@gunesaynasinda In my experience, research is a near constant roller coaster like this. There are periods of huge productivity and right after they are over you often look over your shoulder wondering why the wave didn't continue unabated, and feel like your productivity has "slipped". 1/x
@gunesaynasinda But in reality, there are constant ups and downs, and the long-run average is only visible in a time-frame that's much larger than the gap between conference deadlines. Likewise, we often tend to look only at our peers that are the most productive at the current moment. 2/x
@gunesaynasinda Not paying attention when their previous wave peaks & returning attention again only when their next wave arrives. This leads to a skewed perspective of how "our" research is going in the larger context; everyone's bobbing up & down but we preferentially see those who are up. 3/x
@gunesaynasinda Apart from trying to keep perspective, the best antidote I've found is to set aside the magnum opus, and find a small problem with quick feedback to work on. It's like that Feynman quote: "No problem is too small or too trivial if we can really do something about it." 4/5
@gunesaynasinda Coming from a much more ambitious project, finding a "small, little" problem may seem trivial or silly. But the joy of thinking and getting lost in even the simplest problem can often help kickstart the creative process & and there are often connections to bigger problems. 5/5
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RNA-seq data is often analyzed at the level of genes. This can provide a robust signal, but can also miss out on biologically important information like differences in isoform composition or dominant isoform usage. 1/n
On the other hand, tremendous progress has been made in transcript-level quantification, but certain inherent ambiguity can remain in the abundance estimates. This results from patterns of multi-mapping where no inference procedure can accurately resolve the origin of reads. 2/n
Yet, the total transcriptional output of group of transcripts sharing these complex multi-mapping patterns will have greatly-reduced inferential uncertainty, thus allowing more robust and confident downstream analysis. 3/n