A Bidirectional Fuzzy Index and Approximate Search Algorithm for Next Generation Sequencing Sequence alignment is one of the most important problems in bioinformatics. However, the existing alignment tools may result in a large number of candidate locations which degradate alignment performance. Recent researches discard the high repetitive seeds for improving the alignment speed, which influences alignment accuracy. To this end, we propose a novel fuzzy index ( fBWT ), which is available at https:github.comweiquanappr_bwt. It allows approximate search and extending the length of seeds to reduce the candidate locations and accelerate the sequence alignment. The performance of our tool was compared with BWA using 150bp and 250bp length datasets. The result shows the number of misaligned reads (the correct position is not included in the highscore candidate position set) of the current mainstream tool is 510 times higher than it. The efficiency between the presented and the existing tools are also compared. Under the above conditions, the alignment time of the presented tool is very close. However, the alignment speed of  fBWT  is much faster than BWA under the requirement of similar alignment accuracy.

Efficient Construction of a Complete Index for PanGenomics Read Alignment While short read aligners, which predominantly use the FMindex, are able to easily index one or a few human genomes, they do not scale well to indexing databases containing thousands of genomes. To understand why, it helps to examine the main components of the FMindex in more detail, which is a rank data structure over the BurrowsWheeler Transform ((mathsfBWT)) of the string that will allow us to find the interval in the stringxe2x80x99s suffix array ((mathsfSA)) containing pointers to starting positions of occurrences of a given pattern; second, a sample of the (mathsfSA) thatxe2x80x94when used with the rank data structurexe2x80x94allows us access to the (mathsfSA). The rank data structure can be kept small even for large genomic databases, by runlength compressing the (mathsfBWT), but until recently there was no means known to keep the (mathsfSA) sample small without greatly slowing down access to the (mathsfSA). Now that Gagie et al. (SODA 2018) have defined an (mathsfSA) sample that takes about the same space as the runlength compressed (mathsfBWT)xe2x80x94we have the design for efficient FMindexes of genomic databases but are faced with the problem of building them. In 2018 we showed how to build the (mathsfBWT) of large genomic databases efficiently (WABI 2018) but the problem of building Gagie et al.xe2x80x99s (mathsfSA) sample efficiently was left open. We compare our approach to stateoftheart methods for constructing the (mathsfSA) sample, and demonstrate that it is the fastest and most spaceefficient method on highly repetitive genomic databases. Lastly, we apply our method for indexing partial and whole human genomes and show that it improves over Bowtie with respect to both memory and time.