A web tool for the global identification of pig breeds

Abstract Background Natural and artificial selection for more than 9000 years have led to a variety of domestic pig breeds. Accurate identification of pig breeds is important for breed conservation, sustainable breeding, pork traceability, and local resource registration. Results We evaluated the pe...
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Autor*in:	Jian Miao [verfasserIn] Zitao Chen [verfasserIn] Zhenyang Zhang [verfasserIn] Zhen Wang [verfasserIn] Qishan Wang [verfasserIn] Zhe Zhang [verfasserIn] Yuchun Pan [verfasserIn]

Format:	E-Artikel
Sprache:	Deutsch ; Englisch ; Französisch

Erschienen:	2023

Übergeordnetes Werk:	In: Genetics Selection Evolution - BMC, 2009, 55(2023), 1, Seite 12
Übergeordnetes Werk:	volume:55 ; year:2023 ; number:1 ; pages:12

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1186/s12711-023-00788-0

Katalog-ID:	DOAJ087783010

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520			\|a Abstract Background Natural and artificial selection for more than 9000 years have led to a variety of domestic pig breeds. Accurate identification of pig breeds is important for breed conservation, sustainable breeding, pork traceability, and local resource registration. Results We evaluated the performance of four selectors and six classifiers for breed identification using a wide range of pig breeds (N = 91). The internal cross-validation and external independent testing showed that partial least squares regression (PLSR) was the most effective selector and partial least squares-discriminant analysis (PLS-DA) was the most powerful classifier for breed identification among many breeds. Five-fold cross-validation indicated that using PLSR as the selector and PLS-DA as the classifier to discriminate 91 pig breeds yielded 98.4% accuracy with only 3K single nucleotide polymorphisms (SNPs). We also constructed a reference dataset with 124 pig breeds and used it to develop the web tool iDIGs ( http://alphaindex.zju.edu.cn/iDIGs_en/ ) as a comprehensive application for global pig breed identification. iDIGs allows users to (1) identify pig breeds without a reference population and (2) design small panels to discriminate several specific pig breeds. Conclusions In this study, we proved that breed identification among a wide range of pig breeds is feasible and we developed a web tool for such pig breed identification.
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allfields	10.1186/s12711-023-00788-0 doi (DE-627)DOAJ087783010 (DE-599)DOAJ8fbf1040faa643be96a8cdd16f7345d3 DE-627 ger DE-627 rakwb ger eng fre SF1-1100 QH426-470 Jian Miao verfasserin aut A web tool for the global identification of pig breeds 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Background Natural and artificial selection for more than 9000 years have led to a variety of domestic pig breeds. Accurate identification of pig breeds is important for breed conservation, sustainable breeding, pork traceability, and local resource registration. Results We evaluated the performance of four selectors and six classifiers for breed identification using a wide range of pig breeds (N = 91). The internal cross-validation and external independent testing showed that partial least squares regression (PLSR) was the most effective selector and partial least squares-discriminant analysis (PLS-DA) was the most powerful classifier for breed identification among many breeds. Five-fold cross-validation indicated that using PLSR as the selector and PLS-DA as the classifier to discriminate 91 pig breeds yielded 98.4% accuracy with only 3K single nucleotide polymorphisms (SNPs). We also constructed a reference dataset with 124 pig breeds and used it to develop the web tool iDIGs ( http://alphaindex.zju.edu.cn/iDIGs_en/ ) as a comprehensive application for global pig breed identification. iDIGs allows users to (1) identify pig breeds without a reference population and (2) design small panels to discriminate several specific pig breeds. Conclusions In this study, we proved that breed identification among a wide range of pig breeds is feasible and we developed a web tool for such pig breed identification. Animal culture Genetics Zitao Chen verfasserin aut Zhenyang Zhang verfasserin aut Zhen Wang verfasserin aut Qishan Wang verfasserin aut Zhe Zhang verfasserin aut Yuchun Pan verfasserin aut In Genetics Selection Evolution BMC, 2009 55(2023), 1, Seite 12 (DE-627)312849052 (DE-600)2012369-3 12979686 nnns volume:55 year:2023 number:1 pages:12 https://doi.org/10.1186/s12711-023-00788-0 kostenfrei https://doaj.org/article/8fbf1040faa643be96a8cdd16f7345d3 kostenfrei https://doi.org/10.1186/s12711-023-00788-0 kostenfrei https://doaj.org/toc/1297-9686 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 55 2023 1 12
