mFISHER: a new approach for discovering protein motifs

dc.contributor.author Feng, Jianmin
dc.contributor.department Theses & dissertations (Interdisciplinary)
dc.date 2020-08-05T05:01:35.000
dc.date.accessioned 2021-02-26T08:39:20Z
dc.date.available 2021-02-26T08:39:20Z
dc.date.copyright Tue Jan 01 00:00:00 UTC 2002
dc.date.issued 2002-01-01
dc.description.abstract <p>Motif recognition is a powerful homology based sequence analysis tool for clustering new protein sequences into different families based on characteristic motifs. Compared to BLAST, these approaches typically have lower false positive rates and can reveal more remotely related family members. However, the current motif databases do not cover all the sequences in protein sequence databases. One of the major reasons for the low coverage of motif databases is that there is only a small set of known member sequences available for constructing protein motifs for many gene families. I have designed a new algorithm, "mFISHER", to detect protein motifs from only 2-5 known member sequences by artificial evolution of given sequences based on a position specific PAM evolution model. Based on my test results on 160 motif families, the overall average recall rate or sensitivity (true/(true + false negative)) and specificity (true/(true + false positive)) are 88% and 95%, respectively. Compared with MEME (Multiple EM for Motif Extraction), mFISHER is better based on the recall rate, especially when only 2 or 3 members are available. Both approaches have the similar sensitivity. MFISHER is promising for constructing protein motifs when only a few known members.</p>
dc.format.mimetype application/pdf
dc.identifier archive/lib.dr.iastate.edu/rtd/19846/
dc.identifier.articleid 20845
dc.identifier.contextkey 18779796
dc.identifier.doi https://doi.org/10.31274/rtd-20200803-68
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath rtd/19846
dc.identifier.uri https://dr.lib.iastate.edu/handle/20.500.12876/97213
dc.language.iso en
dc.source.bitstream archive/lib.dr.iastate.edu/rtd/19846/Feng_ISU_2002_F46.pdf|||Fri Jan 14 22:00:27 UTC 2022
dc.subject.keywords Zoology and genetics
dc.subject.keywords Bioinformatics and computational biology
dc.title mFISHER: a new approach for discovering protein motifs
dc.type article
dc.type.genre thesis
dspace.entity.type Publication
thesis.degree.discipline Bioinformatics & Computational Biology
thesis.degree.level thesis
thesis.degree.name Master of Science
File
Original bundle
Now showing 1 - 1 of 1
Name:
Feng_ISU_2002_F46.pdf
Size:
1.36 MB
Format:
Adobe Portable Document Format
Description: