Tweet
How much memory and CPU are required for running bwa with hg19 and a fastq file as small as the 1000 genomes ftp://ftp-trace.ncbi.nih.gov/1000gen...filt.fastq.gz?
Thanks,
Carol
Thanks,
Carol
-
-
You can find the memory use and running time for mapping a 1000Genomes dataset by a few popular aligners including bwa - http://www.ncbi.nlm.nih.gov/pubmed/23558742 -
Bwa-0.5.x uses ~4GB RAM; 0.6.x+ uses ~5.1GB. On ~5-year-old machines, bwa aligns 100bp reads at ~7Gbp per CPU day; bwa-sw at ~3Gbp per day; the new component bwa-mem at ~14Gbp per CPU day (similar to bowtie2). On the latest Intel CPUs, they can be 50-100% faster.Comment
-
It seems that in the construction of bwa index, the step Construct SA from BWT and Occ... after
[BWTIncConstructFromPacked] 680 iterations done. 6177547390 characters processed.
[bwt_gen] Finished constructing BWT in 687 iterations.
[bwa_index] 2546.24 seconds elapse.
[bwa_index] Update BWT... 17.80 sec
[bwa_index] Pack forward-only FASTA... 32.21 sec
is very lengthy. What would be the acceptable amount of time for this step?
Thanks
CarolComment
-
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
Comment