Question: Tophat Results
gravatar for Xiefan Fang
6.2 years ago by
Xiefan Fang30
United States
Xiefan Fang30 wrote:
Dear galaxy users, I aligned my RNA-seq data by using Tophat in galaxy. It generated some "Tophat deletions", "Tophat insertions" and "Tophat splice junctions" results. These are all BED files. Does anyone know how to use/analyze these kind of results? Also, I used illumina RNA-seq. Each biological sample has 36-48 million reads. The data for each sample were divided to 10-12 FASTQ files. When I did the "FASTQ Summary Statistics" and draw "boxplot" for each of the sub-file, the score value is about 9-10. Is it too low? Shall I combine the FASTQ files for each biological sample and do the statistics again? At last, does anyone know how to convert a long list of zebrafish genes (500-1000 genes) to human or mammalian orthologs? Thank you for your replies, Xiefan Fang University of Florida
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ADD COMMENTlink modified 6.2 years ago by Jennifer Hillman Jackson25k • written 6.2 years ago by Xiefan Fang30
gravatar for Jennifer Hillman Jackson
6.2 years ago by
United States
Jennifer Hillman Jackson25k wrote:
Hello Xiefan, Please see 'Tools on the Main server': The RNA-seq tutorial (hosted at Galaxy) and the web sites/paper by the tool authors should give you many good ideas for potential protocols. Combining the files will not change the quality values. If this is a Phred+33 scaled quality score, then yes, this is low. A double check that the 'FASTQ Groomer' was run with the correct options would be the first step. You also may want to run FastQC to generate broader statistics. See the RNA-seq tutorial for details about running this tool and then trimming sequences to improve overall quality. A direct link is: There are a likely many ways to do this, here are some: 1 - 'Get Data -> UCSC Main' Track named "Human Proteins" with the primary table (blastHg18KG). 2 - 'Get Data -> BioMart' Ensemble Genes 68, Danio rerio genes (Zv9). Filters -> Homologs -> Ortholog. Help 'Using Galaxy' Protocol 1 has examples of extracting data from the UCSC Table browser and joining data - the methods can be applied to any similar data. If you need to manipulate files, see Protocol 2, the last example is multi-stepped and demonstrates that just about any file can be converted to interval format and utilized. 3 - 'MAF predictions' 'Using Galaxy' (above) Protocol 5 has an alternate method for predicting "orthologs" (or maybe better described as 'syntenically conserved homologs', since function is not evaluated) from conservation tracks. Full details of MAF functions are in our 'Making whole genome alignments usable for biologists' paper: The ZF Conservation track is not local to Galaxy, so you will need to obtain the data from UCSC and FTP to Galaxy to work with it ('Get Data' is not an option). Review the track description in the UCSC browser (track named "Conservation"), then find the data here: Good luck for the choices you decide on! Jen Galaxy team -- Jennifer Jackson
ADD COMMENTlink written 6.2 years ago by Jennifer Hillman Jackson25k
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