Schmitt, Armin

[["dc.bibliographiccitation.firstpage","790"],["dc.bibliographiccitation.issue","8"],["dc.bibliographiccitation.journal","Biology"],["dc.bibliographiccitation.volume","10"],["dc.contributor.author","Klees, Selina"],["dc.contributor.author","Heinrich, Felix"],["dc.contributor.author","Schmitt, Armin Otto"],["dc.contributor.author","Gültas, Mehmet"],["dc.date.accessioned","2021-10-01T09:58:22Z"],["dc.date.available","2021-10-01T09:58:22Z"],["dc.date.issued","2021"],["dc.description.abstract","Transcription factors (TFs) govern transcriptional gene regulation by specifically binding to short DNA motifs, known as transcription factor binding sites (TFBSs), in regulatory regions, such as promoters. Today, it is well known that single nucleotide polymorphisms (SNPs) in TFBSs can dramatically affect the level of gene expression, since they can cause a change in the binding affinity of TFs. Such SNPs, referred to as regulatory SNPs (rSNPs), have gained attention in the life sciences due to their causality for specific traits or diseases. In this study, we present agReg-SNPdb, a database comprising rSNP data of seven agricultural and domestic animal species: cattle, pig, chicken, sheep, horse, goat, and dog. To identify the rSNPs, we constructed a bioinformatics pipeline and identified a total of 10,623,512 rSNPs, which are located within TFBSs and affect the binding affinity of putative TFs. Altogether, we implemented the first systematic analysis of SNPs in promoter regions and their impact on the binding affinity of TFs for livestock and made it usable via a web interface."],["dc.description.abstract","Transcription factors (TFs) govern transcriptional gene regulation by specifically binding to short DNA motifs, known as transcription factor binding sites (TFBSs), in regulatory regions, such as promoters. Today, it is well known that single nucleotide polymorphisms (SNPs) in TFBSs can dramatically affect the level of gene expression, since they can cause a change in the binding affinity of TFs. Such SNPs, referred to as regulatory SNPs (rSNPs), have gained attention in the life sciences due to their causality for specific traits or diseases. In this study, we present agReg-SNPdb, a database comprising rSNP data of seven agricultural and domestic animal species: cattle, pig, chicken, sheep, horse, goat, and dog. To identify the rSNPs, we constructed a bioinformatics pipeline and identified a total of 10,623,512 rSNPs, which are located within TFBSs and affect the binding affinity of putative TFs. Altogether, we implemented the first systematic analysis of SNPs in promoter regions and their impact on the binding affinity of TFs for livestock and made it usable via a web interface."],["dc.description.sponsorship","Open-Access-Publikationsfonds 2021"],["dc.identifier.doi","10.3390/biology10080790"],["dc.identifier.pii","biology10080790"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/90048"],["dc.language.iso","en"],["dc.notes.intern","DOI Import GROB-469"],["dc.publisher","MDPI"],["dc.relation.eissn","2079-7737"],["dc.rights","https://creativecommons.org/licenses/by/4.0/"],["dc.title","agReg-SNPdb: A Database of Regulatory SNPs for Agricultural Animal Species"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","63"],["dc.bibliographiccitation.firstpage","1"],["dc.bibliographiccitation.issue","3"],["dc.bibliographiccitation.journal","Data"],["dc.bibliographiccitation.lastpage","4"],["dc.bibliographiccitation.volume","5"],["dc.contributor.author","Heinrich, Felix"],["dc.contributor.author","Gültas, Mehmet"],["dc.contributor.author","Link, Wolfgang"],["dc.contributor.author","Schmitt, Armin Otto"],["dc.date.accessioned","2020-08-19T11:42:02Z"],["dc.date.accessioned","2021-10-27T13:11:36Z"],["dc.date.available","2020-08-19T11:42:02Z"],["dc.date.available","2021-10-27T13:11:36Z"],["dc.date.issued","2020"],["dc.description.abstract","The grain faba bean (Vicia faba) which belongs to the family of the Leguminosae, is a crop that is grown worldwide for consumption by humans and livestock. Despite being a rich source of plant-based protein and various agro-ecological advantages its usage is limited due to its anti-nutrients in the form of the seed-compounds vicine and convicine (V+C). While markers for