spelling	10.1186/s12711-023-00788-0 doi (DE-627)DOAJ087783010 (DE-599)DOAJ8fbf1040faa643be96a8cdd16f7345d3 DE-627 ger DE-627 rakwb ger eng fre SF1-1100 QH426-470 Jian Miao verfasserin aut A web tool for the global identification of pig breeds 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Background Natural and artificial selection for more than 9000 years have led to a variety of domestic pig breeds. Accurate identification of pig breeds is important for breed conservation, sustainable breeding, pork traceability, and local resource registration. Results We evaluated the performance of four selectors and six classifiers for breed identification using a wide range of pig breeds (N = 91). The internal cross-validation and external independent testing showed that partial least squares regression (PLSR) was the most effective selector and partial least squares-discriminant analysis (PLS-DA) was the most powerful classifier for breed identification among many breeds. Five-fold cross-validation indicated that using PLSR as the selector and PLS-DA as the classifier to discriminate 91 pig breeds yielded 98.4% accuracy with only 3K single nucleotide polymorphisms (SNPs). We also constructed a reference dataset with 124 pig breeds and used it to develop the web tool iDIGs ( http://alphaindex.zju.edu.cn/iDIGs_en/ ) as a comprehensive application for global pig breed identification. iDIGs allows users to (1) identify pig breeds without a reference population and (2) design small panels to discriminate several specific pig breeds. Conclusions In this study, we proved that breed identification among a wide range of pig breeds is feasible and we developed a web tool for such pig breed identification. Animal culture Genetics Zitao Chen verfasserin aut Zhenyang Zhang verfasserin aut Zhen Wang verfasserin aut Qishan Wang verfasserin aut Zhe Zhang verfasserin aut Yuchun Pan verfasserin aut In Genetics Selection Evolution BMC, 2009 55(2023), 1, Seite 12 (DE-627)312849052 (DE-600)2012369-3 12979686 nnns volume:55 year:2023 number:1 pages:12 https://doi.org/10.1186/s12711-023-00788-0 kostenfrei https://doaj.org/article/8fbf1040faa643be96a8cdd16f7345d3 kostenfrei https://doi.org/10.1186/s12711-023-00788-0 kostenfrei https://doaj.org/toc/1297-9686 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 55 2023 1 12
allfields_unstemmed	10.1186/s12711-023-00788-0 doi (DE-627)DOAJ087783010 (DE-599)DOAJ8fbf1040faa643be96a8cdd16f7345d3 DE-627 ger DE-627 rakwb ger eng fre SF1-1100 QH426-470 Jian Miao verfasserin aut A web tool for the global identification of pig breeds 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Background Natural and artificial selection for more than 9000 years have led to a variety of domestic pig breeds. Accurate identification of pig breeds is important for breed conservation, sustainable breeding, pork traceability, and local resource registration. Results We evaluated the performance of four selectors and six classifiers for breed identification using a wide range of pig breeds (N = 91). The internal cross-validation and external independent testing showed that partial least squares regression (PLSR) was the most effective selector and partial least squares-discriminant analysis (PLS-DA) was the most powerful classifier for breed identification among many breeds. Five-fold cross-validation indicated that using PLSR as the selector and PLS-DA as the classifier to discriminate 91 pig breeds yielded 98.4% accuracy with only 3K single nucleotide polymorphisms (SNPs). We also constructed a reference dataset with 124 pig breeds and used it to develop the web tool iDIGs ( http://alphaindex.zju.edu.cn/iDIGs_en/ ) as a comprehensive application for global pig breed identification. iDIGs allows users to (1) identify pig breeds without a reference population and (2) design small panels to discriminate several specific pig breeds. Conclusions In this study, we proved that breed identification among a wide range of pig breeds is feasible and we developed a web tool for such pig breed identification. Animal culture Genetics Zitao Chen verfasserin aut Zhenyang Zhang verfasserin aut Zhen Wang verfasserin aut Qishan Wang verfasserin aut Zhe Zhang verfasserin aut Yuchun Pan verfasserin aut In Genetics Selection Evolution BMC, 2009 55(2023), 1, Seite 12 (DE-627)312849052 (DE-600)2012369-3 12979686 nnns volume:55 year:2023 number:1 pages:12 https://doi.org/10.1186/s12711-023-00788-0 kostenfrei https://doaj.org/article/8fbf1040faa643be96a8cdd16f7345d3 kostenfrei https://doi.org/10.1186/s12711-023-00788-0 kostenfrei https://doaj.org/toc/1297-9686 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 55 2023 1 12
allfieldsGer	10.1186/s12711-023-00788-0 doi (DE-627)DOAJ087783010 (DE-599)DOAJ8fbf1040faa643be96a8cdd16f7345d3 DE-627 ger DE-627 rakwb ger eng fre SF1-1100 QH426-470 Jian Miao verfasserin aut A web tool for the global identification of pig breeds 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Background Natural and artificial selection for more than 9000 years have led to a variety of domestic pig breeds. Accurate identification of pig breeds is important for breed conservation, sustainable breeding, pork traceability, and local resource registration. Results We evaluated the performance of four selectors and six classifiers for breed identification using a wide range of pig breeds (N = 91). The internal cross-validation and external independent testing showed that partial least squares regression (PLSR) was the most effective selector and partial least squares-discriminant analysis (PLS-DA) was the most powerful classifier for breed identification among many breeds. Five-fold cross-validation indicated that using PLSR as the selector and PLS-DA as the classifier to discriminate 91 pig breeds yielded 98.4% accuracy with only 3K single nucleotide polymorphisms (SNPs). We also constructed