Top Tweets for #featureCounts
5/ With the reads mapped, the next step is #quantification. This involves counting the number of #reads that map to each #gene or #transcript in the reference. Tools such as #featureCounts and #HTSeq are commonly used for this purpose.
This video about RNA-seq read quantification will guide you on how to perform read quantification with the #geneXplain_platform
https://t.co/qpT8Bx7LHr
#RNASeq #FASTQ #FASTA #SAM #BAM #ReadQuantification #ReadCounts #sequencing #NextGenerationSequencing #NGS #featureCounts #htseq
#RNASeq: what happens to multi-mapping reads?
Whoah apparently #htseq and #featureCounts ignore them by default! #Salmon, #Sailfish and #Kallisto are the way, with the Expectation Maximization algorithm! @LabMutwil was right!!!
https://t.co/MTfzHHG3KQ
Funny thing when the program doesn’t run because of my typo... but the error message it returns has a typo of its own 😂😂
#bioinformatics #differentialexpression #trichomonas #protozoans
#featurecounts #AcademicChatter #AcademicTwitter
#AppliedGenomeResearch day6: #STAR & #featureCounts for #RNAseq analysis. #Python, #R #Rstudio, & #DESeq2 for #statistics (#PCA, #TPM, #FPKM ...). Investigating #GeneExpression in #Arabidopsis #MYB #mutants. #CompBio #training for life scientists. #elearning #openresources
First #slides of #AppliedGenomeResearch #course public on @github and more to come: https://t.co/53USzcD6j0
#bioinformatics #education for #life #scientists
#datastorage #slidesharing #reusablecontent #openscience #datasharing #teaching #DataScience
#AppliedGenomeResearch course day5: moving from #genomics to #transcriptomics. #RNAseq background and #experiment design. #gene expression quantification via #STAR & #featureCounts. #RNA #NGS #illumina
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