a low V+C content exist the underlying pathway and the responsible genes have remained unknown for a long time and only recently a possible pathway and enzyme were found. Genetic research into Vicia faba is difficult due to the lack of a reference genome and the near exclusivity of V+C to the species. Here, we present sequence reads obtained through genotyping-by-sequencing of 20 Vicia faba lines with varying V+C contents. For each line, ∼3 million 150 bp paired end reads are available. This data can be useful in the genomic research of Vicia faba in general and its V+C content in particular."],["dc.description.sponsorship","Open-Access-Publikationsfonds 2020"],["dc.identifier.doi","10.3390/data5030063"],["dc.identifier.doi","10.37473/dac/10.3390/data5030063"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/17502"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/91609"],["dc.language.iso","en"],["dc.notes.intern","Migrated from goescholar"],["dc.relation.eissn","2306-5729"],["dc.relation.orgunit","Fakultät für Biologie und Psychologie"],["dc.rights","Goescholar"],["dc.rights.uri","https://goescholar.uni-goettingen.de/licenses"],["dc.subject.ddc","570"],["dc.title","Genotyping by Sequencing Reads of 20 Vicia faba Lines with High and Low Vicine and Convicine Content"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","7512"],["dc.bibliographiccitation.issue","22"],["dc.bibliographiccitation.journal","Sensors"],["dc.bibliographiccitation.volume","21"],["dc.contributor.author","Wutke, Martin"],["dc.contributor.author","Heinrich, Felix"],["dc.contributor.author","Das, Pronaya Prosun"],["dc.contributor.author","Lange, Anita"],["dc.contributor.author","Gentz, Maria"],["dc.contributor.author","Traulsen, Imke"],["dc.contributor.author","Warns, Friederike K."],["dc.contributor.author","Schmitt, Armin Otto"],["dc.contributor.author","Gültas, Mehmet"],["dc.date.accessioned","2022-01-11T14:07:54Z"],["dc.date.available","2022-01-11T14:07:54Z"],["dc.date.issued","2021"],["dc.description.abstract","The identification of social interactions is of fundamental importance for animal behavioral studies, addressing numerous problems like investigating the influence of social hierarchical structures or the drivers of agonistic behavioral disorders. However, the majority of previous studies often rely on manual determination of the number and types of social encounters by direct observation which requires a large amount of personnel and economical efforts. To overcome this limitation and increase research efficiency and, thus, contribute to animal welfare in the long term, we propose in this study a framework for the automated identification of social contacts. In this framework, we apply a convolutional neural network (CNN) to detect the location and orientation of pigs within a video and track their movement trajectories over a period of time using a Kalman filter (KF) algorithm. Based on the tracking information, we automatically identify social contacts in the form of head–head and head–tail contacts. Moreover, by using the individual animal IDs, we construct a network of social contacts as the final output. We evaluated the performance of our framework based on two distinct test sets for pig detection and tracking. Consequently, we achieved a Sensitivity, Precision, and F1-score of 94.2%, 95.4%, and 95.1%, respectively, and a MOTA score of 94.4%. The findings of this study demonstrate the effectiveness of our keypoint-based tracking-by-detection strategy and can be applied to enhance animal monitoring systems."],["dc.description.sponsorship","Open-Access-Publikationsfonds 2021"],["dc.identifier.doi","10.3390/s21227512"],["dc.identifier.pii","s21227512"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/97888"],["dc.language.iso","en"],["dc.notes.intern","DOI-Import GROB-507"],["dc.relation.eissn","1424-8220"],["dc.rights","CC BY 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0/"],["dc.title","Detecting Animal Contacts—A Deep Learning-Based Pig Detection and Tracking Approach for the Quantification of Social Contacts"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.subtype","original_ja"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","e0216475"],["dc.bibliographiccitation.issue","5"],["dc.bibliographiccitation.journal","PLOS