<![CDATA[序列比对工具　对比分析]]>I suppose you should decide what is the purpose of the alignment you are going to carry out and what datasets are used as a query and a subject. It will determine the alignment tools to be used.
Tools like LASTZ or LAST perform whole-genome alignments of large assembled genome fragments (chromosomes, scaffolds, contigs). They are optimized in the way to create so-called chained alignments, that is, alignments which consist of gap-free alignment blocks separated by large gaps. Such alignments are typically used in studying genome rearrangements, synteny blocks and homologous genome regions.
Alignment tools from the NCBI BLAST package (blastn, megablast and others) are used to align short nucleotide or amino acid sequences, like genes or proteins. For example, one may search for gene homologs in the genome of interest using blastn. The search is performed in three steps:
exact matches (seeds or words) are searched between query and subject sequences; the word size is specified by the -word_size option,
the seeds are expanded without using gaps (the -xdrop_ungap option),
the obtained gap-free alignments are expanded using gaps (the -xdrop_gap and -xdrop_gap_final options).
The general rule is that the smaller is the word size, the more sensitive but also slower is the alignment search.
The third group of alignment tools is read aligners like BWA or bowtie. They are optimized to align the large number of reads and usually do well with their default parameters. The read aligners are used in comparative genomics analysis, for example, to detect genomic variants between individuals of the same or related species by aligning the reads to the genome.
As far as I understand, you have an assembled reference genome and a pool of reads from related individuals and you are to study their genomic variability. For that analysis, the read aligners seem to be the most appropriate tools. I would advise you to use bowtie2 - it is a reliable, convenient and fast tool that supports running in parallel mode. Please find the link to the bowtie2 website below.
http://bowtie-bio.sourceforge.net/bowtie2/index.shtml
1.参考
https://www.researchgate.net/post/Any_suggestions_for_a_fast_nucleotide_alignment_tool2

]]>http://an.forum.genostack.com/topic/227/序列比对工具-对比分析RSS for NodeSat, 13 Jun 2026 12:33:17 GMTSat, 20 Feb 2021 03:58:07 GMT60<![CDATA[Reply to 序列比对工具　对比分析 on Sat, 20 Feb 2021 04:07:42 GMT]]>https://en.wikipedia.org/wiki/List_of_sequence_alignment_software

]]>http://an.forum.genostack.com/post/441http://an.forum.genostack.com/post/441Sat, 20 Feb 2021 04:07:42 GMT<![CDATA[Reply to 序列比对工具　对比分析 on Sat, 20 Feb 2021 03:58:18 GMT]]>http://cibiv.github.io/NextGenMap/
NextGenMap (NGM) is a flexible and fast read mapping program that is more than twice as fast as BWA, while achieving a mapping sensitivity similar to Stampy or Bowtie2. NextGenMap uses a memory efficient index structure (hash table) to store the positions of all 13-mers present in the reference genome. This index enables a quick identification of potential mapping regions for every read. Unlike other methods, NextGenMap dynamically determines for each read individually how many of the potential mapping regions have to be evaluated by a pairwise sequence alignment. Moreover, NextGenMap uses fast SIMD instructions (SSE) to accelerate the alignment calculations on the CPU. If available NextGenMap calculates the alignments on the GPU (using OpenCL/CUDA) resulting in a runtime reduction of another 20 - 50 %, depending on the underlying data set.

Our results show that NextGenMap using only the CPU is at least twice as fast as BWA. Using the GPU for the alignment calculations increases the speedup to a factor of three. NextGenMap (GPU) even outperforms Bowtie2 by 10 - 50 % in terms of runtime. More importantly, the number of correctly mapped reads is similar to Stampy, one of the most sensitive methods available.

]]>http://an.forum.genostack.com/post/440http://an.forum.genostack.com/post/440Sat, 20 Feb 2021 03:58:18 GMT