Update: I have managed to find some aid and comfort in a nicely written tutorial. If you're a newbie (like me), you don't know very well what you are doing (like me) and you're still awed by the magic behind cufflink/cuffdiff, you should try and read these:
especially this:
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What does cufflinks compare BAM file to, without reference?
Hello everybody!
Main question: with a minimal syntax like this
cufflinks -p 3 target.bam
what does cufflinks use to assembly the information contained in target.bam file?
I'd also like to know whether using a local reference (the fat 16GB+ files you can download from cufflinks website) cufflinks will name the transcripts with the gene names instead of CUFF.65279 and the like.
I'm a wet biologist trying to learn some bioinformatics. I managed to use bwa and picard to process the raw Illumina reads and get to a .bam file, and now I'm trying to learn how get some DE data out of my samples, but I hadn't found any tutorial clear enough to explain what cufflinks is actually doing. This is also why my thoughts are hazy and my questions are -probably- not outstanding.
Thank you for yuor help and support, as well as anyone who wrote some beautiful tutorials on SEQwiki.
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Genomics studies in neuroscience face a special challenge due to the brain’s complexity and scarcity of samples. Mapping changes in cell type and state using conventional next-generation sequencing methods remains challenging. Advances in technologies like single-cell sequencing, spatial transcriptomics, and long-read sequencing have opened the door to deeper studies of the brain and diseases like Alzheimer’s, amyotrophic lateral sclerosis (ALS), and schizophrenia.
...07-09-2026, 11:10 AM -
Cancer survival rates have significantly increased in the last few decades in the United States, reaching a combined 70% 5-year survival rate by 2021. Behind this number, there are years of research to find new therapies, drug targets, and early detection methods. But there is one core challenge that keeps slowing down these advances, and it’s about drug resistance.
There is no single reason why many patients don’t respond to treatment as expected. Cancer is...07-08-2026, 05:17 AM -
Love this - good data definitely starts from good input, and poor input can only give relatively poor data. I particularly like the mention of Nanodrop/absorbance based methods for quantification. It's such a toss up if you'll get an accurate reading or what amounts to a randomly generated number, and a lot of library/sequencing related issues can be traced back to poor quant.07-01-2026, 11:43 AM
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