ONE"],["dc.bibliographiccitation.volume","14"],["dc.contributor.author","Steuernagel, Lukas"],["dc.contributor.author","Meckbach, Cornelia"],["dc.contributor.author","Heinrich, Felix"],["dc.contributor.author","Zeidler, Sebastian"],["dc.contributor.author","Schmitt, Armin O."],["dc.contributor.author","Gültas, Mehmet"],["dc.date.accessioned","2019-07-09T11:51:30Z"],["dc.date.available","2019-07-09T11:51:30Z"],["dc.date.issued","2019"],["dc.description.abstract","Transcription factors (TFs) are a special class of DNA-binding proteins that orchestrate gene transcription by recruiting other TFs, co-activators or co-repressors. Their combinatorial interplay in higher organisms maintains homeostasis and governs cell identity by finely controlling and regulating tissue-specific gene expression. Despite the rich literature on the importance of cooperative TFs for deciphering the mechanisms of individual regulatory programs that control tissue specificity in several organisms such as human, mouse, or Drosophila melanogaster, to date, there is still need for a comprehensive study to detect specific TF cooperations in regulatory processes of cattle tissues. To address the needs of knowledge about specific combinatorial gene regulation in cattle tissues, we made use of three publicly available RNA-seq datasets and obtained tissue-specific gene (TSG) sets for ten tissues (heart, lung, liver, kidney, duodenum, muscle tissue, adipose tissue, colon, spleen and testis). By analyzing these TSG-sets, tissue-specific TF cooperations of each tissue have been identified. The results reveal that similar to the combinatorial regulatory events of model organisms, TFs change their partners depending on their biological functions in different tissues. Particularly with regard to preferential partner choice of the transcription factors STAT3 and NR2C2, this phenomenon has been highlighted with their five different specific cooperation partners in multiple tissues. The information about cooperative TFs could be promising: i) to understand the molecular mechanisms of regulating processes; and ii) to extend the existing knowledge on the importance of single TFs in cattle tissues."],["dc.identifier.doi","10.1371/journal.pone.0216475"],["dc.identifier.pmid","31095599"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/16139"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/59959"],["dc.language.iso","en"],["dc.notes.intern","Merged from goescholar"],["dc.rights","CC BY 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0"],["dc.subject.ddc","630"],["dc.title","Computational identification of tissue-specific transcription factor cooperation in ten cattle tissues"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","246"],["dc.bibliographiccitation.issue","2"],["dc.bibliographiccitation.journal","Vaccines"],["dc.bibliographiccitation.volume","8"],["dc.contributor.author","Rajavel, Abirami"],["dc.contributor.author","Heinrich, Felix"],["dc.contributor.author","Schmitt, Armin Otto"],["dc.contributor.author","Gültas, Mehmet"],["dc.date.accessioned","2021-04-14T08:25:01Z"],["dc.date.available","2021-04-14T08:25:01Z"],["dc.date.issued","2020"],["dc.identifier.doi","10.3390/vaccines8020246"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/81495"],["dc.language.iso","en"],["dc.notes.intern","DOI Import GROB-399"],["dc.publisher","MDPI"],["dc.relation.eissn","2076-393X"],["dc.rights","https://creativecommons.org/licenses/by/4.0/"],["dc.title","Identifying Cattle Breed-Specific Partner Choice of Transcription Factors during the African Trypanosomiasis Disease Progression Using Bioinformatics Analysis"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","921"],["dc.bibliographiccitation.issue","9"],["dc.bibliographiccitation.journal","Biology"],["dc.bibliographiccitation.volume","10"],["dc.contributor.author","Heinrich, Felix"],["dc.contributor.author","Ramzan, Faisal"],["dc.contributor.author","Rajavel, Abirami"],["dc.contributor.author","Schmitt, Armin Otto"],["dc.contributor.author","Gültas, Mehmet"],["dc.date.accessioned","2021-12-01T09:24:05Z"],["dc.date.available","2021-12-01T09:24:05Z"],["dc.date.issued","2021"],["dc.description.abstract","The interactions between SNPs result in a complex interplay with