Fast and accurate HLA typing from short-read next-generation sequence data with xHLA.
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ABSTRACT:
SUBMITTER: Xie C
PROVIDER: S-EPMC5544337 | biostudies-other | 2017 Jul
REPOSITORIES: biostudies-other
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Project description:BackgroundMulti-locus sequence typing (MLST) has become the gold standard for population analyses of bacterial pathogens. This method focuses on the sequences of a small number of loci (usually seven) to divide the population and is simple, robust and facilitates comparison of results between laboratories and over time. Over the last decade, researchers and population health specialists have invested substantial effort in building up public MLST databases for nearly 100 different bacterial species, and these databases contain a wealth of important information linked to MLST sequence types such as time and place of isolation, host or niche, serotype and even clinical or drug resistance profiles. Recent advances in sequencing technology mean it is increasingly feasible to perform bacterial population analysis at the whole genome level. This offers massive gains in resolving power and genetic profiling compared to MLST, and will eventually replace MLST for bacterial typing and population analysis. However given the wealth of data currently available in MLST databases, it is crucial to maintain backwards compatibility with MLST schemes so that new genome analyses can be understood in their proper historical context.ResultsWe present a software tool, SRST, for quick and accurate retrieval of sequence types from short read sets, using inputs easily downloaded from public databases. SRST uses read mapping and an allele assignment score incorporating sequence coverage and variability, to determine the most likely allele at each MLST locus. Analysis of over 3,500 loci in more than 500 publicly accessible Illumina read sets showed SRST to be highly accurate at allele assignment. SRST output is compatible with common analysis tools such as eBURST, Clonal Frame or PhyloViz, allowing easy comparison between novel genome data and MLST data. Alignment, fastq and pileup files can also be generated for novel alleles.ConclusionsSRST is a novel software tool for accurate assignment of sequence types using short read data. Several uses for the tool are demonstrated, including quality control for high-throughput sequencing projects, plasmid MLST and analysis of genomic data during outbreak investigation. SRST is open-source, requires Python, BWA and SamTools, and is available from http://srst.sourceforge.net.
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Project description:BackgroundThe rapid advancements in the field of genome sequencing are aiding our understanding on many biological systems. In the last five years, computational biologists and bioinformatics specialists have come up with newer, better and more efficient tools towards the discovery, analysis and interpretation of different genomic variants from high-throughput sequencing data. Availability of reliable simulated dataset is essential and is the first step towards testing any newly developed analytical tools for variant discovery. Although there are tools currently available that can simulate variants, none present the possibility of simulating all the three major types of variations (Single Nucleotide Polymorphisms, Insertions and Deletions and Copy Number Variations) and can generate reads taking a realistic error-model into consideration. Therefore, an efficient simulator and read generator is needed that can simulate variants taking the error rates of true biological samples into consideration.ResultsWe report SInC (Snp, Indel and Cnv) an open-source variant simulator and read generator capable of simulating all the three common types of biological variants taking into account a distribution of base quality score from a most commonly used next-generation sequencing instrument from Illumina. SInC is capable of generating single- and paired-end reads with user-defined insert size and with high efficiency compared to the other existing tools. SInC, due to its multi-threaded capability during read generation, has a low time footprint. SInC is currently optimised to work in limited infrastructure setup and can efficiently exploit the commonly used quad-core desktop architecture to simulate short sequence reads with deep coverage for large genomes.ConclusionsWe have come up with a user-friendly multi-variant simulator and read-generator tools called SInC. SInC can be downloaded from http://sourceforge.net/projects/sincsimulator.
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