S/HIC statistics

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S/HIC specifications

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Unique identifier OMICS_27141
Name S/HIC
Alternative name Soft/Hard Inference through Classification
Software type Application/Script
Interface Command line interface
Restrictions to use None
Operating system Unix/Linux
Programming languages C, Python, Shell (Bash)
Computer skills Advanced
Stability Stable
Maintained Yes

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  • person_outline Daniel Schrider <>

Publication for Soft/Hard Inference through Classification

S/HIC in publications

 (2)
PMCID: 5691062
PMID: 29146998
DOI: 10.1038/s41467-017-01658-2

[…] (dm3) using bwa with default parameters. the parameters in pool-hmm were set to be “-n 100 -c 5 -c 400 -q 20 -p -k 0.0000000001”, and “–theta” was set to be the θ estimated for each population. (2) s/hic: it is a machine learning based method and capable of detecting soft and hard sweeps. genotype for each sample was generated using gatk, and the haplotype for each sample was inferred using […]

PMCID: 5850737
PMID: 28482049
DOI: 10.1093/molbev/msx154

[…] the controversy over the impact of adaptation on human genomic variation by conducting a genome-wide scan for both hard and soft selective sweeps across human populations. we previously developed s/hic (soft/hard inference through classification), a machine learning method capable of detecting completed sweeps and inferring their mode of selection with unparalleled accuracy and robustness […]


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S/HIC institution(s)
Department of Genetics, Rutgers University, Piscataway, NJ, USA; Human Genetics Institute of New Jersey, Rutgers University, Piscataway, NJ, USA
S/HIC funding source(s)
Supported by the National Institutes of Health under Ruth L. Kirschstein National Research Service Award F32 GM105231, by National Science Foundation Award MCB-1161367, and by the National Institute of General Medical Sciences of the NIH under award no. R01GM078204.

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