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I am dealing a set of RNA-seq data generated by Illumina, aligned and assembled by tophat-cufflinks pipeline. I wonder why so many of my non-code RNA expression values are zero, but not all of them?
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I"m guessing this is, for you, an early look at rnaseq results. Some genes are expressed, some aren't; this includes non-coding RNA. You'll see this as aligned reads with a gene (mostly the exons) and some genes have no alignments (meaning , likely, that they are not expressed or got filtered out during wet-lab processing). Some protocols only pick up polya mrna. If you can fully explain why some are lighting up and others aren't; then you got a paper.
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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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