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plz suggest me some good topics related to bioinformatics for research...i will be very thankful 
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Could you come up with a vaguer question?
Bioinformatics is a vast and ill-defined field.
Your question is very poorly worded.
Since I'm in a very generous mood, I'll make an attempt to answer.
You posted in a sequencing forum so you could pick as topic the analysis of next-generation sequencing data.
Amongst all the variants of next-generation sequencing, RNA-Seq, or the sequencing of the transcriptome, is the most popular.
So, you could pick as your topic the analysis of RNA-Seq data.
There are several public datasets available.
You could study datasets pertinent to your research field, e.g. related to a particular cancer.
Or, you could study the bioinformatics tools and algorithms used to analyze RNA-Seq data, if you want a focus on bioinformatics.
This is my attempt to answer your question.
Honestly, I wonder if your post should not simply be deleted, as the question is so vague as to be perfectly meaningless. -
you can check a PSI Protein Classifier
i am also interested to discuss , my skype : karimse07 , email [email protected]Comment
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thanks a lot blancha , can we have a discussion about studyComment
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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.Yesterday, 11:43 AM
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I’m not a sequencing expert. I’m a purification scientist who uses NGS to evaluate workflows my group develops. With this perspective, we think about the sample first and the NGS workflow second. The sequencer is an exceptionally honest reporter, but it can only report on what you give it, so whether you get clean, interpretable data from an NGS workflow is largely determined before you begin.
Here are nine questions we think about, in roughly the order they matter, before... -
Data variability is still an issue in sequencing technologies despite the advances in reproducibility and accuracy of these platforms. But the problem does not originate in the sequencing itself, but in the previous steps, before the sample reaches the sequencer.
The first step is collection, followed by preservation and sample preparation for analysis. Most scientists overlook those steps, but not being careful might just be skewing the experiment’s results.
...06-02-2026, 10:05 AM
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