SNPFile – A software library and file format for large scale association mapping and population genetics studies
By J. Nielsen, T. Mailund. BMC Bioinformatics, 2008
873 citations recorded by the source corpus.
BackgroundHigh-throughput genotyping technology has enabled cost effective typing of thousands of individuals in hundred of thousands of markers for use in genome wide studies. This vast improvement in data acquisition technology makes it an informatics challenge to efficiently store and manipulate the data. While spreadsheets and at text files were adequate solutions earlier, the increased data size mandates more efficient solutions.ResultsWe describe a new binary file format for SNP data, together with a software library for file manipulation. The file format stores genotype data together with any kind of additional data, using a flexible serialisation mechanism. The format is designed to be IO efficient for the access patterns of most multi-locus analysis methods.ConclusionThe new file format has been very useful for our own studies where it has significantly reduced the informatics burden in keeping track of various secondary data, and where the memory and IO efficiency has greatly simplified analysis runs. A main limitation with the file format is that it is only supported by the very limited set of analysis tools developed in our own lab. This is somewhat alleviated by a scripting interfaces that makes it easy to write converters to and from the format.
Subfields: Genetics
View Paper: https://www.semanticscholar.org/paper/e24748688f0e6e11866d7ff8af0280bde2804f67
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Citations in the corpus
- Efficient analysis of large-scale genome-wide data with two R packages: bigstatsr and bigsnpr (2018, methodology): “We are aware of the software library SNPFile that uses memory-mapped files to store and efficiently access genotype data, coded in C þþ (Nielsen and Mailund, 2008) and of the R package BEDMatrix (https://github.com/QuantGen/BEDMatrix) which provides memory-mapping directly for binary PLINK files.”
- Benchmarking transposable element annotation methods for creation of a streamlined, comprehensive pipeline (2019, positive): “First, we identified contaminant-like sequences in stage 0 LTR and TIR sublibraries using LTRharvest, MITE-Hunter, and HelitronScanner with default parameters.”
- The genome of Theobroma cacao (2011, neutral): “Cacao chromosome Arabidopsis chromosome C1(1321) A1(63)-A2(85)-A3(69)-A4(116)-A5(64) C2(935) A1(89)-A2(30)-A4(61)-A5(10) C3(1018) A1(70)-A3(17)-A4(63)-A5(130) C4(837) A1(21)-A2(9)-A3(94)-A4(36)-A5(44) C5(820) A1(24)-A2(51)-A3(78)-A5(37) C6(616) A1(59)-A2(15)-A4(66)-A5(26) C7(287) A1(5)-A5(32) C8(605”
- FastPCR Software for PCR Primer and Probe Design and Repeat Search (2009, neutral): “2007), and ‘LTRharvest’ (Ellinghaus et al. 2008).”
- RepeatModeler2 for automated genomic discovery of transposable element families (2020, methodology): “In addition, RepeatModeler2 now includes the LTRharvest (30) and LTR_retriever (34) tools.”
- Assessing genome assembly quality using the LTR Assembly Index (LAI) (2018, methodology): “In this study, LTR-RT candidates were obtained using LTRharvest (11) with parameters ‘-minlenltr 100 -maxlenltr 7000 -mintsd 4 -maxtsd 6 -motif TGCA -motifmis 1 -similar 85 -vic 10 -seed 20 -seqids yes’ and LTR FINDER (10) with parameters ‘-D 15000 -d 1000 -L 7000 -l 100 -p 20 -C -M 0.”
- Insights into Land Plant Evolution Garnered from the Marchantia polymorpha Genome. (2017, methodology): “…Annotation using Small RNAs (TASR) (El Baidouri et al., 2015) https://tasr-pipeline.sourceforge.net/ LTRharvest (Ellinghaus et al., 2008) http://www.zbh.uni-hamburg.de/ forschung/arbeitsgruppegenominformatik/software/ltrharvest.html LTRdigest (Steinbiss et al.,…”