover, the microsatellites have their information present in the GIRI database. So, you can get more information, if you find the microsatellite in your sequences. Hope this helps. ...



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