a reference dataset with 124 pig breeds and used it to develop the web tool iDIGs ( http://alphaindex.zju.edu.cn/iDIGs_en/ ) as a comprehensive application for global pig breed identification. iDIGs allows users to (1) identify pig breeds without a reference population and (2) design small panels to discriminate several specific pig breeds. Conclusions In this study, we proved that breed identification among a wide range of pig breeds is feasible and we developed a web tool for such pig breed identification. Animal culture Genetics Zitao Chen verfasserin aut Zhenyang Zhang verfasserin aut Zhen Wang verfasserin aut Qishan Wang verfasserin aut Zhe Zhang verfasserin aut Yuchun Pan verfasserin aut In Genetics Selection Evolution BMC, 2009 55(2023), 1, Seite 12 (DE-627)312849052 (DE-600)2012369-3 12979686 nnns volume:55 year:2023 number:1 pages:12 https://doi.org/10.1186/s12711-023-00788-0 kostenfrei https://doaj.org/article/8fbf1040faa643be96a8cdd16f7345d3 kostenfrei https://doi.org/10.1186/s12711-023-00788-0 kostenfrei https://doaj.org/toc/1297-9686 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 55 2023 1 12
allfieldsSound	10.1186/s12711-023-00788-0 doi (DE-627)DOAJ087783010 (DE-599)DOAJ8fbf1040faa643be96a8cdd16f7345d3 DE-627 ger DE-627 rakwb ger eng fre SF1-1100 QH426-470 Jian Miao verfasserin aut A web tool for the global identification of pig breeds 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Background Natural and artificial selection for more than 9000 years have led to a variety of domestic pig breeds. Accurate identification of pig breeds is important for breed conservation, sustainable breeding, pork traceability, and local resource registration. Results We evaluated the performance of four selectors and six classifiers for breed identification using a wide range of pig breeds (N = 91). The internal cross-validation and external independent testing showed that partial least squares regression (PLSR) was the most effective selector and partial least squares-discriminant analysis (PLS-DA) was the most powerful classifier for breed identification among many breeds. Five-fold cross-validation indicated that using PLSR as the selector and PLS-DA as the classifier to discriminate 91 pig breeds yielded 98.4% accuracy with only 3K single nucleotide polymorphisms (SNPs). We also constructed a reference dataset with 124 pig breeds and used it to develop the web tool iDIGs ( http://alphaindex.zju.edu.cn/iDIGs_en/ ) as a comprehensive application for global pig breed identification. iDIGs allows users to (1) identify pig breeds without a reference population and (2) design small panels to discriminate several specific pig breeds. Conclusions In this study, we proved that breed identification among a wide range of pig breeds is feasible and we developed a web tool for such pig breed identification. Animal culture Genetics Zitao Chen verfasserin aut Zhenyang Zhang verfasserin aut Zhen Wang verfasserin aut Qishan Wang verfasserin aut Zhe Zhang verfasserin aut Yuchun Pan verfasserin aut In Genetics Selection Evolution BMC, 2009 55(2023), 1, Seite 12 (DE-627)312849052 (DE-600)2012369-3 12979686 nnns volume:55 year:2023 number:1 pages:12 https://doi.org/10.1186/s12711-023-00788-0 kostenfrei https://doaj.org/article/8fbf1040faa643be96a8cdd16f7345d3 kostenfrei https://doi.org/10.1186/s12711-023-00788-0 kostenfrei https://doaj.org/toc/1297-9686 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 55 2023 1 12
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source	In Genetics Selection Evolution 55(2023), 1, Seite 12 volume:55 year:2023 number:1 pages:12
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Accurate identification of pig breeds is important for breed conservation, sustainable breeding, pork traceability, and local resource registration. Results We evaluated the performance of four selectors and six classifiers for breed identification using a wide range of pig breeds (N = 91). The internal cross-validation and external independent testing showed that partial least squares regression (PLSR) was the most effective selector and partial least squares-discriminant analysis (PLS-DA) was the most powerful classifier for breed identification among many breeds. Five-fold cross-validation indicated that using PLSR as the selector and PLS-DA as the classifier to discriminate 91 pig breeds yielded 98.4% accuracy with only 3K single nucleotide polymorphisms (SNPs). We also constructed a reference dataset with 124 pig breeds and used it to develop the web tool iDIGs ( http://alphaindex.zju.edu.cn/iDIGs_en/ ) as a comprehensive application for global pig breed identification. iDIGs allows users to (1) identify pig breeds without a reference population and (2) design small panels to discriminate several specific pig breeds. 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callnumber-first	S - Agriculture
author	Jian Miao
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topic_title	SF1-1100 QH426-470 A web tool for the global identification of pig breeds
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title	A web tool for the global identification of pig breeds
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title_full	A web tool for the global identification of pig breeds
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title_sort	web tool for the global identification of pig breeds
