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I'm trying to compile bowtie for Windows x64 using MinGW64. I actually have a working binary for bowtie x64, but I can only get it to compile with BOWTIE_PTHREADS=0. When that is enabled, I get "Error: incorrect register '%eax' used with 'q' suffix" and make for bowtie fails. Anyone know any solution around this? I'm using pthreads from http://sourceforge.net/apps/trac/min...ile%20pthreads
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I have an operational (with threads) bowtie for Win7-x64 which I could make available if you wish. It comes with the usual disclaimer of warranty. I've run it for about a year with no problems. I will have to locate the notes for compiling though. -
That'd be great if you could provide me with it. If you have compiling notes to share too, that'd be helpful too for future/similar compilations. You wouldn't happen to have a working version of TopHat too?Originally posted by DWW1505 View PostComment
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I will to contact the researcher for whom it was built tonight. Please PM with your contact info as the files will be a bit much for email. TopHat (1.4.1 and 1.3.3), as implemented in my port, requires 64-bit Samtools, 64-bit Bowtie, several supporting files, and does not do .gz compression.Comment
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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.
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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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