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Frequently Asked Questions (FAQ)
常见问题解答

- When loading the reference genome sequence in FANSe program, it shows an error message “Out of memory” or “Stack overflow”.

This problem most likely appears when you run the FANSe program in a 32-bit system and try to use a reference genome larger than 400Mb. If this error constantly appears please switch to 64-bit system, or perform the mapping chromosome by chromosome, since the current FANSe does not stably process multiple chromosomes.
Another reported possibility is that the reference genome file is not in FASTA format.

当在FANSe程序中加载参考基因组序列的时候,出现一条错误信息"out of memory"或"stack overflow"

这个问题最有可能出现在你试图在32位系统上运行FANSe,且使用一个大于400Mb的参考基因组的时候。如果这个错误经常出现,请使用64位的系统,或者每次只比对一条染色体。
另一种可能性是参考基因组序列文件不是标准的FASTA格式。

- I bought the newest hexa-core CPU, however FANSe uses only one core. Is it possible to accelerate the mapping by using all cores?

For stability we do not offer multithreading option currently in version 7.2. Nevertheless you can start several instances of FANSe to map different datasets or chromosomes simultaneously in one computer if you have enough RAM. In our test, 8GB RAM is sufficient for 7 instances of FANSe to map reads to human chromosome 1 in parallel with almost linear performance increase on an 8-core workstation. Moreover, we are working on a novel technical strategy of efficient parallelization to unleash the power of 24-core or 48-core workstation with limited memory and this feature will be added (hopefully) in the next major version of FANSe.

The new generation of FANSe2 supports unlimited parallelization. Please try it out.

我买了最新的6核CPU,而FANSe却只能使用一个核。如果能用上所有的核,是不是能加速比对?

出于对稳定性的考虑,在目前版本中我们并没有提供多线程并行执行的功能。但是如果你有足够的内存,你可以同时在一台计算机中运行多个FANSe来比对不同的数据集或染色体。我们的测试结果表明,8G内存足以运行7个FANSe实例来同时比对人类1号染色体,而且性能几乎是随实例数增加而线性提升的。此外,我们正在尝试一种新的并行策略来在使用有限内存的前提下充分利用24核或48核工作站的计算能力。这些特性有希望加入到下一个主要版本的FANSe里。

现在,新一代FANSe2算法已经发布,支持几乎无限的并行计算能力。您可以使用FANSe2.

- Do I need to use the “masked” genome? When should I use it?

For RNA-seq applications, masked genome is highly recommended. A read map to the repetitive region will be mapped to the whole genome many times, making it impossible to estimate its location. Using masked genome also accelerates the mapping process significantly.
For DNA sequencing applications, e.g. resequencing and methylation analysis, you may use non-masked genome if you are interested also in the repetitive sequence when you have long reads.

我需要使用被遮罩基因组(masked genome)吗?我什么时候该使用它?

对于RNA测序应用,我们强烈建议使用masked基因组。一个比对到重复区域的短读序列将会许多次比对到整个基因组,就不能给出确切比对位置。使用masked基因组同时也能加速比对的过程。
对于在DNA序列上的应用,例如甲基化测序分析,如果你也对重复序列有兴趣,且读长够长的时候,你可以使用非masked基因组。

- My reference genome is not large, containing 16 chromosomes. However the chromosome number in the FANSe result file seems not fully correct. What should I do?

The current FANSe version may make mistakes when exporting the chromosome number in some cases with multiple chromosomes, although the mapping itself is correct. Therefore we recommend you to do the mapping once a chromosome. Alternatively, you can make a program to join all the chromosomes into one entire string as the reference sequence and perform the mapping. We are trying to solve this problem and this bug will be fixed in the next version.

FANSe2 corrected this bug and fully supports multiple chromosomes.

我使用的参考基因组不大,含有16个染色体。但FANSe输出的比对结果中染色体序号有误。怎么办?

目前的FANSe版本对多染色体的支持并不完善,有时会输出错误的染色体号,尽管比对本身是正确的。因此,我们强烈建议每次只比对一个染色体。另一种解决方案是自己编个程序将所有的染色体序列连接成一个长序列作为参考基因组序列,然后进行比对。

新的 FANSe2 算法已发布,完美支持多染色体的比对。请您使用新的FANSe2

 


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