{"data":{"id":"10.6084/m9.figshare.1415804","type":"dois","attributes":{"doi":"10.6084/m9.figshare.1415804","prefix":"10.6084","suffix":"m9.figshare.1415804","identifiers":[],"alternateIdentifiers":[],"creators":[{"name":"McDermott, Jason E.","nameType":"Personal","givenName":"Jason E.","familyName":"McDermott","affiliation":[],"nameIdentifiers":[]},{"name":"Bruillard, Paul","nameType":"Personal","givenName":"Paul","familyName":"Bruillard","affiliation":[],"nameIdentifiers":[]},{"name":"Overall, Christopher C.","nameType":"Personal","givenName":"Christopher C.","familyName":"Overall","affiliation":[],"nameIdentifiers":[]},{"name":"Gosink, Luke","nameType":"Personal","givenName":"Luke","familyName":"Gosink","affiliation":[],"nameIdentifiers":[]},{"name":"Lindemann, Stephen R.","nameType":"Personal","givenName":"Stephen R.","familyName":"Lindemann","affiliation":[],"nameIdentifiers":[]}],"titles":[{"title":"Prediction of multi-drug resistance transporters dataset"}],"publisher":"f1000research.com","container":{},"publicationYear":2015,"subjects":[{"subject":"60102 Bioinformatics","subjectScheme":"FOR"},{"subject":"FOS: Computer and information sciences","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"FOS: Computer and information sciences","subjectScheme":"Fields of Science and Technology (FOS)"}],"contributors":[],"dates":[{"date":"2015-05-15","dateType":"Created"},{"date":"2015-06-05","dateType":"Updated"},{"date":"2015","dateType":"Issued"}],"language":null,"types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"Dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"IsSupplementTo","relatedIdentifier":"10.12688/f1000research.6200.2","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":["28051347 Bytes"],"formats":[],"version":null,"rightsList":[{"rights":"Creative Commons Attribution 4.0 International","rightsUri":"https://creativecommons.org/licenses/by/4.0/legalcode","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"cc-by-4.0","rightsIdentifierScheme":"SPDX"}],"descriptions":[{"description":"Data file 1\u003cbr\u003eTitle: Data File PROSITE_positives_PS000125.fasta.\u003cbr\u003eLegend: Sequence file in FASTA format of all positive examples for the ser/thr phosphatase model. Data file 2\u003cbr\u003eData File PROSITE_negatives_PS000125.fasta.\u003cbr\u003eSequence file in FASTA format of all randomly selected negative examples for the ser/thr phosphatase model.\" Data file 3\u003cbr\u003eData File PROSITE_positives_PS00028.fasta.\u003cbr\u003eSequence file in FASTA format of all positive examples for the zinc finger model. Data file 4\u003cbr\u003eData File PROSITE_negatives_PS00028.fasta.\u003cbr\u003eSequence file in FASTA format of all randomly selected negative examples for the zinc finger model. Data file 5\u003cbr\u003eData File PROSITE_PS00125.txt.\u003cbr\u003ePROSITE record used for the ser/thr phosphatase model. Data file 6\u003cbr\u003eData File PROSITE_PS00028.txt.\u003cbr\u003ePROSITE record used for the zinc finger model. Data file 7\u003cbr\u003eData File MDR_TCDB_positives.fasta.\u003cbr\u003eSequence file of MDR transporters used for training. FASTA format file of positive examples used in this study derived from the TCDB. Data file 8\u003cbr\u003eData File MDR_TCDB_negatives.fasta.\u003cbr\u003eSequence file of non-MDR transporters used for training. FASTA format file of negative examples used in this study derived from the TCDB. Data file 9\u003cbr\u003eData File PILGram_PATTERNS_PS00125.txt.\u003cbr\u003eRegular expression generated by PILGram for the ser/thr phosphatase model. Data file 10\u003cbr\u003eData File PS00125_alignments.out.\u003cbr\u003eSequence alignments of PILGram model matches to the positive examples in the ser/thr phosphatase model. Data file 11\u003cbr\u003eData File PILGram_PATTERNS_PS00028.txt.\u003cbr\u003eRegular expressions generated by PILGram for the zinc finger model. Data file 12\u003cbr\u003eData File PS00028_alignments.out.\u003cbr\u003eSequence alignments of PILGram model matches to the positive examples in the zinc finger model and a summary score line that represents the overlap of the 10 different models for each sequence. Data file 13\u003cbr\u003eData File PILGram_PATTERNS_MDRpred.txt.\u003cbr\u003eThe 36 regular expressions and associated physiochemical properties (where applicable) generated by PILGram for the MDR model . Data file 14\u003cbr\u003eData File MDRpred_alignments.out.\u003cbr\u003eAlignments of 36 PILGram model matches on the MDR positive example sequences. Data file 15\u003cbr\u003eData File Pfam_transporters.txt.\u003cbr\u003eA list of Pfam families that were used to identify transporters in the Hot Lake metagenome. Data file 16\u003cbr\u003eData File HotLake_MDRpred_predictions.fasta.\u003cbr\u003eA FASTA format file of 63 protein sequences from the Hot Lake metagenome that are matched by 30 or more MDRpred individual models (high confidence predictions), match Pfam families for transporters (Pfam e-value less than 1e-20), but are not identified by Pfam as multidrug resistance transporters.","descriptionType":"Abstract"}],"geoLocations":[],"fundingReferences":[],"xml":"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