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S/HIC specifications


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 citations


Supervised Machine Learning for Population Genetics: A New Paradigm

PMCID: 5905713
PMID: 29331490
DOI: 10.1016/j.tig.2017.12.005

[…] rs were recently used to detect selective sweeps and classify them according to whether they have reached fixation (complete vs incomplete) as well as by their timing (recent vs ancient) []. Finally, S/HIC (soft/hard inference through classification), which uses a variant of a random forest [] called an extra-trees classifier [] to detect both classic hard sweeps from de novo mutations and soft sw […]


Regulation of gene expression and RNA editing in Drosophila adapting to divergent microclimates

Nat Commun
PMCID: 5691062
PMID: 29146998
DOI: 10.1038/s41467-017-01658-2

[…] e (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 BEAG […]


Selection plays the hand it was dealt: evidence that human adaptation commonly targets standing genetic variation

Genome Biol
PMCID: 5537971
PMID: 28760139
DOI: 10.1186/s13059-017-1280-5

[…] nd Kern [] used a sophisticated machine learning method that they previously developed for the robust detection of selective sweeps. Their approach, termed soft/hard inference through classification (S/HIC) [], uses supervised machine learning to leverage multiple sweep signatures including reduced haplotype diversity, skews in the allele frequency spectrum, and increased linkage disequilibrium in […]


Soft Sweeps Are the Dominant Mode of Adaptation in the Human Genome

Mol Biol Evol
PMCID: 5850737
PMID: 28482049
DOI: 10.1093/molbev/msx154

[…] Our analysis demonstrates that the impact of linked positive selection on genetic variation is considerable, with roughly half of the genome classified by S/HIC as being influenced by a nearby sweep. This result has important implications for efforts to infer demographic histories from patterns of genetic polymorphism, as most inference methods hinge on […]


Meaning Making Process Related to Temporality During Breast Cancer Traumatic Experience: The Clinical Use of Narrative to Promote a New Continuity of Life

PMCID: 5114876
PMID: 27872670
DOI: 10.5964/ejop.v12i4.1150

[…] the precariousness of meaning-making were predominant in the narratives of women undergoing chemotherapy (; ). The uncertainty and precariousness caused by the diagnosis are revoked in the narration’s hic et nunc, that is, the difficulty of constructing a meaning regarding what has happened and what will happen: “It was as if my head was elsewhere, as if I were muffled; the brain sensed what was […]


Phage selection restores antibiotic sensitivity in MDR Pseudomonas aeruginosa

Sci Rep
PMCID: 4880932
PMID: 27225966
DOI: 10.1038/srep26717

[…] eriments were performed in accordance with The Yale University Human Investigation Committee/Institutional Review Board (HIC/IRB) guidelines, and relevant experimental protocols were approved by Yale’s HIC/IRB committee.P. aeruginosa strains 1845 and 1607 were collected from household sink drains (1845: bathroom sink drain; 1607: Kitchen sink drain) in a previous study, and kindly provided by S. R […]

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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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