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title_auth	A web tool for the global identification of pig breeds
abstract	Abstract Background Natural and artificial selection for more than 9000 years have led to a variety of domestic pig breeds. Accurate identification of pig breeds is important for breed conservation, sustainable breeding, pork traceability, and local resource registration. Results We evaluated the performance of four selectors and six classifiers for breed identification using a wide range of pig breeds (N = 91). The internal cross-validation and external independent testing showed that partial least squares regression (PLSR) was the most effective selector and partial least squares-discriminant analysis (PLS-DA) was the most powerful classifier for breed identification among many breeds. Five-fold cross-validation indicated that using PLSR as the selector and PLS-DA as the classifier to discriminate 91 pig breeds yielded 98.4% accuracy with only 3K single nucleotide polymorphisms (SNPs). We also constructed a reference dataset with 124 pig breeds and used it to develop the web tool iDIGs ( http://alphaindex.zju.edu.cn/iDIGs_en/ ) as a comprehensive application for global pig breed identification. iDIGs allows users to (1) identify pig breeds without a reference population and (2) design small panels to discriminate several specific pig breeds. Conclusions In this study, we proved that breed identification among a wide range of pig breeds is feasible and we developed a web tool for such pig breed identification.
abstractGer	Abstract Background Natural and artificial selection for more than 9000 years have led to a variety of domestic pig breeds. Accurate identification of pig breeds is important for breed conservation, sustainable breeding, pork traceability, and local resource registration. Results We evaluated the performance of four selectors and six classifiers for breed identification using a wide range of pig breeds (N = 91). The internal cross-validation and external independent testing showed that partial least squares regression (PLSR) was the most effective selector and partial least squares-discriminant analysis (PLS-DA) was the most powerful classifier for breed identification among many breeds. Five-fold cross-validation indicated that using PLSR as the selector and PLS-DA as the classifier to discriminate 91 pig breeds yielded 98.4% accuracy with only 3K single nucleotide polymorphisms (SNPs). We also constructed a reference dataset with 124 pig breeds and used it to develop the web tool iDIGs ( http://alphaindex.zju.edu.cn/iDIGs_en/ ) as a comprehensive application for global pig breed identification. iDIGs allows users to (1) identify pig breeds without a reference population and (2) design small panels to discriminate several specific pig breeds. Conclusions In this study, we proved that breed identification among a wide range of pig breeds is feasible and we developed a web tool for such pig breed identification.
abstract_unstemmed	Abstract Background Natural and artificial selection for more than 9000 years have led to a variety of domestic pig breeds. Accurate identification of pig breeds is important for breed conservation, sustainable breeding, pork traceability, and local resource registration. Results We evaluated the performance of four selectors and six classifiers for breed identification using a wide range of pig breeds (N = 91). The internal cross-validation and external independent testing showed that partial least squares regression (PLSR) was the most effective selector and partial least squares-discriminant analysis (PLS-DA) was the most powerful classifier for breed identification among many breeds. Five-fold cross-validation indicated that using PLSR as the selector and PLS-DA as the classifier to discriminate 91 pig breeds yielded 98.4% accuracy with only 3K single nucleotide polymorphisms (SNPs). We also constructed a reference dataset with 124 pig breeds and used it to develop the web tool iDIGs ( http://alphaindex.zju.edu.cn/iDIGs_en/ ) as a comprehensive application for global pig breed identification. iDIGs allows users to (1) identify pig breeds without a reference population and (2) design small panels to discriminate several specific pig breeds. Conclusions In this study, we proved that breed identification among a wide range of pig breeds is feasible and we developed a web tool for such pig breed identification.
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title_short	A web tool for the global identification of pig breeds
url	https://doi.org/10.1186/s12711-023-00788-0 https://doaj.org/article/8fbf1040faa643be96a8cdd16f7345d3 https://doaj.org/toc/1297-9686
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author2	Zitao Chen Zhenyang Zhang Zhen Wang Qishan Wang Zhe Zhang Yuchun Pan
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We also constructed a reference dataset with 124 pig breeds and used it to develop the web tool iDIGs ( http://alphaindex.zju.edu.cn/iDIGs_en/ ) as a comprehensive application for global pig breed identification. iDIGs allows users to (1) identify pig breeds without a reference population and (2) design small panels to discriminate several specific pig breeds. 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