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Genome assembly.
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Please check the Minia program discussed here. You can assemble a 3Gbase genome using about 6-8GB RAM.Originally posted by kenietz View Post
You can also check the slides posted here -
If you like to split the reads into parts, the paper by Titus Brown in the first link should help you.
Please email me (samanta at homolog.us), if you need more explanation of the algorithms, because I do not check the forum frequently. The state of the art is far ahead of Velvet with 512Gb RAM, etc.Comment
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If I classify the reads into different chromosomes using bwa, can I "de novo"ly assemble the chromosomes in a 64GB machine?Comment
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Interesting question.Originally posted by ymc View Post
i) For kind of de novo assembly we talk about, the chromosome sequences are not known. If they were known, why would you need de novo assembly in the first place?
ii) Where chromosomes exist and you are trying to do reassembly, yes it is possible to reduce the RAM requirement by partitioning the reads. However, remember that the RAM requirement for error-free reads is capped no matter how many reads you have. However, in world with errors, RAM requirement goes up linearly with the number of reads.
iii) If you are trying to do reassembly of human genome using BWA, you are most likely interested in parts of chromosome with indels, etc. Unfortunately, BWA may not be able to capture the reads for those regions and assign to reference chromosome.Comment
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You can also request soapdenovo2 from BGI. Its RAM requirement is much better than SOAPdenovo, especially when you use k-mer skipping option.Originally posted by kenietz View PostComment
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I want to have better variant phasing than GATK's ReadBackedPhasing. Will that route do a better job?Originally posted by samanta View PostComment
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In my understanding, that is a different class of problem that none of the solutions suggested above (SGA, diginorm, SOAPdenovo, SGA etc.) is designed to handle. Typical de Bruijn graph-based genome assembly programs are designed to assemble genomes, where none exists. Haplotype difference is a second order issue that those programs are not expected to handle by design. In some situations (long indels), they may assemble two separate contigs for a chromosomal region, but that is fortuitous.Originally posted by ymc View Post
Of late, people are recognizing a need for algorithms to handle problems of type mentioned by you. Please take a look at the following two papers and check their programs freely distributed at their websites.
The paper mentioned in the following link is not directly relevant to your problem, but could be of help in de novo assembling highly polymorphic genome, where the assumption of no haplotype difference breaks down -
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This looks very interesting indeed. It's difficult to compare the results directly but I hope this project continues to develop. Thanks for posting.Originally posted by samanta View PostPlease check the Minia program discussed here. You can assemble a 3Gbase genome using about 6-8GB RAM.
You can also check the slides posted here -
If you like to split the reads into parts, the paper by Titus Brown in the first link should help you.
Please email me (samanta at homolog.us), if you need more explanation of the algorithms, because I do not check the forum frequently. The state of the art is far ahead of Velvet with 512Gb RAM, etc.Comment
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ymc, I wrote this up on HapCompass algorithm that you may find interesting -Originally posted by ymc View Post
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How about Fermi assembler by Heng Li ? Is it not faster and more accurate than SGA or Readjoiner ?Comment
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Readjoiner Features
It was interesting to read article on Readjoiner and notice it has several features as an improvement over SGA. Is Readjoiner MPI compatible. I read it is multithreaded, how good is the scalability ?
However, I notice that the tool does not perform well for erroneous reads as you showed in your e.coli data. Is it possible you integrate data cleaner and filters in Readjoiner itself ?
Also, on Plantagora metrics it seems that Readjoiner performs worse than SGA! It popped up with more number of insertions and deletions and misassembled contig bases than SGA or Edena!Comment
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There is also the IDBA assembler:
Seems to work pretty well and does not use a lot of memory. I used it for denovo RNA-seq. Good sized transcripts.Comment
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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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Introduction
Single-cell sequencing technologies have undergone remarkable advances over the past decade, transitioning from low-throughput experimental approaches to highly scalable platforms capable of...05-22-2026, 06:42 AM
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