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In order to facilitate this, a novel mapping method, KMA (k-mer alignment), was designed. KMA is able to map raw reads directly against redundant databases, it also scales well for large redundant databases. KMA uses k-mer seeding to speed up mapping and the Needleman-Wunsch algorithm to accurately align extensions from k-mer seeds. Multi-mapping reads are resolved using a novel sorting scheme (ConClave scheme), ensuring an accurate selection of templates.

The redundant databases used in genomic epidemiology are however still posing a challenge regarding mapping raw reads directly. When databases are constantly updated with new sequences due to natural evolution, the results are in a constantly changing state, making clustering difficult. This feature of the databases makes direct mapping of raw reads troublesome, as there is no guarantee that the read will cover a unique part of a reference sequence, resulting in a tie for best match. Redundancy is a problem when mapping to bacterial databases, but to a lesser degree since these databases are not as redundant as some of the gene databases, and better use can be made of paired end reads since only one of the ends will often map to single genes. There is a need for new methods to resolve the issue with redundancy in an accurate and fast manner so that acute decisions can be made. In response to this need, a new alignment method, KMA (k-mer alignment), was developed. KMA introduces a novel sorting scheme, ConClave, in order to distinguish homologous templates.

We introduce a novel alignment method, KMA, and scoring scheme, ConClave, which allows for mapping of raw reads directly against redundant databases. KMA diverges from known mappers by allowing redundancy within the databases, and KMA also produces consensus sequences and a result overview. KMA was created to be as intuitive and user friendly as possible, based on our current user demands and analysis challenges. In order to solve this, KMA works in five main steps: trimming of reads, heuristic k-mer mapping, fine alignment, ConClave scoring and reference assembly (see Fig. 1).

As KMA is designed to map sequences against redundant databases, the possibility of a tie for best matching template should be considered more as rule than an exception. For KMA to be able to find the one most likely template for each query sequence, a novel scoring scheme, ConClave, was developed.

In order to measure the performance on larger databases, a core genome MLST (cgMLST) database for E. coli was downloaded from EnteroBase (available at: [Accessed 18 January 2018]. The cgMLST scheme contains 2447741 closely related genes, which together matches the size of the human genome.

As for the antimicrobial resistance genes, BWA-MEM, Bowtie2 and Minimap2 had trouble assigning genes exceeding the thresholds, thus giving a low correlation coefficient (see Table 2). The performance of MGmapper was slightly better than that of BWA-MEM used directly, giving a correlation of 0.062 compared with BWA-MEM with a correlation of 0.021 (see Table 2). As for the resistance gene prediction, the correlation of BWA-MEM was improved when using Salmon. In this case, however, Salmon did take up the majority of the computational resources when compared with the mapping. When estimating the allele abundances, Salmon had a peak memory consumption of 104.2 GB, and used more time on the post-processing than BWA-MEM used on mapping (see Table 2). KMA outperformed all of the methods with a correlation of 0.998, only missing two genes and detecting three duplicates on average.

The E. coli sequences with the following accession number: SRR341549, SRR341551, SRR341552, SRR341555, SRR341557, SRR341559, SRR341561, SRR341563, SRR341565, SRR341567, SRR341569, SRR341571, SRR341580, were downloaded from SRA: The cgMLST database for E. coli was downloaded from Enterobase [online] Enterobase.warwick.ac.uk: [Accessed 18 January 2018]. The ResFinder database, together with in-house scripts used in the comparison and the exact options used with the different methods, have been collected as a tar-gzip archive along with the KMA source code on bitbucket:

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Please note that the FASTA download service at URL /pdb/download/downloadFastaFiles.do?structureIdList=4hhb&compressionType=uncompressedhas been discontinued. Users will need to migrate to the new endpoints below. Note that the output of the new endpoints are per entity (with chain identifiers provided in header) instead of per chain.

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Thus far, although many RSDAs have been identified, little of them exhibit high specific activity toward raw starches [7]. In addition, the production of RSDAs with high substrate affinities and high specific activities toward raw starches remains a great challenge [6]. In our previous study, we reported a novel RSDA from Pontibacillus sp. ZY (named AmyZ1) with broad substrate specificity towards different raw starches at low temperatures (about 35 C) and higher specific activity than other reported RSDAs [8]. Such properties make AmyZ1 an ideal candidate for the raw starch industry. However, when using Escherichia coli as a recombination production host, AmyZ1 primarily presents as insoluble inclusion bodies, thus hampers its industrial application. Therefore, choosing a suitable expression host for AmyZ1 soluble extracellular production is beneficial to promote its industrial application. 2ff7e9595c