the phenotype, known as epistasis. The knowledge of epistasis is a crucial part of understanding genetic causes of complex traits. However, due to the enormous number of SNP pairs and their complex relationship to the phenotype, identification still remains a challenging problem. Many approaches for the detection of epistasis have been developed using mutual information (MI) as an association measure. However, these methods have mainly been restricted to case–control phenotypes and are therefore of limited applicability for quantitative traits. To overcome this limitation of MI-based methods, here, we present an MI-based novel algorithm, MIDESP, to detect epistasis between SNPs for qualitative as well as quantitative phenotypes. Moreover, by incorporating a dataset-dependent correction technique, we deal with the effect of background associations in a genotypic dataset to separate correct epistatic interaction signals from those of false positive interactions resulting from the effect of single SNP×phenotype associations. To demonstrate the effectiveness of MIDESP, we apply it on two real datasets with qualitative and quantitative phenotypes, respectively. Our results suggest that by eliminating the background associations, MIDESP can identify important genes, which play essential roles for bovine tuberculosis or the egg weight of chickens."],["dc.description.sponsorship","Open-Access-Publikationsfonds 2021"],["dc.identifier.doi","10.3390/biology10090921"],["dc.identifier.pii","biology10090921"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/94840"],["dc.language.iso","en"],["dc.notes.intern","DOI-Import GROB-478"],["dc.relation.eissn","2079-7737"],["dc.relation.orgunit","Abteilung Züchtungsinformatik"],["dc.rights","CC BY 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0/"],["dc.title","MIDESP: Mutual Information-Based Detection of Epistatic SNP Pairs for Qualitative and Quantitative Phenotypes"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.subtype","original_ja"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","614"],["dc.bibliographiccitation.issue","6"],["dc.bibliographiccitation.journal","Genes"],["dc.bibliographiccitation.volume","11"],["dc.contributor.author","Heinrich, Felix"],["dc.contributor.author","Wutke, Martin"],["dc.contributor.author","Das, Pronaya Prosun"],["dc.contributor.author","Kamp, Miriam"],["dc.contributor.author","Gültas, Mehmet"],["dc.contributor.author","Link, Wolfgang"],["dc.contributor.author","Schmitt, Armin Otto"],["dc.date.accessioned","2021-04-14T08:25:05Z"],["dc.date.available","2021-04-14T08:25:05Z"],["dc.date.issued","2020"],["dc.identifier.doi","10.3390/genes11060614"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/17444"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/81519"],["dc.language.iso","en"],["dc.notes.intern","DOI Import GROB-399"],["dc.notes.intern","Merged from goescholar"],["dc.publisher","MDPI"],["dc.relation.eissn","2073-4425"],["dc.rights","CC BY 4.0"],["dc.rights.uri","http://creativecommons.org/licenses/by/4.0/"],["dc.title","Identification of Regulatory SNPs Associated with Vicine and Convicine Content of Vicia faba Based on Genotyping by Sequencing Data Using Deep Learning"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","100140"],["dc.bibliographiccitation.journal","Current Plant Biology"],["dc.bibliographiccitation.volume","21"],["dc.contributor.author","Lange, Thomas M."],["dc.contributor.author","Heinrich, Felix"],["dc.contributor.author","Enders, Matthias"],["dc.contributor.author","Wolf, Markus"],["dc.contributor.author","Schmitt, Armin O."],["dc.date.accessioned","2020-12-10T14:23:18Z"],["dc.date.available","2020-12-10T14:23:18Z"],["dc.date.issued","2020"],["dc.identifier.doi","10.1016/j.cpb.2020.100140"],["dc.identifier.issn","2214-6628"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/17351"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/71893"],["dc.language.iso","en"],["dc.notes.intern","DOI Import GROB-354"],["dc.notes.intern","Merged from goescholar"],["dc.rights","CC BY 4.0"],["dc.rights.uri","http://creativecommons.org/licenses/by/4.0/"],["dc.title","In silico quality assessment of SNPs—A case study on the Axiom® Wheat genotyping arrays"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]