By Jinbo Xu, Sheng Wang, Jianzhu Ma
ISBN-10: 331914913X
ISBN-13: 9783319149134
This paintings covers sequence-based protein homology detection, a basic and hard bioinformatics challenge with various real-world functions. The textual content first surveys a couple of well known homology detection equipment, corresponding to Position-Specific Scoring Matrix (PSSM) and Hidden Markov version (HMM) dependent tools, after which describes a singular Markov Random Fields (MRF) established procedure built through the authors. MRF-based equipment are even more delicate than HMM- and PSSM-based equipment for distant homolog detection and fold popularity, as MRFs can version long-range residue-residue interplay. The textual content additionally describes the set up, utilization and end result interpretation of courses enforcing the MRF-based process.
Read Online or Download Protein Homology Detection Through Alignment of Markov Random Fields: Using MRFalign (SpringerBriefs in Computer Science) PDF
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Additional resources for Protein Homology Detection Through Alignment of Markov Random Fields: Using MRFalign (SpringerBriefs in Computer Science)
Sample text
0). 1”. To evaluate the performance of remote homology detection, we compare MRFalign, with PSSM-PSSM based method FFAS [7], sequence-HMM based method hmmscan (comparison) and HMM-HMM based methods HHsearch and HHblits [8]. Meanwhile, HHsearch and hmmscan use HHalign and HMMER, respectively, for protein alignment. Evaluation criteria Three performance metrics are employed including referencedependent alignment recall and precision, and success rate of homology detection and fold recognition. 2 Test Data 39 reference alignment that are aligned by an algorithm.
In: Advances in Neural Information Processing Systems (2009) 13. : Protein threading using context-specific alignment potential. Bioinformatics 29 (13), i257–i265 (2013) 14. : A conditional neural fields model for protein threading. Bioinformatics 28(12), i59–i66 (2012) 15. : Protein structure alignment beyond spatial proximity, vol. 3. Science Report (2013) 16. : Amino acid substitution matrices from protein blocks. Proc. Natl. Acad. Sci. 89(22), 10915–10919 (1992) 17. : Structure-derived substitution matrices for alignment of distantly related sequences.
I; i þ 1; . ; i þ w: In case that one column does not exist (when i w or i þ w [ N), zero is used. See Fig. 4 for an illustration of how to use neural networks to estimate local similarity of two MRF nodes. We train the parameters in Eu by maximizing the occurring probability of a set of reference alignments, which are generated by a structure alignment tool DeepAlign [15]. That is, we optimize the model parameters so that the structure alignment of one training protein pair has the largest probability among all possible alignments.
Protein Homology Detection Through Alignment of Markov Random Fields: Using MRFalign (SpringerBriefs in Computer Science) by Jinbo Xu, Sheng Wang, Jianzhu Ma
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